title: Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-Temporal Path Proposals

abstract: Vehicle re-identification is an important problem and has many applications in video surveillance and intelligent transportation. It gains increasing attention because of the recent advances of person re-identification techniques. However, unlike person re-identification, the visual differences between pairs of vehicle images are usually subtle and even challenging for humans to distinguish. Incorporating additional spatio-temporal information is vital for solving the challenging re-identification task. Existing vehicle re-identification methods ignored or used over-simplified models for the spatio-temporal relations between vehicle images. In this paper, we propose a two-stage framework that incorporates complex spatio-temporal information for effectively regularizing the re-identification results. Given a pair of vehicle images with their spatio-temporal information, a candidate visual-spatio-temporal path is first generated by a chain MRF model with a deeply learned potential function, where each visual-spatio-temporal state corresponds to an actual vehicle image with its spatio-temporal information. A Siamese-CNN+Path-LSTM model takes the candidate path as well as the pairwise queries to generate their similarity score. Extensive experiments and analysis show the effectiveness of our proposed method and individual components.


title: Latent-Space Variational Bayes

abstract: Variational Bayesian expectation-maximization (VBEM), an approximate inference method for probabilistic models based on factorizing over latent variables and model parameters, has been a standard technique for practical Bayesian inference. In this paper, we introduce a more general approximate inference framework for conjugate-exponential family models, which we call latent-space variational Bayes (LSVB). In this approach, we integrate out model parameters in an exact way, leaving only the latent variables. It can be shown that the LSVB approach gives better estimates of the model evidence as well as the distribution over latent variables than the VBEM approach, but in practice, the distribution over latent variables has to be approximated. As a practical implementation, we present a first-order LSVB (FoLSVB) algorithm to approximate this distribution over latent variables. From this approximate distribution, one can estimate the model evidence and the posterior over model parameters. The FoLSVB algorithm is directly comparable to the VBEM algorithm and has the same computational complexity. We discuss how LSVB generalizes the recently proposed collapsed variational methods [20] to general conjugate-exponential families. Examples based on mixtures of Gaussians and mixtures of Bernoullis with synthetic and real-world data sets are used to illustrate some advantages of our method over VBEM.


title: Sentiment Analysis and Summarization of Twitter Data

abstract: Sentiment Analysis (SA) and summarization has recently become the focus of many researchers, because analysis of online text is beneficial and demanded in many different applications. One such application is product-based sentiment summarization of multi-documents with the purpose of informing users about pros and cons of various products. This paper introduces a novel solution to target-oriented (i.e. aspect-based) sentiment summarization and SA of short informal texts with a main focus on Twitter posts known as "tweets". We compare different algorithms and methods for SA polarity detection and sentiment summarization. We show that our hybrid polarity detection system not only outperforms the unigram state-of-the-art baseline, but also could be an advantage over other methods when used as a part of a sentiment summarization system. Additionally, we illustrate that our SA and summarization system exhibits a high performance with various useful functionalities and features.


title: Aspect-augmented Adversarial Networks for Domain Adaptation

abstract: We introduce a neural method for transfer learning between two (source and target) classification tasks or aspects over the same domain. Rather than training on target labels, we use a few keywords pertaining to source and target aspects indicating sentence relevance instead of document class labels. Documents are encoded by learning to embed and softly select relevant sentences in an aspect-dependent manner. A shared classifier is trained on the source encoded documents and labels, and applied to target encoded documents. We ensure transfer through aspect-adversarial training so that encoded documents are, as sets, aspect-invariant. Experimental results demonstrate that our approach outperforms different baselines and model variants on two datasets, yielding an improvement of 27% on a pathology dataset and 5% on a review dataset.


title: Female and male antisocial trajectories: from childhood origins to adult outcomes

abstract: This article reports on the childhood origins and adult outcomes of female versus male antisocial behavior trajectories in the Dunedin longitudinal study. Four antisocial behavior trajectory groups were identified among females and males using general growth mixture modeling and included life-course persistent (LCP), adolescent-onset, childhood-limited, and low trajectory groups. During childhood, both LCP females and males were characterized by social, familial and neurodevelopmental risk factors, whereas those on the adolescent-onset pathway were not. At age 32, women and men on the LCP pathway were engaging in serious violence and experiencing significant mental health, physical health, and economic problems. Females and males on the adolescent-onset pathway were also experiencing difficulties at age 32, although to a lesser extent. Although more males than females followed the LCP trajectory, findings support similarities across gender with respect to developmental trajectories of antisocial behavior and their associated childhood origins and adult consequences. Implications for theory, research, and practice are discussed.


title: Delta-encoder: an effective sample synthesis method for few-shot object recognition

abstract: Learning to classify new categories based on just one or a few examples is a long-standing challenge in modern computer vision. In this work, we proposes a simple yet effective method for few-shot (and one-shot) object recognition. Our approach is based on a modified auto-encoder, denoted Delta-encoder, that learns to synthesize new samples for an unseen category just by seeing few examples from it. The synthesized samples are then used to train a classifier. The proposed approach learns to both extract transferable intra-class deformations, or "deltas", between same-class pairs of training examples, and to apply those deltas to the few provided examples of a novel class (unseen during training) in order to efficiently synthesize samples from that new class. The proposed method improves over the state-of-the-art in one-shot object-recognition and compares favorably in the few-shot case. Upon acceptance code will be made available.


title: Hydrogen peroxide effects on root hydraulic properties and plasma membrane aquaporin regulation in Phaseolus vulgaris

abstract: In the last few years, the role of reactive oxygen species as signaling molecules has emerged, and not only as damage-related roles. Here, we analyzed how root hydraulic properties were modified by different hydrogen peroxide (H2O2) concentrations applied exogenously to the root medium. Two different experimental setups were employed: Phaseolus vulgaris plants growing in hydroponic or in potted soils. In both experimental setups, we found an increase of root hydraulic conductance (L) in response to H2O2 application for the first time. Twenty millimolar was the threshold concentration of H2O2 for observing an effect on L in the soil experiment, while in the hydroponic experiment, a positive effect on L was observed at 0.25 mM H2O2. In the hydroponic experiment, a correlation between increased L and plasma membrane aquaporin amount and their root localization was observed. These findings provide new insights to study how several environmental factors modify L.


Parallel database systems: the future of high performance database systems


title: Network Dissection: Quantifying Interpretability of Deep Visual Representations

abstract: We propose a general framework called Network Dissection for quantifying the interpretability of latent representations of CNNs by evaluating the alignment between individual hidden units and a set of semantic concepts. Given any CNN model, the proposed method draws on a broad data set of visual concepts to score the semantics of hidden units at each intermediate convolutional layer. The units with semantics are given labels across a range of objects, parts, scenes, textures, materials, and colors. We use the proposed method to test the hypothesis that interpretability of units is equivalent to random linear combinations of units, then we apply our method to compare the latent representations of various networks when trained to solve different supervised and self-supervised training tasks. We further analyze the effect of training iterations, compare networks trained with different initializations, examine the impact of network depth and width, and measure the effect of dropout and batch normalization on the interpretability of deep visual representations. We demonstrate that the proposed method can shed light on characteristics of CNN models and training methods that go beyond measurements of their discriminative power.


title: Learning Like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images

abstract: In this paper, we address the task of learning novel visual concepts, and their interactions with other concepts, from a few images with sentence descriptions. Using linguistic context and visual features, our method is able to efficiently hypothesize the semantic meaning of new words and add them to its word dictionary so that they can be used to describe images which contain these novel concepts. Our method has an image captioning module based on [38] with several improvements. In particular, we propose a transposed weight sharing scheme, which not only improves performance on image captioning, but also makes the model more suitable for the novel concept learning task. We propose methods to prevent overfitting the new concepts. In addition, three novel concept datasets are constructed for this new task, and are publicly available on the project page. In the experiments, we show that our method effectively learns novel visual concepts from a few examples without disturbing the previously learned concepts. The project page is: www.stat.ucla.edu/junhua. mao/projects/child_learning.html.


title: A Spatial Mapping Algorithm with Applications in Deep Learning-Based Structure Classification

abstract: Convolutional Neural Network (CNN)-based machine learning systems have made breakthroughs in feature extraction and image recognition tasks in two dimensions (2D). Although there is significant ongoing work to apply CNN technology to domains involving complex 3D data, the success of such efforts has been constrained, in part, by limitations in data representation techniques. Most current approaches rely upon low-resolution 3D models, strategic limitation of scope in the 3D space, or the application of lossy projection techniques to allow for the use of 2D CNNs. To address this issue, we present a mapping algorithm that converts 3D structures to 2D and 1D data grids by mapping a traversal of a 3D space-filling curve to the traversal of corresponding 2D and 1D curves. We explore the performance of 2D and 1D CNNs trained on data encoded with our method versus comparable volumetric CNNs operating upon raw 3D data from a popular benchmarking dataset. Our experiments demonstrate that both 2D and 1D representations of 3D data generated via our method preserve a significant proportion of the 3D data's features in forms learnable by CNNs. Furthermore, we demonstrate that our method of encoding 3D data into lower-dimensional representations allows for decreased CNN training time cost, increased original 3D model rendering resolutions, and supports increased numbers of data channels when compared to purely volumetric approaches. This demonstration is accomplished in the context of a structural biology classification task wherein we train 3D, 2D, and 1D CNNs on examples of two homologous branches within the Ras protein family. The essential contribution of this paper is the introduction of a dimensionality-reduction method that may ease the application of powerful deep learning tools to domains characterized by complex structural data.


title: Mining unstructured log files for recurrent fault diagnosis

abstract: Enterprise software systems are large and complex with limited support for automated root-cause analysis. Avoiding system downtime and loss of revenue dictates a fast and efficient root-cause analysis process. Operator practice and academic research have shown that about 80% of failures in such systems have recurrent causes; therefore, significant efficiency gains can be achieved by automating their identification. In this paper, we present a novel approach to modelling features of log files. This model offers a compact representation of log data that can be efficiently extracted from large amounts of monitoring data. We also use decision-tree classifiers to learn and classify symptoms of recurrent faults. This representation enables automated fault matching and, in addition, enables human investigators to understand manifestations of failure easily. Our model does not require any access to application source code, a specification of log messages, or deep application knowledge. We evaluate our proposal using fault-injection experiments against other proposals in the field. First, we show that the features needed for symptom definition can be extracted more efficiently than does related work. Second, we show that these features enable an accurate classification of recurrent faults using only standard machine learning techniques. This enables us to identify accurately up to 78% of the faults in our evaluation data set.


title: Personality Recognition Based on User Generated Content

abstract: Personality recognition has lots of applications, like business recommendation systems, human resource management, author identification and psychological treatment. Traditionally, identifying one's personality traits depends on questionnaire scales. With the development of the Internet, people can express their opinions, emotions, attitudes, etc., on social networks. From the viewpoint of psychology, people's behaviors and decisions are influenced by their personality characteristics, so it's convenient for us to recognize one's personality based on his social network content. In this paper, we build a new personality recognition model based on a standard dataset, i.e., the Facebook update status content. Firstly, we extract the language features via the Linguistic Inquiry and Word Count (LIWC) dictionary tool. Then all the words in the status content are trained by Google news pre-trained word2vec embedding with 300 dimensions. Next, we construct the personality recognition model by using the Convolutional Neural Network (CNN) method with the LIWC features. We conduct five binary classifications on the most widely used personality model, i.e., the big five personality that contains five traits: extroversion, neuroticism, agreeableness, conscientiousness, and openness. Finally, our experimental results demonstrate that the method we present in this paper achieves very good performances, especially on the openness trait.


title: Human Activity Recognition Using Temporal Convolutional Network

abstract: Human activity recognition using wearable sensors is an area of interest for various domains like healthcare, surveillance etc. Various approaches have been used to solve the problem of activity recognition. Recently deep learning methods like RNNs and LSTMs have been used for this task. But these architectures are unable to capture long term dependencies in time series data. In this work, we propose to use the Temporal Convolutional Network architecture for recognizing the activities from the sensor data obtained from a smartphone. Due to the potential of the architecture to take variable length input sequences along with significantly better ability to capture long term dependencies, it performs better than other deep learning methods. The results of the proposed methods shows an improved performance over the existing methods.


Penile prosthesis implant: scientific advances and technological innovations over the last four decades


A neuromarker of sustained attention from whole-brain functional connectivity


title: Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences

abstract: Recurrent Neural Networks (RNNs) have become the state-of-the-art choice for extracting patterns from temporal sequences. Current RNN models are ill suited to process irregularly sampled data triggered by events generated in continuous time by sensors or other neurons. Such data can occur, for example, when the input comes from novel event-driven artificial sensors which generate sparse, asynchronous streams of events or from multiple conventional sensors with different update intervals. In this work, we introduce the Phased LSTM model, which extends the LSTM unit by adding a new time gate. This gate is controlled by a parametrized oscillation with a frequency range which require updates of the memory cell only during a small percentage of the cycle. Even with the sparse updates imposed by the oscillation, the Phased LSTM network achieves faster convergence than regular LSTMs on tasks which require learning of long sequences. The model naturally integrates inputs from sensors of arbitrary sampling rates, thereby opening new areas of investigation for processing asynchronous sensory events that carry timing information. It also greatly improves the performance of LSTMs in standard RNN applications, and does so with an order-of-magnitude fewer computes.


title: A Novel Method for Measuring the Performance of Software Project Managers

abstract: This paper is focused on providing a novel method for measuring the performance of software project managers. It clarifies the fundamental concepts of software project management, knowledge areas, life cycle phases of software project, and performance metrics. It presents some examples of processes and common performance metrics related to knowledge areas of software project management. The researcher extracts an enhanced list of performance metrics using a questionnaire that is passed to 60 experts and specialists in the field of software projects. Their responses are collected and filtered for reaching to effective performance metrics and the importance degree of each one. The researcher adapts Goal Question Metrics method to include an additional step that dedicated to calculate a performance indicator for each knowledge area of software project management. Finally, the new method has been applied on 3 real software projects to measure the performance of their managers. Measuring the performance of software project managers can be helpful in controlling and improving the performance.


title: EMF Exposure Study Concerning mmWave Phased Array in Mobile Devices for 5G Communication

abstract: The electromagnetic field (EMF) exposure to millimeter-wave (mmWave) phased arrays in mobile devices for 5G communication is analyzed in this letter. Unlike the current cellular band, the EMF exposure in the mmWave band (10–200 GHz) is evaluated by the free-space power density instead of the specific absorption rate. However, current regulations have not been well defined for the mobile device application. In this letter, we present the power density property of phased arrays in mobile devices at 15 and 28 GHz. Uniform linear patch arrays are used, and different array configurations are compared. Suggestions for the power density evaluation are also provided.


title: Current Status of the Scientific Study of the Personality Disorders: an Overview of Epidemiological, Longitudinal, Experimental Psychopathology, and Neurobehavioral Perspectives:

abstract: Research on the nature and development of personality disorders has grown immensely over the past thirty years. A selective summary overview is given of the current status of the scientific study of the personality disorders from several perspectives, including the epidemiological, longitudinal, experimental psychopathology, and neurobehavioral perspectives. From this research, we now know that approximately 10 percent of the general population suffer from a diagnosable personality disorder. Moreover, contrary to nearly a century of theory and clinical pedagogy, modern longitudinal studies clearly suggest that personality disorders decrease in severity over time. The mechanisms by which this change occurs are not understood at present, though it is not likely that change in underlying normal personality systems drives the change in personality disorder. The methods of the experimental psychopathology laboratory, including neuroimaging approaches, are being brought to bear on the nature of personality diso...


title: Activity Theory and System Design: A View from the Trenches

abstract: An activity theory model and a mediating artifacts hierarchy were employed to help identify the needs for tools for customer support engineers who documented solutions to customer problems, a i>knowledge authoring activity. This activity also involves customer support engineers who assist Hewlett-Packard software product users. The particular tools to be designed were knowledge-authoring tools embedded in the customer support tracking application suite, SupportTracker. The research analyzed the role of tensions between the elements of Engestrom's activity theory model. The research also explored the benefits of specific interpretations of Engestrom's refinement of Wartofsky's mediating artifacts hierarchy. The hierarchy contributed to the identification of desired characteristics of mediating artifacts, particularly tools. The findings included an interpretation of the ``where-to'' artifact concept as supporting an understanding of the entire activity system as an evolving entity. Specific interventions were used to achieve a positive impact on the evolution of the activity system.


Attentive Language Models


title: A vision based traffic accident detection method using extreme learning machine

abstract: Over the past years, automatic traffic accident detection (ATAD) based on video has become one of the most promising applications in intelligent transportation and is playing a more and more important role in ensuring travel safety. This paper proposes a classifier-based supervised method by viewing the last seconds before motor vehicle collisions as the detection target. In our method, we devise a novel algorithm called OF-SIFT as the low-level feature. Deriving from the optical flow and Scale Invariant Feature Transform (SIFT), it is designed to extract local motion information from the temporal domain rather than gradient-based local appearance from the spatial domain. The purpose of OF-SIFT is to generate a feature that can capture sufficient and distinctive dynamic motion information for motion detection without using the static state information of moving objects. Further, in order to develop a more compact image representation without considering the explicit vehicle geometry shape, we use the idea of Bag of Feature (BOF) model with spatial information to encode features. Finally, an extreme learning machine (ELM) classifier is introduced as the basic classifier owing to its excellent and fast generalization. Experiments using real-world data have shown that the proposed method has achieved good performance in handling ordinary video scenes.


title: An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis

abstract: A hyperspectral image can be considered as an image cube where the third dimension is the spectral domain represented by hundreds of spectral wavelengths. As a result, a hyperspectral image pixel is actually a column vector with dimension equal to the number of spectral bands and contains valuable spectral information that can be used to account for pixel variability, similarity and discrimination. We present a new hyperspectral measure, the spectral information measure (SIM), to describe spectral variability and two criteria, spectral information divergence and spectral discriminatory probability for spectral similarity and discrimination, respectively. The spectral information measure is an information-theoretic measure which treats each pixel as a random variable using its spectral signature histogram as the desired probability distribution. Spectral information divergence (SID) compares the similarity between two pixels by measuring the probabilistic discrepancy between two corresponding spectral signatures. The spectral discriminatory probability calculates spectral probabilities of a spectral database (library) relative to a pixel to be identified so as to achieve material identification. In order to compare the discriminatory power of one spectral measure relative to another, a criterion is also introduced for performance evaluation, which is based on the power of discriminating one pixel from another relative to a reference pixel. The experimental results demonstrate that the new hyperspectral measure can characterize spectral variability more effectively than the commonly used spectral angle mapper (SAM).


title: Compliant humanoid robot control by the torque transformer

abstract: This paper presents a new control architecture for compliant motion control and safe physical interaction between humanoid robot and human. One of the key technologies in this framework is the Torque Transformer, which enables the implementation of joint torque control on the traditional joint position controlled robots. In this framework, the torque control is accomplished by converting desired joint torque command into instantaneous increments of joint velocity command. Through the transformer, the Operational Space Formulation was applied to account for the dynamics of the system on the current joint position controlled robots. This approach was experimentally implemented on the physical humanoid robot, HONDA ASIMO's upper body control. The ZMP based stable balance controller of ASIMO was integrated to control the lower body of the robot. In this framework, dynamics control by the torque transformer and stable position based balance controller were connected and coordinated together on the current position controlled humanoid robot. The paper presents modeling process of the torque transformer, whole body controller and the results of the implementation which demonstrate the effectiveness of this approach.


title: Excitation Characteristics of Ultrasonic Probe

abstract: the quality of ultrasonic transducer has a direct impact on the performance of ultrasonic flowmeter, the ultrasonic transducer function is converting the electrical signal to mechanical signal and mechanical signal to electrical signal, this process realizes the generation and reception of ultrasonic waves. The electrical signals of the excitation transducer are different, causing the effect of the transmitting transducer to be different. Based on different excitation signals, the transmitting transducer is urged to study the difference of receiving a signal under various excitations. At the same time, a COMSOL simulation method was proposed to simulate different excitation methods for single-channel ultrasound probes, and the difference in sound field distribution during transmission. Experiments verify the rationality of simulation results. The excitation signal is selected as a square wave, sine wave, with different burst number and burst time interval as the signal entry. Discuss the parameters such as the propagation trajectory, propagation time and sound pressure distribution of ultrasonic waves with the variation of excitation mode, and analyze its influence on the received signal.


title: Finding Optimal Skyline Product Combinations under Price Promotion

abstract: Nowadays, with the development of e-commerce, a growing number of customers choose to go shopping online. To find attractive products from online shopping marketplaces, the skyline query is a useful tool which offers more interesting and preferable choices for customers. The skyline query and its variants have been extensively investigated. However, to the best of our knowledge, they have not taken into account the requirements of customers in certain practical application scenarios. Recently, online shopping marketplaces usually hold some price promotion campaigns to attract customers and increase their purchase intention. Considering the requirements of customers in this practical application scenario, we are concerned about product selection under price promotion. We formulate a constrained optimal product combination (COPC) problem. It aims to find out the skyline product combinations which both meet a customer's willingness to pay and bring the maximum discount rate. The COPC problem is significant to offer powerful decision support for customers under price promotion, which is certified by a customer study. To process the COPC problem effectively, we first propose a two list exact (TLE) algorithm. The COPC problem is proven to be NP-hard, and the TLE algorithm is not scalable because it needs to process an exponential number of product combinations. Additionally, we design a lower bound approximate (LBA) algorithm that has a guarantee about the accuracy of the results and an incremental greedy (IG) algorithm that has good performance. The experiment results demonstrate the efficiency and effectiveness of our proposed algorithms.


title: Multi-scale retinex for color image enhancement

abstract: The retinex is a human perception-based image processing algorithm which provides color constancy and dynamic range compression. We have previously reported on a single-scale retinex (SSR) and shown that it can either achieve color/lightness rendition or dynamic range compression, but not both simultaneously. We now present a multi-scale retinex (MSR) which overcomes this limitation for most scenes. Both color rendition and dynamic range compression are successfully accomplished except for some "pathological" scenes that have very strong spectral characteristics in a single band.


title: Historical Development of Research Methods in the Information Systems Discipline

abstract: In this article, we report on an investigation that integrated the results from twenty meta-studies on research methods as identified by a thorough literature review. By conducting this investigation, we seek to reconstruct the historical development of research methods in the Information Systems (IS) discipline. Major results of the investigation are: Only the classical empirical methods (survey, case study, laboratory experiment, and field experiment) have been the subject of intensive discussion. Survey, case study, and laboratory experiment demonstrate an upward tendency in their historical development during the past forty years (1968-2006), whereas the field experiment does not. The investigation reveals an average adoption rate of 24 percent for the survey, 13 percent for the case study, 10 percent for the laboratory experiment, and 3 percent for the field experiment. Finally, we have not observed radical methodological changes in the IS discipline. Key findings and their implications for the future development of the IS discipline are discussed.


title: Local Binary Convolutional Neural Networks

abstract: We propose local binary convolution (LBC), an efficient alternative to convolutional layers in standard convolutional neural networks (CNN). The design principles of LBC are motivated by local binary patterns (LBP). The LBC layer comprises of a set of fixed sparse pre-defined binary convolutional filters that are not updated during the training process, a non-linear activation function and a set of learnable linear weights. The linear weights combine the activated filter responses to approximate the corresponding activated filter responses of a standard convolutional layer. The LBC layer affords significant parameter savings, 9x to 169x in the number of learnable parameters compared to a standard convolutional layer. Furthermore, the sparse and binary nature of the weights also results in up to 9x to 169x savings in model size compared to a standard convolutional layer. We demonstrate both theoretically and experimentally that our local binary convolution layer is a good approximation of a standard convolutional layer. Empirically, CNNs with LBC layers, called local binary convolutional neural networks (LBCNN), achieves performance parity with regular CNNs on a range of visual datasets (MNIST, SVHN, CIFAR-10, and ImageNet) while enjoying significant computational savings.


title: RASCAL: A Domain Specific Language for Source Code Analysis and Manipulation

abstract: Many automated software engineering tools require tight integration of techniques for source code analysis and manipulation. State-of-the-art tools exist for both, but the domains have remained notoriously separate because different computational paradigms fit each domain best. This impedance mismatch hampers the development of new solutions because the desired functionality and scalability can only be achieved by repeated and ad hoc integration of different techniques. Rascal is a domain-specific language that takes away most of this boilerplate by integrating source code analysis and manipulation at the conceptual, syntactic, semantic and technical level. We give an overview of the language and assess its merits by implementing a complex refactoring.


title: RMAS: Relational Multiagent System for CPS Prototyping and Programming

abstract: This paper proposes RMAS (relational model multiagent system), a database-centric architecture for the realization of multiagent systems suitable for the embodiment of reasoning and control in cyber-physical systems. The RMAS uses a tightly coupled association between an active database management system and a host processing language. A first instance of RMAS is proposed by means of the coupling of the Matlab environment and the SQLite database language. The choice of the database language is guided by the keen attention of RMAS to systems that feature scalability down to the tiny embedded systems of the Internet of Things. An example of a typical holonic manufacturing problem with mechatronics entities is conducted by means of the proposed architecture.


title: Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising

abstract: Real-time advertising allows advertisers to bid for each impression for a visiting user. To optimize specific goals such as maximizing revenue and return on investment (ROI) led by ad placements, advertisers not only need to estimate the relevance between the ads and user's interests, but most importantly require a strategic response with respect to other advertisers bidding in the market. In this paper, we formulate bidding optimization with multi-agent reinforcement learning. To deal with a large number of advertisers, we propose a clustering method and assign each cluster with a strategic bidding agent. A practical Distributed Coordinated Multi-Agent Bidding (DCMAB) has been proposed and implemented to balance the tradeoff between the competition and cooperation among advertisers. The empirical study on our industry-scaled real-world data has demonstrated the effectiveness of our methods. Our results show cluster-based bidding would largely outperform single-agent and bandit approaches, and the coordinated bidding achieves better overall objectives than purely self-interested bidding agents.


title: RCDA: Recoverable Concealed Data Aggregation for Data Integrity in Wireless Sensor Networks

abstract: Recently, several data aggregation schemes based on privacy homomorphism encryption have been proposed and investigated on wireless sensor networks. These data aggregation schemes provide better security compared with traditional aggregation since cluster heads (aggregator) can directly aggregate the ciphertexts without decryption; consequently, transmission overhead is reduced. However, the base station only retrieves the aggregated result, not individual data, which causes two problems. First, the usage of aggregation functions is constrained. For example, the base station cannot retrieve the maximum value of all sensing data if the aggregated result is the summation of sensing data. Second, the base station cannot confirm data integrity and authenticity via attaching message digests or signatures to each sensing sample. In this paper, we attempt to overcome the above two drawbacks. In our design, the base station can recover all sensing data even these data has been aggregated. This property is called “recoverable.” Experiment results demonstrate that the transmission overhead is still reduced even if our approach is recoverable on sensing data. Furthermore, the design has been generalized and adopted on both homogeneous and heterogeneous wireless sensor networks.


title: Detecting and correcting malicious data in VANETs

abstract: In order to meet performance goals, it is widely agreed that vehicular ad hoc networks (VANETs) must rely heavily on node-to-node communication, thus allowing for malicious data traffic. At the same time, the easy access to information afforded by VANETs potentially enables the difficult security goal of data validation. We propose a general approach to evaluating the validity of VANET data. In our approach a node searches for possible explanations for the data it has collected based on the fact that malicious nodes may be present. Explanations that are consistent with the node's model of the VANET are scored and the node accepts the data as dictated by the highest scoring explanations. Our techniques for generating and scoring explanations rely on two assumptions: 1) nodes can tell "at least some" other nodes apart from one another and 2) a parsimony argument accurately reflects adversarial behavior in a VANET. We justify both assumptions and demonstrate our approach on specific VANETs.


title: Using A Central Data Repository For Biometric Authentication In Passport Systems

abstract: Passports and visas are currently undergoing a rapid change due to legislation being passed by authorities such as the US requirement that they should have machine readable biometrics embedded within them. What the governments are trying to accomplish is to positively link individuals to these identity documents. But if an impostor is capable of forging a passport, complete with embedded biometric, the document could still pass the biometric authentication step. To prevent the system from being compromised in this manner, a central repository of biometric templates could be incorporated. Then the system could compare its ‘live’ biometric reading with the remote repository, rather than relying solely on the authenticity of the biometric embedded within the identit y document. Using a central repository raises certain questions. Firstly, how will the system know which repository to query? How can the system be sure the passport is not fraudulent and referring queries to a fake repository? How will the repository know it is communicating with a valid port system and not just wasting time with fraudulent queries performing a denial of service attack? This paper proposes a model for the construction of a passport system that employs a set of na tional repositories for biometric identification that addresses these issues.


title: Collaborative Filtering for Implicit Feedback Datasets

abstract: A common task of recommender systems is to improve customer experience through personalized recommendations based on prior implicit feedback. These systems passively track different sorts of user behavior, such as purchase history, watching habits and browsing activity, in order to model user preferences. Unlike the much more extensively researched explicit feedback, we do not have any direct input from the users regarding their preferences. In particular, we lack substantial evidence on which products consumer dislike. In this work we identify unique properties of implicit feedback datasets. We propose treating the data as indication of positive and negative preference associated with vastly varying confidence levels. This leads to a factor model which is especially tailored for implicit feedback recommenders. We also suggest a scalable optimization procedure, which scales linearly with the data size. The algorithm is used successfully within a recommender system for television shows. It compares favorably with well tuned implementations of other known methods. In addition, we offer a novel way to give explanations to recommendations given by this factor model.


title: Elementary actions on an extended entity-relationship database

abstract: Semantic data models have been widely studied for the conceptual specification of databases. However, most of these data models are restricted to the description of the static structure of a database. They do not provide means to specify the dynamic behaviour of a database.


title: Deep Generative Models for Weakly-Supervised Multi-Label Classification

abstract: In order to train learning models for multi-label classification (MLC), it is typically desirable to have a large amount of fully annotated multi-label data. Since such annotation process is in general costly, we focus on the learning task of weakly-supervised multi-label classification (WS-MLC). In this paper, we tackle WS-MLC by learning deep generative models for describing the collected data. In particular, we introduce a sequential network architecture for constructing our generative model with the ability to approximate observed data posterior distributions. We show that how information of training data with missing labels or unlabeled ones can be exploited, which allows us to learn multi-label classifiers via scalable variational inferences. Empirical studies on various scales of datasets demonstrate the effectiveness of our proposed model, which performs favorably against state-of-the-art MLC algorithms.


title: Novel computed tomography-based tools reliably quantify plant reproductive investment

abstract: The flower is a bisexual reproductive unit where both genders compete for resources. Counting pollen and ovules in flowers is essential to understand how much is invested in each gender. Classical methods to count very numerous pollen grains and ovules are inefficient when pollen grains are tightly aggregated, and when fertilization rates of ovules are unknown. In this study we have therefore developed novel counting techniques based on computed tomography. In order to demonstrate the potential of our methods in very difficult cases, we counted pollen and ovules across inflorescences of deceptive and rewarding species of European orchids, which possess both very large numbers of pollen grains (tightly aggregated) and ovules. Pollen counts did not significantly vary across inflorescences and pollination strategies, whereas deceptive flowers had significantly more ovules than rewarding flowers. The within-inflorescence variance of pollen-to-ovule ratios in rewarding flowers was four times higher than in deceptive flowers, possibly demonstrating differences in the constraints acting on both pollination strategies. We demonstrate the inaccuracies and limitations of previously established methods, and the broad applicability of our new techniques: they allow measurement of reproductive investment without restriction on object number or aggregation, and without specimen destruction.


title: Relationship between phosphorus concentration in soil solution and phosphorus in shoots of barley

abstract: Phosphorus concentration in the soil solution of agricultural soils should be a consensus of the agronomic and environmental aspect. Data from literary sources are inconsistent if the method of soil solution extraction from the soil and the method of phosphorus detection are not indicated. In the present paper a simplified procedure of soil solution extraction is used that is derived from the need of water to attain saturated soil paste. Based on barley cultivation in a plant growth chamber on 72 different soils the relationship between P concentration in simulated soil solution and the response of test plant (spring barley) was evaluated. Three approaches were used to derive an adequate P concentration in soil solution. Based on the diagnostics of P content in barley the following adequate P concentrations in soil solution were derived: 0.23–0.86 ppm P for colorimetry and 0.9–1.75 ppm P for ICP-AES. Using the concept of the boundary line of yield the critical P concentration in soil solutions was 0.8 ppm P for colorimetry and 1.3 ppm P for ICP-AES. The concept of the boundary line of P efficiency index enabled to define P concentrations in soil solution that can be considered as the lower limits of suitability from the agronomic aspect: 0.15 ppm P in simulated soil solution for colorimetry and 0.7 ppm P for ICP-AES.


title: Effect of [111] texture on the perpendicular magnetic anisotropy of Co/Ni multilayers

abstract: [111]fcc oriented [Co(2 A)/Ni(7 A)]20 multilayers were prepared by molecular beam epitaxy at room temperature on epitaxial Au/Ag buffer layers grown on chemically etched Si(111) surfaces. NH4F etching of Si(111) leads to a smaller spread in the 〈111〉 orientation of the Au/Ag buffer layers and the Co/Ni multilayers as compared to a similar sample prepared on HF-etched Si(111). This results in a stronger perpendicular magnetic anisotropy as determined from the magnetic hysteresis loops. Cross-sectional transmission electron microscopy studies show that the magnetic multilayer film is not flat but has significant waviness at both top and bottom surfaces. The observed waviness originates in part from the waviness already present on the surface of the Au buffer layer and appears to be further enhanced by the difference in the surface free energies of Au and the magnetic elements. A flatter Au/Ag buffer layer is essential to further improve the [111] texture of the [Co/Ni] multilayers.


title: Cut-and-Paste Text Summarization

abstract: Automatic text summarization provides a concise summary for a document. In this thesis, we present a cut-and-paste approach to addressing the text generation problem in domain-independent, single-document summarization. ::: We found that professional abstractors often reuse the text in an original document for producing the text in a summary. But rather than simply extracting the original text, as in most existing automatic summarizers, humans often edit the extracted sentences. We call such editing operations “revision operations”. Our summarizer simulates two revision operations that are frequently used by humans: sentence reduction and sentence combination. Sentence reduction removes inessential phrases from sentences and sentence combination merges sentences and phrases together. The sentence reduction algorithm we propose relies on multiple sources of knowledge to decide when it is appropriate to delete a phrase from a sentence, including linguistic knowledge, probabilities trained from corpus examples, and context information. The sentence combination module relies on a set of rules to decide how to combine sentences and phrases and when to combine them. Sentence reduction aims to improve the conciseness of generated summaries and sentence combination aims to improve the coherence of generated summaries. We call this approach “cut-and-paste” since it produces summaries by excerpting and combining sentences and phrases from original documents, unlike the extraction technique which produces summaries by simply extracting sentences or passages. ::: Our work also includes a Hidden Markov Model based sentence decomposition program which analyzes human-written summaries. The decomposition program identifies where the phrases of a summary originate in the original document, producing an aligned corpus of summaries and articles that we use to train and evaluate the summarizer. We also built a large-scale, reusable lexicon by combining multiple, heterogeneous resources. The lexicon contains lexical, syntactic, and semantic knowledge. It can be used in many applications.


title: An efficient abnormal beat detection scheme from ECG signals using neural network and ensemble classifiers

abstract: This paper presents an investigation into the development of an efficient scheme to detect abnormal beat from lead II Electro Cardio Gram (ECG) signals. Firstly, a fast ECG feature extraction algorithm was proposed which could extract the locations, amplitudes waves and interval from lead II ECG signal. We then created 11 customized features based on the outputs of the feature extraction algorithm. Then, we used these 11 features to train an artificial neural network and an ensemble classifier respectively for detecting the abnormal ECG beats. Three manually annotated databases were used for training and testing our system: MIT-BIH Arrhythmia, QT and European ST-T database availed from Physionet databank. The results showed that for an abnormal beat detection, the neural network classifier had an overall accuracy of 98.73% and the ensemble classifier with AdaBoost had 99.40%. Using time domain processing approach, the proposed scheme reduced overall computational complexity as compared to the existing methods with an aim to deploy on the mobile devices in the future to promote early and instant abnormal ECG beat detection.


title: Curcumin nanoparticles: physico-chemical fabrication and its in vitro efficacy against human pathogens

abstract: Curcumin is one of the polyphenols, which has been known for its medicinal use since long time. Curcumin shows poor solubility and low absorption, and therefore, its use as nanoparticles is beneficial due to their greater solubility and absorption. The main aim of the present study was the formation of curcumin nanoparticles (Nano curcu), evaluation of their antibacterial activity against human pathogenic bacteria and formulation of Nano curcu-based cream. We synthesized Nano curcu by sonication method. The synthesis of Nano curcu was assessed for their solubility in water and by UV–visible spectrophotometry. Further, the nanoparticles were characterized by Fourier transform infrared spectroscopy, transmission electron microscopy, nanoparticle tracking and analysis, and zeta potential analysis. In vitro antibacterial activity of Nano curcu was evaluated against Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa. The cream containing Nano curcu was found to be effective against human bacterial pathogens and hence can be used for treatment of bacterial diseases.


mOS : An open middlebox platform with programmable network stacks


title: On Horizontal Decomposition of the Operating System.

abstract: As previous OS abstractions and structures fail to explicitly consider the separation between resource users and providers, the shift toward server-side computing poses serious challenges to OS structures, which is aggravated by the increasing many-core scale and workload diversity. ::: This paper presents the horizontal OS model. We propose a new OS abstraction, subOS, an independent OS instance owning physical resources that can be created, destroyed, and resized swiftly. We horizontally decompose the OS into the supervisor for the resource provider and several subOSes for resource users. The supervisor discovers, monitors, and provisions resources for subOSes, while each subOS independently runs applications. We confine state sharing among subOSes, but allow on-demand state sharing if necessary. ::: We present the first implementation, RainForest, which supports unmodified Linux applications binaries. Our comprehensive evaluations using six benchmark suites quantitatively show RainForest outperforms Linux with three different kernels, LXC, and XEN. The RainForest source code is soon available.


title: Scenario Simulation and the Prediction of Land Use and Land Cover Change in Beijing, China

abstract: Land use and land cover (LULC) models are essential for analyzing LULC change and predicting land use requirements and are valuable for guiding reasonable land use planning and management. However, each LULC model has its own advantages and constraints. In this paper, we explore the characteristics of LULC change and simulate future land use demand by combining a CLUE-S model with a Markov model to deal with some shortcomings of existing LULC models. Using Beijing as a case study, we describe the related driving factors from land-adaptive variables, regional spatial variables and socio-economic variables and then simulate future land use scenarios from 2010 to 2020, which include a development scenario (natural development and rapid development) and protection scenarios (ecological and cultivated land protection). The results indicate good consistency between predicted results and actual land use situations according to a Kappa statistic. The conversion of cultivated land to urban built-up land will form the primary features of LULC change in the future. The prediction for land use demand shows the differences under different scenarios. At higher elevations, the geographical environment limits the expansion of urban built-up land, but the conversion of cultivated land to built-up land in mountainous areas will be more prevalent by 2020; Beijing, however, still faces the most pressure in terms of ecological and cultivated land protection.


Comparison of EEG preprocessing methods to improve the performance of the P300 speller


title: Difference Target Propagation

abstract: Back-propagation has been the workhorse of recent successes of deep learning but it relies on infinitesimal effects (partial derivatives) in order to perform credit assignment. This could become a serious issue as one considers deeper and more non-linear functions, e.g., consider the extreme case of non-linearity where the relation between parameters and cost is actually discrete. Inspired by the biological implausibility of back-propagation, a few approaches have been proposed in the past that could play a similar credit assignment role. In this spirit, we explore a novel approach to credit assignment in deep networks that we call target propagation. The main idea is to compute targets rather than gradients, at each layer. Like gradients, they are propagated backwards. In a way that is related but different from previously proposed proxies for back-propagation which rely on a backwards network with symmetric weights, target propagation relies on auto-encoders at each layer. Unlike back-propagation, it can be applied even when units exchange stochastic bits rather than real numbers. We show that a linear correction for the imperfectness of the auto-encoders, called difference target propagation, is very effective to make target propagation actually work, leading to results comparable to back-propagation for deep networks with discrete and continuous units and denoising auto-encoders and achieving state of the art for stochastic networks.


title: Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique

abstract: The papers in this special section focus on the technology and applications supported by deep learning. Deep learning is a growing trend in general data analysis and has been termed one of the 10 breakthrough technologies of 2013. Deep learning is an improvement of artificial neural networks, consisting of more layers that permit higher levels of abstraction and improved predictions from data. To date, it is emerging as the leading machine-learning tool in the general imaging and computer vision domains. In particular, convolutional neural networks (CNNs) have proven to be powerful tools for a broad range of computer vision tasks. Deep CNNs automatically learn mid-level and high-level abstractions obtained from raw data (e.g., images). Recent results indicate that the generic descriptors extracted from CNNs are extremely effective in object recognition and localization in natural images. Medical image analysis groups across the world are quickly entering the field and applying CNNs and other deep learning methodologies to a wide variety of applications.


title: DeepSketch 2: Deep convolutional neural networks for partial sketch recognition

abstract: Freehand sketches are a simple and powerful tool for communication. They are easily recognized across cultures and suitable for various applications. In this paper, we propose a new approach for partial sketch recognition. This could be used to design applications using real-time sketch recognition. We use deep convolutional neural networks (ConvNets), state-of-the-art in the field of sketch recognition. To the best of our knowledge, it is the first ConvNet for partial sketch classification. Our first aim is to build a ConvNet capable of recognizing partial sketches without compromising the accuracy reached for complete sketch recognition. Therefore, we evaluate different approaches and propose an efficient way for partial sketch recognition. Our second aim is improving complete sketch recognition using information about sketching progression. We obtained a ConvNet that outperforms state-of-the-art results in the TU-Berlin sketch benchmark. We reached an accuracy of 77.69%.1


title: Automatic license plate recognition using extracted features

abstract: Automatic License Plate Recognition (ALPR) systems are employed for detection and recognition of license plate/number plate of vehicles. The performance of existing systems is well below the desired level. In this perspective, there is a definite need to propose a system to overcome the limitations of currently available systems. A new approach is being introduced in this paper for fast and efficient implementation of ALPR system. In this approach, the vertical edge detection algorithm is applied and removes unwanted edges by image normalization technique. The LP region is extracted by incorporating statistical and morphological image processing techniques. For character recognition, the template matching is employed for optical character recognition (OCR). The algorithm is tested on 500 real time images, which are acquired under different illumination conditions and from different scenes. Overall efficiency of the proposed method is 84.8% and the execution time is less than 0.5sec.


Hierarchical Energy Efficient Clustering Algorithm for WSN


The European Taxonomy Syllabus: A multi-lingual, multi-level ontology framework to untangle the web of European legal terminology


title: Impact of Biases in Big Data

abstract: The underlying paradigm of big data-driven machine learning reflects the desire of deriving better conclusions from simply analyzing more data, without the necessity of looking at theory and models. Is having simply more data always helpful? In 1936, The Literary Digest collected 2.3M filled in questionnaires to predict the outcome of that year's US presidential election. The outcome of this big data prediction proved to be entirely wrong, whereas George Gallup only needed 3K handpicked people to make an accurate prediction. Generally, biases occur in machine learning whenever the distributions of training set and test set are different. In this work, we provide a review of different sorts of biases in (big) data sets in machine learning. We provide definitions and discussions of the most commonly appearing biases in machine learning: class imbalance and covariate shift. We also show how these biases can be quantified and corrected. This work is an introductory text for both researchers and practitioners to become more aware of this topic and thus to derive more reliable models for their learning problems.


title: Teacher Consultation and Coaching within Mental Health Practice: Classroom and Child Effects in Urban Elementary Schools

abstract: Children in high poverty urban schools experience mental health difficulties and academic underachievement at elevated rates (Atkins, Hoagwood, Kutash, & Seidman, 2010; Whipple, Evans, Barry, & Maxwell, 2010). Underfunded schools struggle to meet student needs and teachers report difficulties motivating and managing students (Reinke, Stormont, Herman, Puri, & Goel, 2011). Observations in elementary classrooms reveal inconsistent access to effective classrooms in schools with high proportions of economically disadvantaged students (Pianta, Belsky, Houts, Morrison, & NICHD Early Child Care Research Network, 2007). Effective classrooms are defined by the presence of supportive teacher-student interactions (instructional, emotional, and behavioral) which together create a classroom environment conducive to academic and social-emotional development (Hamre & Pianta, 2010). When children with adjustment problems are in effective classrooms, their achievement has been shown to match that of their typically developing peers (Hamre & Pianta, 2005). Promoting effective teaching practices in elementary classrooms in low-income schools may thus benefit the adaptation of all students – those with and without behavioral difficulties. ::: ::: Recent efforts to promote effective teaching through teacher consultation and classroom coaching have been promising. Preschool teachers in high poverty schools who received training, coaching, and consultation improved in their management of behavior and provision of emotional support; and, children increased their self-regulation skills and academic performance beyond the skills and performance of comparison children (Raver et al., 2008; Raver et al., 2011). Similarly, in elementary schools assigned to receive training and coaching as a part of a social-emotional and academic learning program (4Rs; Brown, Jones, LaRusso, & Aber, 2010), intervention teachers demonstrated greater instructional and emotional support compared to control group teachers. ::: ::: The promise of these models is tempered by concerns about sustainability. Across studies, researchers employ external coaches and consultants as primary interventionists (e.g., Jones, Brown, & Aber, 2011; Raver et al., 2008). These interventionists are unfamiliar to teachers at the start of the study and depart when the study ends. Recent scholarship suggests the importance of considering issues of intervention feasibility and sustainability from the start (Aarons, Hurlburt, & Horwitz, 2011; Wandersman et al., 2008). Employing individuals already working in the school to deliver novel interventions may increase their sustainability (Atkins et al., 2008). In addition, to advance the learning and development of children with behavioral difficulties and their classmates, school mental health services may need to be re-conceptualized to include a focus on universal prevention (Nastasi, 2004) and effective classrooms (Cappella, Frazier, Atkins, Schoenwald, & Glisson, 2008). ::: ::: Accordingly, we designed and evaluated a teacher consultation and coaching program – Bridging Mental Health and Education in Urban Schools (BRIDGE; Cappella, Jackson, Wagner, Hamre, & Soule, 2011) – to increase effective classroom interactions and improve the functioning of children with behavioral challenges and their classmates. Embedded within the regular activities of school mental health professionals, BRIDGE represents a step toward the broader goal of enabling school and community mental health resources to support effective classrooms. Rather than focus only on the whole classroom or students with behavioral problems, BRIDGE applies a two-tiered approach to improving classroom interactions for all children (universal) and children with behavioral difficulties (targeted). A randomized trial of the initial effects of this program illuminates the extent to which school mental health staff can support teachers to increase effective teaching and promote the academic, behavioral, and social adaptation of children in urban poor schools.


title: In vivo evidence for post-adolescent brain maturation in frontal and striatal regions

abstract: We spatially and temporally mapped brain maturation between adolescence and young adulthood using a whole-brain, voxel-by-voxel statistical analysis of high-resolution structural magnetic resonance images (MRI). The pattern of brain maturation during these years was distinct from earlier development, and was localized to large regions of dorsal, medial and orbital frontal cortex and lenticular nuclei, with relatively little change in any other location. This spatial and temporal pattern agrees with convergent findings from post-mortem studies of brain development and the continued development over this age range of cognitive functions attributed to frontal structures.


title: Eclectic domain mixing for effective adaptation in action spaces

abstract: Although videos appear to be very high-dimensional in terms of duration × frame-rate × resolution, temporal smoothness constraints ensure that the intrinsic dimensionality for videos is much lower. In this paper, we use this idea for investigating Domain Adaptation (DA) in videos, an area that remains under-explored. An approach that has worked well for the image DA is based on the subspace modeling of the source and target domains, which works under the assumption that the two domains share a latent subspace where the domain shift can be reduced or eliminated. In this paper, first we extend three subspace based image DA techniques for human action recognition and then combine it with our proposed Eclectic Domain Mixing (EDM) approach to improve the effectiveness of the DA. Further, we use discrepancy measures such as Symmetrized KL Divergence and Target Density Around Source for empirical study of the proposed EDM approach. While, this work mainly focuses on Domain Adaptation in videos, for completeness of the study, we comprehensively evaluate our approach using both object and action datasets. In this paper, we have achieved consistent improvements over chosen baselines and obtained some state-of-the-art results for the datasets.


title: Weakly Supervised Affordance Detection

abstract: Localizing functional regions of objects or affordances is an important aspect of scene understanding and relevant for many robotics applications. In this work, we introduce a pixel-wise annotated affordance dataset of 3090 images containing 9916 object instances. Since parts of an object can have multiple affordances, we address this by a convolutional neural network for multilabel affordance segmentation. We also propose an approach to train the network from very few keypoint annotations. Our approach achieves a higher affordance detection accuracy than other weakly supervised methods that also rely on keypoint annotations or image annotations as weak supervision.


Imaging-histologic discordance at sonographically guided percutaneous biopsy of breast lesions


title: Secure integration of IoT and Cloud Computing

abstract: Abstract Mobile Cloud Computing is a new technology which refers to an infrastructure where both data storage and data processing operate outside of the mobile device. Another recent technology is Internet of Things. Internet of Things is a new technology which is growing rapidly in the field of telecommunications. More specifically, IoT related with wireless telecommunications. The main goal of the interaction and cooperation between things and objects which sent through the wireless networks is to fulfill the objective set to them as a combined entity. In addition, there is a rapid development of both technologies, Cloud Computing and Internet of Things, regard the field of wireless communications. In this paper, we present a survey of IoT and Cloud Computing with a focus on the security issues of both technologies. Specifically, we combine the two aforementioned technologies (i.e Cloud Computing and IoT) in order to examine the common features, and in order to discover the benefits of their integration. Concluding, we present the contribution of Cloud Computing to the IoT technology. Thus, it shows how the Cloud Computing technology improves the function of the IoT. Finally, we survey the security challenges of the integration of IoT and Cloud Computing.


title: Vitamin A and Infection: Public Health Implications

abstract: Nitroglycerine inclusion compound in cyclodextrin is obtained by contacting cyclodextrin with nitroglycerine in the presence of water. The inclusion compound is very stable as a quick effective drug for treating angina pectoris and easy in handling.


title: A Framework for Modeling and Assessing Security of the Internet of Things

abstract: Internet of Things (IoT) is enabling innovative applications in various domains. Due to its heterogeneous and wide scale structure, it introduces many new security issues. To address the security problem, we propose a framework for security modeling and assessment of the IoT. The framework helps to construct graphical security models for the IoT. Generally, the framework involves five steps to find attack scenarios, analyze the security of the IoT through well-defined security metrics, and assess the effectiveness of defense strategies. The benefits of the framework are presented via a study of two example IoT networks. Through the analysis results, we show the capabilities of the proposed framework on mitigating impacts of potential attacks and evaluating the security of large-scale networks.


A Context-Aware Vertical Handover Decision Algorithm for Multimode Mobile Terminals and Its Performance


title: Trading is hazardous to your wealth: The common stock investment performance of individual investors

abstract: Using account data for over 60,000 households from a large discount brokerage firm, we analyze the common stock investment performance of individual investors from February 1991 through December 1996. The average household tilts their common stock investment toward high-beta, small, value stocks, and turns over 80 per cent of their portfolio annually. On one hand, the gross returns (before accounting for transaction costs) earned by the average household are unremarkable; the average household earned an annualized geometric mean gross return of 17.7 per cent while the value-weighted market index earned 17.1 per cent. On the other hand, the net returns earned by the average household lag reasonable benchmarks by economically and statistically significant amounts; the average household earned an annualized geometric mean net return of 15.3 per cent. The 20 per cent of households that trade most (which average at least 9.6 per cent turnover per month) earned an annualized geometric mean net return of 10.0 per cent. The poor performance of those households that trade frequently is generally consistent with the implications of recent theoretical models of investor overconfidence. Our central message is that trading is hazardous to your wealth.


title: Fast Digital Image Inpainting

abstract: We present a very simple inpainting algorithm for reconstruction of small missing and damaged portions of images that is two to three orders of magnitude faster than current methods while producing comparable results.


title: Transparent and Efficient CFI Enforcement with Intel Processor Trace

abstract: Current control flow integrity (CFI) enforcement approaches either require instrumenting application executables and even shared libraries, or are unable to defend against sophisticated attacks due to relaxed security policies, or both, many of them also incur high runtime overhead. This paper observes that the main obstacle of providing transparent and strong defense against sophisticated adversaries is the lack of sufficient runtime control flow information. To this end, this paper describes FlowGuard, a lightweight, transparent CFI enforcement approach by a novel reuse of Intel Processor Trace (IPT), a recent hardware feature that efficiently captures the entire runtime control flow. The main challenge is that IPT is designed for offline performance analysis and software debugging such that decoding collected control flow traces is prohibitively slow on the fly. FlowGuard addresses this challenge by reconstructing applications' conservative control flow graphs (CFG) to be compatible with the compressed encoding format of IPT, and labeling the CFG edges with credits in the help of fuzzing-like dynamic training. At runtime, FlowGuard separates fast and slow paths such that the fast path compares the labeled CFGs with the IPT traces for fast filtering, while the slow path decodes necessary IPT traces for strong security. We have implemented and evaluated FlowGuard on a commodity Intel Skylake machine with IPT support. Evaluation results show that FlowGuard is effective in enforcing CFI for several applications, while introducing only small performance overhead. We also show that, with minor hardware extensions, the performance overhead can be further reduced.


title: Multisegment annular dielectric elastomer actuators for soft robots

abstract: This paper presents a novel multisegment annular dielectric elastomer actuator (ADEA) which is an annular configuration with six DE segments distributed circumferentially. Each of the DE segments can be actuated independently, thus various active deformations can be achieved by actuating different combinations of DE segments. When opposite DEs are activated, the ADEA presents a unique ability of active deformation (switching between circles and ellipses) for its body controlled by applied voltage. The active deformations of the actuators with different prestretch ratios of the DE films are analyzed and tested. The fabricated actuator weighs 0.88 g with a diameter of 49.7 mm, and exhibits fast response (within 50 ms), good mechanical flexibility and resilience. By taking full advantages of the actuation characteristics of the ADEAs, three soft robots with different locomotion mechanisms (rolling, creeping, and peristalsis) are developed and experimentally tested. The locomotion mechanisms and control patterns of the three soft robots are described in detail. Experimental results show that the rolling soft robot can roll fast on the ground, and the creeping and peristalsis soft robots have good bionic locomotion performance which can be potential for applications in unstructured terrain and narrow space. It demonstrates that the ADEA can be a useful and promising actuator for soft robots.


title: A hybrid GPU rasterized and ray traced rendering pipeline for real time rendering of per pixel effects

abstract: Rendering in 3D games typically uses rasterization approaches in order to guarantee interactive frame rates, since ray tracing, a superior method for rendering photorealistic images, has greater computational cost. With the advent of massively parallel processors in the form of GPUs, parallelized ray tracing have been investigated as an alternative to rasterization techniques. While many works present parallelization methods for the classical ray tracing algorithm, in order to achieve interactive, or even real time ray tracing rendering, we present a rasterized and ray traced hybrid technique, completely done in GPU. While a deferred render model determines the colors of primary rays, a ray tracing phase compute other effects such as specular reflection and transparency, in order to achieve effects that are not easily obtained with rasterization. We also present a heuristic approach that select a subset of relevant objects to be ray traced, avoiding traversing rays for objects that might not have a significant contribution to the real time experience. This selection is capable of maintaining the real time requirement of games, while offering superior visual effects.


title: A Model of Representational Spaces in Human Cortex

abstract: Current models of the functional architecture of human cortex emphasize areas that capture coarse-scale features of cortical topography but provide no account for population responses that encode information in fine-scale patterns of activity. Here, we present a linear model of shared representational spaces in human cortex that captures fine-scale distinctions among population responses with response-tuning basis functions that are common across brains and models cortical patterns of neural responses with individual-specific topographic basis functions. We derive a common model space for the whole cortex using a new algorithm, searchlight hyperalignment, and complex, dynamic stimuli that provide a broad sampling of visual, auditory, and social percepts. The model aligns representations across brains in occipital, temporal, parietal, and prefrontal cortices, as shown by between-subject multivariate pattern classification and intersubject correlation of representational geometry, indicating that structural principles for shared neural representations apply across widely divergent domains of information. The model provides a rigorous account for individual variability of well-known coarse-scale topographies, such as retinotopy and category selectivity, and goes further to account for fine-scale patterns that are multiplexed with coarse-scale topographies and carry finer distinctions.


title: Gender Classification Based on Geometry Features of Palm Image

abstract: This paper presents a novel gender classification method based on geometry features of palm image which is simple, fast, and easy to handle. This gender classification method based on geometry features comprises two main attributes. The first one is feature extraction by image processing. The other one is classification system with polynomial smooth support vector machine (PSSVM). A total of 180 palm images were collected from 30 persons to verify the validity of the proposed gender classification approach and the results are satisfactory with classification rate over 85%. Experimental results demonstrate that our proposed approach is feasible and effective in gender recognition.


title: Coherence: bridging personal, social and global health

abstract: All nature is a continuum. The endless complexity of life is organized into patterns which repeat themselves—theme and variations—at each level of system. These similarities and differences are proper concerns for science. From the ceaseless streaming of protoplasm to the many-vectored activities of supranational systems, there are continuous flows through living systems as they maintain their highly orga nized steady states. Even more basic to this presentation than the concept of “system” are the concepts of “space,” “time,” “matter,” “energy,” and “information,” because the living systems exist in space and are made of matter and energy organized by information. — James Grier Miller, Living Systems, 1978


title: XIS-mobile: a DSL for mobile applications

abstract: Mobile applications are becoming increasingly more present in our daily life, allowing people to perform several tasks through the use of smartphones or tablets. Despite fostering the innovation, the rapid growth of the mobile market resulted in some fragmentation of the mobile platforms. The existence of different mobile operating systems with different programming languages and tools can be a problem when someone wants to release an application in multiple platforms. Rewriting the application for each platform is usually impracticable either in terms of budget or time, requiring a great effort. Therefore, a solution that could generate cross-platform applications without compromising the quality, would decrease the time to market and increase the number of potential users. This paper presents the XIS-Mobile language, defined as a UML profile in the context of the Sparx Systems Enterprise Architect, and discusses its main challenges and benefits in the context of the cross-platform mobile application development.


title: Amphetamine: Effects on meal patterns and macronutrient selection

abstract: Abstract Catecholaminergic systems, specifically in the region of the lateral perifornical hypothalamus (PFH), have been linked to the inhibition of feeding behavior. The present studies examined the effects of d-amphetamine (AMPH), which is believed to act through the release of endogenous catecholamines (CAs), on spontaneous feeding and appetite regulation in rats. Injection of AMPH directly into the PFH caused a marked suppression of food intake; changes in computer-monitored meal patterns were characterized by an increase in the latency to meal onset and a consequent reduction in meal size and duration. This suggests that hypothalamic AMPH administration may influence primarily the initiation, rather than the termination, of feeding. In other experiments, chronic infusion of AMPH directly into the PFH was shown to suppress 24 hr food intake and body weight gain, indicating the effectiveness of lateral hypothalamic CA stimulation in overriding normal long-term patterns of feeding. The effect of hypothalamic CA stimulation on macronutrient selection was also investigated in groups of rats injected either centrally or peripherally with AMPH, or centrally with the CA agonists, dopamine and epinephrine. Each of these manipulations caused a strong inhibition of protein intake with no effect on carbohydrate and only a mild suppression of fat ingestion after peripheral AMPH. These selective effects of AMPH on feeding patterns and diet choice, provide support for a role of CA innervation to the lateral hypothalamus in the modulation of natural feeding behavior and macronutrient selection.


title: Rate, Timing, and Cooperativity Jointly Determine Cortical Synaptic Plasticity

abstract: Cortical long-term plasticity depends on firing rate, spike timing, and cooperativity among inputs, but how these factors interact during realistic patterns of activity is unknown. Here we monitored plasticity while systematically varying the rate, spike timing, and number of coincident afferents. These experiments demonstrate a novel form of cooperativity operating even when postsynaptic firing is evoked by current injection, and reveal a complex dependence of LTP and LTD on rate and timing. Based on these data, we constructed and tested three quantitative models of cortical plasticity. One of these models, in which spike-timing relationships causing LTP "win" out over those favoring LTD, closely fits the data and accurately predicts the build-up of plasticity during random firing. This provides a quantitative framework for predicting the impact of in vivo firing patterns on synaptic strength.


title: Smoothing noisy data with spline functions

abstract: Smoothing splines are well known to provide nice curves which smooth discrete, noisy data. We obtain a practical, effective method for estimating the optimum amount of smoothing from the data. Derivatives can be estimated from the data by differentiating the resulting (nearly) optimally smoothed spline. We consider the model yi=g(ti)+e~, i= 1, 2 . . . . . n, tie[0 , 1], where geW2 ~'~) = {f: j; f , , .... f(mi~ abs. cont., f(m~ ~2 [0, 1 ] }, and the {el} are random errors with E e i=0, E eie~=a z 6~j. The error variance a 2 may be unknown. As an estimate ofg we take the solution g,, a to the problem: Find f ~ W2 ("~ to minimize 1 1_ ~ (f(t j) y~)2 + 2 S (f(")(u)) 2 du. The function g,, a is a smoothing polynomial n j = l 0 spline of degree 2m-1 . The,parameter 2 controls the tradeoff between the 1 "roughness" of the solution, as measured by S [f(m)(u)]2 du, and the infidelity to 0


title: Adapting non-hierarchical multilabel classification methods for hierarchical multilabel classification. Intelligent Data Analysis

abstract: In most classification problems, a classifier assigns a single class to each instance and the classes form a flat non-hierarchical structure, without superclasses or subclasses. In hierarchical multilabel classification problems, the classes are hierarchically structured, with superclasses and subclasses, and instances can be simultaneously assigned to two or more classes at the same hierarchical level. This article proposes two new hierarchical multilabel classification methods based on the well-known local approach for hierarchical classification. The methods are compared with two global methods and one well-known local binary classification method from the literature. The proposed methods presented promising results in experiments performed with bioinformatics datasets.


title: A Survey on Routing in Anonymous Communication Protocols

abstract: The Internet has undergone dramatic changes in the past 2 decades and now forms a global communication platform that billions of users rely on for their daily activities. While this transformation has brought tremendous benefits to society, it has also created new threats to online privacy, such as omnipotent governmental surveillance. As a result, public interest in systems for anonymous communication has drastically increased. In this work, we survey previous research on designing, developing, and deploying systems for anonymous communication. Our taxonomy and comparative assessment provide important insights about the differences between the existing classes of anonymous communication protocols.


Organizing academic work in higher education: teaching, learning and identities


Tutorial on ”An Algorithm for Computing the Capacity of Arbitrary Discrete Memoryless Channels ” by Suguru Arimoto


title: Anti-spoofing and open GNSS signal authentication with signal authentication sequences

abstract: Global Navigation Satellite System (GNSS) signal authentication is a requirement for a number of applications. GNSS authentication has been proposed with aiding techniques that can be applied to the existing GPS and as a new security function for future GNSS. The paper proposes a concept of a new authentication scheme based on signal authentication sequences that can be integrated in GNSS. The method works on systems that provide an open and encrypted service on the same frequency. The scheme would require minimum impact to the system. The architecture is explained in the different components of ground, space and user segment. A simulation of the architecture has been implemented in Matlab and performances and test results are shown. The paper concludes with suggestions of optimal parameters for an hypothetical implementation, explaining the future research steps.


title: Non-reference audio quality assessment for online live music recordings

abstract: Immensely popular video sharing websites such as YouTube have become the most important sources of music information for Internet users and the most prominent platform for sharing live music. The audio quality of this huge amount of live music recordings, however, varies significantly due to factors such as environmental noise, location, and recording device. However, most video search engines do not take audio quality into consideration when retrieving and ranking results. Given the fact that most users prefer live music videos with better audio quality, we propose the first automatic, non-reference audio quality assessment framework for live music video search online. We first construct two annotated datasets of live music recordings. The first dataset contains 500 human-annotated pieces, and the second contains 2,400 synthetic pieces systematically generated by adding noise effects to clean recordings. Then, we formulate the assessment task as a ranking problem and try to solve it using a learning-based scheme. To validate the effectiveness of our framework, we perform both objective and subjective evaluations. Results show that our framework significantly improves the ranking performance of live music recording retrieval and can prove useful for various real-world music applications.


title: Three-dimensional bin packing problem with variable bin height

abstract: This paper studies a variant of the three-dimensional bin packing problem (3D-BPP), where the bin height can be adjusted to the cartons it packs. The bins and cartons to be packed are assumed rectangular in shape. The cartons are allowed to be rotated into any one of the six positions that keep the carton edges parallel to the bin edges. This greatly increases the difficulty of finding a good solution since the search space expands significantly comparing to the 3D-BPP where the cartons have fixed orientations. A mathematical (mixed integer programming) approach is modified based on [Chen, C. S., Lee, S. M., Shen, Q. S., 1995. An analytical model for the container loading problem. European Journal of Operational Research 80 (1), 68-76] and numerical experiments indicate that the mathematical approach is not suitable for the variable bin height 3D-BPP. A special bin packing algorithm based on packing index is designed to utilize the special problem feature and is used as a building block for a genetic algorithm designed for the 3D-BPP. The paper also investigates the situation where more than one type of bin are used and provides a heuristic for packing a batch of cartons using the genetic algorithm. Numerical experiments show that our proposed method yields quick and satisfactory results when benchmarked against the actual packing practice and the MIP model with the latest version of CPLEX.


title: Improved Round Robin CPU scheduling algorithm: Round Robin, Shortest Job First and priority algorithm coupled to increase throughput and decrease waiting time and turnaround time

abstract: In multitasking operating system, processes don't run simultaneously, but switch very expeditiously. Thus, all the processes share the CPU time. It is the job of the scheduler to select a process from the ready queue and place it into the memory based a particular strategy known as Scheduling Algorithm. There exist many Scheduling Algorithms such as First Come First Serve (FCFS), Shortest Job First (SJF), Round Robin (RR), Priority Scheduling, Multilevel Queue Scheduling (MLQ). This paper proposes a new strategy consolidating three of the existing scheduling algorithms to create a new strategy that reduces the time a process spends in waiting state. The proposed algorithm also reduces the number of context switches to provide a fair, efficient and methodical scheduling algorithm. The algorithm is an extended version of Round Robin Algorithm where each of the processes is given a priority level (low, medium or high) and based on the priority level, the Time Quantum for that process is decided and executed. The throughput of the algorithm is increased by executing processes with smaller burst time or smaller remaining burst time by allocating the process to the CPU as soon as it is ready but not pre-empting the running process.


title: Xilinx FPGA implementation of a pixel processor for object detection applications

abstract: This paper describes an FPGA and distributed RAM architecture for an image pixel processor implementing primary elements of an object detection system. A comparison of the system performance with existing DSP processor-based alternatives is detailed. An implementation using the RC1000-PP FPGA-based development platform and Handel-C hardware programming language is outlined. A system architecture is proposed for an image pixel processor for elements of a machine vision based object detection system.


title: Gender Mainstreaming: Productive Tensions in Theory and Practice

abstract: The articles in this special issue of Social Politics take these debates forward in many significant ways. Most of the articles contributed to and drew from a series of seminars funded by the U.K. Economic and Social Research Council on gender mainstreaming. They address the meaning of gender equality as well as the project of gender mainstreaming (Verloo) engage with diverse inequalities and their intersectionality and their implications for theories of democracy (Squires) consider the implications of the wider economic and political context for the potential of gender mainstreaming to create change (Perrons) address the tension between the agenda-setting potential of the strategy and integration into the mainstream (Lombardo) and investigate the relationship between theory and practice in diverse European settings (Daly). (excerpt)


title: Semantic Degrees for Industrie 4.0 Deciding on the degree of semantic formalization to select appropriate technologies

abstract: Under the context of Industrie 4.0 (I4.0), future production systems provide balanced operations between manufacturing flexibility and efficiency, realized in an autonomous, horizontal, and decentralized item-level production control framework. Structured interoperability via precise formulations on an appropriate degree is crucial to achieve engineering efficiency in the system life cycle. However, selecting the degree of formalization can be challenging, as it crucially depends on the desired common understanding (semantic degree) between multiple parties. In this paper, we categorize different semantic degrees and map a set of technologies in industrial automation to their associated degrees. Furthermore, we created guidelines to assist engineers selecting appropriate semantic degrees in their design. We applied these guidelines on publically available scenarios to examine the validity of the approach, and identified semantic elements over internally developed use cases targeting semantically-enabled plug-and-produce.


title: Event detection in baseball video using superimposed caption recognition

abstract: We have developed a novel system for baseball video event detection and summarization using superimposed caption text detection and recognition. The system detects different types of semantic level events in baseball video including scoring and last pitch of each batter. The system has two components: event detection and event boundary detection. Event detection is realized by change detection and recognition of game stat texts (such as text information showing in score box). Event boundary detection is achieved using our previously developed algorithm, which detects the pitch view as the event beginning and nonactive view as potential endings of the event. One unique contribution of the system is its capability to accurately detect the semantic level events by combining video text recognition with camera view recognition. Another unique feature is the real-time processing speed by taking advantage of compressed-domain approaches in part of the algorithms such as caption detection. To the best of our knowledge, this is the first system achieving accurate detection of multiple types of high-level semantic events in baseball videos.


title: Adaptive control of virtualized resources in utility computing environments

abstract: Data centers are often under-utilized due to over-provisioning as well as time-varying resource demands of typical enterprise applications. One approach to increase resource utilization is to consolidate applications in a shared infrastructure using virtualization. Meeting application-level quality of service (QoS) goals becomes a challenge in a consolidated environment as application resource needs differ. Furthermore, for multi-tier applications, the amount of resources needed to achieve their QoS goals might be different at each tier and may also depend on availability of resources in other tiers. In this paper, we develop an adaptive resource control system that dynamically adjusts the resource shares to individual tiers in order to meet application-level QoS goals while achieving high resource utilization in the data center. Our control system is developed using classical control theory, and we used a black-box system modeling approach to overcome the absence of first principle models for complex enterprise applications and systems. To evaluate our controllers, we built a testbed simulating a virtual data center using Xen virtual machines. We experimented with two multi-tier applications in this virtual data center: a two-tier implementation of RUBiS, an online auction site, and a two-tier Java implementation of TPC-W. Our results indicate that the proposed control system is able to maintain high resource utilization and meets QoS goals in spite of varying resource demands from the applications.


title: Personality Stability in Late Adulthood: A Behavioral Genetic Analysis

abstract: Abstract A sample of 833 twins from the Minnesota Twin Study of Adult Development and Aging completed the Multidimensional Personality Questionnaire (MPQ; Tellegen, 1982) twice, averaging 59.4 (sd=9.7) years of age at first and 64.4 (sd=10.2) years of age at second testing (average retest interval 5.0 years, sd=2.36, range 1.0–10.4 years). Both means and standard deviations of scale scores were extremely stable from first to second testing. In addition, sample participants tended to retain their rank order on the scales (average r=.76 across scales). Bivariate biometric analyses showed that the genetic influences on most of the scale scores were almost perfectly correlated across the two waves (range .95 to 1.00). The nonshared environmental influences were also highly correlated across the two waves (range .53 to .73). Models specifying identical variance components at the two time points and fixing the genetic correlation to 1.00 provided improved fit. The results suggest that the high stability of personality in later adulthood has a strong genetic foundation, supplemented by stability of environmental effects.


title: Rel-grams: A Probabilistic Model of Relations in Text

abstract: We introduce the Rel-grams language model, which is analogous to an n-grams model, but is computed over relations rather than over words. The model encodes the conditional probability of observing a relational tuple R, given that R' was observed in a window of prior relational tuples. We build a database of Rel-grams co-occurence statistics from Re-Verb extractions over 1.8M news wire documents and show that a graphical model based on these statistics is useful for automatically discovering event templates. We make this database freely available and hope it will prove a useful resource for a wide variety of NLP tasks.