title: Modeling Cadmium Adsorption by Activated Carbon Using the Langmuir and Freundlich Isotherm Expressions

abstract: Abstract Cadmium adsorption isotherms were conducted using two commercially available powdered activated carbons (PACs). Isotherms were conducted at several pH values and metal and carbon concentrations. Both PACs removed significant amounts of cadmium, and removal was a strong function of solution pH: increasing the solution pH increased cadmium adsorption. Adsorption data at a specific pH were successfully modeled using both the Langmuir and Freundlich isotherms. Adsorption and surface precipitation were hypothesized to be the operative removal mechanisms. Cadmium removal is strongly related to the carbon's pHzpc, acid-base characteristics, and surface charge-pH relationship. Surface area, an important adsorption parameter for organic adsorbates, does not appear to influence metal removal strongly.


title: Personalized Navigation and Random Walk on a Complex Heterogeneous Graph

abstract: As data in many disciplines continues to grow at a frantic pace, a more heterogeneous set of information sources both can and need to be connected in innovative ways to improve information search and recommendation systems. Using information retrieval (IR) as an example, classical search techniques are based on limited information, including query terms, the content of documents, and basic user characteristics. However, recent studies have started to investigate additional information resources, including semantic web data, knowledge graphs, linguistic features, user email account content, social networks, user behavior, the search context, and other sources. Intuitively, a heterogeneous graph integrating all these information sources could enhance search performance. However, the complexity of such graphs challenge most existing graph mining algorithms. In this study, we propose a novel approach for a personalized random walk over a complex heterogeneous graph; which we refer to as Personalized Graph Navigation (PGN). Unlike earlier expert-guided random walk approaches, by using an EM framework, PGN estimates the personalized usefulness probability distribution for each edge type (the latent variable), which can be used as a random walk navigation profile for a user on a heterogeneous graph. While PGN can cope with information retrieval/recommendation problems at a low cost, this method also transforms a complex heterogeneous graph into a homogeneous graph, allowing existing homogeneous graph mining algorithms to be applied to a heterogeneous graph.


title: Effects of non-invasive cortical stimulation on skilled motor function in chronic stroke

abstract: Summary Stroke is a leading cause of adult motor disability. Despite recent progress, recovery of motor function after stroke is usually incomplete. This double blind, Sham-controlled, crossover study was designed to test the hypothesis that non-invasive stimulation of the motor cortex could improve motor function in the paretic hand of patients with chronic stroke. Hand function was measured using the Jebsen–Taylor Hand Function Test (JTT), a widely used, well validated test for functional motor assessment that reflects activities of daily living. JTT measured in the paretic hand improved significantly with non-invasive transcranial direct current stimulation (tDCS), but not with Sham, an effect that outlasted the stimulation period, was present in every single patient tested and that correlated with an increment in motor cortical excitability within the affected hemisphere, expressed as increased recruitment curves (RC) and reduced shortinterval intracortical inhibition. These results document a beneficial effect of non-invasive cortical stimulation on a set of hand functions that mimic activities of daily living in the paretic hand of patients with chronic stroke, and suggest that this interventional strategy in combination with customary rehabilitative treatments may play an adjuvant role in neurorehabilitation.


title: Visualizing work processes in software engineering with developer rivers

abstract: Work processes involving dozens or hundreds of collaborators are complex and difficult to manage. Problems within the process may have severe organizational and financial consequences. Visualization helps monitor and analyze those processes. In this paper, we study the development of large software systems as an example of a complex work process. We introduce Developer Rivers, a timeline-based visualization technique that shows how developers work on software modules. The flow of developers' activity is visualized by a river metaphor: activities are transferred between modules represented as rivers. Interactively switching between hierarchically organized modules and workload metrics allows for exploring multiple facets of the work process. We study typical development patterns by applying our visualization to Python and the Linux kernel.


title: Parser Features for Sentence Grammaticality Classification

abstract: Automatically judging sentences for their grammaticality is potentially useful for several purposes — evaluating language technology systems, assessing language competence of second or foreign language learners, and so on. Previous work has examined parser ‘byproducts’, in particular parse probabilities, to distinguish grammatical sentences from ungrammatical ones. The aim of the present paper is to examine whether the primary output of a parser, which we characterise via CFG production rules embodied in a parse, contains useful information for sentence grammaticality classification; and also to examine which feature selection metrics are most useful in this task. Our results show that using gold standard production rules alone can improve over using parse probabilities alone. Combining parser-produced production rules with parse probabilities further produces an improvement of 1.6% on average in the overall classification accuracy.


title: Challenges in Big Data Application: A Review

abstract: invention of advanced technology, enhanced capacity of storage media, maturity of information technology and popularity of social media, business intelligence and Scientific invention, produces huge amount of data which made ample set of information that is responsible for birth of new concept well known as big data. Big data analytics is the process of examining large amounts of data. The analysis is done on huge amount of data which is structure, semi structure and unstructured. In big data, data is generated at exponentially for reason of increase use of social media, email, document and sensor data. The growth of data has affected all fields, whether it is business sector or the world of science. In this paper, the process of system is reviewed for managing "Big Data" and today's activities on big data tools and techniques.


title: Improved performance of face recognition using CNN with constrained triplet loss layer

abstract: Recognizing human faces is one of the most popular problems in the field of pattern recognition. Many approaches and methods have been tested and applied on the topic, especially neural networks. This paper proposed a new loss layer that can be replaced at the bottom of a neural network architecture in terms of face recognition, called constrained triplet loss layer (CTLL). In order to make more confident predictions and classifications, this loss layer helps the deep learning model to specify further distinguishable clusters between different people (classes) by placing extra constraints on images of the same person (intra-person) while putting margins on images of a different person (inter-person). This proposed constrained triplet loss layer improved the recognition accuracy on faces by 2%.


Using Network Metrics in Soccer: A Macro-Analysis


title: Joint Channel Estimation and Data Detection in MIMO-OFDM Systems: A Sparse Bayesian Learning Approach

abstract: The impulse response of wireless channels between the N-t transmit and N-r receive antennas of a MIMO-OFDM system are group approximately sparse (ga-sparse), i.e., NtNt the channels have a small number of significant paths relative to the channel delay spread and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wireless channels are also group approximately cluster-sparse (gac-sparse), i.e., every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this paper, we cast the problem of estimating the ga-sparse and gac-sparse block-fading and time-varying channels in the sparse Bayesian learning (SBL) framework and propose a bouquet of novel algorithms for pilot-based channel estimation, and joint channel estimation and data detection, in MIMO-OFDM systems. The proposed algorithms are capable of estimating the sparse wireless channels even when the measurement matrix is only partially known. Further, we employ a first-order autoregressive modeling of the temporal variation of the ga-sparse and gac-sparse channels and propose a recursive Kalman filtering and smoothing (KFS) technique for joint channel estimation, tracking, and data detection. We also propose novel, parallel-implementation based, low-complexity techniques for estimating gac-sparse channels. Monte Carlo simulations illustrate the benefit of exploiting the gac-sparse structure in the wireless channel in terms of the mean square error (MSE) and coded bit error rate (BER) performance.


Detecting Cloning Attack in Low-Cost Passive RFID Tags An Analytic Comparison between KILL Passwords and Synchronized Secrets Obinna


title: Analytical method for optimization of maintenance policy based on available system failure data

abstract: An analytical optimization method for preventive maintenance (PM) policy with minimal repair at failure, periodic maintenance, and replacement is proposed for systems with historical failure time data influenced by a current PM policy. The method includes a new imperfect PM model based on Weibull distribution and incorporates the current maintenance interval T0 and the optimal maintenance interval T to be found. The Weibull parameters are analytically estimated using maximum likelihood estimation. Based on this model, the optimal number of PM and the optimal maintenance interval for minimizing the expected cost over an infinite time horizon are also analytically determined. A number of examples are presented involving different failure time data and current maintenance intervals to analyze how the proposed analytical optimization method for periodic PM policy performances in response to changes in the distribution of the failure data and the current maintenance interval.


Development of Measurement-based Time-domain Models and its application to Wafer Level Packaging Approved by:


title: Analyzing Sequence Data Based on Conditional Random Fields with Co-training

abstract: Sequence data plays an important role in data analysis applications, such as sequence classification. One important aspect of sequence data analysis is to obtain the labeled sequence data and use a machine learning model to predict the sequence structures. Conditional Random Fields (CRF) is such a machine learning method which is popular used in sequential data analysis. This is because that CRF can effectively capture the data correlations in context with abundant training data. However, in real applications, the labeled training data is usually difficult to be collected. In order to reduce the requirement of the amount of the labeled training data, a novel model is proposed named Conditional Random Fields with Co-training (Co-CRF). The Co-CRF model can work well even on the reduced labeled training data. Empirical results show that Co-CRF can produce a more accurate analysis than the traditional CRF, especially with very limited training data.


title: Flowchart techniques for structured programming

abstract: With the advent of structured programming and GOTO-less programming a method is needed to model computation in simply ordered structures, each representing a complete thought possibly defined in terms of other thoughts as yet undefined. A model is needed which prevents unrestricted transfers of control and has a control structure closer to languages amenable to structured programming. We present an attempt at such a model.


title: Asynchronous Decentralized Parallel Stochastic Gradient Descent

abstract: Recent work shows that decentralized parallel stochastic gradient decent (D-PSGD) can outperform its centralized counterpart both theoretically and practically. While asynchronous parallelism is a powerful technology to improve the efficiency of parallelism in distributed machine learning platforms and has been widely used in many popular machine learning softwares and solvers based on centralized parallel protocols such as Tensorflow, it still remains unclear how to apply the asynchronous parallelism to improve the efficiency of decentralized parallel algorithms. This paper proposes an asynchronous decentralize parallel stochastic gradient descent algorithm to apply the asynchronous parallelism technology to decentralized algorithms. Our theoretical analysis provides the convergence rate or equivalently the computational complexity, which is consistent with many special cases and indicates we can achieve nice linear speedup when we increase the number of nodes or the batchsize. Extensive experiments in deep learning validate the proposed algorithm.


title: Material Editing Using a Physically Based Rendering Network

abstract: The ability to edit materials of objects in images is desirable by many content creators. However, this is an extremely challenging task as it requires to disentangle intrinsic physical properties of an image. We propose an end-to-end network architecture that replicates the forward image formation process to accomplish this task. Specifically, given a single image, the network first predicts intrinsic properties, i.e. shape, illumination, and material, which are then provided to a rendering layer. This layer performs in-network image synthesis, thereby enabling the network to understand the physics behind the image formation process. The proposed rendering layer is fully differentiable, supports both diffuse and specular materials, and thus can be applicable in a variety of problem settings. We demonstrate a rich set of visually plausible material editing examples and provide an extensive comparative study.


title: Multi-label ensemble learning

abstract: Multi-label learning aims at predicting potentially multiple labels for a given instance. Conventional multi-label learning approaches focus on exploiting the label correlations to improve the accuracy of the learner by building an individual multi-label learner or a combined learner based upon a group of single-label learners. However, the generalization ability of such individual learner can be weak. It is well known that ensemble learning can effectively improve the generalization ability of learning systems by constructing multiple base learners and the performance of an ensemble is related to the both accuracy and diversity of base learners. In this paper, we study the problem of multilabel ensemble learning. Specifically, we aim at improving the generalization ability of multi-label learning systems by constructing a group of multilabel base learners which are both accurate and diverse. We propose a novel solution, called EnML, to effectively augment the accuracy as well as the diversity of multi-label base learners. In detail, we design two objective functions to evaluate the accuracy and diversity of multilabel base learners, respectively, and EnML simultaneously optimizes these two objectives with an evolutionary multi-objective optimization method. Experiments on real-world multi-label learning tasks validate the effectiveness of our approach against other well-established methods.


title: Ontology Evaluation and Ranking using OntoQA

abstract: Ontologies form the cornerstone of the Semantic Web and are intended to help researchers to analyze and share knowledge, and as more ontologies are being introduced, it is difficult for users to find good ontologies related to their work. Therefore, tools for evaluating and ranking the ontologies are needed. In this paper, we present OntoQA, a tool that evaluates ontologies related to a certain set of terms and then ranks them according a set of metrics that captures different aspects of ontologies. Since there are no global criteria defining how a good ontology should be, OntoQA allows users to tune the ranking towards certain features of ontologies to suit the need of their applications. We also show the effectiveness of OntoQA in ranking ontologies by comparing its results to the ranking of other comparable approaches as well as expert users.


title: A simple circularly polarized loop tag antenna for increased reading distance

abstract: A simple circularly polarized (CP) loop tag antenna is proposed to overcome the polarization mismatch problem between reader antenna and tag antenna. Good CP radiation can be achieved by introducing an open gap onto the square loop. Also, the technique of loading a matching stub across the RFID chip is applied to the proposed loop tag antenna, and desirable impedance matching between the antenna and RFID chip can be realized by tuning the matching stub. The proposed loop tag antenna shows good reading distance performance, and its measured reading distance reaches to 17.6 m.


title: Any-angle path planning

abstract: Navigating an agent from a given start coordinate to a given goal coordinate through a continuous environment is one of the most important problems faced by roboticists and video game developers. Traditional edge-constrained find-path algorithms propagate information along graph edges and constrain paths to be formed by graph edges. This constraint is artificial and causes the paths found by traditional edge-constrained find-path algorithms to be both longer and less realistic looking than the true shortest paths (that is, the shortest paths in the continuous environment). While this fact is well known by roboticists and video game developers two important questions remain: Question 1: How much longer can the paths found by traditional edge-constrained find-path algorithms be than the true shortest paths? Question 2: Can more sophisticated find-path algorithms be developed that find shorter and more realistic looking paths than traditional edge-constrained find-path algorithms, while maintaining the desirable properties of traditional edge-constrained find-path algorithms? This dissertation addresses these two questions and therefore our hypotheses are as follows: Hypothesis 1: Analytical bounds can be introduced which compare the lengths of the paths found by traditional edge-constrained find-path algorithms on certain types of graphs with the lengths of the true shortest paths. Hypothesis 2: A new class of any-angle find-path algorithms, that propagate information along graph edges, without constraining paths to be formed by graph edges, can be used to quickly find paths that are shorter than the paths found by traditional edge-constrained find-path algorithms, while maintaining the Simplicity and Generality Properties of traditional edge-constrained find-path algorithms. ::: To validate Hypothesis 1, we introduce a comprehensive set of eight new analytical bounds which compare the lengths of the paths found by traditional edge-constrained find-path algorithms on grid graphs constructed from 2D and 3D regular grids with the lengths of the true shortest paths. ::: To validate Hypothesis 2, we introduce a new class of any-angle find-path algorithms that propagate information along graph edges (to achieve a short runtime) without constraining paths to be formed by graph edges (to find any-angle paths). We introduce new members to this class and evaluate each member in one of three types of continuous environments, namely continuous 2D environments in which agents have complete knowledge of the environment (known 2D environments), continuous 3D environments in which agents have complete knowledge of the environment (known 3D environments) and continuous 2D environments in which agents do not have complete knowledge of the environment (unknown 2D environments). For each new any-angle find-path algorithm, we use the Simplicity, Efficiency and Generality Properties as our evaluation criteria. Specifically, we introduce three new any-angle find-path algorithms, namely Basic Theta*, Lazy Theta* and Incremental Phi*. In known 2D environments, Basic Theta* satisfies the Simplicity and Generality Properties, provides a good tradeoff, relative to traditional edge-constrained find-path algorithms, with respect to the runtime of the search and the length of the resulting path and a dominating tradeoff over existing any-angle find-path algorithms with respect to the runtime of the search and the length of the resulting path (Efficiency Property). Lazy Theta* is a variant of Basic Theta* that is designed for path planning in known 3D environments. In known 3D environments, Lazy Theta* satisfies the Simplicity and Generality Properties, provides a good tradeoff, relative to traditional edge-constrained find-path algorithms, with respect to the runtime of the search and the length of the resulting path and a dominating tradeoff over Basic Theta* with respect to the runtime of the search and the length of the resultingpath (Efficiency Property). Incremental Phi* is a variant of Basic Theta* that is designed for path planning in unknown 2D environments. In unknown 2D environments, Incremental Phi* satisfies the Simplicity Property and provides a dominating tradeoff over Basic Theta* with respect to the runtime of the search and the length of the resulting path (Efficiency Property). These contributions demonstrate that any-angle find-path algorithms represent a promising new technique for path planning in robotics and video games. (Abstract shortened by UMI.)


title: A comparative assessment of malware classification using binary texture analysis and dynamic analysis

abstract: AI techniques play an important role in automated malware classification. Several machine-learning methods have been applied to classify or cluster malware into families, based on different features derived from dynamic review of the malware. While these approaches demonstrate promise, they are themselves subject to a growing array of counter measures that increase the cost of capturing these binary features. Further, feature extraction requires a time investment per binary that does not scale well to the daily volume of binary instances being reported by those who diligently collect malware. Recently, a new type of feature extraction, used by a classification approach called binary-texture analysis, was introduced in [16]. We compare this approach to existing malware classification approaches previously published. We find that, while binary texture analysis is capable of providing comparable classification accuracy to that of contemporary dynamic techniques, it can deliver these results 4000 times faster than dynamic techniques. Also surprisingly, the texture-based approach seems resilient to contemporary packing strategies, and can robustly classify a large corpus of malware with both packed and unpacked samples. We present our experimental results from three independent malware corpora, comprised of over 100 thousand malware samples. These results suggest that binary-texture analysis could be a useful and efficient complement to dynamic analysis.


Procedural generation of balanced levels for a 3D paintball game


title: Junction detection based on line segments

abstract: We present a novel method for junction detection. A junction is defined as the point where several lines intersect. Given the line segments in the image, our junction detector consists of three steps. First, potential junctions are located from a small neighborhood around the intersection of each pair of lines. Second, our detector searches the branches connecting to each potential junction in a circular neighborhood according to its scale. Finally, the actual junctions are selected using a two-step method according to their connecting branches and their distance. We test our algorithm on a variety of images. The experiment results demonstrate that the proposed algorithm can robustly detect junctions in different scenes.


title: From Story Points to COSMIC Function Points in Agile Software Development - A Six Sigma perspective

abstract: The spreading of agile methodologies in software development raises the question of how to measure requirements once more, as it happened in ‘traditional’ software industry development approaches decades ago. The difference is that requirements are not known in advance but detected as User Stories while iterating and enhancing the software product from one agile ‘Sprint’ to the other. Some authors – promoting best practices for agile software development – propose Story Points to size User Stories (e.g., Scrum, with Story Cards), yet not combined with base project estimation. Story Points are not standardized, thus leading to eventual misconceptions and quantitative differences among practitioners and domains. The uncertainty implied in such approach can therefore propagate to any estimate based on it, not to mention the difficulty in accurately tracing requirements and their variation over the project and across project iterations. This work investigates benefits from adopting a standardized Functional Size Measurement (FSM) method, such as COSMIC Function Points, in place of Story Points. Using a Transfer Function (from the Six Sigma practice) that transforms size into effort spent within a particular agile team, defect density prediction can be made using sensitivity analysis.


title: MASCOT: Faster Malicious Arithmetic Secure Computation with Oblivious Transfer

abstract: We consider the task of secure multi-party computation of arithmetic circuits over a finite field. Unlike Boolean circuits, arithmetic circuits allow natural computations on integers to be expressed easily and efficiently. In the strongest setting of malicious security with a dishonest majority --- where any number of parties may deviate arbitrarily from the protocol --- most existing protocols require expensive public-key cryptography for each multiplication in the preprocessing stage of the protocol, which leads to a high total cost. We present a new protocol that overcomes this limitation by using oblivious transfer to perform secure multiplications in general finite fields with reduced communication and computation. Our protocol is based on an arithmetic view of oblivious transfer, with careful consistency checks and other techniques to obtain malicious security at a cost of less than 6 times that of semi-honest security. We describe a highly optimized implementation together with experimental results for up to five parties. By making extensive use of parallelism and SSE instructions, we improve upon previous runtimes for MPC over arithmetic circuits by more than 200 times.


title: Multilingual Distributed Representations without Word Alignment

abstract: Distributed representations of meaning are a natural way to encode covariance relationships between words and phrases in NLP. By overcoming data sparsity problems, as well as providing information about semantic relatedness which is not available in discrete representations, distributed representations have proven useful in many NLP tasks. Recent work has shown how compositional semantic representations can successfully be applied to a number of monolingual applications such as sentiment analysis. At the same time, there has been some initial success in work on learning shared word-level representations across languages. We combine these two approaches by proposing a method for learning distributed representations in a multilingual setup. Our model learns to assign similar embeddings to aligned sentences and dissimilar ones to sentence which are not aligned while not requiring word alignments. We show that our representations are semantically informative and apply them to a cross-lingual document classification task where we outperform the previous state of the art. Further, by employing parallel corpora of multiple language pairs we find that our model learns representations that capture semantic relationships across languages for which no parallel data was used.


title: The nature of unsupervised learning in deep neural networks: A new understanding and novel approach

abstract: Over the last decade, the deep neural networks are a hot topic in machine learning. It is breakthrough technology in processing images, video, speech, text and audio. Deep neural network permits us to overcome some limitations of a shallow neural network due to its deep architecture. In this paper we investigate the nature of unsupervised learning in restricted Boltzmann machine. We have proved that maximization of the log-likelihood input data distribution of restricted Boltzmann machine is equivalent to minimizing the cross-entropy and to special case of minimizing the mean squared error. Thus the nature of unsupervised learning is invariant to different training criteria. As a result we propose a new technique called "REBA" for the unsupervised training of deep neural networks. In contrast to Hinton's conventional approach to the learning of restricted Boltzmann machine, which is based on linear nature of training rule, the proposed technique is founded on nonlinear training rule. We have shown that the classical equations for RBM learning are a special case of the proposed technique. As a result the proposed approach is more universal in contrast to the traditional energy-based model. We demonstrate the performance of the REBA technique using wellknown benchmark problem. The main contribution of this paper is a novel view and new understanding of an unsupervised learning in deep neural networks.


title: Multilingual Semantic Parsing : Parsing Multiple Languages into Semantic Representations

abstract: We consider multilingual semantic parsing ‐ the task of simultaneously parsing semantically equivalent sentences from multiple different languages into their corresponding formal semantic representations. Our model is built on top of the hybrid tree semantic parsing framework, where natural language sentences and their corresponding semantics are assumed to be generated jointly from an underlying generative process. We first introduce a variant of the joint generative process, which essentially gives us a new semantic parsing model within the framework. Based on the different models that can be developed within the framework, we then investigate several approaches for performing the multilingual semantic parsing task. We present our evaluations on a standard dataset annotated with sentences in multiple languages coming from different language families.


title: Geometric flight control of a hovering robotic hummingbird

abstract: Controlled hovering of motor driven flapping wing micro aerial vehicles (FWMAVs) is challenging due to its limited control authority, large inertia, vibration produced by wing strokes, and limited components accuracy due to fabrication methods. In this work, we present a hummingbird inspired FWMAV with 12 grams of weight and 20 grams of maximum lift. We present its full non-linear dynamic model including the full inertia tensor, non-linear input mapping, and damping effect from flapping counter torques (FCTs) and flapping counter forces (FCFs). We also present a geometric flight controller to ensure exponentially stable and globally exponential attractive properties. We experimentally demonstrated the vehicle lifting off and hover with attitude stabilization.


title: Timing, Carrier, and Frame Synchronization of Burst-Mode CPM

abstract: In this paper, we propose a complete synchronization algorithm for continuous phase modulation (CPM) signals in burst-mode transmission over additive white Gaussian noise (AWGN) channels. The timing and carrier recovery are performed through a data-aided (DA) maximum likelihood algorithm, which jointly estimates symbol timing, carrier phase, and frequency offsets based on an optimized synchronization preamble. Our algorithm estimates the frequency offset via a one-dimensional grid search, after which symbol timing and carrier phase are computed via simple closed-form expressions. The mean-square error (MSE) of the algorithm's estimates reveals that it performs very close to the theoretical Cramer-Rao bound (CRB) for various CPMs at signal-to-noise ratios (SNRs) as low as 0 dB. Furthermore, we present a frame synchronization algorithm that detects the arrival of bursts and estimates the start-of-signal. We simulate the performance of the frame synchronization algorithm along with the timing and carrier recovery algorithm. The bit error rate results demonstrate near ideal synchronization performance for low SNRs and short preambles.


title: Combination of generative models and SVM based classifier for speech emotion recognition

abstract: Modeling time series data of varying length is important in different domains. There are two paradigms for modeling the varying length sequential data. Tasks such as speech recognition need modeling the temporal dynamics and the correlations among the features. Hidden Markov models (HMM) are used for these tasks. In tasks such as speaker recognition, audio classification and speech emotion recognition, modeling the temporal dynamics is not critical. Gaussian mixture models (GMM) are commonly used for these tasks. Generative models such as HMMs and GMMs focus on estimating the density of the data and are not suitable for classifying the data of confusable classes. Discriminative classifiers such as support vector machines (SVM) are suitable for the fixed dimensional patterns. In this paper, we propose a hybrid framework where a generative front end is used for representing the varying length time series data and then a discriminative model is used for classification. A score based approach and a segment modeling based approach are proposed in this framework. Both the approaches are applied for speech emotion recognition. The performance is compared with that of an SVM classifier that uses different statistical features and also with that of the GMM classifiers that use maximum likelihood method and the variational Bayes method for parameter estimation. Both the proposed approaches outperform the methods used for comparison.


title: A survey of software testing practices in alberta

abstract: Software organizations have typically de-emphasized the importance of software testing. In this paper, the results of a regional survey of software testing and software quality assurance techniques are described. Researchers conducted the study during the summer and fall of 2002 by surveying software organizations in the Province of Alberta. Results indicate that Alberta-based organizations tend to test less than their counterparts in the United States. The results also indicate that Alberta software organizations tend to train fewer personnel on testing-related topics. This practice has the potential for a two-fold impact: first, the ability to detect trends that lead to reduced quality and to identify the root causes of reductions in product quality may suffer from the lack of testing. This consequence is serious enough to warrant consideration, since overall quality may suffer from the reduced ability to detect and eliminate process or product defects. Second, the organization may have a more difficult time adopting methodologies such as extreme programming. This is significant because other industry studies have concluded that many software organizations have tried or will in the next few years try some form of agile method. Newer approaches to software development like extreme programming increase the extent to which teams rely on testing skills. Organizations should consider their testing skill level as a key indication of their readiness for adopting software development techniques such as test-driven development, extreme programming, agile modelling, or other agile methods.


title: 3Book: a scalable 3D virtual book

abstract: This paper describes the 3Book, a 3D interactive visualization of a codex book as a component for digital library and information-intensive applications. The 3Book is able to represent books of almost unlimited length, allows users to read large format books, and has features to enhance reading and sensemaking.


title: Reliability Comparison of ZrO2-Based DRAM High-k Dielectrics Under DC and AC Stress

abstract: In this paper, the time dependent dielectric breakdown behavior is investigated for production type crystalline ZrO2-based thin films under dc and ac stress. Constant voltage stress measurements over six decades in time show that the voltage acceleration of time-to-breakdown follows the conventional exponential law. The effects of ac stress on time-to-breakdown are studied in detail by changing the experimental parameters including stress voltage, base voltage, and frequency. In general, ac stressing gives rise to a gain in lifetime, which may result from less overall charge trapping. This trap dynamic was investigated by dielectric absorption measurements. Overall, the typical DRAM refresh of the capacitor leads to the most critical reliability concern.


title: Lightweight unmanned aerial vehicles will revolutionize spatial ecology

abstract: *Ecologists require spatially explicit data to relate structure to function. To date, heavy reliance has been placed on obtaining such data from remote-sensing instruments mounted on spacecraft or manned aircraft, although the spatial and temporal resolutions of the data are often not suited to local-scale ecological investigations. Recent technological innovations have led to an upsurge in the availability of unmanned aerial vehicles (UAVs) – aircraft remotely operated from the ground – and there are now many lightweight UAVs on offer at reasonable costs. Flying low and slow, UAVs offer ecologists new opportunities for scale-appropriate measurements of ecological phenomena. Equipped with capable sensors, UAVs can deliver fine spatial resolution data at temporal resolutions defined by the end user. Recent innovations in UAV platform design have been accompanied by improvements in navigation and the miniaturization of measurement technologies, allowing the study of individual organisms and their spatiotemporal dynamics at close range.


title: Mining event-related brain dynamics

abstract: This article provides a new, more comprehensive view of event-related brain dynamics founded on an information-based approach to modeling electroencephalographic (EEG) dynamics. Most EEG research focuses either on peaks ’evoked’ in average eventrelated potentials (ERPs) or on changes ’induced’ in the EEG power spectrum by experimental events. Although these measures are nearly complementary, they do not fully model the event-related dynamics in the data, and cannot isolate the signals of the contributing cortical areas. We propose that many ERPs and other EEG features are better viewed as time/frequency perturbations of underlying field potential processes. The new approach combines independent component analysis (ICA), time/frequency analysis, and trial-by-trial visualization that measures EEG source dynamics without requiring an explicit head model.


title: Speeding up similarity search under dynamic time warping by pruning unpromising alignments

abstract: Similarity search is the core procedure for several time series mining tasks. While different distance measures can be used for this purpose, there is clear evidence that the Dynamic Time Warping (DTW) is the most suitable distance function for a wide range of application domains. Despite its quadratic complexity, research efforts have proposed a significant number of pruning methods to speed up the similarity search under DTW. However, the search may still take a considerable amount of time depending on the parameters of the search, such as the length of the query and the warping window width. The main reason is that the current techniques for speeding up the similarity search focus on avoiding the costly distance calculation between as many pairs of time series as possible. Nevertheless, the few pairs of subsequences that were not discarded by the pruning techniques can represent a significant part of the entire search time. In this work, we adapt a recently proposed algorithm to improve the internal efficiency of the DTW calculation. Our method can speed up the UCR suite, considered the current fastest tool for similarity search under DTW. More important, the longer the time needed for the search, the higher the speedup ratio achieved by our method. We demonstrate that our method performs similarly to UCR suite for small queries and narrow warping constraints. However, it performs up to five times faster for long queries and large warping windows.


title: Design and development of gripping assistive device for post — Stroke rehabilitation

abstract: Report from The National Stroke Association of Malaysia (NASAM), the disease that cause most deaths is stroke which comes after cancer and heart attack. Even though most of the stroke cases is avoidable, rehabilitation will be in high demand. Devices as such has not been created and looking into since the therapist could not fulfil the demand. This project addresses a device to help post — stroke patients to grip and release their fingers as a rehabilitation working process. The system uses leap motion sensor as the input and the output is a servomotor-based exoskeleton. At the moment the system is based on a master-slave mechanism which needs a healthy hand to control the weak hand. The exoskeleton is still being tested on using linear-like actuator mechanisms to perform grasping and extension.


title: Accurate modeling of Modbus/TCP for intrusion detection in SCADA systems

abstract: The Modbus/TCP protocol is commonly used in SCADA systems for communications between a human–machine interface (HMI) and programmable logic controllers (PLCs). This paper presents a model-based intrusion detection system designed specifically for Modbus/TCP networks. The approach is based on the key observation that Modbus traffic to and from a specific PLC is highly periodic; as a result, each HMI-PLC channel can be modeled using its own unique deterministic finite automaton (DFA). An algorithm is presented that can automatically construct the DFA associated with an HMI-PLC channel based on about 100 captured messages. The resulting DFA-based intrusion detection system looks deep into Modbus/TCP packets and produces a very detailed traffic model. This approach is very sensitive and is able to flag anomalies such as a message appearing out of its position in the normal sequence or a message referring to a single unexpected bit. The intrusion detection approach is tested on a production Modbus system. Despite its high sensitivity, the system has a very low false positive rate—perfect matches of the model to the traffic were observed for five of the seven PLCs tested without a single false alarm over 111 h of operation. Furthermore, the intrusion detection system successfully flagged real anomalies that were caused by technicians who were troubleshooting the HMI system. The system also helped identify a PLC that was configured incorrectly.


title: Preprocessing and postprocessing for skeleton-based fingerprint minutiae extraction

abstract: In this paper, we propose to use the fingerprint valley instead of ridge for the binarization-thinning process to extract fingerprint minutiae. We first use several preprocessing steps on the binary image in order to eliminate the spurious lakes and dots, and to reduce the spurious islands, bridges, and spurs in the skeleton image. By removing all the bug pixels introduced at the thinning stage, our algorithm can detect a maximum number of minutiae from the fingerprint skeleton using the Rutovitz Crossing Number. This allows the true minutiae preserved and false minutiae removed in later postprocessing stages. Finally, using the intrinsic duality property of fingerprint image we develop several postprocessing techniques to efficiently remove spurious minutiae. Especially, we define an H-point structure to remove several types of spurious minutiae including bridge, triangle, ladder, and wrinkle all together. Experimental results clearly demonstrate the effectiveness of the new algorithms.


title: Interactive Video Advertising: A Multimodal Affective Approach

abstract: Online video advertising (video-in-video) strategies are typically agnostic to the video content (ex.: advertising on YouTube) and the human viewer’s preferences. How to assess the emotional state and engagement of the viewer to place an advertisement? Where to insert an advertisement based on the content in an advertisement and a specific target video stream? Surely these are relevant questions that should be addressed by a good model for video advertisement placement. In this paper, we propose a novel framework to address two important aspects of (a) multi-modal affective analysis of video content and viewer behavior (b) a method for interactive personalized advertisement insertion for a single user. Our analysis and framework is backed by a systematic study of literature in marketing, consumer psychology and affective analysis of videos. Results from the user-study experiments demonstrate that the proposed method performs better than the state-of-the-art in video-in-video advertising.


abstract: Neurology Haas, N. A. Akshoomoff and R. Y. Courchesne Chisum, P. Moses, K. Pierce, C. Lord, A. J. Lincoln, S. Pizzo, L. Schreibman, R. H. E. Courchesne, C. M. Karns, H. R. Davis, R. Ziccardi, R. A. Carper, Z. D. Tigue, H. J. MRI study Unusual brain growth patterns in early life in patients with autistic disorder: An This information is current as of November 15, 2007 http://www.neurology.org/cgi/content/full/57/2/245 located on the World Wide Web at: The online version of this article, along with updated information and services, is All rights reserved. Print ISSN: 0028-3878. Online ISSN: 1526-632X. since 1951, it is now a weekly with 48 issues per year. Copyright © 2001 by AAN Enterprises, Inc. ® is the official journal of the American Academy of Neurology. Published continuously Neurology


title: Answering Pattern Queries Using Views

abstract: Answering queries using views has proven effective for querying relational and semistructured data. This paper investigates this issue for graph pattern queries based on graph simulation. We propose a notion of pattern containment to characterize graph pattern matching using graph pattern views. We show that a pattern query can be answered using a set of views if and only if it is contained in the views. Based on this characterization, we develop efficient algorithms to answer graph pattern queries. We also study problems for determining (minimal, minimum) containment of pattern queries. We establish their complexity (from cubic-time to NP-complete) and provide efficient checking algorithms (approximation when the problem is intractable). In addition, when a pattern query is not contained in the views, we study maximally contained rewriting to find approximate answers; we show that it is in cubic-time to compute such rewriting, and present a rewriting algorithm. We experimentally verify that these methods are able to efficiently answer pattern queries on large real-world graphs.


title: Serious games as new educational tools: how effective are they? A meta‐analysis of recent studies

abstract: Computer-assisted learning is known to be an effective tool for improving learning in both adults and children. Recent years have seen the emergence of the so-called ‘serious games (SGs)’ that are flooding the educational games market. In this paper, the term ‘serious games’ is used to refer to video games (VGs) intended to serve a useful purpose. The objective was to review the results of experimental studies designed to examine the effectiveness of VGs and SGs on players' learning and engagement. After pointing out the varied nature of the obtained results and the impossibility of reaching any reliable conclusion concerning the effectiveness of VGs and SGs in learning, we stress the limitations of the existing literature and make a number of suggestions for future studies.


title: A Machine Learning Approach to Coreference Resolution of Noun Phrases

abstract: In this paper, we present a learning approach to coreference resolution of noun phrases in unrestricted text. The approach learns from a small, annotated corpus and the task includes resolving not just a certain type of noun phrase (e.g., pronouns) but rather general noun phrases. It also does not restrict the entity types of the noun phrases; that is, coreference is assigned whether they are of "organization," "person," or other types. We evaluate our approach on common data sets (namely, the MUC-6 and MUC-7 coreference corpora) and obtain encouraging results, in-dicating that on the general noun phrase coreference task, the learning approach holds promise and achieves accuracy comparable to that of nonlearning approaches. Our system is the first learning-based system that offers performance comparable to that of state-of-the-art nonlearning systems on these data sets.


title: Bridging the gap between physical location and online social networks

abstract: This paper examines the location traces of 489 users of a location sharing social network for relationships between the users' mobility patterns and structural properties of their underlying social network. We introduce a novel set of location-based features for analyzing the social context of a geographic region, including location entropy, which measures the diversity of unique visitors of a location. Using these features, we provide a model for predicting friendship between two users by analyzing their location trails. Our model achieves significant gains over simpler models based only on direct properties of the co-location histories, such as the number of co-locations. We also show a positive relationship between the entropy of the locations the user visits and the number of social ties that user has in the network. We discuss how the offline mobility of users can have implications for both researchers and designers of online social networks.


title: Adjuvants--a balance between toxicity and adjuvanticity.

abstract: Adjuvants have been used to augment the immune response in experimental immunology as well as in practical vaccination for more than 60 years. The chemical nature of adjuvants, their mode of action and the profile of their side effects are highly variable. Some of the side effects can be ascribed to an unintentional stimulation of different mechanisms of the immune system whereas others may reflect general adverse pharmacological reactions. The most common adjuvants for human use today are still aluminium hydroxide, aluminium phosphate and calcium phosphate although oil emulsions, products from bacteria and their synthetic derivatives as well as liposomes have also been tested or used in humans. In recent years monophosphoryl lipid A, ISCOMs with Quil-A and Syntex adjuvant formulation (SAF) containing the threonyl derivative of muramyl dipeptide have been under consideration for use as adjuvants in humans. At present the choice of adjuvants for human vaccination reflects a compromise between a requirement for adjuvanticity and an acceptable low level of side effects.


title: An electrically tunable plenoptic camera using a liquid crystal microlens array.

abstract: Plenoptic cameras generally employ a microlens array positioned between the main lens and the image sensor to capture the three-dimensional target radiation in the visible range. Because the focal length of common refractive or diffractive microlenses is fixed, the depth of field (DOF) is limited so as to restrict their imaging capability. In this paper, we propose a new plenoptic camera using a liquid crystal microlens array (LCMLA) with electrically tunable focal length. The developed LCMLA is fabricated by traditional photolithography and standard microelectronic techniques, and then, its focusing performance is experimentally presented. The fabricated LCMLA is directly integrated with an image sensor to construct a prototyped LCMLA-based plenoptic camera for acquiring raw radiation of targets. Our experiments demonstrate that the focused region of the LCMLA-based plenoptic camera can be shifted efficiently through electrically tuning the LCMLA used, which is equivalent to the extension of the DOF.


title: Secure and efficient proof of storage with deduplication

abstract: Both security and efficiency are crucial to the success of cloud storage. So far, security and efficiency of cloud storage have been separately investigated as follows: On one hand, security notions such as Proof of Data Possession (PDP) and Proof of Retrievability (POR) have been introduced for detecting that the data stored in the cloud has been tampered with. On the other hand, the notion of Proof of Ownership (POW) has also been proposed to alleviate the cloud server from storing multiple copies of the same data, which could substantially reduce the consumption of both network bandwidth and server storage space. These two aspects are seemingly quite to the opposite of each other. In this paper, we show, somewhat surprisingly, that the two aspects can actually co-exist within the same framework. This is possible fundamentally because of the following insight: The public verifiability offered by PDP/POR schemes can be naturally exploited to achieve POW. This "one stone, two birds" phenomenon not only inspired us to propose the novel notion of Proof of Storage with Deduplication (POSD), but also guided us to design a concrete scheme that is provably secure in the Random Oracle model based on the Computational Diffie-Hellman (CDH) assumption.


title: THERMAL NEAR‐SURFACE EXPRESSION OF SHALLOW GEOLOGICAL FEATURES*

abstract: It has been customary in geothermal studies to assume the surface to be an isothermal plane, though difficulties existed to explain the magnitude of thermal anomalies observed in the field under this assumption. Detailed investigations of temperatures at depths between 5 cm and 230 cm show that—after removal of the strong diurnal variations—sizeable anomalies persist right to the surface. Thus, the effective heat transfer (coefficient) at the surface might be lower than commonly assumed.


Statistical Voice Conversion with WaveNet-Based Waveform Generation.


title: “I Broke Your Game!”: critique among middle schoolers designing computer games about climate change

abstract: There have been increasing calls for integrating computational thinking and computing into school science, mathematics, and engineering classrooms. The learning goals of the curriculum in this study included learning about both computational thinking and climate science. Including computer science in science classrooms also means a shift in the focus on design and creation of artifacts and attendant practices. One such design practice, widespread in the design and arts fields, is critique. This paper explores the role of critique in two urban, heterogenous 8th grade science classrooms in which students engaged in creating computer games on the topic of climate systems and climate change. It explores and compares how practices of critique resulted from curricular decisions to (i) scaffold intentional critique sessions for student game designers and (ii) allow for spontaneous feedback as students interacted with each other and their games during the process of game creation. Although we designed formal opportunities for critique, the participatory dimension of the project meant that students were free to critique each other’s games at any time during the building process and did so voluntarily. Data indicate that students focused much more on the game play dimension of the design than the science, particularly in those critique sessions that were student-initiated. Despite the de-emphasis on science in spontaneous critiques, students still focused on several dimensions of computational thinking, considering user experience, troubleshooting, modeling, and elegance of solutions. Students making games about science topics should have opportunities for both formal and spontaneous critiques. Spontaneous critiques allow for students to be authorities of knowledge and to determine what is acceptable and what is not. However, formal, teacher-designed critiques may be necessary for students to focus on science as part of the critique. Furthermore, one of the benefits to critiquing others was that students were able to see what others had done, how they had set up their games, the content they included, and how they had programmed certain features. Lastly, critiques can help facilitate iteration as students work to improve their games.


title: Smarter Universities A Vision for the Fast Changing Digital Era

abstract: In this paper we analyze the current situation of education in universities, with particular reference to the European scenario. Specifically, we observe that recent evolutions, such as pervasive networking and other enabling technologies, have been dramatically changing human life, knowledge acquisition, and the way works are performed and people learn. In this societal change, universities must maintain their leading role. Historically, they set trends primarily in education but now they are called to drive the change in other aspects too, such as management, safety, and environment protection. The availability of newer and newer technology reflects on how the relevant processes should be performed in the current fast changing digital era. This leads to the adoption of a variety of smart solutions in university environments to enhance the quality of life and to improve the performances of both teachers and students. Nevertheless, we argue that being smart is not enough for a modern university. In fact, universities should better become smarter. By "smarter university" we mean a place where knowledge is shared between employees, teachers, students, and all stakeholders in a seamless way. In this paper we propose, and discuss a smarter university model, derived from the one designed for the development of smart cities. A design model is proposed for the evolution of universities.The model fosters cooperation among employees, teachers, students, and stakeholders.The model fosters knowledge sharing.The model is compliant with the smart cities model.


title: Feature Selection for Sentiment Analysis Based on Content and Syntax Models

abstract: Recent solutions for sentiment analysis have relied on feature selection methods ranging from lexicon-based approaches where the set of features are generated by humans, to approaches that use general statistical measures where features are selected solely on empirical evidence. The advantage of statistical approaches is that they are fully automatic, however, they often fail to separate features that carry sentiment from those that do not. In this paper we propose a set of new feature selection schemes that use a Content and Syntax model to automatically learn a set of features in a review document by separating the entities that are being reviewed from the subjective expressions that describe those entities in terms of polarities. By focusing only on the subjective expressions and ignoring the entities, we can choose more salient features for document-level sentiment analysis. The results obtained from using these features in a maximum entropy classifier are competitive with the state-of-the-art machine learning approaches.


title: Multi-target tracking by online learning of non-linear motion patterns and robust appearance models

abstract: We describe an online approach to learn non-linear motion patterns and robust appearance models for multi-target tracking in a tracklet association framework. Unlike most previous approaches that use linear motion methods only, we online build a non-linear motion map to better explain direction changes and produce more robust motion affinities between tracklets. Moreover, based on the incremental learned entry/exit map, a multiple instance learning method is devised to produce strong appearance models for tracking; positive sample pairs are collected from different track-lets so that training samples have high diversity. Finally, using online learned moving groups, a tracklet completion process is introduced to deal with tracklets not reaching entry/exit points. We evaluate our approach on three public data sets, and show significant improvements compared with state-of-art methods.


title: Measuring Large-Scale Dynamic Graph Similarity by RICom: RWR with Intergraph Compression

abstract: By how much is a large-scale graph transformed over time or by a significant event?' or 'how structurally similar are two large-scale graphs?' are the two questions that this paper attempts to address. The proposed method efficiently calculates and accurately produces graph similarity. Our approach is based on the well-known random walk with restart (RWR) algorithm, which quantifies relevance between nodes to express the structural and connection characteristics of graphs. Intergraph compression, which is inspired by interframe compression, merges two input graphs and reorders their nodes contributing to improved process-data storage efficiency and processing convenience. This is a boon to the RWR algorithm for large-scale graphs. The representation of a graph transformed via intergraph compression can be used to accurately show similarity because sub-matrix blocks are reordered to concentrate nonzero elements. In performing the RWR algorithm, which quantifies inter-node relevance, transformed representation of graph with Intergraph compression is efficient in space requirement and produces results more quickly and accurately over conventional graph transformation schemes. We demonstrate the validity of our method through experiments and apply it to the usage data of public transportation SmartCard in a large metropolitan area to suggest usefulness of the proposed algorithm.


title: IOS Press An Evaluation of Pedagogical Tutorial Tactics for a Natural Language Tutoring System: A Reinforcement Learning

abstract: Pedagogical strategies are policies for a tutor to decide the next action when there are multiple actions available. When the content is controlled to be the same across experimental conditions, there has been little evidence that tutorial decisions have an impact on students' learning. In this paper, we applied Reinforcement Learning (RL) to induce two sets of pedagogical policies from pre-existing human interaction data. The NormGain set was derived with the goal of enhancing tutorial decisions that contribute to learning while the InvNormGain set was derived with the goal of enhancing those decisions that contribute less or even nothing to learning. The two sets were then tested with human students. Our results show that when the content was controlled to be the same, different pedagogical policies did make a difference in learning and more specifically, the NormGain students outperformed their peers. Overall our results suggest that content exposure and practice opportunities can help students to learn even when tutors have poor pedagogical tutorial tactics. However, with effective tutorial tactics, students can learn even more.


The Moral Foundations in New York Times


title: Indoor Tracking Using Undirected Graphical Models

abstract: Indoor tracking and navigation is a fundamental need for pervasive and context-aware smartphone applications. Although indoor maps are becoming increasingly available, there is no practical and reliable indoor map matching solution available at present. We present MapCraft, a novel, robust and responsive technique that is extremely computationally efficient (running in under 10 ms on an Android smartphone), does not require training in different sites, and tracks well even when presented with very noisy sensor data. Key to our approach is expressing the tracking problem as a conditional random field (CRF), a technique which has had great success in areas such as natural language processing. Unlike directed graphical models like Hidden Markov Models, CRFs capture arbitrary constraints that express how well observations support state transitions, given map constraints. In addition, we show how to further improve tracking accuracy, by tuning the parameters of the motion sensing model using an unsupervised EM-style optimization scheme. Extensive experiments in multiple sites show how MapCraft outperforms state-of-the art approaches, demonstrating excellent tracking error and accurate reconstruction of tortuous trajectories with zero training effort. As proof of its robustness, we also demonstrate how it is able to accurately track the position of a user from accelerometer and magnetometer measurements only (i.e., gyro- and Wi-Fi-free). We believe that such an energy-efficient approach will enable always-on background localisation, enabling a new era of location-aware applications to be developed.


title: 360 Degree multi sensor fusion for static and dynamic obstacles

abstract: In this paper an approach for 360 degree multi sensor fusion for static and dynamic obstacles is presented. The perception of static and dynamic obstacles is achieved by combining the advantages of model based object tracking and an occupancy map. For the model based object tracking a novel multi reference point tracking system, called best knowledge model, is introduced. The best knowledge model allows to track and describe objects with respect to a best suitable reference point. It is explained how the object tracking and the occupancy map closely interact and benefit from each other. Experimental results of the 360 degree multi sensor fusion system from an automotive test vehicle are shown.


title: Obstacle detection and mapping in low-cost, low-power multi-robot systems using an Inverted Particle Filter

abstract: There are a vast number of methods for detecting obstacles, though not taking resource consumption into consideration. The proposed method is an obstacle detection and mapping method focused on systems with constrained memory capacity and processing power, and is called the Inverted Particle Filter. This method has small resource requirements, and can be fine tuned according to available resources. The conducted experiments show promising results.


title: Automatic assessment and immediate feedback in first grade mathematics

abstract: In this paper we present a study where automatic assessment and immediate feedback was utilized to support the learning of mathematical concepts and automatization of basic arithmetic calculations. Two first grade classes from a Finnish elementary school participated in the research. One class acted as a treatment group (N=23) and the other as a control group (N=21). Pupils in the treatment group worked for ten weeks one lesson a week with a collaborative education tool called ViLLE. Additionally, the pupils in the group did all of their homework with the system. Pupils in the control group used traditional learning methods. Before the experiment began a pre-test was conducted for both groups. The same test with three additional exercises was given after the ten week period. The pre- and post-tests included exercises to evaluate math skills and a questionnaire about pupils' attitudes towards math. The treatment group got statistically significantly better results compared to the control group in the post-test. In attitudes, the only statistically significant difference between the groups was in a question of how well they liked math. However, the difference in attitudes inside the groups was not statistically significant.


title: 2001 No. 148Living Arrangements of Older Adults in the Developing World: An Analysis of DHS Household Surveys

abstract: Objectives. This study examines living arrangements of older adults across 43 developing countries and compares patterns by gender, world regions, and macro-level indicators of socioeconomic development. Methods. Data are from Demographic and Health Surveys. The country is the unit of analysis. Indicators include household size, headship, relationship to head, and coresidence with spouse, children, and others. Unweighted regional averages and ordinary least-squares regressions determine whether variations exist. Results. Average household sizes are large, but a substantially greater proportion of older adults live alone than do individuals in other age groups. Females are more likely than males to live alone and are less likely to live with a spouse or head of a household. Heading a household and living in a large household and with young children is more prevalent in Africa than elsewhere. Coresidence with adult children is most common in Asia and least in Africa. Coresidence is more frequent with sons than with daughters in both Asia and Africa, but not in Latin America. As a country’s level of schooling rises, most living arrangement indicators change with families becoming more nuclear. Urbanization and gross national product have no significant effects. Discussion. Although living arrangements differ across world regions and genders, within-region variations exist and are explained in part by associations between countrywide levels of education and household structure. These associations may be caused by a variety of intermediating factors, such as migration of children and preferences for privacy.


title: Easy Handling of Sensors and Actuators over TCP/IP Networks by Open Source Hardware/Software

abstract: There are several specific solutions for accessing sensors and actuators present in any process or system through a TCP/IP network, either local or a wide area type like the Internet. The usage of sensors and actuators of different nature and diverse interfaces (SPI, I2C, analogue, etc.) makes access to them from a network in a homogeneous and secure way more complex. A framework, including both software and hardware resources, is necessary to simplify and unify networked access to these devices. In this paper, a set of open-source software tools, specifically designed to cover the different issues concerning the access to sensors and actuators, and two proposed low-cost hardware architectures to operate with the abovementioned software tools are presented. They allow integrated and easy access to local or remote sensors and actuators. The software tools, integrated in the free authoring tool Easy Java and Javascript Simulations (EJS) solve the interaction issues between the subsystem that integrates sensors and actuators into the network, called convergence subsystem in this paper, and the Human Machine Interface (HMI)—this one designed using the intuitive graphical system of EJS—located on the user’s computer. The proposed hardware architectures and software tools are described and experimental implementations with the proposed tools are presented.


title: DW-MAC: a low latency, energy efficient demand-wakeup MAC protocol for wireless sensor networks

abstract: Duty cycling is a widely used mechanism in wireless sensor networks (WSNs) to reduce energy consumption due to idle listening, but this mechanism also introduces additional latency in packet delivery. Several schemes have been proposed to mitigate this latency, but they are mainly optimized for light traffic loads. A WSN, however, could often experience bursty and high traffic loads, such as due to broadcast or convergecast traffic. In this paper, we present a new MAC protocol, called Demand Wakeup MAC (DW-MAC), that introduces a new low-overhead scheduling algorithm that allows nodes to wake up on demand during the Sleep period of an operational cycle and ensures that data transmissions do not collide at their intended receivers. This demand wakeup adaptively increases effective channel capacity during an operational cycle as traffic load increases, allowing DW-MAC to achieve low delivery latency under a wide range of traffic loads including both unicast and broadcast traffic. We compare DW-MAC with S-MAC (with and without adaptive listening) and with RMAC using ns-2 and show that DW-MAC outperforms these protocols, with increasing benefits as traffic load increases. For example, under high unicast traffic load, DW-MAC reduces delivery latency by 70% compared to S-MAC and RMAC, and uses only 50% of the energy consumed with S-MAC with adaptive listening. Under broadcast traffic, DW-MAC reduces latency by more than 50% on average while maintaining higher energy efficiency.


title: Error bounds for approximate value iteration

abstract: Approximate Value Iteration (AVI) is an method for solving a Markov Decision Problem by making successive calls to a supervised learning (SL) algorithm. Sequence of value representations Vn are processed iteratively by Vn+1 = ATVn where T is the Bellman operator and A an approximation operator. Bounds on the error between the performance of the policies induced by the algorithm and the optimal policy are given as a function of weighted Lp-norms (p ≥ 1) of the approximation errors. The results extend usual analysis in L∞-norm, and allow to relate the performance of AVI to the approximation power (usually expressed in Lp-norm, for p = 1 or 2) of the SL algorithm. We illustrate the tightness of these bounds on an optimal replacement problem.


title: A similarity learning approach to content-based image retrieval: application to digital mammography

abstract: In this paper, we describe an approach to content-based retrieval of medical images from a database, and provide a preliminary demonstration of our approach as applied to retrieval of digital mammograms. Content-based image retrieval (CBIR) refers to the retrieval of images from a database using information derived from the images themselves, rather than solely from accompanying text indices. In the medical-imaging context, the ultimate aim of CBIR is to provide radiologists with a diagnostic aid in the form of a display of relevant past cases, along with proven pathology and other suitable information. CBIR may also be useful as a training tool for medical students and residents. The goal of information retrieval is to recall from a database information that is relevant to the user's query. The most challenging aspect of CBIR is the definition of relevance (similarity), which is used to guide the retrieval machine. In this paper, we pursue a new approach, in which similarity is learned from training examples provided by human observers. Specifically, we explore the use of neural networks and support vector machines to predict the user's notion of similarity. Within this framework we propose using a hierarchal learning approach, which consists of a cascade of a binary classifier and a regression module to optimize retrieval effectiveness and efficiency. We also explore how to incorporate online human interaction to achieve relevance feedback in this learning framework. Our experiments are based on a database consisting of 76 mammograms, all of which contain clustered microcalcifications (MCs). Our goal is to retrieve mammogram images containing similar MC clusters to that in a query. The performance of the retrieval system is evaluated using precision-recall curves computed using a cross-validation procedure. Our experimental results demonstrate that: 1) the learning framework can accurately predict the perceptual similarity reported by human observers, thereby serving as a basis for CBIR; 2) the learning-based framework can significantly outperform a simple distance-based similarity metric; 3) the use of the hierarchical two-stage network can improve retrieval performance; and 4) relevance feedback can be effectively incorporated into this learning framework to achieve improvement in retrieval precision based on online interaction with users; and 5) the retrieved images by the network can have predicting value for the disease condition of the query.


title: A cybernetics Social Cloud

abstract: The SocialMedia API has been developed to demonstrate proofs-of-concept.Cybernetics-1 and cybernetics-2 functions are developed for cybernetics Social Cloud.Large scale simulations have been tested for capacity tests. No costs are involved.All the steps involved are fully justified for research contributions. This paper proposes a Social Cloud, which presents the system design, development and analysis. The technology is based on the BOINC open source software, our hybrid Cloud, Facebook Graph API and our development in a new Facebook API, SocialMedia. The creation of SocialMedia API with its four functions can ensure a smooth delivery of Big Data processing in the Social Cloud, with four selected examples provided. The proposed solution is focused on processing the contacts who click like or comment on the author's posts. Outputs result in visualization with their core syntax being demonstrated. Four functions in the SocialMedia API have evaluation test and each client-server API processing can be completed efficiently and effectively within 1.36źs. We demonstrate large scale simulations involved with 50,000 simulations and all the execution time can be completed within 70,000źs. Cybernetics functions are created to ensure that 100% job completion rate for Big Data processing. Results support our case for Big Data processing on Social Cloud with no costs involved. All the steps involved have closely followed system design, implementation, experiments and validation for Cybernetics to ensure a high quality of outputs and services at all times. This offers a unique contribution for Cybernetics to meet Big Data research challenges.


title: Predictive State Temporal Difference Learning

abstract: We propose a new approach to value function approximation which combines linear temporal difference reinforcement learning with subspace identification. In practical applications, reinforcement learning (RL) is complicated by the fact that state is either high-dimensional or partially observable. Therefore, RL methods are designed to work with features of state rather than state itself, and the success or failure of learning is often determined by the suitability of the selected features. By comparison, subspace identification (SSID) methods are designed to select a feature set which preserves as much information as possible about state. In this paper we connect the two approaches, looking at the problem of reinforcement learning with a large set of features, each of which may only be marginally useful for value function approximation. We introduce a new algorithm for this situation, called Predictive State Temporal Difference (PSTD) learning. As in SSID for predictive state representations, PSTD finds a linear compression operator that projects a large set of features down to a small set that preserves the maximum amount of predictive information. As in RL, PSTD then uses a Bellman recursion to estimate a value function. We discuss the connection between PSTD and prior approaches in RL and SSID. We prove that PSTD is statistically consistent, perform several experiments that illustrate its properties, and demonstrate its potential on a difficult optimal stopping problem.


title: A Fast Face Detection Method via Convolutional Neural Network

abstract: Abstract Current face or object detection methods via convolutional neural network (such as OverFeat, R-CNN and DenseNet) explicitly extract multi-scale features based on an image pyramid. However, such a strategy increases the computational burden for face detection. In this paper, we propose a fast face detection method based on discriminative complete features (DCFs) extracted by an elaborately designed convolutional neural network, where face detection is directly performed on the complete feature maps. DCFs have shown the ability of scale invariance, which is beneficial for face detection with high speed and promising performance. Therefore, extracting multi-scale features on an image pyramid employed in the conventional methods is not required in the proposed method, which can greatly improve its efficiency for face detection. Experimental results on several popular face detection datasets show the efficiency and the effectiveness of the proposed method for face detection.


title: Rolling guidance normal filter for geometric processing

abstract: 3D geometric features constitute rich details of polygonal meshes. Their analysis and editing can lead to vivid appearance of shapes and better understanding of the underlying geometry for shape processing and analysis. Traditional mesh smoothing techniques mainly focus on noise filtering and they cannot distinguish different scales of features well, even mixing them up. We present an efficient method to process different scale geometric features based on a novel rolling-guidance normal filter. Given a 3D mesh, our method iteratively applies a joint bilateral filter to face normals at a specified scale, which empirically smooths small-scale geometric features while preserving large-scale features. Our method recovers the mesh from the filtered face normals by a modified Poisson-based gradient deformation that yields better surface quality than existing methods. We demonstrate the effectiveness and superiority of our method on a series of geometry processing tasks, including geometry texture removal and enhancement, coating transfer, mesh segmentation and level-of-detail meshing.


title: Congenital hypotrichosis due to short anagen.

abstract: Two children with congenital hypotrichosis are described. Both presented with persistent short, fine hair since birth. Evidence is provided that the short hair observed in these patients is due to a short anagen phase of the hair cycle, with a normal rate of hair growth. Shortening of the anagen phase of the scalp hair cycle leads to a decrease in the maximal hair length and an increase in the number of hairs in telogen, resulting in an increase in hair shedding. Scanning electron microscopy showed a widely spaced cuticular pattern, a finding typically seen in hair of thin calibre. One patient had affected family members with an apparently autosomal dominant mode of inheritance. The disorder appears to resolve spontaneously during puberty and adulthood.


title: Localization of a putative ClC chloride channel in spinach chloroplasts

abstract: Seven genes seem to encode for putative ClC chloride channels (AtClC-a to AtClC-g) in Arabidopsis thaliana. Their function and localization is still largely unknown. AtClC-f shares considerable sequence similarity with putative ClC channel proteins from Synechocystis, considered to represent the precursor of chloroplasts. We show by biochemical and mass spectrometry analysis that ClC-f is located in the outer envelope membrane of spinach chloroplasts. Consistent with the plastidial localization of ClC-f, p-chlorophenoxy-acetic acid (CPA) reduces photosynthetic activity and the protein is expressed in etioplasts and chloroplasts but not in root tissue. These findings may represent a step toward the molecular identification of ion channel activities in chloroplast membranes.


title: Cache Telepathy: Leveraging Shared Resource Attacks to Learn DNN Architectures

abstract: Deep Neural Networks (DNNs) are fast becoming ubiquitous for their ability to attain good accuracy in various machine learning tasks. A DNN's architecture (i.e., its hyper-parameters) broadly determines the DNN's accuracy and performance, and is often confidential. Attacking a DNN in the cloud to obtain its architecture can potentially provide major commercial value. Further, attaining a DNN's architecture facilitates other, existing DNN attacks. This paper presents Cache Telepathy: a fast and accurate mechanism to steal a DNN's architecture using the cache side channel. Our attack is based on the insight that DNN inference relies heavily on tiled GEMM (Generalized Matrix Multiply), and that DNN architecture parameters determine the number of GEMM calls and the dimensions of the matrices used in the GEMM functions. Such information can be leaked through the cache side channel. This paper uses Prime+Probe and Flush+Reload to attack VGG and ResNet DNNs running OpenBLAS and Intel MKL libraries. Our attack is effective in helping obtain the architectures by very substantially reducing the search space of target DNN architectures. For example, for VGG using OpenBLAS, it reduces the search space from more than $10^{35}$ architectures to just 16.


title: Design and development of remote-operated multi-direction Unmanned Ground Vehicle (UGV)

abstract: The main purpose of this research is to design and develop complete system of a remote-operated multi-direction Unmanned Ground Vehicle (UGV). The development involved PIC microcontroller in remote-controlled and UGV robot, Xbee Pro modules, Graphic LCD 84×84, Vexta brushless DC electric motor and mecanum wheels. This paper show the study the movement of multidirectional UGV by using Mecanum wheels with differences drive configuration. The 16-bits Microchips microcontroller were used in the UGV's system that embed with Xbee Pro through variable baud-rate value via UART protocol and control the direction of wheels. The successful develop UGV demonstrated clearly the potential application of this type of vehicle, and incorporated the necessary technology for further research of this type of vehicle.


title: What drives smartwatch adoption intention? Comparing Apple and non-Apple watches

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this study is twofold. First, an integrated model will be developed based on task-technology fit, innovation diffusion theory and the new product adoption model in order to explore the factors that affect smartwatch adoption. Second, the differences in the factors that affect users’ intention to adopt the Apple Watch and other smartwatches will be examined. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The data for this study were collected via an online survey questionnaire. The responses of 341 potential adopters of smartwatches were used to test the hypotheses in the research model. The casual model was assessed using partial least squares techniques. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The model can account for more than 50 percent of the variance in adoption intention. The research results affirm prior findings that perceived product attributes have relatively strong influence on adoption intention. Among these attributes, relative advantage has the strongest effect. Moreover, this study revealed differences between the antecedents of Apple watches and those of non-Apple watches. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: The insights provided by this study can help smartwatch providers formulate better growth strategies. The findings also provide some directions for further development. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: This study provides a better understanding of how the factors in the theories influence the adoption intentions of Apple watches and non-Apple watches.


title: Virtual Function Placement for Service Chaining with Partial Orders and Anti-Affinity Rules

abstract: Software-Defined Networking and Network Function Virtualization are two paradigms that offer flexible software-based network management. Service providers are instantiating Virtualized Network Functions, for example, firewalls, DPIs, gateways—to highly facilitate the deployment and reconfiguration of network services with reduced time-to-value. They use Service Function Chaining technologies to dynamically reconfigure network paths traversing physical and virtual network functions. Providing a cost-efficient virtual function deployment over the network for a set of service chains is a key technical challenge for service providers, and this problem has recently caught much attention from both Industry and Academia. In this article, we propose a formulation of this problem as an Integer Linear Program that allows one to find the best feasible paths and virtual function placement for a set of services with respect to a total financial cost, while taking into account the (total or partial) order constraints for Service Function Chains of each service and other constraints such as end-to-end latency, anti-affinity rules between network functions on the same physical node and resource limitations in terms of network and processing capacities. Furthermore, we propose a heuristic algorithm based on a linear relaxation of the problem that performs close to optimum for large scale instances. © 2017 Wiley Periodicals, Inc. NETWORKS, 2017


title: A Comparison of Evolutionary Algorithms and Gradient-based Methods for the Optimal Control Problem

abstract: An experimental comparison of evolutionary algorithms and gradient-based methods for the optimal control problem is carried out. The problem is solved separately by Particle swarm optimization, Grey wolf optimizer, Fast gradient descent method, Marquardt method and Adam method. The simulation is performed on a jet aircraft model. The results of each algorithm performance are compared according to the best found value of the fitness function, the mean value and the standard deviation.


Analysing High Level Heuristics within a Graph-Based Hyper Heuristic for Exam Timetabling Problems


title: Grounding Verbs of Motion in Natural Language Commands to Robots

abstract: To be useful teammates to human partners, robots must be able to follow spoken instructions given in natural language. An important class of instructions involve interacting with people, such as “Follow the person to the kitchen” or “Meet the person at the elevators.” These instructions require that the robot fluidly react to changes in the environment, not simply follow a pre-computed plan. We present an algorithm for understanding natural language commands with three components. First, we create a cost function that scores the language according to how well it matches a candidate plan in the environment, defined as the log-likelihood of the plan given the command. Components of the cost function include novel models for the meanings of motion verbs such as “follow,” “meet,” and “avoid,” as well as spatial relations such as “to” and landmark phrases such as “the kitchen.” Second, an inference method uses this cost function to perform forward search, finding a plan that matches the natural language command. Third, a high-level controller repeatedly calls the inference method at each timestep to compute a new plan in response to changes in the environment such as the movement of the human partner or other people in the scene. When a command consists of more than a single task, the controller switches to the next task when an earlier one is satisfied. We evaluate our approach on a set of example tasks that require the ability to follow both simple and complex natural language commands.


title: Terminological ontology learning and population using latent Dirichlet allocation

abstract: The success of Semantic Web will heavily rely on the availability of formal ontologies to structure machine understanding data. However, there is still a lack of general methodologies for ontology automatic learning and population, i.e. the generation of domain ontologies from various kinds of resources by applying natural language processing and machine learning techniques In this paper, the authors present an ontology learning and population system that combines both statistical and semantic methodologies. Several experiments have been carried out, demonstrating the effectiveness of the proposed approach. HighlightsA graph of terms can be effectively used for ontology building.Such a graph is extracted from documents thanks to a LDA based methodology.Ontology learning involves the use of annotated lexicons (WordNet).Proposed method achieves good performances on standard datasets.


title: Falcon: A Graph Manipulation Language for Heterogeneous Systems

abstract: Graph algorithms have been shown to possess enough parallelism to keep several computing resources busy—even hundreds of cores on a GPU. Unfortunately, tuning their implementation for efficient execution on a particular hardware configuration of heterogeneous systems consisting of multicore CPUs and GPUs is challenging, time consuming, and error prone. To address these issues, we propose a domain-specific language (DSL), Falcon, for implementing graph algorithms that (i) abstracts the hardware, (ii) provides constructs to write explicitly parallel programs at a higher level, and (iii) can work with general algorithms that may change the graph structure (morph algorithms). We illustrate the usage of our DSL to implement local computation algorithms (that do not change the graph structure) and morph algorithms such as Delaunay mesh refinement, survey propagation, and dynamic SSSP on GPU and multicore CPUs. Using a set of benchmark graphs, we illustrate that the generated code performs close to the state-of-the-art hand-tuned implementations.


title: Efficiently Verifiable Computation on Encrypted Data

abstract: We study the task of verifiable delegation of computation on encrypted data. We improve previous definitions in order to tolerate adversaries that learn whether or not clients accept the result of a delegated computation. In this strong model, we construct a scheme for arbitrary computations and highly efficient schemes for delegation of various classes of functions, such as linear combinations, high-degree univariate polynomials, and multivariate quadratic polynomials. Notably, the latter class includes many useful statistics. Using our solution, a client can store a large encrypted dataset on a server, query statistics over this data, and receive encrypted results that can be efficiently verified and decrypted. As a key contribution for the efficiency of our schemes, we develop a novel homomorphic hashing technique that allows us to efficiently authenticate computations, at the same cost as if the data were in the clear, avoiding a $10^4$ overhead which would occur with a naive approach. We support our theoretical constructions with extensive implementation tests that show the practical feasibility of our schemes.


title: Self-Generating a Labor Force for Crowdsourcing: Is Worker Confidence a Predictor of Quality?

abstract: When leveraging the crowd to perform complex tasks, it is imperative to identify the most effective worker for a particular job. Demographic profiles provided by workers, skill self-assessments by workers, and past performance as captured by employers all represent viable data points available within labor markets. Employers often question the validity of a worker's self-assessment of skills and expertise level when selecting workers in context of other information. More specifically, employers would like to answer the question, "Is worker confidence a predictor of quality?" In this paper, we discuss the state-of-the-art in recommending crowd workers based on assessment information. A major contribution of our work is an architecture, platform, and push/pull process for categorizing and recommending workers based on available self-assessment information. We present a study exploring the validity of skills input by workers in light of their actual performance and other metrics captured by employers. A further contribution of this approach is the extrapolation of a body of workers to describe the nature of the community more broadly. Through experimentation, within the language-processing domain, we demonstrate a new capability of deriving trends that might help future employers to select appropriate workers.


title: Sub-Sampled Newton Methods II: Local Convergence Rates

abstract: Many data-fitting applications require the solution of an optimization problem involving a sum of large number of functions of high dimensional parameter. Here, we consider the problem of minimizing a sum of $n$ functions over a convex constraint set $\mathcal{X} \subseteq \mathbb{R}^{p}$ where both $n$ and $p$ are large. In such problems, sub-sampling as a way to reduce $n$ can offer great amount of computational efficiency. Within the context of second order methods, we first give quantitative local convergence results for variants of Newton's method where the Hessian is uniformly sub-sampled. Using random matrix concentration inequalities, one can sub-sample in a way that the curvature information is preserved. Using such sub-sampling strategy, we establish locally Q-linear and Q-superlinear convergence rates. We also give additional convergence results for when the sub-sampled Hessian is regularized by modifying its spectrum or Levenberg-type regularization. Finally, in addition to Hessian sub-sampling, we consider sub-sampling the gradient as way to further reduce the computational complexity per iteration. We use approximate matrix multiplication results from randomized numerical linear algebra (RandNLA) to obtain the proper sampling strategy and we establish locally R-linear convergence rates. In such a setting, we also show that a very aggressive sample size increase results in a R-superlinearly convergent algorithm. While the sample size depends on the condition number of the problem, our convergence rates are problem-independent, i.e., they do not depend on the quantities related to the problem. Hence, our analysis here can be used to complement the results of our basic framework from the companion paper, [38], by exploring algorithmic trade-offs that are important in practice.


title: Fine Tuning Based SqueezeNet for Vehicle Classification

abstract: The vehicle classification contributes in many important aspects of road management. In this paper, we convey our result of the vehicle classification based on the deep net prediction. We fine-tuned the recently published SqueezeNet on the Miovision Traffic Camera Dataset (MIO-TCD)1. The imbalanced instance distribution of the MIO-TCD classification training section portrays the nature of traffic scene. To give a better generalization, we redesign the instance distribution of the training dataset by implementing augmentation and reduction. We find that the balanced instance distribution delivers significant minority class prediction accuracy improvement over the imbalanced instance training dataset. Also, we show that fine-tuning a pre-trained SqueezeNet effectively increase the net performance over the baseline models.