ERP system implementation in SMEs: exploring the influences of the SME context

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Job Resources Boost Work Engagement , Particularly When Job Demands Are High

This study of 805 Finnish teachers working in elementary, secondary, and vocational schools tested 2 interaction hypotheses. On the basis of the job demands–resources model, the authors predicted that job resources act as buffers and diminish the negative relationship between pupil misbehavior and work engagement. In addition, using conservation of resources theory, the authors hypothesized that job resources particularly influence work engagement when teachers are confronted with high levels of pupil misconduct. In line with these hypotheses, moderated structural equation modeling analyses resulted in 14 out of 18 possible 2-way interaction effects. In particular, supervisor support, innovativeness, appreciation, and organizational climate were important job resources that helped teachers cope with demanding interactions with students.


Superior thermal conductivity of single-layer graphene.

We report the measurement of the thermal conductivity of a suspended single-layer graphene. The room temperature values of the thermal conductivity in the range approximately (4.84+/-0.44)x10(3) to (5.30+/-0.48)x10(3) W/mK were extracted for a single-layer graphene from the dependence of the Raman G peak frequency on the excitation laser power and independently measured G peak temperature coefficient. The extremely high value of the thermal conductivity suggests that graphene can outperform carbon nanotubes in heat conduction. The superb thermal conduction property of graphene is beneficial for the proposed electronic applications and establishes graphene as an excellent material for thermal management.


Asymmetric Cryptography for Mobile Devices

This paper is meant to give the reader a general overview about the application of asymmetric cryptography in communication, particular in mobile devices. The basics principles of a cryptosystem are addressed, as well as the idea of symmetric and asymmetric cryptography. The functional principles of RSA encryption and the DiffieHellman key exchange scheme, as well as the general idea of digital signatures are shortly described. Furthermore some challenges and solution approaches for the application of asymmetric encryption in low power mobile devices are named. On completing, some the future developments in cryptography are shortly described.


Interpreting Semantic Relations in Noun Compounds via Verb Semantics

We propose a novel method for automatically interpreting compound nouns based on a predefined set of semantic relations. First we map verb tokens in sentential contexts to a fixed set of seed verbs using WordNet::Similarity and Moby’s Thesaurus. We then match the sentences with semantic relations based on the semantics of the seed verbs and grammatical roles of the head noun and modifier. Based on the semantics of the matched sentences, we then build a classifier using TiMBL. The performance of our final system at interpreting NCs is 52.6%.


An Empirical Study on Android-Related Vulnerabilities

Mobile devices are used more and more in everyday life. They are our cameras, wallets, and keys. Basically, they embed most of our private information in our pocket. For this and other reasons, mobile devices, and in particular the software that runs on them, are considered first-class citizens in the software-vulnerabilities landscape. Several studies investigated the software-vulnerabilities phenomenon in the context of mobile apps and, more in general, mobile devices. Most of these studies focused on vulnerabilities that could affect mobile apps, while just few investigated vulnerabilities affecting the underlying platform on which mobile apps run: the Operating System (OS). Also, these studies have been run on a very limited set of vulnerabilities. In this paper we present the largest study at date investigating Android-related vulnerabilities, with a specific focus on the ones affecting the Android OS. In particular, we (i) define a detailed taxonomy of the types of Android-related vulnerability, (ii) investigate the layers and subsystems from the Android OS affected by vulnerabilities, and (iii) study the survivability of vulnerabilities (i.e., the number of days between the vulnerability introduction and its fixing). Our findings could help OS and apps developers in focusing their verification & validation activities, and researchers in building vulnerability detection tools tailored for the mobile world.


Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness

We introduce a new family of fairness definitions that interpolate between statistical and individual notions of fairness, obtaining some of the best properties of each. We show that checking whether these notions are satisfied is computationally hard in the worst case, but give practical oracle-efficient algorithms for learning subject to these constraints, and confirm our findings with experiments.


A Simple Introduction to Maximum Entropy Models for Natural Language Processing

Many problems in natural language processing can be viewed as lin guistic classi cation problems in which linguistic contexts are used to pre dict linguistic classes Maximum entropy models o er a clean way to com bine diverse pieces of contextual evidence in order to estimate the proba bility of a certain linguistic class occurring with a certain linguistic con text This report demonstrates the use of a particular maximum entropy model on an example problem and then proves some relevant mathemat ical facts about the model in a simple and accessible manner This report also describes an existing procedure called Generalized Iterative Scaling which estimates the parameters of this particular model The goal of this report is to provide enough detail to re implement the maximum entropy models described in Ratnaparkhi Reynar and Ratnaparkhi Ratnaparkhi and also to provide a simple explanation of the max imum entropy formalism Introduction Many problems in natural language processing NLP can be re formulated as statistical classi cation problems in which the task is to estimate the probability of class a occurring with context b or p a b Contexts in NLP tasks usually include words and the exact context depends on the nature of the task for some tasks the context b may consist of just a single word while for others b may consist of several words and their associated syntactic labels Large text corpora usually contain some information about the cooccurrence of a s and b s but never enough to completely specify p a b for all possible a b pairs since the words in b are typically sparse The problem is then to nd a method for using the sparse evidence about the a s and b s to reliably estimate a probability model p a b Consider the Principle of Maximum Entropy Jaynes Good which states that the correct distribution p a b is that which maximizes en tropy or uncertainty subject to the constraints which represent evidence i e the facts known to the experimenter Jaynes discusses its advan tages in making inferences on the basis of partial information we must use that probability distribution which has maximum entropy sub ject to whatever is known This is the only unbiased assignment we can make to use any other would amount to arbitrary assumption of information which by hypothesis we do not have More explicitly if A denotes the set of possible classes and B denotes the set of possible contexts p should maximize the entropy H p X


Improved Deep Metric Learning with Multi-class N-pair Loss Objective

Deep metric learning has gained much popularity in recent years, following the success of deep learning. However, existing frameworks of deep metric learning based on contrastive loss and triplet loss often suffer from slow convergence, partially because they employ only one negative example while not interacting with the other negative classes in each update. In this paper, we propose to address this problem with a new metric learning objective called multi-class N -pair loss. The proposed objective function firstly generalizes triplet loss by allowing joint comparison among more than one negative examples – more specifically, N -1 negative examples – and secondly reduces the computational burden of evaluating deep embedding vectors via an efficient batch construction strategy using only N pairs of examples, instead of (N+1)×N . We demonstrate the superiority of our proposed loss to the triplet loss as well as other competing loss functions for a variety of tasks on several visual recognition benchmark, including fine-grained object recognition and verification, image clustering and retrieval, and face verification and identification.


A second order accurate projection method for the incompressible Navier-Stokes equations on non-graded adaptive grids

We present an unconditionally stable second order accurate projection method for the incompressible Navier–Stokes equations on non-graded adaptive Cartesian grids. We employ quadtree and octree data structures as an efficient means to represent the grid. We use the supra-convergent Poisson solver of [C.-H. Min, F. Gibou, H. Ceniceros, A supra-convergent finite difference scheme for the variable coefficient Poisson equation on fully adaptive grids, CAM report 05-29, J. Comput. Phys. (in press)], a second order accurate semi-Lagrangian method to update the momentum equation, an unconditionally stable backward difference scheme to treat the diffusion term and a new method that guarantees the stability of the projection step on highly non-graded grids. We sample all the variables at the grid nodes, producing a scheme that is straightforward to implement. We propose two and three-dimensional examples to demonstrate second order accuracy for the velocity field and the divergence free condition in the L and L1 norms. 2006 Elsevier Inc. All rights reserved.


A Monte Carlo Model of Light Propagation in Tissue

The Monte Carlo method is rapidly becoming the model of choice for simulating light transport in tissue. This paper provides all the details necessary for implementation of a Monte Carlo program. Variance reduction schemes that improve the efficiency of the Monte Carlo method are discussed. Analytic expressions facilitating convolution calculations for finite flat and Gaussian beams are included. Useful validation benchmarks are presented.


A design and development of an intelligent jammer and jamming detection methodologies using machine learning approach

Nowadays, the utilization of mobile phones has increased rapidly. Due to this evolution schools, mosques, prisons, etc., require silence and security. This is achieved by using the mobile phone jammers. An intelligent mobile jammer is designed for allowing only the emergency calls by utilizing the microcontroller. Here, the jammer utilizes the successive approximation for reducing the transmission power. However, it requires few modifications. Therefore in this paper, an improved successive approximation based on divide and conquer algorithm is proposed in order to design the improved intelligent mobile jammer and reduce transmission power. Subsequently, the proposed jammer is analysed based on the different scenarios and frequency bands to illustrate their performance effectiveness. Furthermore, the normal activities are distinguished from jamming by using machine learning-based detection system according to the various parameters. Finally, the proposed algorithm is compared with conventional algorithms to demonstrate its performance efficiency in terms of detection accuracy.


Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition

Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in sequential data modeling. The hidden layers in RNNs can be regarded as the memory units, which are helpful in storing information in sequential contexts. However, when dealing with high dimensional input data, such as video and text, the input-to-hidden linear transformation in RNNs brings high memory usage and huge computational cost. This makes the training of RNNs very difficult. To address this challenge, we propose a novel compact LSTM model, named as TR-LSTM, by utilizing the low-rank tensor ring decomposition (TRD) to reformulate the input-to-hidden transformation. Compared with other tensor decomposition methods, TRLSTM is more stable. In addition, TR-LSTM can complete an end-to-end training and also provide a fundamental building block for RNNs in handling large input data. Experiments on real-world action recognition datasets have demonstrated the promising performance of the proposed TR-LSTM compared with the tensortrain LSTM and other state-of-the-art competitors.


Quo Vadis , Atlas-Based Segmentation?


Temporal Modular Networks for Retrieving Complex Compositional Activities in Videos

A major challenge in computer vision is scaling activity understanding to the long tail of complex activities without requiring collecting large quantities of data for new actions. The task of video retrieval using natural language descriptions seeks to address this through rich, unconstrained supervision about complex activities. However, while this formulation offers hope of leveraging underlying compositional structure in activity descriptions, existing approaches typically do not explicitly model compositional reasoning. In this work, we introduce an approach for explicitly and dynamically reasoning about compositional natural language descriptions of activity in videos. We take a modular neural network approach that, given a natural language query, extracts the semantic structure to assemble a compositional neural network layout and corresponding network modules. We show that this approach is able to achieve state-of-the-art results on the DiDeMo video retrieval dataset.


Multivalued logics: a uniform approach to reasoning in artificial intelligence

This paper describes a uniform formalization of much of the current work in artificial intelligence on inference systems. We show that many of these systems, including first-order theorem provers, assumption-based truth maintenance systems (ATMSS), and unimplemented formal systems such as default logic or circumscription, can be subsumed under a single general framework. We begin by defining this framework, which is based on a mathematical structure known as a bilattice. We present a formal definition of inference using this structure and show that this definition generalizes work involving ATMSS and some simple nonmonotonic logics. Following the theoretical description, we describe a constructive approach to inference in this setting; the resulting generalization of both conventional inference and ATMSS is achieved without incurring any substantial computational overhead. We show that our approach can also be used to implement a default reasoner, and discuss a combination of default and ATMS methods that enables us to formally describe an “incremental” default reasoning system. This incremental system does not need to perform consistency checks before drawing tentative conclusions, but can instead adjust its beliefs when a default premise or conclusion is overturned in the face of convincing contradictory evidence. The system is therefore much more computationally viable than earlier approaches. Finally, we discuss the implementation of our ideas. We begin by considering general issues that need to be addressed when implementing a multivalued approach such as that we are proposing, and then turn to specific examples showing the results of an existing implementation. This single implementation is used to solve a digital simulation task using first-order logic, a diagnostic task using ATMSS as suggested by de Kleer and Williams, a problem in default reasoning as in Reiter’s default logic or McCarthy’s circumscription, and to solve the same problem more efficiently by combining default methods with justification information. All of these applications use the same general-purpose bilattice theorem prover and differ only in the choice of bilattice being considered.


Automics: souvenir generating photoware for theme parks

Automics is a photo-souvenir service which utilises mobile devices to support the capture, sharing and annotation of digital images amongst groups of visitors to theme parks. The prototype service mixes individual and group photo-capture with existing in-park, on-ride photo services, to allow users to create printed photo-stories. Herein we discuss initial fieldwork in theme parks that grounded the design of Automics, our development of the service prototype, and its real-world evaluation with theme park visitors. We relate our findings on user experience of the service to a literature on mobile photoware, finding implications for the design of souvenir services.


A DIRT-T A PPROACH TO U NSUPERVISED D OMAIN

Domain adaptation refers to the problem of leveraging labeled data in a source domain to learn an accurate model in a target domain where labels are scarce or unavailable. A recent approach for finding a common representation of the two domains is via domain adversarial training (Ganin & Lempitsky, 2015), which attempts to induce a feature extractor that matches the source and target feature distributions in some feature space. However, domain adversarial training faces two critical limitations: 1) if the feature extraction function has high-capacity, then feature distribution matching is a weak constraint, 2) in non-conservative domain adaptation (where no single classifier can perform well in both the source and target domains), training the model to do well on the source domain hurts performance on the target domain. In this paper, we address these issues through the lens of the cluster assumption, i.e., decision boundaries should not cross high-density data regions. We propose two novel and related models: 1) the Virtual Adversarial Domain Adaptation (VADA) model, which combines domain adversarial training with a penalty term that punishes violation of the cluster assumption; 2) the Decision-boundary Iterative Refinement Training with a Teacher (DIRT-T)1 model, which takes the VADA model as initialization and employs natural gradient steps to further minimize the cluster assumption violation. Extensive empirical results demonstrate that the combination of these two models significantly improve the state-of-the-art performance on the digit, traffic sign, and Wi-Fi recognition domain adaptation benchmarks.


SCRUM: An extension pattern language for hyper productive software development

The patterns of the SCRUM development method are presented as an extension pattern language to the existing organizational pattern languages. In the last few years, the SCRUM development method has rapidly gained recognition as an effective tool to hyper-productive software development. However, when SCRUM patterns are combined with other existing organizational patterns, they lead to highly adaptive, yet well-structured software development organizations. Also, decomposing SCRUM into patterns can guide adoption of only those parts of SCRUM that are applicable to a specific situation.


Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance

In a typical surveillance installation, a human operator has to constantly monitor a large array of video feeds for suspicious behaviour. As the number of cameras increases, information overload makes manual surveillance increasingly difficult, adding to other confounding factors such as human fatigue and boredom. The objective of an intelligent vision-based surveillance system is to automate the monitoring and event detection components of surveillance, alerting the operator only when unusual behaviour or other events of interest are detected. While most traditional methods for trajectory-based unusual behaviour detection rely on low-level trajectory features such as flow vectors or control points, this paper builds upon a recently introduced approach that makes use of higher-level features of intentionality. Individuals in the scene are modelled as intentional agents, and unusual behaviour is detected by evaluating the explicability of the agent’s trajectory with respect to known spatial goals. The proposed method extends the original goal-based approach in three ways: first, the spatial scene structure is learned in a training phase; second, a region transition model is learned to describe normal movement patterns between spatial regions; and third, classification of trajectories in progress is performed in a probabilistic framework using particle filtering. Experimental validation on three published third-party datasets demonstrates the validity of the proposed approach.


A stochastic model of human-machine interaction for learning dialog strategies

In this paper, we propose a quantitative model for dialog systems that can be used for learning the dialog strategy. We claim that the problem of dialog design can be formalized as an optimization problem with an objective function reflecting different dialog dimensions relevant for a given application. We also show that any dialog system can be formally described as a sequential decision process in terms of its state space, action set, and strategy. With additional assumptions about the state transition probabilities and cost assignment, a dialog system can be mapped to a stochastic model known as Markov decision process(MDP). A variety of data driven algorithms for finding the optimal strategy (i.e., the one that optimizes the criterion) is available within the MDP framework, based on reinforcement learning. For an effective use of the available training data we propose a combination of supervised and reinforcement learning: the supervised learning is used to estimate a model of the user, i.e., the MDP parameters that quantify the user’s behavior. Then a reinforcement learning algorithm is used to estimate the optimal strategy while the system interacts with the simulated user. This approach is tested for learning the strategy in an air travel information system (ATIS) task. The experimental results we present in this paper show that it is indeed possible to find a simple criterion, a state space representation, and a simulated user parameterization in order to automatically learn a relatively complex dialog behavior, similar to one that was heuristically designed by several research groups.


Reinforcement Learning for Spoken Dialogue Systems

Recently, a number of authors have proposed treating dialog ue systems as Markov decision processes (MDPs). However, the practical applica tion of MDP algorithms to dialogue systems faces a number of severe technical chall enges. We have built a general software tool (RLDS, for Reinforcement Learning fo r Dialogue Systems) based on the MDP framework, and have applied it to dialogue co rpora gathered from two dialogue systems built at AT&T Labs. Our experiment s demonstrate that RLDS holds promise as a tool for “browsing” and understandin g correlations in complex, temporally dependent dialogue corpora.


Learning from delayed rewards


An Efficient Algorithm for Fractal Analysis of Textures

In this paper we propose a new and efficient texture feature extraction method: the Segmentation-based Fractal Texture Analysis, or SFTA. The extraction algorithm consists in decomposing the input image into a set of binary images from which the fractal dimensions of the resulting regions are computed in order to describe segmented texture patterns. The decomposition of the input image is achieved by the Two-Threshold Binary Decomposition (TTBD) algorithm, which we also propose in this work. We evaluated SFTA for the tasks of content-based image retrieval (CBIR) and image classification, comparing its performance to that of other widely employed feature extraction methods such as Haralick and Gabor filter banks. SFTA achieved higher precision and accuracy for CBIR and image classification. Additionally, SFTA was at least 3.7 times faster than Gabor and 1.6 times faster than Haralick with respect to feature extraction time.


Voice-based assessments of trustworthiness, competence, and warmth in blind and sighted adults

The study of voice perception in congenitally blind individuals allows researchers rare insight into how a lifetime of visual deprivation affects the development of voice perception. Previous studies have suggested that blind adults outperform their sighted counterparts in low-level auditory tasks testing spatial localization and pitch discrimination, as well as in verbal speech processing; however, blind persons generally show no advantage in nonverbal voice recognition or discrimination tasks. The present study is the first to examine whether visual experience influences the development of social stereotypes that are formed on the basis of nonverbal vocal characteristics (i.e., voice pitch). Groups of 27 congenitally or early-blind adults and 23 sighted controls assessed the trustworthiness, competence, and warmth of men and women speaking a series of vowels, whose voice pitches had been experimentally raised or lowered. Blind and sighted listeners judged both men's and women's voices with lowered pitch as being more competent and trustworthy than voices with raised pitch. In contrast, raised-pitch voices were judged as being warmer than were lowered-pitch voices, but only for women's voices. Crucially, blind and sighted persons did not differ in their voice-based assessments of competence or warmth, or in their certainty of these assessments, whereas the association between low pitch and trustworthiness in women's voices was weaker among blind than sighted participants. This latter result suggests that blind persons may rely less heavily on nonverbal cues to trustworthiness compared to sighted persons. Ultimately, our findings suggest that robust perceptual associations that systematically link voice pitch to the social and personal dimensions of a speaker can develop without visual input.


Place navigation impaired in rats with hippocampal lesions

Electrophysiological studies have shown that single cells in the hippocampus respond during spatial learning and exploration1–4, some firing only when animals enter specific and restricted areas of a familiar environment. Deficits in spatial learning and memory are found after lesions of the hippocampus and its extrinsic fibre connections5,6 following damage to the medial septal nucleus which successfully disrupts the hippocampal theta rhythm7, and in senescent rats which also show a correlated reduction in synaptic enhancement on the perforant path input to the hippocampus8. We now report, using a novel behavioural procedure requiring search for a hidden goal, that, in addition to a spatial discrimination impairment, total hippocampal lesions also cause a profound and lasting placenavigational impairment that can be dissociated from correlated motor, motivational and reinforcement aspects of the procedure.


Performance anomaly of 802.11b

We analyze the performance of the IEEE 802.11b wireless local area networks. We have observed that when some mobile hosts use a lower bit rate than the others, the performance of all hosts is considerably degraded. Such a situation is a common case in wireless local area networks in which a host far away from an Access Point is subject to important signal fading and interference. To cope with this problem, the host changes its modulation type, which degrades its bit rate to some lower value. Typically, 802.11b products degrade the bit rate from 11 Mb/s to 5.5, 2, or 1 Mb/s when repeated unsuccessful frame transmissions are detected. In such a case, a host transmitting for example at 1 Mb/s reduces the throughput of all other hosts transmitting at 11 Mb/s to a low value below 1 Mb/s. The basic CSMA/CA channel access method is at the root of this anomaly: it guarantees an equal long term channel access probability to all hosts. When one host captures the channel for a long time because its bit rate is low, it penalizes other hosts that use the higher rate. We analyze the anomaly theoretically by deriving simple expressions for the useful throughput, validate them by means of simulation, and compare with several performance measurements.


Deadline Guaranteed Service for Multi-Tenant Cloud Storage

It is imperative for cloud storage systems to be able to provide deadline guaranteed services according to service level agreements (SLAs) for online services. In spite of many previous works on deadline aware solutions, most of them focus on scheduling work flows or resource reservation in datacenter networks but neglect the server overload problem in cloud storage systems that prevents providing the deadline guaranteed services. In this paper, we introduce a new form of SLAs, which enables each tenant to specify a percentage of its requests it wishes to serve within a specified deadline. We first identify the multiple objectives (i.e., traffic and latency minimization, resource utilization maximization) in developing schemes to satisfy the SLAs. To satisfy the SLAs while achieving the multi-objectives, we propose a Parallel Deadline Guaranteed (PDG) scheme, which schedules data reallocation (through load re-assignment and data replication) using a tree-based bottom-up parallel process. The observation from our model also motivates our deadline strictness clustered data allocation algorithm that maps tenants with the similar SLA strictness into the same server to enhance SLA guarantees. We further enhance PDG in supplying SLA guaranteed services through two algorithms: i) a prioritized data reallocation algorithm that deals with request arrival rate variation, and ii) an adaptive request retransmission algorithm that deals with SLA requirement variation. Our trace-driven experiments on a simulator and Amazon EC2 show the effectiveness of our schemes for guaranteeing the SLAs while achieving the multi-objectives.


High Quality Uniform Random Number Generation Using LUT Optimised State-transition Matrices

This paper presents a family of uniform random number generators designed for efficient implementation in Lookup table (LUT) based FPGA architectures. A generator with a period of 2j1 can be implemented using k flip-flops and k LUTs, and provides k random output bits each cycle. Each generator is based on a binary linear recurrence, with a state-transition matrix designed to make best use of all available LUT inputs in a given FPGA architecture, and to ensure that the critical path between all registers is a single LUT. This class of generator provides a higher sample rate per area than LFSR and Combined Tausworthe generators, and operates at similar or higher clock-rates. The statistical quality of the generators increases with k, and can be used to pass all common empirical tests such as Diehard, Crush and the NIST cryptographic test suite. Theoretical properties such as global equidistribution can also be calculated, and best and average case statistics shown. Due to the large number of random bits generated per cycle these generators can be used as a basis for generators with even higher statistical quality, and an example involving combination through addition is demonstrated.


Adversarial Text Generation Without Reinforcement Learning

Generative Adversarial Networks (GANs) have experienced a recent surge in popularity, performing competitively in a variety of tasks, especially in computer vision. However, GAN training has shown limited success in natural language processing. This is largely because sequences of text are discrete, and thus gradients cannot propagate from the discriminator to the generator. Recent solutions use reinforcement learning to propagate approximate gradients to the generator, but this is inefficient to train. We propose to utilize an autoencoder to learn a low-dimensional representation of sentences. A GAN is then trained to generate its own vectors in this space, which decode to realistic utterances. We report both random and interpolated samples from the generator. Visualization of sentence vectors indicate our model correctly learns the latent space of the autoencoder. Both human ratings and BLEU scores show that our model generates realistic text against competitive baselines.


Fast bayesian matching pursuit

A low-complexity recursive procedure is presented for minimum mean squared error (MMSE) estimation in linear regression models. A Gaussian mixture is chosen as the prior on the unknown parameter vector. The algorithm returns both an approximate MMSE estimate of the parameter vector and a set of high posterior probability mixing parameters. Emphasis is given to the case of a sparse parameter vector. Numerical simulations demonstrate estimation performance and illustrate the distinctions between MMSE estimation and MAP model selection. The set of high probability mixing parameters not only provides MAP basis selection, but also yields relative probabilities that reveal potential ambiguity in the sparse model.


Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines

Image processing pipelines combine the challenges of stencil computations and stream programs. They are composed of large graphs of different stencil stages, as well as complex reductions, and stages with global or data-dependent access patterns. Because of their complex structure, the performance difference between a naive implementation of a pipeline and an optimized one is often an order of magnitude. Efficient implementations require optimization of both parallelism and locality, but due to the nature of stencils, there is a fundamental tension between parallelism, locality, and introducing redundant recomputation of shared values.
 We present a systematic model of the tradeoff space fundamental to stencil pipelines, a schedule representation which describes concrete points in this space for each stage in an image processing pipeline, and an optimizing compiler for the Halide image processing language that synthesizes high performance implementations from a Halide algorithm and a schedule. Combining this compiler with stochastic search over the space of schedules enables terse, composable programs to achieve state-of-the-art performance on a wide range of real image processing pipelines, and across different hardware architectures, including multicores with SIMD, and heterogeneous CPU+GPU execution. From simple Halide programs written in a few hours, we demonstrate performance up to 5x faster than hand-tuned C, intrinsics, and CUDA implementations optimized by experts over weeks or months, for image processing applications beyond the reach of past automatic compilers.


Biomedical Event Extraction using Abstract Meaning Representation

We propose a novel, Abstract Meaning Representation (AMR) based approach to identifying molecular events/interactions in biomedical text. Our key contributions are: (1) an empirical validation of our hypothesis that an event is a subgraph of the AMR graph, (2) a neural network-based model that identifies such an event subgraph given an AMR, and (3) a distant supervision based approach to gather additional training data. We evaluate our approach on the 2013 Genia Event Extraction dataset1 (Kim et al., 2013) and show promising results.


Deep generative models: Survey

Generative models have found their way to the forefront of deep learning the last decade and so far, it seems that the hype will not fade away any time soon. In this paper, we give an overview of the most important building blocks of most recent revolutionary deep generative models such as RBM, DBM, DBN, VAE and GAN. We will also take a look at three of state-of-the-art generative models, namely PixelRNN, DRAW and NADE. We will delve into their unique architectures, the learning procedures and their potential and limitations. We will also review some of the known issues that arise when trying to design and train deep generative architectures using shallow ones and how different models deal with these issues. This paper is not meant to be a comprehensive study of these models, but rather a starting point for those who bear an interest in the field.


Critical event prediction for proactive management in large-scale computer clusters

As the complexity of distributed computing systems increases, systems management tasks require significantly higher levels of automation; examples include diagnosis and prediction based on real-time streams of computer events, setting alarms, and performing continuous monitoring. The core of autonomic computing, a recently proposed initiative towards next-generation IT-systems capable of 'self-healing', is the ability to analyze data in real-time and to predict potential problems. The goal is to avoid catastrophic failures through prompt execution of remedial actions.This paper describes an attempt to build a proactive prediction and control system for large clusters. We collected event logs containing various system reliability, availability and serviceability (RAS) events, and system activity reports (SARs) from a 350-node cluster system for a period of one year. The 'raw' system health measurements contain a great deal of redundant event data, which is either repetitive in nature or misaligned with respect to time. We applied a filtering technique and modeled the data into a set of primary and derived variables. These variables used probabilistic networks for establishing event correlations through prediction algorithms. We also evaluated the role of time-series methods, rule-based classification algorithms and Bayesian network models in event prediction.Based on historical data, our results suggest that it is feasible to predict system performance parameters (SARs) with a high degree of accuracy using time-series models. Rule-based classification techniques can be used to extract machine-event signatures to predict critical events with up to 70% accuracy.


Small business credit scoring: a comparison of logistic regression, neural network, and decision tree models

The paper compares the models for small business credit scoring developed by logistic regression, neural networks, and CART decision trees on a Croatian bank dataset. The models obtained by all three methodologies were estimated; then validated on the same hold-out sample, and their performance is compared. There is an evident significant difference among the best neural network model, decision tree model, and logistic regression model. The most successful neural network model was obtained by the probabilistic algorithm. The best model extracted the most important features for small business credit scoring from the observed data


SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine

We publicly release a new large-scale dataset, called SearchQA, for machine comprehension, or question-answering. Unlike recently released datasets, such as DeepMind CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to reflect a full pipeline of general question-answering. That is, we start not from an existing article and generate a question-answer pair, but start from an existing question-answer pair, crawled from J! Archive, and augment it with text snippets retrieved by Google. Following this approach, we built SearchQA, which consists of more than 140k question-answer pairs with each pair having 49.6 snippets on average. Each question-answer-context tuple of the SearchQA comes with additional meta-data such as the snippet’s URL, which we believe will be valuable resources for future research. We conduct human evaluation as well as test two baseline methods, one simple word selection and the other deep learning based, on the SearchQA. We show that there is a meaningful gap between the human and machine performances. This suggests that the proposed dataset could well serve as a benchmark for question-answering.


Learning Structured Representation for Text Classification via Reinforcement Learning

Representation learning is a fundamental problem in natural language processing. This paper studies how to learn a structured representation for text classification. Unlike most existing representation models that either use no structure or rely on pre-specified structures, we propose a reinforcement learning (RL) method to learn sentence representation by discovering optimized structures automatically. We demonstrate two attempts to build structured representation: Information Distilled LSTM (ID-LSTM) and Hierarchically Structured LSTM (HS-LSTM). ID-LSTM selects only important, taskrelevant words, and HS-LSTM discovers phrase structures in a sentence. Structure discovery in the two representation models is formulated as a sequential decision problem: current decision of structure discovery affects following decisions, which can be addressed by policy gradient RL. Results show that our method can learn task-friendly representations by identifying important words or task-relevant structures without explicit structure annotations, and thus yields competitive performance.


Privacy Paradox Revised: Pre-Existing Attitudes, Psychological Ownership, and Actual Disclosure

Prior research has pointed to discrepancies between users' privacy concerns and disclosure behaviors, denoted as the privacy paradox, and repeatedly highlighted the importance to find explanations for this dichotomy. In this regard, three approaches have been proposed by prior literature: (1) use of actual disclosure behavior rather than behavioral intentions, (2) systematic distinction between pre-existing attitudes and situation-specific privacy considerations, and (3) limited and irrational cognitive processes during decision-making. The current research proposes an experiment capable to test these three assumptions simultaneously. More precisely, the authors aim to explore the contextual nature of privacy-related decisions by systematically manipulating (1) individuals’ psychological ownership with regard to own private information, and (2) individuals’ affective states, while measuring (3) pre-existing attitudes as well as situation-specific risk and benefit perceptions, and (4) intentions as well as actual disclosure. Thus, the proposed study strives to uniquely add to the understanding of the privacy paradox.


Anomaly Detection in Noisy Images

Title of dissertation: Anomaly Detection in Noisy Images Xavier Gibert Serra, Ph.D. Examination, Fall 2015 Dissertation directed by: Professor Rama Chellappa Department of Electrical and Computer Engineering Finding rare events in multidimensional data is an important detection problem that has applications in many fields, such as risk estimation in insurance industry, finance, flood prediction, medical diagnosis, quality assurance, security, or safety in transportation. The occurrence of such anomalies is so infrequent that there is usually not enough training data to learn an accurate statistical model of the anomaly class. In some cases, such events may have never been observed, so the only information that is available is a set of normal samples and an assumed pairwise similarity function. Such metric may only be known up to a certain number of unspecified parameters, which would either need to be learned from training data, or fixed by a domain expert. Sometimes, the anomalous condition may be formulated algebraically, such as a measure exceeding a predefined threshold, but nuisance variables may complicate the estimation of such a measure. Change detection methods used in time series analysis are not easily extendable to the multidimensional case, where discontinuities are not localized to a single point. On the other hand, in higher dimensions, data exhibits more complex interdependencies, and there is redundancy that could be exploited to adaptively model the normal data. In the first part of this dissertation, we review the theoretical framework for anomaly detection in images and previous anomaly detection work done in the context of crack detection and detection of anomalous components in railway tracks. In the second part, we propose new anomaly detection algorithms. The fact that curvilinear discontinuities in images are sparse with respect to the frame of shearlets, allows us to pose this anomaly detection problem as basis pursuit optimization. Therefore, we pose the problem of detecting curvilinear anomalies in noisy textured images as a blind source separation problem under sparsity constraints, and propose an iterative shrinkage algorithm to solve it. Taking advantage of the parallel nature of this algorithm, we describe how this method can be accelerated using graphical processing units (GPU). Then, we propose a new method for finding defective components on railway tracks using cameras mounted on a train. We describe how to extract features and use a combination of classifiers to solve this problem. Then, we scale anomaly detection to bigger datasets with complex interdependencies. We show that the anomaly detection problem naturally fits in the multitask learning framework. The first task consists of learning a compact representation of the good samples, while the second task consists of learning the anomaly detector. Using deep convolutional neural networks, we show that it is possible to train a deep model with a limited number of anomalous examples. In sequential detection problems, the presence of time-variant nuisance parameters affect the detection performance. In the last part of this dissertation, we present a method for adaptively estimating the threshold of sequential detectors using Extreme Value Theory on a Bayesian framework. Finally, conclusions on the results obtained are provided, followed by a discussion of possible future work. Anomaly Detection in Noisy Images by Xavier Gibert Serra Dissertation submitted to the Faculty of the Graduate School of the University of Maryland, College Park in partial fulfillment of the requirements for the degree of Doctor of Philosophy 2015 Advisory Committee: Professor Rama Chellappa, Chair/Advisor Professor Piya Pal Professor Shuvra Bhattacharyya Professor Vishal M. Patel Professor Amitabh Varshney, Dean’s Representative


Building population mapping with aerial imagery and GIS data

Geospatial distribution of population at a scale of individual buildings is needed for analysis of people’s interaction with their local socio-economic and physical environments. High resolution aerial images are capable of capturing urban complexities and considered as a potential source for mapping urban features at this fine scale. This paper studies population mapping for individual buildings by using aerial imagery and other geographic data. Building footprints and heights are first determined from aerial images, digital terrain and surface models. City zoning maps allow the classification of the buildings as residential and non-residential. The use of additional ancillary geographic data further filters residential utility buildings out of the residential area and identifies houses and apartments. In the final step, census block population, which is publicly available from the U.S. Census, is disaggregated and mapped to individual residential buildings. This paper proposes a modified building population mapping model that takes into account the effects of different types of residential buildings. Detailed steps are described that lead to the identification of residential buildings from imagery and other GIS data layers. Estimated building populations are evaluated per census block with reference to the known census records. This paper presents and evaluates the results of building population mapping in areas of West Lafayette, Lafayette, and Wea Township, all in the state of Indiana, USA. © 2011 Published by Elsevier B.V.


Toward a Universal Cortical Algorithm: Examining Hierarchical Temporal Memory in Light of Frontal Cortical Function

Representations One aspect of frontal cortical function that has been given less attention than it is due, both in this review and in the literature in general, is the biological basis of the generation and manipulation of abstract frontal representations. Response properties of some PFC cells have been shown to correspond with abstract rules (Wallis et al, 2001), and models such as those of Norman & Shallice (1980, 1986; Shallice, 1982; Shallice & Burgess, 1991, 1996), Grafman (2002) and Fuster (1997) have emphasized the requirement that frontal cortex dynamically generate and manipulate abstract


An Adaption of BIOASQ Question Answering dataset for Machine Reading systems by Manual Annotations of Answer Spans

BIOASQ Task B Phase B challenge focuses on extracting answers from snippets for a given question. The dataset provided by the organizers contains answers, but not all their variants. Henceforth a manual annotation was performed to extract all forms of correct answers. This article shows the impact of using all occurrences of correct answers for training on the evaluation scores which are improved significantly.


QoE in Video Transmission: A User Experience-Driven Strategy

The increasing popularity of video (i.e., audio-visual) applications or services over both wired and wireless links has prompted recent growing interests in the investigations of quality of experience (QoE) in online video transmission. Conventional video quality metrics, such as peak-signal-to-noise-ratio and quality of service, only focus on the reception quality from the systematic perspective. As a result, they cannot represent the true visual experience of an individual user. Instead, the QoE introduces a user experience-driven strategy which puts special emphasis on the contextual and human factors in addition to the transmission system. This advantage has raised the popularity and widespread usage of QoE in video transmission. In this paper, we present an overview of selected issues pertaining to QoE and its recent applications in video transmission, with consideration of the compelling features of QoE (i.e., context and human factors). The selected issues include QoE modeling with influence factors in the end-to-end chain of video transmission, QoE assessment (including subjective test and objective QoE monitoring) and QoE management of video transmission over different types of networks. Through the literature review, we observe that the context and human factors in QoE-aware video transmission have attracted significant attentions since the past two to three years. A vast number of high quality works were published in this area, and will be highlighted in this survey. In addition to a thorough summary of recent progresses, we also present an outlook of future developments on QoE assessment and management in video transmission, especially focusing on the context and human factors that have not been addressed yet and the technical challenges that have not been completely solved so far. We believe that our overview and findings can provide a timely perspective on the related issues and the future research directions in QoE-oriented services over video communications.


Grounded Theory

Grounded theory was originally expounded by Barney Glaser and Anselm Strauss in their 1967 book The Discovery of Grounded Theory (Glaser and Strauss 1967). Reacting against what they saw as the dominance of hypothetico-deductive, theory-testing approaches, Glaser and Strauss proposed grounded theory as a way of building theory systematically using data obtained from social research. Since its first appearance, grounded theory has gone on to become ‘currently the most widely used and popular qualitative research method across a wide range of disciplines and subject areas’ (Bryant and Charmaz 2007: 1).


Optimal assay design for determining the in vitro sensitivity of ring stage Plasmodium falciparum to artemisinins.

Recent reports demonstrate that failure of artemisinin-based antimalarial therapies is associated with an altered response of early blood stage Plasmodium falciparum. This has led to increased interest in the use of pulse assays that mimic clinical drug exposure for analysing artemisinin sensitivity of highly synchronised ring stage parasites. We report a methodology for the reliable execution of drug pulse assays and detail a synchronisation strategy that produces well-defined tightly synchronised ring stage cultures in a convenient time-frame.


An MDP-based Recommender System

Typical Recommender systems adopt a static view of the recommendation process and treat it as a prediction problem. We argue that it is more appropriate to view the problem of generating recommendations as a sequential decision problem and, consequently, that Markov decision processes (MDP) provide a more appropriate model for Recommender systems. MDPs introduce two benefits: they take into account the long-term effects of each recommendation, and they take into account the expected value of each recommendation. To succeed in practice, an MDP-based Recommender system must employ a strong initial model; and the bulk of this paper is concerned with the generation of such a model. In particular, we suggest the use of an n-gram predictive model for generating the initial MDP. Ourn-gram model induces a Markovchain model of user behavior whose predictive accuracy is greater than that of existing predictive models. We describe our predictive model in detail and evaluate its performance on real data. In addition, we show how the model can be used in an MDP-based Recommender system.


From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews

Recommending products to consumers means not only understanding their tastes, but also understanding their level of experience. For example, it would be a mistake to recommend the iconic film Seven Samurai simply because a user enjoys other action movies; rather, we might conclude that they will eventually enjoy it---once they are ready. The same is true for beers, wines, gourmet foods---or any products where users have acquired tastes: the `best' products may not be the most 'accessible'. Thus our goal in this paper is to recommend products that a user will enjoy now, while acknowledging that their tastes may have changed over time, and may change again in the future. We model how tastes change due to the very act of consuming more products---in other words, as users become more experienced. We develop a latent factor recommendation system that explicitly accounts for each user's level of experience. We find that such a model not only leads to better recommendations, but also allows us to study the role of user experience and expertise on a novel dataset of fifteen million beer, wine, food, and movie reviews.


Item-based top-N recommendation algorithms

The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems---a personalized information filtering technology used to identify a set of items that will be of interest to a certain user. User-based collaborative filtering is the most successful technology for building recommender systems to date and is extensively used in many commercial recommender systems. Unfortunately, the computational complexity of these methods grows linearly with the number of customers, which in typical commercial applications can be several millions. To address these scalability concerns model-based recommendation techniques have been developed. These techniques analyze the user--item matrix to discover relations between the different items and use these relations to compute the list of recommendations.In this article, we present one such class of model-based recommendation algorithms that first determines the similarities between the various items and then uses them to identify the set of items to be recommended. The key steps in this class of algorithms are (i) the method used to compute the similarity between the items, and (ii) the method used to combine these similarities in order to compute the similarity between a basket of items and a candidate recommender item. Our experimental evaluation on eight real datasets shows that these item-based algorithms are up to two orders of magnitude faster than the traditional user-neighborhood based recommender systems and provide recommendations with comparable or better quality.


Dynamic Conversion Behavior at E-Commerce Sites

T paper develops a model of conversion behavior (i.e., converting store visits into purchases) that predicts each customer’s probability of purchasing based on an observed history of visits and purchases. We offer an individual-level probability model that allows for different forms of customer heterogeneity in a very flexible manner. Specifically, we decompose an individual’s conversion behavior into two components: one for accumulating visit effects and another for purchasing threshold effects. Each component is allowed to vary across households as well as over time. Visit effects capture the notion that store visits can play different roles in the purchasing process. For example, some visits are motivated by planned purchases, while others are associated with hedonic browsing (akin to window shopping); our model is able to accommodate these (and several other) types of visit-purchase relationships in a logical, parsimonious manner. The purchasing threshold captures the psychological resistance to online purchasing that may grow or shrink as a customer gains more experience with the purchasing process at a given website. We test different versions of the model that vary in the complexity of these two key components and also compare our general framework with popular alternatives such as logistic regression. We find that the proposed model offers excellent statistical properties, including its performance in a holdout validation sample, and also provides useful managerial diagnostics about the patterns underlying online buyer behavior.


Content-Based, Collaborative Recommendation

The problem of recommending items from some fixed database has been studied extensively, and two main paradigms have emerged. In content-based recommendation one tries to recommend items similar to those a given user has liked in the past, whereas in collaborative recommendation one identifies users whose tastes are similar to those of the given user and recommends items they have liked. Our approach in Fab has been to combine these two methods. Here, we explain how a hybrid system can incorporate the advantages of both methods while inheriting the disadvantages of neither. In addition to what one might call the “generic advantages” inherent in any hybrid system, the particular design of the Fab architecture brings two additional benefits. First, two scaling problems common to all Web services are addressed—an increasing number of users and an increasing number of documents. Second, the system automatically identifies emergent communities of interest in the user population, enabling enhanced group awareness and communications. Here we describe the two approaches for contentbased and collaborative recommendation, explain how a hybrid system can be created, and then describe Fab, an implementation of such a system. For more details on both the implemented architecture and the experimental design the reader is referred to [1]. The content-based approach to recommendation has its roots in the information retrieval (IR) community, and employs many of the same techniques. Text documents are recommended based on a comparison between their content and a user profile. Data


Splitting approaches for context-aware recommendation: an empirical study

User and item splitting are well-known approaches to context-aware recommendation. To perform item splitting, multiple copies of an item are created based on the contexts in which it has been rated. User splitting performs a similar treatment with respect to users. The combination of user and item splitting: UI splitting, splits both users and items in the data set to boost context-aware recommendations. In this paper, we perform an empirical comparison of these three context-aware splitting approaches (CASA) on multiple data sets, and we also compare them with other popular context-aware collaborative filtering (CACF) algorithms. To evaluate those algorithms, we propose new evaluation metrics specific to contextual recommendation. The experiments reveal that CASA typically outperform other popular CACF algorithms, but there is no clear winner among the three splitting approaches. However, we do find some underlying patterns or clues for the application of CASA.


Asynchronous Distributed Semi-Stochastic Gradient Optimization

Lemma 2 At a specific stage s and for a worker p, let g∗ i = ∇fi(w)−∇fi(w̃) +∇F (w̃), i ∈ Dp, then the following inequality holds, Ep [ Ei‖g i ‖ ] ≤ 2L [F (w̃)− F (w∗)] , Proof 2 Ei‖g i ‖ =Ei‖∇fi(w̃)−∇fi(w)−∇F (w̃)‖ =Ei‖∇fi(w̃)−∇fi(w)‖ − 2Ei〈∇F (w̃),∇fi(w̃)−∇fi(w)〉+ ‖∇F (w̃)‖ ≤2L (Fp(w̃)− Fp(w)− 〈∇Fp(w), w̃ − w∗〉) − 2〈∇F (w̃),∇Fp(w̃)−∇Fp(w)〉+ ‖∇F (w̃)‖, where the inequality is the result of applying Lemma 1. Further, taking expectation on both sides w.r.t. worker p, we get


Teaching-to-Learn and Learning-to-Teach for Multi-label Propagation

Multi-label propagation aims to transmit the multi-label information from labeled examples to unlabeled examples based on a weighted graph. Existing methods ignore the specific propagation difficulty of different unlabeled examples and conduct the propagation in an imperfect sequence, leading to the error-prone classification of some difficult examples with uncertain labels. To address this problem, this paper associates each possible label with a “teacher”, and proposes a “Multi-Label Teaching-to-Learn and Learning-toTeach” (ML-TLLT) algorithm, so that the entire propagation process is guided by the teachers and manipulated from simple examples to more difficult ones. In the teaching-to-learn step, the teachers select the simplest examples for the current propagation by investigating both the definitiveness of each possible label of the unlabeled examples, and the dependencies between labels revealed by the labeled examples. In the learning-to-teach step, the teachers reversely learn from the learner’s feedback to properly select the simplest examples for the next propagation. Thorough empirical studies show that due to the optimized propagation sequence designed by the teachers, ML-TLLT yields generally better performance than seven state-of-the-art methods on the typical multi-label benchmark datasets.


A 1-V, 16.9 ppm/$^{\circ}$C, 250 nA Switched-Capacitor CMOS Voltage Reference

An ultra low-power, precise voltage reference using a switched-capacitor technique in 0.35-μm CMOS is presented in this paper. The temperature dependence of the carrier mobility and channel length modulation effect can be effectively minimized by using 3.3 and 5 V N-type transistors to operate in the saturation and subthreshold regions, respectively. In place of resistors, a precise reference voltage with flexible trimming capability is achieved by using capacitors. When the supply voltage is 1 V and the temperature is 80°C, the supply current is 250 nA. The line sensitivity is 0.76%/V; the PSRR is -41 dB at 100 Hz and -17 dB at 10 MHz. Moreover, the occupied die area is 0.049 mm2.


Morphological Segmentation with Window LSTM Neural Networks

Morphological segmentation, which aims to break words into meaning-bearing morphemes, is an important task in natural language processing. Most previous work relies heavily on linguistic preprocessing. In this paper, we instead propose novel neural network architectures that learn the structure of input sequences directly from raw input words and are subsequently able to predict morphological boundaries. Our architectures rely on Long Short Term Memory (LSTM) units to accomplish this, but exploit windows of characters to capture more contextual information. Experiments on multiple languages confirm the effectiveness of our models on this task.


An overview of 3D object grasp synthesis algorithms

This overview presents computational algorithms for generating 3D object grasps with autonomous multi-fingered robotic hands. Robotic grasping has been an active research subject for decades, and a great deal of effort has been spent on grasp synthesis algorithms. Existing papers focus on reviewing the mechanics of grasping and the finger-object contact interactions [7] or robot hand design and their control [1]. Robot grasp synthesis algorithms have been reviewed in [63], but since then an important progress has been made toward applying learning techniques to the grasping problem. This overview focuses on analytical as well as empirical grasp synthesis approaches.


Assessing the effects of service quality and justice on customer satisfaction and the continuance intention of mobile value-added services: An empirical test of a multidimensional model

a r t i c l e i n f o Understanding the antecedents and consequences of customer satisfaction in the mobile communications market is important. This study explores the effects of service quality and justice on customer satisfaction, which, in turn, affects continuance intention of mobile services. Service quality, justice and customer satisfaction were measured by multiple dimensions. A research model was developed based on this multidimen-sional approach and was empirically examined with data collected from about one thousand users of mobile value-added services in China. Results show that all three dimensions of service quality (interaction quality, environment quality and outcome quality) have significant and positive effects on cumulative satisfaction while only one dimension of service quality (interaction quality) has a significant and positive effect on transaction-specific satisfaction. Besides procedural justice, the other two dimensions of justice (distribu-tive justice and interactional justice) significantly influence both transaction-specific satisfaction and cumulative satisfaction. Furthermore, both types of customer satisfaction have significant and positive effects on continuance intention. Implications for research and practice are discussed. With the rapid advancements of mobile network technologies, provision of various kinds of value-added services by mobile service providers is on the rise around the world. As the market becomes more and more mature, value-added services become more homogeneous and the competition for acquiring new customers and retaining existing customers becomes more intense. In this environment, customer satisfaction is a critical factor for mobile service providers to maintain or improve their market share and profitability. Prior studies have found that customer satisfaction contributes to a firm's profitability and customer retention [33,35]. In a reorganization of the communications industry in China between 2008 and 2009, the original six mobile network operators were reduced to three. Meanwhile, the availability of third-generation telecommunications technologies suggested that more mobile value-added services would be provided to the customers. A recent value-added services survey report on mobile communications conducted by Analysys in 2010 predicted that, the competition among existing mobile network operators would become fierce after the reorganization of the industry and the introduction of third-generation services. Thus, for these mobile network operators, in order to retain customers, enhancing customer satisfaction is an urgent task to tackle with. Moreover, as new mobile value-added services are released, service providers need to focus on if these new services appeal to customers and on the willingness of customers to continue to use the services. Therefore, understanding the …


A Low-Noise Amplifier With Tunable Interference Rejection for 3.1- to 10.6-GHz UWB Systems

An ultrawideband common-gate low noise amplifier with tunable interference rejection is presented. The proposed LNA embeds a tunable active notch filter to eliminate interferer at 5-GHz WLAN and employs a common-gate input stage and dual-resonant loads for wideband implementation. This LNA has been fabricated in a 0.18-¿m CMOS process. The measured maximum power gain is 13.2 dB and noise figure is 4.5-6.2 dB with bandwidth of 3.1-10.6 GHz. The interferer rejection is 8.2 dB compared to the maximum gain and 7.6 dB noise figure at 5.2 GHz , respectively. The measured input P1dB is -11 dBm at 10.3 GHz. It consumes 12.8 mA from 1.8-V supply voltage.


The effect of small disjuncts and class distribution on decision tree learning

.............................................................................................................................ii Acknowledgements........................................................................................................... iv Dedication .........................................................................................................................vi List of Tables....................................................................................................................xii List of Figures .................................................................................................................xiv


A strategic framework for good governance through e-governance optimization: A case study of Punjab in India

Purpose – The purpose of this paper is to attempt to find out whether the new information and communication technologies can make a significant contribution to the achievement of the objective of good governance. The study identifies the factors responsible for creating a conducive environment for effective and successful implementation of e-governance for achieving good governance and the possible barriers in the implementation of e governance applications. Based on the comprehensive analysis it proposes a strategic policy framework for good governance in Punjab in India. Punjab is a developed state ranked amongst some of the top states of India in terms of per capita income and infrastructure. Design/methodology/approach – The study designs a framework for good governance by getting the shared vision of all stakeholders about providing good quality administration and governance in the Indian context through “Participatory Stakeholder Assessment”. The study uses descriptive statistics, perception gap, ANOVA and factor analysis to identify the key factors for good governance, the priorities of public regarding e-services, the policy makers’ perspectives regarding good governance to be achieved through e-governance. Findings – The study captures the good governance factors mainly contributing to the shared vision. The study further highlights that most Indian citizens in Punjab today believe in the power of information and communication technology (ICT) and want to access e-governance services. Major factors causing pain and harassment to the citizens in getting the services from various government departments include: unreasonable delay, multiple visits even for small services; poor public infrastructure and its maintenance in government offices. In the understanding of citizens the most important factors for the success of e-governance services are: overall convenience and experience of the citizens; reduction in the corruption levels by improvement in the transparency of government functioning and awareness about the availability of service amongst general masses. Originality/value – The present study has evolved a shared vision of all stakeholders on good governance in the Indian context. It has opened up many new possibilities for the governments, not only to use ICTs and help them in prioritizing the governance areas for focused attention, but also help to understand the mindset of the modern citizenry, their priorities and what they consider as good governance. The study will help policy makers focus on these factors for enhancing speedy delivery of prioritized services and promote good governance in developing countries similar to India.


Deanonymizing mobility traces: using social network as a side-channel

Location-based services, which employ data from smartphones, vehicles, etc., are growing in popularity. To reduce the threat that shared location data poses to a user's privacy, some services anonymize or obfuscate this data. In this paper, we show these methods can be effectively defeated: a set of location traces can be deanonymized given an easily obtained social network graph. The key idea of our approach is that a user may be identified by those she meets: a "contact graph" identifying meetings between anonymized users in a set of traces can be structurally correlated with a social network graph, thereby identifying anonymized users. We demonstrate the effectiveness of our approach using three real world datasets: University of St Andrews mobility trace and social network (27 nodes each), SmallBlue contact trace and Facebook social network (125 nodes), and Infocom 2006 bluetooth contact traces and conference attendees' DBLP social network (78 nodes). Our experiments show that 80% of users are identified precisely, while only 8% are identified incorrectly, with the remainder mapped to a small set of users.


A topology control approach for utilizing multiple channels in multi-radio wireless mesh networks

We consider the channel assignment problem in a multi-radio wireless mesh network that involves assigning channels to radio interfaces for achieving efficient channel utilization. We propose the notion of a traffic-independent base channel assignment to ease coordination and enable dynamic, efficient and flexible channel assignment. We present a novel formulation of the base channel assignment as a topology control problem, and show that the resulting optimization problem is NP-complete. We then develop a new greedy heuristic channel assignment algorithm (termed CLICA) for finding connected, low interference topologies by utilizing multiple channels. Our extensive simulation studies show that the proposed CLICA algorithm can provide large reduction in interference (even with a small number of radios per node), which in turn leads to significant gains in both link layer and multihop performance in 802.11-based multi-radio mesh networks.


Pattern frequency representation for time series classification

The paper presents a new method for data transformation. The obtained data format enhances the efficiency of the time series classification. The transformation is realized in four steps, namely: time series segmentation, segment's feature representation, segments' binning and pattern frequency representation. The method is independent of the nature of the data, and it works well with temporal data from diverse areas. Its ability is proved by classification of different real datasets and is also compared with the results of other classification methods.


Unsupervised document classification using sequential information maximization

We present a novel sequential clustering algorithm which is motivated by the Information Bottleneck (IB) method. In contrast to the agglomerative IB algorithm, the new sequential (sIB) approach is guaranteed to converge to a local maximum of the information with time and space complexity typically linear in the data size. information, as required by the original IB principle. Moreover, the time and space complexity are significantly improved. We apply this algorithm to unsupervised document classification. In our evaluation, on small and medium size corpora, the sIB is found to be consistently superior to all the other clustering methods we examine, typically by a significant margin. Moreover, the sIB results are comparable to those obtained by a supervised Naive Bayes classifier. Finally, we propose a simple procedure for trading cluster's recall to gain higher precision, and show how this approach can extract clusters which match the existing topics of the corpus almost perfectly.


The Optimal Control of Partially Observable Markov Processes over a Finite Horizon


Contextual RNN-GANs for Abstract Reasoning Diagram Generation

Education University of California, Berkeley (2008-2013) Ph.D. in Computer Science Thesis: Surface Web Semantics for Structured Natural Language Processing Advisor: Dan Klein. Committee members: Dan Klein, Marti Hearst, Line Mikkelsen, Nelson Morgan University of California, Berkeley (2012) Master of Science (M.S.) in Computer Science Thesis: An All-Fragments Grammar for Simple and Accurate Parsing Advisor: Dan Klein Indian Institute of Technology, Kanpur (2004-2008) Bachelor of Technology (B.Tech.) in Computer Science and Engineering GPA: 3.96/4.00 (Institute and Department Rank 2) Cornell University (Summer 2007) CS490 (Independent Research and Reading) GPA: 4.00/4.00 Advisors: Lillian Lee, Claire Cardie


An Energy-Efficient and Wide-Range Voltage Level Shifter With Dual Current Mirror

This brief presents an energy-efficient level shifter (LS) to convert a subthreshold input signal to an above-threshold output signal. In order to achieve a wide range of conversion, a dual current mirror (CM) structure consisting of a virtual CM and an auxiliary CM is proposed. The circuit has been implemented and optimized in SMIC 40-nm technology. The postlayout simulation demonstrates that the new LS can achieve voltage conversion from 0.2 to 1.1 V. Moreover, at the target voltage of 0.3 V, the proposed LS exhibits an average propagation delay of 66.48 ns, a total energy per transition of 72.31 fJ, and a static power consumption of 88.4 pW, demonstrating an improvement of <inline-formula> <tex-math notation="LaTeX">$6.0\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$13.1\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$89.0\times $ </tex-math></inline-formula>, respectively, when compared with Wilson CM-based LS.


A 60 V Auto-Zero and Chopper Operational Amplifier With 800 kHz Interleaved Clocks and Input Bias Current Trimming

An auto-zero and chopper operational amplifier with a 4.5-60 V supply voltage range is realized, using a 0.18 μm CMOS process augmented by 5 V CMOS and 60 V DMOS transistors. It achieves a maximum offset voltage drift of 0.02 μV/°C, a minimum CMRR of 145 dB, a noise PSD of 6.8 nV/√Hz, and a 3.1 MHz unity gain bandwidth, while dissipating 840 μA of current. Up-modulated chopper ripple is suppressed by auto- zeroing. Furthermore, glitches from the charge injection of the input switches are mitigated by employing six parallel input stages with 800 kHz interleaved clocks. This moves the majority of the glitch energy up to 4.8 MHz, while leaving little energy at 800 kHz. As a result, the requirements on an external low-pass glitch filter is relaxed, and a wider usable signal bandwidth can be obtained. Maximum input bias current due to charge injection mismatch is reduced from 1.5 nA to 150 pA by post production trimming with an on-chip charge mismatch compensation circuit.


Laser hair removal.

The extended theory of selective photothermolysis enables the laser surgeon to target and destroy hair follicles, thereby leading to hair removal. Today, laser hair removal (LHR) is the most commonly requested cosmetic procedure in the world and is routinely performed by dermatologists, other physicians, and non-physician personnel with variable efficacy. The ideal candidate for LHR is fair skinned with dark terminal hair; however, LHR can today be successfully performed in all skin types. Knowledge of hair follicle anatomy and physiology, proper patient selection and preoperative preparation, principles of laser safety, familiarity with the various laser/light devices, and a thorough understanding of laser-tissue interactions are vital to optimizing treatment efficacy while minimizing complications and side effects.


Psychological Well-Being in Adult Life

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ENN: Extended Nearest Neighbor Method for Pattern Recognition [Research Frontier]

This article introduces a new supervised classification method - the extended nearest neighbor (ENN) - that predicts input patterns according to the maximum gain of intra-class coherence. Unlike the classic k-nearest neighbor (KNN) method, in which only the nearest neighbors of a test sample are used to estimate a group membership, the ENN method makes a prediction in a "two-way communication" style: it considers not only who are the nearest neighbors of the test sample, but also who consider the test sample as their nearest neighbors. By exploiting the generalized class-wise statistics from all training data by iteratively assuming all the possible class memberships of a test sample, the ENN is able to learn from the global distribution, therefore improving pattern recognition performance and providing a powerful technique for a wide range of data analysis applications.


Fast and Accurate Template Matching Using Pixel Rearrangement on the GPU

A GPU (Graphics Processing Unit) is a specialized processor for graphics processing. GPUs have the ability to perform high-speed parallel processing using its many processing cores. To utilize the powerful computing ability, GPUs are widely used for general purpose processing. The main contribution of this paper is to show a new template matching algorithm using pixel rearrangement. Template Matching is a technique for finding small parts of an image which match a template image. The feature of our proposed algorithm is that using pixel rearrangement, multiple low-resolution images are generated and template matching for the low-resolution images is performed to reduce the computing time. Also, we implemented our algorithm on a GPU system. The experimental results show that, for an input image with size of 4096 $\times$ 4096 and a template image with size of 256 $\times$ 256, our implementation can achieve a speedup factor of approximately 78 times over the conventional sequential implementation.


Visual Digital Signature Scheme: A New Approach

—A digital signature is an important public-key primitive that performs the function of conventional handwritten signatures for entity authentication, data integrity, and non-repudiation, especially within the electronic commerce environment. Currently, most conventional digital signature schemes are based on mathematical hard problems. These mathematical algorithms require computers to perform the heavy and complex computations to generate and verify the keys and signatures. In 1995, Naor and Shamir proposed a visual cryptography (VC) for binary images. VC has high security and requires simple computations. The purpose of this paper is to provide an alternative to the current digital signature technology. In this paper, we introduce a new digital signature scheme based on the concept of a non-expansion visual cryptography. A visual digital signature scheme is a method to enable visual verification of the authenticity of an image in an insecure environment without the need to perform any complex computations. Our proposed scheme generates visual shares and manipulates them using the simple Boolean operations OR rather than generating and computing large and long random integer values as in the conventional digital signature schemes currently in use.


Analyzing Stability in Wide-Area Network Performance

The Internet is a very large scale, complex, dynamical system that is hard to model and analyze. In this paper, we develop and analyze statistical models for the observed end-to-end network performance based on extensive packet-level traces (consisting of approximately 1.5 billion packets) collected from the primary Web site for the Atlanta Summer Olympic Games in 1996. We find that observed mean throughputs for these transfers measured over 60 million complete connections vary widely as a function of end-host location and time of day, confirming that the Internet is characterized by a large degree of heterogeneity. Despite this heterogeneity, we find (using best-fit linear regression techniques) that we can express the throughput for Web transfers to most hosts as a random variable with a log-normal distribution. Then, using observed throughput as the control parameter, we attempt to quantify the spatial (statistical similarity across neighboring hosts) and temporal (persistence over time) stability of network performance. We find that Internet hosts that are close to each other often have almost identically distributed probability distributions of throughput. We also find that throughputs to individual hosts often do not change appreciably for several minutes. Overall, these results indicate that there is promise in protocol mechanisms that cache and share network characteristics both within a single host and amongst nearby hosts.


Learning to Grade Short Answer Questions using Semantic Similarity Measures and Dependency Graph Alignments

In this work we address the task of computerassisted assessment of short student answers. We combine several graph alignment features with lexical semantic similarity measures using machine learning techniques and show that the student answers can be more accurately graded than if the semantic measures were used in isolation. We also present a first attempt to align the dependency graphs of the student and the instructor answers in order to make use of a structural component in the automatic grading of student answers.


Tuned Models of Peer Assessment in MOOCs

In massive open-access online courses (MOOCs), peer grading serves as a critical tool for scaling the grading of complex, open-ended assignments to courses with tens or hundreds of thousands of students. But despite promising initial trials, it does not always deliver accurate results compared to human experts. In this paper, we develop algorithms for estimating and correcting for grader biases and reliabilities, showing significant improvement in peer grading accuracy on real data with 63,199 peer grades from Coursera’s HCI course offerings — the largest peer grading networks analysed to date. We relate grader biases and reliabilities to other student factors such as engagement, performance as well as commenting style. We also show that our model can lead to more intelligent assignment of graders to gradees.


The critical importance of retrieval for learning.

Learning is often considered complete when a student can produce the correct answer to a question. In our research, students in one condition learned foreign language vocabulary words in the standard paradigm of repeated study-test trials. In three other conditions, once a student had correctly produced the vocabulary item, it was repeatedly studied but dropped from further testing, repeatedly tested but dropped from further study, or dropped from both study and test. Repeated studying after learning had no effect on delayed recall, but repeated testing produced a large positive effect. In addition, students' predictions of their performance were uncorrelated with actual performance. The results demonstrate the critical role of retrieval practice in consolidating learning and show that even university students seem unaware of this fact.


Personalized Online Education - A Crowdsourcing Challenge

Interest in online education is surging, as dramatized by the success of Khan Academy and recent Stanford online courses, but the technology for online education is in its infancy. Crowdsourcing mechanisms will likely be essential in order to reach the full potential of this medium. This paper sketches some of the challenges and directions we hope HCOMP researchers will ad-


Powergrading: a Clustering Approach to Amplify Human Effort for Short Answer Grading

We introduce a new approach to the machine-assisted grading of short answer questions. We follow past work in automated grading by first training a similarity metric between student responses, but then go on to use this metric to group responses into clusters and subclusters. The resulting groupings allow teachers to grade multiple responses with a single action, provide rich feedback to groups of similar answers, and discover modalities of misunderstanding among students; we refer to this amplification of grader effort as “powergrading.” We develop the means to further reduce teacher effort by automatically performing actions when an answer key is available. We show results in terms of grading progress with a small “budget” of human actions, both from our method and an LDA-based approach, on a test corpus of 10 questions answered by 698 respondents.


EARLY DEVELOPMENTS OF A PARALLELLY ACTUATED HUMANOID , SAFFIR

This paper presents the design of our new 33 degree of freedom full size humanoid robot, SAFFiR (Shipboard Autonomous Fire Fighting Robot). The goal of this research project is to realize a high performance mixed force and position controlled robot with parallel actuation. The robot has two 6 DOF legs and arms, a waist, neck, and 3 DOF hands/fingers. The design is characterized by a central lightweight skeleton actuated with modular ballscrew driven force controllable linear actuators arranged in a parallel fashion around the joints. Sensory feedback on board the robot includes an inertial measurement unit, force and position output of each actuator, as well as 6 axis force/torque measurements from the feet. The lower body of the robot has been fabricated and a rudimentary walking algorithm implemented while the upper body fabrication is completed. Preliminary walking experiments show that parallel actuation successfully minimizes the loads through individual actuators.


QuizRDF: search technology for the semantic Web

An information-seeking system is described which combines traditional keyword querying of WWW resources with the ability to browse and query against RDF annotations of those resources. RDF(S) and RDF are used to specify and populate an ontology and the resultant RDF annotations are then indexed along with the full text of the annotated resources. The resultant index allows both keyword querying against the full text of the document and the literal values occurring in the RDF annotations, along with the ability to browse and query the ontology. We motivate our approach as a key enabler for fully exploiting the semantic Web in the area of knowledge management and argue that the ability to combine searching and browsing behaviours more fully supports a typical information-seeking task. The approach is characterised as "low threshold, high ceiling" in the sense that where RDF annotations exist they are exploited for an improved information-seeking experience but where they do not yet exist, a search capability is still available.


Squadron: Incentivizing Quality-Aware Mission-Driven Crowd Sensing

Recent years have witnessed the success of mobile crowd sensing systems, which outsource sensory data collection to the public crowd equipped with various mobile devices in a wide spectrum of civilian applications. We envision that crowd sensing could as well be very useful in a whole host of mission-driven scenarios, such as peacekeeping operations, non-combatant evacuations, and humanitarian missions. However, the power of crowd sensing could not be fully unleashed in mission-driven crowd sensing (MiCS) systems, unless workers are effectively incentivized to participate. Therefore, in this paper, taking into consideration workers' diverse quality of information (QoI), we propose Squadron, a quality-aware incentive mechanism for MiCS systems. Squadron adopts the reverse auction framework. It approximately minimizes the platform's total payment for worker recruiting in a computationally efficient manner, and recruits workers who potentially could provide high quality data. Furthermore, it also satisfies the desirable properties of truth-fulness and individual rationality. Through rigorous theoretical analysis, as well as extensive simulations, we validate the various aforementioned desirable properties held by Squadron.


Where to Add Actions in Human-in-the-Loop Reinforcement Learning

In order for reinforcement learning systems to learn quickly in vast action spaces such as the space of all possible pieces of text or the space of all images, leveraging human intuition and creativity is key. However, a human-designed action space is likely to be initially imperfect and limited; furthermore, humans may improve at creating useful actions with practice or new information. Therefore, we propose a framework in which a human adds actions to a reinforcement learning system over time to boost performance. In this setting, however, it is key that we use human effort as efficiently as possible, and one significant danger is that humans waste effort adding actions at places (states) that aren’t very important. Therefore, we propose Expected Local Improvement (ELI), an automated method which selects states at which to query humans for a new action. We evaluate ELI on a variety of simulated domains adapted from the literature, including domains with over a million actions and domains where the simulated experts change over time. We find ELI demonstrates excellent empirical performance, even in settings where the synthetic “experts” are quite poor.


Bad Subsequences of Well-Known Linear Congruential Pseudo-Random Number Generators

We present a spectral test analysis of full-period subsequences with small step sizes generated by well-known linear congruential pseudorandom number generators. Subsequences may occur in certain simulation problems or as a method to get parallel streams of pseudorandom numbers. Applying the spectral test, it is possible to find bad subsequences with small step sizes for almost all linear pseudorandom number generators currently in use.


An Introduction to Temporal Graphs: An Algorithmic Perspective

A temporal graph is, informally speaking, a graph that changes with time. When time is discrete and only the relationships between the participating entities may change and not the entities themselves, a temporal graph may be viewed as a sequence G1, G2 . . . , Gl of static graphs over the same (static) set of nodes V . Though static graphs have been extensively studied, for their temporal generalization we are still far from having a concrete set of structural and algorithmic principles. Recent research shows that many graph properties and problems become radically different and usually substantially more difficult when an extra time dimension is added to them. Moreover, there is already a rich and rapidly growing set of modern systems and applications that can be naturally modeled and studied via temporal graphs. This, further motivates the need for the development of a temporal extension of graph theory. We survey here recent results on temporal graphs and temporal graph problems that have appeared in the Computer Science community.


Analytical Solution of Air-Gap Field in Permanent-Magnet Motors Taking Into Account the Effect of Pole Transition Over Slots

We present an analytical method to study magnetic fields in permanent-magnet brushless motors, taking into consideration the effect of stator slotting. Our attention concentrates particularly on the instantaneous field distribution in the slot regions where the magnet pole transition passes over the slot opening. The accuracy in the flux density vector distribution in such regions plays a critical role in the prediction of the magnetic forces, i.e., the cogging torque and unbalanced magnetic pull. However, the currently available analytical solutions for calculating air-gap fields in permanent magnet motors can estimate only the distribution of the flux density component in the radial direction. Magnetic field and forces computed by the new analytical method agree well with those obtained by the finite-element method. The analytical method provides a useful tool for design and optimization of permanent-magnet motors.


Learning to learn with the informative vector machine

This paper describes an efficient method for learning the parameters of a Gaussian process (GP). The parameters are learned from multiple tasks which are assumed to have been drawn independently from the same GP prior. An efficient algorithm is obtained by extending the informative vector machine (IVM) algorithm to handle the multi-task learning case. The multi-task IVM (MTIVM) saves computation by greedily selecting the most informative examples from the separate tasks. The MT-IVM is also shown to be more efficient than random sub-sampling on an artificial data-set and more effective than the traditional IVM in a speaker dependent phoneme recognition task.


Test setup for multi-finger gripper control based on robot operating system (ROS)

This paper presents the concept for a test setup to prototype control algorithms for a multi-finger gripper. The human-robot interface has to provide enough degrees of freedom (DOF) to intuitively control advanced gripper and to accomplish this task a simple sensor glove equipped with flex and force sensors has been prepared for this project. The software architecture has to support both real hardware and simulation, as well as flexible communication standards, therefore, ROS architecture was employed in this matter. Paper presents some preliminary results for using sensor glove and simulated model of the three finger gripper.