title: Adversarial approach to diagnostic quality volumetric image enhancement

abstract: We propose a method based on the generative adversarial networks to obtain diagnostic-quality volumetric images (magnetic resonance imaging (MRI) and computed tomography (CT)) from fast data acquisition protocols and lower radiation dosage respectively. These types of acquisition protocols are beneficial to patients, but often inadequate for diagnostic purposes due to the loss in image quality. This work extends the approach presented in [1] to volumetric medical imaging by using perceptual and clinically meaningful loss function. The proposed loss function consists of adversarial and perceptual loss components. The adversarial loss is trained to differentiate between the estimated and the diagnostic quality image while the perceptual loss incorporates perceptual similarity instead of pixel-wise similitude. The adversarial architecture with the perceptual loss was separately trained on 40 CT and 40 MR images, respectively and subsequently used to enhance low resolution scans to obtain diagnostic quality image data. The proposed framework produced an average peak signal-to-noise ratio (PSNR) of 32.09 ± 0.24 dB and 33.76 ± 0.28 dB for CT and MR images respectively in our experiments. The quality obtained is greater than the minimum discernible PSNR threshold of 31.7 dB of the human visual system, indicating high quality for diagnosis [2].


title: Stabilization of Inverse Perspective Mapping Images based on Robust Vanishing Point Estimation

abstract: In this work, a new inverse perspective mapping (IPM) technique is proposed based on a robust estimation of the vanishing point, which provide bird-view images of the road, so that facilitating the tasks of road modeling and vehicle detection and tracking. This new approach has been design to cope with the instability that cameras mounted on a moving vehicle suffer. The estimation of the vanishing point relies on a novel and efficient feature extraction strategy, which segmentates the lane markings of the images by combining a histogram-based segmentation with temporal and frequency filtering. Then, the vanishing point of each image is stabilized by means of a temporal filtering along the estimates of previous images. In a last step, the IPM image is computed based on the stabilized vanishing point. Tests have been carried out on several long video sequences captured from cameras inside a vehicle being driven along highways and local roads, with different illumination and weather conditions, presence of shadows, occluding vehicles, and slope changes. Results have shown a significant improvement in terms of lane width constancy and parallelism between lane markings over non-stabilized IPM algorithms.


title: AGENCY, SURRENDER, AND GRACE IN PSYCHOANALYSIS

abstract: There is a vital dialectic between agency and surrender in life and in the analytic process. Without an ability to will, one cannot choose one’s actions; one becomes a passive victim of circumstances rather than an agent who can influence one’s own destiny. On the other hand, an exaggerated sense of agency fails to take into account the limits of our ability to control life, and is associated with a type of narcissistic omnipotence that can be linked with an experience of isolation. In this article I explore some of the subtleties of the interplay between willing and surrendering in the analytic process. I also examine the way in which an inability to surrender can impede the patient’s ability to take in what the analyst has to offer. And finally I adapt the concept of grace from theological discourse to highlight a dimension of the analytic process that involves an emergence of the patient’s capacity to make constructive use of the analyst’s interventions.


title: RGBD camera monitoring system for Alzheimer's disease assessment using Recurrent Neural Networks with Parametric Bias action recognition

abstract: Abstract The present paper proposes a computer vision system to diagnose the stage of illness in patients affected by Alzheimer's disease. In the context of Ambient Assisted Living (AAL), the system monitors people in home environment during daily personal care activities. The aim is to evaluate the dementia stage, observing actions listed in the Direct Assessment of Funcional Status (DAFS) index and detecting anomalies during the performance, in order to assign a score explaining if the action is correct or not. In this work brushing teeth and grooming hair by a hairbrush are analysed. The technology consists of the application of a Recurrent Neural Network with Parametric Bias (RNNPB) that is able to learn movements connected with a specific action and recognize human activities by parametric bias that work like mirror neurons. This study has been conducted using Microsoft Kinect to collect data about the actions observed and oversee the user tracking and gesture recognition. Experiments prove that the proposed computer vision system can learn and recognize complex human activities and evaluates DAFS score.


abstract: The Giger and Davidhizar Transcultural Assessment Model was developed in 1988 in response to the need for nursing students in an undergraduate program to assess and provide care for patients that were culturally diverse. The model includes six cultural phenomena: communication, time, space, social organization, environmental control, and biological variations. These provide a framework for patient assessment and from which culturally sensitive care can be designed.


title: A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping

abstract: A Markov-chain Monte Carlo based algorithm is provided to solve the Simultaneous localization and mapping (SLAM) problem with general dynamics and observation model under open-loop control and provided that the map-representation is finite dimensional. To our knowledge this is the first provably consistent yet (close-to) practical solution to this problem. The superiority of our algorithm over alternative SLAM algorithms is demonstrated in a difficult loop closing situation.


title: Appliance classification across multiple high frequency energy datasets

abstract: Non-intrusive load monitoring (NILM) provides several techniques for demand information retrieval to support consumers saving energy usage. Research in NILM often focuses on closed environments, such as single datasets or single households. Disaggregation results are typically not suitable to represent the classification performance under real circumstances due to its data homogeneity of a single dataset. We apply a classification system across four commonly available high frequency energy datasets. The experiments include classification tasks with four different classifiers on 36 spectral and temporal features to perform a cross-, mixed-, and intra-dataset validation. The outcome of this work is a reliable benchmark for appliance recognition in the high frequency domain and its efficiency in smart meters for different use cases and appliance features.


title: Comparative Measurements of Hypoxia in Human Brain Tumors Using Needle Electrodes and EF5 Binding

abstract: Hypoxia is known to be an important prognostic marker in many human cancers. We report the use of two oxygen measurement techniques in human brain tumors and compare these data with semiquantitative histological end points. Oxygenation was measured using the Eppendorf needle electrode and/or EF5 binding in 28 brain tumors. These data were compared with necrosis, mitosis, and endothelial proliferation. In some tumors, absolute EF5 binding was converted to tissue pO2 based on in vitro calibrations. Eppendorf electrode readings could not be used to identify WHO grade 1/2 versus WHO grade 3/4 tumors, they could not differentiate grade 3 versus grade 4 glial-derived neoplasms, nor did they correlate with necrosis or endothelial proliferation scores. EF5 binding increased as the tumor grade increased and was significantly associated with necrosis and endothelial proliferation. There was no statistically significant correlation between the two hypoxia detection techniques, although both methods indicated similar absolute ranges of tissue pO2. There was substantial inter- and intratumoral heterogeneity of EF5 binding in WHO grade 4 glial neoplasms. The majority of cells in glial-derived tumor had levels of hypoxia that were mild to moderate (defined herein as 10% to 0.5% pO2) rather than severe (defined as approximately 0.1% pO2). Immunohistochemical detection of EF5 binding tracks histological parameters in adult brain tumors, with increased binding associated with increasing necrosis and endothelial proliferation. The proportion of moderately to severely hypoxic cells is relatively low, even in the high-grade tumors. Human brain tumors are dominated by oxic to moderately hypoxic cells.


A robust audio-visual speech recognition using audio-visual voice activity detection.


Adaptation of Convolutional Neural Networks for the 2017 DAVIS Challenge on Video Object Segmentation


title: Personalizing Human Video Pose Estimation

abstract: We propose a personalized ConvNet pose estimator that automatically adapts itself to the uniqueness of a person's appearance to improve pose estimation in long videos. We make the following contributions: (i) we show that given a few high-precision pose annotations, e.g. from a generic ConvNet pose estimator, additional annotations can be generated throughout the video using a combination of image-based matching for temporally distant frames, and dense optical flow for temporally local frames; (ii) we develop an occlusion aware self-evaluation model that is able to automatically select the high-quality and reject the erroneous additional annotations; and (iii) we demonstrate that these high-quality annotations can be used to fine-tune a ConvNet pose estimator and thereby personalize it to lock on to key discriminative features of the person's appearance. The outcome is a substantial improvement in the pose estimates for the target video using the personalized ConvNet compared to the original generic ConvNet. Our method outperforms the state of the art (including top ConvNet methods) by a large margin on two standard benchmarks, as well as on a new challenging YouTube video dataset. Furthermore, we show that training from the automatically generated annotations can be used to improve the performance of a generic ConvNet on other benchmarks.


title: Real-Time Traffic-Sign Recognition Using Tree Classifiers

abstract: Traffic-sign recognition (TSR) is an essential component of a driver assistance system (DAS), providing drivers with safety and precaution information. In this paper, we evaluate the performance of k-d trees, random forests, and support vector machines (SVMs) for traffic-sign classification using different-sized histogram-of-oriented-gradient (HOG) descriptors and distance transforms (DTs). We also use the Fisher's criterion and random forests for the feature selection to reduce the memory requirements and enhance the performance. We use the German Traffic Sign Recognition Benchmark (GTSRB) data set containing 43 classes and more than 50 000 images.


title: Clustering and profiling of customers using RFM for customer relationship management recommendations

abstract: The problem faced by the company is how to determine potential customers and apply CRM (Customer Relationship Management) in order to perform the right marketing strategy, so it can bring benefits to the company. This research aims to perform clustering and profiling customer by using the model of Recency Frequency and Monetary (RFM) to provide customer relationship management (CRM) recommendation to middle industrial company. The method used in this study consists of four steps: data mining from transaction history data of customer sales, data mining modeling using RFM with K-Means algorithm and customer classification with desicion tree (J48), determination of customer loyalty level and recommendation of customer relationship management (CRM) on the medium-sized industry. This research produces RFM data mining model for medium industrial companies. In addition, the results of the study provide recommendations of four customer segments and characteristics of each customer to perform customer relationship strategy.


title: Gender Classification: A Convolutional Neural Network Approach

abstract: An approach using a convolutional neural network (CNN) is proposed for real-time gender classification based on facial images. The proposed CNN architecture exhibits a much reduced design complexity when compared with other CNN solutions applied in pattern recognition. The number of processing layers in the CNN is reduced to only four by fusing the convolutional and subsampling layers. Unlike in conventional CNNs, we replace the convolution operation with cross-correlation, hence reducing the computational load. The network is trained using a second-order backpropagation learning algorithm with annealed global learning rates. Performance evaluation of the proposed CNN solution is conducted on two publicly available face databases of SUMS and AT&T. We achieve classification accuracies of 98.75% and 99.38% on the SUMS and AT&T databases, respectively. The neural network is able to process and classify a 32 × 32 pixel face image in less than 0.27 ms, which corresponds to a very high throughput of over 3700 images per second. Training converges within less than 20 epochs. These results correspond to a superior classification performance, verifying that the proposed CNN is an effective real-time solution for gender recognition.


title: Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion

abstract: Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost.


title: Deep Active Learning for Named Entity Recognition

abstract: Deep learning has yielded state-of-the-art performance on many natural language processing tasks including named entity recognition (NER). However, this typically requires large amounts of labeled data. In this work, we demonstrate that the amount of labeled training data can be drastically reduced when deep learning is combined with active learning. While active learning is sample-efficient, it can be computationally expensive since it requires iterative retraining. To speed this up, we introduce a lightweight architecture for NER, viz., the CNN-CNN-LSTM model consisting of convolutional character and word encoders and a long short term memory (LSTM) tag decoder. The model achieves nearly state-of-the-art performance on standard datasets for the task while being computationally much more efficient than best performing models. We carry out incremental active learning, during the training process, and are able to nearly match state-of-the-art performance with just 25\% of the original training data.


title: Utilising artificial intelligence in software defined wireless sensor network

abstract: Software Defined Wireless Sensor Network (SDWSN) is realised by infusing Software Defined Network (SDN) model in Wireless Sensor Network (WSN), Reason for that is to overcome the challenges of WSN. Artificial Intelligence (AI) and machine learning play an important role in our society, give rise to systems that can manage themselves. WSNs have been used in various industrial applications, where reliability and network performance are critical success factors. Many advanced AI techniques can be utilised to improve the performance and reliability of these applications. Investigating the AI algorithms applied to SDN may bring improved network management, security or routing in SDWSN which may result in a more reliable network. We look at machine learning algorithms applied in SDN and discuss the possibility of using these AI in SDWSN to address the WSN challenges and improve its performance and reliability.


title: Big IoT and social networking data for smart cities: Algorithmic improvements on Big Data Analysis in the context of RADICAL city applications

abstract: In this paper we present a SOA (Service Oriented Architecture)-based platform, enabling the retrieval and analysis of big datasets stemming from social networking (SN) sites and Internet of Things (IoT) devices, collected by smart city applications and socially-aware data aggregation services. A large set of city applications in the areas of Participating Urbanism, Augmented Reality and Sound-Mapping throughout participating cities is being applied, resulting into produced sets of millions of user-generated events and online SN reports fed into the RADICAL platform. Moreover, we study the application of data analytics such as sentiment analysis to the combined IoT and SN data saved into an SQL database, further investigating algorithmic and configurations to minimize delays in dataset processing and results retrieval.


title: Link prediction in multiplex online social networks

abstract: Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.


title: Cheetah: Just-in-Time Taint Analysis for Android Apps

abstract: Current static-analysis tools are often long-running, which causes them to be sidelined into nightly build checks. As a result, developers rarely use such tools to detect bugs when writing code, because they disrupt their workflow. In this paper, we present Cheetah, a static taint analysis tool for Android apps that interleaves bug fixing and code development in the Eclipse integrated development environment. Cheetah is based on the novel concept of Just-in-Time static analysis that discovers and reports the most relevant results to the developer fast, and computes the more complex results incrementally later. Unlike traditional batch-style static-analysis tools, Cheetah causes minimal disruption to the developer's workflow. This video demo showcases the main features of Cheetah: https://www.youtube.com/watch?v=i_KQD-GTBdA.


title: NetCo: Reliable Routing With Unreliable Routers

abstract: Software-Defined Networks (SDNs) are typicallydesigned and operated under the assumption that the underlyingrouters (and switches) are trustworthy. Recent incidents, however, suggest that this assumption is questionable. The possibility ofincorrect or even malicious router behavior introduces a widerange of security problems. The problem is exacerbated by thefact that governments and companies do not have the expertisenor budget to build their own trusted high-performance routinghardware. This paper presents NetCo, an approach to build securerouting using insecure routers. NetCo is inspired by the robustcombiner concept known from cryptography, and leveragesredundancy to compile a secure whole from insecure parts. Wepresent the basic design of NetCo, and report on a prototypeimplementation in OpenFlow.


title: A Survey on Chip to System Reverse Engineering

abstract: The reverse engineering (RE) of electronic chips and systems can be used with honest and dishonest intentions. To inhibit RE for those with dishonest intentions (e.g., piracy and counterfeiting), it is important that the community is aware of the state-of-the-art capabilities available to attackers today. In this article, we will be presenting a survey of RE and anti-RE techniques on the chip, board, and system levels. We also highlight the current challenges and limitations of anti-RE and the research needed to overcome them. This survey should be of interest to both governmental and industrial bodies whose critical systems and intellectual property (IP) require protection from foreign enemies and counterfeiters who possess advanced RE capabilities.


Pseudo-contractive mappings and the Leray-Schauder boundary condition


title: Registration Techniques for Using Imperfect and Par tially Calibrated Devices in Planar Multi-Projector Displays

abstract: Multi-projector displays today are automatically registered, both geometrically and photometrically, using cameras. Existing registration techniques assume pre-calibrated projectors and cameras that are devoid of imperfections such as lens distortion. In practice, however, these devices are usually imperfect and uncalibrated. Registration of each of these devices is often more challenging than the multi-projector display registration itself. To make tiled projection-based displays accessible to a layman user we should allow the use of uncalibrated inexpensive devices that are prone to imperfections. In this paper, we make two important advances in this direction. First, we present a new geometric registration technique that can achieve geometric alignment in the presence of severe projector lens distortion using a relatively inexpensive low-resolution camera. This is achieved via a closed-form model that relates the projectors to cameras, in planar multi-projector displays, using rational Bezier patches. This enables us to geometrically calibrate a 3000 times 2500 resolution planar multi-projector display made of 3 times 3 array of nine severely distorted projectors using a low resolution (640 times 480) VGA camera. Second, we present a photometric self-calibration technique for a projector-camera pair. This allows us to photometrically calibrate the same display made of nine projectors using a photometrically uncalibrated camera. To the best of our knowledge, this is the first work that allows geometrically imperfect projectors and photometrically uncalibrated cameras in calibrating multi-projector displays.


title: Sustainability of Social Housing in Asia: A Holistic Multi-Perspective Development Process for Bamboo-Based Construction in the Philippines

abstract: This paper highlights the need for a more inclusive and sustainable development of social housing in rapidly developing countries of Asia, Latin America, and Africa. At the example of the Philippines, a multi-perspective development process for a bamboo-based building system is developed. Sustainability Assessment Criteria are defined through literature review, field observations and interviews with three stakeholder clusters: (1) Builders and users of traditional bamboo houses in the Philippines; (2) Stakeholders involved in using forest products for housing in other countries around the world; and (3) Stakeholders in the field of social housing in the Philippines. Through coding and sorting of data in a qualitative content analysis, 15 sustainability assessment criteria are identified clustered into the dimensions society, ecology, economy, governance, and technology. Guided by the sustainability criteria and four implementation strategies: (A) Research about and (B) Implementation of the building technology; (C) Participation and Capacity Building of Stakeholders; and (D) Sustainable Supply Chains, a strategic roadmap was created naming, in total, 28 action items. Through segmentation of the complex problem into these action items, the paper identifies one-dimensional methods leading to measurable, quantitative endpoints. In this way, qualitative stakeholder data is translated into quantitative methods, forming a pathway for a holistic assessment of the building technologies. A mid-point, multi-criteria, or pareto decision-making method comparing the 28 endpoints of the alternative to currently practiced conventional solutions is suggested as subject for further research. This framework paper is a contribution to how sustainable building practices can become more inclusive, incorporating the building stock of low-income dwellers. It bridges the gap between theoretical approach and practical applications of sustainability and underlines the strength of combining multi-dimensional development with stakeholder participation.


title: Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models

abstract: Learning the past tense of English verbs - a seemingly minor aspect of language acquisition - has generated heated debates since 1986, and has become a landmark task for testing the adequacy of cognitive modeling. Several artificial neural networks (ANNs) have been implemented, and a challenge for better symbolic models has been posed. In this paper, we present a general-purpose Symbolic Pattern Associator (SPA) based upon the decision-tree learning algorithm ID3. We conduct extensive head-to-head comparisons on the generalization ability between ANN models and the SPA under different representations. We conclude that the SPA generalizes the past tense of unseen verbs better than ANN models by a wide margin, and we offer insights as to why this should be the case. We also discuss a new default strategy for decision-tree learning algorithms.


title: Object tracking and dynamic estimation on evidential grids

abstract: Autonomous driving is one of the most challenging tasks of the automotive industry. As a subtask, the estimation of driveable and non-driveable space is often solved by applying occupancy grids. The information about non-driveable space can be used to improve object tracking. This paper presents an approach for object tracking and modelling in an occupancy grid map. Tracking objects on grid cells yields the advantage of a consistent environmental model on the occupancy grid map. The authors introduce the occupancy grid map as the only information source for the object tracking module. Taking advantage of the Dempster Shafer theory, a dynamic belief of conflicting cells can be estimated. This dynamic belief is then accumulated in a tracked object model. This is a grid based free form object model that uses detached grid cells to model vehicles in urban environments. The authors reduce false positives and initialization time by maintaining a dynamic belief for each object.


title: Graph-Based Decoding for Event Sequencing and Coreference Resolution

abstract: Events in text documents are interrelated in complex ways. In this paper, we study two types of relation: Event Coreference and Event Sequencing. We show that the popular tree-like decoding structure for automated Event Coreference is not suitable for Event Sequencing. To this end, we propose a graph-based decoding algorithm that is applicable to both tasks. The new decoding algorithm supports flexible feature sets for both tasks. Empirically, our event coreference system has achieved state-of-the-art performance on the TAC-KBP 2015 event coreference task and our event sequencing system beats a strong temporal-based, oracle-informed baseline. We discuss the challenges of studying these event relations.


title: OntoQA: Metric-based ontology quality analysis

abstract: As the Semantic Web gains importance for sharing knowledge on the Internet this has lead to the development and publishing of many ontologies in different domains. When trying to reuse existing ontologies into their applications, users are faced with the problem of determining if an ontology is suitable for their needs. In this paper, we introduce OntoQA, an approach that analyzes ontology schemas and their populations (i.e. knowledgebases) and describes them through a well defined set of metrics. These metrics can highlight key characteristics of an ontology schema as well as its population and enable users to make an informed decision quickly. We present an evaluation of several ontologies using these metrics to demonstrate their applicability.


title: Mapping CMMI Project Management Process Areas to SCRUM Practices

abstract: Over the past years, the capability maturity model (CMM) and capability maturity model integration (CMMI) have been broadly used for assessing organizational maturity and process capability throughout the world. However, the rapid pace of change in information technology has caused increasing frustration to the heavyweight plans, specifications, and other documentation imposed by contractual inertia and maturity model compliance criteria. In light of that, agile methodologies have been adopted to tackle this challenge. The aim of our paper is to present mapping between CMMI and one of these methodologies, Scrum. It shows how Scrum addresses the Project Management Process Areas of CMMI. This is useful for organizations that have their plan-driven process based on the CMMI model and are planning to improve its processes toward agility or to help organizations to define a new project management framework based on both CMMI and Scrum practices.


title: Topics in dynamic research communities: An exploratory study for the field of information retrieval

abstract: Research topics and research communities are not disconnected from each other: communities and topics are interwoven and co-evolving. Yet, scientometric evaluations of topics and communities have been conducted independently and synchronically, with researchers often relying on homogeneous unit of analysis, such as authors, journals, institutions, or topics. Therefore, new methods are warranted that examine the dynamic relationship between topics and communities. This paper examines how research topics are mixed and matched in evolving research communities by using a hybrid approach which integrates both topic identification and community detection techniques. Using a data set on information retrieval (IR) publications, two layers of enriched information are constructed and contrasted: one is the communities detected through the topology of coauthorship network and the other is the topics of the communities detected through the topic model. We find evidence to support the assumption that IR communities and topics are interwoven and co-evolving, and topics can be used to understand the dynamics of community structures. We recommend the use of the hybrid approach to study the dynamic interactions of topics and communities.


title: DSM-5 ASD Moves Forward into the Past

abstract: The fifth edition of the diagnostic and statistical manual of mental disorders (DSM-5) (APA in diagnostic and statistical manual of mental disorders, Author, Washington, 2013) has decided to merge the subtypes of pervasive developmental disorders into a single category of autism spectrum disorder (ASD) on the assumption that they cannot be reliably differentiated from one another. The purpose of this review is to analyze the basis of this assumption by examining the comparative studies between Asperger’s disorder (AsD) and autistic disorder (AD), and between pervasive developmental disorder not otherwise specified (PDDNOS) and AD. In all, 125 studies compared AsD with AD. Of these, 30 studies concluded that AsD and AD were similar conditions while 95 studies found quantitative and qualitative differences between them. Likewise, 37 studies compared PDDNOS with AD. Nine of these concluded that PDDNOS did not differ significantly from AD while 28 reported quantitative and qualitative differences between them. Taken together, these findings do not support the conceptualization of AD, AsD and PDDNOS as a single category of ASD. Irrespective of the changes proposed by the DSM-5, future research and clinical practice will continue to find ways to meaningfully subtype the ASD.


title: Privacy With Estimation Guarantees

abstract: We study the central problem in data privacy: how to share data with an analyst while providing both privacy and utility guarantees to the user that owns the data. In this setting, we present an estimation-theoretic analysis of the privacy-utility trade-off (PUT). Here, an analyst is allowed to reconstruct (in a mean-squared error sense) certain functions of the data (utility), while other private functions should not be reconstructed with distortion below a certain threshold (privacy). We demonstrate how chi-square information captures the fundamental PUT in this case and provide bounds for the best PUT. We propose a convex program to compute privacy-assuring mappings when the functions to be disclosed and hidden are known a priori and the data distribution is known. We derive lower bounds on the minimum mean-squared error of estimating a target function from the disclosed data and evaluate the robustness of our approach when an empirical distribution is used to compute the privacy-assuring mappings instead of the true data distribution. We illustrate the proposed approach through two numerical experiments.


title: Superposition coding for side-information channels

abstract: We present simple, practical codes designed for the binary and Gaussian dirty-paper channels. We show that the dirty-paper decoding problem can be transformed into an equivalent multiple-access decoding problem, for which we apply superposition coding. Our concept is a generalization of the nested lattices approach of Zamir, Shamai, and Erez. In a theoretical setting, our constructions are capable of achieving capacity using random component codes and maximum-likelihood decoding. We also present practical implementations of the constructions, and simulation results for both dirty-paper channels. Our results for the Gaussian dirty-paper channel are on par with the best known results for nested lattices. We discuss the binary dirty- tape channel, for which we present a simple, effective coding technique. Finally, we propose a framework for extending our approach to general Gel'fand-Pinsker channels.


title: A symbolic representation of time series, with implications for streaming algorithms

abstract: The parallel explosions of interest in streaming data, and data mining of time series have had surprisingly little intersection. This is in spite of the fact that time series data are typically streaming data. The main reason for this apparent paradox is the fact that the vast majority of work on streaming data explicitly assumes that the data is discrete, whereas the vast majority of time series data is real valued.Many researchers have also considered transforming real valued time series into symbolic representations, nothing that such representations would potentially allow researchers to avail of the wealth of data structures and algorithms from the text processing and bioinformatics communities, in addition to allowing formerly "batch-only" problems to be tackled by the streaming community. While many symbolic representations of time series have been introduced over the past decades, they all suffer from three fatal flaws. Firstly, the dimensionality of the symbolic representation is the same as the original data, and virtually all data mining algorithms scale poorly with dimensionality. Secondly, although distance measures can be defined on the symbolic approaches, these distance measures have little correlation with distance measures defined on the original time series. Finally, most of these symbolic approaches require one to have access to all the data, before creating the symbolic representation. This last feature explicitly thwarts efforts to use the representations with streaming algorithms.In this work we introduce a new symbolic representation of time series. Our representation is unique in that it allows dimensionality/numerosity reduction, and it also allows distance measures to be defined on the symbolic approach that lower bound corresponding distance measures defined on the original series. As we shall demonstrate, this latter feature is particularly exciting because it allows one to run certain data mining algorithms on the efficiently manipulated symbolic representation, while producing identical results to the algorithms that operate on the original data. Finally, our representation allows the real valued data to be converted in a streaming fashion, with only an infinitesimal time and space overhead.We will demonstrate the utility of our representation on the classic data mining tasks of clustering, classification, query by content and anomaly detection.


title: Injecting Logical Background Knowledge into Embeddings for Relation Extraction

abstract: Matrix factorization approaches to relation extraction provide several attractive features: they support distant supervision, handle open schemas, and leverage unlabeled data. Unfortunately, these methods share a shortcoming with all other distantly supervised approaches: they cannot learn to extract target relations without existing data in the knowledge base, and likewise, these models are inaccurate for relations with sparse data. Rule-based extractors, on the other hand, can be easily extended to novel relations and improved for existing but inaccurate relations, through first-order formulae that capture auxiliary domain knowledge. However, usually a large set of such formulae is necessary to achieve generalization. In this paper, we introduce a paradigm for learning low-dimensional embeddings of entity-pairs and relations that combine the advantages of matrix factorization with first-order logic domain knowledge. We introduce simple approaches for estimating such embeddings, as well as a novel training algorithm to jointly optimize over factual and first-order logic information. Our results show that this method is able to learn accurate extractors with little or no distant supervision alignments, while at the same time generalizing to textual patterns that do not appear in the formulae.


title: A Communication Means for Totally Locked-in ALS Patients Based on Changes in Cerebral Blood Volume Measured with Near-Infrared Light

abstract: A communication means is presented for patients with amyotrophic lateral sclerosis in totally locked-in state who are completely unable to move any part of the body and have no usual communication means. The method utilizes changes in cerebral blood volume accompanied with changes in brain activity. When a patient is asked a question and the answer to it is ‘yes’, the patient makes his or her brain active. The change in blood volume at the frontal lobe is detected with near-infrared light. The instantaneous amplitude and phase of the change are calculated, and the maximum amplitude and phase change are obtained. The answer ‘yes’ or ‘no’ of the patient is detected using a discriminant analysis with these two quantities as variables. The rate of correct detection is 80% on average.


title: Characteristics Associated with Presence of Depressive Symptoms in Adults with Autism Spectrum Disorder

abstract: Evidence suggests that individuals with autism spectrum disorders (ASD) often exhibit associated psychiatric symptoms, particularly related to depression. The current study investigated whether individual characteristics, specifically, severity of ASD symptoms, level of cognitive ability, and/or presence of other psychiatric disorders, are associated with occurrence of depressive symptoms in adults with ASD. Forty-six adults with ASD were administered a standardized psychiatric history interview. Twenty participants (43%) endorsed depressive symptoms. It was found that individuals with less social impairment, higher cognitive ability, and higher rates of other psychiatric symptoms, were more likely to report depressive symptoms. These characteristics may be vulnerability factors for the development of depression, and should be considered when screening and treating adults with ASD.


title: Dialectometric analysis of language variation in Twitter

abstract: In the last few years, microblogging platforms such as Twitter have given rise to a deluge of textual data that can be used for the analysis of informal communication between millions of individuals. In this work, we propose an information-theoretic approach to geographic language variation using a corpus based on Twitter. We test our models with tens of concepts and their associated keywords detected in Spanish tweets geolocated in Spain. We employ dialectometric measures (cosine similarity and Jensen-Shannon divergence) to quantify the linguistic distance on the lexical level between cells created in a uniform grid over the map. This can be done for a single concept or in the general case taking into account an average of the considered variants. The latter permits an analysis of the dialects that naturally emerge from the data. Interestingly, our results reveal the existence of two dialect macrovarieties. The first group includes a region-specific speech spoken in small towns and rural areas whereas the second cluster encompasses cities that tend to use a more uniform variety. Since the results obtained with the two different metrics qualitatively agree, our work suggests that social media corpora can be efficiently used for dialectometric analyses.


title: A Pyramidal Neural Network For Visual Pattern Recognition

abstract: In this paper, we propose a new neural architecture for classification of visual patterns that is motivated by the two concepts of image pyramids and local receptive fields. The new architecture, called pyramidal neural network (PyraNet), has a hierarchical structure with two types of processing layers: Pyramidal layers and one-dimensional (1-D) layers. In the new network, nonlinear two-dimensional (2-D) neurons are trained to perform both image feature extraction and dimensionality reduction. We present and analyze five training methods for PyraNet [gradient descent (GD), gradient descent with momentum, resilient backpropagation (RPROP), Polak-Ribiere conjugate gradient (CG), and Levenberg-Marquadrt (LM)] and two choices of error functions [mean-square-error (mse) and cross-entropy (CE)]. In this paper, we apply PyraNet to determine gender from a facial image, and compare its performance on the standard facial recognition technology (FERET) database with three classifiers: The convolutional neural network (NN), the k-nearest neighbor (k-NN), and the support vector machine (SVM)


title: Adaptation in CRISPR-Cas Systems.

abstract: Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins constitute an adaptive immune system in prokaryotes. The system preserves memories of prior infections by integrating short segments of foreign DNA, termed spacers, into the CRISPR array in a process termed adaptation. During the past 3 years, significant progress has been made on the genetic requirements and molecular mechanisms of adaptation. Here we review these recent advances, with a focus on the experimental approaches that have been developed, the insights they generated, and a proposed mechanism for self- versus non-self-discrimination during the process of spacer selection. We further describe the regulation of adaptation and the protein players involved in this fascinating process that allows bacteria and archaea to harbor adaptive immunity.


title: Matching Entities Across Online Social Networks

abstract: Online social networks (OSNs), such as Facebook and Twitter, have become an integral part of our daily lives. There are hundreds of OSNs, and each offers particular services and functionalities. Recent studies show that many OSN users create accounts on multiple OSNs, using the same or different personal information. Collecting all the available data on an individual from several OSNs to fuse into a single profile can provide valuable information. In this paper, we introduce novel machine learning based methods for solving entity resolution (ER), a problem for matching user profiles across multiple OSNs. By using extracted features and supervised learning techniques, we developed classifiers which can perform entity matching between two profiles for the following scenarios: (a) matching users across two OSNs; (b) searching for a user by similar name; and (c) de-anonymizing a user's identity. The constructed classifiers were tested using data collected from two popular OSNs, Facebook and Xing. We then evaluated the classifiers' performances using measures such as true and false positive rates, accuracy, and the area under the receiver operator curve (AUC). The classification performance measured by AUC was quite remarkable, with an AUC of up to 0.982 and an accuracy of up to 95.9% in identifying user profiles across two OSNs.


title: Context-Aware Semantic Inpainting

abstract: In recent times, image inpainting has witnessed rapid progress due to the generative adversarial networks (GANs) that are able to synthesize realistic contents. However, most existing GAN-based methods for semantic inpainting apply an auto-encoder architecture with a fully connected layer, which cannot accurately maintain spatial information. In addition, the discriminator in existing GANs struggles to comprehend high-level semantics within the image context and yields semantically consistent content. Existing evaluation criteria are biased toward blurry results and cannot well characterize edge preservation and visual authenticity in the inpainting results. In this paper, we propose an improved GAN to overcome the aforementioned limitations. Our proposed GAN-based framework consists of a fully convolutional design for the generator which helps to better preserve spatial structures and a joint loss function with a revised perceptual loss to capture high-level semantics in the context. Furthermore, we also introduce two novel measures to better assess the quality of image inpainting results. The experimental results demonstrate that our method outperforms the state-of-the-art under a wide range of criteria.


title: Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge

abstract: We present a deep learning framework for computer-aided lung cancer diagnosis. Our multi-stage framework detects nodules in 3D lung CAT scans, determines if each nodule is malignant, and finally assigns a cancer probability based on these results. We discuss the challenges and advantages of our framework. In the Kaggle Data Science Bowl 2017, our framework ranked 41st out of 1972 teams.


title: Applying OLSR routing in FANETs

abstract: MANETs are getting exposure due to their versatile applications in the last few years. New networking paradigms like VANETs and FANETs have evolved by using the concept of MANETs. FANETs provide a distinguished approach to tackle with the situations like emergency, natural disaster, military battle fields, UAVs etc. Due to the high mobility in FANETs nodes and rapid topology change, it is a big challenge for researcher to apply routing in FANETs. Mobility models also play a very important role in optimizing the performance of routing protocol in FANET. The research presented aims to apply OLSR routing protocol in FANETs and study of OLSR under different mobility models to optimize the performance of OLSR in FANETs.


title: Genome-Scale CRISPR-Cas9 Knockout Screening in Human Cells

abstract: The simplicity of programming the CRISPR (clustered regularly interspaced short palindromic repeats)-associated nuclease Cas9 to modify specific genomic loci suggests a new way to interrogate gene function on a genome-wide scale. We show that lentiviral delivery of a genome-scale CRISPR-Cas9 knockout (GeCKO) library targeting 18,080 genes with 64,751 unique guide sequences enables both negative and positive selection screening in human cells. First, we used the GeCKO library to identify genes essential for cell viability in cancer and pluripotent stem cells. Next, in a melanoma model, we screened for genes whose loss is involved in resistance to vemurafenib, a therapeutic RAF inhibitor. Our highest-ranking candidates include previously validated genes NF1 and MED12, as well as novel hits NF2, CUL3, TADA2B, and TADA1. We observe a high level of consistency between independent guide RNAs targeting the same gene and a high rate of hit confirmation, demonstrating the promise of genome-scale screening with Cas9.


Predicting circulatory system deterioration in intensive care unit patients


title: Slack Selection for Unintentional Islanding: Practical Validation in a Benchmark Microgrid

abstract: Upon an intentional or emergency disconnection from the main grid, a microgrid is expected to continue working in islanded mode. Thus, (at least) one resource needs to act as slack and compensate for power variations to keep the power balance, and ensure the security of supply. Although several resources might be eligible to become slack, some are more suitable than others (energy storage systems in particular) depending on the state of both the resources and the grid before the islanding transition. In this paper, we validate a recently proposed method to select in real-time the best slack-candidate using an abstract representation of the internal state of the available resources. The same method can be used to actively switch the slack during islanded operation to accommodate the intrinsic stochastic nature of the microgrid's resources. Our main contribution is the validation of the method in a real-scale microgrid, including a discussion of implementation and deployment aspects. To support our findings, we present extensive experimental results in different operating conditions.


title: A Smart Home Automation Via Facebook Chatbot and Raspberry Pi

abstract: This work demonstrates an experimental implementation of smart home automation using Raspberry Pi, Facebook chatbot and Google Maps APIs. The development process was shortened and additional functionalities like estimated time of arrival, interactive chat and secure communications were incorporated. More importantly, this home automation system is extendable and scalable without needing an architectural redesign and other complications.


title: Human factors in information security: The insider threat - Who can you trust these days?

abstract: This paper examines some of the key issues relating to insider threats to information security and the nature of loyalty and betrayal in the context of organisational, cultural factors and changing economic and social factors. It is recognised that insiders pose security risks due to their legitimate access to facilities and information, knowledge of the organisation and the location of valuable assets. Insiders will know how to achieve the greatest impact whilst leaving little evidence. However, organisations may not have employed effective risk management regimes to deal with the speed and scale of change, for example the rise of outsourcing. Outsourcing can lead to the fragmentation of protection barriers and controls and increase the number of people treated as full time employees. Regional and cultural differences will manifest themselves in differing security threat and risk profiles. At the same time, the recession is causing significant individual (and organisational) uncertainty and may prompt an increase in abnormal behaviour in long-term employees and managers - those traditionally most trusted - including members of the security community. In this environment, how can organisations know who to trust and how to maintain this trust? The paper describes a practitioner's view of the issue and the approaches used by BT to assess and address insider threats and risks. Proactive measures need to be taken to mitigate against insider attacks rather than reactive measures after the event. A key priority is to include a focus on insiders within security risk assessments and compliance regimes. The application of technology alone will not provide solutions. Security controls need to be workable in a variety of environments and designed, implemented and maintained with people's behaviour in mind. Solutions need to be agile and build and maintain trust and secure relationships over time. This requires a focus on human factors, education and awareness and greater attention on the security 'aftercare' of employees and third parties.


title: Neurobiology of suicidal behaviour

abstract: About one million suicides and ten million suicide attempts occur worldwide each year. Suicide is not simply a response to stress, but generally a complication of a psychiatric disorder. A proposed stress–diathesis model is described in clinical and neurobiological terms. Neurobiological correlates of the diathesis for suicidal acts point to the involvement of the serotonergic and noradrenergic systems, and the ventromedial prefrontal cortex. Some treatments seem to reduce suicide risk independently of an effect on the primary psychiatric disorder, perhaps by reducing the diathesis.


title: Accelerated focused crawling through online relevance feedback

abstract: The organization of HTML into a tag tree structure, which is rendered by browsers as roughly rectangular regions with embedded text and HREF links, greatly helps surfers locate and click on links that best satisfy their information need. Can an automatic program emulate this human behavior and thereby learn to predict the relevance of an unseen HREF target page w.r.t. an information need, based on information limited to the HREF source page? Such a capability would be of great interest in focused crawling and resource discovery, because it can fine-tune the priority of unvisited URLs in the crawl frontier, and reduce the number of irrelevant pages which are fetched and discarded.


title: Sexual and Relationship Satisfaction Among Heterosexual Men and Women: The Importance of Desired Frequency of Sex

abstract: Little is known of the extent to which heterosexual couples are satisfied with their current frequency of sex and the degree to which this predicts overall sexual and relationship satisfaction. A population-based survey of 4,290 men and 4,366 women was conducted among Australians aged 16 to 64 years from a range of sociodemographic backgrounds, of whom 3,240 men and 3,304 women were in regular heterosexual relationships. Only 46% of men and 58% of women were satisfied with their current frequency of sex. Dissatisfied men were overwhelmingly likely to desire sex more frequently; among dissatisfied women, only two thirds wanted sex more frequently. Age was a significant factor but only for men, with those aged 35–44 years tending to be least satisfied. Men and women who were dissatisfied with their frequency of sex were also more likely to express overall lower sexual and relationship satisfaction. The authors’ findings not only highlight desired frequency of sex as a major factor in satisfaction, but also reveal important gender and other sociodemographic differences that need to be taken into account by researchers and therapists seeking to understand and improve sexual and relationship satisfaction among heterosexual couples. Other issues such as length of time spent having sex and practices engaged in may also be relevant, particularly for women.


title: Modeling the coupled return-spread high frequency dynamics of large tick assets

abstract: Large tick assets, i.e. assets where one tick movement is a significant fraction of the price and bid-ask spread is almost always equal to one tick, display a dynamics in which price changes and spread are strongly coupled. We introduce a Markov-switching modeling approach for price change, where the latent Markov process is the transition between spreads. We then use a finite Markov mixture of logit regressions on past squared returns to describe the dependence of the probability of price changes. The model can thus be seen as a Double Chain Markov Model. We show that the model describes the shape of return distribution at different time aggregations, volatility clustering, and the anomalous decrease of kurtosis of returns. We calibrate our models on Nasdaq stocks and we show that this model reproduces remarkably well the statistical properties of real data.


Detection of Fraud Ranking for Mobile App Using Fuzzy Logic


title: Techniques for analysis, sorting, and dispensing of C. elegans on the COPAS flow-sorting system.

abstract: The COPAS Biosorter is a flow cytometer designed to accommodate large objects the size of Caenorhabditis elegans. This instrumentation brings high-speed automated analysis and sorting to this small model organism. The Biosort system optically analyzes and sorts living multicellular organisms on the basis of fluorescent protein expression patterns and other optical signatures, at rates up to about 100 organisms per second. The Biosort is capable of fluorescently analyzing and sorting multicellular organisms that are many-fold larger than single cells. Animals pass through a laser beam focused to the center of the flow cell. This beam is narrower than the animal so that multiple measurements are made per animal, which means that the organism is optically scanned along its long axis as it flows. Stable laminar flow in the flow cell acts to orientate the animal with the flow stream. Fluorescent locations along the axis of the animal are sequentially excited as the organism flows through the line of focus. The fluorescent properties of commonly used reagents in the research field allow the user to detect fluorescent protein expression, lectin and antibody binding, and autofluorescence. The ability to dispense organisms as they emerge from the flow cell allows for the collection of those organisms that have certain optical properties defined by the researcher. Also, dispensing allows for the precise distribution of specific numbers of animals for analysis that can vary with organism numbers.


title: Time Aware Knowledge Extraction for Microblog Summarization on Twitter

abstract: Topic-based microblog summarization framework applied on Twitter considering both time and meaning of tweets.Definition of Time Aware Knowledge Extraction methodology relying on temporal extension of Fuzzy Formal Concept Analysis.Tweets' content annotated via wikification service that enables to represent a sentence with a set of Wikipedia concepts.Summaries are extracted with different level of detail exploiting the peculiar properties of timed fuzzy lattice.The framework outperforms compared approaches based ensuring good tradeoff between quality, completeness and redundancy. Microblogging services like Twitter and Facebook collect millions of user generated content every moment about trending news, occurring events, and so on. Nevertheless, it is really a nightmare to find information of interest through the huge amount of available posts that are often noisy and redundant. In the era of Big Data, social media analytics services have caught increasing attention from both research and industry. Specifically, the dynamic context of microblogging requires to manage not only meaning of information but also the evolution of knowledge over the timeline. This work defines Time Aware Knowledge Extraction (briefly TAKE) methodology that relies on temporal extension of Fuzzy Formal Concept Analysis. In particular, a microblog summarization algorithm has been defined filtering the concepts organized by TAKE in a time-dependent hierarchy. The algorithm addresses topic-based summarization on Twitter. Besides considering the timing of the concepts, another distinguishing feature of the proposed microblog summarization framework is the possibility to have more or less detailed summary, according to the user's needs, with good levels of quality and completeness as highlighted in the experimental results.


title: Asymmetrical effects of positive and negative events: The mobilization - minimization hypothesis

abstract: Negative (adverse or threatening) events evoke strong and rapid physiological, cognitive, emotional, and social responses. This mobilization of the organism is followed by physiological, cognitive, and behavioral responses that damp down, minimize, and even erase the impact of that event. This pattern of mobilization-minimization appears to be greater for negative events than for neutral or positive events. Theoretical accounts of this response pattern are reviewed


title: Throughput and Packet Error Rate Analysis Using Routing Algorithm

abstract: In now a day's explosion of digital world results in faster, easily accessible, most efficient, reliable communication over the network communication to exchange the information. Considering this advantages, this paper introduced the network on which shortest path algorithm implemented to communicate some nodes on network in shortest, efficient path with efficient throughput mechanism. The nodes carry the data in the form of packets which consists packet header with IP addresses of source and destination and data payload with user information. In the simulation, it estimates the throughput, arrival and blocking rate with packet error rate by implementing ARQ protocol with cooperative diversity system.


title: Exact sampling with integer linear programs and random perturbations

abstract: We consider the problem of sampling from a discrete probability distribution specified by a graphical model. Exact samples can, in principle, be obtained by computing the mode of the original model perturbed with an exponentially many i.i.d. random variables. We propose a novel algorithm that views this as a combinatorial optimization problem and searches for the extreme state using a standard integer linear programming (ILP) solver, appropriately extended to account for the random perturbation. Our technique, GumbelMIP, leverages linear programming (LP) relaxations to evaluate the quality of samples and prune large portions of the search space, and can thus scale to large tree-width models beyond the reach of current exact inference methods. Further, when the optimization problem is not solved to optimality, our method yields a novel approximate sampling technique. We empirically demonstrate that our approach parallelizes well, our exact sampler scales better than alternative approaches, and our approximate sampler yields better quality samples than a Gibbs sampler and a low-dimensional perturbation method.


title: Group saliency propagation for large scale and quick image co-segmentation

abstract: Most of the existing co-segmentation methods are usually complex, and require pre-grouping of images, fine-tuning a few parameters and initial segmentation masks etc. These limitations become serious concerns for their application on large scale datasets. In this paper, Group Saliency Propagation (GSP) model is proposed where a single group saliency map is developed, which can be propagated to segment the entire group. In addition, it is also shown how a pool of these group saliency maps can help in quickly segmenting new input images. Experiments demonstrate that the proposed method can achieve competitive performance on several benchmark co-segmentation datasets including ImageNet, with the added advantage of speed up.


title: The computational modeling of analogy-making

abstract: Abstract Our ability to see a particular object or situation in one context as being ‘the same as' another object or situation in another context is the essence of analogy-making. It encompasses our ability to explain new concepts in terms of already-familiar ones, to emphasize particular aspects of situations, to generalize, to characterize, to explain or describe new phenomena, to serve as a basis for how to act in unfamiliar surroundings, and to understand many types of humor. Within this framework, the importance of analogy-making in modeling cognition becomes clear.


title: Never too old: engaging retired people inventing the future with MaKey MaKey

abstract: Within HCI, aging is often viewed in terms of designing assistive technologies to improve the lives of older people, such as those who are suffering from frailty or memory loss. Our research adopts a very different approach, reframing the relationship in terms of wisdom, creativity and invention. We ran a series of workshops where groups of retirees, aged between early 60s and late 80s, used the MaKey MaKey inventor's toolkit. We asked them to think about inventing the future and suggest ideas for new technologies. Our findings showed that they not only rose to the challenge but also mastered the technology, collaborated intensely together while using it and freely and at length discussed their own, their family's and others' relationship with technology. We discuss the value of empowering people in this way and consider what else could be invented to enable more people to be involved in the design and use of creative technologies.


title: Cool Cloud: A Practical Dynamic Virtual Machine Placement Framework for Energy Aware Data Centers

abstract: With the continuing growth of cloud computing services, power consumption has become one of the most challenging issues in data center environments. With the support of today's virtualization technology, the efficiency and flexibility of data center management can be greatly enhanced, creating great energy saving opportunities. However, effective energy aware design is a non-trivial task, considering the size of the data center, the dynamic fluctuation of workloads and the variation of computing resource requests. In this paper, we propose Cool Cloud: a practical solution for managing the mappings of VMs to physical servers. This framework solves the problem of finding the most energy efficient way (least resource wastage and least power consumption) of placing the VMs considering their resource requirements. Experiment result demonstrates our design can effectively improve data center energy efficiency and scales well to large size data centers. Comparing with industry leading product VMware's Distributed Resource Scheduler (DRS), our design offers better performance in both load balancing and power consumption.


title: Categorization and Segmentation of Intestinal Content Frames for Wireless Capsule Endoscopy

abstract: Wireless capsule endoscopy (WCE) is a device that allows the direct visualization of gastrointestinal tract with minimal discomfort for the patient, but at the price of a large amount of time for screening. In order to reduce this time, several works have proposed to automatically remove all the frames showing intestinal content. These methods label frames as {intestinal content - clear} without discriminating between types of content (with different physiological meaning) or the portion of image covered. In addition, since the presence of intestinal content has been identified as an indicator of intestinal motility, its accurate quantification can show a potential clinical relevance. In this paper, we present a method for the robust detection and segmentation of intestinal content in WCE images, together with its further discrimination between turbid liquid and bubbles. Our proposal is based on a twofold system. First, frames presenting intestinal content are detected by a support vector machine classifier using color and textural information. Second, intestinal content frames are segmented into {turbid, bubbles, and clear} regions. We show a detailed validation using a large dataset. Our system outperforms previous methods and, for the first time, discriminates between turbid from bubbles media.


title: Modeling, sensing and control of manufacturing processes

abstract: This book contains 23 papers. Some of the titles are: Computer-assisted Prediction of Drill-Failure Using in-Process Measurements of Thrust Force; Automated Diagnostics for Robot-Based Assembly Systems; Acoustic Inspection of Flow Paths in Evaporator Cores; and A Group Tool-Replacement Model for Transfer Lines.


title: Cloud-assisted Industrial Internet of Things (IIoT) - Enabled framework for health monitoring

abstract: The promising potential of the emerging Internet of Things (IoT) technologies for interconnected medical devices and sensors has played an important role in the next-generation healthcare industry for quality patient care. Because of the increasing number of elderly and disabled people, there is an urgent need for a real-time health monitoring infrastructure for analyzing patients' healthcare data to avoid preventable deaths. Healthcare Industrial IoT (HealthIIoT) has significant potential for the realization of such monitoring. HealthIIoT is a combination of communication technologies, interconnected apps, Things (devices and sensors), and people that would function together as one smart system to monitor, track, and store patients' healthcare information for ongoing care. This paper presents a HealthIIoT-enabled monitoring framework, where ECG and other healthcare data are collected by mobile devices and sensors and securely sent to the cloud for seamless access by healthcare professionals. Signal enhancement, watermarking, and other related analytics will be used to avoid identity theft or clinical error by healthcare professionals. The suitability of this approach has been validated through both experimental evaluation, and simulation by deploying an IoT-driven ECG-based health monitoring service in the cloud.


title: An Analysis of the t-SNE Algorithm for Data Visualization

abstract: A first line of attack in exploratory data analysis is data visualization, i.e., generating a 2-dimensional representation of data that makes clusters of similar points visually identifiable. Standard Johnson-Lindenstrauss dimensionality reduction does not produce data visualizations. The t-SNE heuristic of van der Maaten and Hinton, which is based on non-convex optimization, has become the de facto standard for visualization in a wide range of applications. This work gives a formal framework for the problem of data visualization - finding a 2-dimensional embedding of clusterable data that correctly separates individual clusters to make them visually identifiable. We then give a rigorous analysis of the performance of t-SNE under a natural, deterministic condition on the"ground-truth"clusters (similar to conditions assumed in earlier analyses of clustering) in the underlying data. These are the first provable guarantees on t-SNE for constructing good data visualizations. We show that our deterministic condition is satisfied by considerably general probabilistic generative models for clusterable data such as mixtures of well-separated log-concave distributions. Finally, we give theoretical evidence that t-SNE provably succeeds in partially recovering cluster structure even when the above deterministic condition is not met.


title: Querying Wikidata: Comparing SPARQL, Relational and Graph Databases

abstract: In this paper, we experimentally compare the efficiency of various database engines for the purposes of querying the Wikidata knowledge-base, which can be conceptualised as a directed edge-labelled graph where edges can be annotated with meta-information called qualifiers. We take two popular SPARQL databases (Virtuoso, Blazegraph), a popular relational database (PostgreSQL), and a popular graph database (Neo4J) for comparison and discuss various options as to how Wikidata can be represented in the models of each engine. We design a set of experiments to test the relative query performance of these representations in the context of their respective engines. We first execute a large set of atomic lookups to establish a baseline performance for each test setting, and subsequently perform experiments on instances of more complex graph patterns based on real-world examples. We conclude with a summary of the strengths and limitations of the engines observed.


The market for 'lemons': quality uncertainty and the market mechanism


title: Effects of national culture on bank risk-taking behavior

abstract: Theory suggests that national culture influences bank risk-taking behavior directly by conditioning the decision-making of human participants. This study uses an international sample of banks from 75 countries and examines the direct effects of national culture on bank risk-taking behavior. We measure national culture with four dimensions – uncertainty avoidance, individualism vs. collectivism, masculinity vs. femininity and power distance – from Hofstede's framework of national culture. We find strong evidence that bank risk-taking is significantly higher in countries which have high individualism, low uncertainty avoidance, and low power distance cultural values. We confirm main results using alternate cultural dimensions from House et al. (2004. Culture, Leadership, and Organizations. Sage)’s framework of national culture, using alternative bank risk-taking proxies, and using instrumental variables analysis for endogeneity issues. This paper adds to our understanding by finding that cultural values lead to bank risk-taking decisions that may deviate in systematic and geographically predictable ways.


title: A 2.5-dB Insertion Loss, DC-60 GHz CMOS SPDT Switch in 45-nm SOI

abstract: This paper presents a single-pole double-throw (SPDT), transmit/receive (T/R) switch operating from DC to 60 GHz. The SPDT switch is based on a series-shunt circuit with broadband input and output matching circuits and is implemented in a partially-depleted, 45-nm silicon-on-insulator (SOI) process. A buried oxide (BOX) layer is demonstrated to minimize substrate coupling. The switch exhibits a measured insertion loss of less than 1.7 dB at 45 GHz and less than 2.5 dB at 60 GHz with an isolation of greater than 25 dB at 45 GHz. To our knowledge, this is the lowest insertion loss demonstrated for an SPDT switch at 60 GHz in a CMOS process. With a control voltage of 1.2 V, the measured P1dB and IIP3 are 7.1 dBm and 18.2 dBm, respectively. The active chip area is 0.18×0.22 mm2.


title: Saw palmetto ethanol extract inhibits adipocyte differentiation

abstract: The fruits of saw palmetto have been used for the treatment of a variety of urinary and reproductive system problems. In this study we investigated whether the fruit extracts affect in vitro adipogenesis. Saw palmetto ethanol extract inhibited the lipid droplet accumulation by induction media in a dose-dependent manner, and it also attenuated the protein expressions of C-EBPα and PPARγ. Phosphorylation of Erk1/2 and Akt1 were also decreased by saw palmetto ethanol extract. This report suggests that saw palmetto extracts selectively affect the adipocyte differentiation through the modulation of several key factors that play a critical role during adipogenesis.


title: Of, for, and by the people: the legal lacuna of synthetic persons

abstract: Conferring legal personhood on purely synthetic entities is a very real legal possibility, one under consideration presently by the European Union. We show here that such legislative action would be morally unnecessary and legally troublesome. While AI legal personhood may have some emotional or economic appeal, so do many superficially desirable hazards against which the law protects us. We review the utility and history of legal fictions of personhood, discussing salient precedents where such fictions resulted in abuse or incoherence. We conclude that difficulties in holding "electronic persons" accountable when they violate the rights of others outweigh the highly precarious moral interests that AI legal personhood might protect.


title: Analysis of classifiers’ robustness to adversarial perturbations

abstract: The goal of this paper is to analyze an intriguing phenomenon recently discovered in deep networks, namely their instability to adversarial perturbations (Szegedy et. al., 2014). We provide a theoretical framework for analyzing the robustness of classifiers to adversarial perturbations, and show fundamental upper bounds on the robustness of classifiers. Specifically, we establish a general upper bound on the robustness of classifiers to adversarial perturbations, and then illustrate the obtained upper bound on the families of linear and quadratic classifiers. In both cases, our upper bound depends on a distinguishability measure that captures the notion of difficulty of the classification task. Our results for both classes imply that in tasks involving small distinguishability, no classifier in the considered set will be robust to adversarial perturbations, even if a good accuracy is achieved. Our theoretical framework moreover suggests that the phenomenon of adversarial instability is due to the low flexibility of classifiers, compared to the difficulty of the classification task (captured by the distinguishability). Moreover, we show the existence of a clear distinction between the robustness of a classifier to random noise and its robustness to adversarial perturbations. Specifically, the former is shown to be larger than the latter by a factor that is proportional to \sqrt{d} (with d being the signal dimension) for linear classifiers. This result gives a theoretical explanation for the discrepancy between the two robustness properties in high dimensional problems, which was empirically observed in the context of neural networks. To the best of our knowledge, our results provide the first theoretical work that addresses the phenomenon of adversarial instability recently observed for deep networks. Our analysis is complemented by experimental results on controlled and real-world data.


title: A fast algorithm for computing longest common subsequences

abstract: Previously published algorithms for finding the longest common subsequence of two sequences of length n have had a best-case running time of O(n 2 ). An algorithm for this problem is presented which has a running time of O((r + n) log n), where r is the total number of ordered pairs of positions at which the two sequences match. Thus in the worst case the algorithm has a running time of O(n 2 log n). However, for those applications where most positions of one sequence match relatively few positions in the other sequence, a running time of O(n log n) can be expected.


title: Constant Fraction Discriminator Involving Automatic Gain Control to Reduce Time Walk

abstract: A constant fraction discriminator (CFD) is a widely used time-pickoff circuit because it ideally produces no time walk for signals with equal rise times. However, a practical CFD produces some time walk because of the dependency of the response time in an involved comparator on the input signal size. In this paper, a CFD combined with an automatic gain control (AGC) circuit at the input stage is proposed to reduce this dependence. The AGC has four step gains that are inversely proportional to the amplitude of their input signal. Accordingly, the AGC reduces the dynamic range of the signal entering the CFD and consequently reduces the dependency of the response time on the input signal, i.e., time walk. For a short resolving time, this gain setting is performed at one time by amplitude comparison. The proposed CFD is evaluated by experiment for input signals with a rise time of 2 ns and a dynamic range of 40 dB, and the time walk is reduced to 277 ps, a decrease of more than 42%.


Communicating in the language of mathematics


title: Walking from thought

abstract: Online analysis and classification of single electroencephalogram (EEG) trials during motor imagery were used for navigation in the virtual environment (VE). The EEG was recorded bipolarly with electrode placement over the hand and foot representation areas. The aim of the study was to demonstrate for the first time that it is possible to move through a virtual street without muscular activity when the participant only imagines feet movements. This is achieved by exploiting a brain-computer interface (BCI) which transforms thought-modulated EEG signals into an output signal that controls events within the VE. The experiments were carried out in an immersive projection environment, commonly referred to as a "Cave" (Cruz-Neira, C., Sandin, D.J., DeFanti, T.A., Surround-screen projection-based virtual reality: the design and implementation of the CAVE. Proceedings of the 20th annual conference on Computer graphics and interactive techniques, ACM Press, 1993, pp. 135-142) where participants were able to move through a virtual street by foot imagery only. Prior to the final experiments in the Cave, the participants underwent an extensive BCI training.


title: Adaptive Patient-Cooperative Control of a Compliant Ankle Rehabilitation Robot (CARR) With Enhanced Training Safety

abstract: This paper proposes a new adaptive patient-cooperative control strategy for improving the effectiveness and safety of robot-assisted ankle rehabilitation. This control strategy has been developed and implemented on a compliant ankle rehabilitation robot (CARR). The CARR is actuated by four Festo Fluidic muscles located to the calf in parallel, has three rotational degrees of freedom. The control scheme consists of a position controller implemented in joint space and a high-level admittance controller in task space. The admittance controller adaptively modifies the predefined trajectory based on real-time ankle measurement, which enhances the training safety of the robot. Experiments were carried out using different modes to validate the proposed control strategy on the CARR. Three training modes include: 1) a passive mode using a joint-space position controller, 2) a patient–robot cooperative mode using a fixed-parameter admittance controller, and 3) a cooperative mode using a variable-parameter admittance controller. Results demonstrate satisfactory trajectory tracking accuracy, even when externally disturbed, with a maximum normalized root mean square deviation less than 5.4%. These experimental findings suggest the potential of this new patient-cooperative control strategy as a safe and engaging control solution for rehabilitation robots.


title: Serverless Computing: Design, Implementation, and Performance

abstract: We present the design of a novel performance-oriented serverless computing platform implemented in. NET, deployed in Microsoft Azure, and utilizing Windows containers as function execution environments. Implementation challenges such as function scaling and container discovery, lifecycle, and reuse are discussed in detail. We propose metrics to evaluate the execution performance of serverless platforms and conduct tests on our prototype as well as AWS Lambda, Azure Functions, Google Cloud Functions, and IBM's deployment of Apache OpenWhisk. Our measurements show the prototype achieving greater throughput than other platforms at most concurrency levels, and we examine the scaling and instance expiration trends in the implementations. Additionally, we discuss the gaps and limitations in our current design, propose possible solutions, and highlight future research.


title: Data Augmentation by Pairing Samples for Images Classification

abstract: Data augmentation is a widely used technique in many machine learning tasks, such as image classification, to virtually enlarge the training dataset size and avoid overfitting. Traditional data augmentation techniques for image classification tasks create new samples from the original training data by, for example, flipping, distorting, adding a small amount of noise to, or cropping a patch from an original image. In this paper, we introduce a simple but surprisingly effective data augmentation technique for image classification tasks. With our technique, named SamplePairing, we synthesize a new sample from one image by overlaying another image randomly chosen from the training data (i.e., taking an average of two images for each pixel). By using two images randomly selected from the training set, we can generate N^2 new samples from N training samples. This simple data augmentation technique significantly improved classification accuracy for all the tested datasets; for example, the top-1 error rate was reduced from 33.5% to 29.0% for the ILSVRC 2012 dataset with GoogLeNet and from 8.22% to 6.93% in the CIFAR-10 dataset. We also show that our SamplePairing technique largely improved accuracy when the number of samples in the training set was very small. Therefore, our technique is more valuable for tasks with a limited amount of training data, such as medical imaging tasks.


title: Digitally Controlled Low-Dropout Regulator with Fast-Transient and Autotuning Algorithms

abstract: A digitally controlled low-dropout voltage regulator (LDO) that can perform fast-transient and autotuned voltage is introduced in this paper. Because there are still several arguments regarding the digital implementation on the LDOs, pros and cons of the digital control are first discussed in this paper to illustrate its opportunity in the LDO applications. Following that, the architecture and configuration of the digital scheme are demonstrated. The working principles and design flows of the functional algorithms are also illustrated and then verified by the simulation before the circuit implementation. The proposed LDO was implemented by the 0.18-μm manufacturing process for the performance test. Experimental results show that the LDO's output voltage Vout can accurately perform the dynamic voltage scaling function at various Vout levels (1/2, 5/9, 2/3, and 5/6 of the input voltage VDD) from a wide VDD range (from 1.8 to 0.9 V). The transient time is within 2 μs and the voltage spikes are within 50 mV when a 1-μF output capacitor is used. Test of the autotuning algorithm shows that the proposed LDO is able to work at its optimal performance under various uncertain conditions.


title: AC-coupled front-end for biopotential measurements

abstract: AC coupling is essential in biopotential measurements. Electrode offset potentials can be several orders of magnitude larger than the amplitudes of the biological signals of interest, thus limiting the admissible gain of a dc-coupled front end to prevent amplifier saturation. A high-gain input stage needs ac input coupling. This can be achieved by series capacitors, but in order to provide a bias path, grounded resistors are usually included, which degrade the common mode rejection ratio (CMRR). This paper proposes a novel balanced input ac-coupling network that provides a bias path without any connection to ground, thus resulting in a high CMRR. The circuit being passive, it does not limit the differential dc input voltage. Furthermore, differential signals are ac coupled, whereas common-mode voltages are dc coupled, thus allowing the closed-loop control of the dc common mode voltage by means of a driven-right-leg circuit. This makes the circuit compatible with common-mode dc shifting strategies intended for single-supply biopotential amplifiers. The proposed circuit allows the implementation of high-gain biopotential amplifiers with a reduced number of parts, thus resulting in low power consumption. An electrocardiogram amplifier built according to the proposed design achieves a CMRR of 123 dB at 50 Hz.


title: Measuring #GamerGate: A Tale of Hate, Sexism, and Bullying

abstract: Over the past few years, online aggression and abusive behaviors have occurred in many different forms and on a variety of platforms. In extreme cases, these incidents have evolved into hate, discrimination, and bullying, and even materialized into real-world threats and attacks against individuals or groups. In this paper, we study the Gamergate controversy. Started in August 2014 in the online gaming world, it quickly spread across various social networking platforms, ultimately leading to many incidents of cyberbullying and cyberaggression. We focus on Twitter, presenting a measurement study of a dataset of 340k unique users and 1.6M tweets to study the properties of these users, the content they post, and how they differ from random Twitter users. We find that users involved in this ``Twitter war'' tend to have more friends and followers, are generally more engaged and post tweets with negative sentiment, less joy, and more hate than random users. We also perform preliminary measurements on how the Twitter suspension mechanism deals with such abusive behaviors. While we focus on Gamergate, our methodology to collect and analyze tweets related to aggressive and bullying activities is of independent interest.


title: Gamification and student motivation

abstract: The literature suggests that gamified learning interventions may increase student engagement and enhance learning. We empirically investigate this by exploring the impact of intrinsic and extrinsic motivation on the participation and performance of over 100 undergraduate students in an online gamified learning intervention. The paper makes a number of contributions. First, by synthesizing the literature the central concepts required for a learning intervention to be considered gamified are mapped and the development of an online gamified learning intervention is described. Second, the effect of gamification on learning outcomes is examined using a pre- and post-intervention survey. We find that gamified learning interventions have a positive impact on student learning. Third, our results show that while generally positive, the impact of gamified intervention*ns on student participation varies depending on whether the student is motivated intrinsically or extrinsically. These findings will be of practical interest to teaching and learning practitioners working in a range of educational contexts, and at all levels of education, who wish to increase student engagement and enhance learning.


title: Mindfulness Meditation and Physical Activity: Evidence From 2012 National Health Interview Survey

abstract: OBJECTIVE ::: Physical activity is critical for health, yet only about half of the U.S. adult population meets basic aerobic physical activity recommendations and almost a third are inactive. Mindfulness meditation is gaining attention for its potential to facilitate health-promoting behavior and may address some limitations of existing interventions for physical activity. However, little evidence exists on mindfulness meditation and physical activity. This study assessed whether mindfulness meditation is uniquely associated with physical activity in a nationally representative sample. ::: ::: ::: METHOD ::: Cross-sectional data from the adult sample (N = 34,525) of the 2012 National Health Interview Survey were analyzed. Logistic regression models tested whether past-year use of mindfulness meditation was associated with (a) inactivity and (b) meeting aerobic physical activity recommendations, after accounting for sociodemographics, another health-promoting behavior, and 2 other types of meditation. Data were weighted to represent the U.S. civilian, noninstitutionalized adult population. ::: ::: ::: RESULTS ::: Accounting for covariates, U.S. adults who practiced mindfulness meditation in the past year were less likely to be inactive and more likely to meet physical activity recommendations. Mindfulness meditation showed stronger associations with these indices of physical activity than the 2 other types of meditation. ::: ::: ::: CONCLUSIONS ::: These results suggest that mindfulness meditation specifically, beyond meditation in general, is associated with physical activity in U.S adults. Future research should test whether intervening with mindfulness meditation-either as an adjunctive component or on its own-helps to increase or maintain physical activity. (PsycINFO Database Record


title: Big-Crypto: Big Data, Blockchain and Cryptocurrency

abstract: Cryptocurrency has been a trending topic over the past decade, pooling tremendous technological power and attracting investments valued over trillions of dollars on a global scale. The cryptocurrency technology and its network have been endowed with many superior features due to its unique architecture, which also determined its worldwide efficiency, applicability and data intensive characteristics. This paper introduces and summarises the interactions between two significant concepts in the digitalized world, i.e., cryptocurrency and Big Data. Both subjects are at the forefront of technological research, and this paper focuses on their convergence and comprehensively reviews the very recent applications and developments after 2016. Accordingly, we aim to present a systematic review of the interactions between Big Data and cryptocurrency and serve as the one stop reference directory for researchers with regard to identifying research gaps and directing future explorations.


title: Cellulo: Versatile Handheld Robots for Education

abstract: In this article, we present Cellulo, a novel robotic platform that investigates the intersection of three ideas for robotics in education: designing the robots to be versatile and generic tools; blending robots into the classroom by designing them to be pervasive objects and by creating tight interactions with (already pervasive) paper; and finally considering the practical constraints of real classrooms at every stage of the design. Our platform results from these considerations and builds on a unique combination of technologies: groups of handheld haptic-enabled robots, tablets and activity sheets printed on regular paper. The robots feature holonomic motion, haptic feedback capability and high accuracy localization through a microdot pattern overlaid on top of the activity sheets, while remaining affordable (robots cost about EUR 125 at the prototype stage) and classroom-friendly. We present the platform and report on our first interaction studies, involving about 230 children.


title: Automatic detection of anger in telephone speech with robust autoregressive modulation filtering

abstract: A new system for automatic detection of angry speech is proposed. Using simulation of far-end-noise-corrupted telephone speech and the widely used Berlin database of emotional speech, autoregressive prediction of features across speech frames is shown to contribute significantly to both the clean speech performance and the robustness of the system. The autoregressive models are learned from the training data in order to capture long-term temporal dynamics of the features. Additionally, linear predictive spectrum analysis outperforms conventional Fourier spectrum analysis in terms of robustness in the computation of mel-frequency cepstral coefficients in the feature extraction stage.


title: A Novel Security Metrics Taxonomy for R&D Organisations

abstract: In order to obtain evidence of the security and privacy issues of products, services or an organization, systematic approaches to measuring security are needed. In this study we survey the emerging security metrics approaches from the academic, governmental and industrial perspectives. We aim to bridge the gaps between business management, information security management and ICT product security practices. If appropriate security metrics can be to offer a quantitative and objective basis for security assurance, it would be easier to make business and engineering decisions concerning information security. We believe that being able to express a high-level taxonomy of security metrics will help the actual process of developing feasible composite metrics even for complex situations. A welldefined taxonomy can be used to enhance the composition of feasible security metrics all the way from business management to the lowest level of technical detail. Information security management, business management and, on the other hand, software security and network security engineering have been handled as separate areas. Common metrics approaches can be used to bridge the gaps in between.


title: Full-wave analysis and equivalent circuit modeling of substrate integrated waveguide (SIW) circuits

abstract: The modeling and design of substrate integrated waveguide (SIW) circuits requires sophisticated electromagnetic solvers, which provide reliable results, fast computing time, and high flexibility to fully exploit the potential of SIW technology. This paper presents the outline of the full-wave analysis of SIW circuits by the Boundary Integral-Resonant Mode Expansion (BI-RME) method. In addition, the derivation of multimodal and parametric equivalent circuit modeling of SIW discontinuities. The use of equivalent circuit models is particularly useful for the fast design of SIW circuits, as well as for the sensitivity and yield analysis.