title: GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models

abstract: Modeling and generating graphs is fundamental for studying networks in biology, engineering, and social sciences. However, modeling complex distributions over graphs and then efficiently sampling from these distributions is challenging due to the non-unique, high-dimensional nature of graphs and the complex, non-local dependencies that exist between edges in a given graph. Here we propose GraphRNN, a deep autoregressive model that addresses the above challenges and approximates any distribution of graphs with minimal assumptions about their structure. GraphRNN learns to generate graphs by training on a representative set of graphs and decomposes the graph generation process into a sequence of node and edge formations, conditioned on the graph structure generated so far. ::: In order to quantitatively evaluate the performance of GraphRNN, we introduce a benchmark suite of datasets, baselines and novel evaluation metrics based on Maximum Mean Discrepancy, which measure distances between sets of graphs. Our experiments show that GraphRNN significantly outperforms all baselines, learning to generate diverse graphs that match the structural characteristics of a target set, while also scaling to graphs 50 times larger than previous deep models.


title: A Tri-Partite Neural Document Language Model for Semantic Information Retrieval

abstract: Previous work in information retrieval have shown that using evidence, such as concepts and relations, from external knowledge sources could enhance the retrieval performance. Recently, deep neural approaches have emerged as state-of-the art models for capturing word semantics. This paper presents a new tri-partite neural document language framework that leverages explicit knowledge to jointly constrain word, concept, and document learning representations to tackle a number of issues including polysemy and granularity mismatch. We show the effectiveness of the framework in various IR tasks.


title: Past Visions of Artificial Futures: One Hundred and Fifty Years under the Spectre of Evolving Machines

abstract: The influence of Artificial Intelligence (AI) and Artificial Life (ALife) technologies upon society, and their potential to fundamentally shape the future evolution of humankind, are topics very mu...


title: Anatomy of GPU Memory System for Multi-Application Execution

abstract: As GPUs make headway in the computing landscape spanning mobile platforms, supercomputers, cloud and virtual desktop platforms, supporting concurrent execution of multiple applications in GPUs becomes essential for unlocking their full potential. However, unlike CPUs, multi-application execution in GPUs is little explored. In this paper, we study the memory system of GPUs in a concurrently executing multi-application environment. We first present an analytical performance model for many-threaded architectures and show that the common use of misses-per-kilo-instruction (MPKI) as a proxy for performance is not accurate without considering the bandwidth usage of applications. We characterize the memory interference of applications and discuss the limitations of existing memory schedulers in mitigating this interference. We extend the analytical model to multiple applications and identify the key metrics to control various performance metrics. We conduct extensive simulations using an enhanced version of GPGPU-Sim targeted for concurrently executing multiple applications, and show that memory scheduling decisions based on MPKI and bandwidth information are more effective in enhancing throughput compared to the traditional FR-FCFS and the recently proposed RR FR-FCFS policies.


title: Like It or Not: A Survey of Twitter Sentiment Analysis Methods

abstract: Sentiment analysis in Twitter is a field that has recently attracted research interest. Twitter is one of the most popular microblog platforms on which users can publish their thoughts and opinions. Sentiment analysis in Twitter tackles the problem of analyzing the tweets in terms of the opinion they express. This survey provides an overview of the topic by investigating and briefly describing the algorithms that have been proposed for sentiment analysis in Twitter. The presented studies are categorized according to the approach they follow. In addition, we discuss fields related to sentiment analysis in Twitter including Twitter opinion retrieval, tracking sentiments over time, irony detection, emotion detection, and tweet sentiment quantification, tasks that have recently attracted increasing attention. Resources that have been used in the Twitter sentiment analysis literature are also briefly presented. The main contributions of this survey include the presentation of the proposed approaches for sentiment analysis in Twitter, their categorization according to the technique they use, and the discussion of recent research trends of the topic and its related fields.


title: Anisotropic Diffusion-based Kernel Matrix Model for Face Liveness Detection

abstract: Facial recognition and verification is a widely used biometric technology in security system. Unfortunately, face biometrics is vulnerable to spoofing attacks using photographs or videos. In this paper, we present an anisotropic diffusion-based kernel matrix model (ADKMM) for face liveness detection to prevent face spoofing attacks. We use the anisotropic diffusion to enhance the edges and boundary locations of a face image, and the kernel matrix model to extract face image features which we call the diffusion-kernel (D-K) features. The D-K features reflect the inner correlation of the face image sequence. We introduce convolution neural networks to extract the deep features, and then, employ a generalized multiple kernel learning method to fuse the D-K features and the deep features to achieve better performance. Our experimental evaluation on the two publicly available datasets shows that the proposed method outperforms the state-of-art face liveness detection methods.


Global Optimization Using Interval Analysis : The One-Dimensional Case


title: Shot Orientation Controls for Interactive Cinematography with 360 Video

abstract: Virtual reality filmmakers creating 360-degree video currently rely on cinematography techniques that were developed for traditional narrow field of view film. They typically edit together a sequence of shots so that they appear at a fixed-orientation irrespective of the viewer's field of view. But because viewers set their own camera orientation they may miss important story content while looking in the wrong direction. We present new interactive shot orientation techniques that are designed to help viewers see all of the important content in 360-degree video stories. Our viewpoint-oriented technique reorients the shot at each cut so that the most important content lies in the the viewer's current field of view. Our active reorientation technique, lets the viewer press a button to immediately reorient the shot so that important content lies in their field of view. We present a 360-degree video player which implements these techniques and conduct a user study which finds that users spend 5.2-9.5% more time viewing the important points (manually labelled) of the scene with our techniques compared to the traditional fixed-orientation cuts. In practice, 360-degree video creators may label important content, but we also provide an automatic method for determining important content in existing 360-degree videos.


title: Overview of the TREC 2005 Enterprise Track

abstract: The goal of the enterprise track is to conduct experiments with enterprise data — intranet pages, email archives, document repositories — that reflect the experiences of users in real organisations, such that for example, an email ranking technique that is effective here would be a good choice for deployment in a real multi-user email search application. This involves both understanding user needs in enterprise search and development of appropriate IR techniques.


title: Active torque control of electric power steering system using composite nonlinear feedback control

abstract: Electric power steering (EPS) system plays an important role in assisting the driver to achieve better handling, steering feel and response in securing vehicle stability. In EPS control system, controlling the torque of the EPS motor is crucial where tracking of the robust command of motor angle to generate desired assist torque is very challenging. In this paper, a reference model is adopted to generate an ideal motor angle that can guarantee the desired performance and composite nonlinear feedback (CNF) control is applied to achieve a faster transient response in tracking the robust reference motor angle. The H2 state optimization is used to obtain the linear control parameters whereas Hooke-Jeeves auto-tuning algorithm is used to fine-tune the nonlinear control parameters to achieve the best transient performance. The proposed control strategy is validated with CarSim Software by interfacing Simulink model with CarSim full nonlinear vehicle model under different test procedures and compared to other controllers based on ITAE performance criteria.


title: Technological Paradigms and Technological Trajectories : A Suggested Interpretation of the Determinants and the Directions of Technical Change : Research Policy

abstract: Similarities between technological research and science have been cited in past research, indicating that both technological research paradigms and basic scientific paradigms share many commonalities. Past research has sought to determine the causal relationship between a firm's technological progress and economic success. The current research seeks to define the directionality of this relationship, as well as the significance of innovative activities upon a firm's performance. Considering the impact of both institutional and economic factors, several "technological paradigms" within firms are presented, as are "technological trajectories" which explain the progressive paths for each paradigm. In contrast to current unidirectional models of the innovative process, the research seeks to present a more broadly applicable model for determining advantageous technological paths and innovative processes within firms. Based upon the presented theorized relationships and paradigms, several implications are considered, including differentiating between the development of new technological patterns and their incorporation within an industry. Also considered are "Schumpeterian" phases within industries, and how those phases relate to the incorporation of new technologies within established industries. The findings of the study are discussed in terms of the implications of the paradigms and trajectories upon technical advancement, as well as the potential impact of these relationships upon shaping or contradicting existing economic theories and public policies. (AKP)


title: A Supervised Learning Framework for Modeling Director Agent Strategies in Educational Interactive Narrative

abstract: Computational models of interactive narrative offer significant potential for creating educational game experiences that are procedurally tailored to individual players and support learning. A key challenge posed by interactive narrative is devising effective director agent models that dynamically sequence story events according to players' actions and needs. In this paper, we describe a supervised machine-learning framework to model director agent strategies in an educational interactive narrative Crystal Island. Findings from two studies with human participants are reported. The first study utilized a Wizard-of-Oz paradigm where human “wizards” directed participants through Crystal Island's mystery storyline by dynamically controlling narrative events in the game environment. Interaction logs yielded training data for machine learning the conditional probabilities of a dynamic Bayesian network (DBN) model of the human wizards' directorial actions. Results indicate that the DBN model achieved significantly higher precision and recall than naive Bayes and bigram model techniques. In the second study, the DBN director agent model was incorporated into the runtime version of Crystal Island, and its impact on students' narrative-centered learning experiences was investigated. Results indicate that machine-learning director agent strategies from human demonstrations yield models that positively shape players' narrative-centered learning and problem-solving experiences.


title: Effect of substitution of concentrate mix with Sesbania sesban on feed intake, digestibility, body weight change, and carcass parameters of Arsi-Bale sheep fed a basal diet of native grass hay

abstract: An experiment was conducted to assess the effect of substitution of concentrate mix with Sesbania sesban on feed intake, digestibility, average daily gain (ADG), and carcass parameters of Arsi-Bale sheep. The experiment employed 25 male sheep with mean (±standard error) initial body live weight (BLW) of 19.1 ± 0.09 kg. The experiment consisted of 7 days of digestibility and 90 days of feeding trials followed by carcass evaluation. The experiment employed a randomized complete block design with five treatments and five blocks. Treatments comprised of grass hay alone fed ad libitum (GHA; control), GHA + 100 % concentrate mix (CM) consisting of wheat bran and noug seed cake at a ratio of 2:1 (0 S. sesban), GHA + 67 % CM + 33 % S. sesban (33 S. sesban), GHA + 33 % CM + 67 % S. sesban (67 S. sesban), and GHA + 100 % S. sesban (100 S. sesban). Total dry matter intake (DMI) was higher (p < 0.001) for sheep in 0 S. sesban-100 S. sesban (800-821 g/day) compared to sheep in control (611 g/day). However, the effect of S. sesban inclusion (0 S. sesban-100 S. sesban) on total DMI was quadratic, and DMI declined after 67 S. sesban. Digestibility of DM, organic matter (p < 0.01), and crude protein were higher (p < 0.001) in supplemented group compared to the control. ADG, feed conversion efficiency (ADG/DMI), slaughter BLW, hot carcass weight, and total edible offals were higher (p < 0.05-0.001) for sheep in 0 S. sesban-100 S. sesban than those in control. Increased level of S. sesban inclusion, in general, reduced growth and carcass parameters in this study. However, there was no difference between 0 S. sesban and 33 S. sesban in most parameters studied. Thus, it can be concluded that S. sesban could substitute a concentrate when it accounted for up to 33 % of the mix.


title: Accelerating Strassen-Winograd's matrix multiplication algorithm on GPUs

abstract: In this paper, we report on the development of an efficient GPU implementation of the Strassen-Winograd matrix multiplication algorithm for matrices of arbitrary sizes. We utilize multi-kernel streaming to exploit concurrency across sub-matrix operations in addition to intra-operation parallelism. We evaluate the performance of the implementation in comparison with CUBLAS-5.0 on Fermi and Kepler GPUs. The experimental results demonstrate the usefulness of Strassen's algorithm for practically relevant matrix sizes on GPUs, with up to 1.27X speedup for single-precision and 1.42X speedup for double-precision floating point computation.


title: Stock trend prediction using regression analysis–a data mining approach

abstract: Organizations have been collecting data for decades, building massive data warehouses in which to store the data. Even though this data is available, very few of these organizations have been able to realize the actual value stored in it. The question these organizations are asking is how to extract meaningful data and uncover patterns and relationship from their databases. This paper presents a study of regression analysis for use in stock price prediction. Data were obtained from the daily official list of the prices of all shares traded on the stock exchange published by the Nigerian Stock Exchange using banking sector of Nigerian economy with three banks namely:- First Bank of Nigeria Plc, Zenith Bank Plc, and Skye Bank Plc to build a database. A data mining software tool was used to uncover patterns and relationships and also to extract values of variables from the database to predict the future values of other variables through the use of time series data that employed moving average method. The tools were found capable technique to describe the trends of stock market prices and predict the future stock market prices of three banks sampled.


title: A fully-synthesizable 0.6V digital LDO with dual-loop control using digital standard cells

abstract: A fully synthesizable digital low drop-out regulator (LDO) with dual (coarse/fine) loops control is proposed. This structure employs a synchronized comparator to compare the feedback voltage (Vfb) and the desired reference voltage (Vref). A coarse loop is used to simplify the control logic circuit and increase the LDO response speed during the output dynamic transit. A fine loop is used to minimize the output ripple voltage of the LDO in the steady-state. A dynamic detection circuit using dual offset comparators is employed to detect the output voltage variation. To minimize the current consumption, the switching frequency is scaled to 1/32 of original frequency in steady-state. The circuit is implemented by purely digital standard cells. Simulation results at 130nm CMOS technology shows that this structure can achieve a peak current efficiency of 99.9% using a 1 MHz clock and a 0.6 V supply.


title: Q-L/MRP: a buffer management mechanism for QoS support in a multimedia DBMS

abstract: Multimedia database systems (MMDBSs) have to be capable of efficiently handing time-dependent and time-independent data, and of supporting quality of service (QoS). To support continuous playout of time-dependent data, reservations of the resource-limited disk I/O bandwidth and network bandwidth have to be combined with appropriate buffer management. Based on the special requirements of the DEDICATION (DatabasE support for DIstance eduCATION) pre-study, i.e. building a cheap prototype system for asynchronous distance education, we have designed the buffer management mechanism Q-L/MRP (QoS-Least/Most Relevant for Presentation). Q-L/MRP is a buffer preloading and page replacement mechanism for multimedia applications with heterogeneous QoS requirements. Q-L/MRP extends L/MRP with two features: (1) it supports multiple concurrent users, and (2) it supports QoS with a dynamic prefetching daemon. This daemon is able to dynamically adapt to the changes in network and disk I/O load. Furthermore, QoS requirements from the users, such as the frame rate, are mapped into the buffer mechanism. A performance analysis shows that Q-L/MRP is very suitable for the special environment in DEDICATION and outperforms other buffer management mechanisms. Since we have implemented Q-L/MRP in software only, it is also suitable for other multimedia applications on other systems with different hardware configurations and workloads.


title: MMH: Software Message Authentication in the Gbit/second Rates

abstract: We describe a construction of almost universal hash functions suitable for very fast software implementation and applicable to the hashing of variable size data and fast cryptographic message authentication. Our construction uses fast single precision arithmetic which is increasingly supported by modern processors due to the growing needs for fast arithmetic posed by multimedia applications.


Improving graph drawing readability by incorporating readability metrics: A software tool for network analysts


title: Dous et al. Knowledge Management Capabilities in CRM Knowledge Management Capabilities in CRM: Making Knowledge For, From and About Customers Work ABSTRACT

abstract: In recent years, companies have integrated their customer relationship management (CRM) and knowledge management (KM) efforts because they realize that KM plays a key role in CRM success. In this article we present the findings of a survey of customer-related KM initiatives' status quo within organizations as the first step of a two-stage research approach. In a second step we build on the survey's findings by presenting a cross-case analysis. The case studies that we conducted can be considered good practices that enhance CRM initiatives by applying knowledge for, from or about customers. Based on the case study analysis, we derive a conceptual framework for successfully implementing knowledge-based CRM initiatives in practice. Apart from these practical recommendations, we also discuss further research issues in the domain of customer knowledge management.


title: Predicting the Optimal Spacing of Study: A Multiscale Context Model of Memory

abstract: When individuals learn facts (e.g., foreign language vocabulary) over multiple study sessions, the temporal spacing of study has a significant impact on memory retention. Behavioral experiments have shown a nonmonotonic relationship between spacing and retention: short or long intervals between study sessions yield lower cued-recall accuracy than intermediate intervals. Appropriate spacing of study can double retention on educationally relevant time scales. We introduce a Multiscale Context Model (MCM) that is able to predict the influence of a particular study schedule on retention for specific material. MCM's prediction is based on empirical data characterizing forgetting of the material following a single study session. MCM is a synthesis of two existing memory models (Staddon, Chelaru, & Higa, 2002; Raaijmakers, 2003). On the surface, these models are unrelated and incompatible, but we show they share a core feature that allows them to be integrated. MCM can determine study schedules that maximize the durability of learning, and has implications for education and training. MCM can be cast either as a neural network with inputs that fluctuate over time, or as a cascade of leaky integrators. MCM is intriguingly similar to a Bayesian multiscale model of memory (Kording, Tenenbaum, & Shadmehr, 2007), yet MCM is better able to account for human declarative memory.


title: Towards a Robust Solution to People Counting

abstract: Estimating the number of persons in a public place provides useful information for video-based surveillance and monitoring applications. In the case of oblique camera setup, counting is either achieved by detecting individuals or by statistically establishing relations between values of simple image features (e.g. amount of moving pixels, edge density, etc.) to the number of people. While the methods of the first category exhibit poor accuracy in cases of occlusions, the second category of methods are sensitive to perspective distortions, and require people to move in order to be counted. In this paper we investigate the possibilities of developing a robust statistical method for people counting. To maximize its applicability scope, we choose ? in contrast to the majority of existing methods from this category ? not to require prior learning of categories corresponding to different number of people. Second, we search for a suitable way of correcting the perspective distortion. Finally, we link the estimation to a confidence value that takes into account the known factors being of influence on the result. The confidence is then used to refine final results.


title: Tools to Support Systematic Literature Reviews in Software Engineering: A Mapping Study

abstract: Background: Systematic literature reviews (SLRs) have become an established methodology in software engineering (SE) research however they can be very time consuming and error prone. Aim: The aims of this study are to identify and classify tools that can help to automate part or all of the SLR process within the SE domain. Method: A mapping study was performed using an automated search strategy plus snowballing to locate relevant papers. A set of known papers was used to validate the search string. Results: 14 papers were accepted into the final set. Eight presented text mining tools and six discussed the use of visualisation techniques. The stage most commonly targeted was study selection. Only two papers reported an independent evaluation of the tool presented. The majority were evaluated through small experiments and examples of their use. Conclusions: A variety of tools are available to support the SLR process although many are in the early stages of development and usage.


title: 3D Urban Scene Modeling Integrating Recognition and Reconstruction

abstract: Supplying realistically textured 3D city models at ground level promises to be useful for pre-visualizing upcoming traffic situations in car navigation systems. Because this pre-visualization can be rendered from the expected future viewpoints of the driver, the required maneuver will be more easily understandable. 3D city models can be reconstructed from the imagery recorded by surveying vehicles. The vastness of image material gathered by these vehicles, however, puts extreme demands on vision algorithms to ensure their practical usability. Algorithms need to be as fast as possible and should result in compact, memory efficient 3D city models for future ease of distribution and visualization. For the considered application, these are not contradictory demands. Simplified geometry assumptions can speed up vision algorithms while automatically guaranteeing compact geometry models. In this paper, we present a novel city modeling framework which builds upon this philosophy to create 3D content at high speed. ::: ::: Objects in the environment, such as cars and pedestrians, may however disturb the reconstruction, as they violate the simplified geometry assumptions, leading to visually unpleasant artifacts and degrading the visual realism of the resulting 3D city model. Unfortunately, such objects are prevalent in urban scenes. We therefore extend the reconstruction framework by integrating it with an object recognition module that automatically detects cars in the input video streams and localizes them in 3D. The two components of our system are tightly integrated and benefit from each other's continuous input. 3D reconstruction delivers geometric scene context, which greatly helps improve detection precision. The detected car locations, on the other hand, are used to instantiate virtual placeholder models which augment the visual realism of the reconstructed city model.


title: Design and Sensorless Control of a Novel Axial-Flux Permanent Magnet Machine for In-Wheel Applications

abstract: This paper proposes an axial-flux-modulation permanent magnet (PM) machine and its sensorless control strategy for in-wheel application in electrical vehicles. A Vernier structure is integrated with the axial-flux PM machine to include the magnetic gear effect and improve output torque. The sensorless control strategy of the proposed machine, including initial rotor position estimation and rotating position estimation, is proposed for flux-modulation motors in this paper. The initial rotor position estimation is based on the technique of rectangular pulse voltage injection and the rotating rotor position estimation is based on the sliding mode observer (SMO). The saturation effect on inductances, which is the theoretical basis of the rectangular pulse voltage injection, makes the stator parameter variation in different loads and affects the SMO estimation. To overcome this problem, a novel online parameter self-adjustment procedure for the SMO is introduced. The machine design and its sensorless control performance are verified by simulation and prototype experiments.


The Plinius ontology of ceramic materials


title: Unsupervised P2P Rental Recommendations via Integer Programming

abstract: Due to the sparseness of quality rating data, unsupervised recommender systems are used in many applications in Peer to Peer (P2P) rental marketplaces such as Airbnb, FlipKey, and HomeAway. We present an integer programming based recommender systems, where both accommodation benefits and community risks of lodging places are measured and incorporated into an objective function as utility measurements. More specifically, we first present an unsupervised fused scoring method for quantifying the accommodation benefits and community risks of a lodging with crowd-sourced geo-tagged data. In order to the utility of recommendations, we formulate the unsupervised P2P rental recommendations as a constrained integer programming problem, where the accommodation benefits of recommendations are maximized and the community risks of recommendations are minimized, while maintaining constraints on personalization. Furthermore, we provide an efficient solution for the optimization problem by developing a learning-to-integer-programming method for combining aggregated listwise learning to rank into branching variable selection. We apply the proposed approach to the Airbnb data of New York City and provide lodging recommendations to travelers. In our empirical experiments, we demonstrate both the efficiency and effectiveness of our method in terms of striving a trade-off between the user satisfaction, time on market, and the number of reviews, and achieving a balance between positive and negative sides.


title: A robust pose graph approach for city scale LiDAR mapping

abstract: This paper presents a method for reconstructing globally consistent 3D High-Definition (HD) maps at city scale. Current approaches for eliminating cumulative drift are mainly based on the pose graph optimization under the constraint of scan-matching factors. The misaligned edges in the graph may have negative impacts on the results. To address this problem and further handle inconsistency caused by multi-task acquisitions in urban environments, we introduce a refined structure of the factor graph considering systematical initialization bias, where the scan-matching factors are twice validated through a novel classifier and a robust optimization strategy. In addition, we incorporate a multi-hypothesis extended Kalman filter (MH-EKF) to remove dynamic objects. Quantitative experimental results demonstrate that the proposed method outperforms state-of-the-art techniques in terms of map quality.


title: Quantitative Projection Coverage for Testing ML-enabled Autonomous Systems

abstract: Systematically testing models learned from neural networks remains a crucial unsolved barrier to successfully justify safety for autonomous vehicles engineered using data-driven approach. We propose quantitative k-projection coverage as a metric to mediate combinatorial explosion while guiding the data sampling process. By assuming that domain experts propose largely independent environment conditions and by associating elements in each condition with weights, the product of these conditions forms scenarios, and one may interpret weights associated with each equivalence class as relative importance. Achieving full k-projection coverage requires that the data set, when being projected to the hyperplane formed by arbitrarily selected k-conditions, covers each class with number of data points no less than the associated weight. For the general case where scenario composition is constrained by rules, precisely computing k-projection coverage remains in NP. In terms of finding minimum test cases to achieve full coverage, we present theoretical complexity for important sub-cases and an encoding to 0-1 integer programming. We have implemented a research prototype that generates test cases for a visual object defection unit in automated driving, demonstrating the technological feasibility of our proposed coverage criterion.


title: 3D Printed Flexure Hinges for Soft Monolithic Prosthetic Fingers

abstract: Abstract Mechanical compliance is one of the primary properties of structures in nature playing a key role in their efficiency. This study investigates a number of commonly used flexure hinges to determine a flexure hinge morphology, which generates large displacements under a lowest possible force input. The aim of this is to design a soft and monolithic robotic finger. Fused deposition modeling, a low-cost 3D printing technique, was used to fabricate the flexure hinges and the soft monolithic robotic fingers. Experimental and finite element analyses suggest that a nonsymmetric elliptical flexure hinge is the most suitable type for use in the soft monolithic robotic finger. Having estimated the effective elastic modulus, flexion of the soft monolithic robotic fingers was simulated and this showed a good correlation with the actual experimental results. The soft monolithic robotic fingers can be employed to handle objects with unknown shapes and are also potential low-cost candidates for establishing soft...


DIFFERENT APPROACHES TO DISCOVER THE REPETITIVE PART OF THE BANANA NUCLEAR GENOME


title: uTrack: 3D input using two magnetic sensors

abstract: While much progress has been made in wearable computing in recent years, input techniques remain a key challenge. In this paper, we introduce uTrack, a technique to convert the thumb and fingers into a 3D input system using magnetic field (MF) sensing. A user wears a pair of magnetometers on the back of their fingers and a permanent magnet affixed to the back of the thumb. By moving the thumb across the fingers, we obtain a continuous input stream that can be used for 3D pointing. Specifically, our novel algorithm calculates the magnet's 3D position and tilt angle directly from the sensor readings. We evaluated uTrack as an input device, showing an average tracking accuracy of 4.84 mm in 3D space - sufficient for subtle interaction. We also demonstrate a real-time prototype and example applications allowing users to interact with the computer using 3D finger input.


title: Deeper, Broader and Artier Domain Generalization

abstract: The problem of domain generalization is to learn from multiple training domains, and extract a domain-agnostic model that can then be applied to an unseen domain. Domain generalization (DG) has a clear motivation in contexts where there are target domains with distinct characteristics, yet sparse data for training. For example recognition in sketch images, which are distinctly more abstract and rarer than photos. Nevertheless, DG methods have primarily been evaluated on photo-only benchmarks focusing on alleviating the dataset bias where both problems of domain distinctiveness and data sparsity can be minimal. We argue that these benchmarks are overly straightforward, and show that simple deep learning baselines perform surprisingly well on them. In this paper, we make two main contributions: Firstly, we build upon the favorable domain shift-robust properties of deep learning methods, and develop a low-rank parameterized CNN model for end-to-end DG learning. Secondly, we develop a DG benchmark dataset covering photo, sketch, cartoon and painting domains. This is both more practically relevant, and harder (bigger domain shift) than existing benchmarks. The results show that our method outperforms existing DG alternatives, and our dataset provides a more significant DG challenge to drive future research.


title: Online Credit Card Fraud Detection: A Hybrid Framework with Big Data Technologies

abstract: In this paper, we focus on designing an online credit card fraud detection framework with big data technologies, by which we want to achieve three major goals: 1) the ability to fuse multiple detection models to improve accuracy, 2) the ability to process large amount of data and 3) the ability to do the detection in real time. To accomplish that, we propose a general workflow, which satisfies most design ideas of current credit card fraud detection systems. We further implement the workflow with a new framework which consists of four layers: distributed storage layer, batch training layer, key-value sharing layer and streaming detection layer. With the four layers, we are able to support massive trading data storage, fast detection model training, quick model data sharing and real-time online fraud detection, respectively. We implement it with latest big data technologies like Hadoop, Spark, Storm, HBase, etc. A prototype is implemented and tested with a synthetic dataset, which shows great potentials of achieving the above goals.


title: Towards Semi-Supervised Learning for Deep Semantic Role Labeling

abstract: Neural models have shown several state-of-the-art performances on Semantic Role Labeling (SRL). However, the neural models require an immense amount of semantic-role corpora and are thus not well suited for low-resource languages or domains. The paper proposes a semi-supervised semantic role labeling method that outperforms the state-of-the-art in limited SRL training corpora. The method is based on explicitly enforcing syntactic constraints by augmenting the training objective with a syntactic-inconsistency loss component and uses SRL-unlabeled instances to train a joint-objective LSTM. On CoNLL-2012 English section, the proposed semi-supervised training with 1%, 10% SRL-labeled data and varying amounts of SRL-unlabeled data achieves +1.58, +0.78 F1, respectively, over the pre-trained models that were trained on SOTA architecture with ELMo on the same SRL-labeled data. Additionally, by using the syntactic-inconsistency loss on inference time, the proposed model achieves +3.67, +2.1 F1 over pre-trained model on 1%, 10% SRL-labeled data, respectively.


title: Adaptive Deep Brain Stimulation In Advanced Parkinson Disease

abstract: Brain-computer interfaces (BCIs) could potentially be used to interact with pathological brain signals to intervene and ameliorate their effects in disease states. Here, we provide proof-of-principle of this approach by using a BCI to interpret pathological brain activity in patients with advanced Parkinson disease (PD) and to use this feedback to control when therapeutic deep brain stimulation (DBS) is delivered. Our goal was to demonstrate that by personalizing and optimizing stimulation in real time, we could improve on both the efficacy and efficiency of conventional continuous DBS. We tested BCI-controlled adaptive DBS (aDBS) of the subthalamic nucleus in 8 PD patients. Feedback was provided by processing of the local field potentials recorded directly from the stimulation electrodes. The results were compared to no stimulation, conventional continuous stimulation (cDBS), and random intermittent stimulation. Both unblinded and blinded clinical assessments of motor effect were performed using the Unified Parkinson's Disease Rating Scale. Motor scores improved by 66% (unblinded) and 50% (blinded) during aDBS, which were 29% (p = 0.03) and 27% (p = 0.005) better than cDBS, respectively. These improvements were achieved with a 56% reduction in stimulation time compared to cDBS, and a corresponding reduction in energy requirements (p < 0.001). aDBS was also more effective than no stimulation and random intermittent stimulation. BCI-controlled DBS is tractable and can be more efficient and efficacious than conventional continuous neuromodulation for PD. Copyright © 2013 American Neurological Association.


title: 3D MRI brain tumor segmentation using autoencoder regularization

abstract: Automated segmentation of brain tumors from 3D magnetic resonance images (MRIs) is necessary for the diagnosis, monitoring, and treatment planning of the disease. Manual delineation practices require anatomical knowledge, are expensive, time consuming and can be inaccurate due to human error. Here, we describe a semantic segmentation network for tumor subregion segmentation from 3D MRIs based on encoder-decoder architecture. Due to a limited training dataset size, a variational auto-encoder branch is added to reconstruct the input image itself in order to regularize the shared decoder and impose additional constraints on its layers. The current approach won 1st place in the BraTS 2018 challenge.


title: Compact Generalized Non-local Network

abstract: The non-local module is designed for capturing long-range spatio-temporal dependencies in images and videos. Although having shown excellent performance, it lacks the mechanism to model the interactions between positions across channels, which are of vital importance in recognizing fine-grained objects and actions. To address this limitation, we generalize the non-local module and take the correlations between the positions of any two channels into account. This extension utilizes the compact representation for multiple kernel functions with Taylor expansion that makes the generalized non-local module in a fast and low-complexity computation flow. Moreover, we implement our generalized non-local method within channel groups to ease the optimization. Experimental results illustrate the clear-cut improvements and practical applicability of the generalized non-local module on both fine-grained object recognition and video classification. Code is available at: https://github.com/KaiyuYue/cgnl-network.pytorch.


Selective Harmonic Elimination Technique for a Cascaded Multilevel Inverter Using Intelligent Systems


title: Unsupervised feature selection for multi-cluster data

abstract: In many data analysis tasks, one is often confronted with very high dimensional data. Feature selection techniques are designed to find the relevant feature subset of the original features which can facilitate clustering, classification and retrieval. In this paper, we consider the feature selection problem in unsupervised learning scenario, which is particularly difficult due to the absence of class labels that would guide the search for relevant information. The feature selection problem is essentially a combinatorial optimization problem which is computationally expensive. Traditional unsupervised feature selection methods address this issue by selecting the top ranked features based on certain scores computed independently for each feature. These approaches neglect the possible correlation between different features and thus can not produce an optimal feature subset. Inspired from the recent developments on manifold learning and L1-regularized models for subset selection, we propose in this paper a new approach, called Multi-Cluster Feature Selection (MCFS), for unsupervised feature selection. Specifically, we select those features such that the multi-cluster structure of the data can be best preserved. The corresponding optimization problem can be efficiently solved since it only involves a sparse eigen-problem and a L1-regularized least squares problem. Extensive experimental results over various real-life data sets have demonstrated the superiority of the proposed algorithm.


title: Assigning AS relationships to satisfy the Gao-Rexford conditions

abstract: Compliance with the Gao-Rexford conditions [1] is perhaps the most realistic explanation of Internet routing stability, although BGP is renowned to be prone to oscillations. Informally, the Gao-Rexford conditions assume that (i) the business relationships between Internet Service Providers (ISPs) yield a hierarchy, (ii) each ISP behaves in a rational way, i.e., it does not offer transit to other ISPs for free, and (iii) each ISP ranks routes through customers better than routes through providers and peers.


title: Robust moving objects detection in lidar data exploiting visual cues

abstract: Detecting moving objects in dynamic scenes from sequences of lidar scans is an important task in object tracking, mapping, localization, and navigation. Many works focus on changes detection in previously observed scenes, while a very limited amount of literature addresses moving objects detection. The state-of-the-art method exploits Dempster-Shafer Theory to evaluate the occupancy of a lidar scan and to discriminate points belonging to the static scene from moving ones. In this paper we improve both speed and accuracy of this method by discretizing the occupancy representation, and by removing false positives through visual cues. Many false positives lying on the ground plane are also removed thanks to a novel ground plane removal algorithm. Efficiency is improved through an octree indexing strategy. Experimental evaluation against the KITTI public dataset shows the effectiveness of our approach, both qualitatively and quantitatively with respect to the state- of-the-art.


title: Convolutional neural networks for lung cancer screening in computed tomography (CT) scans

abstract: Diagnosis and cure of cancer has been one of the biggest challenges faced by mankind in the last few decades. Early detection of cancer would facilitate in saving millions of lives across the globe every year. This paper presents an approach which uses a Convolutional Neural Network (CNNs) to classify tumours seen in lung cancer screening computed tomography scans as malignant or benign. CNNs have special properties such as spatial invariance, and allow for multiple feature extraction. When such layers are cascaded, leading to Deep CNNs, it has been shown widely that the accuracy of prediction increases dramatically. In this work, we have designed a CNN suitable for the analysis of CT scans with tumours, using domain knowledge from both medicine and neural networks. The results show that the accuracy of classification for our network performs better than both the traidtional neural networks, and also existing CNNs built for image classification purposes.


Adaptive Spammer Detection with Sparse Group Modeling.


title: Computing Reeb Graphs as a Union of Contour Trees

abstract: The Reeb graph of a scalar function tracks the evolution of the topology of its level sets. This paper describes a fast algorithm to compute the Reeb graph of a piecewise-linear (PL) function defined over manifolds and non-manifolds. The key idea in the proposed approach is to maximally leverage the efficient contour tree algorithm to compute the Reeb graph. The algorithm proceeds by dividing the input into a set of subvolumes that have loop-free Reeb graphs using the join tree of the scalar function and computes the Reeb graph by combining the contour trees of all the subvolumes. Since the key ingredient of this method is a series of union-find operations, the algorithm is fast in practice. Experimental results demonstrate that it outperforms current generic algorithms by a factor of up to two orders of magnitude, and has a performance on par with algorithms that are catered to restricted classes of input. The algorithm also extends to handle large data that do not fit in memory.


title: Pessimistic Uplift Modeling

abstract: Uplift modeling is a machine learning technique that aims to model treatment effects heterogeneity. It has been used in business and health sectors to predict the effect of a specific action on a given individual. Despite its advantages, uplift models show high sensitivity to noise and disturbance, which leads to unreliable results. In this paper we show different approaches to address the problem of uplift modeling, we demonstrate how disturbance in data can affect uplift measurement. We propose a new approach, we call it Pessimistic Uplift Modeling, that minimizes disturbance effects. We compared our approach with the existing uplift methods, on simulated and real data-sets. The experiments show that our approach outperforms the existing approaches, especially in the case of high noise data environment.


title: Learning Distributed Representations for Structured Output Prediction

abstract: In recent years, distributed representations of inputs have led to performance gains in many applications by allowing statistical information to be shared across inputs. However, the predicted outputs (labels, and more generally structures) are still treated as discrete objects even though outputs are often not discrete units of meaning. In this paper, we present a new formulation for structured prediction where we represent individual labels in a structure as dense vectors and allow semantically similar labels to share parameters. We extend this representation to larger structures by defining compositionality using tensor products to give a natural generalization of standard structured prediction approaches. We define a learning objective for jointly learning the model parameters and the label vectors and propose an alternating minimization algorithm for learning. We show that our formulation outperforms structural SVM baselines in two tasks: multiclass document classification and part-of-speech tagging.


Grounded Action Transformation for Robot Learning in Simulation.


title: Hand posture recognition based on bottom-up structured deep convolutional neural network with curriculum learning

abstract: Hand posture recognition has tremendous potential in the field of natural user interactions. There were many advances in research in recent years but there are still limitations regarding its usage in unfavorable live situations where hand posture variation, illumination change or background complexity are an issue. In cases like these, recognizing the hand posture is a difficult task. As such, we considered reducing the difficulty of the task by using curriculum learning with intermediate information. We proceeded to divide the complex architecture of the hand posture recognition task into two easier ones: 1) Extraction of the hand shape under clutter background with illumination change, 2) Recognition of the hand posture from a binary image. In order to do so, we propose here a bottom-up structured deep convolutional neural network incorporating a special layer for binary image extraction. Our proposed method also employs state-of-the art techniques for deep learning to obtain generalization. As a result, we achieved better recognition performances of the hand posture under clutter background compared to the baseline method.


title: UbiGreen: investigating a mobile tool for tracking and supporting green transportation habits

abstract: The greatest contributor of CO2 emissions in the average American household is personal transportation. Because transportation is inherently a mobile activity, mobile devices are well suited to sense and provide feedback about these activities. In this paper, we explore the use of personal ambient displays on mobile phones to give users feedback about sensed and self-reported transportation behaviors. We first present results from a set of formative studies exploring our respondents' existing transportation routines, willingness to engage in and maintain green transportation behavior, and reactions to early mobile phone "green" application design concepts. We then describe the results of a 3-week field study (N=13) of the UbiGreen Transportation Display prototype, a mobile phone application that semi-automatically senses and reveals information about transportation behavior. Our contributions include a working system for semi-automatically tracking transit activity, a visual design capable of engaging users in the goal of increasing green transportation, and the results of our studies, which have implications for the design of future green applications.


title: Fast Supervised Hashing with Decision Trees for High-Dimensional Data

abstract: Supervised hashing aims to map the original features to compact binary codes that are able to preserve label based similarity in the Hamming space. Non-linear hash functions have demonstrated their advantage over linear ones due to their powerful generalization capability. In the literature, kernel functions are typically used to achieve non-linearity in hashing, which achieve encouraging retrieval perfor- mance at the price of slow evaluation and training time. Here we propose to use boosted decision trees for achieving non-linearity in hashing, which are fast to train and evalu- ate, hence more suitable for hashing with high dimensional data. In our approach, we first propose sub-modular for- mulations for the hashing binary code inference problem and an efficient GraphCut based block search method for solving large-scale inference. Then we learn hash func- tions by training boosted decision trees to fit the binary codes. Experiments demonstrate that our proposed method significantly outperforms most state-of-the-art methods in retrieval precision and training time. Especially for high- dimensional data, our method is orders of magnitude faster than many methods in terms of training time.


title: Segmentation as selective search for object recognition

abstract: For object recognition, the current state-of-the-art is based on exhaustive search. However, to enable the use of more expensive features and classifiers and thereby progress beyond the state-of-the-art, a selective search strategy is needed. Therefore, we adapt segmentation as a selective search by reconsidering segmentation: We propose to generate many approximate locations over few and precise object delineations because (1) an object whose location is never generated can not be recognised and (2) appearance and immediate nearby context are most effective for object recognition. Our method is class-independent and is shown to cover 96.7% of all objects in the Pascal VOC 2007 test set using only 1,536 locations per image. Our selective search enables the use of the more expensive bag-of-words method which we use to substantially improve the state-of-the-art by up to 8.5% for 8 out of 20 classes on the Pascal VOC 2010 detection challenge.


title: MobilityGraphs: Visual Analysis of Mass Mobility Dynamics via Spatio-Temporal Graphs and Clustering

abstract: Learning more about people mobility is an important task for official decision makers and urban planners. Mobility data sets characterize the variation of the presence of people in different places over time as well as movements (or flows) of people between the places. The analysis of mobility data is challenging due to the need to analyze and compare spatial situations (i.e., presence and flows of people at certain time moments) and to gain an understanding of the spatio-temporal changes (variations of situations over time). Traditional flow visualizations usually fail due to massive clutter. Modern approaches offer limited support for investigating the complex variation of the movements over longer time periods.


title: Adaptive Bayesian network classifiers

abstract: This paper is concerned with adaptive learning algorithms for Bayesian network classifiers in a prequential (on-line) learning scenario. In this scenario, new data is available over time. An efficient supervised learning algorithm must be able to improve its predictive accuracy by incorporating the incoming data, while optimizing the cost of updating. However, if the process is not strictly stationary, the target concept could change over time. Hence, the predictive model should be adapted quickly to these changes. The main contribution of this work is a proposal of an unified, adaptive prequential framework for supervised learning called AdPreqFr4SL, which attempts to handle the cost-performance trade-off and deal with concept drift. Starting with the simple Naive Bayes, we scale up the complexity by gradually increasing the maximum number of allowable attribute dependencies, and then by searching for new dependences in the extended search space. Since updating the structure is a costly task, we use new data to primarily adapt the parameters. We adapt the structure only when is actually necessary. The method for handling concept drift is based on the Shewhart P-Chart. We experimentally prove the advantages of using the AdPreqFr4SL in comparison with its non-adaptive versions.


title: Towards a Security Lifecycle Model against Social Engineering Attacks: SLM-SEA

abstract: This research considers the impact of social engineering security attacks which are noted as taking opportunities for critically exploiting user awareness and behavior. The research proposes in this respect a managerial method in an attempt to enhance or even ensure protection. The aim of this study is to construct a security lifecycle model against these eventualities and to analyze the test results that have been carried out within the context of the Turkish public sector. The main objective of the study is to determine why employees shared sensitive information by stating fallacies and related amendments through interviews and thus to understand user actions when they are face to face with a real social engineering attack. The research findings demonstrate that employees in Turkish public organizations are not sufficiently aware of information security and they generally ignore critically important security procedures. This represents an important illustration of the increasing need for further generalized user awareness and responsibilities where individuals and not simply software form a critical element of the security protection portfolio.


title: Research on personalized recommendation based on web usage mining using collaborative filtering technique

abstract: Collaborative filtering is the most successful technology for building personalized recommendation system and is extensively used in many fields. This paper presents a system architecture of personalized recommendation using collaborative filtering based on web usage mining and describes detailedly data preparation process. To improve recommending quantity, a new personalized recommendaton model is proposed in which takes the good consideration of URL related analysis and combines the K-means algorithm. Experimental results show that our proposed model is effective and can enhance the performance of recommendation.


title: Shoulder impingement revisited: evolution of diagnostic understanding in orthopedic surgery and physical therapy

abstract: “Impingement syndrome” is a common diagnostic label for patients presenting with shoulder pain. Historically, it was believed to be due to compression of the rotator cuff tendons beneath the acromion. It has become evident that “impingement syndrome” is not likely an isolated condition that can be easily diagnosed with clinical tests or most successfully treated surgically. Rather, it is likely a complex of conditions involving a combination of intrinsic and extrinsic factors. A mechanical impingement phenomenon as an etiologic mechanism of rotator cuff disease may be distinct from the broad diagnostic label of “impingement syndrome”. Acknowledging the concepts of mechanical impingement and movement-related impairments may better suit the diagnostic and interventional continuum as they support the existence of potentially modifiable impairments within the conservative treatment paradigm. Therefore, it is advocated that the clinical diagnosis of “impingement syndrome” be eliminated as it is no more informative than the diagnosis of “anterior shoulder pain”. While both terms are ambiguous, the latter is less likely to presume an anatomical tissue pathology that may be difficult to isolate either with a clinical examination or with diagnostic imaging and may prevent potentially inappropriate surgical interventions. We further recommend investigation of mechanical impingement and movement patterns as potential mechanisms for the development of shoulder pain, but clearly distinguished from a clinical diagnostic label of “impingement syndrome”. For shoulder researchers, we recommend investigations of homogenous patient groups with accurately defined specific pathologies, or with subgrouping or classification based on specific movement deviations. Diagnostic labels based on the movement system may allow more effective subgrouping of patients to guide treatment strategies.


title: Cyber-Risk Assessment for Autonomous Ships

abstract: As a $183.3 Billion industry controlling 90 % of all world trade, the shipping community is continuously looking for methods to increase profits while still considering human and environmental safety. As a result of developing technologies and policy that make autonomy a feasible solution, at least three separate organizations are aiming to produce and sail their first autonomous ships by 2020. Thus it is essential to begin assessing their cyber-risk profiles in order to rank and mitigate any vulnerabilities. As existing risk models for physical ship safety and autonomous cars do not adequately represent the unique nature of cyber-threats for autonomous vessels within the maritime sector, this article applies a model-based risk assessment framework named MaCRA which had previous only been used to model existing ships, not those of the near-future.


title: Latent Low-Rank Representation for subspace segmentation and feature extraction

abstract: Low-Rank Representation (LRR) [16, 17] is an effective method for exploring the multiple subspace structures of data. Usually, the observed data matrix itself is chosen as the dictionary, which is a key aspect of LRR. However, such a strategy may depress the performance, especially when the observations are insufficient and/or grossly corrupted. In this paper we therefore propose to construct the dictionary by using both observed and unobserved, hidden data. We show that the effects of the hidden data can be approximately recovered by solving a nuclear norm minimization problem, which is convex and can be solved efficiently. The formulation of the proposed method, called Latent Low-Rank Representation (LatLRR), seamlessly integrates subspace segmentation and feature extraction into a unified framework, and thus provides us with a solution for both subspace segmentation and feature extraction. As a subspace segmentation algorithm, LatLRR is an enhanced version of LRR and outperforms the state-of-the-art algorithms. Being an unsupervised feature extraction algorithm, LatLRR is able to robustly extract salient features from corrupted data, and thus can work much better than the benchmark that utilizes the original data vectors as features for classification. Compared to dimension reduction based methods, LatLRR is more robust to noise.


title: Systemic Acquired Resistance and Induced Systemic Resistance in Conventional Agriculture

abstract: Plants possess a range of defenses that can be actively expressed in response to pathogens and parasites of various scales, ranging from microscopic viruses to insect herbivores. The timing of these defense responses is critical and can be the difference between being able to cope or succumbing to the challenge of a pathogen or parasite. Systemic acquired resistance (SAR) and induced systemic resistance (ISR) are two forms of induced resistance; in both SAR and ISR, plant defenses are preconditioned by prior infection or treatment that results in resistance (or tolerance) against subsequent challenge by a pathogen or parasite. Great strides have been made over the past 20 yr in understanding the physiological and biochemical basis of SAR and ISR. Much of this knowledge is due to the identification of a number of chemical and biological elicitors, some of which are commercially available for use in conventional agriculture. However, the effectiveness of these elicitors to induce SAR and ISR as a practical means to control various plant diseases is just being realized. In this review, we first briefly summarize the fundamentals of ISR and SAR, for which a number of critical reviews already exist. We then examine the efficacy of SAR and ISR in published field-based studies. We place special emphasis on the benefits, drawbacks, and future considerations for the improved use of chemical and biological elicitors of induced resistance in conventional agriculture; this includes the potential to exploit genetic variability within populations of crop species to improve the utility of SAR and ISR in the field.


title: A digital design flow for secure integrated circuits

abstract: Small embedded integrated circuits (ICs) such as smart cards are vulnerable to the so-called side-channel attacks (SCAs). The attacker can gain information by monitoring the power consumption, execution time, electromagnetic radiation, and other information leaked by the switching behavior of digital complementary metal-oxide-semiconductor (CMOS) gates. This paper presents a digital very large scale integrated (VLSI) design flow to create secure power-analysis-attack-resistant ICs. The design flow starts from a normal design in a hardware description language such as very-high-speed integrated circuit (VHSIC) hardware description language (VHDL) or Verilog and provides a direct path to an SCA-resistant layout. Instead of a full custom layout or an iterative design process with extensive simulations, a few key modifications are incorporated in a regular synchronous CMOS standard cell design flow. The basis for power analysis attack resistance is discussed. This paper describes how to adjust the library databases such that the regular single-ended static CMOS standard cells implement a dynamic and differential logic style and such that 20 000+ differential nets can be routed in parallel. This paper also explains how to modify the constraints and rules files for the synthesis, place, and differential route procedures. Measurement-based experimental results have demonstrated that the secure digital design flow is a functional technique to thwart side-channel power analysis. It successfully protects a prototype Advanced Encryption Standard (AES) IC fabricated in an 0.18-mum CMOS


title: Parameter extraction for a physics-based circuit simulator IGBT model

abstract: A practical parameter extraction method is presented for the Fourier-based-solution physics-based IGBT model. In the extraction procedure, only one simple clamped inductive load test is needed for the extraction of the eleven and thirteen parameters required for the NPT and PT IGBT models, respectively. Validation with experimental results from various structure IGBTs demonstrates the accuracy of the proposed IGBT model and the robustness of the parameter extraction method.


title: Amandroid: A Precise and General Inter-component Data Flow Analysis Framework for Security Vetting of Android Apps

abstract: We propose a new approach to conduct static analysis for security vetting of Android apps, and built a general framework, called Amandroid for determining points-to information for all objects in an Android app in a flow- and context-sensitive way across Android apps components. We show that: (a) this type of comprehensive analysis is completely feasible in terms of computing resources needed with modern hardware, (b) one can easily leverage the results from this general analysis to build various types of specialized security analyses -- in many cases the amount of additional coding needed is around 100 lines of code, and (c) the result of those specialized analyses leveraging Amandroid is at least on par and often exceeds prior works designed for the specific problems, which we demonstrate by comparing Amandroid's results with those of prior works whenever we can obtain the executable of those tools. Since Amandroid's analysis directly handles inter-component control and data flows, it can be used to address security problems that result from interactions among multiple components from either the same or different apps. Amandroid's analysis is sound in that it can provide assurance of the absence of the specified security problems in an app with well-specified and reasonable assumptions on Android runtime system and its library.


title: Opensmile: the munich versatile and fast open-source audio feature extractor

abstract: We introduce the openSMILE feature extraction toolkit, which unites feature extraction algorithms from the speech processing and the Music Information Retrieval communities. Audio low-level descriptors such as CHROMA and CENS features, loudness, Mel-frequency cepstral coefficients, perceptual linear predictive cepstral coefficients, linear predictive coefficients, line spectral frequencies, fundamental frequency, and formant frequencies are supported. Delta regression and various statistical functionals can be applied to the low-level descriptors. openSMILE is implemented in C++ with no third-party dependencies for the core functionality. It is fast, runs on Unix and Windows platforms, and has a modular, component based architecture which makes extensions via plug-ins easy. It supports on-line incremental processing for all implemented features as well as off-line and batch processing. Numeric compatibility with future versions is ensured by means of unit tests. openSMILE can be downloaded from http://opensmile.sourceforge.net/.


title: Recognizing human faces under disguise and makeup

abstract: The accuracy of automated human face recognition algorithms can significantly degrade while recognizing same subjects under make-up and disguised appearances. Increasing constraints on enhanced security and surveillance requires enhanced accuracy from face recognition algorithms for faces under disguise and/or makeup. This paper presents a new database for face images under disguised and make-up appearances the development of face recognition algorithms under such covariates. This database has 2460 images from 410 different subjects and is acquired under real environment, focuses on make-up and disguises covariates and also provides ground truth (eye glass, goggle, mustache, beard) for every image. This can enable developed algorithms to automatically quantify their capability for identifying such important disguise attribute during the face recognition We also present comparative experimental results from two popular commercial matchers and from recent publications. Our experimental results suggest significant performance degradation in the capability of these matchers in automatically recognizing these faces. We also analyze face detection accuracy from these matchers. The experimental results underline the challenges in recognizing faces under these covariates. Availability of this new database in public domain will help to advance much needed research and development in recognizing make-up and disguised faces.


title: Smart cameras as embedded systems

abstract: Recent technological advances are enabling a new generation of smart cameras that represent a quantum leap in sophistication. While today's digital cameras capture images, smart cameras capture high-level descriptions of the scene and analyze what they see. These devices could support a wide variety of applications including human and animal detection, surveillance, motion analysis, and facial identification. Video processing has an insatiable demand for real-time performance. Smart cameras leverage very large-scale integration to meet this need in a low-cost, low-power system with substantial memory. Moving well beyond pixel processing and compression, these VLSI systems run a wide range of algorithms to extract meaning from streaming video. Recently, Princeton University researchers developed a first-generation smart camera system that can detect people and analyze their movement in real time. Because they push the design space in so many dimensions, these smart cameras are a leading-edge application for embedded system research.


title: A common criteria based security requirements engineering process for the development of secure information systems

abstract: In order to develop security critical Information Systems, specifying security quality requirements is vitally important, although it is a very difficult task. Fortunately, there are several security standards, like the Common Criteria (ISO/IEC 15408), which help us handle security requirements. This article will present a Common Criteria centred and reuse-based process that deals with security requirements at the early stages of software development in a systematic and intuitive way, by providing a security resources repository as well as integrating the Common Criteria into the software lifecycle, so that it unifies the concepts of requirements engineering and security engineering.


title: Reflections on Frequency Effects in Language Processing

abstract: This response addresses the following points raised in the commentaries: (a) complementary learning mechanisms, the distinction between explicit and implicit memory, and the neuroscience of “noticing”; (b) what must and what need not be noticed for learning; (c) when frequency fails to drive learning, which addresses factors such as failing to notice cues, perseveration, transfer from L1, developmental readiness, thinking too hard, pedagogical input, and practicing; (d) attention and form-focused instruction; (e) conscious and unconscious knowledge of frequency; (f) sequences of acquisition—from formula, through low-scope pattern, to construction; (g) the Fundamental Difference hypothesis; (h) the blind faith of categorical grammar; (i) Labovian variationist perspectives; (j) parsimony and theory testing; (k) universals and predispositions; and (l) wanna -contractions. It concludes by emphasizing that language acquisition is a process of dynamic emergence and that learners' language is a product of their history of usage in communicative interaction.


title: Dietary Practices and Nutrient Intakes of Pregnant Women in Accra, Ghana

abstract: The aim of the study was to assess potential changes in dietary habits during pregnancy, nutrient intakes of pregnant women and the effect of socio-demographic factors on nutrient intakes of pregnant women. The research design was cross-sectional. A systematic random sampling technique was used to recruit 279 pregnant women from the antenatal clinic of the Korle-Bu Teaching Hospital and Osu Maternity Home in Accra. In-depth interviews were performed to assess perceptions and attitudes regarding food consumption and dietary habits during pregnancy. In addition, an interactive 24 h recall repeated for three days was used to obtain data on food and nutrient intakes of the women. Information on foods consumed was converted into quantitative data of nutrients using Ghana Food Composition Tables and FOOD PROCESSOR PLUS Software. Data were analyzed using the Statistical Package for Social Sciences (SPSS, 17). The mean energy, vitamin B12, folate, iron and zinc intakes were found to be inadequate compared with the Recommended Intakes. There was also a significant difference (p<0.001) in the mean intake of protein across the different trimesters. On the average protein, fat and carbohydrate contributed 13.2, 34.7 and 52.1%, respectively to total calories. Significant differences were observed between educational level and mean intake of protein (p<0.001), as well as income levels and mean intake of protein (p<0.001) and zinc (p<0.02). Surprisingly women with larger household size had high intake of energy and nutrients. Most of the women (67.7%) reported food craving, 57.3% practiced pica and 48.8% reported dietary restrictions during pregnancy. However no significant difference was found in nutrient intakes between women who reported food craving, food avoidance and pica and women who did not. In conclusion, pregnant women in the study had lower intake of energy and nutrients compared with the RNI. Food craving and aversion as well as pica practice are common dietary practices among pregnant women in the study. Protein and zinc are associated with income and educational levels of the pregnant women in the study. It is recommended that more attention should be focused on nutrition education and intervention programs during pregnancy as well as dietary approaches such as fortification of foods to increase both macronutrient and micronutrient intakes.


title: Voice recognition based wireless home automation system

abstract: Home Automation industry is growing rapidly; this is fuelled by the need to provide supporting systems for the elderly and the disabled, especially those who live alone. Coupled with this, the world population is confirmed to be getting older. Home automation systems must comply with the household standards and convenience of usage. This paper details the overall design of a wireless home automation system (WHAS) which has been built and implemented. The automation centres on recognition of voice commands and uses low-power RF ZigBee wireless communication modules which are relatively cheap. The home automation system is intended to control all lights and electrical appliances in a home or office using voice commands. The system has been tested and verified. The verification tests included voice recognition response test, indoor ZigBee communication test, and the compression and decompression tests of DPCM (Differential Pulse Code Modulation) speech signals. The tests involved a mix of 35 male and female subjects with different English accents. 35 different voice commands were sent by each person. Thus the test involved sending a total of 1225 commands and 79.8% of these commands were recognised correctly.


title: Community challenges in biomedical text mining over 10 years: success, failure and the future

abstract: One effective way to improve the state of the art is through competitions. Following the success of the Critical Assessment of protein Structure Prediction (CASP) in bioinformatics research, a number of challenge evaluations have been organized by the text-mining research community to assess and advance natural language processing (NLP) research for biomedicine. In this article, we review the different community challenge evaluations held from 2002 to 2014 and their respective tasks. Furthermore, we examine these challenge tasks through their targeted problems in NLP research and biomedical applications, respectively. Next, we describe the general workflow of organizing a Biomedical NLP (BioNLP) challenge and involved stakeholders (task organizers, task data producers, task participants and end users). Finally, we summarize the impact and contributions by taking into account different BioNLP challenges as a whole, followed by a discussion of their limitations and difficulties. We conclude with future trends in BioNLP challenge evaluations.


title: Developing Mathematical Thinking with Scratch An Experiment with 6th Grade Students

abstract: One of the latest trends in the educational landscape is the introduction of computer programming in the K-12 classroom to develop computational thinking in students. As computational thinking is not a skill exclusively related to computer science, it is assumed - but not yet scientifically proven - that the problem solving process may be general- ized and transferred to a wide variety of problems. This paper presents a research designed to test whether the use of coding in Maths classes could have a positive impact on learning outcomes of students in their mathematical skills. Therefore, the questions we want to investigate in this paper are if the use of programming in Maths classes improves (a) modeling process and reality phenomena, (b) reasoning, (c) prob- lem formulation and problem solving, and (d) comparison and execution of procedures and algorithms. We have therefore designed a quantita- tive, quasi-experimental experiment with 42 participating 6th grade (11 and 12 years old) students. Results show that there is a statistically sig- nificant increase in the understanding of mathematical processes in the experimental group, which received training in Scratch.


title: Use of dietary supplements among active-duty US Army soldiers

abstract: Background: US Army soldiers engage in strenuous activities and must maintain fitness and body weight to retain their jobs. Anecdotal reports suggest that the use of dietary supplements (DSs) by soldiers may reflect their unique occupational requirements and the complexity of their job and family responsibilities. Objective: We assessed the use of DSs by soldiers. Design: We conducted a survey of 990 randomly selected soldiers at 11 army bases globally. Data were weighted by age, sex, rank, and Special Forces status to represent the active-duty army. Results: Overall, 53% of soldiers reported the use of DSs 1 time/wk; 23% of soldiers used sports beverages, 6% of soldiers used sports bars or gels, and 3% of soldiers reported the use of meal-replacement beverages. Most commonly used DSs were multivitamins or multiminerals (37.5%), protein and amino acids (18.7%), individual vitamins and minerals (17.9%), combination products (9.1%), and herbal supplements (8.3%). Many soldiers reported the use of performanceenhancement and weight-reduction products, and 22% of soldiers consumed 3 different DSs/wk. Logistic regression modeling indicated that older age, educational attainment, higher body mass index, and strength training were associated with DS use (P , 0.05). Reported reasons for DS use were to improve health (64%), provide more energy (31%), increase muscle strength (25%), and enhance performance (17%). Among DS users, mean monthly expenditures on DSs were $38, whereas 23% of soldiers spent .$50/mo. Conclusions: Soldiers, like civilians, use large amounts of DSs, often in combination. Soldiers use more DSs purported to enhance performance than civilians use when matched for key demographic factors. These differences may reflect the unique occupational demands and stressors of military service. Am J Clin Nutr doi: 10. 3945/ajcn.2010.29274.


title: 60 V rating split gate trench MOSFETs having best-in-class specific resistance and figure-of-merit

abstract: A new 60 V rating split gate trench MOSFET fabricated with the state-of-the-art process technology has been investigated and evaluated in the secondary synchronous rectifier board. It shows the best-in-class specific resistance of 10.9 mΩ·mm2 and the figure-of-merit (FOM) of 85 mΩ·nC which is the product of the on-resistance and the gate charge.


title: Dual-band dual-polarized antenna array for beam selection MIMO WLAN

abstract: A novel compact size dual-band dual-polarized antenna array for MIMO WLAN is reported. The array element comprises a periodic antenna with bowtie dipoles and a printed dipole antenna with a reflector and a director. The presented design is characterized by dual-band operation, good front-to-back ratios, average gains of 4 and 5 dBi over the 2.4 and 5.2 GHz bands respectively. A prototype 12-element array exhibits dual broad bandwidth and low mutual coupling among 2.25GHz–2.6GHz and 4.95GHz–6GHz. The measured results indicate the array is suitable for 802.11a/b/g/n systems employing MIMO and beam selection techniques.


title: Time-Frequency Training OFDM with High Spectral Efficiency and Improved Performance over Fast Fading Channels

abstract: Time domain synchronous OFDM (TDS-OFDM) has higher spectral efficiency than cyclic prefix OFDM (CP-OFDM), but suffers from severe performance loss over fast fading channels. In this paper, a novel transmission scheme called time-frequency training OFDM (TFT-OFDM) is proposed. The time-frequency joint channel estimation for TFT-OFDM utilizes the time-domain training sequence without interference cancellation to merely acquire the time delay profile of the channel, while the path coefficients are estimated by using the frequency-domain group pilots. The redundant group pilots only occupy about 1% of the useful subcarriers, thus TFT-OFDM still has much higher spectral efficiency than CP-OFDM by about 10%. Simulation results also demonstrate that TFT-OFDM outperforms CP-OFDM and TDS-OFDM over time-varying channels.


title: Automated Extraction of GUI Models for Testing

abstract: Abstract A significant challenge in applying model-based testing on software systems is that manually designing the test models requires considerable amount of effort and deep expertise in formal modeling. When an existing system is being modeled and tested, there are various techniques to automate the process of producing the models based on the implementation. Some approaches aim to fully automated creation of the models, while others aim to automate the first steps to create an initial model to serve as a basis to start the manual modeling process. Especially graphical user interface (GUI) applications, including mobile and Web applications, have been a good domain for model extraction, reverse engineering, and specification mining approaches. In this chapter, we survey various automated modeling techniques, with a special focus on GUI models and their usefulness in analyzing and testing of the modeled GUI applications.


title: HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

abstract: Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. We introduce HotpotQA, a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowing QA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems' ability to extract relevant facts and perform necessary comparison. We show that HotpotQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions.


title: From Gene Trees to a Dated Allopolyploid Network: Insights from the Angiosperm Genus Viola (Violaceae)

abstract: Allopolyploidization accounts for a significant fraction of speciation events in many eukaryotic lineages. However, existing phylogenetic and dating methods require tree-like topologies and are unable to handle the network-like phylogenetic relationships of lineages containing allopolyploids. No explicit framework has so far been established for evaluating competing network topologies, and few attempts have been made to date phylogenetic networks. We used a four-step approach to generate a dated polyploid species network for the cosmopolitan angiosperm genus Viola L. (Violaceae Batch.). The genus contains ca 600 species and both recent (neo-) and more ancient (meso-) polyploid lineages distributed over 16 sections. First, we obtained DNA sequences of three low-copy nuclear genes and one chloroplast region, from 42 species representing all 16 sections. Second, we obtained fossil-calibrated chronograms for each nuclear gene marker. Third, we determined the most parsimonious multilabeled genome tree and its corresponding network, resolved at the section (not the species) level. Reconstructing the "correct" network for a set of polyploids depends on recovering all homoeologs, i.e., all subgenomes, in these polyploids. Assuming the presence of Viola subgenome lineages that were not detected by the nuclear gene phylogenies ("ghost subgenome lineages") significantly reduced the number of inferred polyploidization events. We identified the most parsimonious network topology from a set of five competing scenarios differing in the interpretation of homoeolog extinctions and lineage sorting, based on (i) fewest possible ghost subgenome lineages, (ii) fewest possible polyploidization events, and (iii) least possible deviation from expected ploidy as inferred from available chromosome counts of the involved polyploid taxa. Finally, we estimated the homoploid and polyploid speciation times of the most parsimonious network. Homoploid speciation times were estimated by coalescent analysis of gene tree node ages. Polyploid speciation times were estimated by comparing branch lengths and speciation rates of lineages with and without ploidy shifts. Our analyses recognize Viola as an old genus (crown age 31 Ma) whose evolutionary history has been profoundly affected by allopolyploidy. Between 16 and 21 allopolyploidizations are necessary to explain the diversification of the 16 major lineages (sections) of Viola, suggesting that allopolyploidy has accounted for a high percentage—between 67% and 88%—of the speciation events at this level. The theoretical and methodological approaches presented here for (i) constructing networks and (ii) dating speciation events within a network, have general applicability for phylogenetic studies of groups where allopolyploidization has occurred. They make explicit use of a hitherto underexplored source of ploidy information from chromosome counts to help resolve phylogenetic cases where incomplete sequence data hampers network inference. Importantly, the coalescent-based method used herein circumvents the assumption of tree-like evolution required by most techniques for dating speciation events. (Dating; low-copy nuclear gene; polyploidy; species network; Viola; violaceae.)


title: Separating style and content on a nonlinear manifold

abstract: Bilinear and multi-linear models have been successful in decomposing static image ensembles into perceptually orthogonal sources of variations, e.g., separation of style and content. If we consider the appearance of human motion such as gait, facial expression and gesturing, most of such activities result in nonlinear manifolds in the image space. The question that we address in this paper is how to separate style and content on manifolds representing dynamic objects. In this paper we learn a decomposable generative model that explicitly decomposes the intrinsic body configuration (content) as a function of time from the appearance (style) of the person performing the action as time-invariant parameter. The framework we present in this paper is based on decomposing the style parameters in the space of nonlinear functions which map between a learned unified nonlinear embedding of multiple content manifolds and the visual input space.


title: Thermal Flow-Sensor Drift Reduction by Thermopile Voltage Cancellation via Power Feedback Control

abstract: The research question that is addressed in this paper relates to the performance limitations of thermal flow sensors due to miniaturization. Sensor elements in current microflow sensors are mostly made by metal thin films. The problem is that thin-films reproduce poorly and that practically all material properties are subject to drift. This drift and poor reproducibility translates directly into the accuracy of thermal microflow sensors. This paper presents a thermal flow sensor consisting of freely suspended silicon-rich silicon-nitride microchannels with an integrated Al/poly-Si++ thermopile in combination with up ::: and downstream Al heater resistors. The drift-free zero offset of a thermopile at uniform temperature is exploited in a feedback loop controlling the dissipated powers in Al heater resistors, reducing inevitable influences of resistance drift, and mismatch of thin-film metal resistors. The control system attempts to cancel the flow-induced temperature imbalance across the thermopile by controlling a power difference between both heater resistors, thereby giving a measure of the flow rate nearly independent of material drift.


title: Advanced transformer winding deformation diagnosis: moving from off-line to on-line

abstract: On-line monitoring and diagnosis of transformers have been investigated and discussed significantly in last decade. This study has concentrated on issues arising while on-line transformer winding deformation diagnosis is going to be applied on transformers with various kinds of techniques. From technical perspective, before replacing off-line methods by on-line methods and eventually by intelligent approaches, practical challenges must be addressed and overcome. Hence, available off-line transformer winding deformation diagnosis methods are discussed precisely. Mathematical calculation in on-line short circuit impedance measurement is investigated. On-line transformer transfer function measurement setup is presented. A profound insight to the problems pertaining on-line transformer winding deformation recognition methods, characterizes existing online methods, explains the concepts behind online measurements and striving to open the discussion doors towards challenges are discussed. In the end a 400 MVA step up transformer has been taken as a case in order to clarify the capability of Frequency Response Analysis (FRA) method in fault detection while short circuit impedance could only demonstrate some rough understanding about transformer condition.


title: Variable structure methods in hydraulic servo systems control

abstract: In the general framework of hydraulic servo systems, this paper addresses the problem of position control in the presence of important friction nonlinearities. The accent falls on the variable structure methodology, as we try to use its intrinsic robustness properties. Several friction observers, including the one based on a variable structure approach, were incorporated and tested in an acceleration feedback control. Next, we present a novel implementation of a variable structure controller, which lumps friction and load as an external disturbance. Results of extensive experimental testing encourage the use of variable structure methods in a class of highly nonlinear hydraulic servo systems.


title: Asymmetric anisotropic diffusion

abstract: We introduce a new anisotropic diffusion based approach for image filtering and enhancement. The main novelty of our method consists in considering a modulo 2π based definition of the diffusion axis along the main directions of the processed image. Such an asymmetric formulation of the filter induces better junction and corner preservation properties coupled with a good noise removal capacity.


title: Anatomical variability of the lateral femoral cutaneous nerve: findings from a surgical series.

abstract: The lateral femoral cutaneous nerve (LFCN) is a branch of the lumbar plexus and supplies the skin of the lateral thigh region. This entrapment-compressive syndrome is named meralgia paresthetica or Roth's meralgia and depends, on a vast majority of cases, on the entrapment of the nerve in proximity of the inguinal ligament. Surgical decompression of the nerve is an option when conservative treatments fail and is usually performed through a 3-cm infrainguinal skin incision. Available data on anatomical variations of the LFCN derive from extensive cadaver dissections and lack many features relevant to the surgeon. This study was conducted to investigate anatomical details of the LFCN at the site of surgery for meralgia paresthetica. We reviewed retrospective data regarding the anatomical features of LFCN from 148 consecutive patients operated on for Roth's meralgia. In the majority of the cases the LFCN was a single trunk, deep to the thigh superficial fascia and to the inguinal ligament and coursing inferior-lateral to the anterior superior iliac spine. Less frequent findings were early nerve bifurcation, epifascial position, inferior-medial direction, and exit from the pelvis through an iliac bone canal. In 13 cases (8.8%) the nerve was not found at surgery. Anatomical variations of the LFCN must be considered at the time of surgery to maximize success rates and avoid nerve damage during surgical dissection.


title: Mobile Unmanned Aerial Vehicles (UAVs) for Energy-Efficient Internet of Things Communications

abstract: In this paper, the efficient deployment and mobility of multiple unmanned aerial vehicles (UAVs), used as aerial base stations to collect data from ground Internet of Things (IoT) devices, is investigated. In particular, to enable reliable uplink communications for IoT devices with a minimum total transmit power, a novel framework is proposed for jointly optimizing the three-dimensional (3D) placement and mobility of the UAVs, device-UAV association, and uplink power control. First, given the locations of active IoT devices at each time instant, the optimal UAVs' locations and associations are determined. Next, to dynamically serve the IoT devices in a time-varying network, the optimal mobility patterns of the UAVs are analyzed. To this end, based on the activation process of the IoT devices, the time instances at which the UAVs must update their locations are derived. Moreover, the optimal 3D trajectory of each UAV is obtained in a way that the total energy used for the mobility of the UAVs is minimized while serving the IoT devices. Simulation results show that, using the proposed approach, the total transmit power of the IoT devices is reduced by 45% compared to a case in which stationary aerial base stations are deployed. In addition, the proposed approach can yield a maximum of 28% enhanced system reliability compared to the stationary case. The results also reveal an inherent tradeoff between the number of update times, the mobility of the UAVs, and the transmit power of the IoT devices. In essence, a higher number of updates can lead to lower transmit powers for the IoT devices at the cost of an increased mobility for the UAVs.


title: Recurrent Convolutional Neural Networks for Discourse Compositionality

abstract: The compositionality of meaning extends beyond the single sentence. Just as words combine to form the meaning of sentences, so do sentences combine to form the meaning of paragraphs, dialogues and general discourse. We introduce both a sentence model and a discourse model corresponding to the two levels of compositionality. The sentence model adopts convolution as the central operation for composing semantic vectors and is based on a novel hierarchical convolutional neural network. The discourse model extends the sentence model and is based on a recurrent neural network that is conditioned in a novel way both on the current sentence and on the current speaker. The discourse model is able to capture both the sequentiality of sentences and the interaction between different speakers. Without feature engineering or pretraining and with simple greedy decoding, the discourse model coupled to the sentence model obtains state of the art performance on a dialogue act classification experiment.


title: A Pattern Decomposition Algorithm for Data Mining of Frequent Patterns

abstract: Efficient algorithms to mine frequent patterns are crucial to many tasks in data mining. Since the Apriori algorithm was proposed in 1994, there have been several methods proposed to improve its performance. However, most still adopt its candidate set generation-and-test approach. In addition, many methods do not generate all frequent patterns, making them inadequate to derive association rules. We propose a pattern decomposition (PD) algorithm that can significantly reduce the size of the dataset on each pass, making it more efficient to mine all frequent patterns in a large dataset. The proposed algorithm avoids the costly process of candidate set generation and saves time by reducing the size of the dataset. Our empirical evaluation shows that the algorithm outperforms Apriori by one order of magnitude and is faster than FP-tree algorithm.