title: Intelligent assistant carer for active aging

abstract: We present the concept of an Intelligent Assistant Carer system for the elderly, designed to help with active aging and to facilitate the interactions with carers. The system is modular, allowing the users to choose the appropriate functions according to their needs, and is built on an open platform in order to make it compatible with third-party products and services. Currently, the system consists of a wearable device (a smartwatch) and an internet portal that manages the data and takes care of the interactions between the user, the carers, and the support services. We present in detail one of the modules, i.e., fall detection, and the results of a pilot study for the system on 150 users over the course of 3 months.


title: Bovine aortic arch: predictor of entry site and risk factor for neurologic injury in acute type a dissection.

abstract: BACKGROUND ::: The aim of this study was to evaluate if the presence of a bovine aortic arch (BAA)- the most common aortic arch anomaly-influences the location of the primary entry tear, the surgical procedure, and the outcome of patients undergoing operation for type A acute aortic dissection (AAD). ::: ::: ::: METHODS ::: A total of 157 patients underwent emergency operations because of AAD (71% men, mean age 59.5 ± 13 years). Preoperative computed tomographic scans were screened for the presence of BAA. Patients were separated into 2 groups: presenting with BAA (BAA+, n = 22) or not (BAA-, n = 135). Location of the primary tear, surgical treatment, outcome, and risk factors for postoperative neurologic injury and in-hospital mortality were analyzed. ::: ::: ::: RESULTS ::: Fourteen percent (22 of 157) of all patients operated on for AAD had a concomitant BAA. Location of the primary entry tear was predominantly in the aortic arch in patients with BAA (BAA+, 59.1% versus BAA-, 13.3%; p < 0.001). Multivariate analysis revealed the presence of a BAA to be an independent risk factor for having the primary tear in the aortic arch (odds ratio [OR], 14.79; 95% confidence interval [CI] 4.54-48.13; p < 0.001) but not for in-hospital mortality. Patients with BAA had a higher rate of postoperative neurologic injury (BAA+, 35% versus BAA-, 7.9%; p = 0.004). Multivariate analysis identified the presence of BAA as an independent risk factor for postoperative neurologic injury (OR, 4.9; 95% CI, 1.635-14.734; p = 0.005). ::: ::: ::: CONCLUSIONS ::: In type A AAD, the presence of a BAA predicts the location of the primary entry site in the aortic arch and is an independent risk factor for a poor neurologic outcome.


title: Query-based Attention CNN for Text Similarity Map

abstract: In this paper, we introduce Query-based Attention CNN(QACNN) for Text Similarity Map, an end-to-end neural network for question answering. This network is composed of compare mechanism, two-staged CNN architecture with attention mechanism, and a prediction layer. First, the compare mechanism compares between the given passage, query, and multiple answer choices to build similarity maps. Then, the two-staged CNN architecture extracts features through word-level and sentence-level. At the same time, attention mechanism helps CNN focus more on the important part of the passage based on the query information. Finally, the prediction layer find out the most possible answer choice. We conduct this model on the MovieQA dataset using Plot Synopses only, and achieve 79.99% accuracy which is the state of the art on the dataset.


title: An IoT based intelligent building management system for ambient assisted living

abstract: Ambient Assisted Living (AAL) describes an ICT based environment that exposes personalized and context-aware intelligent services, thus creating an appropriate experience to the end user to support independent living and improvement of the everyday quality of life of both healthy elderly and disabled people. The social and economic impact of AAL systems have boosted the research activities that combined with the advantages of enabling technologies such as Wireless Sensor Networks (WSNs) and Internet of Things (IoT) can greatly improve the performance and the efficiency of such systems. Sensors and actuators inside buildings can create an intelligent sensing environments that help gather realtime data for the patients, monitor their vital signs and identify abnormal situations that need medical attention. AAL applications might be life critical and therefore have very strict requirements for their performance with respect to the reliability of the devices, the ability of the system to gather data from heterogeneous devices, the timeliness of the data transfer and their trustworthiness. This work presents the functional architecture of SOrBet (Marie Curie IAPP project) that provides a framework for interconnecting efficiently smart devices, equipping them with intelligence that helps automating many of the everyday activities of the inhabitants. SOrBet is a paradigm shift of traditional AAL systems based on a hybrid architecture, including both distributed and centralized functionalities, extensible, self-organising, robust and secure, built on the concept of “reliability by design”, thus being capable of meeting the strict Quality of Service (QoS) requirements of demanding applications such as AAL.


title: Mining Association Rules with Multiple Minimum Supports Using Maximum Constraints

abstract: Abstract Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most of the previous approaches set a single minimum support threshold for all the items or itemsets. But in real applications, different items may have different criteria to judge its importance. The support requirements should then vary with different items. In this paper, we provide another point of view about defining the minimum supports of itemsets when items have different minimum supports. The maximum constraint is used, which is well explained and may be suitable to some mining domains. We then propose a simple algorithm based on the Apriori approach to find the large-itemsets and association rules under this constraint. The proposed algorithm is easy and efficient when compared to Wang et al.’s under the maximum constraint. The numbers of association rules and large itemsets obtained by the proposed mining algorithm using the maximum constraint are also less than those using the minimum constraint. Whether to adopt the proposed approach thus depends on the requirements of mining problems. Besides, the granular computing technique of bit strings is used to speed up the proposed data mining algorithm.


gOCCF: Graph-Theoretic One-Class Collaborative Filtering Based on Uninteresting Items


title: Opportunities and challenges for formal methods tools in the certification of avionics software

abstract: Formal methods tools, whose underlying principles are based on mathematics and formal logic, are considered one of the most effective and rigorous means of verifying system properties and assuring the absence of undesirable system behavior. The use of such tools seem to squarely fit the needs of those aiming to develop and certify avionic software as per the DO-178C standard, the set of objectives laid out by FAA to achieve a high level of confidence on the systems. However, our recent work on a NASA-funded research project revealed that there are practical considerations and additional complexities involved in using formal method tools to provide the level of assurance as exemplified by the DO-178C. In this paper we discuss one of the key concerns with formal tools: its soundness — the characteristic of a tool to never permit the verified system property be declared true when it is actually not true. We explored two major classes of formal methods tools — namely model checkers and static analyzers — and observed several threats to their soundness such as tool fallacies and failure modes that could lead to misplaced confidence in the verified system. We present various strategies to mitigate them, including an assurance case framework to verify that potential risks are all mitigated. The intent of this paper is not to discourage but encourage scrupulous use of formal tools to certify critical avionic software by being wary of the subtle but serious issues that may be overlooked.


title: On the application of VTOL UAVs to the inspection of power utility assets

abstract: This paper presents a review of the state of the art and a discussion on vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) applied to the inspection of power utility assets and other similar civil applications. The first part of the paper presents the authors' view on specific benefits and operation constraints associated with the use of UAVs in power industry applications. The second part cites more than 70 recent publications related to this field of application. Among them, some present complete technologies while others deal with specific subsystems relevant to the application of such mobile platforms to power line inspection. The authors close with a discussion of key factors for successful application of VTOL UAVs to power industry infrastructure inspection.


title: Some mathematical notes on three-mode factor analysis

abstract: The model for three-mode factor analysis is discussed in terms of newer applications of mathematical processes including a type of matrix process termed the Kronecker product and the definition of combination variables. Three methods of analysis to a type of extension of principal components analysis are discussed. Methods II and III are applicable to analysis of data collected for a large sample of individuals. An extension of the model is described in which allowance is made for unique variance for each combination variable when the data are collected for a large sample of individuals.


title: Culture, Gender, and Self: A Perspective From Individualism-Collectivism Research

abstract: Individualism and collectivism are often equated with independent vs. interdependent, agentic vs. communal, and separate vs. relational self-construals. Although these same concepts have been used to characterize both cultural and gender differences, a perspective of cultural evolution suggests it is unlikely. A division of labor within society may produce gender differences, but this cannot explain cultural differences. A study of self-construal involving 5 cultures (Australia, the United States, Hawaii, Japan, and Korea) shows that differences between these cultures are captured mostly by the extent to which people see themselves as acting as independent agents, whereas gender differences are best summarized by the extent to which people regard themselves as emotionally related to others.


title: Assessing the Relationship among Service Quality, Customer Satisfaction and Behavioral Intentions in Online Shopping Environments

abstract: This paper seeks to investigate the nature of the service quality construct in online shipping environments and its relationship with customer satisfaction and behavioral intentions. After a factor analysis used a sample from college student respondents, we assessed the relationship among all variables with correlation analysis and regression analysis. And then we introduced a test of the mediating effects of customer satisfaction to study the approach of service quality on behavioral intentions. Empirical results show that reliability and assurance are important for service quality in online shopping industry while responsiveness and tangibles fail to play a dominant role. This study also demonstrates that while service quality has an important influence on behavioral intentions, its indirect effect through customer satisfaction is larger than the direct effect on behavioral intentions.


title: Mindfulness meditation improves cognition: Evidence of brief mental training

abstract: Although research has found that long-term mindfulness meditation practice promotes executive functioning and the ability to sustain attention, the effects of brief mindfulness meditation training have not been fully explored. We examined whether brief meditation training affects cognition and mood when compared to an active control group. After four sessions of either meditation training or listening to a recorded book, participants with no prior meditation experience were assessed with measures of mood, verbal fluency, visual coding, and working memory. Both interventions were effective at improving mood but only brief meditation training reduced fatigue, anxiety, and increased mindfulness. Moreover, brief mindfulness training significantly improved visuo-spatial processing, working memory, and executive functioning. Our findings suggest that 4 days of meditation training can enhance the ability to sustain attention; benefits that have previously been reported with long-term meditators.


title: Book recommendation system based on combine features of content based filtering, collaborative filtering and association rule mining

abstract: Recommendation systems are widely used to recommend products to the end users that are most appropriate. Online book selling websites now-a-days are competing with each other by many means. Recommendation system is one of the stronger tools to increase profit and retaining buyer. The book recommendation system must recommend books that are of buyer's interest. This paper presents book recommendation system based on combined features of content filtering, collaborative filtering and association rule mining.


Convolutional Neural Networks-based Plankton Image Classification System ∗


title: Face detection, pose estimation, and landmark localization in the wild

abstract: We present a unified model for face detection, pose estimation, and landmark estimation in real-world, cluttered images. Our model is based on a mixtures of trees with a shared pool of parts; we model every facial landmark as a part and use global mixtures to capture topological changes due to viewpoint. We show that tree-structured models are surprisingly effective at capturing global elastic deformation, while being easy to optimize unlike dense graph structures. We present extensive results on standard face benchmarks, as well as a new “in the wild” annotated dataset, that suggests our system advances the state-of-the-art, sometimes considerably, for all three tasks. Though our model is modestly trained with hundreds of faces, it compares favorably to commercial systems trained with billions of examples (such as Google Picasa and face.com).


title: Soundness of workflow nets: classification, decidability, and analysis

abstract: Workflow nets, a particular class of Petri nets, have become one of the standard ways to model and analyze workflows. Typically, they are used as an abstraction of the workflow that is used to check the so-called soundness property. This property guarantees the absence of livelocks, deadlocks, and other anomalies that can be detected without domain knowledge. Several authors have proposed alternative notions of soundness and have suggested to use more expressive languages, e.g., models with cancellations or priorities. This paper provides an overview of the different notions of soundness and investigates these in the presence of different extensions of workflow nets. We will show that the eight soundness notions described in the literature are decidable for workflow nets. However, most extensions will make all of these notions undecidable. These new results show the theoretical limits of workflow verification. Moreover, we discuss some of the analysis approaches described in the literature.


title: A Transformerless High-Voltage DC–DC Converter for DC Grid Interconnection

abstract: This paper presents a transformerless high-voltage dc–dc converter based on cascaded sub-modules. It is intended for interconnecting high-voltage or medium-voltage dc grids. The proposed dc–dc converter consists of two phase legs, each of which consists of an upper arm and a lower arm with their middle nodes crossly connected through a branch of active sub-modules. By properly controlling the output voltage of the cross-connected branch, a trapezoidal ac current is induced to interact with the ac voltage components in the upper and lower arm for rebalancing the power amongst converter arms. The features of modular design, single-stage power conversion, and transformerless structure make the proposed dc–dc converter gain the outstanding merits of wide voltage ratio range, high system efficiency, and light converter weight. A control scheme is also elaborated for guaranteeing the normal operation of the proposed dc–dc converter. A 100-kV 100-MW simulation model performed in MATLAB/Simulink verifies the feasibility of the proposed dc–dc converter. Experimental results obtained from a 100-V 1-kW laboratory setup also confirm the validation of the proposal.


title: How to Design Valid Simulator Studies for Investigating User Experience in Automated Driving: Review and Hands-On Considerations

abstract: Simulator studies have been conducted in the automotive domain since the 1960s. Recently, automated driving studies have become more popular as real-world automated cars start to emerge but at this time not all levels of automation can be realized. A simulation does not entail all details of real driving, creating a realistic simulation experience - both on a psychological and physical level - proposes recurring challenges. These are among others: sample acquisition, simulator sickness, simulator training, interface design, take-over requests and secondary tasks in automated driving simulator studies. In this paper, we review existing literature and summarize important lessons from simulations in the domain of driving automation to provide considerations for studies investigating driver behavior in the age of highly automated driving.


title: Secure Sharing of Personal Health Records in Cloud Computing using Attribute-based Broadcast Encryption

abstract: Personal Health Record (PHR) service is an emerging model for health information exchange. It allows patients to create, update and manage personal and medical information. Also they can control and share their medical information with other users as well as health care providers. PHR data is hosted to the third party cloud service providers in order to enhance its interoperability. However, there have been serious security and privacy issues in outsourcing these data to cloud server. For security, encrypt the PHRs before outsourcing. So many issues such as risks of privacy exposure, scalability in key management, flexible access and efficient user revocation, have remained the most important challenges toward achieving fine-grained, cryptographically enforced data access control. To achieve fine-grained and scalable data access control for client’s data, a novel patient-centric framework is used. This frame work is mainly focus on the multiple data owner scenario. A high degree of patient privacy is guaranteed simultaneously by exploiting multi authority ABE. This scheme also enables dynamic modification of access policies or file attributes, support efficient on demand user/attribute revocation. However some practical limitations are in building PHR system. If consider the workflow based access control scenarios, the data access right could be given based on users identities rather than their attributes, while ABE does not handle that efficiently. For solving these problem in this thesis proposed PHR system, based on Attribute Based Broadcast Encryption (ABBE).


title: Learning with labeled and unlabeled data

abstract: In this paper, on the one hand, we aim to give a review on literature dealing with the problem of supervised learning aided by additional unlabeled data. On the other hand, being a part of the author's first year PhD report, the paper serves as a frame to bundle related work by the author as well as numerous suggestions for potential future work. Therefore, this work contains more speculative and partly subjective material than the reader might expect from a literature review. We give a rigorous definition of the problem and relate it to supervised and unsupervised learning. The crucial role of prior knowledge is put forward, and we discuss the important notion of input-dependent regularization. We postulate a number of baseline methods, being algorithms or algorithmic schemes which can more or less straightforwardly be applied to the problem, without the need for genuinely new concepts. However, some of them might serve as basis for a genuine method. In the literature review, we try to cover the wide variety of (recent) work and to classify this work into meaningful categories. We also mention work done on related problems and suggest some ideas towards synthesis. Finally, we discuss some caveats and tradeoffs of central importance to the problem.


title: Perceived coping as a mediator between attachment and psychological distress: A structural equation modeling approach.

abstract: This study examined perceived coping (perceived problem-solving ability and progress in coping with problems) as a mediator between adult attachment (anxiety and avoidance) and psychological distress (depression, hopelessness, anxiety, anger, and interpersonal problems). Survey data from 515 undergraduate students were analyzed using structural equation modeling. Results indicated that perceived coping fully mediated the relationship between attachment anxiety and psychological distress and partially mediated the relationship between attachment avoidance and psychological distress. These findings suggest not only that it is important to consider attachment anxiety or avoidance in understanding distress but also that perceived coping plays an important role in these relationships. Implications for these more complex relations are discussed for both counseling interventions and further research.


title: Achieving Higher Resolution ISAR Imaging With Limited Pulses via Compressed Sampling

abstract: Recent theory of compressed sampling (CS) suggests that exact recovery of an unknown sparse signal with overwhelming probability can be achieved from very limited number of samples. In this letter, we adapt this idea and present a framework of high-resolution inverse synthetic aperture radar imaging with limited measured data. During the framework, we mathematically convert the imaging into a problem of signal reconstruction with orthogonal basis; hence, a conceptive upper bound of the cross-range resolution is presented based on the CS theory. Real data results show that the CS imaging approach outperforms the conventional range-Doppler one in resolution.


title: One-class support vector machines: analysis of outlier detection for wireless sensor networks in harsh environments

abstract: Machine learning, like its various applications, has received a great interest in outlier detection in Wireless Sensor Networks. Support Vector Machines (SVM) are a special type of Machine learning techniques which are computationally inexpensive and provide a sparse solution. This work presents a detailed analysis of various formulations of one-class SVMs, like, hyper-plane, hyper-sphere, quarter-sphere and hyper-ellipsoidal. These formulations are used to separate the normal data from anomalous data. Various techniques based on these formulations have been analyzed in terms of a number of characteristics for harsh environments. These characteristics include input data type, spatio-temporal and attribute correlations, user specified thresholds, outlier types, outlier identification(event/error), outlier degree, susceptibility to dynamic topology, non-stationarity and inhomogeneity. A tabular description of improvement and feasibility of various techniques for deployment in the harsh environments has also been presented.


title: Impulsivity and risk-taking behavior in focal frontal lobe lesions

abstract: Frontal lobe dysfunction may underlie excessively impulsive and risky behavior observed in a range of neurological disorders. We devised a gambling task to examine these behavior tendencies in a sample of patients who had sustained focal damage to the frontal lobes or nonfrontal cortical regions as well as in a matched sample of healthy control subjects. The main objectives of the study were: (1) to behaviorally dissociate impulsivity and risk-taking; (2) to examine potential associations between specific frontal lesion sites and impulsivity or risk-taking; (3) to investigate the influence of reinforcement and trial timing on both behaviors. Our results indicated that patients and controls were equally likely to perform impulsively. Risk-taking performance strategies, however, were related to left ventrolateral and orbital lesion sites. Moreover, risk-taking was also associated with blunted response alteration following a nonrewarded trial. Patients and control subjects showed identical responses to reward-timing manipulations consistent with formal decision-making theory. These findings suggest that ventrolateral and orbital lesions are related to the reward-based aspects of decision-making (risk-taking) rather than to simple response disinhibition (impulsivity). Reduced reaction to the negative consequences of one's actions may underlie this behavior pattern.


title: Workload Management in Dynamic IT Service Delivery Organizations

abstract: Enterprises and service providers are increasingly looking to global service delivery as a means for containing costs while improving the quality of service delivery. However, it is often difficult to effectively manage the conflicting needs associated with dynamic customer workload, strict service level constraints, and efficient service personnel organization. In this paper we propose a dynamic approach for workload and personnel management, where organization of personnel is dynamically adjusted based upon differences between observed and target service level metrics. Our approach consists of constructing a dynamic service delivery organization and developing a feedback control mechanism for dynamic workload management. We demonstrate the effectiveness of the proposed approach in an IT incident management example designed based on a large service delivery environment handling more than ten thousand service requests over a period of six months.


title: Design of a biologically inspired humanoid neck

abstract: This paper presents the design of a novel anthropomorphic robotic neck. It mimics the range of movements found in the human neck, actuated by pneumatic artificial muscles. The proposed humanoid neck simulates the anatomical functionality and structure of a human neck. Specifications are made according to biological, anatomical and behavioural data. The preliminary results show that the proposed humanoid neck is able to deliver the range of movements and head velocities comparable to those observed in human necks. These results also demonstrate that biologically inspired musculoskeletal robotic systems represent a reliable and robust platform to investigate motion development.


title: Regulating Feelings During Interpersonal Conflicts by Changing Voice Self-perception

abstract: Emotions play a major role in how interpersonal conflicts unfold. Although several strategies and technological approaches have been proposed for emotion regulation, they often require conscious attention and effort. This often limits their efficacy in practice. In this paper, we propose a different approach inspired by self-perception theory: noticing that people are often reacting to the perception of their own behavior, we artificially change their perceptions to influence their emotions. We conducted two studies to evaluate the potential of this approach by automatically and subtly altering how people perceive their own voice. In one study, participants that received voice feedback with a calmer tone during relationship conflicts felt less anxious. In the other study, participants who listened to their own voices with a lower pitch during contentious debates felt more powerful. We discuss the implications of our findings and the opportunities for designing automatic and less perceptible emotion regulation systems.


title: Audio-Visual Speaker Diarization Based on Spatiotemporal Bayesian Fusion

abstract: Speaker diarization consists of assigning speech signals to people engaged in a dialogue. An audio-visual spatiotemporal diarization model is proposed. The model is well suited for challenging scenarios that consist of several participants engaged in multi-party interaction while they move around and turn their heads towards the other participants rather than facing the cameras and the microphones. Multiple-person visual tracking is combined with multiple speech-source localization in order to tackle the speech-to-person association problem. The latter is solved within a novel audio-visual fusion method on the following grounds: binaural spectral features are first extracted from a microphone pair, then a supervised audio-visual alignment technique maps these features onto an image, and finally a semi-supervised clustering method assigns binaural spectral features to visible persons. The main advantage of this method over previous work is that it processes in a principled way speech signals uttered simultaneously by multiple persons. The diarization itself is cast into a latent-variable temporal graphical model that infers speaker identities and speech turns, based on the output of an audio-visual association process, executed at each time slice, and on the dynamics of the diarization variable itself. The proposed formulation yields an efficient exact inference procedure. A novel dataset, that contains audio-visual training data as well as a number of scenarios involving several participants engaged in formal and informal dialogue, is introduced. The proposed method is thoroughly tested and benchmarked with respect to several state-of-the art diarization algorithms.


Determining Graduation Rates in Engineering for Community College Transfer Students Using Data Mining


title: Action Recognition with Joint Attention on Multi-Level Deep Features

abstract: We propose a novel deep supervised neural network for the task of action recognition in videos, which implicitly takes advantage of visual tracking and shares the robustness of both deep Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). In our method, a multi-branch model is proposed to suppress noise from background jitters. Specifically, we firstly extract multi-level deep features from deep CNNs and feed them into 3d-convolutional network. After that we feed those feature cubes into our novel joint LSTM module to predict labels and to generate attention regularization. We evaluate our model on two challenging datasets: UCF101 and HMDB51. The results show that our model achieves the state-of-art by only using convolutional features.


Deep Reinforcement Learning for Simulated Autonomous Vehicle Control


title: Activity-based ridesharing: increasing flexibility by time geography

abstract: Ridesharing is an emerging travel mode that reduces the total amount of traffic on the road by combining people's travels together. While present ridesharing algorithms are trip-based, this paper aims to achieve significantly higher matching chances by a novel, activity-based algorithm. The algorithm expands the potential destination choice set by considering alternative destinations that are within given space-time budgets and would provide a similar activity function as the originals. In order to address the increased combinatorial complexity of trip chains, the paper introduces an efficient space-time filter on the foundations of time geography to search for accessible resources. Globally optimal matching is achieved by binary linear programming. The ridesharing algorithm is tested with a series of realistic scenarios of different population sizes. The encouraging results demonstrate that the matching rate by activity-based ridesharing is significantly increased from the baseline scenario of traditional trip-based ridesharing.


title: Multi-modal Convolutional Neural Networks for Activity Recognition

abstract: Convolutional neural network (CNN), which comprises one or more convolutional and pooling layers followed by one or more fully-connected layers, has gained popularity due to its ability to learn fruitful representations from images or speeches, capturing local dependency and slight-distortion invariance. CNN has recently been applied to the problem of activity recognition, where 1D kernels are applied to capture local dependency over time in a series of observations measured at inertial sensors (3-axis accelerometers and gyroscopes). In this paper we present a multi-modal CNN where we use 2D kernels in both convolutional and pooling layers, to capture local dependency over time as well as spatial dependency over sensors. Experiments on benchmark datasets demonstrate the high performance of our multi-modal CNN, compared to several state of the art methods.


title: Optimal User Scheduling and Power Allocation for Millimeter Wave NOMA Systems

abstract: This paper investigates the application of non-orthogonal multiple access (NOMA) in millimeter wave (mmWave) communications by exploiting beamforming, user scheduling and power allocation. Random beamforming is invoked for reducing the feedback overhead of considered systems. A nonconvex optimization problem for maximizing the sum rate is formulated, which is proved to be NP-hard. The branch and bound (BB) approach is invoked to obtain the optimal power allocation policy, which is proved to converge to a global optimal solution. To elaborate further, low complexity suboptimal approach is developed for striking a good computational complexity-optimality tradeoff, where matching theory and successive convex approximation (SCA) techniques are invoked for tackling the user scheduling and power allocation problems, respectively. Simulation results reveal that: i) the proposed low complexity solution achieves a near-optimal performance; and ii) the proposed mmWave NOMA systems is capable of outperforming conventional mmWave orthogonal multiple access (OMA) systems in terms of sum rate and the number of served users.


title: Neural processing associated with cognitive and affective Theory of Mind in adolescents and adults

abstract: Theory of Mind (ToM) is the ability to attribute thoughts, intentions and beliefs to others. This involves component processes, including cognitive perspective taking (cognitive ToM) and understanding emotions (affective ToM). This study assessed the distinction and overlap of neural processes involved in these respective components, and also investigated their development between adolescence and adulthood. While data suggest that ToM develops between adolescence and adulthood, these populations have not been compared on cognitive and affective ToM domains. Using fMRI with 15 adolescent (aged 11–16 years) and 15 adult (aged 24–40 years) males, we assessed neural responses during cartoon vignettes requiring cognitive ToM, affective ToM or physical causality comprehension (control). An additional aim was to explore relationships between fMRI data and self-reported empathy. Both cognitive and affective ToM conditions were associated with neural responses in the classic ToM network across both groups, although only affective ToM recruited medial/ventromedial PFC (mPFC/vmPFC). Adolescents additionally activated vmPFC more than did adults during affective ToM. The specificity of the mPFC/vmPFC response during affective ToM supports evidence from lesion studies suggesting that vmPFC may integrate affective information during ToM. Furthermore, the differential neural response in vmPFC between adult and adolescent groups indicates developmental changes in affective ToM processing.


title: The Best of Both Worlds: Learning Geometry-based 6D Object Pose Estimation

abstract: We address the task of estimating the 6D pose of known rigid objects, from RGB and RGB-D input images, in scenarios where the objects are heavily occluded. Our main contribution is a new modular processing pipeline. The first module localizes all known objects in the image via an existing instance segmentation network. The next module densely regresses the object surface positions in its local coordinate system, using an encoder-decoder network. The third module is purely a geometry-based algorithm to output the final 6D object poses. While the first two modules are learned from data, and the last one not, we believe that this is the best of both worlds: geometry-based and learning-based algorithms for object 6D pose estimation. This is validated by achieving state-of-the-art results for RGB input and a slight improvement over state-of-the-art for RGB-D input. However, in contrast to previous work, we achieve these results with the same pipeline for RGB and RGB-D input. Furthermore, to obtain these results, we give a second contribution of a new 3D occlusion-aware and object-centric data augmentation procedure.


title: Structured Image Classification from Conditional Random Field with Deep Class Embedding.

abstract: This paper presents a novel deep learning architecture to classify structured objects in datasets with a large number of visually similar categories. Our model extends the CRF objective function to a nonlinear form, by factorizing the pairwise potential matrix, to learn neighboring-class embedding. The embedding and the classifier are jointly trained to optimize this highly nonlinear CRF objective function. The non-linear model is trained on object-level samples, which is much faster and more accurate than the standard sequence-level training of the linear model. This model overcomes the difficulties of existing CRF methods to learn the contextual relationships thoroughly when there is a large number of classes and the data is sparse. The performance of the proposed method is illustrated on a huge dataset that contains images of retail-store product displays, taken in varying settings and viewpoints, and shows significantly improved results compared to linear CRF modeling and sequence-level training.


title: STD-PD: Generating Synthetic Training Data for Pedestrian Detection in Unannotated Videos

abstract: We present a new method for training pedestrian detectors on an unannotated image set, which is captured by a moving camera with a fixed height and angle from the ground. Our approach is general and robust and makes no other assumptions about the image dataset or the number of pedestrians. We automatically extract the vanishing point and the scale of the pedestrians to calibrate the virtual camera and generate a probability map for the pedestrians to spawn. Using these features, we overlay synthetic human-like agents in proper locations on the images from the unannotated dataset. We also present novel techniques to increase the realism of these synthetic agents and use the augmented images to train a Faster R-CNN detector. Our approach improves the accuracy by 12 to 13 percent over prior methods for unannotated image datasets.


Handling Multiword Expressions in Causality Estimation.


title: Collaborative Analytics and Brokering in Digital Humanitarian Response

abstract: During large scale humanitarian crises, relief practitioners identify data used for decision making and coordination, as critical to their operations. Implicit in this need is the required capabilities for analyzing data. Given the rapidly evolving systems of collaborative data management and analysis in digital humanitarian efforts, information scientists and practitioners alike are keen to understand the role of data analytics in response operations. Through a case study of a digital humanitarian collaborative effort, we examine the processes for big and small data analytics, specifically focusing on data development, sharing, and collaborative analytics. Informed by theories of articulation work and collaborative analytics, we analyze data from in-depth interviews with digital humanitarians. Our findings identify key practices and processes for collaborative analytics in resource constrained environments, particularly the role of brokering, and in turn generate design recommendation for collaborative analytic platforms.


title: Matrix factorization algorithms for the identification of muscle synergies : evaluation on simulated and experimental data sets

abstract: Several recent studies have used matrix factorization algorithms to assess the hypothesis that behaviors might be produced through the combination of a small number of muscle synergies. Although generally agreeing in their basic conclusions, these studies have used a range of different algorithms, making their interpretation and integration difficult. We therefore compared the performance of these different algorithms on both simulated and experimental data sets. We focused on the ability of these algorithms to identify the set of synergies underlying a data set. All data sets consisted of nonnegative values, reflecting the nonnegative data of muscle activation patterns. We found that the performance of principal component analysis (PCA) was generally lower than that of the other algorithms in identifying muscle synergies. Factor analysis (FA) with varimax rotation was better than PCA, and was generally at the same levels as independent component analysis (ICA) and nonnegative matrix factorization (NMF). ICA performed very well on data sets corrupted by constant variance Gaussian noise, but was impaired on data sets with signal-dependent noise and when synergy activation coefficients were correlated. Nonnegative matrix factorization (NMF) performed similarly to ICA and FA on data sets with signal-dependent noise and was generally robust across data sets. The best algorithms were ICA applied to the subspace defined by PCA (ICAPCA) and a version of probabilistic ICA with nonnegativity constraints (pICA). We also evaluated some commonly used criteria to identify the number of synergies underlying a data set, finding that only likelihood ratios based on factor analysis identified the correct number of synergies for data sets with signal-dependent noise in some cases. We then proposed an ad hoc procedure, finding that it was able to identify the correct number in a larger number of cases. Finally, we applied these methods to an experimentally obtained data set. The best performing algorithms (FA, ICA, NMF, ICAPCA, pICA) identified synergies very similar to one another. Based on these results, we discuss guidelines for using factorization algorithms to analyze muscle activation patterns. More generally, the ability of several algorithms to identify the correct muscle synergies and activation coefficients in simulated data, combined with their consistency when applied to physiological data sets, suggests that the muscle synergies found by a particular algorithm are not an artifact of that algorithm, but reflect basic aspects of the organization of muscle activation patterns underlying behaviors.


title: Efficient Compressive Sensing with Deterministic Guarantees Using Expander Graphs

abstract: Compressive sensing is an emerging technology which can recover a sparse signal vector of dimension n via a much smaller number of measurements than n. However, the existing compressive sensing methods may still suffer from relatively high recovery complexity, such as O(n3), or can only work efficiently when the signal is super sparse, sometimes without deterministic performance guarantees. In this paper, we propose a compressive sensing scheme with deterministic performance guarantees using expander-graphs-based measurement matrices and show that the signal recovery can be achieved with complexity O(n) even if the number of nonzero elements k grows linearly with n. We also investigate compressive sensing for approximately sparse signals using this new method. Moreover, explicit constructions of the considered expander graphs exist. Simulation results are given to show the performance and complexity of the new method.


title: Image-driven simplification

abstract: We introduce the notion of image-driven simplification , a framework that uses images to decide which portions of a model to simplify. This is a departure from approaches that make polygonal simplification decisions based on geometry. As with many methods, we use the edge collapse operator to make incremental changes to a model. Unique to our approach, however, is the use at comparisons between images of the original model against those of a simplified model to determine the cost of an ease collapse. We use common graphics rendering hardware to accelerate the creation of the required images. As expected, this method produces models that are close to the original model according to image differences. Perhaps more surprising, however, is that the method yields models that have high geometric fidelity as well. Our approach also solves the quandary of how to weight the geometric distance versus appearance properties such as normals, color, and texture. All of these trade-offs are balanced by the image metric. Benefits of this approach include high fidelity silhouettes, extreme simplification of hidden portions of a model, attention to shading interpolation effects, and simplification that is sensitive to the content of a texture. In order to better preserve the appearance of textured models, we introduce a novel technique for assigning texture coordinates to the new vertices of the mesh. This method is based on a geometric heuristic that can be integrated with any edge collapse algorithm to produce high quality textured surfaces.


title: Evolving cellular automata to perform computations: Mechanisms and impediments

abstract: Abstract We present results from experiments in which a genetic algorithm (GA) was used to evolve cellular automata (CAs) to perform a particular computational task - one-dimensional density classification. We look in detail at the evolutionary mechanisms producing the GA's behavior on this task and the impediments faced by the GA. In particular, we identify four “epochs of innovation” in which new CA strategies for solving the problem are discovered by the GA, describe how these strategies are implemented in CA rule tables, and identify the GA mechanisms underlying their discovery. The epochs are characterized by a breaking of the task's symmetries on the part of the GA. The symmetry breaking results in a short-term fitness gain but ultimately prevents the discovery of the most highly fit strategies. We discuss the extent to which symmetry breaking and other impediments are general phenomena in any GA search.


title: Toward Sensor-Based Random Number Generation for Mobile and IoT Devices

abstract: The importance of random number generators (RNGs) to various computing applications is well understood. To ensure a quality level of output, high-entropy sources should be utilized as input. However, the algorithms used have not yet fully evolved to utilize newer technology. Even the Android pseudo RNG (APRNG) merely builds atop the Linux RNG to produce random numbers. This paper presents an exploratory study into methods of generating random numbers on sensor-equipped mobile and Internet of Things devices. We first perform a data collection study across 37 Android devices to determine two things-how much random data is consumed by modern devices, and which sensors are capable of producing sufficiently random data. We use the results of our analysis to create an experimental framework called SensoRNG, which serves as a prototype to test the efficacy of a sensor-based RNG. SensoRNG employs collection of data from on-board sensors and combines them via a lightweight mixing algorithm to produce random numbers. We evaluate SensoRNG with the National Institute of Standards and Technology statistical testing suite and demonstrate that a sensor-based RNG can provide high quality random numbers with only little additional overhead.


title: Cultivation, photobioreactor design and harvesting of microalgae for biodiesel production: a critical review.

abstract: Microalgae have the ability to mitigate CO(2) emission and produce oil with a high productivity, thereby having the potential for applications in producing the third-generation of biofuels. The key technologies for producing microalgal biofuels include identification of preferable culture conditions for high oil productivity, development of effective and economical microalgae cultivation systems, as well as separation and harvesting of microalgal biomass and oil. This review presents recent advances in microalgal cultivation, photobioreactor design, and harvesting technologies with a focus on microalgal oil (mainly triglycerides) production. The effects of different microalgal metabolisms (i.e., phototrophic, heterotrophic, mixotrophic, and photoheterotrophic growth), cultivation systems (emphasizing the effect of light sources), and biomass harvesting methods (chemical/physical methods) on microalgal biomass and oil production are compared and critically discussed. This review aims to provide useful information to help future development of efficient and commercially viable technology for microalgae-based biodiesel production.


title: Embodied and disembodied cognition: Spatial perspective-taking

abstract: Although people can take spatial perspectives different from their own, it is widely assumed that egocentric perspectives are natural and have primacy. Two studies asked respondents to describe the spatial relations between two objects on a table in photographed scenes; in some versions, a person sitting behind the objects was either looking at or reaching for one of the objects. The mere presence of another person in a position to act on the objects induced a good proportion of respondents to describe the spatial relations from that person’s point of view (Experiment 1). When the query about the spatial relations was phrased in terms of action, more respondents took the other’s perspective than their own (Experiment 2). The implication of action elicits spontaneous spatial perspective-taking, seemingly in the service of understanding the other’s actions.


title: Inward FDI in seven transitional countries of South-Eastern Europe: a quest of institution-based attractiveness

abstract: The main question we address is whether the weak FDI level in the SEE-7 is linked to ill-adapted institutions or not. In order to answer it, we need to understand the role of institutions in shaping a strong localization advantage for FDI. We develop a theoretical framework to understand the relationship between Transition, Institutions and inward FDI. We assume that the ability to attract FDI depends on the local institutional arrangement. We present our pattern of institutional arrangement that may help us understand why, in spite of identical institutions, countries attract a different level of FDI. We split the SEE into two categories of host countries, each category being characterized by a specific institutional arrangement and level of FDI. We conclude with the relevance of our proposition to develop an analytical framework where FDI is the outcome of a new and well-adapted institutional arrangement


title: Synchronous Bidirectional Inference for Neural Sequence Generation

abstract: Abstract In sequence to sequence generation tasks (e.g. machine translation and abstractive summarization), inference is generally performed in a left-to-right manner to produce the result token by token. The neural approaches, such as LSTM and self-attention networks, are now able to make full use of all the predicted history hypotheses from left side during inference, but cannot meanwhile access any future (right side) information and usually generate unbalanced outputs (e.g. left parts are much more accurate than right ones in Chinese-English translation). In this work, we propose a synchronous bidirectional inference model to generate outputs using both left-to-right and right-to-left decoding simultaneously and interactively. First, we introduce a novel beam search algorithm that facilitates synchronous bidirectional decoding. Then, we present the core approach which enables left-to-right and right-to-left decoding to interact with each other, so as to utilize both the history and future predictions simultaneously during inference. We apply the proposed model to both LSTM and self-attention networks. Furthermore, we propose a novel fine-tuning based parameter optimization algorithm in addition to the simple two-pass strategy. The extensive experiments on machine translation and abstractive summarization demonstrate that our synchronous bidirectional inference model can achieve remarkable improvements over the strong baselines.


title: Semi-supervised multimodal deep learning for RGB-D object recognition

abstract: This paper studies the problem of RGB-D object recognition. Inspired by the great success of deep convolutional neural networks (DCNN) in AI, researchers have tried to apply it to improve the performance of RGB-D object recognition. However, DCNN always requires a large-scale annotated dataset to supervise its training. Manually labeling such a large RGB-D dataset is expensive and time consuming, which prevents DCNN from quickly promoting this research area. To address this problem, we propose a semi-supervised multimodal deep learning framework to train DCNN effectively based on very limited labeled data and massive unlabeled data. The core of our framework is a novel diversity preserving co-training algorithm, which can successfully guide DCNN to learn from the unlabeled RGB-D data by making full use of the complementary cues of the RGB and depth data in object representation. Experiments on the benchmark RGB-D dataset demonstrate that, with only 5% labeled training data, our approach achieves competitive performance for object recognition compared with those state-of-the-art results reported by fully-supervised methods.


title: Autonomous Vehicle Guidance on Braunschweig's inner ring road within the Stadtpilot Project

abstract: With the “Stadtpilot”-Project the Technische Universit at Braunschweig transfers the knowledge gained from its participation in the DARPA Urban Challenge Event to the real urban environment of Braunschweig's inner ring road. The goal is to drive fully autonomously in the traffic flow and to behave according to traffic rules. On October 8th, 2010, the first “Stadtpilot”-vehicle called “Leonie” has shown its abilities to the public while driving fully autonomously on a selected part in the northeast of the ring road. This paper introduces the vehicle guidance system of “Leonie” that has proven its functionality on several hundred kilometers in real urban traffic by the end of 2010. The overall system functionality is shown as well as several driving situations that can be handled fully autonomously. In addition, the limitations of the current system are also discussed.


Machine Learning Methods for Optical Character Recognition


title: Deep r -th Root of Rank Supervised Joint Binary Embedding for Multivariate Time Series Retrieval

abstract: Multivariate time series data are becoming increasingly common in numerous real world applications, e.g., power plant monitoring, health care, wearable devices, automobile, etc. As a result, multivariate time series retrieval, i.e., given the current multivariate time series segment, how to obtain its relevant time series segments in the historical data (or in the database), attracts significant amount of interest in many fields. Building such a system, however, is challenging since it requires a compact representation of the raw time series which can explicitly encode the temporal dynamics as well as the correlations (interactions) between different pairs of time series (sensors). Furthermore, it requires query efficiency and expects a returned ranking list with high precision on the top. Despite the fact that various approaches have been developed, few of them can jointly resolve these two challenges. To cope with this issue, in this paper we propose a Deep r-th root of Rank Supervised Joint Binary Embedding (Deep r-RSJBE) to perform multivariate time series retrieval. Given a raw multivariate time series segment, we employ Long Short-Term Memory (LSTM) units to encode the temporal dynamics and utilize Convolutional Neural Networks (CNNs) to encode the correlations (interactions) between different pairs of time series (sensors). Subsequently, a joint binary embedding is pursued to incorporate both the temporal dynamics and the correlations. Finally, we develop a novel r-th root ranking loss to optimize the precision at the top of a Hamming distance ranking list. Thoroughly empirical studies based upon three publicly available time series datasets demonstrate the effectiveness and the efficiency of Deep r-RSJBE.


title: High-order Proximity Preserving Information Network Hashing

abstract: Information network embedding is an effective way for efficient graph analytics. However, it still faces with computational challenges in problems such as link prediction and node recommendation, particularly with increasing scale of networks. Hashing is a promising approach for accelerating these problems by orders of magnitude. However, no prior studies have been focused on seeking binary codes for information networks to preserve high-order proximity. Since matrix factorization (MF) unifies and outperforms several well-known embedding methods with high-order proximity preserved, we propose a MF-based \underlineI nformation \underlineN etwork \underlineH ashing (INH-MF) algorithm, to learn binary codes which can preserve high-order proximity. We also suggest Hamming subspace learning, which only updates partial binary codes each time, to scale up INH-MF. We finally evaluate INH-MF on four real-world information network datasets with respect to the tasks of node classification and node recommendation. The results demonstrate that INH-MF can perform significantly better than competing learning to hash baselines in both tasks, and surprisingly outperforms network embedding methods, including DeepWalk, LINE and NetMF, in the task of node recommendation. The source code of INH-MF is available online\footnote\urlhttps://github.com/DefuLian/network .


Accelerating SVRG via second-order information


title: Causal Mediation Analysis with Multiple Mediators

abstract: In diverse fields of empirical research-including many in the biological sciences-attempts are made to decompose the effect of an exposure on an outcome into its effects via a number of different pathways. For example, we may wish to separate the effect of heavy alcohol consumption on systolic blood pressure (SBP) into effects via body mass index (BMI), via gamma-glutamyl transpeptidase (GGT), and via other pathways. Much progress has been made, mainly due to contributions from the field of causal inference, in understanding the precise nature of statistical estimands that capture such intuitive effects, the assumptions under which they can be identified, and statistical methods for doing so. These contributions have focused almost entirely on settings with a single mediator, or a set of mediators considered en bloc; in many applications, however, researchers attempt a much more ambitious decomposition into numerous path-specific effects through many mediators. In this article, we give counterfactual definitions of such path-specific estimands in settings with multiple mediators, when earlier mediators may affect later ones, showing that there are many ways in which decomposition can be done. We discuss the strong assumptions under which the effects are identified, suggesting a sensitivity analysis approach when a particular subset of the assumptions cannot be justified. These ideas are illustrated using data on alcohol consumption, SBP, BMI, and GGT from the Izhevsk Family Study. We aim to bridge the gap from "single mediator theory" to "multiple mediator practice," highlighting the ambitious nature of this endeavor and giving practical suggestions on how to proceed.


title: Data processing for large database using feature selection

abstract: Big data is a term for huge amount of data sets that are becoming more complex for data processing applications and are inadequate to deal with them. Big data need a set of techniques and technologies which can easily handle the complex data set and can be easily processed. Feature selection techniques have become an apparent need in many applications to identify the required information from a large set of data. Existing systems processes the same data repeatedly each time when a user request is submitted even for a small task. In this work, a feature selection technique using spark streaming is proposed which can get live stream of input data and process it in batches to extract the required feature from the given input stream data. The proposed method will use the feature selection technique to extract the required data from the large dataset based on the user requirements. The algorithm proposed will also help to increase the throughput of the system.


title: Policy Advocacy in Science: Prevalence, Perspectives, and Implications for Conservation Biologists

abstract: J. MICHAEL SCOTT,∗‡§§ JANET L. RACHLOW,∗ ROBERT T. LACKEY,§ ANNA B. PIDGORNA,† JOCELYN L. AYCRIGG,∗ GABRIELLE R. FELDMAN,∗∗ LEONA K. SVANCARA,∗ DAVID A. RUPP,† DAVID I. STANISH,∗ AND R. KIRK STEINHORST†† ∗Department of Fish and Wildlife Resources, University of Idaho, Moscow, ID 83844-1136, U.S.A. †Environmental Science Program, University of Idaho, Moscow, ID 83844-1136, U.S.A. ‡U.S. Geological Survey, Idaho Cooperative Fish and Wildlife Research Unit, University of Idaho, Moscow, ID 83844-1136, U.S.A. §U.S. Environmental Protection Agency, 200 SW 35th Street, Corvallis, OR 97333-4902, U.S.A. ∗∗Department of Forest Resources, University of Idaho, Moscow, ID 83844-1136, U.S.A. ††Department of Statistics, University of Idaho, Moscow, ID 83844-1136, U.S.A.


title: Interactive storytelling: approaches and techniques to achieve dynamic stories

abstract: In this paper we review different techniques which can be used to achieve interactive storytelling in games, most notably the employment of planning algorithms to decide which and how events should be presented to the player at a given time, and the definition of non playing characters actions and behaviours as a response to the player's actions. Furthermore we will consider game world with more interesting characters that react to other characters actions and behaviour, while forming bonds and relationships with them and the player. We will consider the use of emotional characters to this effect. The incorporation of narrative techniques into the storytelling used in games can help not only to increment the level of interaction between the player and the world and characters, but also to keep the story fluent, thus achieving more realistic narrative. We will examine and discuss some of them, along with some existing approaches and possible applications for interactive storyline generation. We will also review and discuss some areas in which emotional characters have been used to support the player, and in the development of interactive storytelling.


title: On convergence of double sequences of closed sets

abstract: In this paper, we extend the definitions of various kinds of convergence from ordinary (single) sequences to double sequences of closed sets. We examine the relationship among them. Also, we introduce monotone double sequences of sets and analyze the limit of monotone double sequences of sets.


title: Mining frequent closed graphs on evolving data streams

abstract: Graph mining is a challenging task by itself, and even more so when processing data streams which evolve in real-time. Data stream mining faces hard constraints regarding time and space for processing, and also needs to provide for concept drift detection. In this paper we present a framework for studying graph pattern mining on time-varying streams. Three new methods for mining frequent closed subgraphs are presented. All methods work on coresets of closed subgraphs, compressed representations of graph sets, and maintain these sets in a batch-incremental manner, but use different approaches to address potential concept drift. An evaluation study on datasets comprising up to four million graphs explores the strength and limitations of the proposed methods. To the best of our knowledge this is the first work on mining frequent closed subgraphs in non-stationary data streams.


title: An Experimental Investigation of Hyperbolic Routing with a Smart Forwarding Plane in NDN

abstract: Routing in NDN networks must scale in terms of forwarding table size and routing protocol overhead. Hyperbolic routing (HR) presents a potential solution to address the routing scalability problem, because it does not use traditional forwarding tables or exchange routing updates upon changes in network topologies. Although HR has the drawbacks of producing sub-optimal routes or local minima for some destinations, these issues can be mitigated by NDN's intelligent data forwarding plane. However, HR's viability still depends on both the quality of the routes HR provides and the overhead incurred at the forwarding plane due to HR's sub-optimal behavior. We designed a new forwarding strategy called Adaptive Smoothed RTT-based Forwarding (ASF) to mitigate HR's sub-optimal path selection. This paper describes our experimental investigation into the packet delivery delay and overhead under HR as compared with Named-Data Link State Routing (NLSR), which calculates shortest paths. We run emulation experiments using various topologies with different failure scenarios, probing intervals, and maximum number of next hops for a name prefix. Our results show that HR's delay stretch has a median close to 1 and a 95th-percentile around or below 2, which does not grow with the network size. HR's message overhead in dynamic topologies is nearly independent of the network size, while NLSR's overhead grows polynomially at least. These results suggest that HR offers a more scalable routing solution with little impact on the optimality of routing paths.


title: Recognition of Handwritten Textual Annotations using Tesseract Open Source OCR Engine for information Just In Time (iJIT)

abstract: Objective of the current work is to develop an Optical Character Recognition (OCR) engine for information Just In Time (iJIT) system that can be used for recognition of handwritten textual annotations of lower case Roman script. Tesseract open source OCR engine under Apache License 2.0 is used to develop user-specific handwriting recognition models, viz., the language sets, for the said system, where each user is identified by a unique identification tag associated with the digital pen. To generate the language set for any user, Tesseract is trained with labeled handwritten data samples of isolated and free-flow texts of Roman script, collected exclusively from that user. The designed system is tested on five different language sets with free- flow handwritten annotations as test samples. The system could successfully segment and subsequently recognize 87.92%, 81.53%, 92.88%, 86.75% and 90.80% handwritten characters in the test samples of five different users.


title: Current Status of Knowledge, Attitude and Practice (KAP) and Screening for Cervical Cancer in Countries at Different Levels of Development

abstract: Cancer of the uterine cervix is a worldwide menace taking innumerable womens' lives. The literature is vast and a large number of studies have been conducted in this field. Analyses have shown significant differences exist in terms of screening and HPV testing facilities among high income and low to middle income countries. In addition, acute lack of awareness and knowledge among the concerned population is particularly noted in rural areas of the low income countries. A detailed review of Indian case studies revealed that early age of marriage and childbirth, multiparity, poor personal hygiene and low socio-economic status among others are the principal risk factors for this disease. This review concludes that a two pronged strategy involving strong government and NGO action is necessary to minimize the occurrence of cervical cancer especially in low and medium income countries.


title: Query Segmentation Based on Eigenspace Similarity

abstract: Query segmentation is essential to query processing. It aims to tokenize query words into several semantic segments and help the search engine to improve the precision of retrieval. In this paper, we present a novel unsupervised learning approach to query segmentation based on principal eigenspace similarity of query-word-frequency matrix derived from web statistics. Experimental results show that our approach could achieve superior performance of 35.8% and 17.7% in F-measure over the two baselines respectively, i.e. MI (Mutual Information) approach and EM optimization approach.


title: The Many Shades of Anonymity: Characterizing Anonymous Social Media Content

abstract: Recently, there has been a significant increase in the popularity of anonymous social media sites like Whisper and Secret. Unlike traditional social media sites like Facebook and Twitter, posts on anonymous social media sites are not associated with well-defined user identities or profiles. In this study, our goals are two-fold: (i) to understand the nature (sensitivity, types) of content posted on anonymous social media sites and (ii) to investigate the differences between content posted on anonymous and non-anonymous social media sites like Twitter. To this end, we gather and analyze extensive content traces from Whisper (anonymous) and Twitter (non-anonymous) social media sites. We introduce the notion of anonymity sensitivity of a social media post, which captures the extent to which users think the post should be anonymous. We also propose a human annotator based methodology to measure the same for Whisper and Twitter posts. Our analysis reveals that anonymity sensitivity of most whispers (unlike tweets) is not binary. Instead, most whispers exhibit many shades or different levels of anonymity. We also find that the linguistic differences between whispers and tweets are so significant that we could train automated classifiers to distinguish between them with reasonable accuracy. Our findings shed light on human behavior in anonymous media systems that lack the notion of an identity and they have important implications for the future designs of such systems.


title: Pixel-based methods for widget state and style in a runtime implementation of sliding widgets

abstract: Pixel-based methods offer unique potential for modifying existing interfaces independent of their underlying implementation. Prior work has demonstrated a variety of modifications to existing interfaces, including accessibility enhancements, interface language translation, testing frameworks, and interaction techniques. But pixel-based methods have also been limited in their understanding of the interface and therefore the complexity of modifications they can support. This work examines deeper pixel-level understanding of widgets and the resulting capabilities of pixel-based runtime enhancements. Specifically, we present three new sets of methods: methods for pixel-based modeling of widgets in multiple states, methods for managing the combinatorial complexity that arises in creating a multitude of runtime enhancements, and methods for styling runtime enhancements to preserve consistency with the design of an existing interface. We validate our methods through an implementation of Moscovich et al.'s Sliding Widgets, a novel runtime enhancement that could not have been implemented with prior pixel-based methods.


title: Rotorigami: A rotary origami protective system for robotic rotorcraft

abstract: Applications of aerial robots are progressively expanding into complex urban and natural environments. Despite remarkable advancements in the field, robotic rotorcraft is still drastically limited by the environment in which they operate. Obstacle detection and avoidance systems have functionality limitations and substantially add to the computational complexity of the onboard equipment of flying vehicles. Furthermore, they often cannot identify difficult-to-detect obstacles such as windows and wires. Robustness to physical contact with the environment is essential to mitigate these limitations and continue mission completion. However, many current mechanical impact protection concepts are either not sufficiently effective or too heavy and cumbersome, severely limiting the flight time and the capability of flying in constrained and narrow spaces. Therefore, novel impact protection systems are needed to enable flying robots to navigate in confined or heavily cluttered environments easily, safely, and efficiently while minimizing the performance penalty caused by the protection method. Here, we report the development of a protection system for robotic rotorcraft consisting of a free-to-spin circular protector that is able to decouple impact yawing moments from the vehicle, combined with a cyclic origami impact cushion capable of reducing the peak impact force experienced by the vehicle. Experimental results using a sensor-equipped miniature quadrotor demonstrated the impact resilience effectiveness of the Rotary Origami Protective System (Rotorigami) for a variety of collision scenarios. We anticipate this work to be a starting point for the exploitation of origami structures in the passive or active impact protection of robotic vehicles.


title: IT Capability and Organizational Performance: The Roles of Business Process Agility and Environmental Factors

abstract: The business value of information technology (IT) has been one of the top concerns of both practitioners and scholars for decades. Numerous studies have documented the positive effects of IT capability on organizational performance but our knowledge of the processes through which such gains are achieved remains limited due to a lack of focus on the business environment. Such a linkage therefore remains the subject of debate in the information systems literature. In this study, we fill this gap by investigating the mediating role of business process agility and the moderating roles of environmental factors. On the basis of matched survey data obtained from 214 IT and business executives from manufacturing firms in China, our analyses show that even though firm-wide IT capability presents the characteristics of rarity, appropriability, non-reproducibility, and non-substitutability, its impact on organizational performance is fully mediated by business process agility. Our results also show that the impact of the environment is multifaceted and nuanced. In particular, environmental hostility weakens the effect of IT capability on business process agility, while environmental complexity strengthens it. The theoretical and practical implications of this study, and its limitations, are also discussed.


title: Exploiting emoticons in sentiment analysis

abstract: As people increasingly use emoticons in text in order to express, stress, or disambiguate their sentiment, it is crucial for automated sentiment analysis tools to correctly account for such graphical cues for sentiment. We analyze how emoticons typically convey sentiment and demonstrate how we can exploit this by using a novel, manually created emoticon sentiment lexicon in order to improve a state-of-the-art lexicon-based sentiment classification method. We evaluate our approach on 2,080 Dutch tweets and forum messages, which all contain emoticons and have been manually annotated for sentiment. On this corpus, paragraph-level accounting for sentiment implied by emoticons significantly improves sentiment classification accuracy. This indicates that whenever emoticons are used, their associated sentiment dominates the sentiment conveyed by textual cues and forms a good proxy for intended sentiment.


title: Geraniol, E-3,7-dimethyl-2,6-octadien-1-ol, as the Alarm Pheromone of the Sycamore Lace Bug Corythucha ciliata (Say)

abstract: Although adult sycamore lace bugs Corythucha ciliata (Say) show no sign of aggregation, nymphs at all developing stages are gregarious. When an individual nymph in the center of a colony was squashed with a needlepoint, proximate nymphs showed evasive behavior. The same evasive reaction was produced by exposing aggregated nymphs to nymph hexane extract. The active component, E-3,7-dimethyl-2,6-octadien-1-ol, geraniol, was responsible for the evasive behavior, and identified as the alarm pheromone. One nanogram of the alarm pheromone elicited activity similar to that in a third instar nymph. Presence of 2-acylcyclohexane-1,3-diones and their 4-hydroxy analogues are reconfirmed as nymph-specific components, though their biological significance remains unknown.


title: SocialFilter: Introducing social trust to collaborative spam mitigation

abstract: We propose SocialFilter, a trust-aware collaborative spam mitigation system. Our proposal enables nodes with no email classification functionality to query the network on whether a host is a spammer. It employs Sybil-resilient trust inference to weigh the reports concerning spamming hosts that collaborating spam-detecting nodes (reporters) submit to the system. It weighs the spam reports according to the trustworthiness of their reporters to derive a measure of the system's belief that a host is a spammer. SocialFilter is the first collaborative unwanted traffic mitigation system that assesses the trustworthiness of spam reporters by both auditing their reports and by leveraging the social network of the reporters' administrators. The design and evaluation of our proposal offers us the following lessons: a) it is plausible to introduce Sybil-resilient Online-Social-Network-based trust inference mechanisms to improve the reliability and the attack-resistance of collaborative spam mitigation; b) using social links to obtain the trustworthiness of reports concerning spammers can result in comparable spam-blocking effectiveness with approaches that use social links to rate-limit spam (e.g., Ostra [27]); c) unlike Ostra, in the absence of reports that incriminate benign email senders, SocialFilter yields no false positives.


title: Learning parameterized models of image motion

abstract: A framework for learning parameterized models of optical flow from image sequences is presented. A class of motions is represented by a set of orthogonal basis flow fields that are computed from a training set using principal component analysis. Many complex image motions can be represented by a linear combination of a small number of these basis flows. The learned motion models may be used for optical flow estimation and for model-based recognition. For optical flow estimation we describe a robust, multi-resolution scheme for directly computing the parameters of the learned flow models from image derivatives. As examples we consider learning motion discontinuities, non-rigid motion of human mouths, and articulated human motion.


title: Anomaly detection on the edge

abstract: Anomaly detection is the process of identifying unusual signals in a set of observations. This is a vital task in a variety of fields including cybersecurity and the battlefield. In many scenarios, observations are gathered from a set of distributed mobile or small form factor devices. Traditionally, the observations are sent to centralized servers where large-scale systems perform analytics on the data gathered from all devices. However, the compute capability of these small form factor devices is ever increasing with annual improvements to hardware. A new model, known as edge computing, takes advantage of this compute capability and performs local analytics on the distributed devices. This paper presents an approach to anomaly detection that uses autoencoders, specialized deep learning neural networks, deployed on each edge device, to perform analytics and identify anomalous observations in a distributed fashion. Simultaneously, the autoencoders learn from the new observations in order to identify new trends. A centralized server aggregates the updated models and distributes them back to the edge devices when a connection is available. This architecture reduces the bandwidth and connectivity requirements between the edge devices and the central server as only the autoencoder model and anomalous observations must be sent to the central servers, rather than all observation data.


title: Emotion recognition: The role of facial movement and the relative importance of upper and lower areas of the face.

abstract: In order to investigate the role of facial movement in the recognition of emotions, faces were covered with black makeup and white spots. Video recordings of such faces were played back so that only the white spots were visible. The results demonstrated that moving displays of happiness, sadness, fear, surprise, anger and disgust were recognized more accurately than static displays of the white spots at the apex of the expressions. This indicated that facial motion, in the absence of information about the shape and position of facial features, is informative about these basic emotions. Normally illuminated dynamic displays of these expressions, however, were recognized more accurately than displays of moving spots. The relative effectiveness of upper and lower facial areas for the recognition of these six emotions was also investigated using normally illuminated and spots-only displays. In both instances the results indicated that different facial regions are more informative for different emitions. The movement patterns characterizing the various emotional expressions as well as common confusions between emotions are also discussed.


title: AlN contour-mode resonators for narrow-band filters above 3 GHz

abstract: This paper reports on the design and experimental verification of a new class of thin-film (250 nm) Super High Frequency (SHF) laterally-vibrating piezoelectric microelectromechanical (MEMS) resonators suitable for the fabrication of narrow-band MEMS filters operating at frequencies above 3 GHz. The device dimensions have been opportunely scaled both in the lateral and vertical dimensions in order to excite a contour-extensional mode of vibration in nano features of an ultra-thin (250 nm) Aluminum Nitride (AlN) film. In this first demonstration two-port resonators vibrating up to 4.5 GHz were fabricated on the same die and attained electromechanical coupling, k t 2, in excess of 1.5 %. These devices were employed to synthesize the highest frequency ever reported MEMS filter (3.7 GHz) based on AlN contour-mode resonator (CMR) technology.


title: Artificial intelligence: Learning to see and act

abstract: An artificial-intelligence system uses machine learning from massive training sets to teach itself to play 49 classic computer games, demonstrating that it can adapt to a variety of tasks. See Letter p.529


title: Hybrid position and orientation tracking for a passive rehabilitation table-top robot

abstract: This paper presents a real time hybrid 2D position and orientation tracking system developed for an upper limb rehabilitation robot. Designed to work on a table-top, the robot is to enable home-based upper-limb rehabilitative exercise for stroke patients. Estimates of the robot's position are computed by fusing data from two tracking systems, each utilizing a different sensor type: laser optical sensors and a webcam. Two laser optical sensors are mounted on the underside of the robot and track the relative motion of the robot with respect to the surface on which it is placed. The webcam is positioned directly above the workspace, mounted on a fixed stand, and tracks the robot's position with respect to a fixed coordinate system. The optical sensors sample the position data at a higher frequency than the webcam, and a position and orientation fusion scheme is proposed to fuse the data from the two tracking systems. The proposed fusion scheme is validated through an experimental set-up whereby the rehabilitation robot is moved by a humanoid robotic arm replicating previously recorded movements of a stroke patient. The results prove that the presented hybrid position tracking system can track the position and orientation with greater accuracy than the webcam or optical sensors alone. The results also confirm that the developed system is capable of tracking recovery trends during rehabilitation therapy.


title: Variation of Preamplifier Noise Figure With B0 Field Strength

abstract: Introduction High-field arrays with large numbers of coils require preamplifiers located close to the coils to minimize SNR losses incurred in cables. The design of low-noise preamplifiers for high-field MRI must thus ensure there is no performance degradation due to the presence or orientation of high magnetic fields. Preamplifier scattering (S) parameters have been known to vary with B0 field strength and orientation due to the field’s effects on the motion of charge carriers (i.e., the Hall effect) in semiconductor devices [1]. The objective of the present work is to measure the accompanying changes in noise figure (NF) [2], which describes the loss of SNR due to noise added by the preamp. The “Y factor” measurement of NF [2] requires a noise source whose equivalent noise temperature can switch between room (“cold”) and a much higher (“hot”) temperature. Typical coaxial RF noise sources use a PN diode reverse biased into avalanche breakdown [3] to produce the excess noise required for the hot state. The diode current is zero in the cold state, and an attenuator is placed between the diode and the output to control its impedance and to provide room temperature noise. Methods Due to the incorporation of a semiconductor diode, magnetic fields are expected to influence the noise source’s hot temperature, thereby rendering its excess noise ratio (ENR) calibration table (measured at zero field in a certified laboratory), and consequent NF measurements, unreliable. Our approach circumvents this problem by locating a source (NW1M500-6-CS, NoiseWave, USA) with nominal ENR = 6 dB well outside the 5 G line of the magnet (9.4 T, 205 mm bore, Magnex, UK) as shown in Fig. 1. The optional “tuner board” [4] required to measure how NF varies with the impedance seen by the preamplifier's input, is unaffected by the field since it contains only passive components and can be located with the device under test (DUT) inside the bore. The NF measurement accounts for losses in the length of cable (RG223, approximate length 6 m) between source and tuner board by incorporating it into the “tuner loss” [5], while that of the corresponding cable at the DUT’s output is calibrated out by the second stage correction [2]. A network analyzer measures S parameters required for the “available gain” correction [5]. The preamp is located within the MR system’s RF screen for all measurements. Noise figure measurements (50 Ω) were performed at 128 MHz on a monolithic amplifier (MAR-8A+, Mini-Circuits, USA) based on InGaP bipolar transistors. Bias current was set to ~32 mA at zero field using a 10 V regulated supply and a resistive current limiter; subsequent changes in bias current with field strength were monitored. The amplifier was placed in the magnet bore with the field parallel to the ground pins that exit the device’s circular body at diametrically opposite sides (Fig. 2). In this orientation the insertion gain (S21) of the amplifier is reduced by ~1.5 dB at 9.4 T compared to 0 T. Noise figure was measured with the amplifier at axial positions of 0 cm, 35 cm and out to 75 cm (magnet edge, ~1.5 T) in 5 cm increments relative to iso-center. In a second experiment at zero field the supply voltage was varied to adjust the bias current in the range 25–37 mA; NF measurements were performed at 2 mA increments. Results A variation in noise figure with field strength is readily observed in Fig. 2. As shown in Fig. 3 changes in bias current only partially explain this behaviour, and thus active control of the bias current is not likely to be an effective method to limit such variations in this device. Conclusions This approach allows precise measurements of the noise performance of MRI preamplifiers at any field strength. Variations in NF are readily observed even for relatively insensitive devices such as bipolar transistors. Detailed device models are needed to describe, and subsequently control, variations in NF with field and orientation. References [1] Possanzini, Boutelje., Proc. ISMRM, p. 1123 (2008). [2] Vendelin et al. Microwave Circuit Design Using Linear and Nonlinear Techniques, 2 ed., Wiley (2005). [3] Tiemeijer et al. IEEE Transactions Microwave Theory and Techniques 53(9) pp. 2917–2925 (2005). [4] Massner et al., Proc. ISMRM, p. 328 (2007). [5] Pak et al. Microwaves & RF 29(7) pp. 103–108 (1990). Funding Natural Sciences and Engineering Research Council (Canada); Alberta Cancer Research Institute; Canada Foundation for Innovation. Figure 1: measurement setup


title: Web Entities Extraction Based on Semi- Structured Semantic Database

abstract: Web is the biggest source of information and contains many entities and relationships between them, extracting these data from Massive Web pages and Integrating to a Semi-Structured Data with rich semantics will be more conducive to the management and use of these web data. On this premise, a comprehensive method is proposed to perform extraction the entities and relationships from the webpages. The method consists of two steps: 1) The target Web pages which contains these entities will be found based on the combination of vision information and content of keyword, meanwhile recording the relationship between father and children target Web pages; 2) Extracting the entities with analysis of DOM tree structure of the obtained Web pages and definitions of some extraction rules. At last, the extracted data is organized into a Semi-Structured Data with special relationships. Experiments on a large number of HTML pages have showed that this method can get a high correct rate and coverage.


title: Estimating Crowd Density in an RF-Based Dynamic Environment

abstract: Crowd density estimating is a crucial service in many applications (e.g., smart guide, crowd control, etc.), which is often conducted using pattern recognition technologies based on video surveillance. However, these kinds of methods are high cost, and cannot work well in low-light environments. Radio frequency based technologies are adopted more and more in indoor application, since radio signal strength (RSS) can be easily obtained by various wireless devices without additional cost. In this paper, we introduce a low cost crowd density estimating method using wireless sensor networks. The proposed approach is a device-free crowd counting approach without objects carrying any assistive device. It is hard to count objects based on RSS measurement, since different number of mobile people at different positions often generates different RSS due to the multipath phenomenon. This paper utilizes the space-time relativity of crowd distribution to reduce the estimation errors. The proposed approach is an iterative process, which contains three phases: the training phase, the monitoring phase, and the calibrating phase. Our experiments are implemented based on TelosB sensor platform. We also do some large-scale simulations to verify the feasibility and the effectiveness of our crowd density estimating approach.


title: Design and performance analysis of a single phase PWM inverter

abstract: Power Electronic equipment that converts a DC power into AC power at required voltage and frequency level is known as Inverter. Voltage Source inverters produce an output voltage or current with levels either zero to positive voltage or zero to negative voltage that means two levels. These bi-level inverters are powered by different type of gating signals like square, quasi - square and sinusoidal Pulse width modulated signals. Different gating signals result in different harmonic levels in the output signal. In this paper, a hardware prototype of a single phase inverter using MOSFETs as the power switches has been developed. The MOSFETs are driven by square and quasi-square gating signals. These gating signals have been generated by designing the driver circuit using a MOSFET driver IC. In order to maintain the voltage level, the conduction time interval of MOSFETs has been maintained by controlling the pulse width of the gating pulses. Performance of the inverter has been studied for resistive load. Simulation has been done in SIMULINK, results of simulation and experimental model has been compared on the basis of various parameters like output voltage, load current and THD levels.


of Class Attendance With Grades and Student Characteristics Class Attendance in College : A Meta-Analytic Review of the Relationship


title: Clustered balanced minimum spanning tree for routing and energy reduction in wireless sensor networks

abstract: Wireless sensor networks are thousands micro sensor nodes that energy consumption is one of the most important constraints about them. These nodes have very small dimensions so for producing their energy; we can only use batteries with limited power. We know that the problem of energy reduction is always challenging in these networks. Routing is one of the issues that can reduce energy reduction. We proposed a new method for routing in wireless sensor networks in this paper that attempts to add nodes which have the minimum distances from each other instead of using the traditional routing methods. In our method, we divide nodes to some clusters according to LEACH algorithm then cluster heads create minimum spanning tree according to Prim algorithm. Continuously the tree is balanced according to AVL algorithm. Simulation results show that our proposed algorithm is very well for big area.


title: Autonomous takeoff and landing of a quadcopter

abstract: This paper presents an implementation method for an autonomous take-off and landing with a quadcopter on a stationary platform. The altitude measurement is improved with a Lidar, an inertial unit and a Kalman filter. Equations shown for a take-off and landing path use position and velocity commands. A non linear Backsteping robust control law is used. The entire system is programmed in C/C++ with ROS.


title: A robust music genre classification approach for global and regional music datasets evaluation

abstract: This paper deals with two problems: (1) the selection of a set of music features in order to achieve high genre classification accuracies; (2) the absence of a representative music dataset of regional Brazilian music. In this paper, we propose a set of features to classify genres of music. The features proposed were obtained by a methodical selection of important features used in the literature of Music Information Retrieval (MIR) and Music Emotion Recognition (MER). Besides, we propose a new music dataset called BMD (Brazilian Music Dataset)1, containing 120 songs labeled in 7 musical genres: FoFFo, Rock, Repente, MPB(Musica Popular Brasileira — Brazilian Popular Music), Brega, Sertanejo and Disco. An important characteristic of this new dataset compared with others, is the presence of three popular genres in Brazil Northeast region: Repente, Brega and a characteristic genre similar to MPB, which we also call as MPB. We evaluated our proposed features on both datasets: GTZAN and BMD. The proposed approach achieved average accuracy (after 30 runs of 5-fold-cross-validations) of 79.7% for GTZAN and 86.11% for the BMD. Another important contribution of this work is random repetition of cross-validation executions. Most of the papers performs only a single n-fold cross-validation. We criticize that practice and propose, at least, 30 random executions to compute the average accuracy.


title: TransmiR: a transcription factor–microRNA regulation database

abstract: MicroRNAs (miRNAs) regulate gene expression at the posttranscriptional level and are therefore important cellular components. As is true for protein-coding genes, the transcription of miRNAs is regulated by transcription factors (TFs), an important class of gene regulators that act at the transcriptional level. The correct regulation of miRNAs by TFs is critical, and increasing evidence indicates that aberrant regulation of miRNAs by TFs can cause phenotypic variations and diseases. Therefore, a TF–miRNA regulation database would be helpful for understanding the mechanisms by which TFs regulate miRNAs and understanding their contribution to diseases. In this study, we manually surveyed approximately 5000 reports in the literature and identified 243 TF–miRNA regulatory relationships, which were supported experimentally from 86 publications. We used these data to build a TF–miRNA regulatory database (TransmiR, http://cmbi.bjmu.edu.cn/transmir), which contains 82 TFs and 100 miRNAs with 243 regulatory pairs between TFs and miRNAs. In addition, we included references to the published literature (PubMed ID) information about the organism in which the relationship was found, whether the TFs and miRNAs are involved with tumors, miRNA function annotation and miRNA-associated disease annotation. TransmiR provides a user-friendly interface by which interested parties can easily retrieve TF–miRNA regulatory pairs by searching for either a miRNA or a TF.


Analyzing the Semantic Types of Claims and Premises in an Online Persuasive Forum


title: Application-only call graph construction

abstract: Since call graphs are an essential starting point for all inter-procedural analyses, many tools and frameworks have been developed to generate the call graph of a given program. The majority of these tools focus on generating the call graph of the whole program (i.e., both the application and the libraries that the application depends on). A popular compromise to the excessive cost of building a call graph for the whole program is to ignore all the effects of the library code and any calls the library makes back into the application. This results in potential unsoundness in the generated call graph and therefore in any analysis that uses it. In this paper, we present Cgc, a tool that generates a sound call graph for the application part of a program without analyzing the code of the library.