title: Recommendations using linked data

abstract: Linked Data offers new opportunities for Semantic Web-based application development by connecting structured information from various domains. These technologies allow machines and software agents to automatically interpret and consume Linked Data and provide users with intelligent query answering services. In order to enable advanced and innovative semantic applications of Linked Data such as recommendation, social network analysis, and information clustering, a fundamental requirement is systematic metrics that allow comparison between resources. In this research, we develop a hybrid similarity metric based on the characteristics of Linked Data. In particular, we develop and demonstrate metrics for providing recommendations of closely related resources. The results of our preliminary experiments and future directions are also presented.


title: Understanding Loneliness in Social Awareness Streams: Expressions and Responses

abstract: We studied the experience of loneliness as communicated by thousands of people on Twitter. Using a data set of public Twitter posts containing explicit expressions of loneliness, we qualitatively developed a categorization scheme for these expressions, showing how the context of loneliness expressed on Twitter relates to existing theories about loneliness. A quantitative analysis of the data exposed categories and patterns in communication practices around loneliness. For example, users expressing more severe, enduring loneliness are more likely to be female, and less likely to include requests for social interaction in their tweets. Further, we studied the responses to expressions of loneliness in Twitter’s social settings. Deriving from the same dataset, we examined factors that correlate with the existence and type of response, showing, for example, that men were more likely to receive responses to lonely tweets, and expressions of enduring loneliness are critically less likely to receive responses.


title: Biomarkers of Therapeutic Response in Multiple Sclerosis: Current Status

abstract: Multiple sclerosis (MS) is an autoimmune disease of unknown cause, in which chronic inflammation drives multifocal demyelination of axons in both white and gray matter in the CNS. The pathological course of the disease is heterogeneous and involves an early, predominantly inflammatory demyelinating disease phase of relapsing–remitting MS (RRMS), which, over a variable period of time, evolves into a progressively degenerative stage associated with axonal loss and scar formation, causing physical and cognitive disability. For patients with RRMS, there is a growing arsenal of disease-modifying agents (DMAs), with varying degrees of efficacy, as defined by reduced relapse rates, improved magnetic resonance imaging outcomes, and preservation of neurological function. Establishment of personalized treatment plans remains one of the biggest challenges in therapeutic decision-making in MS because the disease prognosis and individual therapeutic outcomes are extremely difficult to predict. Current research is aimed at discovery and validation of biomarkers that reliably measure disease progression and effective therapeutic intervention. Individual biomarker candidates with evident clinical utility are highlighted in this review and include neutralizing autoantibodies against DMAs, fetuin-A, osteopontin, isoprostanes, chemokine (C-X-C motif) ligand 13 (CXCL13), neurofilament light and heavy, and chitinase 3-like protein. In addition, application of more advanced screening technologies has opened up new categories of biomarkers that move beyond detection of individual soluble proteins, including gene expression and autoantibody arrays, microRNAs, and circulating microvesicles/exosomes. Development of clinically useful biomarkers in MS will not only shape the practice of personalized medicine but will also serve as surrogate markers to enable investigation of innovative treatments within clinical trials that are less costly, are of shorter duration, and have more certainty of outcomes.


title: Hierarchical clustering and visualization of aggregate cyber data

abstract: Most commercial intrusion detections systems (IDS) can produce a very high volume of alerts, and are typically plagued by a high false positive rate. The approach described here uses Splunk to aggregate IDS alerts. The aggregated IDS alerts are retrieved from Splunk programmatically and are then clustered using text analysis and visualized using a sunburst diagram to provide an additional understanding of the data. The equivalent of what the cluster analysis and visualization provides would require numerous detailed queries using Splunk and considerable manual effort.


title: Next-term student grade prediction

abstract: An enduring issue in higher education is student retention to successful graduation. To further this goal, we develop a system for the task of predicting students' course grades for the next enrollment term in a traditional university setting. Each term, students enroll in a limited number of courses and earn grades in the range A-F for each course. Given historical grade data, our task is to predict the grades for each student in the courses they will enroll in during the next term. With this problem formulation, the next-term student grade prediction problem becomes quite similar to a rating prediction or next-basket recommendation problem. The factorization machine (FM), a general-purpose matrix factorization (MF) algorithm suitable for this task, is leveraged as the state-of-the-art method and compared to a variety of other methods. Our experiments show that FMs achieve the lowest prediction error. Results for both cold-start and non-cold-start prediction demonstrate that FMs can be used to accurately predict in both settings. Finally, we identify limitations observed in FMs and the other models tested and discuss directions for future work. To our knowledge, this is the first study that applies state-of-the-art collaborative filtering algorithms to solve the next-term student grade prediction problem.


A Novel Technique for Opus Vermiculatum Mosaic Rendering


title: Exploring transfer function nonlinearity in echo state networks

abstract: Supralinear and sublinear pre-synaptic and dendritic integration is considered to be responsible for nonlinear computation power of biological neurons, emphasizing the role of nonlinear integration as opposed to nonlinear output thresholding. How, why, and to what degree the transfer function nonlinearity helps biologically inspired neural network models is not fully understood. Here, we study these questions in the context of echo state networks (ESN). ESN is a simple neural network architecture in which a fixed recurrent network is driven with an input signal, and the output is generated by a readout layer from the measurements of the network states. ESN architecture enjoys efficient training and good performance on certain signal-processing tasks, such as system identification and time series prediction. ESN performance has been analyzed with respect to the connectivity pattern in the network structure and the input bias. However, the effects of the transfer function in the network have not been studied systematically. Here, we use an approach tanh on the Taylor expansion of a frequently used transfer function, the hyperbolic tangent function, to systematically study the effect of increasing nonlinearity of the transfer function on the memory, nonlinear capacity, and signal processing performance of ESN. Interestingly, we find that a quadratic approximation is enough to capture the computational power of ESN with tanh function. The results of this study apply to both software and hardware implementation of ESN.


title: Approaching the Skyline in Z Order

abstract: Given a set of multidimensional data points, skyline query retrieves a set of data points that are not dominated by any other points. This query is useful for multi-preference analysis and decision making. By analyzing the skyline query, we observe a close connection between Z-order curve and skyline processing strategies and propose to use a new index structure called ZBtree, to index and store data points based on Z-order curve. We develop a suite of novel and efficient skyline algorithms, which scale very well to data dimensionality and cardinality, including (1) ZSearch, which processes skyline queries and supports progressive result delivery; (2) ZUpdate, which facilitates incremental skyline result maintenance; and (3) k-ZSearch, which answers k-dominant skyline query (a skyline variant that retrieves a representative subset of skyline results). Extensive experiments have been conducted to evaluate our proposed algorithms and compare them against the best available algorithms designed for skyline search, skyline result update, and k-dominant skyline search, respectively. The result shows that our algorithms, developed coherently based on the same ideas and concepts, soundly outperforms the state-of-the-art skyline algorithms in their specialized domains.


title: A Novel Transformer Structure for High power, High Frequency converter

abstract: Power transformer structure is a key factor for the high power, high frequency converter performance which includes efficiency, thermal performance and power density. This paper proposes a novel transformer structure for the kilo-watt level, high frequency converter which is reinforce insulation needed for the secondary side to primary side. The transformer has spiral-wound primary layers using TIW (triple insulation wire) and PCB-winding secondary layers. All the windings are arranged by full interleaving structure to minimize the leakage inductance and eddy current loss. Further more, the secondary rectifiers and filter capacitors are mounted in PCB-winding secondary layers to further minimize the termination effect. A 1.2 KW (O/P: 12 V/100 A, I/P: 400 V) Mega Hz LLC converter prototype employed the proposed transformer structure is constructed, and over 96% efficiency achieved.


title: Control of hybrid electric vehicles

abstract: Global optimization techniques, such as dynamic programming, serve mainly to evaluate the potential fuel economy of a given powertrain configuration. Unless the future driving conditions can be predicted during real-time operation but the results obtained using this noncausal approach establish a benchmark for evaluating the optimality of realizable control strategies. Real-time controllers must be simple in order to be implementable with limited computation and memory resources. Moreover, manual tuning of control parameters should be avoided. This article has analyzed two approaches, namely, feedback controllers and ECMS. Both of these approaches can lead to system behavior that is close to optimal, with feedback controllers based on dynamic programming. Additional challenges stem from the need to apply optimal energy-management controllers to advanced HEV architectures, such as combined and plug-in HEVs, as well as to optimization problems that include performance indices in addition to fuel economy, such as pollutant emissions, driveability, and thermal comfort


title: Stochastic Forecasting of Vehicle Dynamics Using Sequential Monte Carlo Simulation

abstract: Predictive control is a popular approach for further improving the efficiency and performance of vehicular systems enabling intelligent systems behavior appropriate to the driving situation. To calculate such control strategies, the future vehicle dynamics or subsequent states have to be predicted. We introduce a stochastic framework based on an explanatory model and stochastic processes to predict future vehicle dynamics with road network data. The distributions of the future states are approximated using sequential Monte Carlo simulation. The proposed approach enables stochastic forecasts incorporating uncertain driver behavior and available road data. Parameter inference is shown for exemplary real-drive test data, and predictive performance is evaluated using commonly used reference models. The results show that the explanatory model provides more specific information than time-series models do, still considering the uncertainty in the driver's behavior or the situation. The framework can be applied with predictive control algorithms enabling intelligent control of vehicular systems. Furthermore, the framework or parts of it may be usable for other applications like predicting behavior of traffic participants or general characterization of driver behavior.


title: Video capture of the circumstances of falls in elderly people residing in long-term care: an observational study

abstract: BACKGROUND ::: Falls in elderly people are a major health burden, especially in the long-term care environment. Yet little objective evidence is available for how and why falls occur in this population. We aimed to provide such evidence by analysing real-life falls in long-term care captured on video. ::: ::: ::: METHODS ::: We did this observational study between April 20, 2007, and June 23, 2010, in two long-term care facilities in British Columbia, Canada. Digital video cameras were installed in common areas (dining rooms, lounges, hallways). When a fall occurred, facility staff completed an incident report and contacted our teams so that we could collect video footage. A team reviewed each fall video with a validated questionnaire that probed the cause of imbalance and activity at the time of falling. We then tested whether differences existed in the proportion of participants falling due to the various causes, and while engaging in various activities, with generalised linear models, repeated measures logistic regression, and log-linear Poisson regression. ::: ::: ::: FINDINGS ::: We captured 227 falls from 130 individuals (mean age 78 years, SD 10). The most frequent cause of falling was incorrect weight shifting, which accounted for 41% (93 of 227) of falls, followed by trip or stumble (48, 21%), hit or bump (25, 11%), loss of support (25, 11%), and collapse (24, 11%). Slipping accounted for only 3% (six) of falls. The three activities associated with the highest proportion of falls were forward walking (54 of 227 falls, 24%), standing quietly (29 falls, 13%), and sitting down (28 falls, 12%). Compared with previous reports from the long-term care setting, we identified a higher occurrence of falls during standing and transferring, a lower occurrence during walking, and a larger proportion due to centre-of-mass perturbations than base-of-support perturbations. ::: ::: ::: INTERPRETATION ::: By providing insight into the sequences of events that most commonly lead to falls, our results should lead to more valid and effective approaches for balance assessment and fall prevention in long-term care. ::: ::: ::: FUNDING ::: Canadian Institutes for Health Research.


title: Unsupervised learning by program synthesis

abstract: We introduce an unsupervised learning algorithm that combines probabilistic modeling with solver-based techniques for program synthesis. We apply our techniques to both a visual learning domain and a language learning problem, showing that our algorithm can learn many visual concepts from only a few examples and that it can recover some English inflectional morphology. Taken together, these results give both a new approach to unsupervised learning of symbolic compositional structures, and a technique for applying program synthesis tools to noisy data.


title: Method of verifying declared identity in optical answer sheets

abstract: In the use of optical answer sheet examinations, occasionally the identity of the examinee is in question. A novel method for characterizing the personal quality of mark shapes in optically read answer sheets is described. The method may be used to identify imposters in multiple choice examinations. All the marks are segmented and measured in multiple parameters including area, dimensions, perimeter and optical density. Imposter decisions are made on the collected data by comparing an identified test form against the form in question in comparison with a population using SVM (support vector machine) modeling. In testing the method, 300 test forms from 100 examinees from past tests were used. An EER (equal error rate) of 15–17% was found. While performance of the presented method is currently insufficient for practical purposes, future research options have been mentioned.


title: Projective dictionary pair learning for pattern classification

abstract: Discriminative dictionary learning (DL) has been widely studied in various pattern classification problems. Most of the existing DL methods aim to learn a synthesis dictionary to represent the input signal while enforcing the representation coefficients and/or representation residual to be discriminative. However, the l0 or l1-norm sparsity constraint on the representation coefficients adopted in most DL methods makes the training and testing phases time consuming. We propose anew discriminative DL framework, namely projective dictionary pair learning (DPL), which learns a synthesis dictionary and an analysis dictionary jointly to achieve the goal of signal representation and discrimination. Compared with conventional DL methods, the proposed DPL method can not only greatly reduce the time complexity in the training and testing phases, but also lead to very competitive accuracies in a variety of visual classification tasks.


title: Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary

abstract: We describe a model of object recognition as machine translation. In this model, recognition is a process of annotating image regions with words. Firstly, images are segmented into regions, which are classified into region types using a variety of features. A mapping between region types and keywords supplied with the images, is then learned, using a method based around EM. This process is analogous with learning a lexicon from an aligned bitext. For the implementation we describe, these words are nouns taken from a large vocabulary. On a large test set, the method can predict numerous words with high accuracy. Simple methods identify words that cannot be predicted well. We show how to cluster words that individually are difficult to predict into clusters that can be predicted well -- for example, we cannot predict the distinction between train and locomotive using the current set of features, but we can predict the underlying concept. The method is trained on a substantial collection of images. Extensive experimental results illustrate the strengths and weaknesses of the approach.


title: Load Prediction for Energy-Aware Scheduling for Cloud Computing Platforms

abstract: We address online scheduling for servers of Cloud service providers. Each server is composed of several variable-speed processors whose power function is convex. The servers may be busy, idle or switched off. The objective of our scheduling is to minimize the energy consumed by a Cloud computing platform. To achieve this goal, we try to anticipate computing demands by predicting a workload, then we modify the set of available servers to fit this prediction and finally we schedule our jobs on the available servers. To schedule jobs we have developed the POD (Predict Optimize Dispatch) algorithm. We evaluate its performance for real-life traces in the presence of different types of prediction. The analysis shows that our scheduling reduces energy consumption considerably.


Elastic Scaling of Stateful Network Functions


title: Structural insights into the secretin PulD and its trypsin-resistant core.

abstract: Limited proteolysis, secondary structure and biochemical analyses, mass spectrometry, and mass measurements by scanning transmission electron microscopy were combined with cryo-electron microscopy to generate a three-dimensional model of the homomultimeric complex formed by the outer membrane secretin PulD, an essential channel-forming component of the type II secretion system from Klebsiella oxytoca. The complex is a dodecameric structure composed of two rings that sandwich a closed disc. The two rings form chambers on either side of a central plug that is part of the middle disc. The PulD polypeptide comprises two major, structurally quite distinct domains; an N domain, which forms the walls of one of the chambers, and a trypsin-resistant C domain, which contributes to the outer chamber, the central disc, and the plug. The C domain contains a lower proportion of potentially transmembrane beta-structure than classical outer membrane proteins, suggesting that only a small part of it is embedded within the outer membrane. Indeed, the C domain probably extends well beyond the confines of the outer membrane bilayer, forming a centrally plugged channel that penetrates both the peptidoglycan on the periplasmic side and the lipopolysaccharide and capsule layers on the cell surface. The inner chamber is proposed to constitute a docking site for the secreted exoprotein pullulanase, whereas the outer chamber could allow displacement of the plug to open the channel and permit the exoprotein to escape.


title: Automatic classification of bengali sentences based on sense definitions present in bengali wordnet

abstract: Based on the sense definition of words available in the Bengali WordNet, an attempt is made to classify the Bengali sentences automatically into different groups in accordance with their underlying senses. The input sentences are collected from 50 different categories of the Bengali text corpus developed in the TDIL project of the Govt. of India, while information about the different senses of particular ambiguous lexical item is collected from Bengali WordNet. In an experimental basis we have used Naive Bayes probabilistic model as a useful classifier of sentences. We have applied the algorithm over 1747 sentences that contain a particular Bengali lexical item which, because of its ambiguous nature, is able to trigger different senses that render sentences in different meanings. In our experiment we have achieved around 84% accurate result on the sense classification over the total input sentences. We have analyzed those residual sentences that did not comply with our experiment and did affect the results to note that in many cases, wrong syntactic structures and less semantic information are the main hurdles in semantic classification of sentences. The applicational relevance of this study is attested in automatic text classification, machine learning, information extraction, and word sense disambiguation.


title: Real-Time Analysis of a Sensor’s Data for Automated Decision Making in an IoT-Based Smart Home

abstract: IoT devices frequently generate large volumes of streaming data and in order to take advantage of this data, their temporal patterns must be learned and identified. Streaming data analysis has become popular after being successfully used in many applications including forecasting electricity load, stock market prices, weather conditions, etc. Artificial Neural Networks (ANNs) have been successfully utilized in understanding the embedded interesting patterns/behaviors in the data and forecasting the future values based on it. One such pattern is modelled and learned in the present study to identify the occurrence of a specific pattern in a Water Management System (WMS). This prediction aids in making an automatic decision support system, to switch OFF a hydraulic suction pump at the appropriate time. Three types of ANN, namely Multi-Input Multi-Output (MIMO), Multi-Input Single-Output (MISO), and Recurrent Neural Network (RNN) have been compared, for multi-step-ahead forecasting, on a sensor’s streaming data. Experiments have shown that RNN has the best performance among three models and based on its prediction, a system can be implemented to make the best decision with 86% accuracy.


title: Capability-based Financial Instruments

abstract: Every novel cooperative arrangement of mutually suspicious parties interacting electronically -- every smart contract -- effectively requires a new cryptographic protocol. However, if every new contract requires new cryptographic protocol design, our dreams of cryptographically enabled electronic commerce would be unreachable. Cryptographic protocol design is too hard and expensive, given our unlimited need for new contracts.Just as the digital logic gate abstraction allows digital circuit designers to create large analog circuits without doing analog circuit design, we present cryptographic capabilities as an abstraction allowing a similar economy of engineering effort in creating smart contracts. We explain the E system, which embodies these principles, and show a covered-call-option as a smart contract written in a simple security formalism independent of cryptography, but automatically implemented as a cryptographic protocol coordinating five mutually suspicious parties.


title: Adversarial Stain Transfer for Histopathology Image Analysis

abstract: It is generally recognized that color information is central to the automatic and visual analysis of histopathology tissue slides. In practice, pathologists rely on color, which reflects the presence of specific tissue components, to establish a diagnosis. Similarly, automatic histopathology image analysis algorithms rely on color or intensity measures to extract tissue features. With the increasing access to digitized histopathology images, color variation and its implications have become a critical issue. These variations are the result of not only a variety of factors involved in the preparation of tissue slides but also in the digitization process itself. Consequently, different strategies have been proposed to alleviate stain-related tissue inconsistencies in automatic image analysis systems. Such techniques generally rely on collecting color statistics to perform color matching across images. In this work, we propose a different approach for stain normalization that we refer to as stain transfer. We design a discriminative image analysis model equipped with a stain normalization component that transfers stains across datasets. Our model comprises a generative network that learns data set-specific staining properties and image-specific color transformations as well as a task-specific network (e.g., classifier or segmentation network). The model is trained end-to-end using a multi-objective cost function. We evaluate the proposed approach in the context of automatic histopathology image analysis on three data sets and two different analysis tasks: tissue segmentation and classification. The proposed method achieves superior results in terms of accuracy and quality of normalized images compared to various baselines.


title: Adaptive median filters: new algorithms and results.

abstract: Based on two types of image models corrupted by impulse noise, we propose two new algorithms for adaptive median filters. They have variable window size for removal of impulses while preserving sharpness. The first one, called the ranked-order based adaptive median filter (RAMF), is based on a test for the presence of impulses in the center pixel itself followed by a test for the presence of residual impulses in the median filter output. The second one, called the impulse size based adaptive median filter (SAMF), is based on the detection of the size of the impulse noise. It is shown that the RAMF is superior to the nonlinear mean L(p) filter in removing positive and negative impulses while simultaneously preserving sharpness; the SAMF is superior to Lin's (1988) adaptive scheme because it is simpler with better performance in removing the high density impulsive noise as well as nonimpulsive noise and in preserving the fine details. Simulations on standard images confirm that these algorithms are superior to standard median filters.


title: Monaural Sound Source Separation by Nonnegative Matrix Factorization With Temporal Continuity and Sparseness Criteria

abstract: An unsupervised learning algorithm for the separation of sound sources in one-channel music signals is presented. The algorithm is based on factorizing the magnitude spectrogram of an input signal into a sum of components, each of which has a fixed magnitude spectrum and a time-varying gain. Each sound source, in turn, is modeled as a sum of one or more components. The parameters of the components are estimated by minimizing the reconstruction error between the input spectrogram and the model, while restricting the component spectrograms to be nonnegative and favoring components whose gains are slowly varying and sparse. Temporal continuity is favored by using a cost term which is the sum of squared differences between the gains in adjacent frames, and sparseness is favored by penalizing nonzero gains. The proposed iterative estimation algorithm is initialized with random values, and the gains and the spectra are then alternatively updated using multiplicative update rules until the values converge. Simulation experiments were carried out using generated mixtures of pitched musical instrument samples and drum sounds. The performance of the proposed method was compared with independent subspace analysis and basic nonnegative matrix factorization, which are based on the same linear model. According to these simulations, the proposed method enables a better separation quality than the previous algorithms. Especially, the temporal continuity criterion improved the detection of pitched musical sounds. The sparseness criterion did not produce significant improvements


title: A meta-analysis of the technology acceptance model

abstract: A statistical meta-analysis of the technology acceptance model (TAM) as applied in various fields was conducted using 88 published studies that provided sufficient data to be credible. The results show TAM to be a valid and robust model that has been widely used, but which potentially has wider applicability. A moderator analysis involving user types and usage types was performed to investigate conditions under which TAM may have different effects. The study confirmed the value of using students as surrogates for professionals in some TAM studies, and perhaps more generally. It also revealed the power of meta-analysis as a rigorous alternative to qualitative and narrative literature review methods.


title: A Submicrowatt 1.1-MHz CMOS Relaxation Oscillator With Temperature Compensation

abstract: A 1.1-MHz submicrowatt current-mode relaxation oscillator with temperature compensation is presented. In this oscillator, the current-starving inverters are biased by using the current sources with positive and negative temperature coefficients. It relaxes the temperature variations. This oscillator is fabricated in a 0.18- μm CMOS process, and its area is 0.075 mm2. The power consumption is 0.859 μW, with a supply voltage of 1.8 V, and the calculated figure of merit is 0.78 nW/kHz. The measured output relative frequency variation is less than 3%/V for the supply voltage of 1.2-2.4 V and ±0.5% for the temperature of -20°C-80°C. The average temperature coefficient is 64.3 ppm/°C.


The chlamydia problem in New Zealand.


title: Graph Classification with Geometric Scattering

abstract: One of the most notable contributions of deep learning is the application of convolutional neural networks (ConvNets) to structured signal classification, and in particular image classification. Beyond their impressive performances in supervised learning, the structure of such networks inspired the development of deep filter banks referred to as scattering transforms. These transforms apply a cascade of wavelet transforms and complex modulus operators to extract features that are invariant to group operations and stable to deformations. Furthermore, ConvNets inspired recent advances in geometric deep learning, which aim to generalize these networks to graph data by applying notions from graph signal processing to learn deep graph filter cascades. We further advance these lines of research by proposing a geometric scattering transform using graph wavelets defined in terms of random walks on the graph. We demonstrate the utility of features extracted with this designed deep filter bank in graph classification, and show its competitive performance relative to other methods, including graph kernel methods and geometric deep learning ones, on both social and biochemistry data.


title: Compositional Sequence Labeling Models for Error Detection in Learner Writing

abstract: In this paper, we present the first experiments using neural network models for the task of error detection in learner writing. We perform a systematic comparison of alternative compositional architectures and propose a framework for error detection based on bidirectional LSTMs. Experiments on the CoNLL-14 shared task dataset show the model is able to outperform other participants on detecting errors in learner writing. Finally, the model is integrated with a publicly deployed self-assessment system, leading to performance comparable to human annotators.


title: Functional Distributional Semantics

abstract: Vector space models have become popular in distributional semantics, despite the challenges they face in capturing various semantic phenomena. We propose a novel probabilistic framework which draws on both formal semantics and recent advances in machine learning. In particular, we separate predicates from the entities they refer to, allowing us to perform Bayesian inference based on logical forms. We describe an implementation of this framework using a combination of Restricted Boltzmann Machines and feedforward neural networks. Finally, we demonstrate the feasibility of this approach by training it on a parsed corpus and evaluating it on established similarity datasets.


title: Datalog± ontology consolidation

abstract: Knowledge bases in the form of ontologies are receiving increasing attention as they allow to clearly represent both the available knowledge, which includes the knowledge in itself and the constraints imposed to it by the domain or the users. In particular, Datalog± ontologies are attractive because of their property of decidability and the possibility of dealing with the massive amounts of data in real world environments; however, as it is the case with many other ontological languages, their application in collaborative environments often lead to inconsistency related issues. In this paper we introduce the notion of incoherence regarding Datalog± ontologies, in terms of satisfiability of sets of constraints, and show how under specific conditions incoherence leads to inconsistent Datalog± ontologies. The main contribution of this work is a novel approach to restore both consistency and coherence in Datalog± ontologies. The proposed approach is based on kernel contraction and restoration is performed by the application of incision functions that select formulas to delete. Nevertheless, instead of working over minimal incoherent/inconsistent sets encountered in the ontologies, our operators produce incisions over non-minimal structures called clusters. We present a construction for consolidation operators, along with the properties expected to be satisfied by them. Finally, we establish the relation between the construction and the properties by means of a representation theorem. Although this proposal is presented for Datalog± ontologies consolidation, these operators can be applied to other types of ontological languages, such as Description Logics, making them apt to be used in collaborative environments like the Semantic Web.


title: I Know What You Saw Last Minute - Encrypted HTTP Adaptive Video Streaming Title Classification

abstract: Desktops can be exploited to violate privacy. There are two main types of attack scenarios: active and passive. We consider the passive scenario where the adversary does not interact actively with the device, but is able to eavesdrop on the network traffic of the device from the network side. In the near future, most Internet traffic will be encrypted and thus passive attacks are challenging. Previous research has shown that information can be extracted from encrypted multimedia streams. This includes video title classification of non HTTP adaptive streams. This paper presents algorithms for encrypted HTTP adaptive video streaming title classification. We show that an external attacker can identify the video title from video HTTP adaptive streams sites, such as YouTube. To the best of our knowledge, this is the first work that shows this. We provide a large data set of 15000 YouTube video streams of 2100 popular video titles that was collected under real-world network conditions. We present several machine learning algorithms for the task and run a thorough set of experiments, which shows that our classification accuracy is higher than 95%. We also show that our algorithms are able to classify video titles that are not in the training set as unknown and some of the algorithms are also able to eliminate false prediction of video titles and instead report unknown. Finally, we evaluate our algorithm robustness to delays and packet losses at test time and show that our solution is robust to these changes.


title: A 3-RSR Haptic Wearable Device for Rendering Fingertip Contact Forces

abstract: A novel wearable haptic device for modulating contact forces at the fingertip is presented. Rendering of forces by skin deformation in three degrees of freedom (DoF), with contact—no contact capabilities, was implemented through rigid parallel kinematics. The novel asymmetrical three revolute-spherical-revolute (3-RSR) configuration allowed compact dimensions with minimum encumbrance of the hand workspace. The device was designed to render constant to low frequency deformation of the fingerpad in three DoF, combining light weight with relatively high output forces. A differential method for solving the non-trivial inverse kinematics is proposed and implemented in real time for controlling the device. The first experimental activity evaluated discrimination of different fingerpad stretch directions in a group of five subjects. The second experiment, enrolling 19 subjects, evaluated cutaneous feedback provided in a virtual pick-and-place manipulation task. Stiffness of the fingerpad plus device was measured and used to calibrate the physics of the virtual environment. The third experiment with 10 subjects evaluated interaction forces in a virtual lift-and-hold task. Although with different performance in the two manipulation experiments, overall results show that participants better controlled interaction forces when the cutaneous feedback was active, with significant differences between the visual and visuo-haptic experimental conditions.


title: Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization

abstract: Automatic localization and labeling of vertebra in 3D medical images plays an important role in many clinical tasks, including pathological diagnosis, surgical planning and postoperative assessment. However, the unusual conditions of pathological cases, such as the abnormal spine curvature, bright visual imaging artifacts caused by metal implants, and the limited field of view, increase the difficulties of accurate localization. In this paper, we propose an automatic and fast algorithm to localize and label the vertebra centroids in 3D CT volumes. First, we deploy a deep image-to-image network (DI2IN) to initialize vertebra locations, employing the convolutional encoder-decoder architecture together with multi-level feature concatenation and deep supervision. Next, the centroid probability maps from DI2IN are iteratively evolved with the message passing schemes based on the mutual relation of vertebra centroids. Finally, the localization results are refined with sparsity regularization. The proposed method is evaluated on a public dataset of 302 spine CT volumes with various pathologies. Our method outperforms other state-of-the-art methods in terms of localization accuracy. The run time is around 3 seconds on average per case. To further boost the performance, we retrain the DI2IN on additional 1000+ 3D CT volumes from different patients. To the best of our knowledge, this is the first time more than 1000 3D CT volumes with expert annotation are adopted in experiments for the anatomic landmark detection tasks. Our experimental results show that training with such a large dataset significantly improves the performance and the overall identification rate, for the first time by our knowledge, reaches 90%.


title: Theme Based Clustering of Tweets

abstract: In this paper, we present overview of our approach for clustering tweets. Due to short text of tweets, traditional text clustering mechanisms alone may not produce optimal results. We believe that there is an underlying theme/topic present in majority of tweets which is evident in growing usage of hashtag feature in the Twitter network. Clustering tweets based on these themes seems a more natural way for grouping. We propose to use Wikipedia topic taxonomy to discover the themes from the tweets and use the themes along with traditional word based similarity metric for clustering. We show some of our initial results to demonstrate the effectiveness of our approach.


Roentgen: A case-based approach to radiation therapy planning


title: Semi-Implicit Variational Inference

abstract: Semi-implicit variational inference (SIVI) is introduced to expand the commonly used analytic variational distribution family, by mixing the variational parameter with a flexible distribution. This mixing distribution can assume any density function, explicit or not, as long as independent random samples can be generated via reparameterization. Not only does SIVI expand the variational family to incorporate highly flexible variational distributions, including implicit ones that have no analytic density functions, but also sandwiches the evidence lower bound (ELBO) between a lower bound and an upper bound, and further derives an asymptotically exact surrogate ELBO that is amenable to optimization via stochastic gradient ascent. With a substantially expanded variational family and a novel optimization algorithm, SIVI is shown to closely match the accuracy of MCMC in inferring the posterior in a variety of Bayesian inference tasks.


title: Redundancy Resolution of the Human Arm and an Upper Limb Exoskeleton

abstract: The human arm has 7 degrees of freedom (DOF) while only 6 DOF are required to position the wrist and orient the palm. Thus, the inverse kinematics of an human arm has a nonunique solution. Resolving this redundancy becomes critical as the human interacts with a wearable robot and the inverse kinematics solution of these two coupled systems must be identical to guarantee an seamless integration. The redundancy of the arm can be formulated by defining the swivel angle, the rotation angle of the plane defined by the upper and lower arm around a virtual axis that connects the shoulder and wrist joints. Analyzing reaching tasks recorded with a motion capture system indicates that the swivel angle is selected such that when the elbow joint is flexed, the palm points to the head. Based on these experimental results, a new criterion is formed to resolve the human arm redundancy. This criterion was implemented into the control algorithm of an upper limb 7-DOF wearable robot. Experimental results indicate that by using the proposed redundancy resolution criterion, the error between the predicted and the actual swivel angle adopted by the motor control system is less then 5°.


title: The effect of exercise training on cognitive function in older adults with mild cognitive impairment: a meta-analysis of randomized controlled trials.

abstract: Objectives Investigations of exercise and cognition have primarily focused on healthy or demented older adults, and results have been equivocal in individuals with mild cognitive impairment (MCI). Our aim was to evaluate efficacy of exercise on cognition in older adults with MCI. Design We conducted a meta-analysis of random controlled trials (RCTs) of exercise effects on cognitive outcomes in adults with MCI. Searches were conducted in Medline, EMBASE, CINAHL, PEDro, SPORTSDICUS, PsychInfo, and PubMed. Participants Adults aged over 65 years with MCI or Mini-Mental State Exam mean score 24–28 inclusive. Measurements Study quality was assessed using the PEDro scale; data on participant and intervention characteristics and outcomes were extracted, followed by meta-analysis. Results Fourteen RCTs (1,695 participants; age 65–95 years) met inclusion criteria. Quality was modest and under-powering for small effects prevalent. Overall, 42% of effect sizes (ESs) were potentially clinically relevant (ES >0.20) with only 8% of cognitive outcomes statistically significant. Meta-analysis revealed negligible but significant effects of exercise on verbal fluency (ES: 0.17 [0.04, 0.30]). No significant benefit was found for additional executive measures, memory, or information processing. Overall results were inconsistent with benefits varying across exercise types and cognitive domains. Conclusions There is very limited evidence that exercise improves cognitive function in individuals with MCI, although published research is of moderate quality and inconclusive due to low statistical power. Questions remain regarding the magnitude, generalization, persistence, and mechanisms of benefits. Large-scale, high-quality RCTs are required to determine if exercise improves cognition or reduces dementia incidence in those with MCI.


title: Security Analysis of Smartphone and Cloud Computing Authentication Frameworks and Protocols

abstract: We live in a digital world where every detail of our information is being transferred from one smart device to another via cross-platform, third-party cloud services. Smart technologies, such as, smartphones are playing dynamic roles in order to successfully complete our daily routines and official tasks that require access to all types of critical data. Before the advent of these smart technologies, securing critical information was quite a challenge. However, after the advent and global adoption of such technologies, information security has become one of the primary and most fundamental task for security professionals. The integration of social media has made this task even more challenging to undertake successfully. To this day, there are plentiful studies in which numerous authentication and security techniques were proposed and developed for smartphone and cloud computing technologies. These studies have successfully addressed multiple authentication threats and other related issues in existing the smartphone and cloud computing technologies. However, to the best of our understanding and knowledge, these studies lack many aspects in terms of authentication attacks, logical authentication analysis, and the absence of authentication implementation scenarios. Due to these authentication anomalies and ambiguities, such studies cannot be fully considered for successful implementation. Therefore, in this paper, we have performed a comprehensive security analysis and review of various smartphone and cloud computing authentication frameworks and protocols to outline up-to-date authentication threats and issues in the literature. These authentication challenges are further summarized and presented in the form of different graphs to illustrate where the research is currently heading. Finally, based on those outcomes, we identify the latest and existing authentication uncertainties, threats, and other related issues to address future directions and open research issues in the domain of the smartphone-and cloud-computing authentication.


title: A Joint Detection-Classification Model for Audio Tagging of Weakly Labelled Data

abstract: Audio tagging aims to assign one or several tags to an audio clip. Most of the datasets are weakly labelled, which means only the tags of the clip are known, without knowing the occurrence time of the tags. The labeling of an audio clip is often based on the audio events in the clip and no event level label is provided to the user. Previous works have used the bag of frames model assume the tags occur all the time, which is not the case in practice. We propose a joint detection-classification (JDC) model to detect and classify the audio clip simultaneously. The JDC model has the ability to attend to informative and ignore uninformative sounds. Then only informative regions are used for classification. Experimental results on the “CHiME Home” dataset show that the JDC model reduces the equal error rate (EER) from 19.0% to 16.9%. More interestingly, the audio event detector is trained successfully without needing the event level label.


title: Enhancement of Multispectral Images of Degraded Documents by Employing Spatial Information

abstract: This work aims at enhancing ancient and degraded writings, which are captured by MultiSpectral Imaging systems. The manuscripts captured, contain faded out characters and are partly corrupted by mold and hardly legible. Several works have shown that such writings can be enhanced by applying unsupervised dimension reduction tools - like Principal Component Analysis (PCA) or Independent Component Analysis (ICA). In this work the Fisher Linear Discriminate Analysis (LDA) is applied in order to reduce the dimension of the multispectral scan and to enhance the degraded writings. Since Fisher LDA is a supervised dimension reduction tool, it is necessary to label a subset of multispectral data. For this purpose, a semi-automated label generation step is conducted, which is based on an automated detection of text lines. Thus, the approach is not only based on spectral information - like PCA and ICA - but also on spatial information. The method has been tested on two Slavonic manuscripts. A qualitative analysis shows, that the LDA based dimension reduction gains better performance, compared to unsupervised techniques.


title: Fingerprint liveness detection based on multi-scale LPQ and PCA

abstract: Fingerprint authentication system is used to verify users' identification according to the characteristics of their fingerprints. However, this system has some security and privacy problems. For example, some artificial fingerprints can trick the fingerprint authentication system and access information using real users' identification. Therefore, a fingerprint liveness detection algorithm needs to be designed to prevent illegal users from accessing privacy information. In this paper, a new software-based liveness detection approach using multi-scale local phase quantity (LPQ) and principal component analysis (PCA) is proposed. The feature vectors of a fingerprint are constructed through multi-scale LPQ. PCA technology is also introduced to reduce the dimensionality of the feature vectors and gain more effective features. Finally, a training model is gained using support vector machine classifier, and the liveness of a fingerprint is detected on the basis of the training model. Experimental results demonstrate that our proposed method can detect the liveness of users' fingerprints and achieve high recognition accuracy. This study also confirms that multi-resolution analysis is a useful method for texture feature extraction during fingerprint liveness detection.


title: Comparison between SPM and IPM motor drives for EV application

abstract: Electric Vehicles make use of permanent magnet synchronous traction motors for their high torque density and efficiency. A comparison between interior permanent magnet (IPM) and surface mounted permanent magnet (SPM) motors is carried out, in terms of performance at given inverter ratings. A simplified analytical approach is proposed and its results are confirmed by actual motor design, validated through FEA. The main result of the proposed analysis is to put in evidence the different behavior of IPM and SPM motors in terms of overload capability: as expected, the overload current results in a power overload of the IPM motor, while in the SPM motor the maximum power is upper limited irrespectively of the current overload.


A decomposition storage model


title: Learning Deep Features for Discriminative Localization

abstract: In this work, we revisit the global average pooling layer proposed in [13], and shed light on how it explicitly enables the convolutional neural network (CNN) to have remarkable localization ability despite being trained on imagelevel labels. While this technique was previously proposed as a means for regularizing training, we find that it actually builds a generic localizable deep representation that exposes the implicit attention of CNNs on an image. Despite the apparent simplicity of global average pooling, we are able to achieve 37.1% top-5 error for object localization on ILSVRC 2014 without training on any bounding box annotation. We demonstrate in a variety of experiments that our network is able to localize the discriminative image regions despite just being trained for solving classification task1.


Managing External Stakeholder Relationships in PPP Projects - A Multidimensional Approach


title: Using Speakers ’ Referential Intentions to Model Early Cross-Situational Word Learning

abstract: Word learning is a ''chicken and egg'' prob- lem. If a child could understand speakers' utterances, it would be easy to learn the meanings of individual words, and once a child knows what many words mean, it is easy to infer speakers' intended meanings. To the beginning learner, however, both individual word meanings and speakers' intentions are unknown. We describe a com- putational model of word learning that solves these two inference problems in parallel, rather than relying exclu- sively on either the inferred meanings of utterances or cross-situational word-meaning associations. We tested our model using annotated corpus data and found that it inferred pairings between words and object concepts with higher precision than comparison models. Moreover, as the result of making probabilistic inferences about speak- ers' intentions, our model explains a variety of behavioral phenomena described in the word-learning literature. These phenomena include mutual exclusivity, one-trial learning, cross-situational learning, the role of words in object individuation, and the use of inferred intentions to disambiguate reference.


title: Applications Know Best: Performance-Driven Memory Overcommit with Ginkgo

abstract: Memory over commitment enables cloud providers to host more virtual machines on a single physical server, exploiting spare CPU and I/O capacity when physical memory becomes the bottleneck for virtual machine deployment. However, over commiting memory can also cause noticeable application performance degradation. We present Ginkgo, a policy framework for over omitting memory in an informed and automated fashion. By directly correlating application-level performance to memory, Ginkgo automates the redistribution of scarce memory across all virtual machines, satisfying performance and capacity constraints. Ginkgo also achieves memory gains for traditionally fixed-size Java applications by coordinating the redistribution of available memory with the activities of the Java Virtual Machine heap. When compared to a non-over commited system, Ginkgo runs the Day Trader 2.0 and SPEC Web 2009 benchmarks with the same number of virtual machines while saving up to 73% (50% omitting free space) of a physical server's memory while keeping application performance degradation within 7%.


title: EchoSafe: Sonar-based Verifiable Interaction with Intelligent Digital Agents

abstract: Voice controlled interactive smart speakers, such as Google Home, Amazon Echo, and Apple HomePod are becoming commonplace in today's homes. These devices listen continually for the user commands, that are triggered by special keywords, such as "Alexa" and "Hey Siri". Recent research has shown that these devices are vulnerable to attacks through malicious voice commands from nearby devices. The commands can be sent easily during unoccupied periods, so that the user may be unaware of such attacks. We present EchoSafe, a user-friendly sonar-based defense against these attacks. When the user sends a critical command to the smart speaker, EchoSafe sends an audio pulse followed by post processing to determine if the user is present in the room. We can detect the user's presence during critical commands with 93.13% accuracy, and our solution can be extended to defend against other attack scenarios, as well.


title: PCB integrated inductors for low power DC/DC converter

abstract: This paper discusses the use of PCB integrated inductors for low power DC/DC buck converters. Coreless, magnetic plates and closed core structures are compared in terms of achievable inductance, power handling and efficiency in a footprint of 10 /spl times/ 10 mm/sup 2/. Analytic and FEM methods are applied and typical current waveforms encountered in a buck converter are included. The magnetic layers consist of electroplated NiFe, so that the process is fully compatible with standard PCB process. A 4.7 /spl mu/H PCB integrated inductor with DC current handling of up to 500 mA is presented. This inductor is employed in a 1.5 W buck converter using a commercial available IC. The footprint of the entire converter measures 10 /spl times/ 10 mm/sup 2/ and is built on top of the integrated inductor to demonstrate the concept of integrated passives in power electronic circuits to achieve ultra flat and compact converter design.


title: Modelling and gain scheduled control of shape memory alloy actuators

abstract: This paper describes a gain-scheduled controller for a shape memory alloy (SMA) actuator. For accurate control of an SMA actuator, it is important to develop a precise model for the SMA. A model has been proposed based on concepts from physics. The equations include Joules heating - convectional cooling to explain the dynamics of temperature, Fermi-Dirac statistics to explain the variation of mole fraction with temperature, and a stress-strain constitutive equation to relate changes in mole fraction and temperature to changes in stress and strain of the SMA. This model is then used to develop a gain-scheduled controller to control the strain in the SMA. Simulation and experimental results show fast and accurate control of the strain in the SMA actuator


title: Convolutional Recurrent Neural Networks for Glucose Prediction

abstract: Control of blood glucose is essential for diabetes management. Current digital therapeutic approaches for subjects with type 1 diabetes mellitus such as the artificial pancreas and insulin bolus calculators leverage machine learning techniques for predicting subcutaneous glucose for improved control. Deep learning has recently been applied in healthcare and medical research to achieve state-of-the-art results in a range of tasks including disease diagnosis, and patient state prediction among others. In this paper, we present a deep learning model that is capable of forecasting glucose levels with leading accuracy for simulated patient cases (root-mean-square error (RMSE) = 9.38 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 0.71 [mg/dL] over a 30-min horizon, RMSE = 18.87 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 2.25 [mg/dL] over a 60-min horizon) and real patient cases (RMSE = 21.07 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 2.35 [mg/dL] for 30 min, RMSE = 33.27 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 4.79% for 60 min). In addition, the model provides competitive performance in providing effective prediction horizon (<inline-formula><tex-math notation="LaTeX">$\text{PH}_{\text{eff}}$</tex-math></inline-formula>) with minimal time lag both in a simulated patient dataset (<inline-formula><tex-math notation="LaTeX">$\text{PH}_{\text{eff}}$</tex-math></inline-formula> = 29.0 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 0.7 for 30 min and <inline-formula><tex-math notation="LaTeX">$\text{PH}_{\text{eff}}$</tex-math></inline-formula> = 49.8 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 2.9 for 60 min) and in a real patient dataset (<inline-formula><tex-math notation="LaTeX">$\text{PH}_{\text{eff}}$</tex-math></inline-formula> = 19.3 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 3.1 for 30 min and <inline-formula><tex-math notation="LaTeX">$\text{PH}_{\text{eff}}$</tex-math></inline-formula> = 29.3 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 9.4 for 60 min). This approach is evaluated on a dataset of ten simulated cases generated from the UVA/Padova simulator and a clinical dataset of ten real cases each containing glucose readings, insulin bolus, and meal (carbohydrate) data. Performance of the recurrent convolutional neural network is benchmarked against four algorithms. The proposed algorithm is implemented on an Android mobile phone, with an execution time of 6 ms on a phone compared to an execution time of 780 ms on a laptop.


title: Instance-Dependent PU Learning by Bayesian Optimal Relabeling

abstract: When learning from positive and unlabelled data, it is a strong assumption that the positive observations are randomly sampled from the distribution of $X$ conditional on $Y = 1$, where X stands for the feature and Y the label. Most existing algorithms are optimally designed under the assumption. However, for many real-world applications, the observed positive examples are dependent on the conditional probability $P(Y = 1|X)$ and should be sampled biasedly. In this paper, we assume that a positive example with a higher $P(Y = 1|X)$ is more likely to be labelled and propose a probabilistic-gap based PU learning algorithms. Specifically, by treating the unlabelled data as noisy negative examples, we could automatically label a group positive and negative examples whose labels are identical to the ones assigned by a Bayesian optimal classifier with a consistency guarantee. The relabelled examples have a biased domain, which is remedied by the kernel mean matching technique. The proposed algorithm is model-free and thus do not have any parameters to tune. Experimental results demonstrate that our method works well on both generated and real-world datasets.


title: Effective Neural Solution for Multi-Criteria Word Segmentation

abstract: We present a novel and elegant deep learning solution to train a single joint model on multi-criteria corpora for Chinese Word Segmentation (CWS) challenge. Our innovative design requires no private layers in model architecture, instead, introduces two artificial tokens at the beginning and ending of input sentence to specify the required target criteria. The rest of the model including Long Short-Term Memory (LSTM) layer and Conditional Random Fields (CRFs) layer remains unchanged and is shared across all datasets, keeping the size of parameter collection minimal and constant. On Bakeoff 2005 and Bakeoff 2008 datasets, our innovative design has surpassed the previous multi-criteria learning results. Testing results on two out of four datasets even have surpassed the latest state-of-the-art single-criterion learning scores. To the best knowledge, our design is the first one that has achieved the latest state-of-the-art performance on such large-scale datasets. Source codes and corpora of this paper are available on GitHub (https://github.com/hankcs/multi-criteria-cws).


title: Mobile augmented reality survey: a bottom-up approach

abstract: Augmented Reality (AR) is becoming mobile. Mobile devices have many constraints but also rich new features that traditional desktop computers do not have. There are several survey papers on AR, but none is dedicated to Mobile Augmented Reality (MAR). Our work serves the purpose of closing this gap. The contents are organized with a bottom-up approach. We first present the state-of-the-art in system components including hardware platforms, software frameworks and display devices, follows with enabling technologies such as tracking and data management. We then survey the latest technologies and methods to improve run-time performance and energy efficiency for practical implementation. On top of these, we further introduce the application fields and several typical MAR applications. Finally we conclude the survey with several challenge problems, which are under exploration and require great research efforts in the future.


title: Combining RGB and ToF cameras for real-time 3D hand gesture interaction

abstract: Time-of-Flight (ToF) and other IR-based cameras that register depth are becoming more and more affordable in consumer electronics. This paper aims to improve a realtime hand gesture interaction system by augmenting it with a ToF camera. First, the ToF camera and the RGB camera are calibrated, and a mapping is made from the depth data to the RGB image. Then, a novel hand detection algorithm is introduced based on depth and color. This not only improves detection rates, but also allows for the hand to overlap with the face, or with hands from other persons in the background. The hand detection algorithm is evaluated in these settings, and compared to previous algorithms. Furthermore, the depth information allows us to track the position of the hand in 3D, allowing for more interesting modes of interaction. Finally, the hand gesture recognition algorithm is applied to the depth data as well, and compared to the recognition based on the RGB images. The result is a real-time hand gesture interaction system that allows for complex 3D gestures and is not disturbed by objects or persons in the background.


title: Small Talk in Industry

abstract: Small talk means a great deal in a manufacturing plant. Fourteen super visors indicate that it has some bearing on establishing a positive climate for future communication. The effective supervisor knows when to en gage a worker in small talk in order to influence the worker's attitudes.


title: Breaking the News: First Impressions Matter on Online News

abstract: A growing number of people are changing the way they consume news, replacing the traditional physical newspapers and magazines by their virtual online versions or/and weblogs. The interactivity and immediacy present in online news are changing the way news are being produced and exposed by media corporations. News websites have to create effective strategies to catch people's attention and attract their clicks. In this paper we investigate possible strategies used by online news corporations in the design of their news headlines. We analyze the content of 69,907 headlines produced by four major global media corporations during a minimum of eight consecutive months in 2014. In order to discover strategies that could be used to attract clicks, we extracted features from the text of the news headlines related to the sentiment polarity of the headline. We discovered that the sentiment of the headline is strongly related to the popularity of the news and also with the dynamics of the posted comments on that particular news.


title: Delivery of adaptive bit rate video: balancing fairness, efficiency and quality

abstract: HTTP streaming currently dominates Internet traffic. It is increasingly common that video players employ adaptive bitrate (ABR) streaming strategies to maximise the user experience by selecting the highest video representation while targeting stall-free playback. Our interest lies in the common situation where a set of video flows are competing for access to a shared bottleneck link, such as in a cellular radio access network. We observe that ISPs (e.g. cellular operators) are considering innetwork techniques for resource allocation and sharing among different users. Buoyed by the ability of software defined networks (SDN) to offer flow-specific control and traffic shaping, we focus on traffic shaping techniques, and experimentally analyse the effect on ABR video flows when sharing a bottleneck link. We conduct experiments using the GPAC video player operating over a Mininet virtual network. We conclude that traffic shaping can allow a balance of fairness, efficiency and quality. Traffic shaping ABR videos reduce the number of stalls and quality switches, while also reducing the peaks for the aggregate network traffic.


title: Bidirectional Tree-Structured LSTM with Head Lexicalization

abstract: Sequential LSTM has been extended to model tree structures, giving competitive results for a number of tasks. Existing methods model constituent trees by bottom-up combinations of constituent nodes, making direct use of input word information only for leaf nodes. This is different from sequential LSTMs, which contain reference to input words for each node. In this paper, we propose a method for automatic head-lexicalization for tree-structure LSTMs, propagating head words from leaf nodes to every constituent node. In addition, enabled by head lexicalization, we build a tree LSTM in the top-down direction, which corresponds to bidirectional sequential LSTM structurally. Experiments show that both extensions give better representations of tree structures. Our final model gives the best results on the Standford Sentiment Treebank and highly competitive results on the TREC question type classification task.


title: Electrophysiological signatures of phonological and semantic maintenance in sentence repetition

abstract: Abstract Verbal short-term memory comprises resources for phonological rehearsal, which have been characterized anatomically, and for maintenance of semantic information, which are less understood. Sentence repetition tasks tap both processes interactively. To distinguish brain activity involved in phonological vs. semantic maintenance, we recorded magnetoencephalography during a sentence repetition task, incorporating three manipulations emphasizing one mechanism over the other. Participants heard sentences or word lists and attempted to repeat them verbatim after a 5-second delay. After MEG, participants completed a cued recall task testing how much they remembered of each sentence. Greater semantic engagement relative to phonological rehearsal was hypothesized for 1) sentences vs. word lists, 2) concrete vs. abstract sentences, and 3) well recalled vs. poorly recalled sentences. During auditory perception and the memory delay period, we found highly left-lateralized activation in the form of 8–30 Hz event-related desynchronization. Compared to abstract sentences, concrete sentences recruited posterior temporal cortex bilaterally, demonstrating a neural signature for the engagement of visual imagery in sentence maintenance. Maintenance of arbitrary word lists recruited right hemisphere dorsal regions, reflecting increased demands on phonological rehearsal. Sentences that were ultimately poorly recalled in the post-test also elicited extra right hemisphere activation when they were held in short-term memory, suggesting increased demands on phonological resources. Frontal midline theta oscillations also reflected phonological rather than semantic demand, being increased for word lists and poorly recalled sentences. These findings highlight distinct neural resources for phonological and semantic maintenance, with phonological maintenance associated with stronger oscillatory modulations.


title: An end-to-end domain-driven software development framework

abstract: This paper presents a comprehensive, domain-driven framework for software development. It consists of a meta-programmable domain-specific modeling environment and a model transformation generator toolset based on graph transformations. The framework allows the creation of custom, domain-oriented programming environments that support end-user programmability. In addition, the framework could be considered an early, end-to-end implementation of the concepts advocated by the OMG's Model Driven Architecture initiative.


title: Data-aggregation techniques in sensor networks: a survey

abstract: Wireless sensor networks consist of sensor nodes with sensing and com- munication capabilities. We focus on data-aggregation problems in energy- constrained sensor networks. The main goal of data-aggregation algorithms is to gather and aggregate data in an energy efficient manner so that net- work lifetime is enhanced. In this article we present a survey of data-aggre- gation algorithms in wireless sensor networks. We compare and contrast different algorithms on the basis of performance measures such as lifetime, latency, and data accuracy. We conclude with possible future research directions.


title: A Survey of Spam Detection Methods on Twitter

abstract: Twitter is one of the most popular social media platforms that has 313 million monthly active users which post 500 million tweets per day. This popularity attracts the attention of spammers who use Twitter for their malicious aims such as phishing legitimate users or spreading malicious software and advertises through URLs shared within tweets, aggressively follow/unfollow legitimate users and hijack trending topics to attract their attention, propagating pornography. In August of 2014, Twitter revealed that 8.5% of its monthly active users which equals approximately 23 million users have automatically contacted their servers for regular updates. Thus, detecting and filtering spammers from legitimate users are mandatory in order to provide a spam-free environment in Twitter. In this paper, features of Twitter spam detection presented with discussing their effectiveness. Also, Twitter spam detection methods are categorized and discussed with their pros and cons. The outdated features of Twitter which are commonly used by Twitter spam detection approaches are highlighted. Some new features of Twitter which, to the best of our knowledge, have not been mentioned by any other works are also presented.


title: Learning Role-based Graph Embeddings

abstract: Random walks are at the heart of many existing network embedding methods. However, such algorithms have many limitations that arise from the use of random walks, e.g., the features resulting from these methods are unable to transfer to new nodes and graphs as they are tied to vertex identity. In this work, we introduce the Role2Vec framework which uses the flexible notion of attributed random walks, and serves as a basis for generalizing existing methods such as DeepWalk, node2vec, and many others that leverage random walks. Our proposed framework enables these methods to be more widely applicable for both transductive and inductive learning as well as for use on graphs with attributes (if available). This is achieved by learning functions that generalize to new nodes and graphs. We show that our proposed framework is effective with an average AUC improvement of 16:55% while requiring on average 853x less space than existing methods on a variety of graphs.


title: Controlling byte pair encoding for neural machine translation

abstract: Byte pair encoding(BPE) is an approach that segments the corpus in such a way that frequent sequence of characters are combined; it results to having word surface forms divided into its' root word and affix. It alone handles out-of-vocabulary words, but tends to not consistently segment inflected words. Controlled byte pair encoding (CBPE) allowed our word-level neural machine translation (NMT) model to easily recognize inflected words which are prevalent in morphologically-rich languages. It prevented BPE from merging affixes in a word to other characters in the word. Our resulting NMT models from CBPE consistently evaluates affixes that could've been segmented with variations in BPE. In our experiments, we considered 119,969 English-Filipino parallel language pairs from an existing dataset, with Filipino as a morphologically-rich language. The results show that BPE and CBPE both showed improvements in the BLEU scores from 38.31 to 44.82 and 44.07 for English→Filipino, and from 32.17 to 35.25 and 35.98 for Filipino→English, respectively. The lower scores in the Filipino→English can be attributed to other language characteristics of Filipino such as free word order, one-to-many relationship in translating from English to Filipino, and some transliterations in the parallel corpus. CBPE also performed slightly better for English→Filipino than for Filipino→English.


title: Architecture comparisons between Nvidia and ATI GPUs: Computation parallelism and data communications

abstract: In recent years, modern graphics processing units have been widely adopted in high performance computing areas to solve large scale computation problems. The leading GPU manufacturers Nvidia and ATI have introduced series of products to the market. While sharing many similar design concepts, GPUs from these two manufacturers differ in several aspects on processor cores and the memory subsystem. In this paper, we conduct a comprehensive study to characterize the architectural differences between Nvidia's Fermi and ATI's Cypress and demonstrate their impact on performance. Our results indicate that these two products have diverse advantages that are reflected in their performance for different sets of applications. In addition, we also compare the energy efficiencies of these two platforms since power/energy consumption is a major concern in the high performance computing.


title: Beyond streams and graphs: dynamic tensor analysis

abstract: How do we find patterns in author-keyword associations, evolving over time? Or in Data Cubes, with product-branch-customer sales information? Matrix decompositions, like principal component analysis (PCA) and variants, are invaluable tools for mining, dimensionality reduction, feature selection, rule identification in numerous settings like streaming data, text, graphs, social networks and many more. However, they have only two orders, like author and keyword, in the above example.We propose to envision such higher order data as tensors,and tap the vast literature on the topic. However, these methods do not necessarily scale up, let alone operate on semi-infinite streams. Thus, we introduce the dynamic tensor analysis (DTA) method, and its variants. DTA provides a compact summary for high-order and high-dimensional data, and it also reveals the hidden correlations. Algorithmically, we designed DTA very carefully so that it is (a) scalable, (b) space efficient (it does not need to store the past) and (c) fully automatic with no need for user defined parameters. Moreover, we propose STA, a streaming tensor analysis method, which provides a fast, streaming approximation to DTA.We implemented all our methods, and applied them in two real settings, namely, anomaly detection and multi-way latent semantic indexing. We used two real, large datasets, one on network flow data (100GB over 1 month) and one from DBLP (200MB over 25 years). Our experiments show that our methods are fast, accurate and that they find interesting patterns and outliers on the real datasets.


title: How Do Developers Fix Cross-Project Correlated Bugs? A Case Study on the GitHub Scientific Python Ecosystem

abstract: GitHub, a popular social-software-development platform, has fostered a variety of software ecosystems where projects depend on one another and practitioners interact with each other. Projects within an ecosystem often have complex inter-dependencies that impose new challenges in bug reporting and fixing. In this paper, we conduct an empirical study on cross-project correlated bugs, i.e., causally related bugs reported to different projects, focusing on two aspects: 1) how developers track the root causes across projects, and 2) how the downstream developers coordinate to deal with upstream bugs. Through manual inspection of bug reports collected from the scientific Python ecosystem and an online survey with developers, this study reveals the common practices of developers and the various factors in fixing cross-project bugs. These findings provide implications for future software bug analysis in the scope of ecosystem, as well as shed light on the requirements of issue trackers for such bugs.


title: Effective Collaboration with Information Sharing in Virtual Universities

abstract: A global education system, as a key area in future IT, has fostered developers to provide various learning systems with low cost. While a variety of e-learning advantages has been recognized for a long time and many advances in e-learning systems have been implemented, the needs for effective information sharing in a secure manner have to date been largely ignored, especially for virtual university collaborative environments. Information sharing of virtual universities usually occurs in broad, highly dynamic network-based environments, and formally accessing the resources in a secure manner poses a difficult and vital challenge. This paper aims to build a new rule-based framework to identify and address issues of sharing in virtual university environments through role-based access control (RBAC) management. The framework includes a role-based group delegation granting model, group delegation revocation model, authorization granting, and authorization revocation. We analyze various revocations and the impact of revocations on role hierarchies. The implementation with XML-based tools demonstrates the feasibility of the framework and authorization methods. Finally, the current proposal is compared with other related work.


title: Mechanical design of a novel Hand Exoskeleton for accurate force displaying

abstract: This paper deals with the mechanical design of a novel haptic Hand Exoskeleton (HE) that allows exerting controlled forces on the fingertip of the index and thumb of the operator. The proposed device includes several design solutions for optimizing the accuracy and mechanical performances. Remote Centers of Motion mechanisms have been adopted for delocalizing the encumbrance of linkages of the structure away from the operator's fingers. An improved stiffness of the transmission and reduced requirements for the actuators have been achieved thanks to a novel Patent Pending principle for integrating speed reduction ratio with the transmission system.


title: Honeypot based signature generation for defense against polymorphic worm attacks in networks

abstract: With the growing sophistication of computer worms, information security has become a prime concern for individuals, community and organizations. Traditional signature based IDS, though effective for known attacks but failed to handle the unknown attack promptly. This paper describes a novel honeypot system which capture worm based on their characteristics of self replication. We introduce combination of unlimited and limited outbound connections to capture different payload of single or multiple worms. The proposed system isolate the suspicious traffic and able to effectively control the malicious traffic and capture most useful information regarding the worm's activities, without attacker's knowledge. Our system will be used for critical study of structure and behavior of most sophisticated worms and then forwards the necessary input to Signature Generation Module for automatically generating signature of unknown worms. Our attempt is to generate signature of unknown especially polymorphic worms with low false positive and high coverage. Our system is able to enhance the capability of IDS signature library and increases the probability of detecting most variant of unknown worms.


title: Improvements in the Life Expectancy of Type 1 Diabetes The Pittsburgh Epidemiology of Diabetes Complications Study Cohort

abstract: Survival in type 1 diabetes has improved, but the impact on life expectancy in the U.S. type 1 diabetes population is not well established. Our objective was to estimate the life expectancy of the Pittsburgh Epidemiology of Diabetes Complications (EDC) study cohort and quantify improvements by comparing two subcohorts based on year of diabetes diagnosis (1950–1964 [ n = 390] vs. 1965–1980 [ n = 543]). The EDC study is a prospective cohort study of 933 participants with childhood-onset (aged P


title: Intrusion and intrusion detection

abstract: Assurance technologies for computer security have failed to have significant impacts in the marketplace, with the result that most of the computers connected to the internet are vulnerable to attack. This paper looks at the problem of malicious users from both a historical and practical standpoint. It traces the history of intrusion and intrusion detection from the early 1970s to the present day, beginning with a historical overview. The paper describes the two primary intrusion detection techniques, anomaly detection and signature-based misuse detection, in some detail and describes a number of contemporary research and commercial intrusion detection systems. It ends with a brief discussion of the problems associated with evaluating intrusion detection systems and a discussion of the difficulties associated with making further progress in the field. With respect to the latter, it notes that, like many fields, intrusion detection has been based on a combination of intuition and brute-force techniques. We suspect that these have carried the field as far as they can and that further significant progress will depend on the development of an underlying theoretical basis for the field.


title: A source code plagiarism detecting method using alignment with abstract syntax tree elements

abstract: Learning to program is an important subject in computer science courses. During programming exercises, plagiarism by copying and pasting can lead to problems for fair evaluation. Some methods of plagiarism detection are currently available, such as sim. However, because sim is easily influenced by changing the identifier or program statement order, it fails to do enough to support plagiarism detection. In this paper, we propose a plagiarism detection method which is not influenced by changing the identifier or program statement order. We also explain our method's capabilities by comparing it to the sim plagiarism detector. Furthermore, we reveal how our method successfully detects the presence of plagiarism.


title: LawStats – Large-Scale German Court Decision Evaluation Using Web Service Classifiers

abstract: LawStats provides quantitative insights into court decisions from the Bundesgerichtshof - Federal Court of Justice (BGH), the Federal Court of Justice in Germany. Using Watson Web Services and approaches from Sentiment Analysis (SA), we can automatically classify the revision outcome and offer statistics on judges, senates, previous instances etc. via faceted search. These statistics are accessible through a open web interface to aid law professionals. With a clear focus on interpretability, users can not only explore statistics, but can also understand, which sentences in the decision are responsible for the machine’s decision; links to the original texts provide more context. This is the first large-scale application of Machine Learning (ML) based Natural Language Processing (NLP) for German in the analysis of ordinary court decisions in Germany that we are aware of. We have analyzed over 50,000 court decisions and extracted the outcomes and relevant entities. The modular architecture of the application allows continuous improvements of the ML model as more annotations become available over time. The tool can provide a critical foundation for further quantitative research in the legal domain and can be used as a proof-of-concept for similar efforts.


title: Neural Survival Recommender

abstract: The ability to predict future user activity is invaluable when it comes to content recommendation and personalization. For instance, knowing when users will return to an online music service and what they will listen to increases user satisfaction and therefore user retention. We present a model based on Long-Short Term Memory to estimate when a user will return to a site and what their future listening behavior will be. In doing so, we aim to solve the problem of Just-In-Time recommendation, that is, to recommend the right items at the right time. We use tools from survival analysis for return time prediction and exponential families for future activity analysis. We show that the resulting multitask problem can be solved accurately, when applied to two real-world datasets.


Tackling the Cornell NLVR task using seq 2 seq model with weak supervision


title: Prospective Clinical Study of Zirconia Full-coverage Restorations on Teeth Prepared With Biologically Oriented Preparation Technique on Gingival Health: Results After Two-year Follow-up

abstract: Clinical Relevance Tooth preparation with the biologically oriented preparation technique prior to restoration by zirconia fixed prostheses is a safe treatment option that provides excellent clinical outcomes, with greater gingival thickness and gingival margin stability.


title: Applying Epidemic Algorithm for Financial Service Based on Blockchain Technology

abstract: Our global market is emerging transformation strategy that can make the difference between success and failure. Smart contract systems through developments of technological innovations are increasingly seen as alternative technologies to impact transactional processes significantly. Blockchain is a smart contract protocol with trust offering the potential for creating new transaction platforms and thus shows a radical change of the current core value creation in third parties. These results in enormous cost and time savings and the reduced risk for the parties. This study proposed a method to improve the efficiency of distributed consensus in blockchains using epidemic algorithm. The results showed that epidemic protocols can distribute the information similar to blockchain.


title: Group Consistent Similarity Learning via Deep CRF for Person Re-identification

abstract: Person re-identification benefits greatly from deep neural networks (DNN) to learn accurate similarity metrics and robust feature embeddings. However, most of the current methods impose only local constraints for similarity learning. In this paper, we incorporate constraints on large image groups by combining the CRF with deep neural networks. The proposed method aims to learn the "local similarity" metrics for image pairs while taking into account the dependencies from all the images in a group, forming "group similarities". Our method involves multiple images to model the relationships among the local and global similarities in a unified CRF during training, while combines multi-scale local similarities as the predicted similarity in testing. We adopt an approximate inference scheme for estimating the group similarity, enabling end-to-end training. Extensive experiments demonstrate the effectiveness of our model that combines DNN and CRF for learning robust multi-scale local similarities. The overall results outperform those by state-of-the-arts with considerable margins on three widely-used benchmarks.


title: Chapter 9 A Hybrid Genetic Algorithm, Simulated Annealing and Tabu Search Heuristic for Vehicle Routing Problems with Time Windows

abstract: The vehicle routing problem with time windows (VRPTW) is an extension of the vehicle routing problem (VRP) with earliest, latest, and service times for customers. The VRPTW routes a set of vehicles to service customers having earliest, latest service times. The objective of the problem is to minimize the number of vehicles and the distance travelled to service the customers. The constraints of the problem are to service all the customers after the earliest release time and before the latest service time of each customer without exceeding the route time of the vehicle and overloading the vehicle. The route time of the vehicle is the sum total of the waiting time, the service time and distance travelled by the vehicle. A vehicle that reaches a customer before the earliest release time incurs waiting time. If a vehicle services a customer after the latest delivery time the vehicle is considered to be tardy. The service time is the time taken by a vehicle to service a customer. A vehicle is said to be overloaded if the sum total of the customer demands exceed the total capacity of the vehicle. The quality of the solution is measured in terms of the minimization of the number of vehicles followed by the minimization of the total distance travelled respectively in that order. That is, a solution for the VRPTW with a lower total number of vehicles and greater total distance travelled is preferred over a solution that requires greater number of vehicles and smaller total distance travelled.


title: A Wideband Magnetoelectric Dipole Antenna With Microstrip Line Aperture-Coupled Excitation

abstract: In this communication, a novel wideband magnetoelectric (ME) dipole antenna is proposed and investigated. A microstrip line aperture-coupled feeding structure is used to excite the antenna. This simple feeding structure can significantly enhance the impedance bandwidth. An antenna prototype is fabricated and exemplified to validate the proposed concept. Measured results show that the proposed ME dipole antenna with microstrip line aperture-coupled excitation achieves an impedance bandwidth of 85% for VSWR < 2 from 2.5 to 6.2 GHz. Further, a measured maximum boresight gain up to 8 dBi is obtained and a 1 dB gain bandwidth is about 67% from 2.5 to 5 GHz. The antenna radiation patterns perform well over the 1 dB gain bandwidth range. A low cross-polarization level is obtained over the operating band.


title: Trait conscientiousness and the personality meta-trait stability are associated with regional white matter microstructure

abstract: Establishing the neural bases of individual differences in personality has been an enduring topic of interest. However, while a growing literature has sought to characterize grey matter correlates of personality traits, little attention to date has been focused on regional white matter correlates of personality, especially for the personality traits agreeableness, conscientiousness and openness. To rectify this gap in knowledge we used a large sample (n > 550) of older adults who provided data on both personality (International Personality Item Pool) and white matter tract-specific fractional anisotropy (FA) from diffusion tensor MRI. Results indicated that conscientiousness was associated with greater FA in the left uncinate fasciculus (b ¼ 0.17, P < 0.001). We also examined links between FA and the personality meta-trait ‘stability’, which is defined as the common variance underlying agreeableness, conscientiousness, and neuroticism/emotional stability. We observed an association between left uncinate fasciculus FA and stability (b ¼ 0.27, P < 0.001), which fully accounted for the link between left uncinate fasciculus FA and conscientiousness. In sum, these results provide novel evidence for links between regional white matter microstructure and key traits of human personality, specifically conscientiousness and the meta-trait, stability. Future research is recommended to replicate and address the causal directions of these associations.


title: The Rise of Emotion-aware Conversational Agents

abstract: A future where the conversation with machines can potentially involve mutual emotions between the parties may be not so far in time. Inspired by the episode of Black Mirror "Be Right Back'' and Replika, a futuristic app that promises to be "your best friend'', in this work we are considering the positive and negative points of including an automated learning conversational agent inside the personal world of feelings and emotions. These systems can impact both single individuals and society, worsening an already critical situation. Our conclusion is that a regulation on the artificial emotional content should be considered before actually going beyond some one-way-only limits.


title: Tight bounds for online vector bin packing

abstract: In the d-dimensional bin packing problem (VBP), one is given vectors x1,x2, ... ,xn ∈ Rd and the goal is to find a partition into a minimum number of feasible sets: {1,2 ... ,n} = ∪is Bi. A set Bi is feasible if ∑j ∈ Bi xj ≤ 1, where 1 denotes the all 1's vector. For online VBP, it has been outstanding for almost 20 years to clarify the gap between the best lower bound Ω(1) on the competitive ratio versus the best upper bound of O(d). We settle this by describing a Ω(d1-e) lower bound. We also give strong lower bounds (of Ω(d1/B-e) ) if the bin size B ∈ Z+ is allowed to grow. Finally, we discuss almost-matching upper bound results for general values of B; we show an upper bound whose exponent is additively "shifted by 1" from the lower bound exponent.


title: Automated vision system for fabric defect inspection using Gabor filters and PCNN

abstract: In this study, an embedded machine vision system using Gabor filters and Pulse Coupled Neural Network (PCNN) is developed to identify defects of warp-knitted fabrics automatically. The system consists of smart cameras and a Human Machine Interface (HMI) controller. A hybrid detection algorithm combing Gabor filters and PCNN is running on the SOC processor of the smart camera. First, Gabor filters are employed to enhance the contrast of images captured by a CMOS sensor. Second, defect areas are segmented by PCNN with adaptive parameter setting. Third, smart cameras will notice the controller to stop the warp-knitting machine once defects are found out. Experimental results demonstrate that the hybrid method is superior to Gabor and wavelet methods on detection accuracy. Actual operations in a textile factory verify the effectiveness of the inspection system.


title: A 7-µW 2.4-GHz wake-up receiver with -80 dBm sensitivity and high co-channel interferer tolerance

abstract: An ultra-low power wake-up receiver for 2.4-GHz wireless sensor networks, based on a fast sampling method, is presented. A novel multi-branch receiver architecture covers a wide range of interferer scenarios for highly occupied radio channels. The scalability of current consumption versus data rate at a constant sensitivity is another useful feature that fits a multitude of applications, requiring both short reaction times and ultra-low power consumption. The 2.4-GHz OOK receiver comprises a 3-branch analog superheterodyne front-end and six digital 31-bit correlating decoders. It is fabricated in a 130-nm CMOS technology. The current consumption is 2.9 µA at 2.5 V supply voltage and a reaction time of 30 ms. The receiver sensitivity is −80 dBm. Among other sub-100 µW state-of-the-art receivers, the presented implementation shows the best reported sensitivity.