title: AIS-based vessel trajectory prediction

abstract: In order for autonomous surface vessels (ASVs) to avoid collisions at sea it is necessary to predict the future trajectories of surrounding vessels. This paper investigate the use of historical automatic identification system (AIS) data to predict such trajectories. The availability of AIS data have steadily increased in the last years as a result of more regulations, together with wider coverage through AIS integration on satellites and more land based receivers. Several AIS-based methods for predicting vessel trajectories already exist. However, these prediction techniques tend to focus on time horizons in the level of hours. The prediction time of our interest typically ranges from a few minutes up to about 15 minutes, depending on the maneuverability of the ASV. This paper presents a novel datadriven approach which recursively use historical AIS data in the neighborhood of a predicted position to predict next position and time. Three course and speed prediction methods are compared for one time step predictions. Lastly, the algorithm is briefly tested for multiple time steps in curved environments and shows good potential.


title: Real Time Characteristics of Ethernet and Its Improvement

abstract: There is a growing interest in using Ethernet to support time constrained communication in process control, factory automation and other real-time applications. The paper first presents a comprehensive analysis of the temporal characteristics of traditional Ethernet and the improvements for them, then summarizes the interesting features offered by a switched Ethernet for supporting time constrained communication. Finally, the paper points that there is still lack of general means to handle the capability of switched Ethernet after investigating the QoS of switched Ethernet.


title: Studying Smartphone Usage: Lessons from a Four-Month Field Study

abstract: Many emerging mobile applications and services are based on smartphones. We have performed a four-month field study of the adoption and usage of smartphone-based services by 14 novice teenage users. From the field study, we present the application usage and usage characteristics of our participants. We show that their usage is highly mobile, location-dependent, and serves multiple social purposes. Furthermore, we report qualitative lessons regarding the evaluation of smartphone-based services. In particular, we highlight the cases that an accurate evaluation would require a long-term and/or field study instead of a short or lab-based study, and the cases where studying a particular application independently is insufficient and a holistic study, i.e., involving the whole device, is necessary. We further present guidelines on effectively shortening the length of a study. These lessons are supported in part by five identified contributing factors to usage evolution.


title: A Classification Framework for Software Component Models

abstract: In the last decade, a large number of different software component models have been developed, with different aims and using different principles and technologies. This has resulted in a number of models which have many similarities, but also principal differences, and in many cases unclear concepts. Component-based development has not succeeded in providing standard principles, as has, for example, object-oriented development. In order to increase the understanding of the concepts and to differentiate component models more easily, this paper identifies, discusses, and characterizes fundamental principles of component models and provides a Component Model Classification Framework based on these principles. Further, the paper classifies a large number of component models using this framework.


title: Morphosys: an integrated reconfigurable system for data-parallel and computation-intensive applications

abstract: This paper introduces MorphoSys, a reconfigurable computing system developed to investigate the effectiveness of combining reconfigurable hardware with general-purpose processors for word-level, computation-intensive applications. MorphoSys is a coarse-grain, integrated, and reconfigurable system-on-chip, targeted at high-throughput and data-parallel applications. It is comprised of a reconfigurable array of processing cells, a modified RISC processor core, and an efficient memory interface unit. This paper describes the MorphoSys architecture, including the reconfigurable processor array, the control processor, and data and configuration memories. The suitability of MorphoSys for the target application domain is then illustrated with examples such as video compression, data encryption and target recognition. Performance evaluation of these applications indicates improvements of up to an order of magnitude (or more) on MorphoSys, in comparison with other systems.


title: Domain Adaptation for Automatic Detection of Speculative Sentences

abstract: The use of speculative, uncertain or vague sentences is common in both spoken and written language. Automatic detection of such sentences plays significant role in natural language processing and social sciences as it could enhance information extraction systems and lead to faster and more reliable analyses in social sciences. However, this problem has only been addressed in biomedical and encyclopedic domains. In this paper, we address automatic speculation detection (as a binary sentence classification task) in monetary policy domain, and for the first time, on the transcripts of spoken language. We build two new speculation detection datasets and a dictionary of speculation triggers using expert annotations, and benchmark the performance of automatic speculation detection systems in this new domain.


title: Active behavioral fingerprinting of wireless devices

abstract: We propose a simple active method for discovering facts about the chipset, the firmware or the driver of an 802.11 wireless device by observing its responses (or lack thereof) to a series of crafted non-standard or malformed 802.11 frames. We demonstrate that such responses can differ significantly enough to distinguish between a number of popular chipsets and drivers. We expect to significantly expand the number of recognized device types through community contributions of signature data for the proposed open fingerprinting framework. Our method complements known fingerprinting approaches, and can be used to interrogate and spot devices that may be spoofing their MAC addresses in order to conceal their true architecture from other stations, such as a fake AP seeking to engage clients in complex protocol frame exchange (e.g., in order to exploit a driver vulnerability). In particular, it can be used to distinguish rogue APs from legitimate APs before association.


title: How forgetting aids heuristic inference.

abstract: Some theorists, ranging from W. James (1890) to contemporary psychologists, have argued that forgetting is the key to proper functioning of memory. The authors elaborate on the notion of beneficial forgetting by proposing that loss of information aids inference heuristics that exploit mnemonic information. To this end, the authors bring together 2 research programs that take an ecological approach to studying cognition. Specifically, they implement fast and frugal heuristics within the ACT-R cognitive architecture. Simulations of the recognition heuristic, which relies on systematic failures of recognition to infer which of 2 objects scores higher on a criterion value, demonstrate that forgetting can boost accuracy by increasing the chances that only 1 object is recognized. Simulations of the fluency heuristic, which arrives at the same inference on the basis of the speed with which objects are recognized, indicate that forgetting aids the discrimination between the objects' recognition speeds.


Applying Quantum Optimization Algorithms for Linear Programming


title: Bristle-sensors – low-cost flexible passive dry EEG electrodes for neurofeedback and BCI applications

abstract: In this paper, we present a new, low-cost dry electrode for EEG that is made of flexible metal-coated polymer bristles. We examine various standard EEG paradigms, such as capturing occipital alpha rhythms, testing for event-related potentials in an auditory oddball paradigm and performing a sensory motor rhythm-based event-related (de-) synchronization paradigm to validate the performance of the novel electrodes in terms of signal quality. Our findings suggest that the dry electrodes that we developed result in high-quality EEG recordings and are thus suitable for a wide range of EEG studies and BCI applications. Furthermore, due to the flexibility of the novel electrodes, greater comfort is achieved in some subjects, this being essential for long-term use.


title: Design of a Compact X-Band Substrate Integrated Waveguide Directional Coupler

abstract: Abstract— This paper presented the design of four-port network directional coupler at X-band frequency ::: (8.2-12.4 GHz) by using substrate integrated waveguide (SIW) technique. SIW appears few years back ::: which provides an excellent platform in order to design millimeter-wave circuits such as filter, antenna, ::: resonator, coupler and power divider. It offers great compensation for smaller size and can be easily ::: integrated with other planar circuits. The fabrication process can simply be done by using standard ::: Printed Circuit Board (PCB) process where the cost of the manufacturing process will be reduced ::: compared to the conventional waveguide. The directional coupler basically implemented at radar, ::: satellite and point-to-point radio. The simulations for this SIW directional coupler design shows good ::: performances with low insertion loss, low return loss, broad operational bandwidth and have high ::: isolation.


title: Probabilistic fibre tract analysis of cytoarchitectonically defined human inferior parietal lobule areas reveals similarities to macaques

abstract: Abstract The human inferior parietal lobule (IPL) is a multimodal brain region, subdivided in several cytoarchitectonic areas which are involved in neural networks related to spatial attention, language, and higher motor processing. Tracer studies in macaques revealed differential connectivity patterns of IPL areas as the respective structural basis. Evidence for comparable differential fibre tracts of human IPL is lacking. Here, anatomical connectivity of five cytoarchitectonic human IPL areas to 64 cortical targets was investigated using probabilistic tractography. Connection likelihood was assessed by evaluating the number of traces between seed and target against the distribution of traces from that seed to voxels in the same distance as the target. The main fibre tract pattern shifted gradually from rostral to caudal IPL: Rostral areas were predominantly connected to somatosensory and superior parietal areas while caudal areas more strongly connected with auditory, anterior temporal and higher visual cortices. All IPL areas were strongly connected with inferior frontal, insular and posterior temporal areas. These results showed striking similarities with connectivity patterns in macaques, providing further evidence for possible homologies between these two species. This shift in fibre tract pattern supports a differential functional involvement of rostral (higher motor functions) and caudal IPL (spatial attention), with probable overlapping language involvement. The differential functional involvement of IPL areas was further supported by hemispheric asymmetries of connection patterns which showed left–right differences especially with regard to connections to sensorimotor, inferior frontal and temporal areas.


title: Relative distance measurement between moving vehicles for manless driving

abstract: This study aims to design a distance warning system to help drive safely and avoid collisions. The system design is made in two stages, i.e. detecting vehicles and estimating the detected vehicles distance. The method used to detect vehicles is Haar method and HOG method. Whereas, the method used to estimate distance is Width-based method. The result showed that Haar method was better in detecting vehicles with the average of TPR by 75%. Based on the result of distance estimation, the value MSE (Mean Squared Error) obtained was 0.333 for van type vehicle.


title: Efficient query answering in probabilistic RDF graphs

abstract: In this paper, we tackle the problem of efficiently answering queries on probabilistic RDF data graphs. Specifically, we model RDF data by probabilistic graphs, and an RDF query is equivalent to a search over subgraphs of probabilistic graphs that have high probabilities to match with a given query graph. To efficiently processqueries on probabilistic RDF graphs, we propose effective pruning mechanisms, structural and probabilistic pruning. For the structural pruning, we carefully design synopses for vertex/edge labels by considering their distributions and other structural information, in order to improve the pruning power. For the probabilistic pruning, we derive a cost model to guide the pre-computation of probability upper bounds such that the query cost is expected to be low. We construct an index structure that integrates synopses/statistics for structural and robabilistic pruning, and propose an efficient approach to answer queries on probabilistic RDF graph data. The efficiency of our solutions has been verified through extensive experiments.


title: Hybrid Corporate Performance Prediction Model Considering Technical Capability

abstract: Many studies have tried to predict corporate performance and stock prices to enhance investment profitability using qualitative approaches such as the Delphi method. However, developments in data processing technology and machine-learning algorithms have resulted in efforts to develop quantitative prediction models in various managerial subject areas. We propose a quantitative corporate performance prediction model that applies the support vector regression (SVR) algorithm to solve the problem of the overfitting of training data and can be applied to regression problems. The proposed model optimizes the SVR training parameters based on the training data, using the genetic algorithm to achieve sustainable predictability in changeable markets and managerial environments. Technology-intensive companies represent an increasing share of the total economy. The performance and stock prices of these companies are affected by their financial standing and their technological capabilities. Therefore, we apply both financial indicators and technical indicators to establish the proposed prediction model. Here, we use time series data, including financial, patent, and corporate performance information of 44 electronic and IT companies. Then, we predict the performance of these companies as an empirical verification of the prediction performance of the proposed model.


title: Mechanisms of Endocytosis

abstract: Endocytic mechanisms control the lipid and protein composition of the plasma membrane, thereby regulating how cells interact with their environments. Here, we review what is known about mammalian endocytic mechanisms, with focus on the cellular proteins that control these events. We discuss the well-studied clathrin-mediated endocytic mechanisms and dissect endocytic pathways that proceed independently of clathrin. These clathrin-independent pathways include the CLIC/GEEC endocytic pathway, arf6-dependent endocytosis, flotillindependent endocytosis, macropinocytosis, circular doral ruffles, phagocytosis, and trans-endocytosis. We also critically review the role of caveolae and caveolin1 in endocytosis. We highlight the roles of lipids, membrane curvature-modulating proteins, small G proteins, actin, and dynamin in endocytic pathways. We discuss the functional relevance of distinct endocytic pathways and emphasize the importance of studying these pathways to understand human disease processes.


title: To Go or Not To Go? A Near Unsupervised Learning Approach For Robot Navigation

abstract: It is important for robots to be able to decide whether they can go through a space or not, as they navigate through a dynamic environment. This capability can help them avoid injury or serious damage, e.g., as a result of running into people and obstacles, getting stuck, or falling off an edge. To this end, we propose an unsupervised and a near-unsupervised method based on Generative Adversarial Networks (GAN) to classify scenarios as traversable or not based on visual data. Our method is inspired by the recent success of data-driven approaches on computer vision problems and anomaly detection, and reduces the need for vast amounts of negative examples at training time. Collecting negative data indicating that a robot should not go through a space is typically hard and dangerous because of collisions, whereas collecting positive data can be automated and done safely based on the robot's own traveling experience. We verify the generality and effectiveness of the proposed approach on a test dataset collected in a previously unseen environment with a mobile robot. Furthermore, we show that our method can be used to build costmaps (we call as "GoNoGo" costmaps) for robot path planning using visual data only.


title: Selective immunolesioning of the basal forebrain cholinergic neurons in rats: effect on attention using the 5-choice serial reaction time task

abstract: RATIONALE: Excitotoxic lesions of the nucleus basalis magnocellularis (nbm) in rats produce deficits in performance of the 5-choice serial reaction time (5CSRT) task, suggesting that basal forebrain cholinergic projections to the neocortex play an important role in visuospatial attention. However, non-selective damage induced by excitotoxins may have confounded the interpretation of the specific contribution of the corticopetal cholinergic neurons of the nbm to attentional processes. OBJECTIVE: The purpose of the present study was to produce selective immunolesions of the cholinergic neurons of the nbm in order to examine more precisely the role of the cholinergic projections of the basal forebrain on attentional performance in a 5CSRT task. METHODS: Rats received bilateral injections of the selective cholinergic immunotoxin 192 IgG-saporin (0.067 microg/ microl, 1 microl) into the nbm after baseline training in the 5CSRT task. Performance of sham and nbm lesion groups was then assessed during baseline and increased task difficulty conditions. RESULTS: Contrary to results previously reported, accuracy of responding and behavioral inhibition were unaffected by the immunotoxin. Rats with nbm lesions showed, however, significant increases in omissions relative to control rats, most markedly during sessions with increased difficulty of signal detection, e.g., decreased stimulus intensity or duration. Magazine and correct latencies were unaffected, suggesting that the lesion-induced omissions were not due to changes in motivation. Omissions were highly correlated with percentage of choline acetylcholine transferase depletion. Reduced premature responses were also observed when the target stimulus was made less predictable. CONCLUSIONS: Although the 192 IgG-saporin lesion produced a different array of behavioral deficits than previously reported, these effects nevertheless are consistent with an important role of the basal forebrain cholinergic system in attentional function, in particular with accurate timing of stimulus presentation and target detection.


title: Intrinsically motivated reinforcement learning

abstract: Psychologists distinguish between extrinsically motivated behavior, which is behavior undertaken to achieve some externally supplied reward, such as a prize, a high grade, or a high-paying job, and intrinsically motivated behavior, which is behavior done for its own sake. Is an analogous distinction meaningful for machine learning systems? Can we say of a machine learning system that it is motivated to learn, and if so, is it possible to provide it with an analog of intrinsic motivation? Despite the fact that a formal distinction between extrinsic and intrinsic motivation is elusive, this chapter argues that the answer to both questions is assuredly “yes” and that the machine learning framework of reinforcement learning is particularly appropriate for bringing learning together with what in animals one would call motivation. Despite the common perception that a reinforcement learning agent’s reward has to be extrinsic because the agent has a distinct input channel for reward signals, reinforcement learning provides a natural framework for incorporating principles of intrinsic motivation.


title: Towards robust distributed systems (abstract)

abstract: Current distributed systems, even the ones that work, tend to be very fragile: they are hard to keep up, hard to manage, hard to grow, hard to evolve, and hard to program. In this talk, I look at several issues in an attempt to clean up the way we think about these systems. These issues include the fault model, high availability, graceful degradation, data consistency, evolution, composition, and autonomy. These are not (yet) provable principles, but merely ways to think about the issues that simplify design in practice. They draw on experience at Berkeley and with giant-scale systems built at Inktomi, including the system that handles 50% of all web searches.


title: Joint Input-Label Embedding for Neural Text Classification

abstract: Neural text classification methods typically treat output classes as categorical labels which lack description and semantics. This leads to an inability to train them well on large label sets or to generalize to unseen labels and makes speed and parameterization dependent on the size of the label set. Joint input-label space methods ameliorate the above issues by exploiting label texts or descriptions, but often at the expense of weak performance on the labels seen frequently during training. In this paper, we propose a label-aware text classification model which addresses these issues without compromising performance on the seen labels. The model consists of a joint input-label multiplicative space and a label-set-size independent classification unit and is trained with cross-entropy loss to optimize accuracy. We evaluate our model on text classification for multilingual news and for biomedical text with a large label set. The label-aware model consistently outperforms both monolingual and multilingual classification models which do not leverage label semantics and previous joint input-label space models.


title: StegoTorus: a camouflage proxy for the Tor anonymity system

abstract: Internet censorship by governments is an increasingly common practice worldwide. Internet users and censors are locked in an arms race: as users find ways to evade censorship schemes, the censors develop countermeasures for the evasion tactics. One of the most popular and effective circumvention tools, Tor, must regularly adjust its network traffic signature to remain usable. We present StegoTorus, a tool that comprehensively disguises Tor from protocol analysis. To foil analysis of packet contents, Tor's traffic is steganographed to resemble an innocuous cover protocol, such as HTTP. To foil analysis at the transport level, the Tor circuit is distributed over many shorter-lived connections with per-packet characteristics that mimic cover-protocol traffic. Our evaluation demonstrates that StegoTorus improves the resilience of Tor to fingerprinting attacks and delivers usable performance.


title: Automatic recommendation of prognosis measures for mechanical components based on massive text mining

abstract: Automatically providing suggestions for predicting the likely status of a mechanical component is a key challenge in a wide variety of industrial domains. Existing solutions based on ontological models have proven to be appropriate for fault diagnosis, but they fail when suggesting activities leading to a successful prognosis of mechanical components. The major reason is that fault prognosis is an activity that, unlike fault diagnosis, involves a lot of uncertainty and it is not always possible to envision a model for predicting possible faults. In this work, we propose a solution based on massive text mining for automatically suggesting prognosis activities concerning mechanical components. The great advantage of text mining is that it is possible to automatically analyze vast amounts of unstructured information in order to find strategies that have been successfully exploited, and formally or informally documented, in the past in any part of the world.


title: Scalable Energy Management for Low Voltage Microgrids Using Multi-Agent Storage System Aggregation

abstract: This paper proposes multi-agent energy storage system aggregation as a means of scaling energy management to low voltage microgrids with distributed energy storage systems. Based on this concept, a hierarchical control strategy is developed for an AC microgrid with distributed battery and ultracapacitor energy storage systems. On the tertiary control level, the energy management problem is made scalable by considering each type of energy storage system in aggregate. This addresses the “curse of dimensionality,” since additional energy storage systems do not increase the optimization problem dimension, and allows nonlinear energy storage models to be used for optimization, accounting for variable efficiency, self-discharge, and lifetime degradation. On the secondary control level, multi-agent state of charge balancing, reactive power sharing, frequency restoration, and voltage restoration are combined, to aggregate energy storage systems for the tertiary control. This includes the novel use of multi-agent sliding mode control for state of charge balancing between AC microgrid energy storage systems. Unlike a linear state of charge balancing strategy, circulating currents are prevented, increasing efficiency and reducing lifetime degradation. An RTDS Technologies real-time digital simulator was used to verify the performance of the proposed control strategy.


title: Curiosity-Driven Reinforcement Learning with Homeostatic Regulation

abstract: We propose a curiosity reward based on information theory principles and consistent with the animal instinct to maintain certain critical parameters within a bounded range. Our experimental validation shows the added value of the additional homeostatic drive to enhance the overall information gain of a reinforcement learning agent interacting with a complex environment using continuous actions. Our method builds upon two ideas: i) To take advantage of a new Bellman-like equation of information gain and ii) to simplify the computation of the local rewards by avoiding the approximation of complex distributions over continuous states and actions.


The Signal-Noise Problem: A Solution for the Case That Signal and Noise Are Gaussian and Independent


title: A modular crawler-driven robot: Mechanical design and preliminary experiments

abstract: This paper presents a tracked robot composed of the proposed crawler mechanism, in which a planetary gear reducer is employed as the transmission device and provides two outputs in different forms with only one actuator. When the crawler moves in a rough environment, collision between mechanism and environment inevitably occurs. This under-actuated crawler can absorb the impact energy that should be transmitted to the actuator. A modular concept for the crawler is proposed for enlarging its use in robot systems and mechanical design of a modular crawler is conducted. Using this crawler module, a four-crawler-driven robot is realized by easily assembling. Experiments are conducted to verify the proposed concept and mechanical design. A single crawler module can well perform the proposed three locomotion modes. The four-crawler-driven robot has good adaptability to the environment which can get over obstacles both passively and actively.


title: Data Mining: An AI Perspective

abstract: Data mining, or knowledge discovery in databases (KDD), is an interdisciplinary area that integrates techniques from several fields including machine learning, statistics, and database systems, for the analysis of large volumes of data. This paper reviews the topics of interest from the IEEE International Conference on Data Mining (ICDM) from an AI perspective. We discuss common topics in data mining and AI, including key AI ideas that have been used in both data mining and machine learning.


title: Open Question Answering with Weakly Supervised Embedding Models

abstract: Building computers able to answer questions on any subject is a long standing goal of artificial intelligence. Promising progress has recently been achieved by methods that learn to map questions to logical forms or database queries. Such approaches can be effective but at the cost of either large amounts of human-labeled data or by defining lexicons and grammars tailored by practitioners. In this paper, we instead take the radical approach of learning to map questions to vectorial feature representations. By mapping answers into the same space one can query any knowledge base independent of its schema, without requiring any grammar or lexicon. Our method is trained with a new optimization procedure combining stochastic gradient descent followed by a fine-tuning step using the weak supervision provided by blending automatically and collaboratively generated resources. We empirically demonstrate that our model can capture meaningful signals from its noisy supervision leading to major improvements over paralex, the only existing method able to be trained on similar weakly labeled data.


title: Power assist method for HAL-3 using EMG-based feedback controller

abstract: We have developed the exoskeletal robotics suite HAL (Hybrid Assistive Leg) which is integrated with human and assists suitable power for lower limb of people with gait disorder. This study proposes the method of assist motion and assist torque to realize a power assist corresponding to the operator's intention. In the method of assist motion, we adopted Phase Sequence control which generates a series of assist motions by transiting some simple basic motions called Phase. We used the feedback controller to adjust the assist torque to maintain myoelectricity signals which were generated while performing the power assist walking. The experiment results showed the effective power assist according to operator's intention by using these control, methods.


title: Predictive analytics with aviation big data

abstract: In this paper, we describe a novel analytics system that enables query processing and predictive analytics over streams of big aviation data. As part of an Internal Research and Development project, Boeing Research and Technology (BR&T) Advanced Air Traffic Management (AATM) built a system that makes predictions based upon descriptive patterns of massive aviation data. Boeing AATM has been receiving live Aircraft Situation Display to Industry (ASDI) data and archiving it for over two years. At the present time, there is not an easy mechanism to perform analytics on the data. The incoming ASDI data is large, compressed, and requires correlation with other flight data before it can be analyzed. The service exposes this data once it has been uncompressed, correlated, and stored in a data warehouse for further analysis using a variety of descriptive, predictive, and possibly prescriptive analytics tools. The service is being built partially in response to requests from Boeing Commercial Aviation (BCA) for analysis of capacity and flow in the US National Airspace System (NAS). The service utilizes a custom tool developed by Embry Riddle Aeronautical University (ERAU) that correlates the raw ASDI feed, IBM Warehouse with DB2 for data management, WebSphere Message Broker for real-time message brokering, SPSS Modeler for statistical analysis, and Cognos BI for front-end business intelligence (BI) visualization tools. This paper describes a scalable service architecture, implementation and value it adds to the aviation domain.


title: CAYLEYNETS: SPECTRAL GRAPH CNNS WITH COMPLEX RATIONAL FILTERS

abstract: The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains. In this paper, we introduce a new spectral domain convolutional architecture for deep learning on graphs. The core ingredient of our model is a new class of parametric rational complex functions (Cayley polynomials) allowing to efficiently compute spectral filters on graphs that specialize on frequency bands of interest. Our model generates rich spectral filters that are localized in space, scales linearly with the size of the input data for sparsely-connected graphs, and can handle different constructions of Laplacian operators. Extensive experimental results show the superior performance of our approach on spectral image classification, community detection, vertex classification and matrix completion tasks.


title: A coronary heart disease prediction model: the Korean Heart Study

abstract: Objective The objectives of this study were to develop a coronary heart disease (CHD) risk model among the Korean Heart Study (KHS) population and compare it with the Framingham CHD risk score. Design A prospective cohort study within a national insurance system. Setting 18 health promotion centres nationwide between 1996 and 2001 in Korea. Participants 268 315 Koreans between the ages of 30 and 74 years without CHD at baseline. Outcome measure Non-fatal or fatal CHD events between 1997 and 2011. During an 11.6-year median follow-up, 2596 CHD events (1903 non-fatal and 693 fatal) occurred in the cohort. The optimal CHD model was created by adding high-density lipoprotein (HDL)-cholesterol, low-density lipoprotein (LDL)-cholesterol and triglycerides to the basic CHD model, evaluating using the area under the receiver operating characteristic curve (ROC) and continuous net reclassification index (NRI). Results The optimal CHD models for men and women included HDL-cholesterol (NRI=0.284) and triglycerides (NRI=0.207) from the basic CHD model, respectively. The discrimination using the CHD model in the Korean cohort was high: the areas under ROC were 0.764 (95% CI 0.752 to 0.774) for men and 0.815 (95% CI 0.795 to 0.835) for women. The Framingham risk function predicted 3–6 times as many CHD events than observed. Recalibration of the Framingham function using the mean values of risk factors and mean CHD incidence rates of the KHS cohort substantially improved the performance of the Framingham functions in the KHS cohort. Conclusions The present study provides the first evidence that the Framingham risk function overestimates the risk of CHD in the Korean population where CHD incidence is low. The Korean CHD risk model is well-calculated alternations which can be used to predict an individual9s risk of CHD and provides a useful guide to identify the groups at high risk for CHD among Koreans.


title: Brain-Inspired Cognitive Model With Attention for Self-Driving Cars

abstract: The perception-driven approach and end-to-end system are two major vision-based frameworks for self-driving cars. However, it is difficult to introduce attention and historical information into the autonomous driving process, which are essential for achieving human-like driving in these two methods. In this paper, we propose a novel model for self-driving cars called the brain-inspired cognitive model with attention. This model comprises three parts: 1) a convolutional neural network for simulating the human visual cortex; 2) a cognitive map to describe the relationships between objects in a complex traffic scene; and 3) a recurrent neural network, which is combined with the real-time updated cognitive map to implement the attention mechanism and long-short term memory. An advantage of our model is that it can accurately solve three tasks simultaneously: 1) detecting the free space and boundaries for the current and adjacent lanes; 2) estimating the distances to obstacles and vehicle attitude; and 3) learning the driving behavior and decision-making process of a human driver. Importantly, the proposed model can accept external navigation instructions during an end-to-end driving process. To evaluate the model, we built a large-scale road-vehicle dataset containing over 40 000 labeled road images captured by three cameras placed on our self-driving car. Moreover, human driving activities and vehicle states were recorded at the same time.


title: Joint Layout Estimation and Global Multi-view Registration for Indoor Reconstruction

abstract: In this paper, we propose a novel method to jointly solve scene layout estimation and global registration problems for accurate indoor 3D reconstruction. Given a sequence of range data, we first build a set of scene fragments using KinectFusion and register them through pose graph optimization. Afterwards, we alternate between layout estimation and layout-based global registration processes in iterative fashion to complement each other. We extract the scene layout through hierarchical agglomerative clustering and energy-based multi-model fitting in consideration of noisy measurements. Having the estimated scene layout in one hand, we register all the range data through the global iterative closest point algorithm where the positions of 3D points that belong to the layout such as walls and a ceiling are constrained to be close to the layout. We experimentally verify the proposed method with the publicly available synthetic and real-world datasets in both quantitative and qualitative ways.


title: Ontology-Based Social Media Analysis for Urban Planning

abstract: This paper reports a social media analysis studyproposed by the Beijing Municipal Institute of Urban Planningand Design. The purpose is to explore techniques that can help theurban planning administrations to improve the social sensing andsocial perception abilities under the evolving data and technologyenvironments. A framework integrating a comprehensive set oftext mining algorithms is presented to conduct topic modeling, text clustering, event evolution detection, sentiment analysis, opinion mining, and information extraction on user-generatedcontents in Chinese social media. A domain ontology of Beijingurban planning is constructed to facilitate the text mining processes. Evaluations on two large, real-world datasets composed ofmicroblogs and WeChat articles about the residential communityand school education in Beijing demonstrate the effectiveness ofour framework. The study illustrates the power of combiningmachine learning with knowledge-based, semantic approaches inanalyzing social media for the domain of interest.


Stochastic versus Robust Optimization for a Transportation Problem


title: A Discriminative Representation of Convolutional Features for Indoor Scene Recognition

abstract: Indoor scene recognition is a multi-faceted and challenging problem due to the diverse intra-class variations and the confusing inter-class similarities that characterize such scenes. This paper presents a novel approach that exploits rich mid-level convolutional features to categorize indoor scenes. Traditional convolutional features retain the global spatial structure, which is a desirable property for general object recognition. We, however, argue that the structure-preserving property of the convolutional neural network activations is not of substantial help in the presence of large variations in scene layouts, e.g., in indoor scenes. We propose to transform the structured convolutional activations to another highly discriminative feature space. The representation in the transformed space not only incorporates the discriminative aspects of the target data set but also encodes the features in terms of the general object categories that are present in indoor scenes. To this end, we introduce a new large-scale data set of 1300 object categories that are commonly present in indoor scenes. Our proposed approach achieves a significant performance boost over the previous state-of-the-art approaches on five major scene classification data sets.


title: Indoor Experiment of SAR Interferometry with 79 GHz MIMO Sensor

abstract: Synthetic Aperture Radar (SAR) has proven to be useful in surveying, and particularly change detection. When used with interferometric techniques, SAR can detect phase-correlated target movement up to a small fraction of carrier wavelength. Studies have shown a huge advantages in urban area surveying using this technique on satellite-borne SAR sensors over conventional surveying [1]. Some on-going research has also shown possible application of Interferometric Ground-based SAR (GBSAR) sensors that works on a linear rail and around X-band to Ku-band [2]. With the recent advancement in autonomous driving, 77–81 GHz MIMO sensors has been developed for vehicles [3]. While these sensors are not originally intended for SAR, it has been shown a decent SAR image can be formed using these sensor with interferometric measurements. This paper reveals the preliminary result of interferometric SAR imaging using these 77–81 GHz MIMO sensors in a confined indoor environment with corner reflectors.


title: Cujo: efficient detection and prevention of drive-by-download attacks

abstract: The JavaScript language is a core component of active and dynamic web content in the Internet today. Besides its great success in enhancing web applications, however, JavaScript provides the basis for so-called drive-by downloads---attacks exploiting vulnerabilities in web browsers and their extensions for unnoticeably downloading malicious software. Due to the diversity and frequent use of obfuscation in these attacks, static code analysis is largely ineffective in practice. While dynamic analysis and honeypots provide means to identify drive-by-download attacks, current approaches induce a significant overhead which renders immediate prevention of attacks intractable. In this paper, we present Cujo, a system for automatic detection and prevention of drive-by-download attacks. Embedded in a web proxy, Cujo transparently inspects web pages and blocks delivery of malicious JavaScript code. Static and dynamic code features are extracted on-the-fly and analysed for malicious patterns using efficient techniques of machine learning. We demonstrate the efficacy of Cujo in different experiments, where it detects 94% of the drive-by downloads with few false alarms and a median run-time of 500 ms per web page---a quality that, to the best of our knowledge, has not been attained in previous work on detection of drive-by-download attacks.


title: The elusive memristor: properties of basic electrical circuits

abstract: We present a tutorial on the properties of the new ideal circuit element, a memristor. By definition, a memristor M relates the charge q and the magnetic flux $\phi$ in a circuit, and complements a resistor R, a capacitor C, and an inductor L as an ingredient of ideal electrical circuits. The properties of these three elements and their circuits are a part of the standard curricula. The existence of the memristor as the fourth ideal circuit element was predicted in 1971 based on symmetry arguments, but was clearly experimentally demonstrated just this year. We present the properties of a single memristor, memristors in series and parallel, as well as ideal memristor-capacitor (MC), memristor-inductor (ML), and memristor-capacitor-inductor (MCL) circuits. We find that the memristor has hysteretic current-voltage characteristics. We show that the ideal MC (ML) circuit undergoes non-exponential charge (current) decay with two time-scales, and that by switching the polarity of the capacitor, an ideal MCL circuit can be tuned from overdamped to underdamped. We present simple models which show that these unusual properties are closely related to the memristor's internal dynamics. This tutorial complements the pedagogy of ideal circuit elements (R,C, and L) and the properties of their circuits.


title: Social Bots: Human-Like by Means of Human Control?

abstract: Social bots are currently regarded an influential but also somewhat mysterious factor in public discourse and opinion making. They are considered to be capable of massively distributing propaganda in social and online media and their application is even suspected to be partly responsible for recent election results. Astonishingly, the term `Social Bot' is not well defined and different scientific disciplines use divergent definitions. This work starts with a balanced definition attempt, before providing an overview of how social bots actually work (taking the example of Twitter) and what their current technical limitations are. Despite recent research progress in Deep Learning and Big Data, there are many activities bots cannot handle well. We then discuss how bot capabilities can be extended and controlled by integrating humans into the process and reason that this is currently the most promising way to go in order to realize effective interactions with other humans.


title: Information centric services in Smart Cities

abstract: HighlightsWe propose a service platform for Smart City based on the emerging ICN paradigm.The platform offers an efficient interface between service and technology layers.We designed the service execution by means of three consecutive phases.A hierarchical and flexible name space is conceived to map a wide range of services.The value of our idea is demonstrated through the description of real use cases. A "Smart City" is intended as an urban environment which, supported by pervasive ICT systems, is able to offer advanced and innovative services to citizens in order to improve the overall quality of their life. In this context, the present contribution formulates a pioneering proposal, by drawing an advanced information centric platform for supporting the typical ICT services of a Smart City. It can easily embrace all available and upcoming wireless technologies, while enforcing, at the same time, ubiquitous and secure applications in many domains, such as, e-government and public administration, intelligent transportation systems, public safety, social, health-care, educational, building and urban planning, environmental, and energy and water management applications. All the details of the proposed approach have been carefully described by means of pragmatical use-cases, such as the management of administrative procedures, the starting of a new business in a given country, the navigation assistance, the signaling of an urban accident aimed at improving the public safety, the reservation of a medical examination, the remote assistance of patients, and the management of waste in a city. This description makes evident the real effectiveness of the present proposal in future urban environments.


title: Consumer acceptance of online banking: an extension of the . . .

abstract: Advances in electronic banking technology have created novel ways of handling daily banking affairs, especially via the online banking channel. The acceptance of online banking services has been rapid in many parts of the world, and in the leading e‐banking countries the number of e‐banking contracts has exceeded 50 percent. Investigates online banking acceptance in the light of the traditional technology acceptance model (TAM), which is leveraged into the online environment. On the basis of a focus group interview with banking professionals, TAM literature and e‐banking studies, we develop a model indicating online‐banking acceptance among private banking customers in Finland. The model was tested with a survey sample (n=268). The findings of the study indicate that perceived usefulness and information on online banking on the Web site were the main factors influencing online‐banking acceptance.


title: A New Approach to Compute CNNs for Extremely Large Images

abstract: CNN (Convolution Neural Network) is widely used in visual analysis and achieves exceptionally high performances in image classification, face detection, object recognition, image recoloring, and other learning jobs. Using deep learning frameworks, such as Torch and Tensorflow, CNN can be efficiently computed by leveraging the power of GPU. However, one drawback of GPU is its limited memory which prohibits us from handling large images. Passing a 4K resolution image to the VGG network will result in an exception of out-of-memory for Titan-X GPU. In this paper, we propose a new approach that adopts the BSP (bulk synchronization parallel) model to compute CNNs for images of any size. Before fed to a specific CNN layer, the image is split into smaller pieces which go through the neural network separately. Then, a specific padding and normalization technique is adopted to merge sub-images back into one image. Our approach can be easily extended to support distributed multi-GPUs. In this paper, we use neural style network as our example to illustrate the effectiveness of our approach. We show that using one Titan-X GPU, we can transfer the style of an image with 10,000×10,000 pixels within 1 minute.


title: Design and simulation of X Band microstrip circulator

abstract: Microwave circulator is an important ferrite device which is widely used in wireless transceivers. This paper presents design and simulation of X-Band microstrip circulator. Major application of microstrip junction circulator presented here is as duplexer in RADAR. The circulator designed here is centered at 9.6 GHz with 800 MHz bandwidth. CST Microwave Studio Suite is used as simulation software. Here, the design equations given by Fay and Comstock are followed and prototype of X-band microstrip circulator is prepared. Yttrium iron garnet (YIG) is used as ferrite material. Isolation and return loss of more than 20 dB, and insertion loss of less than 0.1 dB are achieved in simulation results.


title: Design of a 600 GHz fresnel lens antenna for passive and active imaging

abstract: This paper presents design information and methodology for a high frequency (600 GHz) Fresnel lens that can significantly reduce the thickness and weight of dielectric based lenses for imaging and spectroscopy applications in the submillimeter wave bands.


title: Doppler based speed estimation of vehicles using passive sensor

abstract: This paper aims to develop a system for estimating a vehicle's speed by analyzing its drive by acoustics with a passive audio microphone. Analysis of the vehicle's acoustics would primarily use the phenomenon of Doppler shift, and the instant at which vehicle is at closest-point-of approach. This approach uses a technique called Seam carving to track harmonics formed by vehicle particularly its engine noise. The method proposed is computationally inexpensive and can very easily be developed into mobile application.


title: Parallel Grasp With Path-Relinking For Job Shop Scheduling

abstract: In the job shop scheduling problem (JSP), a finite set of jobs is processed on a finite set of machines under certain constraints, such that the maximum completion time of the jobs is minimized. In this paper, we describe a parallel greedy randomized adaptive search procedure (GRASP) with path-relinking for the JSP. Independent and cooperative parallelization strategies are described and implemented. Computational experience on a large set of standard test problems indicates that the parallel GRASP with path-relinking finds good-quality approximate solutions of the JSP.


title: Deep Spectral Correspondence for Matching Disparate Image Pairs

abstract: A novel, non-learning-based, saliency-aware, shape-cognizant correspondence determination technique is proposed for matching image pairs that are significantly disparate in nature. Images in the real world often exhibit high degrees of variation in scale, orientation, viewpoint, illumination and affine projection parameters, and are often accompanied by the presence of textureless regions and complete or partial occlusion of scene objects. The above conditions confound most correspondence determination techniques by rendering impractical the use of global contour-based descriptors or local pixel-level features for establishing correspondence. The proposed deep spectral correspondence (DSC) determination scheme harnesses the representational power of local feature descriptors to derive a complex high-level global shape representation for matching disparate images. The proposed scheme reasons about correspondence between disparate images using high-level global shape cues derived from low-level local feature descriptors. Consequently, the proposed scheme enjoys the best of both worlds, i.e., a high degree of invariance to affine parameters such as scale, orientation, viewpoint, illumination afforded by the global shape cues and robustness to occlusion provided by the low-level feature descriptors. While the shape-based component within the proposed scheme infers what to look for, an additional saliency-based component dictates where to look at thereby tackling the noisy correspondences arising from the presence of textureless regions and complex backgrounds. In the proposed scheme, a joint image graph is constructed using distances computed between interest points in the appearance (i.e., image) space. Eigenspectral decomposition of the joint image graph allows for reasoning about shape similarity to be performed jointly, in the appearance space and eigenspace.


title: Double Threshold Digraphs

abstract: A semiorder is a model of preference relations where each element $x$ is associated with a utility value $\alpha(x)$, and there is a threshold $t$ such that $y$ is preferred to $x$ iff $\alpha(y)>\alpha(x)+t$. These are motivated by the notion that there is some uncertainty in the utility values we assign an object or that a subject may be unable to distinguish a preference between objects whose values are close. However, they fail to model the well-known phenomenon that preferences are not always transitive. Also, if we are uncertain of the utility values, it is not logical that preference is determined absolutely by a comparison of them with an exact threshold. We propose a new model in which there are two thresholds, $t_1$ and $t_2$; if the difference $\alpha(y) - \alpha(x)$ less than $t_1$, then $y$ is not preferred to $x$; if the difference is greater than $t_2$ then $y$ is preferred to $x$; if it is between $t_1$ and $t_2$, then then $y$ may or may not be preferred to $x$. We call such a relation a double-threshold semiorder, and the corresponding directed graph $G = (V,E)$ a double threshold digraph. Every directed acyclic graph is a double threshold graph; bounds on $t_2/t_1$ give a nested hierarchy of subclasses of the directed acyclic graphs. In this paper we characterize the subclasses in terms of forbidden subgraphs, and give algorithms for finding an assignment of of utility values that explains the relation in terms of a given $(t_1,t_2)$ or else produces a forbidden subgraph, and finding the minimum value $\lambda$ of $t_2/t_1$ that is satisfiable for a given directed acyclic graph. We show that $\lambda$ gives a measure of the complexity of a directed acyclic graph with respect to several optimization problems that are NP-hard on arbitrary directed acyclic graphs.


title: A single-stage isolated bidirectional matrix based AC-DC converter for energy storage

abstract: This paper presents a three-phase single-stage bidirectional isolated matrix based AC-DC converter for energy storage. The matrix (3 × 1) topology directly converts the three-phase line voltages into high-frequency AC voltage which is subsequently, processed using a high-frequency transformer followed by a controlled rectifier. A modified Space Vector Modulation (SVM) based switching scheme is proposed to achieve high input power quality with high power conversion efficiency. Compared to the conventional two stage converter, the proposed converter provides single-stage conversion resulting in higher power conversion efficiency and higher power density. The operating principles of the proposed converter in both AC-DC and DC-AC mode are explained followed by steady state analysis. Simulation results are presented for 230 V, 50 Hz to 48 V isolated bidirectional converter at 2 kW output power to validate the theoretical claims.


title: Geostatistical Modeling of Environmental Sound Propagation

abstract: The atmospheric influence on sound propagation along the ground surface is a critical issue for estimating the noise impact of industrial plants or road networks. Indeed, sound refraction in the surface layer has a dramatic impact on the geographical acoustic exposure. Many analytical and numerical models and studies based on the laws of physics are available in scientific papers whereas very few works in statistical analysis have been attempted. However several important practical issues need to be considered. Among these, time and space representativity of “in situ” measurements, sampling design, influence of meteorological and ground parameters on acoustic exposure show to be a few challenges. They need to be investigated with statistical tools taking into account space and time autocorrelation.


A heuristic approach to determine an appropriate number of topics in topic modeling


title: Trust Networks on the Semantic Web

abstract: The so-called “Web of Trust” is one of the ultimate goals of the Semantic Web. Research on the topic of trust in this domain has focused largely on digital signatures, certificates, and authentication. At the same time, there is a wealth of research into trust and social networks in the physical world. In this paper, we describe an approach for integrating the two to build a web of trust in a more social respect. This paper describes the applicability of social network analysis to the semantic web, particularly discussing the multi-dimensional networks that evolve from ontological trust specifications. As a demonstration of algorithms used to infer trust relationships, we present several tools that allow users to take advantage of trust metrics that use the network.


title: Learning to See by Moving

abstract: The current dominant paradigm for feature learning in computer vision relies on training neural networks for the task of object recognition using millions of hand labelled images. Is it also possible to learn features for a diverse set of visual tasks using any other form of supervision? In biology, living organisms developed the ability of visual perception for the purpose of moving and acting in the world. Drawing inspiration from this observation, in this work we investigated if the awareness of egomotion(i.e. self motion) can be used as a supervisory signal for feature learning. As opposed to the knowledge of class labels, information about egomotion is freely available to mobile agents. We found that using the same number of training images, features learnt using egomotion as supervision compare favourably to features learnt using class-label as supervision on the tasks of scene recognition, object recognition, visual odometry and keypoint matching.


title: Predicting Parameters in Deep Learning

abstract: We demonstrate that there is significant redundancy in the parameterization of several deep learning models. Given only a few weight values for each feature it is possible to accurately predict the remaining values. Moreover, we show that not only can the parameter values be predicted, but many of them need not be learned at all. We train several different architectures by learning only a small number of weights and predicting the rest. In the best case we are able to predict more than 95% of the weights of a network without any drop in accuracy.


title: A Taxonomy of Clutter Reduction for Information Visualisation

abstract: Information visualisation is about gaining insight into data through a visual representation. This data is often multivariate and increasingly, the datasets are very large. To help us explore all this data, numerous visualisation applications, both commercial and research prototypes, have been designed using a variety of techniques and algorithms. Whether they are dedicated to geo-spatial data or skewed hierarchical data, most of the visualisations need to adopt strategies for dealing with overcrowded displays, brought about by too much data to fit in too small a display space. This paper analyses a large number of these clutter reduction methods, classifying them both in terms of how they deal with clutter reduction and more importantly, in terms of the benefits and losses. The aim of the resulting taxonomy is to act as a guide to match techniques to problems where different criteria may have different importance, and more importantly as a means to critique and hence develop existing and new techniques.


title: Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM

abstract: This paper investigates the convergence properties and consistency of extended Kalman filter (EKF) based simultaneous localization and mapping (SLAM) algorithms. Proofs of convergence are provided for the nonlinear two-dimensional SLAM problem with point landmarks observed using a range-and-bearing sensor. It is shown that the robot orientation uncertainty at the instant when landmarks are first observed has a significant effect on the limit and/or the lower bound of the uncertainties of the landmark position estimates. This paper also provides some insights to the inconsistencies of EKF based SLAM that have been recently observed. The fundamental cause of EKF SLAM inconsistency for two basic scenarios are clearly stated and associated theoretical proofs are provided.


title: A robust and imperceptible steganography technique for SD and HD videos

abstract: In this era of internet transfer of information is in digital form using multimedia files such as image, video, audio, etc. which relies on secure communication techniques to convey information safely. Due to the frequent transfer of videos over the internet nowadays they have become a good cover media for secure and covert communication in the form of video steganography. For efficient video steganography, it must fulfill its basic requirements such as capacity, imperceptibility, and robustness. In order to make a balance between imperceptibility and robustness, an efficient video steganography scheme is proposed for Standard Definition (SD) and High Definition (HD) videos. This scheme employs DWT (discrete wavelet transforms) for embedding the secret message inside the video frames utilizing only luminance (Y) component, and the security of the proposed scheme is strengthen by pre-processing the secret message with encryption before embedding. The embedding process is done by utilizing the middle-frequency sub-bands after applying second level 2-D DWT to the video frames to decompose it into 16 sub-bands. The performance of the proposed scheme is tested on different videos with quality metrics including peak signal to noise ratio (PSNR), structural similarity (SSIM) index, bit error rate (BER) and also by applying Gaussian and salt & pepper noise attacks. Moreover, the scheme is tested for the different level of compression on stego-video and also compared with U and V components used while embedding. Experimental results show that for both types of videos (HD and SD) the proposed scheme is able to achieve high imperceptibility. Further, it also provides robustness against different types of noise attacks and different compression levels which makes the proposed scheme evident for secure data transmission.


An Algebraic Method For Public-Key Cryptography


title: A model of Korean sentence similarity measurement using sense-based morpheme embedding and RNN sentence encoding

abstract: Sentence similarity measurement is an important technology that can be apply to various applications in the natural language processing. Recently, an encoder-decoder model using recurrent neural network (RNN) has achieved remarkable results. This paper proposes a model for measuring Korean sentence similarity based on sense-based morpheme embedding and gated recurrent units (GRU) encoder. We evaluate the measurement model consist of experimentally optimized morpheme embedding and sentence encoding models. In the measurement of sentence similarity, the proposed model encoded using the pre-trained morpheme embedding improves the performance compared with the character-embedding model. In addition, it can be used effectively in the question and answering (Q&A) system.


title: Towards Practical Attacks on Argon2i and Balloon Hashing

abstract: The algorithm Argon2i-B of Biryukov, Dinu and Khovratovich is currently being considered by the IRTF (Internet Research Task Force) as a new de-facto standard for password hashing. An older version (Argon2i-A) of the same algorithm was chosen as the winner of the recent Password Hashing Competition. An important competitor to Argon2i-B is the recently introduced Balloon Hashing (BH) algorithm of Corrigan-Gibs, Boneh and Schechter. A key security desiderata for any such algorithm is that evaluating it (even using a custom device) requires a large amount of memory amortized across multiple instances. Alwen and Blocki (CRYPTO 2016) introduced a class of theoretical attacks against Argon2i-A and BH. While these attacks yield large asymptotic reductions in the amount of memory, it was not, a priori, clear if (1) they can be extended to the newer Argon2i-B, (2) the attacks are effective on any algorithm for practical parameter ranges (e.g., 1GB of memory) and (3) if they can be effectively instantiated against any algorithm under realistic hardware constrains. In this work we answer all three of these questions in the affirmative for all three algorithms. This is also the first work to analyze the security of Argon2i-B. In more detail, we extend the theoretical attacks of Alwen and Blocki (CRYPTO 2016) to the recent Argon2i-B proposal demonstrating severe asymptotic deficiencies in its security. Next we introduce several novel heuristics for improving the attack's concrete memory efficiency even when on-chip memory bandwidth is bounded. We then simulate our attacks on randomly sampled Argon2i-A, Argon2i-B and BH instances and measure the resulting memory consumption for various practical parameter ranges and for a variety of upperbounds on the amount of parallelism available to the attacker. Finally we describe, implement, and test a new heuristic for applying the Alwen-Blocki attack to functions employing a technique developed by Corrigan-Gibs et al. for improving concrete security of memory-hard functions. We analyze the collected data and show the effects various parameters have on the memory consumption of the attack. In particular, we can draw several interesting conclusions about the level of security provided by these functions. ● For the Alwen-Blocki attack to fail against practical memory parameters, Argon2i-B must be instantiated with more than 10 passes on memory — beyond the "paranoid" parameter setting in the current IRTF proposal. ● The technique of Corrigan-Gibs for improving security can also be overcome by the Alwen-Blocki attack under realistic hardware constraints. ● On a positive note, both the asymptotic and concrete security of Argon2i-B seem to improve on that of Argon2i-A.


title: Nioh: Hardening The Hypervisor by Filtering Illegal I/O Requests to Virtual Devices

abstract: Vulnerabilities in hypervisors are crucial in multi-tenant clouds since they can undermine the security of all virtual machines (VMs) consolidated on a vulnerable hypervisor. Unfortunately, 107 vulnerabilitiesin KVM+QEMU and 38 vulnerabilities in Xen have been reported in 2016. The device-emulation layer in hypervisors is a hotbed of vulnerabilities because the code for virtualizing devices is complicated and requires knowledge on the device internals. We propose a "device request filter", called Nioh, that raises the bar for attackers to exploit the vulnerabilities in hypervisors. The key insight behind Nioh is that malicious I/O requests attempt to exploit vulnerabilities and violate device specifications in many cases. Nioh inspects I/O requests from VMs and rejects those that do not conform to a device specification. A device specification is modeled as a device automaton in Nioh, an extended automaton to facilitate the description of device specifications. The software framework is also provided to encapsulate the interactions between the device request filter and the underlying hypervisors. The results of our attack evaluation suggests that Nioh can defend against attacks that exploit vulnerabilities in device emulation, i.e., CVE-2015-5158, CVE-2016-1568, CVE-2016-4439, and CVE-2016-7909. This paper shows that the notorious VENOM attack can be detected and rejected by using Nioh.


title: Citalopram therapy for depression: a review of 10 years of European experience and data from U.S. clinical trials.

abstract: Background: This review summarizes and evaluates clinical experience with citalopram, the latest selective serotonin reuptake inhibitor (SSRI) to be approved for the treatment of depression in the United States. Data Sources: Published reports of randomized, double-blind, controlled clinical studies of citalopram were retrieved using a MEDLINE literature search. Search terms included citalopram, SSRI, TCA (tricylic antidepressant), depression, and clinical. For each study, data on antidepressant efficacy and adverse events were evaluated. Pharmacokinetic studies and case reports were reviewed to supplement the evaluation of citalopram's safety and tolerability. Data presented at major medical conferences and published in abstract form also were reviewed. Study Findings: Thirty randomized, double-blind, controlled studies of the antidepressant efficacy of citalopram were located and reviewed. In 11 studies, citalopram was compared with placebo (1 of these studies also included comparison with another SSRI). In 4 additional studies, the efficacy of citalopram in preventing depression relapse or recurrence was investigated. In another 11 studies (including I meta-analysis of published and unpublished trials), citalopram was compared with tricyclic and tetracyclic antidepressants. Finally, results are available from 4 studies in which citalopram was compared with other SSRIs. A placebo-controlled study of citalopram for the treatment of panic disorder was reviewed for data on long-term adverse events. Conclusion: Data published over the last decade suggest that citalopram is (1) superior to placebo in the treatment of depression, (2) has efficacy similar to that of the tricyclic and tetracyclic antidepressants and to other SSRIs, and (3) is safe and well tolerated in the therapeutic dose range of 20 to 60 mg/day. Distinct from some other agents in its class, citalopram exhibits linear pharmacokinetics and minimal drug interaction potential. These features make citalopram an attractive agent for the treatment of depression, especially among the elderly and patients with comorbid illness.


title: Swarm Intelligence: Past, Present and Future

abstract: Many optimization problems in science and engineering are challenging to solve, and the current trend is to use swarm intelligence (SI) and SI-based algorithms to tackle such challenging problems. Some significant developments have been made in recent years, though there are still many open problems in this area. This paper provides a short but timely analysis about SI-based algorithms and their links with self-organization. Different characteristics and properties are analyzed here from both mathematical and qualitative perspectives. Future research directions are outlined and open questions are also highlighted.


title: CaPaR: A Career Path Recommendation Framework

abstract: In today's world, recommendation systems are used to solve the problem of information overload in many areas allowing users to focus on important information based on their interests. One of the areas where such systems can play a major role is in helping students achieve their career goals by generating personalized job and skill recommendations. At present, there are many job posting websites providing a huge amount of information and students need to spend hours to find jobs that match their interests. At the same time, existing job recommendation systems only consider the user's field of interest, but do not take into consideration the user's profile and skills, which can generate more relevant career recommendations for users. In this work, we propose CaPaR, a Career Path Recommendation framework, which addresses such shortcomings. Using text mining and collaborative filtering techniques the system first scans the user's profile and resume, identifies the key skills of the candidate and generates personalized job recommendations. Moreover, the system recommends additional skills to students required for related job openings, as well as learning resources for each skill. In this way, the system not only allows its users to explore large amounts of information, but also expand their portfolio and resume to be able to advance their careers further. We experiment and evaluate the various recommendation algorithms with real-world data collected from the San Jose State University career center web site.


title: A framework for rendering complex scattering effects on hair

abstract: The appearance of hair plays a critical role in synthesizing realistic looking human characters. However, due to the high complexity in hair geometry and the scattering nature of hair fibers, rendering hair with photorealistic quality and at interactive speeds remains as an open problem in computer graphics. Previous approaches attempt to simplify the scattering model to only tackle a specific aspect of the scattering effects. In this paper, we present a new approach to simultaneously render complex scattering effects including volumetric shadows, transparency, and antialiasing under a unified framework. Our solution uses a shadow-ray path to produce volumetric self-shadows and an additional view-ray path to produce transparency. To compute and accumulate the contribution of individual hair fibers along each (shadow or view) path, we develop a new GPU-based k-buffer technique that can efficiently locate the K nearest scattering locations and combine them in the correct order. Compared with existing multi-layer based approaches[Kim and Neumann 2001; Yuksel and Keyser 2008; Sintorn and Assarsson 2009], we show that our k-buffer solution can more accurately reproduce the shadowing and transparency effects. Further, we present an anti-aliasing scheme that directly builds upon the k-buffer. We implement all three effects (volumetric shadows, transparency, and anti-aliasing) under a unified rendering pipeline. Experiments on complex hair models demonstrate that our new solution produces near photorealistic hair rendering at very interactive speed.


title: Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization

abstract: We consider the problem of optimizing the sum of a smooth convex function and a non-smooth convex function using proximal-gradient methods, where an error is present in the calculation of the gradient of the smooth term or in the proximity operator with respect to the non-smooth term. We show that both the basic proximal-gradient method and the accelerated proximal-gradient method achieve the same convergence rate as in the error-free case, provided that the errors decrease at appropriate rates.Using these rates, we perform as well as or better than a carefully chosen fixed error level on a set of structured sparsity problems.


title: Phospho-specific flow cytometry in drug discovery

abstract: Here we present phospho-specific flow cytometry as a new tool for drug discovery with applications throughout the drug development pipeline, from target identification to library screening, disease model assessment and clinical screening and diagnostics. The single cell, multiparameter nature of flow cytometry generates high-content datasets, and current improvements in the technology are rapidly increasing its high-throughput capacity, making it a valuable platform in modern drug discovery.


title: An intrusion detection system against malicious attacks on the communication network of driverless cars

abstract: Vehicular ad hoc networking (VANET) have become a significant technology in the current years because of the emerging generation of self-driving cars such as Google driverless cars. VANET have more vulnerabilities compared to other networks such as wired networks, because these networks are an autonomous collection of mobile vehicles and there is no fixed security infrastructure, no high dynamic topology and the open wireless medium makes them more vulnerable to attacks. It is important to design new approaches and mechanisms to rise the security these networks and protect them from attacks. In this paper, we design an intrusion detection mechanism for the VANETs using Artificial Neural Networks (ANNs) to detect Denial of Service (DoS) attacks. The main role of IDS is to detect the attack using a data generated from the network behavior such as a trace file. The IDSs use the features extracted from the trace file as auditable data. In this paper, we propose anomaly and misuse detection to detect the malicious attack.


title: An overview of the HDF5 technology suite and its applications

abstract: In this paper, we give an overview of the HDF5 technology suite and some of its applications. We discuss the HDF5 data model, the HDF5 software architecture and some of its performance enhancing capabilities.


title: Rehearsal in spatial working memory

abstract: This article reports 3 experiments that tested a hypothesis regarding the nature of rehearsal in spatial working memory, one in which discrete shifts of spatial selective attention mediate the maintenance of location-specific representations. Experiment 1 demonstrated increases in visual processing efficiency for locations held in working memory, which suggested that attention was oriented toward these locations. Experiment 2 eliminated key alternative explanations for Experiment 1 by using an identical stimulus display with a nonspatial memory task, and little or no facilitation of processing at memorized locations was found under these conditions. Finally, Experiment 3 showed that spatial working memory was impaired when participants were hindered in their ability to attend to memorized locations. It is argued that these results implicate selective spatial attention as a rehearsal mechanism for spatial working memory. Much of the research concerning spatial working memory has focused on its independence from other working memory systems. For instance, the influential model of working memory introduced by Baddeley and Hitch (1974) emphasizes the separability of visuospatial and verbal storage mechanisms. Consistent with this model, there is now a substantial body of behavioral and neuropsychological evidence supporting a functional dissociation between spatial and verbal working memory systems (for a review, see


title: Audio Word2Vec: Unsupervised Learning of Audio Segment Representations using Sequence-to-sequence Autoencoder

abstract: The vector representations of fixed dimensionality for words (in text) offered by Word2Vec have been shown to be very useful in many application scenarios, in particular due to the semantic information they carry. This paper proposes a parallel version, the Audio Word2Vec. It offers the vector representations of fixed dimensionality for variable-length audio segments. These vector representations are shown to describe the sequential phonetic structures of the audio segments to a good degree, with very attractive real world applications such as query-by-example Spoken Term Detection (STD). In this STD application, the proposed approach significantly outperformed the conventional Dynamic Time Warping (DTW) based approaches at significantly lower computation requirements. We propose unsupervised learning of Audio Word2Vec from audio data without human annotation using Sequence-to-sequence Audoencoder (SA). SA consists of two RNNs equipped with Long Short-Term Memory (LSTM) units: the first RNN (encoder) maps the input audio sequence into a vector representation of fixed dimensionality, and the second RNN (decoder) maps the representation back to the input audio sequence. The two RNNs are jointly trained by minimizing the reconstruction error. Denoising Sequence-to-sequence Autoencoder (DSA) is furthered proposed offering more robust learning.


Collision and Proximity Queries


title: The 3M syndrome.

abstract: 3M syndrome (MIM 273750) is an autosomal recessive disorder characterized by pre- and post-natal growth retardation ( Uptill now, mutations in either CUL7 or OBSL1 genes have been identified in this rare disorder. There are no clinical or radiological differences between patients with CUL7 or OBSL1 mutations. CUL7 appears to be the major gene responsible for 3M syndrome accounting for 77.5% of cases while OBSL1 mutations accounts for 16.3%. A few patients have no mutations in these genes suggesting the involvement of a third gene.


Assessment of FY 3 A and FY 3 B / MWHS observations


Molecular Graph Encoding Convolutional Neural Networks for Automatic Chemical Feature Extraction


title: Financial Stock Market Forecast using Data Mining Techniques

abstract: The automated computer programs using data mining and predictive technologies do a fare amount of trades in the markets. Data mining is well founded on the theory that the historic data holds the essential memory for predicting the future direction. This technology is designed to help investors discover hidden patterns from the historic data that have probable predictive capability in their investment decisions. The prediction of stock markets is regarded as a challenging task of financial time series prediction. Data analysis is one way of predicting if future stocks prices will increase or decrease. Five methods of analyzing stocks were combined to predict if the day's closing price would increase or decrease. These methods were Typical Price (TP), Bollinger Bands, Relative Strength Index (RSI), CMI and Moving Average (MA). This paper discussed various techniques which are able to predict with future closing stock price will increase or decrease better than level of significance. Also, it investigated various global events and their issues predicting on stock markets. It supports numerically and graphically.


title: Fuzzy Local Binary Patterns for Ultrasound Texture Characterization

abstract: B-scan ultrasound provides a non-invasive low-cost imaging solution to primary care diagnostics. The inherent speckle noise in the images produced by this technique introduces uncertainty in the representation of their textural characteristics. To cope with the uncertainty, we propose a novel fuzzy feature extraction method to encode local texture. The proposed method extends the Local Binary Pattern (LBP) approach by incorporating fuzzy logic in the representation of local patterns of texture in ultrasound images. Fuzzification allows a Fuzzy Local Binary Pattern (FLBP) to contribute to more than a single bin in the distribution of the LBP values used as a feature vector. The proposed FLBP approach was experimentally evaluated for supervised classification of nodular and normal samples from thyroid ultrasound images. The results validate its effectiveness over LBP and other common feature extraction methods.


title: Robust Intensity-Based Localization Method for Autonomous Driving on Snow–Wet Road Surface

abstract: Autonomous vehicles are being developed rapidly in recent years. In advance implementation stages, many particular problems must be solved to bring this technology into the market place. This paper focuses on the problem of driving in snow and wet road surface environments. First, the quality of laser imaging detection and ranging (LIDAR) reflectivity decreases on wet road surfaces. Therefore, an accumulation strategy is designed to increase the density of online LIDAR images. In order to enhance the texture of the accumulated images, principal component analysis is used to understand the geometrical structures and texture patterns in the map images. The LIDAR images are then reconstructed using the leading principal components with respect to the variance distribution accounted by each eigenvector. Second, the appearance of snow lines deforms the expected road context in LIDAR images. Accordingly, the edge profiles of the LIDAR and map images are extracted to encode the lane lines and roadside edges. Edge matching between the two profiles is then calculated to improve localization in the lateral direction. The proposed method has been tested and evaluated using real data that are collected during the winter of 2016–2017 in Suzu and Kanazawa, Japan. The experimental results show that the proposed method increases the robustness of autonomous driving on wet road surfaces, provides a stable performance in laterally localizing the vehicle in the presence of snow lines, and significantly reduces the overall localization error at a speed of 60 km/h.


title: An overview of Multimodal Sentiment Analysis research: Opportunities and Difficulties

abstract: The scatter form of multimedia data such as text, image, audio, and video posted regularly in the social media may contain useful information for the organizations. But, this information should be derived with the use of some form of analysis known as Multimodal Sentiment Analysis (MSA). But, there is a lack of proper analytic tools for such analysis. This paper presents a thorough overview of more than fifty most recent MSA research articles to find the gaps in terms of tasks, approaches theories and applications used till date. There seems to be no single approach, theory, and tool which can support MSA. The study showed that each and every mode presents different difficulties which have not bee n fully solved yet, such as feature points of a face, voice clarity in audio, video summarization and so on, and are great research opportunities for the future researchers. Also, this research recommends a list of existing and upcoming difficulties and opportunities of MSA research.


Forward and inverse problems in the mechanics of soft filaments


title: Multi-vendor penetration testing in the advanced metering infrastructure

abstract: The advanced metering infrastructure (AMI) is revolutionizing electrical grids. Intelligent AMI "smart meters" report real time usage data that enables efficient energy generation and use. However, aggressive deployments are outpacing security efforts: new devices from a dizzying array of vendors are being introduced into grids with little or no understanding of the security problems they represent. In this paper we develop an archetypal attack tree approach to guide penetration testing across multiple-vendor implementations of a technology class. In this, we graft archetypal attack trees modeling broad adversary goals and attack vectors to vendor-specific concrete attack trees. Evaluators then use the grafted trees as a roadmap to penetration testing. We apply this approach within AMI to model attacker goals such as energy fraud and denial of service. Our experiments with multiple vendors generate real attack scenarios using vulnerabilities identified during directed penetration testing, e.g., manipulation of energy usage data, spoofing meters, and extracting sensitive data from internal registers. More broadly, we show how we can reuse efforts in penetration testing to efficiently evaluate the increasingly large body of AMI technologies being deployed in the field.


title: Urban building damage detection from very high resolution imagery using one-class SVM and spatial relations

abstract: In this paper, we propose a method for urban building damage detection from multitemporal high resolution images using spectral and spatial information combined. Given the spectral similarity between damaged and undamaged areas in the images, two spatial features are used in the damage detection, i.e. invariant moments and LISA (local indicator of spatial association) index. These two spatial features were computed for each image object, which is produced by image segmentation. The One-Class Support Vector Machine (OCSVM), a recently developed one-class classifier was used to classify the multitemporal data to obtain building damage information. The uses of spectral data alone and plus obtained spatial features for building damage detection were separately evaluated using bitemporal Quickbird images acquired in Dujiangyan area of China, which was heavily hit by the Wenchuan earthquake. The results show that the combined use of spectral and spatial features significantly improved the damage detection accuracy, compared to that of using spectral information alone.


A Guided Genetic Algorithm for the Planning in Lunar Lander Games


title: Iris Recognition with a Database of Iris Images Obtained in Visible Light Using Smartphone Camera

abstract: This paper delivers a new database of iris images collected in visible light using a mobile phone's camera and presents results of experiments involving existing commercial and open-source iris recognition methods, namely: Iri-Core, VeriEye, MIRLIN and OSIRIS. Several important observations are made. First, we manage to show that after simple preprocessing, such images offer good visibility of iris texture even in heavily-pigmented irides. Second, for all four methods, the enrollment stage is not much affected by the fact that different type of data is used as input. This translates to zero or close-to-zero Failure To Enroll, i.e., cases when templates could not be extracted from the samples. Third, we achieved good matching accuracy, with correct genuine match rate exceeding 94.5% for all four methods, while simultaneously being able to maintain zero false match rate in every case. Correct genuine match rate of over 99.5% was achieved using one of the commercial methods, showing that such images can be used with the existing biometric solutions with minimum additional effort required. Finally, the experiments revealed that incorrect image segmentation is the most prevalent cause of recognition accuracy decrease. To our best knowledge, this is the first database of iris images captured using a mobile device, in which image quality exceeds this of a near-infrared illuminated iris images, as defined in ISO/IEC 19794-6 and 29794-6 documents. This database will be publicly available to all researchers.


title: Understanding User Behavior Through Log Data and Analysis

abstract: HCI researchers are increasingly collecting rich behavioral traces of user interactions with online systems in situ at a scale not previously possible. These logs can be used to characterize user interactions with existing systems and compare different designs. Large-scale log studies give rise to new challenges in experimental design, data collection and interpretation, and ethics. The chapter discusses how to address these challenges using search engine logs, but the methods are applicable to other types of log data.


title: A Zero-Voltage transition full bridge DC-DC converter for photovoltaic applications

abstract: The paper focuses on a high efficiency Zero Voltage Transition (ZVT) DC-DC full-bridge converter using a high-frequency transformer. A novel structure with recovery of energy on load side is proposed. Such solution is particularly suitable for photovoltaic applications which require low losses and cannot recovery energy on source side. Moreover, a suitable architecture of snubber circuit that permits to improve significantly the efficiency is discussed. Analysis of its operating modes and a comparison of the conversion efficiencies with conventional ZVT-PWM converters are given. Design example of a 750 W 100 kHz DC-DC converter and different PSPICE simulation tests are provided.


title: GraphX: a resilient distributed graph system on Spark

abstract: From social networks to targeted advertising, big graphs capture the structure in data and are central to recent advances in machine learning and data mining. Unfortunately, directly applying existing data-parallel tools to graph computation tasks can be cumbersome and inefficient. The need for intuitive, scalable tools for graph computation has lead to the development of new graph-parallel systems (e.g., Pregel, PowerGraph) which are designed to efficiently execute graph algorithms. Unfortunately, these new graph-parallel systems do not address the challenges of graph construction and transformation which are often just as problematic as the subsequent computation. Furthermore, existing graph-parallel systems provide limited fault-tolerance and support for interactive data mining. We introduce GraphX, which combines the advantages of both data-parallel and graph-parallel systems by efficiently expressing graph computation within the Spark data-parallel framework. We leverage new ideas in distributed graph representation to efficiently distribute graphs as tabular data-structures. Similarly, we leverage advances in data-flow systems to exploit in-memory computation and fault-tolerance. We provide powerful new operations to simplify graph construction and transformation. Using these primitives we implement the PowerGraph and Pregel abstractions in less than 20 lines of code. Finally, by exploiting the Scala foundation of Spark, we enable users to interactively load, transform, and compute on massive graphs.


title: The Review of Management Accounting

abstract: Recently management accounting has become a popular topic in the field of accounting, this article traces back to the origin of management accounting and illustrates the history of management accounting. Four stages of managerial accounting's evolution defined by The International Federation of Accountants (IFAC) are also explained. After describing the history of management accounting, various definitions are given by different authoritative organization such as American Accounting Association (AAA), the Institute of Management Accountants (IMA) and so on. Exploring the scope of management accounting can deepen the comprehension about the definition. Consequently, the large scope emphasizes the important status of management accounting. However, there is more area about management accounting can be researched and discussed. In the near future, management accounting will develop rapidly and prosperously with no doubt.


title: Hemizygous deletions on chromosome 1p21.3 involving the DPYD gene in individuals with autism spectrum disorder

abstract: We describe the identification and clinical presentation of four individuals from three unrelated families with hemizygous deletions involving the DPYD gene at chromosome 1p21.3. DPYD encodes dihydropyrimidine dehydrogenase, which is the initial and rate-limiting enzyme in the catabolism of pyrimidine bases. All four individuals described met diagnostic criteria for autism spectrum disorder with severe speech delay. Patient 1's deletion was originally reported in 2008, and more detailed clinical information is provided. Subsequently, this male individual was found to have a missense mutation in the X-linked PTCHD1 autism susceptibility gene, which may also contribute to the phenotype. Patients 2 and 3 are siblings with a novel deletion encompassing the DPYD gene. In their mother, the genomic region deleted from chromosome 1p21.3 was inserted into chromosome 10. A fourth proband had a novel 10-kb intragenic deletion of exon 6 of the DPYD gene detected on a higher resolution microarray. Our study suggests that hemizygous deletions involving the DPYD locus present with variable phenotypes which can include speech delay and autistic features, and may also be influenced by additional mutations in other genes, issues which need to be considered in genetic counseling.


title: Risk-Based Attack Surface Approximation: How Much Data Is Enough?

abstract: Proactive security reviews and test efforts are a necessary component of the software development lifecycle. Resource limitations often preclude reviewing the entire code base. Making informed decisions on what code to review can improve a team's ability to find and remove vulnerabilities. Risk-based attack surface approximation (RASA) is a technique that uses crash dump stack traces to predict what code may contain exploitable vulnerabilities. The goal of this research is to help software development teams prioritize security efforts by the efficient development of a risk-based attack surface approximation. We explore the use of RASA using Mozilla Firefox and Microsoft Windows stack traces from crash dumps. We create RASA at the file level for Firefox, in which the 15.8% of the files that were part of the approximation contained 73.6% of the vulnerabilities seen for the product. We also explore the effect of random sampling of crashes on the approximation, as it may be impractical for organizations to store and process every crash received. We find that 10-fold random sampling of crashes at a rate of 10% resulted in 3% less vulnerabilities identified than using the entire set of stack traces for Mozilla Firefox. Sampling crashes in Windows 8.1 at a rate of 40% resulted in insignificant differences in vulnerability and file coverage as compared to a rate of 100%.


title: Efficient Locking Techniques for Databases on Modern Hardware

abstract: Traditional database systems are driven by the assumption that disk I/O is the primary bottleneck, overshadowing all other costs. However, future database systems will be dominated by many-core processors, large main memory, and low-latency semiconductor mass storage. In the increasingly common case that the working data set fits in memory or low-latency storage, new bottlenecks emerge: locking, latching, logging, and critical sections in the buffer manager. Prior work has addressed two of these – latching and logging. This paper addresses locking and proposes new mechanisms optimized for modern hardware. We devised new algorithms and methods to improve all components of database locking, including key range locking, intent locks, detection and recovery from deadlocks, and early lock release. Most of the techniques are easily applicable to other database systems. Experiments with Shore-MT, the transaction processing engine we used as the implementation basis, show throughput improvement by factors of 5 to 50.