title: Latent Fingerprint Matching Using Descriptor-Based Hough Transform

abstract: Identifying suspects based on impressions of fingers lifted from crime scenes (latent prints) is extremely important to law enforcement agencies. Latents are usually partial fingerprints with small area, contain nonlinear distortion, and are usually smudgy and blurred. Due to some of these characteristics, they have a significantly smaller number of minutiae points (one of the most important features in fingerprint matching) and therefore it can be extremely difficult to automatically match latents to plain or rolled fingerprints that are stored in law enforcement databases. Our goal is to develop a latent matching algorithm that uses only minutiae information. The proposed approach consists of following three modules: (i) align two sets of minutiae by using a descriptor-based Hough Transform; (ii) establish the correspondences between minutiae; and (iii) compute a similarity score. Experimental results on NIST SD27 show that the proposed algorithm outperforms a commercial fingerprint matcher.


title: Lying or longing for likes? Narcissism, peer belonging, loneliness and normative versus deceptive like-seeking on Instagram in emerging adulthood

abstract: Abstract We examined the extent to which emerging adults engage in different behaviors on Instagram, a popular social networking site, to gain attention and validation from others via “likes.” We also examined individual differences in the frequency of like-seeking behavior and motives for Instagram use as mediators of these relationships. Participants ( N = 198 and 265 (replication study)) were recruited via an online crowdsourcing portal to complete a survey. Results demonstrated that, as predicted, participants engaged in an assortment of different like-seeking behaviors. Further, a two-factor solution emerged, with like-seeking behavior separated by whether they were normative (i.e., common or accepted, e.g., using filters or hashtags) or deceptive (e.g., buying likes or changing one’s appearance in photos using software). Deceptive like-seeking was predicted by stronger narcissism and a weaker sense of peer belonging, whereas normative like-seeking was predicted by stronger narcissism and a stronger sense of peer belonging. Further, consistent with hypotheses, significant mediators of the relation between narcissism and deceptive like-seeking included motives to use Instagram to increase popularity and showcase creativity. Results help to identify young people who are more susceptible to engaging in deceptive, potentially harmful acts to gain attention and validation on Instagram.


Tutorial on real-time scheduling


Text Categorization


title: YAGO2: A Spatially and Temporally Enhanced Knowledge Base from Wikipedia (Extended Abstract)

abstract: We present YAGO2, an extension of the YAGO knowledge base, in which entities, facts, and events are anchored in both time and space. YAGO2 is built automatically from Wikipedia, GeoNames, and WordNet. It contains 447 million facts about 9.8 million entities. Human evaluation confirmed an accuracy of 95% of the facts in YAGO2. In this paper, we present the extraction methodology, the integration of the spatio-temporal dimension, and our knowledge representation SPOTL, an extension of the original SPO-triple model to time and space.


title: The Improvisation Effect: A Case Study of User Improvisation and Its Effects on Information System Evolution

abstract: Few studies have examined interactions between IT change and organizational change during information systems evolution (ISE). We propose a dynamic model of ISE where change dynamics are captured in four dimensions: planned change, improvised change, organizational change and IT change. This inductively-generated model yields a rich account of ISE and its drivers by integrating the four change dimensions. The model shows how incremental adjustments in IT and organizational processes often grow into a profound change as users improvise. We demonstrate the value of the dynamic model by illustrating ISE processes in the context of two manufacturing organizations implementing the same system over a study period of five years. This paper makes its contribution by holistically characterizing improvisation in the context of IT and organizational change. Our ISE model moves research in organizational and IT change towards a common framing by showing how each affects the other’s form, function and evolution.


title: Type-2 Fuzzistics for Symmetric Interval Type-2 Fuzzy Sets: Part 2, Inverse Problems

abstract: In Part 1 of this two-part paper, we bounded the centroid of a symmetric interval type-2 fuzzy set (T2 FS), and consequently its uncertainty, using geometric properties of its footprint of uncertainty (FOU). We then used these bounds to solve forward problems, i.e., to go from parametric interval T2 FS models to data. The main purpose of the present paper is to formulate and solve inverse problems, i.e., to go from uncertain data to parametric interval T2 FS models, which we call type-2 fuzzistics. Given interval data collected from people about a phrase, and the inherent uncertainties associated with that data, which can be described statistically using the first- and second-order statistics about the end-point data, we establish parametric FOUs such that their uncertainty bounds are directly connected to statistical uncertainty bounds. These results should find applicability in computing with words


title: Numerical data on neocortical neurons in adult rat, with special reference to the GABA population

abstract: The disector method was used to estimate the numerical density of neurons (number per unit volume) and their actual number per column (number under a given area of pial surface), in the occipital (monocular segment of the primary visual area, Oc1M), the parietal (somatosensory barrelfield area, Par1) and the frontal cortex (primary motor area, Fr1) of adult rat. Values were first obtained for all neurons in each layer, and then for GABA neurons as identified with postembedding immunocytochemistry on semithin sections. The numerical density of neurons in the frontal cortex (34,000/mm3) was significantly lower than in the two other neocortical areas (occipital: 52,000; parietal: 48,000/mm3). The GABA population showed a similar difference and consequently represented an equivalent proportion of total (15%) in the three cortical areas. Across layers, there was an alternate distribution of low and high density of neurons from layers II–III to VI in the three cortical areas, with the highest density in layer IV of the two sensory areas. The laminar changes in density of the GABA neurons were not as pronounced as those of the overall population. Consequently, the layers with the highest overall neuronal densities tended to have a lower proportion of GABA neurons and vice versa. There were more neurons under 1 mm2 of surface in the parietal (90,000) than the occipital or the frontal cortex (71,000), which was also true of the GABA neurons. The greater number of neurons per column in the parietal cortex was mostly imputable to layer IV, the main recipient of thalamic axons. Comparing these values from the rat with those previously obtained in cat and monkey, it seemed that the number of neurons per cortical column was the highest in the sensory area preferentially used by each species.


Comparative Study of Frequent Itemset Mining Algorithms : FP growth , FIN , Prepost + and study of Efficiency in terms of Memory Consumption , Scalability and Runtime


title: Examining Sentiments and Popularity of Pro- and Anti-Vaccination Videos on YouTube

abstract: Vaccine misinformation on social media poses significant drawbacks to the efforts of vaccine coverage rates. This research studies the interlinkages between pro- and anti-vaccine YouTube videos to help public health professionals explore new ways to reach anti-vaccine and vaccine-hesitant audiences. Using YouTube's API, we retrieved 9,489 recommended videos from 250 seeds using keywords such as "vaccines" and its derivatives. We then manually identified 1,984 videos directly related to vaccination and then categorized their vaccine sentiment into pro-, anti-, and neutral. Results show that 65.02% of the videos were anti-vaccine, and only 20.87% were pro-vaccine, 14.11% were neutral. Anti-vaccine videos were significantly more prevalent in the "News & Politics" and "People & Blogs" video categories; while pro-vaccine videos were more prevalent in the "Education" and "Science & Technology" categories. Results also showed that anti-vaccine sentiment videos have higher values of closeness centrality (p


title: UNPU: A 50.6TOPS/W unified deep neural network accelerator with 1b-to-16b fully-variable weight bit-precision

abstract: Deep neural network (DNN) accelerators [1-3] have been proposed to accelerate deep learning algorithms from face recognition to emotion recognition in mobile or embedded environments [3]. However, most works accelerate only the convolutional layers (CLs) or fully-connected layers (FCLs), and different DNNs, such as those containing recurrent layers (RLs) (useful for emotion recognition) have not been supported in hardware. A combined CNN-RNN accelerator [1], separately optimizing the computation-dominant CLs, and memory-dominant RLs or FCLs, was reported to increase overall performance, however, the number of processing elements (PEs) for CLs and RLs was limited by their area and consequently, performance was suboptimal in scenarios requiring only CLs or only RLs. Although the PEs for RLs can be reconfigured into PEs for CLs or vice versa, only a partial reconfiguration was possible resulting in marginal performance improvement. Moreover, previous works [1-2] supported a limited set of weight bit precisions, such as either 4b or 8b or 16b. However, lower weight bit-precisions can achieve better throughput and higher energy efficiency, and the optimal bit-precision can be varied according to different accuracy/performance requirements. Therefore, a unified DNN accelerator with fully-variable weight bit-precision is required for the energy-optimal operation of DNNs within a mobile environment.


title: Wasserstein Distance Guided Representation Learning for Domain Adaptation

abstract: Domain adaptation aims at generalizing a high-performance learner on a target domain via utilizing the knowledge distilled from a source domain which has a different but related data distribution. One solution to domain adaptation is to learn domain invariant feature representations while the learned representations should also be discriminative in prediction. To learn such representations, domain adaptation frameworks usually include a domain invariant representation learning approach to measure and reduce the domain discrepancy, as well as a discriminator for classification. Inspired by Wasserstein GAN, in this paper we propose a novel approach to learn domain invariant feature representations, namely Wasserstein Distance Guided Representation Learning (WDGRL). WDGRL utilizes a neural network, denoted by the domain critic, to estimate empirical Wasserstein distance between the source and target samples and optimizes the feature extractor network to minimize the estimated Wasserstein distance in an adversarial manner. The theoretical advantages of Wasserstein distance for domain adaptation lie in its gradient property and promising generalization bound. Empirical studies on common sentiment and image classification adaptation datasets demonstrate that our proposed WDGRL outperforms the state-of-the-art domain invariant representation learning approaches.


title: Introduction to the 1st Place Winning Model of OpenImages Relationship Detection Challenge

abstract: This article describes the model we built that achieved 1st place in the OpenImage Visual Relationship Detection Challenge on Kaggle. Three key factors contribute the most to our success: 1) language bias is a powerful baseline for this task. We build the empirical distribution $P(predicate|subject,object)$ in the training set and directly use that in testing. This baseline achieved the 2nd place when submitted; 2) spatial features are as important as visual features, especially for spatial relationships such as "under" and "inside of"; 3) It is a very effective way to fuse different features by first building separate modules for each of them, then adding their output logits before the final softmax layer. We show in ablation study that each factor can improve the performance to a non-trivial extent, and the model reaches optimal when all of them are combined.


title: Bayesian Analysis of Empirical Software Engineering Cost Models

abstract: Many parametric software estimation models have evolved in the last two decades (L.H. Putnam and W. Myers, 1992; C. Jones, 1997; R.M. Park et al., 1992). Almost all of these parametric models have been empirically calibrated to actual data from completed software projects. The most commonly used technique for empirical calibration has been the popular classical multiple regression approach. As discussed in the paper, the multiple regression approach imposes a few assumptions frequently violated by software engineering datasets. The paper illustrates the problems faced by the multiple regression approach during the calibration of one of the popular software engineering cost models, COCOMO II. It describes the use of a pragmatic 10 percent weighted average approach that was used for the first publicly available calibrated version (S. Chulani et al., 1998). It then moves on to show how a more sophisticated Bayesian approach can be used to alleviate some of the problems faced by multiple regression. It compares and contrasts the two empirical approaches, and concludes that the Bayesian approach was better and more robust than the multiple regression approach.


title: A multi-agent approach for generating ontologies and composing services into executable workflows

abstract: This paper proposes rethinking how ontologies are used to compose web services into business processes. Unlike handcrafted ontologies, we describe using a multi-agent system (MAS) to automatically generate semantic mappings from service interfaces. Comparing synonyms and contextual clues, we infer meanings of input and output parameters with no explicit semantics (as in a Web Services Description Language document). We further describe how this semantic mapping can be used to derive executable processes by comparing the derived ontologies of each service interface and mapping each service's outputs to inputs of every other service and finding the paths through the resulting graph.


title: Classification of healthcare data using genetic fuzzy logic system and wavelets

abstract: Introduce GSAM model by incorporating genetic algorithm in SAM learning process.GSAM learning has lower computational costs and higher efficiency compared to SAM.Employ wavelet transformation for feature extraction in high-dimensional datasets.This is the first application of fuzzy SAM method in medical diagnosis.This is the first combination of wavelets and fuzzy SAM applied in classification. Healthcare plays an important role in promoting the general health and well-being of people around the world. The difficulty in healthcare data classification arises from the uncertainty and the high-dimensional nature of the medical data collected. This paper proposes an integration of fuzzy standard additive model (SAM) with genetic algorithm (GA), called GSAM, to deal with uncertainty and computational challenges. GSAM learning process comprises three continual steps: rule initialization by unsupervised learning using the adaptive vector quantization clustering, evolutionary rule optimization by GA and parameter tuning by the gradient descent supervised learning. Wavelet transformation is employed to extract discriminative features for high-dimensional datasets. GSAM becomes highly capable when deployed with small number of wavelet features as its computational burden is remarkably reduced. The proposed method is evaluated using two frequently-used medical datasets: the Wisconsin breast cancer and Cleveland heart disease from the UCI Repository for machine learning. Experiments are organized with a five-fold cross validation and performance of classification techniques are measured by a number of important metrics: accuracy, F-measure, mutual information and area under the receiver operating characteristic curve. Results demonstrate the superiority of the GSAM compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. The proposed approach is thus helpful as a decision support system for medical practitioners in the healthcare practice.


title: A 2.45 GHz smart antenna for location-aware single-anchor indoor applications

abstract: This paper proposes a new switched beam array optimized for 2.45 GHz wireless indoor applications. The antenna supports directional communication to enable spatial reusability, and polarization diversity to mitigate multipath propagation. It also supports absolute 2D target localization using measurements from a single anchor node. The paper describes the antenna design, the implementation and the experimental characterization, along with its positioning applications. The localization results obtained with data collected from indoor measurements, showing an average localization error as low as 1.7 m.


title: Limbic systems for emotion and for memory, but no single limbic system

abstract: The concept of a (single) limbic system is shown to be outmoded. Instead, anatomical, neurophysiological, functional neuroimaging, and neuropsychological evidence is described that anterior limbic and related structures including the orbitofrontal cortex and amygdala are involved in emotion, reward valuation, and reward-related decision-making (but not memory), with the value representations transmitted to the anterior cingulate cortex for action–outcome learning. In this ‘emotion limbic system’ a computational principle is that feedforward pattern association networks learn associations from visual, olfactory and auditory stimuli, to primary reinforcers such as taste, touch, and pain. In primates including humans this learning can be very rapid and rule-based, with the orbitofrontal cortex overshadowing the amygdala in this learning important for social and emotional behaviour. Complementary evidence is described showing that the hippocampus and limbic structures to which it is connected including the posterior cingulate cortex and the fornix-mammillary body-anterior thalamus-posterior cingulate circuit are involved in episodic or event memory, but not emotion. This ‘hippocampal system’ receives information from neocortical areas about spatial location, and objects, and can rapidly associate this information together by the different computational principle of autoassociation in the CA3 region of the hippocampus involving feedback. The system can later recall the whole of this information in the CA3 region from any component, a feedback process, and can recall the information back to neocortical areas, again a feedback (to neocortex) recall process. Emotion can enter this memory system from the orbitofrontal cortex etc., and be recalled back to the orbitofrontal cortex etc. during memory recall, but the emotional and hippocampal networks or ‘limbic systems’ operate by different computational principles, and operate independently of each other except insofar as an emotional state or reward value attribute may be part of an episodic memory.


A process model for integrated IT governance , risk , and compliance management


Stance Detection for the Fake News Challenge : Identifying Textual Relationships with Deep Neural Nets


Modelling the stock market using twitter


title: Upscaledb: Efficient Integer-Key Compression in a Key-Value Store using SIMD Instructions

abstract: Compression can sometimes improve performance by making more of the data available to the processors faster. We consider the compression of integer keys in a B+-tree index. For this purpose, systems such as IBM DB2 use variable-byte compression over differentially coded keys. We revisit this problem with various compression alternatives such as Google's VarIntGB, Binary Packing and Frame-of-Reference. In all cases, we describe algorithms that can operate directly on compressed data. Many of our alternatives exploit the single-instruction-multiple-data (SIMD) instructions supported by modern CPUs. We evaluate our techniques in a database environment provided by Upscaledb, a production-quality key-value database. Our best techniques are SIMD accelerated: they simultaneously reduce memory usage while improving single-threaded speeds. In particular, a differentially coded SIMD binary-packing techniques (BP128) can offer a superior query speed (e.g., 40% better than an uncompressed database) while providing the best compression (e.g., by a factor of ten). For analytic workloads, our fast compression techniques offer compelling benefits. Our software is available as open source.


title: Should Smaller Commercial Trucks Be Subject to Safety Regulations?

abstract: Motor-carrier safety is a critical and challenging issue in the US motorcarrier industry. Despite the vast safety improvements in the industry, a continuing challenge is the economic costs associated with regulatory oversight of safety inspection programs. The purpose of this article is to compute the costs and associated benefits of the Federal Motor Carrier Safety Administration's driver and vehicle inspection program of commercial motor vehicles in the 10,001- to 26,000-lb category. Our analysis reveals that for every dollar spent in program costs, the benefits are $8.86. Language: en


title: Efficient Spare Allocation in Reconfigurable Arrays

abstract: The issue of yield degradation due to physical failures in large memory and processor arrays is of significant importance to semiconductor manufacturers. One method of increasing the yield for iterated arrays of memory cells or processing elements is by incorporating spare rows and columns in the die or wafer which can be programmed into the array. This paper addresses the issue of computer-aided design approaches to optimal reconfiguration of such arrays. The paper presents the first formal analysis of the problem. The complexity of optimal reconfiguration is shown to be NP-complete for rectangular arrays utilizing spare rows and columns. In contrast to previously proposed exhaustive search and greedy algorithms, this paper develops a heuristic branch and bound approach based on the complexity analysis, which allows for flexible and highly efficient reconfiguration. Initial screening is performed by a bipartite graph matching algorithm.


title: Prediction-based portfolio optimization model using neural networks

abstract: This work presents a new prediction-based portfolio optimization model that can capture short-term investment opportunities. We used neural network predictors to predict stocks' returns and derived a risk measure, based on the prediction errors, that have the same statistical foundation of the mean-variance model. The efficient diversification effects hold thanks to the selection of predictors with low and complementary pairwise error profiles. We employed a large set of experiments with real data from the Brazilian stock market to examine our portfolio optimization model, which included the evaluation of the Normality of the prediction errors. Our results showed that it is possible to obtain Normal prediction errors with non-Normal time series of stock returns and that the prediction-based portfolio optimization model took advantage of short-term opportunities, outperforming the mean-variance model and beating the market index.


title: Soft Drink Consumption and Risk of Developing Cardiometabolic Risk Factors and the Metabolic Syndrome in Middle-Aged Adults in the Community

abstract: Background—Consumption of soft drinks has been linked to obesity in children and adolescents, but it is unclear whether it increases metabolic risk in middle-aged individuals. Methods and Results—We related the incidence of metabolic syndrome and its components to soft drink consumption in participants in the Framingham Heart Study (6039 person-observations, 3470 in women; mean age 52.9 years) who were free of baseline metabolic syndrome. Metabolic syndrome was defined as the presence of 3o f the following: waist circumference 35 inches (women) or 40 inches (men); fasting blood glucose 100 mg/dL; serum triglycerides 150 mg/dL; blood pressure 135/85 mm Hg; and high-density lipoprotein cholesterol 40 mg/dL (men) or 50 mg/dL (women). Multivariable models included adjustments for age, sex, physical activity, smoking, dietary intake of saturated fat, trans fat, fiber, magnesium, total calories, and glycemic index. Cross-sectionally, individuals consuming 1 soft drink per day had a higher prevalence of metabolic syndrome (odds ratio [OR], 1.48; 95% CI, 1.30 to 1.69) than those consuming 1 drink per day. On follow-up (mean of 4 years), new-onset metabolic syndrome developed in 765 (18.7%) of 4095 participants consuming 1 drink per day and in 474 (22.6%) of 2059 persons consuming 1 soft drink per day. Consumption of 1 soft drink per day was associated with increased odds of developing metabolic syndrome (OR, 1.44; 95% CI, 1.20 to 1.74), obesity (OR, 1.31; 95% CI, 1.02 to 1.68), increased waist circumference (OR, 1.30; 95% CI, 1.09 to 1.56), impaired fasting glucose (OR, 1.25; 95% CI, 1.05 to 1.48), higher blood pressure (OR, 1.18; 95% CI, 0.96 to 1.44), hypertriglyceridemia (OR, 1.25; 95% CI, 1.04 to 1.51), and low high-density lipoprotein cholesterol (OR, 1.32; 95% CI 1.06 to 1.64). Conclusions—In middle-aged adults, soft drink consumption is associated with a higher prevalence and incidence of multiple metabolic risk factors. (Circulation. 2007;116:480-488.)


title: Robust dialog state tracking using delexicalised recurrent neural networks and unsupervised adaptation

abstract: Tracking the user's intention throughout the course of a dialog, called dialog state tracking, is an important component of any dialog system. Most existing spoken dialog systems are designed to work in a static, well-defined domain, and are not well suited to tasks in which the domain may change or be extended over time. This paper shows how recurrent neural networks can be effectively applied to tracking in an extended domain with new slots and values not present in training data. The method is evaluated in the third Dialog State Tracking Challenge, where it significantly outperforms other approaches in the task of tracking the user's goal. A method for online unsupervised adaptation to new domains is also presented. Unsupervised adaptation is shown to be helpful in improving word-based recurrent neural networks, which work directly from the speech recognition results. Word-based dialog state tracking is attractive as it does not require engineering a spoken language understanding system for use in the new domain and it avoids the need for a general purpose intermediate semantic representation.


title: ATLaS: A Native Extension of SQL for Data Mining

abstract: A lack of power and extensibility in their query languages has seriously limited the generality of DBMSs and hampered their ability to support data mining applications. Thus, there is a pressing need for more general mechanisms for extending DBMSs to support efficiently database-centric data mining appliacations. To satisfy this need, we propose a new extensibility mechanism for SQL-compliant DBMSs, and demonstrate its power in supporting decision support applications. The key extension is the ability of defining new table functions and aggregate functions in SQL— rather than in external procedural languages as Object-Relational (O-R) DBMSs currently do. This simple extension turns SQL into a powerful language for decision-support applications, including ROLAPs, time-series queries, stream-oriented processing, and data mining functions. First, we discuss the use of ATLaS for data mining applications, and then the architecture and techniques used


Supervised Mind Reading : Uncovering Text from Neural Data


title: Vivace: a practical gauss-seidel method for stable soft body dynamics

abstract: The solution of large sparse systems of linear constraints is at the base of most interactive solvers for physically-based animation of soft body dynamics. We focus on applications with hard and tight per-frame resource budgets, such as video games, where the solution of soft body dynamics needs to be computed in a few milliseconds. Linear iterative methods are preferred in these cases since they provide approximate solutions within a given error tolerance and in a short amount of time. We present a parallel randomized Gauss-Seidel method which can be effectively employed to enable the animation of 3D soft objects discretized as large and irregular triangular or tetrahedral meshes. At the beginning of each frame, we partition the set of equations governing the system using a randomized graph coloring algorithm. The unknowns in the equations belonging to the same partition are independent of each other. Then, all the equations belonging to the same partition are solved at the same time in parallel. Our algorithm runs completely on the GPU and can support changes in the constraints topology. We tested our method as a solver for soft body dynamics within the Projective Dynamics and Position Based Dynamics frameworks. We show how the algorithmic simplicity of this iterative strategy enables great numerical stability and fast convergence speed, which are essential features for physically based animations with fixed and small hard time budgets. Compared to the state of the art, we found our method to be faster and scale better while providing stabler solutions for very small time budgets.


Privacy-Preserving Classification on Deep Neural Network.


title: Eight-Degrees-of-Freedom Remote Actuation of Small Magnetic Mechanisms

abstract: Magnetically-driven micrometer to millimeter-scale robotic devices have recently shown great capabilities for remote applications in medical procedures, in microfluidic tools and in microfactories. Significant effort recently has been on the creation of mobile or stationary devices with multiple independently-controllable degrees of freedom (DOF) for multiagent or complex mechanism motions. In most applications of magnetic microrobots, however, the relatively large distance from the field generation source and the microscale devices results in controlling magnetic field signals which are applied homogeneously over all agents. While some progress has been made in this area allowing up to six independent DOF to be individually commanded, there has been no rigorous effort in determining the maximum achievable number of DOF for systems with homogeneous magnetic field input. In this work, we show that this maximum is eight and we introduce the theoretical basis for this conclusion, relying on the number of independent usable components in a magnetic field at a point. In order to verify the claim experimentally, we develop a simple demonstration mechanism with 8 DOF designed specifically to show independent actuation. Using this mechanism with $500 \mu \mathrm{m}$ magnetic elements, we demonstrate eight independent motions of 0.6 mm with 8.6 % coupling using an eight coil system. These results will enable the creation of richer outputs in future microrobotic devices.


title: 3D representations for software visualization

abstract: The paper presents a new 3D representation for visualizing large software systems. The origins of this representation can be directly traced to the SeeSoft metaphor. This work extends these visualization mechanisms by utilizing the third dimension, texture, abstraction mechanism, and by supporting new manipulation techniques and user interfaces. By utilizing a 3D representation we can better represent higher dimensional data than previous 2D views. An overview of our prototype tool and its basic functionality is given. Applications of this method to particular software engineering tasks are also discussed.


title: Radio Access Network sharing in cellular networks

abstract: Mobile operators are witnessing a dramatic increase in traffic spurred by a combination of popularity of smartphones, innovative applications and diverse services. As mobile traffic transitions from being voice dominated to video and data dominated, the revenue per byte for the mobile operators is declining at an unhealthy rate. To counter the traffic growth and build cost-effective networks, many operators are now forging alliances for RAN (Radio Access Network) sharing to improve coverage and capacity at reasonable investments and operational costs. This paper presents the design and implementation of NetShare, a network-wide radio resource management framework that provides effective RAN Sharing. NetShare introduces a novel two-level scheduler split between the mobile gateway and the cellular basestations to effectively manage and allocate the wireless resources of the radio access network composed of multiple basestations among multiple different entities (such as operators, content providers, etc.) that share the network. Firstly, NetShare provides performance isolation across entities with a minimum guaranteed resource allocation to each entity across the network. Secondly, NetShare optimally distributes the resources to each entity across the network proportional to the resource demand at each basestation. Through extensive LTE-based system simulations and prototype evaluations on a WiMAX testbed, we show the efficacy of NetShare in (a) providing isolation across entities and (b) efficiently distributing resources for each entity across the network thus achieving high utilization of resources for an entity.


title: Automatic Radio Map Adaptation for Indoor Localization Using Smartphones

abstract: The proliferation of mobile computing has prompted WiFi-based indoor localization to be one of the most attractive and promising techniques for ubiquitous applications. A primary concern for these technologies to be fully practical is to combat harsh indoor environmental dynamics, especially for long-term deployment. Despite numerous research on WiFi fingerprint-based localization, the problem of radio map adaptation has not been sufficiently studied and remains open. In this work, we propose AcMu, an automatic and continuous radio map self-updating service for wireless indoor localization that exploits the static behaviors of mobile devices. By accurately pinpointing mobile devices with a novel trajectory matching algorithm, we employ them as mobile reference points to collect real-time RSS samples when they are static. With these fresh reference data, we adapt the complete radio map by learning an underlying relationship of RSS dependency between different locations, which is expected to be relatively constant over time. Extensive experiments for 20 days across six months demonstrate that AcMu effectively accommodates RSS variations over time and derives accurate prediction of fresh radio map with average errors of less than 5dB, outperforming existing approaches. Moreover, AcMu provides 2 $\times$ improvement on localization accuracy by maintaining an up-to-date radio map.


title: Virtual Resource Allocation for Heterogeneous Services in Full Duplex-Enabled SCNs With Mobile Edge Computing and Caching

abstract: In the area of full duplex (FD) enabled small cell networks (SCNs), only limited works have been done on the consideration of mobile edge computing (MEC) and caching. In this paper, a virtual FD-enabled SCN framework with MEC and caching is investigated for two kinds of heterogeneous services, high-data-rate service and computation-sensitive service. In our proposed framework, content caching and FD communication are jointly considered to provide high-data-rate service without the cost of backhaul resource. And computation-sensitive service is offloaded to MEC, guaranteeing the delay requirement of users. From the view point of heterogeneous services, we formulate a virtual resource allocation problem, in which quality of experience of users and corresponding resource consumption are recognized as system revenue and cost, respectively. Particularly, user association, power control and resources (including spectrum, caching, and computing) allocation are jointly considered. Since the optimized problem is nonconvex, necessary variable relaxation and reformulation are conducted to transfer the original problem to a convex problem. Furthermore, alternating direction method of multipliers algorithm is adopted to obtain the optimal solution with low computation complexity. Finally, extensive simulations are conducted with different system parameter configurations to verify the effectiveness of our proposed scheme.


title: ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks

abstract: We propose to learn a curriculum or a syllabus for supervised learning and deep reinforcement learning with deep neural networks by an attachable deep neural network, called ScreenerNet. Specifically, we learn a weight for each sample by jointly training the ScreenerNet and the main network in an end-to-end self-paced fashion. The ScreenerNet neither has sampling bias nor requires to remember the past learning history. We show the networks augmented with the ScreenerNet achieve early convergence with better accuracy than the state-of-the-art curricular learning methods in extensive experiments using three popular vision datasets such as MNIST, CIFAR10 and Pascal VOC2012, and a Cart-pole task using Deep Q-learning. Moreover, the ScreenerNet can extend other curriculum learning methods such as Prioritized Experience Replay (PER) for further accuracy improvement.


title: OCR for bilingual documents using language modeling

abstract: Script based features are highly discriminative for text segmentation and recognition. Thus they are widely used in Optical Character Recognition(OCR) problems. But usage of script dependent features restricts the adaptation of such architectures directly for another script. With script independent systems, this problem can be solved to a certain extent for monolingual documents. But the problem aggravates in case of multilingual documents as it is very difficult for a single classifier to learn many scripts. Generally a script identification module identifies text segments and accordingly the script-dependent classifier is selected. This paper presents a unified framework of language model and multiple preprocessing hypotheses for word recognition from bilingual document images. Prior to text recognition, preprocessing steps such as binarization and segmentation are required for ease of recognition. But these steps induce huge combinatorial error propagating to final recognition accuracy. In this paper we use multiple preprocessing routines as alternate hypotheses and use a language model to verify each alternative and choose the best recognized sequence. We test this architecture for word recognition of Kannada-English and Telugu-English bilingual documents and achieved better recognition rates than single methods using same classifier.


title: The structure of the Social Self-concept (SSC) Questionnaire

abstract: The aim of this study is to examine the factor structure of the Social Self-Concept Questionnaire (SSQ), newly, made up of two scales: social responsibility and social competence. A review of both theories of human social development as previous attempts to measure social self-concept a theoretical proposal that the social self is the conjunction of two basic self-perceptions was born: competence in social relationships and response to the demands of social functioning. Participated in research 818 students aged between 17 and 52 years. The results obtained by confirmatory factor analysis, endorse the hypothesized two interrelated factors structure. This work, in addition to providing a new measuring instrument with adequate psychometric characteristics and validity that justify its use both in practice as applied in the field of research, helps to better understand the inner nature of the social domain of the self-concept.


title: A 3-D Micromechanical Multi-Loop Magnetometer Driven Off-Resonance by an On-Chip Resonator

abstract: This paper presents the principle and complete characterization of a single-chip unit formed by microelectromechanical system magnetometers to sense the 3-D magnetic field vector and a Tang resonator. The three sensors, nominally with the same resonance frequency, are operated 200-Hz off-resonance through an ac current whose reference frequency is provided by the resonator embedded in an oscillating circuit. The sensors gain is increased by adopting a current recirculation strategy using metal strips directly deposited on the structural polysilicon. At a driving value of 100 $\mu \text{A}_{\mathrm{ rms}}$ flowing in series through the three devices, the magnetometers show a sub-185 nT/ $\vphantom {{\sqrt {\mathrm{ Hz}}}^{a}}\sqrt {\mathrm{ Hz}}$ resolution with a selectable bandwidth up to 50 Hz. Over a ±5-mT full-scale range, the sensitivity curves show linearity errors lower than 0.2%, with high cross-axis rejection and immunity to external accelerations. Under temperature changes, the stability of the 200-Hz difference between the magnetometers and the resonator frequency is within 55 ppm/K. Offset is trimmed down to the microtesla range, with an overall measured Allan stability of about 100 nT at 20-s observation time. [2016-0030]


title: The Inverse Regression Topic Model

abstract: Taddy (2013) proposed multinomial inverse regression (MNIR) as a new model of annotated text based on the influence of metadata and response variables on the distribution of words in a document. While effective, MNIR has no way to exploit structure in the corpus to improve its predictions or facilitate exploratory data analysis. On the other hand, traditional probabilistic topic models (like latent Dirichlet allocation) capture natural heterogeneity in a collection but do not account for external variables. In this paper, we introduce the inverse regression topic model (IRTM), a mixed-membership extension of MNIR that combines the strengths of both methodologies. We present two inference algorithms for the IRTM: an efficient batch estimation algorithm and an online variant, which is suitable for large corpora. We apply these methods to a corpus of 73K Congressional press releases and another of 150K Yelp reviews, demonstrating that the IRTM outperforms both MNIR and supervised topic models on the prediction task. Further, we give examples showing that the IRTM enables systematic discovery of in-topic lexical variation, which is not possible with previous supervised topic models.


title: Zero-voltage-switching PWM hybrid full-bridge three-level converter

abstract: This paper proposes a zero-voltage-switching (ZVS) pulse-width modulation (PWM) hybrid full-bridge three-level converter, which has a three-level leg and a two-level leg. The switches of the three-level leg sustain only the half of the input voltage, and they can realize ZVS in a wide load range. The switches of the two-level leg sustain the input voltage, and they can realize ZVS with the use of the resonant inductance. The secondary rectified voltage is a three-level waveform having lower high-frequency content compared with that of the traditional full-bridge converters, which can reduce the output filter, and as a result, the dynamic response of the converter is improved. The voltage stress of the rectifier diode is reduced also. The input current of the converter has quite little ripple, so the input filter can also be significantly reduced. The operation principle of the proposed converter is analyzed and verified by the experimental results.


title: Hierarchical Discriminative Classification for Text-Based Geolocation

abstract: Text-based document geolocation is commonly rooted in language-based information retrieval techniques over geodesic grids. These methods ignore the natural hierarchy of cells in such grids and fall afoul of independence assumptions. We demonstrate the effectiveness of using logistic regression models on a hierarchy of nodes in the grid, which improves upon the state of the art accuracy by several percent and reduces mean error distances by hundreds of kilometers on data from Twitter, Wikipedia, and Flickr. We also show that logistic regression performs feature selection effectively, assigning high weights to geocentric terms.


title: WiseMAC: An Ultra Low Power MAC Protocol for Multi-hop Wireless Sensor Networks

abstract: WiseMAC is a medium access control protocol designed for wireless sensor networks. This protocol is based on non-persistent CSMA and uses the preamble sampling technique to minimize the power consumed when listening to an idle medium. The novelty in this protocol consists in exploiting the knowledge of the sampling schedule of one’s direct neighbors to use a wake-up preamble of minimized size. This scheme allows not only to reduce the transmit and the receive power consumption, but also brings a drastic reduction of the energy wasted due to overhearing. WiseMAC requires no set-up signalling, no network-wide synchronization and is adaptive to the traffic load. It presents an ultra-low power consumption in low traffic conditions and a high energy efficiency in high traffic conditions. The performance of the WiseMAC protocol is evaluated using simulations and mathematical analysis, and compared with S-MAC, T-MAC, CSMA/CA and an ideal protocol.


title: A Survey on Anomaly detection in Evolving Data: [with Application to Forest Fire Risk Prediction]

abstract: Traditionally most of the anomaly detection algorithms have been designed for 'static' datasets, in which all the observations are available at one time. In non-stationary environments on the other hand, the same algorithms cannot be applied as the underlying data distributions change constantly and the same models are not valid. Hence, we need to devise adaptive models that take into account the dynamically changing characteristics of environments and detect anomalies in 'evolving' data. Over the last two decades, many algorithms have been proposed to detect anomalies in evolving data. Some of them consider scenarios where a sequence of objects (called data streams) with one or multiple features evolves over time. Whereas the others concentrate on more complex scenarios, where streaming objects with one or multiple features have causal/non-causal relationships with each other. The latter can be represented as evolving graphs. In this paper, we categorize existing strategies for detecting anomalies in both scenarios including the state-of-the-art techniques. Since label information is mostly unavailable in real-world applications when data evolves, we review the unsupervised approaches in this paper. We then present an interesting application example, i.e., forest re risk prediction, and conclude the paper with future research directions in this eld for researchers and industry.


title: Twelve Years of National Football League Concussion Data

abstract: Background: Concussion in the National Football League (NFL) remains an important issue. An initial description of the injury epidemiology involved 6 years from 1996 to 2001. Hypothesis: The increased attention to concussions may have resulted in team physicians being more conservative in treating players in recent years. Study Design: Two consecutive 6-year periods (1996-2001 and 2002-2007) were compared to determine changes in the circumstances associated with the injury, the patterns of signs and symptoms, and the players’ time loss from participation in the NFL. Methods: During 2002-2007, concussions were recorded by NFL team physicians and athletic trainers using the same standardized reporting form used from 1996 to 2001. Player position, type of play, concussion signs and symptoms, loss of consciousness, and medical action taken were recorded. Results: There were 0.38 documented concussions per NFL game in 2002-2007—7.6% lower than the 0.42 in the earlier period (1996-2001). The injury rate was lower in quarterbacks and wide receivers but significantly higher in tight ends during the second 6 years. The most frequent symptoms were headaches and dizziness; the most common signs were problems with information processing and immediate recall. During 2002-2007, a significantly lower fraction of concussed players returned to the same game, and more were removed from play. Most concussed players (83.5%) returned to play in < 7 days; the percentage decreased to 57.4% with loss of consciousness. The number of players returning in < 7 days was 8% lower during 2002-2007 and 25% lower for those with loss of consciousness. Conclusion: The most recent 6 years of NFL concussion data show a remarkable similarity to the earlier period. However, there was a significant decrease in the percentage of players returning to the same game, and players were held out of play longer. Clinical Relevance: There was a more conservative management of concussion in NFL players from 2002 to 2007 even though the clinical signs and symptoms remained similar to the earlier 6-year period.


title: Adding a Solar-Radiance Function to the Hošek-Wilkie Skylight Model

abstract: One prerequisite for realistic renderings of outdoor scenes is the proper capturing of the sky's appearance. Currently, an explicit simulation of light scattering in the atmosphere isn't computationally feasible, and won't be in the foreseeable future. Captured luminance patterns have proven their usefulness in practice but can't meet all user needs. To fill this capability gap, computer graphics technology has employed analytical models of sky-dome luminance patterns for more than two decades. For technical reasons, such models deal with only the sky dome's appearance, though, and exclude the solar disc. The widely used model proposed by Arcot Preetham and colleagues employed a separately derived analytical formula for adding a solar emitter of suitable radiant intensity. Although this yields reasonable results, the formula is derived in a manner that doesn't exactly match the conditions in their sky-dome model. But the more sophisticated a skylight model is and the more subtly it can represent different conditions, the more the solar radiance should exactly match the skylight's conditions. Toward that end, researchers propose a solar-radiance function that exactly matches a recently published high-quality analytical skylight model.


title: Fundamentals of Microbial Community Resistance and Resilience

abstract: Microbial communities are at the heart of all ecosystems, and yet microbial community behavior in disturbed environments remains difficult to measure and predict. Understanding the drivers of microbial community stability, including resistance (insensitivity to disturbance) and resilience (the rate of recovery after disturbance) is important for predicting community response to disturbance. Here, we provide an overview of the concepts of stability that are relevant for microbial communities. First, we highlight insights from ecology that are useful for defining and measuring stability. To determine whether general disturbance responses exist for microbial communities, we next examine representative studies from the literature that investigated community responses to press (long-term) and pulse (short-term) disturbances in a variety of habitats. Then we discuss the biological features of individual microorganisms, of microbial populations, and of microbial communities that may govern overall community stability. We conclude with thoughts about the unique insights that systems perspectives – informed by meta-omics data – may provide about microbial community stability.


title: Two-Stage Differential Charge and Transresistance Amplifiers

abstract: A novel approach to the design of high-performance operational-amplifier-based differential charge and transresistance amplifiers is proposed. It is based on a two-stage topology: The first stage performs a differential measurement to single-ended signal conversion, providing a common-mode rejection that only depends on the matching between two resistors; the second stage filters the signal. Three novel topologies that are based on this technique are presented, analyzed, and measured, and design criteria are finally given. Their performance is compared with that of a state-of-the-art topology that is used as the benchmark, and it results in a better common-mode rejection ratio (CMRR).


title: Multi-Connectivity in 5G mmWave Cellular Networks

abstract: The millimeter wave (mmWave) frequencies offer the potential of orders of magnitude increases in capacity for next-generation cellular wireless systems. However, links in mmWave networks are highly susceptible to blocking and may suffer from rapid variations in quality. Connectivity to multiple cells - both in the mmWave and in the traditional lower frequencies - is thus considered essential for robust connectivity. However, one of the challenges in supporting multi-connectivity in the mmWave space is the requirement for the network to track the direction of each link in addition to its power and timing. With highly directional beams and fast varying channels, this directional tracking may be the main bottleneck in realizing robust mmWave networks. To address this challenge, this paper proposes a novel measurement system based on (i) the UE transmitting sounding signals in directions that sweep the angular space, (ii) the mmWave cells measuring the instantaneous received signal strength along with its variance to better capture the dynamics and, consequently, the reliability of a channel/direction and, finally, (iii) a centralized controller making handover and scheduling decisions based on the mmWave cell reports and transmitting the decisions either via a mmWave cell or conventional microwave cell (when control signaling paths are not available). We argue that the proposed scheme enables efficient and highly adaptive cell selection in the presence of the channel variability expected at mmWave frequencies.


title: A Group Acceptance Sampling Plans for Lifetimes Following a Generalized Exponential Distribution

abstract: Abstract In this paper, a group acceptance sampling plan is developed based on truncated lifetimes when the lifetime of an item follows a generalized exponential distribution. For a given group size, the minimum number of groups and the acceptance number required are determined for specified consumer's risk and the test termination time. The values of operating characteristic function for various quality levels are calculated and the minimum ratios of the true average life to the specified life at given producer's risk are obtained. The results are illustrated by an examples.


title: New multilevel Flying-Capacitor inverters with coupled-inductors

abstract: In the paper a new five-level (5L) inverter based on unidirectional Flying-Capacitor switching cells is presented. It is named 5L-Flying-Capacitor with Coupled-Inductor (5L-FC-CI) and consists in parallel connection of two unidirectional (P and N) three-level (3L) Flying-Capacitor switching cells with a coupled-inductor. The PWM output voltage is vastly improved and the switch control dead times is eliminated, particularly when compared with the 3L inverter obtained with bidirectional Flying-Capacitor cells. The phase-shifted carrier (PSC) modulation is used to control the new 5L topology. Thus, the proposed structure ensures an apparent switching frequency in each winding of coupled-inductors twice of switching frequency. The analysis of the new inverter is verified by numerical simulations both for single- and for three-phase topologies and their features are generalized for N odd voltage-levels (NL).


title: A novel STAP method for the detection of fast air moving targets from high speed platform

abstract: Space-time adaptive processing (STAP) is an effective method for detecting moving target in airborne/spaceborne radar. However, the detection of fast air moving targets from high speed platform is challenged by the range walk of both the clutter and moving targets. It is well known that keystone formatting can be used to compensate for the range walk of multiple moving targets simultaneously without using the knowledge of the motion parameters. However, in the presence of serious Doppler ambiguity of fast air moving targets, distribution of the clutter will be affected by the keystone formatting matched to the ambiguity number of targets, and as a result the STAP performance will degrade. In this paper, a novel STAP method is proposed for the detection of fast air moving targets from high speed platform, which can deal with the range walk of both clutter and targets. Effectiveness of the new method is verified via simulation examples.


The ecological approach to text visualization


title: The multidimensional database system RasDaMan

abstract: RasDaMan is a universal — i.e., domain-independent — array DBMS for multidimensional arrays of arbitrary size and structure. A declarative, SQL-based array query language offers flexible retrieval and manipulation. Efficient server-based query evaluation is enabled by an intelligent optimizer and a streamlined storage architecture based on flexible array tiling and compression. RasDaMan is being used in several international projects for the management of geo and healthcare data of various dimensionality.


title: A Review of Attachment Theory in the Context of Adolescent Parenting

abstract: Abstract The purpose of this article is to review attachment theory and relate the attachment perspective to adolescent mothers and their children. Attachment theory explains positive maternal-infant attachment as a dyadic relationship between the infant and mother that provides the infant with a secure base from which to explore the world. With respect to cognitive, social, and behavioral domains, securely attached infants tend to have more favorable long-term outcomes, while insecurely attached infants are more likely to have adverse outcomes. Adolescent parenthood can disrupt normal adolescent development, and this disruption influences development of the emotional and cognitive capacities necessary for maternal behaviors that foster secure attachment. However, it appears that if specialized supports are in place to facilitate the process of developing attachment, infants of adolescent mothers can obtain higher rates of secure attachment than normative samples in this population.


title: HOW CULTURAL VALUES INFLUENCE DIAGNOSIS, TREATMENT AND THE WELFARE OF FAMILIES WITH AN AUTISTIC CHILD

abstract: While the cultural diversity in the United States is simultaneously increasing with the rate autism is being diagnosed, it’s imperative that educators understand the role of culture. Religion and family values determine how a culture perceives etiology, signs and symptoms, diagnosis and treatment of autism and thus determines the welfare of families.


title: Numbers Are Not Enough. Why e-Learning Analytics Failed to Inform an Institutional Strategic Plan

abstract: Learning analytics offers higher education valuable insights that can inform strategic decision-making regarding resource allocation for educational excellence. Research demonstrates that learning management systems (LMSs) can increase student sense of community, support learning communities and enhance student engagement and success, and LMSs have therefore become core enterprise component in many universities. We were invited to undertake a current state analysis of enterprise LMS use in a large research-intensive university, to provide data to inform and guide an LMS review and strategic planning process. Using a new e-learning analytics platform, combined with data visualization and participant observation, we prepared a detailed snapshot of current LMS use patterns and trends and their relationship to student learning outcomes. This paper presents selected data from this "current state analysis" and comments on what it reveals about the comparative effectiveness of this institution's LMS integration in the service of learning and teaching. More critically, it discusses the reality that the institutional planning process was nonetheless dominated by technical concerns, and made little use of the intelligence revealed by the analytics process. To explain this phenomenon we consider theories of change management and resistance to innovation, and argue that to have meaningful impact, learning analytics proponents must also delve into the socio-technical sphere to ensure that learning analytics data are presented to those involved in strategic institutional planning in ways that have the power to motivate organizational adoption and cultural change.


title: Integrated Speech Enhancement Method Based on Weighted Prediction Error and DNN for Dereverberation and Denoising

abstract: Both reverberation and additive noises degrade the speech quality and intelligibility. Weighted prediction error (WPE) method performs well on the dereverberation but with limitations. First, WPE doesn't consider the influence of the additive noise which degrades the performance of dereverberation. Second, it relies on a time-consuming iterative process, and there is no guarantee or a widely accepted criterion on its convergence. In this paper, we integrate deep neural network (DNN) into WPE for dereverberation and denoising. DNN is used to suppress the background noise to meet the noise-free assumption of WPE. Meanwhile, DNN is applied to directly predict spectral variance of the target speech to make the WPE work without iteration. The experimental results show that the proposed method has a significant improvement in speech quality and runs fast.


title: Pre-trained Deep Convolutional Neural Networks for Face Recognition

abstract: Pre-training of models is important because of the unavailability of datasets and models that are becoming more complex. We investigate two aspects of pre-training using face recognition tasks. The first is the use of models that are pre-trained on face datasets and non-face datasets. We will evaluate five pre-trained models based on their results with freezing multiple layers and on robustness. The second aspect is to investigate universal features in pre-trained deep models. This is done by evaluating the performance using only the first few layers for pre-training. This is also investigated by swapping the first layers of the models. ::: ::: We show that models pre-trained on face datasets achieve better results and are more robust in three face recognition tasks than models pre-trained on non-face datasets. The results with pre-training and swapping only the first layers show a significant difference between models that are pre-trained on face datasets and non-face datasets. From this, we conclude that it is important which dataset is used for pre-training the models and used for testing in face recognition. We also conclude that the first few layers of pre-trained models affect performance on face recognition.


title: Collection flow

abstract: Computing optical flow between any pair of Internet face photos is challenging for most current state of the art flow estimation methods due to differences in illumination, pose, and geometry. We show that flow estimation can be dramatically improved by leveraging a large photo collection of the same (or similar) object. In particular, consider the case of photos of a celebrity from Google Image Search. Any two such photos may have different facial expression, lighting and face orientation. The key idea is that instead of computing flow directly between the input pair (I, J), we compute versions of the images (I', J') in which facial expressions and pose are normalized while lighting is preserved. This is achieved by iteratively projecting each photo onto an appearance subspace formed from the full photo collection. The desired flow is obtained through concatenation of flows (I → I') o (J' → J). Our approach can be used with any two-frame optical flow algorithm, and significantly boosts the performance of the algorithm by providing invariance to lighting and shape changes.


title: Automatic Selection of t-SNE Perplexity

abstract: t-Distributed Stochastic Neighbor Embedding (t-SNE) is one of the most widely used dimensionality reduction methods for data visualization, but it has a perplexity hyperparameter that requires manual selection. In practice, proper tuning of t-SNE perplexity requires users to understand the inner working of the method as well as to have hands-on experience. We propose a model selection objective for t-SNE perplexity that requires negligible extra computation beyond that of the t-SNE itself. We empirically validate that the perplexity settings found by our approach are consistent with preferences elicited from human experts across a number of datasets. The similarities of our approach to Bayesian information criteria (BIC) and minimum description length (MDL) are also analyzed.


title: Altered passive eruption: an etiology of short clinical crowns.

abstract: The gingival complex plays a vital role in the overall beauty of a smile. To predictably achieve a successful esthetic and functional result, the dentist must be able to precisely predict the treatment outcome based on biologic determinants. In this article, the biologic requirements for gingival health are discussed. In addition, a differential diagnosis for excess gingival display and treatment options for this condition are discussed.


title: Music acquisition: effects of enculturation and formal training on development

abstract: Musical structure is complex, consisting of a small set of elements that combine to form hierarchical levels of pitch and temporal structure according to grammatical rules. As with language, different systems use different elements and rules for combination. Drawing on recent findings, we propose that music acquisition begins with basic features, such as peripheral frequency-coding mechanisms and multisensory timing connections, and proceeds through enculturation, whereby everyday exposure to a particular music system creates, in a systematic order of acquisition, culture-specific brain structures and representations. Finally, we propose that formal musical training invokes domain-specific processes that affect salience of musical input and the amount of cortical tissue devoted to its processing, as well as domain-general processes of attention and executive functioning.


title: Best-First Fixed-Depth Minimax Algorithms

abstract: Abstract This article has three main contributions to our understanding of minimax search: First, a new formulation for Stockman's SSS ∗ algorithm, based on Alpha-Beta, is presented. It solves all the perceived drawbacks of SSS ∗ , finally transforming it into a practical algorithm. In effect, we show that SSS ∗ = Alpha-Beta + transposition tables. The crucial step is the realization that transposition tables contain so-called solution trees, structures that are used in best-first search algorithms like SSS ∗ . Having created a practical version, we present performance measurements with tournament game-playing programs for three different minimax games, yielding results that contradict a number of publications. Second, based on the insights gained in our attempts at understanding SSS ∗ , we present a framework that facilitates the construction of several best-first fixed-depth game-tree search algorithms, known and new. The framework is based on depth-first null-window Alpha-Beta search, enhanced with storage to allow for the refining of previous search results. It focuses attention on the essential differences between algorithms. Third, a new instance of this framework is presented. It performs better than algorithms that are currently used in most state-of-the-art game-playing programs. We provide experimental evidence to explain why this new algorithm, MTD( f ), performs better than other fixed-depth minimax algorithms.


title: Global exponential stability of impulsive cellular neural networks with time-varying delays via fixed point theory

abstract: This work is devoted to studying the application of fixed point theory to the stability analysis of complex neural networks. We employ the new method of contraction mapping principle to investigate the stability of impulsive cellular neural networks with time-varying delays. Some novel and concise sufficient conditions are presented to ensure the existence and uniqueness of a solution and the global exponential stability of the considered system at the same time. These conditions are easily checked and do not require the differentiability of delays.


title: www.elsevier.com/locate/compedu In-class laptop use and its eVects on student learning

abstract: Recently, a debate has begun over whether in-class laptops aid or hinder learning. While some research demonstrates that laptops can be an important learning tool, anecdotal evidence suggests more and more faculty are banning laptops from their classrooms because of perceptions that they distract students and detract from learning. The current research examines the nature of in-class laptop use in a large lecture course and how that use is related to student learning. Students completed weekly surveys of attendance, laptop use, and aspects of the classroom environment. Results showed that students who used laptops in class spent considerable time multitasking and that the laptop use posed a significant distraction to both users and fellow students. Most importantly, the level of laptop use was negatively related to several measures of student learning, including self-reported understanding of course material and overall course performance. The practical implications of these findings are discussed.


title: Adaptive local contrast enhancement for the visualization of high dynamic range images

abstract: In this paper, we present a new tone mapping algorithm for the display of high dynamic range images, inspired by adaptive process of the human visual system. The proposed algorithm is based on the center-surround Retinex processing. In our method, the local details are enhanced according to a non-linear adaptive spatial filter (Gaussian filter), whose shape (filter variance) is adapted to high-contrast edges of the image. Thus our method does not generate halo artifacts meanwhile preserves visibility and contrast impression of high dynamic range scenes in the common display devices. The proposed method is tested on a variety of HDR images and the results show the good performance of our method in terms of visual quality.


title: Market Basket Analysis for a Supermarket based on Frequent Itemset Mining

abstract: Market basket analysis is an important component of analytical system in retail organizations to determine the placement of goods, designing sales promotions for different segments of customers to improve customer satisfaction and hence the profit of the supermarket. These issues for a leading supermarket are addressed here using frequent itemset mining. The frequent itemsets are mined from the market basket database using the efficient K-Apriori algorithm and then the association rules are generated.


Learning-to-Rank in Research Paper CBF recommendation: Leveraging Irrelevant Papers


title: The influence of operating temperature on the efficiency of a combined heat and power fuel cell plant

abstract: Abstract It is generally accepted that the ideal operating temperature of a molten carbonate fuel cell (MCFC) is 650 °C. Nevertheless, when waste heat utilization in the form of an expander and steam production cycle is introduced in the system, another temperature level might prove more productive. This article is a first attempt to the optimization of MCFC operating temperatures of a MCFC system by presenting a case study in which the efficiency of a combined heat and power (CHP) plant is analyzed. The fuel cell plant under investigation is designed around a 250 kW-class MCFC fuelled by natural gas, which is externally reformed by a heat exchange reformer (HER). The operating temperature of the MCFC is varied over a temperature range between 600 and 700 °C while keeping the rest of the system the same as far as possible. Changes in energetic efficiency are given and the causes of these changes are further analyzed. Furthermore, the exergetic efficiencies of the system and the distribution of exergy losses in the system are given. Flowsheet calculations show that there is little dependency on the temperature in the first order. Both the net electrical performance and the overall exergetic performance show a maximum at approximately 675 °C, with an electrical efficiency of 51.9% (LHV), and an exergy efficiency of 58.7%. The overall thermal efficiency of this CHP plant increases from 87.1% at 600 °C to 88.9% at 700 °C. Overall, the change in performance is small in this typical range of MCFC operating temperature.


title: Ethics of Social Media Research: Common Concerns and Practical Considerations

abstract: Abstract Social media Websites (SMWs) are increasingly popular research tools. These sites provide new opportunities for researchers, but raise new challenges for Institutional Review Boards (IRBs) that review these research protocols. As of yet, there is little-to-no guidance regarding how an IRB should review the studies involving SMWs. The purpose of this article was to review the common risks inherent in social media research and consider how researchers can consider these risks when writing research protocols. We focused this article on three common research approaches: observational research, interactive research, and survey/interview research. Concomitant with these research approaches, we gave particular attention to the issues pertinent to SMW research, including privacy, consent, and confidentiality. After considering these challenges, we outlined key considerations for both researchers and reviewers when creating or reviewing SMW IRB protocols. Our goal in this article was to provide a detailed...


title: VLSI design and analysis of low power 6T SRAM cell using cadence tool

abstract: CMOS SRAM cell is very less power consuming and have less read and write time. Higher cell ratios can decrease the read and write time and improve stability. PMOS transistor with less width reduces the power consumption. This paper implements 6T SRAM cell with reduced read and write time, area and power consumption. It has been noticed often that increased memory capacity increases the bit-line parasitic capacitance which in turn slows down voltage sensing and make bit-line voltage swings energy expensive. This result in slower and more energy hungry memories.. In this paper Two SRAM cell is being designed for 4 Kb of memory core with supply voltage 1.8 V. A technique of global bit line is used for reducing the power consumption and increasing the memory capacity.


The Effect of Vocal and Instrumental Background Music on Primary School Pupils’ Verbal Memory Using a Sentence Recall Task


title: Thermal Vision for Sleep Apnea Monitoring

abstract: The present paper proposes a novel methodology to monitor sleep apnea through thermal imaging. First, the nostril region is segmented and it is tracked over time via a network of cooperating probabilistic trackers. Then, the mean thermal signal of the nostril region, carrying the breathing information, is analyzed through wavelet decomposition. The experimental set included 22 subjects (12 men and 10 women). The sleep-disordered incidents were detected by both thermal and standard polysomnographic methodologies. The high accuracy confirms the validity of the proposed approach, and brings non-obtrusive clinical monitoring of sleep disorders within reach.


FORMULATION OF SITE-SPECIFIC MUCOADHESIVE LIQUID SUPPOSITORIES AS A METHOD TO DECREASE HEPATOTOXICITY IN RABBITS


title: Optimizing piezoelectric inkjet printing of silica sols for biosensor production

abstract: Oscillations during jetting can cause printhead clogging when inkjet printing silica sols. ::: ::: ::: Use of low-pH sols (pH 3.1) allows printing with minimal clogging of nozzles. ::: ::: ::: Low-pH silica sols can be printed reproducibly to create enzyme-based paper sensor strips.


title: User Interface Optimization using Genetic Programming with an Application to Landing Pages

abstract: The design of user interfaces (UIs), such as World Wide Web pages, usually consists in a human designer mapping one particular problem (e.g., the demands of a customer) to one particular solution (i.e., the UI). In this article, a technology based on Genetic Programming is proposed to automate critical parts of the design process. In this approach, designers are supposed to define basic content elements and ways to combine them, which are then automatically composed and tested with real users by a genetic algorithm in order to find optimized compositions. Such a strategy enables the exploration of large design state-spaces in a systematic manner, hence going beyond traditional A/B testing approaches. In relation to similar techniques also based on genetic algorithms, this system has the advantage of being more general, providing the basis of an overall programmatic UI design workflow, and of calculating the fitness of solutions incrementally. To illustrate and evaluate the approach, an experiment based on the optimization of landing pages is provided. The empirical result obtained, though preliminary, is statistically significant and corroborates the hypothesis that the technique works.


title: The situation and development research of internet finance

abstract: Internet finance is not clearly defined by widely accepted. With the rapid development and mature of Internet finance, people's awareness and understanding of its connotation is gradually deepened. Internet finance is a combination of Internet and finance, which is using the Internet and mobile communications technology. Internet finance is a new financial model of joining the financing, payment and information agent together. In essence, the Internet is the use of large financial data, cloud computing, social networking and other Internet search engine technology for financing a new financial services model. Internet finance both non-financial institutions as Internet companies engaged in financial operations, including financial institutions conduct business via the Internet. Internet and financial depth of integration is the further trend, which has a more profound impact on financial products, business, organization and services. Promote the healthy development of Internet finance will help enhance the quality of financial services and efficiency, deepen financial reform, promote financial innovation and development, thus can expand domestic and the financial sector to build multi-level financial system. The paper will discuss the financial situation of the Internet, and then give the future prospects and possible directions of Internet finance based on the core issue-depth analysis.


title: Granting and managing loans for micro-entrepreneurs: New developments and practical experiences

abstract: We present a methodology to grant and follow-up credits for micro-entrepreneurs. This segment of grantees is very relevant for many economies, especially in developing countries, but shows a behavior different to that of classical consumers where established credit scoring systems exist. Parts of our methodology follow a proven procedure we have applied successfully in several credit scoring projects. Other parts, such as cut-off point construction and model follow-up, however, had to be developed and constitute original contributions of the present paper. The results from two credit scoring projects we developed in Chile, one for a private bank and one for a governmental credit granting institution, provide interesting insights into micro-entrepreneurs’ repayment behavior which could also be interesting for the respective segment in countries with similar characteristics.


PD-1 blockade induces responses by inhibiting adaptive immune resistance


title: A concurrent low-area dual band 0.9/2.4 GHz LNA in 0.13µm RF CMOS technology for multi-band wireless receiver

abstract: This article presents the design of a low area novel concurrent dual-band LNA operating in the GSM 0.9GHz/BLUETOOTH 2.4GHz communication standards. The concurrent LNA is designed and simulated in CADENCE using 130nm UMC technology. A conventional source degeneration inductor is eliminated for higher signal gain while providing reasonable input impedance. Also by adding a capacitor between the gate and the source of the input transistor, a noise source from the gate resistance is partly suppressed. The output matching network is constructed of shunt peaking. It's easy to achieve matching and reduced chip size. The current design is especially suitable for use in multi-standard wireless receiver frontends as it saves die area and reduces power consumption by replacing parallel LNAs for each channel frequency. Simulation results indicate a Noise Figure below 2dB and S 21 above 14 dB in all frequency bands and also input and output return loss are below −10 dB for all desired frequency band while drawing 10mA current from a 1.2V power supply.


title: Knowledge Discovery in Databases: an Overview

abstract: After a decade of fundamental interdisciplinary research in machine learning, the spadework in this field has been done; the 1990s should see the widespread exploitation of knowledge discovery as an aid to assembling knowledge bases. The contributors to the AAAI Press book Knowledge Discovery in Databases were excited at the potential benefits of this research. The editors hope that some of this excitement will communicate itself to "AI Magazine readers of this article.


SentiWordNet for Bangla SentiWordNet for Bangla 2


title: Effects of combined strength and kick coordination training on soccer kick biomechanics in amateur players

abstract: The aim of this study was to examine the effect of a soccer (strength and technique) training program on kinematics and electromyographic (EMG) muscle activity during a instep kick. Ten amateur soccer players (aged 19.9+/-0.4 years, body mass 74.8+/-9.1 kg, height 177.4+/-6.7 cm) constituted the experimental group (EG) whereas 10 players (age 21.6+/-1.3 years, weight 71.5+/-6.7 kg, height 175.2+/-3.4 cm) served as controls (CG). The EG followed a 10-week soccer-specific training program combining strength and technique exercises. All participants performed an instep soccer kick using a two-step approach while three-dimensional data and EMG from six muscles of swinging and support legs were recorded prior to and after training. Maximum isometric leg press strength, 10-m sprint performance and maximum speed performance on a bicycle ergometer were also measured. Analysis of variance designs with repeated measures showed that the EG improved significantly (P<0.05) maximum ball speed, the linear velocity of the foot, ankle and angular velocity of all joints during the final phase of the kick. Training had insignificant effects on EMG values, apart from an increase in the averaged EMG of the vastus medialis whereas maximum isometric strength and sprint times significantly improved after training (P<0.05). The present results suggest that the application of the training programs using soccer-specific strength exercises would be particularly effective in improving of soccer kick performance.


title: A Survey of Smart Parking Solutions

abstract: Considering the increase of urban population and traffic congestion, smart parking is always a strategic issue to work on, not only in the research field, but also from economic interests. Thanks to information and communication technology evolution, drivers can more efficiently find satisfying parking spaces with smart parking services. The existing and ongoing works on smart parking are complicated and transdisciplinary. While deploying a smart parking system, cities, as well as urban engineers, need to spend a very long time to survey and inspect all the possibilities. Moreover, many varied works involve multiple disciplines, which are closely linked and inseparable. To give a clear overview, we introduce a smart parking ecosystem and propose a comprehensive and thoughtful classification by identifying their functionalities and problematic focuses. We go through the literature over the period of 2000–2016 on parking solutions as they were applied to smart parking development and evolution, and propose three macro-themes: information collection, system deployment, and service dissemination. In each macro-theme, we explain and synthesize the main methodologies used in the existing works and summarize their common goals and visions to solve current parking difficulties. Finally, we give our engineering insights and show some challenges and open issues. Our survey gives an exhaustive study and a prospect in a multidisciplinary approach. Besides, the main findings of the current state-of-the-art throw out recommendations for future research on smart cities and the Internet architecture.


title: PathCutter : Severing the Self-Propagation Path of XSS JavaScript Worms in Social Web Networks

abstract: Worms exploiting JavaScript XSS vulnerabilities rampantly infect millions of web pages, while drawing the ire of helpless users. To date, users across all the popular social networks, including Facebook, MySpace, Orkut and Twitter, have been vulnerable to XSS worms. We propose PathCutter as a new approach to severing the self-propagation path of JavaScript worms. PathCutter works by blocking two critical steps in the propagation path of an XSS worm: (i) DOM access to different views at the client side and (ii) unauthorized HTTP request to the server. As a result, although an XSS vulnerability is successfully exercised at the client, the XSS worm is prevented from successfully propagating to the would-be victim’s own social network page. PathCutter is effective against all the current forms of XSS worms, including those that exploit traditional XSS, DOM-based XSS, and content sniffing XSS vulnerabilities. We present and evaluate both a server-side and proxyside deployment of PathCutter. We implement PathCutter on WordPress and Elgg and demonstrate its resilience against two proof-of-concept attacks. We also evaluate the PathCutter implementation on five real-world worms: Boonana, MySpace Samy, Renren, SpaceFlash, and the Yamanner worm. We show that although the worms themselves exploit different vulnerabilities, at either the client side or server side, they are successfully thwarted by PathCutter as it is vulnerability agnostic and blocks the propagation path of the infection. Our performance evaluation shows that rendering overhead of PathCutter is less than 4%, and memory overhead for one additional view is less than 1%.


title: GoldMiner: A genetic programming based algorithm applied to Brazilian Stock Market

abstract: The possibility of obtaining financial gain by investing in the Stock Markets is a hard task since it is under constant influence of economical, political and social factors. This paper aims to address the financial technical analysis of Stock Markets, focusing on time series data instead of subjective parameters. An algorithm based on genetic programming, named GoldMiner, has been proposed to perform retrospective study in order to get predictions about the best time for trading top stocks on the BOVESPA, the Brazilian stock exchange market.


title: Using Genetic Algorithms for Multi-depot Vehicle Routing

abstract: Efficient routing and scheduling of vehicles has significant economic implications for both the public and private sectors. Although other variants of the classical vehicle routing problem (VRP) have received much attention from the genetic algorithms (GAs) community, we find it surprising to identify only one GA in the literature for the fixed destination multi-depot vehicle routing problem (MDVRP). This paper aims to bridge this gap by proposing an application of genetic algorithms approach for MDVRP. The proposed GA employs an indirect encoding and an adaptive inter-depot mutation exchange strategy for the MDVRP with capacity and route-length restrictions. The algorithm is tested on a set of 23 classic MDVRP benchmark problems from 50 to 360 customers. Computational results show that the approach is competitive with the existing GA upon which it improves the solution quality for a number of instances. A comparison of the GA’s approach with other non-GA approaches show that although GAs are competitive for the MDVRP, there is still room for further research on GAs for MDVRP, compared to Tabu search.


title: The small world of shakespeare’s plays

abstract: Drama, at least according to the Aristotelian view, is effective inasmuch as it successfully mirrors real aspects of human behavior. This leads to the hypothesis that successful dramas will portray fictional social networks that have the same properties as those typical of human beings across ages and cultures. We outline a methodology for investigating this hypothesis and use it to examine ten of Shakespeare’s plays. The cliques and groups portrayed in the plays correspond closely to those which have been observed in spontaneous human interaction, including in hunter-gatherer societies, and the networks of the plays exhibit “small world” properties of the type which have been observed in many human-made and natural systems.


title: Comparing the Visual Representations and Performance of Humans and Deep Neural Networks

abstract: Although deep neural networks (DNNs) are state-of-the-art artificial intelligence systems, it is unclear what insights, if any, they provide about human intelligence. We address this issue in the d...


title: Classification Of Discourse Coherence Relations: An Exploratory Study Using Multiple Knowledge Sources

abstract: In this paper we consider the problem of identifying and classifying discourse coherence relations. We report initial results over the recently released Discourse GraphBank (Wolf and Gibson, 2005). Our approach considers, and determines the contributions of, a variety of syntactic and lexico-semantic features. We achieve 81% accuracy on the task of discourse relation type classification and 70% accuracy on relation identification.


title: Effect of Electronic Word-of-Mouth on Consumer Purchase Intention: The Perspective of Gender Differences

abstract: Electronic word of mouth is available to customers in different types of online consumer reviews, which can be used to help them make e-commerce purchasing decisions. Customers acknowledge that online consumer reviews help them to determine eWOM credibility and to make purchasing decisions. This study uses surveys and multiple regression analysis to create an extended Elaboration Likelihood Model that describes the relationship between customer expertise, involvement, and rapport to acceptance and use of electronic word of mouth in making purchasing decisions. The study focuses on the cultural effects of gender on the extended Elaboration Likelihood Model and purchasing decisions in e-commerce virtual communities. Study results show that involvement has the most significant effect on perceived eWOM credibility. Study results show that perceived eWOM credibility has a significant effect on eWOM acceptance and intent to purchase. Study results also show the male customers have different e-commerce shopping behaviors than female customers.


title: Driving Control Algorithm for Maneuverability, Lateral Stability, and Rollover Prevention of 4WD Electric Vehicles With Independently Driven Front and Rear Wheels

abstract: This paper describes a driving control algorithm for four-wheel-drive (4WD) electric vehicles equipped with two motors at front and rear driving shafts to improve vehicle maneuverability, lateral stability, and rollover prevention. The driving control algorithm consists of the following three parts: 1) a supervisory controller that determines the control mode, the admissible control region, and the desired dynamics, such as the desired speed and yaw rate; 2) an upper level controller that computes the traction force input and the yaw moment input to track the desired dynamics; and 3) a lower level controller that determines actual actuator commands, such as the front/rear driving motor torques and independent brake torques. The supervisory controller computes the admissible control region, namely, the relationship between the vehicle speed and the maximum curvature of the vehicle considering the maximum steering angle, lateral stability, and rollover prevention. In the lower level controller, a wheel slip controller is designed to keep the slip ratio at each wheel below a limit value. In addition, an optimization-based control allocation strategy is used to map the upper level and wheel slip control inputs to actual actuator commands, taking into account the actuator constraints. Numerical simulation studies have been conducted to evaluate the proposed driving control algorithm. It has been shown from simulation studies that vehicle maneuverability, lateral stability, and rollover mitigation performance can be significantly improved by the proposed driving controller.


title: Optimal Power Flow of Multiple Energy Carriers

abstract: This paper presents an approach for combined optimization of coupled power flows of different energy infrastructures such as electricity, gas, and district heating systems. A steady state power flow model is presented that includes conversion and transmission of an arbitrary number of energy carriers. The couplings between the different infrastructures are explicitly taken into account based on the new concept of energy hubs. With this model, combined economic dispatch and optimal power flow problems are stated covering transmission and conversion of energy. A general optimality condition for optimal dispatch of multiple energy carriers is derived, and the approach is compared with the standard method used for electrical power systems. Finally, the developed tools are demonstrated in examples


title: Basal Cell Hyperplasia in the Peripheral Zone of the Prostate

abstract: Basal cell hyperplasia in the prostate is often viewed as a transition zone proliferation, related to usual, nodular glandular, and stromal hyperplasia. Basal cell hyperplasia in the prostatic peripheral zone, the most common site for development of prostatic intraepithelial neoplasia and carcinoma, has not been previously characterized. We characterized the incidence and histomorphological attributes of basal cell hyperplasia in a series of 500 consecutive sextant needle core biopsy samples and in 26 completely embedded prostate glands from radical prostatectomy specimens. Comparative proliferation indices (by MIB-1 staining) and apoptotic indices (by TUNEL labeling) were quantitated for peripheral zone versus transition zone basal cell hyperplasia versus normal basal cells. The incidence of basal cell hyperplasia in prostate needle biopsy tissue was 10.2% (51 of 500 cases). Usual basal cell hyperplasia was detected in 8.2% of the 500 cases, and basal cell hyperplasia with prominent nucleoli, in 2.0% of cases. Basal cell hyperplasia in needle biopsy tissue was typically focal and associated with inflammation, which was usually lymphocytic, in 84% of cases. Peripheral zone basal cell hyperplasia was found in 23% of whole prostate glands. Peripheral zone basal cell hyperplasia was not observed to be in direct physical continuity with intraepithelial or invasive neoplasia. Peripheral zone and transition zone basal cell hyperplasia exhibited similar mean proliferation and apoptotic indices, at 1% and 0.07%, respectively. This proliferation index was elevated, and apoptotic index was decreased, relative to normal basal cells (P = 1 × 10−7). Basal cell hyperplasia in the peripheral zone is present in a significant minority of prostate needle biopsy samples and whole prostate glands. The presence of prominent nucleoli in basal cell hyperplasia may cause diagnostic concern for a neoplastic proliferation. The increase in cell number in basal cell hyperplasia appears to be due to a coordinate increase in proliferation index coupled with a diminished apoptotic index. The presence of inflammation in the majority of basal cell hyperplasia foci suggests that peripheral zone basal cell hyperplasia in untreated patients may represent a stereotyped response to injury such as that sustained because of inflammation.


title: Identifying Patterns For Short Answer Scoring Using Graph-based Lexico-Semantic Text Matching

abstract: Short answer scoring systems typically use regular expressions, templates or logic expressions to detect the presence of specific terms or concepts among student responses. Previous work has shown that manually developed regular expressions can provide effective scoring, however manual development can be quite time consuming. In this work we present a new approach that uses word-order graphs to identify important patterns from humanprovided rubric texts and top-scoring student answers. The approach also uses semantic metrics to determine groups of related words, which can represent alternative answers. We evaluate our approach on two datasets: (1) the Kaggle Short Answer dataset (ASAP-SAS, 2012), and (2) a short answer dataset provided by Mohler et al. (2011). We show that our automated approach performs better than the best performing Kaggle entry and generalizes as a method to the Mohler dataset.