Automated personalized feedback for physical activity and dietary behavior change with mobile phones: a randomized controlled trial on adults.

BACKGROUND
A dramatic rise in health-tracking apps for mobile phones has occurred recently. Rich user interfaces make manual logging of users' behaviors easier and more pleasant, and sensors make tracking effortless. To date, however, feedback technologies have been limited to providing overall statistics, attractive visualization of tracked data, or simple tailoring based on age, gender, and overall calorie or activity information. There are a lack of systems that can perform automated translation of behavioral data into specific actionable suggestions that promote healthier lifestyle without any human involvement.

OBJECTIVE
MyBehavior, a mobile phone app, was designed to process tracked physical activity and eating behavior data in order to provide personalized, actionable, low-effort suggestions that are contextualized to the user's environment and previous behavior. This study investigated the technical feasibility of implementing an automated feedback system, the impact of the suggestions on user physical activity and eating behavior, and user perceptions of the automatically generated suggestions.

METHODS
MyBehavior was designed to (1) use a combination of automatic and manual logging to track physical activity (eg, walking, running, gym), user location, and food, (2) automatically analyze activity and food logs to identify frequent and nonfrequent behaviors, and (3) use a standard machine-learning, decision-making algorithm, called multi-armed bandit (MAB), to generate personalized suggestions that ask users to either continue, avoid, or make small changes to existing behaviors to help users reach behavioral goals. We enrolled 17 participants, all motivated to self-monitor and improve their fitness, in a pilot study of MyBehavior. In a randomized two-group trial, investigators randomly assigned participants to receive either MyBehavior's personalized suggestions (n=9) or nonpersonalized suggestions (n=8), created by professionals, from a mobile phone app over 3 weeks. Daily activity level and dietary intake was monitored from logged data. At the end of the study, an in-person survey was conducted that asked users to subjectively rate their intention to follow MyBehavior suggestions.

RESULTS
In qualitative daily diary, interview, and survey data, users reported MyBehavior suggestions to be highly actionable and stated that they intended to follow the suggestions. MyBehavior users walked significantly more than the control group over the 3 weeks of the study (P=.05). Although some MyBehavior users chose lower-calorie foods, the between-group difference was not significant (P=.15). In a poststudy survey, users rated MyBehavior's personalized suggestions more positively than the nonpersonalized, generic suggestions created by professionals (P<.001).

CONCLUSIONS
MyBehavior is a simple-to-use mobile phone app with preliminary evidence of efficacy. To the best of our knowledge, MyBehavior represents the first attempt to create personalized, contextualized, actionable suggestions automatically from self-tracked information (ie, manual food logging and automatic tracking of activity). Lessons learned about the difficulty of manual logging and usability concerns, as well as future directions, are discussed.

TRIAL REGISTRATION
ClinicalTrials.gov NCT02359981; https://clinicaltrials.gov/ct2/show/NCT02359981 (Archived by WebCite at http://www.webcitation.org/6YCeoN8nv).


Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study.

Qualitative content analysis and thematic analysis are two commonly used approaches in data analysis of nursing research, but boundaries between the two have not been clearly specified. In other words, they are being used interchangeably and it seems difficult for the researcher to choose between them. In this respect, this paper describes and discusses the boundaries between qualitative content analysis and thematic analysis and presents implications to improve the consistency between the purpose of related studies and the method of data analyses. This is a discussion paper, comprising an analytical overview and discussion of the definitions, aims, philosophical background, data gathering, and analysis of content analysis and thematic analysis, and addressing their methodological subtleties. It is concluded that in spite of many similarities between the approaches, including cutting across data and searching for patterns and themes, their main difference lies in the opportunity for quantification of data. It means that measuring the frequency of different categories and themes is possible in content analysis with caution as a proxy for significance.


A hybrid bipartite graph based recommendation algorithm for mobile games

With the rapid development of the mobile games, mobile game recommendation has become a core technique to mobile game marketplaces. This paper proposes a bipartite graph based recommendation algorithm PKBBR (Prior Knowledge Based for Bipartite Graph Rank). We model the user's interest in mobile game based on bipartite graph structure and use the users' mobile game behavior to describe the edge weights of the graph, then incorporate users' prior knowledge into the projection procedure of the bipartite graph to enrich the information among the nodes. Because the popular games have a great influence on mobile game marketplace, we design a hybrid recommendation algorithm to incorporate popularity recommendation based on users' behaviors. The experiment results show that this hybrid method could achieve a better performance than other approaches.


Active Object Categorization on a Humanoid Robot

We present a Bag of Words-based active object categorization technique implemented and tested on a humanoid robot. The robot is trained to categorize objects that are handed to it by a human operator. The robot uses hand and head motions to actively acquire a number of different views. A view planning scheme using entropy minimization reduces the number of views needed to achieve a valid decision. Categorization results are significantly improved by active elimination of background features using robot arm motion. Our experiments cover both, categorization when the object is handed to the robot in a fixed pose at training and testing, and object pose independent categorization. Results on a 4-class object database demonstrate the classification efficiency, a significant gain from multi-view compared to single-view classification, and the advantage of view planning. We conclude that humanoid robotic systems can be successfully applied to actively categorize objects a task with many potential applications ranging from edutainment to active surveillance.


Real Time Text Mining on Twitter Data

Social media constitute a challenging new source of information for intelligence gathering and decision making. Twitter is one of the most popular social media sites and often becomes the primary source of information. Twitter messages are short and well suited for knowledge discovery. Twitter provides both researchers and practitioners a free Application Programming Interface (API) which allows them to gather and analyse large data sets of tweets. Twitter information aren't solely tweet texts, as Twitter’s API provides a lot of info to perform attention-grabbing analysis studies. The paper concisely describes method of knowledge gathering and therefore the main areas of knowledge mining, information discovery and information visual image from Twitter information. In this paper we can create a twitter app from which we can fetch the real time twitter tweets on a particular topic and stored it into R and then we can apply several text mining steps on the tweets to pre-process the tweets text and than we


A Low-Power Speech Recognizer and Voice Activity Detector Using Deep Neural Networks

This paper describes digital circuit architectures for automatic speech recognition (ASR) and voice activity detection (VAD) with improved accuracy, programmability, and scalability. Our ASR architecture is designed to minimize off-chip memory bandwidth, which is the main driver of system power consumption. A SIMD processor with 32 parallel execution units efficiently evaluates feed-forward deep neural networks (NNs) for ASR, limiting memory usage with a sparse quantized weight matrix format. We argue that VADs should prioritize accuracy over area and power, and introduce a VAD circuit that uses an NN to classify modulation frequency features with 22.3-<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> power consumption. The 65-nm test chip is shown to perform a variety of ASR tasks in real time, with vocabularies ranging from 11 words to 145 000 words and full-chip power consumption ranging from 172 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> to 7.78 mW.


A federated Edge Cloud-IoT architecture

The provisioning of powerful decentralized services in the Internet of Things (IoT), as well as the integration of innovative IoT platforms, requires ubiquity, reliability, high-performance, efficiency, and scalability. Towards the accomplishment of the above requirements, the contemporary technological trends leaded to the IoT and Cloud convergence. This combination created the approach of federated Cloud-IoT architectures. The Cloud-IoT architectural vision is paving the way to the vital step, beyond the IoT cognitive capabilities, that refers to the IoT world leveraging cloud principles. For the success of the deployment of such innovative and high-performance solutions, the introduction and the exploitation of 5G technological principles will empower the way forward. Towards this direction, this paper presents a federated architecture combined by distributed Edge Cloud-IoT platforms, that enables the semantic-based service combination and provisioning. The 5G technological features, such as low-latency, zero-losses, high-bitrates are studied and proposed for the empowerment of the federation of the Edge Cloud-IoT architectures. A smart health use case is presented for the evaluation of the proposed solution.


GPCAD: a tool for CMOS op-amp synthesis

We present a method for optimizing and automating component and transistor sizing for CMOS operational amplifiers. We observe that a wide variety of performance measures can be formulated as posynomial functions of the design variables. As a result, amplifier design problems can be formulated as a geometric program, a special type of convex optimization problem for which very efficient global optimization methods have recently been developed. The synthesis method is therefore fast, and determines the globally optimal design; in particular the final solution is completely independent of the starting point (which can even be infeasible), and infeasible specifications are unambiguously detected. After briefly introducing the method, which is described in more detail by M. Hershenson et al., we show how the method can be applied to six common op-amp architectures, and give several example designs.


Applying graph-based anomaly detection approaches to the discovery of insider threats

The ability to mine data represented as a graph has become important in several domains for detecting various structural patterns. One important area of data mining is anomaly detection, but little work has been done in terms of detecting anomalies in graph-based data. In this paper we present graph-based approaches to uncovering anomalies in applications containing information representing possible insider threat activity: e-mail, cell-phone calls, and order processing.


Talla at SemEval-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection

This paper describes our approach to the SemEval-2017 shared task of determining question-question similarity in a community question-answering setting (Task 3B). We extracted both syntactic and semantic similarity features between candidate questions, performed pairwise-preference learning to optimize for ranking order, and then trained a random forest classifier to predict whether the candidate questions were paraphrases of each other. This approach achieved a MAP of 45.7% out of max achievable 67.0% on the test set.


DDD: A New Ensemble Approach for Dealing with Concept Drift

Online learning algorithms often have to operate in the presence of concept drifts. A recent study revealed that different diversity levels in an ensemble of learning machines are required in order to maintain high generalization on both old and new concepts. Inspired by this study and based on a further study of diversity with different strategies to deal with drifts, we propose a new online ensemble learning approach called Diversity for Dealing with Drifts (DDD). DDD maintains ensembles with different diversity levels and is able to attain better accuracy than other approaches. Furthermore, it is very robust, outperforming other drift handling approaches in terms of accuracy when there are false positive drift detections. In all the experimental comparisons we have carried out, DDD always performed at least as well as other drift handling approaches under various conditions, with very few exceptions.


Semi-supervised Sequence Learning

We present two approaches that use unlabeled data to improve sequ nce learning with recurrent networks. The first approach is to predict wha t comes next in a sequence, which is a conventional language model in natural language processing. The second approach is to use a sequence autoencoder, which r eads the input sequence into a vector and predicts the input sequence again. T hese two algorithms can be used as a “pretraining” step for a later supervised seq uence learning algorithm. In other words, the parameters obtained from the unsu pervised step can be used as a starting point for other supervised training model s. In our experiments, we find that long short term memory recurrent networks after b eing pretrained with the two approaches are more stable and generalize bette r. With pretraining, we are able to train long short term memory recurrent network s up to a few hundred timesteps, thereby achieving strong performance in ma ny text classification tasks, such as IMDB, DBpedia and 20 Newsgroups.


Crime Scene Reconstruction: Online Gold Farming Network Analysis

Many online games have their own ecosystems, where players can purchase in-game assets using game money. Players can obtain game money through active participation or “real money trading” through official channels: converting real money into game money. The unofficial market for real money trading gave rise to gold farming groups (GFGs), a phenomenon with serious impact in the cyber and real worlds. GFGs in massively multiplayer online role-playing games (MMORPGs) are some of the most interesting underground cyber economies because of the massive nature of the game. To detect GFGs, there have been various studies using behavioral traits. However, they can only detect gold farmers, not entire GFGs with internal hierarchies. Even worse, GFGs continuously develop techniques to hide, such as forming front organizations, concealing cyber-money, and changing trade patterns when online game service providers ban GFGs. In this paper, we analyze the characteristics of the ecosystem of a large-scale MMORPG, and devise a method for detecting GFGs. We build a graph that characterizes virtual economy transactions, and trace abnormal trades and activities. We derive features from the trading graph and physical networks used by GFGs to identify them in their entirety. Using their structure, we provide recommendations to defend effectively against GFGs while not affecting the existing virtual ecosystem.


Learning methods for generic object recognition with invariance to pose and lighting

We assess the applicability of several popular learning methods for the problem of recognizing generic visual categories with invariance to pose, lighting, and surrounding clutter. A large dataset comprising stereo image pairs of 50 uniform-colored toys under 36 azimuths, 9 elevations, and 6 lighting conditions was collected (for a total of 194,400 individual images). The objects were 10 instances of 5 generic categories: four-legged animals, human figures, airplanes, trucks, and cars. Five instances of each category were used for training, and the other five for testing. Low-resolution grayscale images of the objects with various amounts of variability and surrounding clutter were used for training and testing. Nearest neighbor methods, support vector machines, and convolutional networks, operating on raw pixels or on PCA-derived features were tested. Test error rates for unseen object instances placed on uniform backgrounds were around 13% for SVM and 7% for convolutional nets. On a segmentation/recognition task with highly cluttered images, SVM proved impractical, while convolutional nets yielded 16/7% error. A real-time version of the system was implemented that can detect and classify objects in natural scenes at around 10 frames per second.


A discriminatively trained, multiscale, deformable part model

This paper describes a discriminatively trained, multiscale, deformable part model for object detection. Our system achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge. It also outperforms the best results in the 2007 challenge in ten out of twenty categories. The system relies heavily on deformable parts. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL challenge. Our system also relies heavily on new methods for discriminative training. We combine a margin-sensitive approach for data mining hard negative examples with a formalism we call latent SVM. A latent SVM, like a hidden CRF, leads to a non-convex training problem. However, a latent SVM is semi-convex and the training problem becomes convex once latent information is specified for the positive examples. We believe that our training methods will eventually make possible the effective use of more latent information such as hierarchical (grammar) models and models involving latent three dimensional pose.


Learning to Forget: Continual Prediction with LSTM

Long short-term memory (LSTM; Hochreiter & Schmidhuber, 1997) can solve numerous tasks not solvable by previous learning algorithms for recurrent neural networks (RNNs). We identify a weakness of LSTM networks processing continual input streams that are not a priori segmented into subsequences with explicitly marked ends at which the network's internal state could be reset. Without resets, the state may grow indefinitely and eventually cause the network to break down. Our remedy is a novel, adaptive forget gate that enables an LSTM cell to learn to reset itself at appropriate times, thus releasing internal resources. We review illustrative benchmark problems on which standard LSTM outperforms other RNN algorithms. All algorithms (including LSTM) fail to solve continual versions of these problems. LSTM with forget gates, however, easily solves them, and in an elegant way.


The anatomy of the posterior aspect of the knee. An anatomic study.

BACKGROUND
The orthopaedic literature contains relatively little quantitative information regarding the anatomy of the posterior aspect of the knee. The purpose of the present study was to provide a detailed description of, and to propose a standard nomenclature for, the anatomy of the posterior aspect of the knee.

METHODS
Detailed dissection of twenty nonpaired, fresh-frozen knees was performed. Posterior knee structures were measured according to length, width, and/or distance to reproducible osseous landmarks.

RESULTS
The semimembranosus tendon had eight attachments distal to the main common tendon. The main components were a lateral expansion to the oblique popliteal ligament; a direct arm, which attached to the tibia; and an anterior arm. The oblique popliteal ligament, the largest posterior knee structure, formed a broad fascial sheath over the posterior aspect of the knee and measured 48.0 mm in length and 9.5 mm wide at its medial origin and 16.4 mm wide at its lateral attachment. It had two lateral attachments, one to the meniscofemoral portion of the posterolateral joint capsule and one to the tibia, along the lateral border of the posterior cruciate ligament facet. The semimembranosus also had a distal tibial expansion, which formed a posterior fascial layer over the popliteus muscle. A thickening of the posterior joint capsule, the proximal popliteus capsular expansion, which in this study averaged 40.5 mm in length, connected the posteromedial knee capsule at its attachment at the intercondylar notch to the medial border of the popliteus musculotendinous junction. The plantaris muscle, popliteofibular ligament, fabellofibular ligament, and semimembranosus bursa were present in all specimens.

CONCLUSIONS
The anatomy of the posterior aspect of the knee is quite complex. This study provides information that can lead to further biomechanical, radiographic imaging, and clinical studies of the importance of these posterior knee structures.


The discrete wavelet transform: wedding the a trous and Mallat algorithms


Benefit of Direct Charge Measurement (DCM) on Interconnect Capacitance Measurement

This paper discusses application of direct charge measurement (DCM) on characterizing on-chip interconnect capacitance. Measurement equipment and techniques are leveraged from Flat Panel Display testing. On-chip active device is not an essential necessity for DCM test structure and it is easy to implement parallel measurements. Femto-Farad measurement sensitivity is achieved without having on-chip active device. Measurement results of silicon and glass substrates, including parallel measurements, are presented.


Adaptive Sharing for Online Social Networks: A Trade-off Between Privacy Risk and Social Benefit

Online social networks such as Facebook allow users to control which friend sees what information, but it can be a laborious process for users to specify every receiver for each piece of information they share. Therefore, users usually group their friends into social circles, and select the most appropriate social circle to share particular information with. However, social circles are not formed for setting privacy policies, and even the most appropriate social circle still cannot adapt to the changes of users' privacy requirements influenced by the changes in context. This problem drives the need for better privacy control which can adaptively filter the members in a selected social circle to satisfy users' requirements while maintaining users' social needs. To enable such adaptive sharing, this paper proposes a utility-based trade-off framework that models users' concerns (i.e. Potential privacy risks) and incentives of sharing (i.e. Potential social benefits), and quantifies users' requirements as a trade-off between these two types of utilities. By balancing these two metrics, our framework suggests a subset of a selected circle that aims to maximise users' overall utility of sharing. Numerical simulation results compare the outcome of three sharing strategies in randomly changing contexts.


Evaluating Reinforcement Learning Algorithms in Observational Health Settings

Omer Gottesman, Fredrik Johansson, Joshua Meier, Jack Dent, Donghun Lee, Srivatsan Srinivasan, Linying Zhang, Yi Ding, David Wihl, Xuefeng Peng, Jiayu Yao, Isaac Lage, Christopher Mosch, Li-wei H. Lehman, Matthieu Komorowski, Aldo Faisal, Leo Anthony Celi, David Sontag, and Finale Doshi-Velez Paulson School of Engineering and Applied Sciences, Harvard University Institute for Medical Engineering and Science, MIT T.H. Chan School of Public Health, Harvard University Department of Statistics, Harvard University Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, MIT Department of Surgery and Cancer, Faculty of Medicine, Imperial College London Department of Bioengineering, Imperial College London Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center MIT Critical Data


Personality traits and group-based information behaviour: an exploratory study

Although the importance of individual characteristics and psychological factors has been conceptualized in many models of information seeking behaviour (e.g., Case 2007; Wilson 1999) research into personality issues has only recently attracted attention in information science. This may be explained by the work by Heinström (2002; 2003; 2006a; 2006b; 2006c), but may also be explained by the emerging interest and research into affective dimensions of information behaviour (Nahl and Bilal 2007). Hitherto, the research focus in information science VOL. 14 NO. 2, JUNE, 2009


Design of Neuro-Fuzzy System Controller for DC ServomotorBased Satellite Tracking System

A parabolic dish antenna positioning system is a control unit which directs the antenna to desired target automatically. In all its aspects, automation has become widespread and promises to represent the future for satellite tracking antenna systems. The term tracking is used to mean that the control system should utilize suitable algorithms to automate the process of pointing the antenna to a selected satellite thereby establishing the desired line of sight (LOS). This paper aims to present and discuss the results obtained from the development of a DC servomotor-based Neuro-Fuzzy System Controller (NFSC) algorithm which can be applied in the positioning of the parabolic reflector antennas. The advantage of using NFSC method is that it has a high degree of nonlinearity tolerance, learning ability and solves problems that are difficult to address with the conventional techniques such as Proportional Integral Derivative (PID) strategy. The architecture of the proposed antenna control system employs Adaptive Neuro-Fuzzy Inference System (ANFIS) design environment of MATLAB/SIMULINK package. The results obtained indicated that the proposed NFSC was able to achieve desired output DC motor position with reduced rise time and overshoot. Future work is to apply the controller in real time to achieve automatic satellite tracking with parabolic antenna using data derived from the signal strength.


Google effects on memory: cognitive consequences of having information at our fingertips.

The advent of the Internet, with sophisticated algorithmic search engines, has made accessing information as easy as lifting a finger. No longer do we have to make costly efforts to find the things we want. We can "Google" the old classmate, find articles online, or look up the actor who was on the tip of our tongue. The results of four studies suggest that when faced with difficult questions, people are primed to think about computers and that when people expect to have future access to information, they have lower rates of recall of the information itself and enhanced recall instead for where to access it. The Internet has become a primary form of external or transactive memory, where information is stored collectively outside ourselves.


Rigid scene flow for 3D LiDAR scans

The perception of the dynamic aspects of the environment is a highly relevant precondition for the realization of autonomous robot system acting in the real world. In this paper, we propose a novel method for estimating dense rigid scene flow in 3D LiDAR scans. We formulate the problem as an energy minimization problem, where we assume local geometric constancy and incorporate regularization for smooth motion fields. Analyzing the dynamics at point level helps in inferring the fine-grained details of motion. We show results on multiple sequences of the KITTI odometry dataset, where we seamlessly estimate multiple motions pertaining to different dynamic objects. Furthermore, we test our approach on a dataset with pedestrians to show how our method adapts to a case with non-rigid motion. For comparison we use the ground truth from KITTI and show how our method outperforms different ICP-based methods.


Dialogue-act-driven Conversation Model : An Experimental Study

The utility of additional semantic information for the task of next utterance selection in an automated dialogue system is the focus of study in this paper. In particular, we show that additional information available in the form of dialogue acts –when used along with context given in the form of dialogue history– improves the performance irrespective of the underlying model being generative or discriminative. In order to show the model agnostic behavior of dialogue acts, we experiment with several well-known models such as sequence-to-sequence encoder-decoder model, hierarchical encoder-decoder model, and Siamese-based models with and without hierarchy; and show that in all models, incorporating dialogue acts improves the performance by a significant margin. We, furthermore, propose a novel way of encoding dialogue act information, and use it along with hierarchical encoder to build a model that can use the sequential dialogue act information in a natural way. Our proposed model achieves an MRR of about 84.8% for the task of next utterance selection on a newly introduced DailyDialog dataset, and outperform the baseline models. We also provide a detailed analysis of results including key insights that explain the improvement in MRR because of dialogue act information.


Evaluation of Objective Quality Measures for Speech Enhancement

In this paper, we evaluate the performance of several objective measures in terms of predicting the quality of noisy speech enhanced by noise suppression algorithms. The objective measures considered a wide range of distortions introduced by four types of real-world noise at two signal-to-noise ratio levels by four classes of speech enhancement algorithms: spectral subtractive, subspace, statistical-model based, and Wiener algorithms. The subjective quality ratings were obtained using the ITU-T P.835 methodology designed to evaluate the quality of enhanced speech along three dimensions: signal distortion, noise distortion, and overall quality. This paper reports on the evaluation of correlations of several objective measures with these three subjective rating scales. Several new composite objective measures are also proposed by combining the individual objective measures using nonparametric and parametric regression analysis techniques.


A Proposed Digital Forensics Business Model to Support Cybercrime Investigation in Indonesia

Digital forensics will always include at least human as the one who performs activities, digital evidence as the main object, and process as a reference for the activities followed. The existing framework has not provided a description of the interaction between human, interaction between human and digital evidence, as well as interaction between human and the process itself. A business model approach can be done to provide the idea regarding the interaction in question. In this case, what has been generated by the author in the previous study through a business model of the digital chain of custody becomes the first step in constructing a business model of a digital forensics. In principle, the proposed business model already accommodates major components of digital forensics (human, digital evidence, process) and also considers the interactions among the components. The business model suggested has contained several basic principles as described in The Regulation of Chief of Indonesian National Police (Perkap) No 10/2010. This will give support to law enforcement to deal with cybercrime cases that are more frequent and more sophisticated, and can be a reference for each institution and organization to implement digital forensics activities.


Compact slow-wave millimeter-wave bandpass filter (BPF) using open-loop resonator

A compact slow-wave millimeter-wave bandpass filter for wireless communication systems is developed and proposed in this paper. The filter is based on an open-loop resonator that is composed of a microstrip line with both ends loaded with folded open stubs. The folded arms of the open stubs are utilized to increase the loading capacitance to ground. In addition, the folded arms of the open stubs are also used to introduce cross couplings. The cross couplings can create transmission zeros at the edges of the desired passband. As a result, the filter can exhibit a passband with sharp rejection skirts. The filter is designed to have a fractional bandwidth of about 3.3% at a center frequency of 30.0 GHz. The filter design is realized using Rogers RT6002 substrate with a dielectric constant of 2.94 and a thickness of 0.127 mm. The filter design is successfully fabricated using the printed circuit board (PCB) technology. The fabricated filter is measured and an excellent agreement between the simulated and measured results is achieved. The fabricated filter has the advantages of compact size and excellent performance.


RDF-4X: a scalable solution for RDF quads store in the cloud

Resource Description Framework (RDF) represents a flexible and concise model for representing the metadata of resources on the web. Over the past years, with the increasing amount of RDF data, efficient and scalable RDF data management has become a fundamental challenge to achieve the Semantic Web vision. However, multiple approaches for RDF storage have been suggested, ranging from simple triple stores to more advanced techniques like vertical partitioning on the predicates or centralized approaches. Unfortunately, it is still a challenge to store a huge quantity of RDF quads due, in part, to the query processing for RDF data. This paper proposes a scalable solution for RDF data management that uses Apache Accumulo. We focus on introducing storage methods and indexing techniques that scale to billions of quads across multiple nodes, while providing fast and easy access to the data through conventional query mechanisms such as SPARQL. Our performance evaluation shows that in most cases our approach works well against large RDF datasets.


Global, Dense Multiscale Reconstruction for a Billion Points

We present a variational approach for surface reconstruction from a set of oriented points with scale information. We focus particularly on scenarios with nonuniform point densities due to images taken from different distances. In contrast to previous methods, we integrate the scale information in the objective and globally optimize the signed distance function of the surface on a balanced octree grid. We use a finite element discretization on the dual structure of the octree minimizing the number of variables. The tetrahedral mesh is generated efficiently with a lookup table which allows to map octree cells to the nodes of the finite elements. We optimize memory efficiency by data aggregation, such that robust data terms can be used even on very large scenes. The surface normals are explicitly optimized and used for surface extraction to improve the reconstruction at edges and corners.


Teachers’ Performance Evaluation in Higher Educational Institution using Data Mining Technique

Educational Data Mining (EDM) is an evolving field exploring pedagogical data by applying different machine learning techniques/tools. It can be considered as interdisciplinary research field which provides intrinsic knowledge of teaching and learning process for effective education. The main objective of any educational institution is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge that predicts teachers’ performance. This study presents an efficient system model for evaluation and prediction of teachers’ performance in higher institutions of learning using data mining technologies. To achieve the objectives of this work, a two-layered classifier system was designed; it consists of an Artificial Neural Network (ANN) and Decision Tree. The classifier system was tested successfully using case study data from a Nigerian University in the South West of Nigeria. The data consists of academic qualifications for teachers as well as their experiences and grades of students in courses they taught among others. The attribute selected were evaluated using two feature selection methods in order to get a subset of the attributes that would make for a compact and accurate predictive model. The WEKA machine learning tool was used for the mining. The results show that, among the six attributes used, Working Experience, and Rank are rated the best two attributes that contributed mostly to the performance of teachers in this study. Also, considering the time taken to build the models and performance accuracy level, C4.5 decision tree outperformed the other two algorithms (ID3 and MLP) with good performance of 83.5% accuracy level and acceptable kappa statistics of 0.743. It does mean that C4.5 decision tree is best algorithm suitable for predicting teachers’ performance in relation to the other two algorithms in this work. General Terms Data Mining


Parsimonious module inference in large networks.

We investigate the detectability of modules in large networks when the number of modules is not known in advance. We employ the minimum description length principle which seeks to minimize the total amount of information required to describe the network, and avoid overfitting. According to this criterion, we obtain general bounds on the detectability of any prescribed block structure, given the number of nodes and edges in the sampled network. We also obtain that the maximum number of detectable blocks scales as sqrt[N], where N is the number of nodes in the network, for a fixed average degree ⟨k⟩. We also show that the simplicity of the minimum description length approach yields an efficient multilevel Monte Carlo inference algorithm with a complexity of O(τNlogN), if the number of blocks is unknown, and O(τN) if it is known, where τ is the mixing time of the Markov chain. We illustrate the application of the method on a large network of actors and films with over 10(6) edges, and a dissortative, bipartite block structure.


Discriminative Optimization: Theory and Applications to Computer Vision

Many computer vision problems are formulated as the optimization of a cost function. This approach faces two main challenges: designing a cost function with a local optimum at an acceptable solution, and developing an efficient numerical method to search for this optimum. While designing such functions is feasible in the noiseless case, the stability and location of local optima are mostly unknown under noise, occlusion, or missing data. In practice, this can result in undesirable local optima or not having a local optimum in the expected place. On the other hand, numerical optimization algorithms in high-dimensional spaces are typically local and often rely on expensive first or second order information to guide the search. To overcome these limitations, we propose Discriminative Optimization (DO), a method that learns search directions from data without the need of a cost function. DO explicitly learns a sequence of updates in the search space that leads to stationary points that correspond to the desired solutions. We provide a formal analysis of DO and illustrate its benefits in the problem of 3D registration, camera pose estimation, and image denoising. We show that DO outperformed or matched state-of-the-art algorithms in terms of accuracy, robustness, and computational efficiency.


Pervasive Motion Tracking and Muscle Activity Monitor

This paper introduces a novel human activity monitoring system combining Inertial Measurement Units (IMU) and Mechanomyographic (MMG) muscle sensing technology. While other work has recognised and implemented systems for combined muscle activity and motion recording, they have focused on muscle activity through EMG sensors, which have limited use outside controlled environments. MMG is a low frequency vibration emitted by skeletal muscle whose measurement does not require gel or adhesive electrodes, potentially offering much more efficient implementation for pervasive use. We have developed a combined IMU and MMG sensor the fusion of which provides a new dimension in human activity monitoring and bridges the gap between human dynamics and muscle activity. Results show that synergy between motion and muscle detection is not only viable, but straightforward, inexpensive, and can be packaged in a lightweight wearable package.


Recognition for Handwritten English Letters : A Review

Character recognition is one of the most interesting and challenging research areas in the field of Image processing. English character recognition has been extensively studied in the last half century. Nowadays different methodologies are in widespread use for character recognition. Document verification, digital library, reading bank deposit slips, reading postal addresses, extracting information from cheques, data entry, applications for credit cards, health insurance, loans, tax forms etc. are application areas of digital document processing. This paper gives an overview of research work carried out for recognition of hand written English letters. In Hand written text there is no constraint on the writing style. Hand written letters are difficult to recognize due to diverse human handwriting style, variation in angle, size and shape of letters. Various approaches of hand written character recognition are discussed here along with their performance.


Anomaly Detection for Astronomical Data

Modern astronomical observatories can produce massive amount of data that are beyond the capability of the researchers to even take a glance. These scientific observations present both great opportunities and challenges for astronomers and machine learning researchers. In this project we address the problem of detecting anomalies/novelties in these large-scale astronomical data sets. Two types of anomalies, the point anomalies and the group anomalies, are considered. The point anomalies include individual anomalous objects, such as single stars or galaxies that present unique characteristics. The group anomalies include anomalous groups of objects, such as unusual clusters of the galaxies that are close together. They both have great values for astronomical studies, and our goal is to detect them automatically in un-supervised ways. For point anomalies, we adopt the subspace-based detection strategy and proposed a robust low-rank matrix decomposition algorithm for more reliable results. For group anomalies, we use hierarchical probabilistic models to capture the generative mechanism of the data, and then score the data groups using various probability measures. Experimental evaluation on both synthetic and real world data sets shows the effectiveness of the proposed methods. On a real astronomical data sets, we obtained several interesting anecdotal results. Initial inspections by the astronomers confirm the usefulness of these machine learning methods in astronomical research.


Graph SLAM based mapping for AGV localization in large-scale warehouses

The operation of industrial Automated Guided Vehicles (AGV) today requires designated infrastructure and readily available maps for their localization. In logistics, high effort and investment is necessary to enable the introduction of AGVs. Within the SICK AG coordinated EU-funded research project PAN-Robots we aim to reduce the installation time and costs dramatically by semi-automated plant exploration and localization based on natural landmarks. In this paper, we present our current mapping and localization results based on measurement data acquired at the site of our project partner Coca-Cola Iberian Partners in Bilbao, Spain. We evaluate our solution in terms of accuracy of the map, i.e. comparing landmark position estimates with a ground truth map of millimeter accuracy. The localization results are shown based on artificial landmarks as well as natural landmarks (gridmaps) based on the same graph based optimization solution.


Evaluation of output embeddings for fine-grained image classification

Image classification has advanced significantly in recent years with the availability of large-scale image sets. However, fine-grained classification remains a major challenge due to the annotation cost of large numbers of fine-grained categories. This project shows that compelling classification performance can be achieved on such categories even without labeled training data. Given image and class embeddings, we learn a compatibility function such that matching embeddings are assigned a higher score than mismatching ones; zero-shot classification of an image proceeds by finding the label yielding the highest joint compatibility score. We use state-of-the-art image features and focus on different supervised attributes and unsupervised output embeddings either derived from hierarchies or learned from unlabeled text corpora. We establish a substantially improved state-of-the-art on the Animals with Attributes and Caltech-UCSD Birds datasets. Most encouragingly, we demonstrate that purely unsupervised output embeddings (learned from Wikipedia and improved with finegrained text) achieve compelling results, even outperforming the previous supervised state-of-the-art. By combining different output embeddings, we further improve results.


Advice weaving in AspectJ

This paper describes the implementation of advice weaving in AspectJ. The AspectJ language picks out dynamic join points in a program's execution with pointcuts and uses advice to change the behavior at those join points. The core task of AspectJ's advice weaver is to statically transform a program so that at runtime it will behave according to the AspeetJ language semantics. This paper describes the 1.1 implementation which is based on transforming bytecode. We describe how AspectJ's join points are mapped to regions of bytecode, how these regions are efficiently matched by AspectJ's pointcuts, and how pieces of advice are efficiently implemented at these regions. We also discuss both run-time and compile-time performance of this implementation.


Scalable production of large quantities of defect-free few-layer graphene by shear exfoliation in liquids.

To progress from the laboratory to commercial applications, it will be necessary to develop industrially scalable methods to produce large quantities of defect-free graphene. Here we show that high-shear mixing of graphite in suitable stabilizing liquids results in large-scale exfoliation to give dispersions of graphene nanosheets. X-ray photoelectron spectroscopy and Raman spectroscopy show the exfoliated flakes to be unoxidized and free of basal-plane defects. We have developed a simple model that shows exfoliation to occur once the local shear rate exceeds 10(4) s(-1). By fully characterizing the scaling behaviour of the graphene production rate, we show that exfoliation can be achieved in liquid volumes from hundreds of millilitres up to hundreds of litres and beyond. The graphene produced by this method performs well in applications from composites to conductive coatings. This method can be applied to exfoliate BN, MoS2 and a range of other layered crystals.


Large-area synthesis of high-quality and uniform graphene films on copper foils.

Graphene has been attracting great interest because of its distinctive band structure and physical properties. Today, graphene is limited to small sizes because it is produced mostly by exfoliating graphite. We grew large-area graphene films of the order of centimeters on copper substrates by chemical vapor deposition using methane. The films are predominantly single-layer graphene, with a small percentage (less than 5%) of the area having few layers, and are continuous across copper surface steps and grain boundaries. The low solubility of carbon in copper appears to help make this growth process self-limiting. We also developed graphene film transfer processes to arbitrary substrates, and dual-gated field-effect transistors fabricated on silicon/silicon dioxide substrates showed electron mobilities as high as 4050 square centimeters per volt per second at room temperature.


Measurement of the elastic properties and intrinsic strength of monolayer graphene.

We measured the elastic properties and intrinsic breaking strength of free-standing monolayer graphene membranes by nanoindentation in an atomic force microscope. The force-displacement behavior is interpreted within a framework of nonlinear elastic stress-strain response, and yields second- and third-order elastic stiffnesses of 340 newtons per meter (N m(-1)) and -690 Nm(-1), respectively. The breaking strength is 42 N m(-1) and represents the intrinsic strength of a defect-free sheet. These quantities correspond to a Young's modulus of E = 1.0 terapascals, third-order elastic stiffness of D = -2.0 terapascals, and intrinsic strength of sigma(int) = 130 gigapascals for bulk graphite. These experiments establish graphene as the strongest material ever measured, and show that atomically perfect nanoscale materials can be mechanically tested to deformations well beyond the linear regime.


Raman spectrum of graphene and graphene layers.

Graphene is the two-dimensional building block for carbon allotropes of every other dimensionality. We show that its electronic structure is captured in its Raman spectrum that clearly evolves with the number of layers. The D peak second order changes in shape, width, and position for an increasing number of layers, reflecting the change in the electron bands via a double resonant Raman process. The G peak slightly down-shifts. This allows unambiguous, high-throughput, nondestructive identification of graphene layers, which is critically lacking in this emerging research area.


Synthesis of graphene-based nanosheets via chemical reduction of exfoliated graphite oxide

Reduction of a colloidal suspension of exfoliated graphene oxide sheets in water with hydrazine hydrate results in their aggregation and subsequent formation of a high-surface-area carbon material which consists of thin graphene-based sheets. The reduced material was characterized by elemental analysis, thermo-gravimetric analysis, scanning electron microscopy, X-ray photoelectron spectroscopy, NMR spectroscopy, Raman spectroscopy, and by electrical conductivity measurements. 2007 Elsevier Ltd. All rights reserved.


Microphone Array Processing for Distant Speech Recognition: From Close-Talking Microphones to Far-Field Sensors

Distant speech recognition (DSR) holds the promise of the most natural human computer interface because it enables man-machine interactions through speech, without the necessity of donning intrusive body- or head-mounted microphones. Recognizing distant speech robustly, however, remains a challenge. This contribution provides a tutorial overview of DSR systems based on microphone arrays. In particular, we present recent work on acoustic beam forming for DSR, along with experimental results verifying the effectiveness of the various algorithms described here; beginning from a word error rate (WER) of 14.3% with a single microphone of a linear array, our state-of-the-art DSR system achieved a WER of 5.3%, which was comparable to that of 4.2% obtained with a lapel microphone. Moreover, we present an emerging technology in the area of far-field audio and speech processing based on spherical microphone arrays. Performance comparisons of spherical and linear arrays reveal that a spherical array with a diameter of 8.4 cm can provide recognition accuracy comparable or better than that obtained with a large linear array with an aperture length of 126 cm.


The Impact on Family Functioning of Social Media Use by Depressed Adolescents: A Qualitative Analysis of the Family Options Study

BACKGROUND
Adolescent depression is a prevalent mental health problem, which can have a major impact on family cohesion. In such circumstances, excessive use of the Internet by adolescents may exacerbate family conflict and lack of cohesion. The current study aims to explore these patterns within an intervention study for depressed adolescents.

METHOD
The current study draws upon data collected from parents within the family options randomized controlled trial that examined family based interventions for adolescent depression (12-18 years old) in Melbourne, Australia (2012-2014). Inclusion in the trial required adolescents to meet diagnostic criteria for a major depressive disorder via the Structured Clinical Interview for DSM-IV Childhood Disorders. The transcripts of sessions were examined using qualitative thematic analysis. The transcribed sessions consisted of 56 h of recordings in total from 39 parents who took part in the interventions.

RESULTS
The thematic analysis explored parental perceptions of their adolescent's use of social media (SM) and access to Internet content, focusing on the possible relationship between adolescent Internet use and the adolescent's depressive disorder. Two overarching themes emerged as follows: the sense of loss of parental control over the family environment and parents' perceived inability to protect their adolescent from material encountered on the Internet and social interactions via SM.

CONCLUSION
Parents within the context of family based treatments felt that prolonged exposure to SM exposed their already vulnerable child to additional stressors and risks. The thematic analysis uncovered a sense of parental despair and lack of control, which is consistent with their perception of SM and the Internet as relentless and threatening to their parental authority and family cohesion.


A Type-Based Compiler for Standard ML

Compile-time type information should be valuable in efficient compilation of statically typed functional languages such as Standard ML. But how should type-directed compilation work in real compilers, and how much performance gain will type-based optimizations yield? In order to support more efficient data representations and gain more experience about type-directed compilation, we have implemented a new type-based middle end and back end for the Standard ML of New Jersey compiler. We describe the basic design of the new compiler, identify a number of practical issues, and then compare the performance of our new compiler with the old non-type-based compiler. Our measurement shows that a combination of several simple type-based optimizations reduces heap allocation by 36%; and improves the already-efficient code generated by the old non-type-based compiler by about 19% on a DECstation 500.


Using Neural Networks to Generate Inferential Roles for Natural Language

Neural networks have long been used to study linguistic phenomena spanning the domains of phonology, morphology, syntax, and semantics. Of these domains, semantics is somewhat unique in that there is little clarity concerning what a model needs to be able to do in order to provide an account of how the meanings of complex linguistic expressions, such as sentences, are understood. We argue that one thing such models need to be able to do is generate predictions about which further sentences are likely to follow from a given sentence; these define the sentence's "inferential role." We then show that it is possible to train a tree-structured neural network model to generate very simple examples of such inferential roles using the recently released Stanford Natural Language Inference (SNLI) dataset. On an empirical front, we evaluate the performance of this model by reporting entailment prediction accuracies on a set of test sentences not present in the training data. We also report the results of a simple study that compares human plausibility ratings for both human-generated and model-generated entailments for a random selection of sentences in this test set. On a more theoretical front, we argue in favor of a revision to some common assumptions about semantics: understanding a linguistic expression is not only a matter of mapping it onto a representation that somehow constitutes its meaning; rather, understanding a linguistic expression is mainly a matter of being able to draw certain inferences. Inference should accordingly be at the core of any model of semantic cognition.


Double Thompson Sampling for Dueling Bandits

In this paper, we propose a Double Thompson Sampling (D-TS) algorithm for dueling bandit problems. As its name suggests, D-TS selects both the first and the second candidates according to Thompson Sampling. Specifically, D-TS maintains a posterior distribution for the preference matrix, and chooses the pair of arms for comparison according to two sets of samples independently drawn from the posterior distribution. This simple algorithm applies to general Copeland dueling bandits, including Condorcet dueling bandits as a special case. For general Copeland dueling bandits, we show that D-TS achieves O(K log T ) regret. Moreover, using a back substitution argument, we refine the regret to O(K log T +K log log T ) in Condorcet dueling bandits and most practical Copeland dueling bandits. In addition, we propose an enhancement of D-TS, referred to as D-TS, to reduce the regret in practice by carefully breaking ties. Experiments based on both synthetic and real-world data demonstrate that D-TS and D-TS significantly improve the overall performance, in terms of regret and robustness.


A Proposal for a Set of Level 3 Basic Linear Algebra Subprograms

This paper describes a proposal for Level 3 Basic Linear Algebra Subprograms (Level 3 BLAS). The Level 3 BLAS are targeted at matrix-matrix operations with the aim of providing more efficient, but portable, implementations of algorithms on high-performance computers, especially those with hierarchical memory and parallel processing capability.


Accurate vertical road profile estimation using v-disparity map and dynamic programming

Detecting obstacles on the road is crucial for the advanced driver assistance systems. Obstacle detection on the road can be greatly facilitated if we have a vertical road profile. Therefore, in this paper, we present a novel method that can estimate an accurate vertical road profile of the scene from the stereo images. Unlike conventional stereo-based road profile estimation methods that heavily rely on a parametric model of the road surface, our method can obtain a road profile for an arbitrary complicated road. To this end, an energy function that includes the stereo matching fidelity and spatio-temporal smoothness of the road profile is presented, and thus the road profile is extracted by maximizing the energy function via dynamic programming. The experimental results demonstrate the effectiveness of the proposed method.


Position control of Robotino mobile robot using fuzzy logic

The appearance of fuzzy logic theory led to new trends in robotics field and solves various typical problems. In fact, fuzzy logic is a good alternative for increasing the capabilities of autonomous mobile robots in an unknown dynamic environment by integrating human experience. The paper presents the design and implementation of the position control using fuzzy logic for an omni directional mobile robot. The position control of the mobile robot was tested remotely using the Festo Robotino platform and Matlab environment. The obstacle avoidance and mobile robot odometry were also studied in this work.


Phase III clinical trial of thalidomide plus dexamethasone compared with dexamethasone alone in newly diagnosed multiple myeloma: a clinical trial coordinated by the Eastern Cooperative Oncology Group.

PURPOSE
To determine if thalidomide plus dexamethasone yields superior response rates compared with dexamethasone alone as induction therapy for newly diagnosed multiple myeloma.

PATIENTS AND METHODS
Patients were randomly assigned to receive thalidomide plus dexamethasone or dexamethasone alone. Patients in arm A received thalidomide 200 mg orally for 4 weeks; dexamethasone was administered at a dose of 40 mg orally on days 1 to 4, 9 to 12, and 17 to 20. Cycles were repeated every 4 weeks. Patients in arm B received dexamethasone alone at the same schedule as in arm A.

RESULTS
Two hundred seven patients were enrolled: 103 were randomly assigned to thalidomide plus dexamethasone and 104 were randomly assigned to dexamethasone alone; eight patients were ineligible. The response rate with thalidomide plus dexamethasone was significantly higher than with dexamethasone alone (63% v 41%, respectively; P = .0017). The response rate allowing for use of serum monoclonal protein levels when a measurable urine monoclonal protein was unavailable at follow-up was 72% v 50%, respectively. The incidence rates of grade 3 or higher deep vein thrombosis (DVT), rash, bradycardia, neuropathy, and any grade 4 to 5 toxicity in the first 4 months were significantly higher with thalidomide plus dexamethasone compared with dexamethasone alone (45% v 21%, respectively; P < .001). DVT was more frequent in arm A than in arm B (17% v 3%); grade 3 or higher peripheral neuropathy was also more frequent (7% v 4%, respectively).

CONCLUSION
Thalidomide plus dexamethasone demonstrates significantly superior response rates in newly diagnosed myeloma compared with dexamethasone alone. However, this must be balanced against the greater toxicity seen with the combination.


The impact of visual attributes on online image diffusion

Little is known on how visual content affects the popularity on social networks, despite images being now ubiquitous on the Web, and currently accounting for a considerable fraction of all content shared. Existing art on image sharing focuses mainly on non-visual attributes. In this work we take a complementary approach, and investigate resharing from a mainly visual perspective. Two sets of visual features are proposed, encoding both aesthetical properties (brightness, contrast, sharpness, etc.), and semantical content (concepts represented by the images). We collected data from a large image-sharing service (Pinterest) and evaluated the predictive power of different features on popularity (number of reshares). We found that visual properties have low predictive power compared that of social cues. However, after factoring-out social influence, visual features show considerable predictive power, especially for images with higher exposure, with over 3:1 accuracy odds when classifying highly exposed images between very popular and unpopular.


Finding Structural Similarity in Time Series Data Using Bag-of-Patterns Representation

For more than one decade, time series similarity search has been given a great deal of attention by data mining researchers. As a result, many time series representations and distance measures have been proposed. However, most existing work on time series similarity search focuses on finding shape-based similarity. While some of the existing approaches work well for short time series data, they typically fail to produce satisfactory results when the sequence is long. For long sequences, it is more appropriate to consider the similarity based on the higher-level structures. In this work, we present a histogram-based representation for time series data, similar to the “bag of words” approach that is widely accepted by the text mining and information retrieval communities. We show that our approach outperforms the existing methods in clustering, classification, and anomaly detection on several real datasets.


An Evaluation of Techniques for Grabbing and Manipulating Remote Objects in Immersive Virtual Environments

Grabbing and manipulating virtual objects is an important user interaction for immersive virtual environments. We present implementations and discussion of six techniques which allow manipulation of remote objects. A user study of these techniques was performed which revealed their characteristics and deficiencies, and led to the development of a new class of techniques. These hybrid techniques provide istinct advantages in terms of ease of use and efficiency because they consider the tasks of grabbing and manipulation separately. CR Categories and Subject Descr iptors: I.3.7 [Computer Graphics]:Three-Dimensional Graphics and Realism Virtual Reality; I.3.6 [Computer Graphics]:Methodology and Techniques Interaction Techniques.


Maladaptive and adaptive emotion regulation through music: a behavioral and neuroimaging study of males and females

Music therapists use guided affect regulation in the treatment of mood disorders. However, self-directed uses of music in affect regulation are not fully understood. Some uses of music may have negative effects on mental health, as can non-music regulation strategies, such as rumination. Psychological testing and functional magnetic resonance imaging (fMRI) were used explore music listening strategies in relation to mental health. Participants (n = 123) were assessed for depression, anxiety and Neuroticism, and uses of Music in Mood Regulation (MMR). Neural responses to music were measured in the medial prefrontal cortex (mPFC) in a subset of participants (n = 56). Discharge, using music to express negative emotions, related to increased anxiety and Neuroticism in all participants and particularly in males. Males high in Discharge showed decreased activity of mPFC during music listening compared with those using less Discharge. Females high in Diversion, using music to distract from negative emotions, showed more mPFC activity than females using less Diversion. These results suggest that the use of Discharge strategy can be associated with maladaptive patterns of emotional regulation, and may even have long-term negative effects on mental health. This finding has real-world applications in psychotherapy and particularly in clinical music therapy.


Presentation Attack Detection Methods for Face Recognition Systems: A Comprehensive Survey

The vulnerability of face recognition systems to presentation attacks (also known as direct attacks or spoof attacks) has received a great deal of interest from the biometric community. The rapid evolution of face recognition systems into real-time applications has raised new concerns about their ability to resist presentation attacks, particularly in unattended application scenarios such as automated border control. The goal of a presentation attack is to subvert the face recognition system by presenting a facial biometric artifact. Popular face biometric artifacts include a printed photo, the electronic display of a facial photo, replaying video using an electronic display, and 3D face masks. These have demonstrated a high security risk for state-of-the-art face recognition systems. However, several presentation attack detection (PAD) algorithms (also known as countermeasures or antispoofing methods) have been proposed that can automatically detect and mitigate such targeted attacks. The goal of this survey is to present a systematic overview of the existing work on face presentation attack detection that has been carried out. This paper describes the various aspects of face presentation attacks, including different types of face artifacts, state-of-the-art PAD algorithms and an overview of the respective research labs working in this domain, vulnerability assessments and performance evaluation metrics, the outcomes of competitions, the availability of public databases for benchmarking new PAD algorithms in a reproducible manner, and finally a summary of the relevant international standardization in this field. Furthermore, we discuss the open challenges and future work that need to be addressed in this evolving field of biometrics.


Deep Fusion Net for Multi-atlas Segmentation: Application to Cardiac MR Images

Atlas selection and label fusion are two major challenges in multi-atlas segmentation. In this paper, we propose a novel deep fusion net for better solving these challenges. Deep fusion net is a deep architecture by concatenating a feature extraction subnet and a non-local patchbased label fusion (NL-PLF) subnet in a single network. This network is trained end-to-end for automatically learning deep features achieving optimal performance in a NL-PLF framework. The learned deep features are further utilized in defining a similarity measure for atlas selection. Experimental results on Cardiac MR images for left ventricular segmentation demonstrate that our approach is effective both in atlas selection and multi-atlas label fusion, and achieves state of the art in performance.


A New Distance Measure for Face Recognition System

This paper proposes a new powerful distance measure called Normalized Unmatched Points (NUP). This measure can be used in a face recognition system to discriminate facial images. It works by counting the number of unmatched pixels between query and database images. A face recognition system has been proposed which makes use of this proposed distance measure for taking the decision on matching. This system has been tested on four publicly available databases, viz. ORL, YALE, BERN and CALTECH databases. Experimental results show that the proposed measure achieves recognition rates more than 98.66% for the first five likely matched faces. It is observed that the NUP distance measure performs better than other existing similar variants on these databases.


Fusing Iris, Palmprint and Fingerprint in a Multi-biometric Recognition System

This paper presents a trimodal biometric recognition system based on iris, palmprint and fingerprint. Wavelet transform and Gabor filter are used to extract features in different scales and orientations from iris and palmprint. Minutiae extraction and alignment is used for fingerprint matching. Six different fusion algorithms including score-based, rank-based and decision-based methods are used to combine the results of three modules. We also propose a new rank-based fusion algorithm Maximum Inverse Rank (MIR) which is robust with respect to variations in scores and also bad ranking from a module. CASIA datasets for iris, palmprint and fingerprint are used in this study. The experiments show the effectiveness of our fusion method and our trimodal biometric recognition system in comparison to existing multi-modal recognition systems.


Dermoscopy in differentiating palmar syphiloderm from palmar papular psoriasis.

Palmar syphiloderm is one of the most common presentations of secondary syphilis and its recognition is of utmost importance in order to promptly identify such a disease and initiate appropriate workup/management. However, the differential diagnosis with palmar papular psoriasis often poses some difficulties, with consequent possible diagnostic errors/delays and prescription of improper therapies. In this report, we underline the role of dermoscopy as a supportive tool to facilitate the non-invasive recognition of palmar syphiloderm and its distinction from palmar papular psoriasis.


Semantically Guided Depth Upsampling

We present a novel method for accurate and efficient upsampling of sparse depth data, guided by high-resolution imagery. Our approach goes beyond the use of intensity cues only and additionally exploits object boundary cues through structured edge detection and semantic scene labeling for guidance. Both cues are combined within a geodesic distance measure that allows for boundary-preserving depth interpolation while utilizing local context. We model the observed scene structure by locally planar elements and formulate the upsampling task as a global energy minimization problem. Our method determines globally consistent solutions and preserves fine details and sharp depth boundaries. In our experiments on several public datasets at different levels of application, we demonstrate superior performance of our approach over the state-of-the-art, even for very sparse measurements.


A master-slave blockchain paradigm and application in digital rights management

Upon flaws of current blockchain platforms of heavyweight, large capacity of ledger, and time-consuming of synchronization of data, in this paper, we proposed a new paradigm of master-slave blockchain scheme (MSB) for pervasive computing that suitable for general PC, mobile device such as smart phones or PADs to participants in the working of mining and verification, in which we separated traditional blockchain model in 2 layer defined as master node layer and a series of slavery agents layer, then we proposed 2 approaches for partially computing model(P-CM) and non-computing of model(NCM) in the MSB blockchain, Finally large amounts of simulations manifest the proposed master-slave blockchain scheme is feasible, extendible and suitable for pervasive computing especially in the 5G generation environment, and can apply in the DRM-related applications.


Analysis of GDI Technique for Digital Circuit Design

Power Dissipation of Digital circuits can be reduced by 15% 25% by using appropriate logic restructuring and also it can be reduced by 40% 60% by lowering switching activity. Here, Gate Diffusion Input Technique which is based on a Shannon expansion is analyzed for minimizing the power consumption and delay of static digital circuits. This technique as compare to other currently used logic design style, allows less power consumption and reduced propagation delay for low-power design of combinatorial digital circuits with minimum number of transistors. In this paper, basic building blocks of digital system and few combinational circuits are analyzed using GDI and other CMOS techniques. All circuits are designed at 180nm technology in CADENCE and simulate using VIRTUOSO SPECTRE simulator at 100 MHz frequency. Comparative analysis has been done among GDI and other parallel design styles for designing ripple adder, CLA adder and bit magnitude comparator. Simulation result shows GDI technique saves 53. 3%, 55. 6% and 75. 6% power in ripple adder, CLA adder and bit magnitude comparator respectively as compare to CMOS. Also delay is reduced with 25. 2%, 3. 4% and 6. 9% as compare to CMOS. Analysis conclude that GDI is revolutionary high speed and low power consumption technique.


Cooperative Inverse Reinforcement Learning

For an autonomous system to be helpful to humans and to pose no unwarranted risks, it needs to align its values with those of the humans in its environment in such a way that its actions contribute to the maximization of value for the humans. We propose a formal definition of the value alignment problem as cooperative inverse reinforcement learning (CIRL). A CIRL problem is a cooperative, partialinformation game with two agents, human and robot; both are rewarded according to the human’s reward function, but the robot does not initially know what this is. In contrast to classical IRL, where the human is assumed to act optimally in isolation, optimal CIRL solutions produce behaviors such as active teaching, active learning, and communicative actions that are more effective in achieving value alignment. We show that computing optimal joint policies in CIRL games can be reduced to solving a POMDP, prove that optimality in isolation is suboptimal in CIRL, and derive an approximate CIRL algorithm.


On the Folly of Rewarding A, While Hoping for B

This publication contains reprint articles for which IEEE does not hold copyright. Full text is not available on IEEE Xplore for these articles.


Network Intrusion Detection System using attack behavior classification

Intrusion Detection Systems (IDS) have become a necessity in computer security systems because of the increase in unauthorized accesses and attacks. Intrusion Detection is a major component in computer security systems that can be classified as Host-based Intrusion Detection System (HIDS), which protects a certain host or system and Network-based Intrusion detection system (NIDS), which protects a network of hosts and systems. This paper addresses Probes attacks or reconnaissance attacks, which try to collect any possible relevant information in the network. Network probe attacks have two types: Host Sweep and Port Scan attacks. Host Sweep attacks determine the hosts that exist in the network, while port scan attacks determine the available services that exist in the network. This paper uses an intelligent system to maximize the recognition rate of network attacks by embedding the temporal behavior of the attacks into a TDNN neural network structure. The proposed system consists of five modules: packet capture engine, preprocessor, pattern recognition, classification, and monitoring and alert module. We have tested the system in a real environment where it has shown good capability in detecting attacks. In addition, the system has been tested using DARPA 1998 dataset with 100% recognition rate. In fact, our system can recognize attacks in a constant time.


Mid-level deep pattern mining

Mid-level visual element discovery aims to find clusters of image patches that are both representative and discriminative. In this work, we study this problem from the prospective of pattern mining while relying on the recently popularized Convolutional Neural Networks (CNNs). Specifically, we find that for an image patch, activation extracted from the first fully-connected layer of a CNN have two appealing properties which enable its seamless integration with pattern mining. Patterns are then discovered from a large number of CNN activations of image patches through the well-known association rule mining. When we retrieve and visualize image patches with the same pattern (See Fig. 1), surprisingly, they are not only visually similar but also semantically consistent. We apply our approach to scene and object classification tasks, and demonstrate that our approach outperforms all previous works on mid-level visual element discovery by a sizeable margin with far fewer elements being used. Our approach also outperforms or matches recent works using CNN for these tasks. Source code of the complete system is available online.


Broadband Reflectarray Antenna Using Subwavelength Elements Based on Double Square Meander-Line Rings

A linearly polarized broadband reflectarray is presented employing a novel single layer subwavelength phase shifting element. The size of the element is a fifth of a wavelength at the center frequency of 10 GHz and the element consists of double concentric square rings of meander lines. By changing the length of the meander line, a 420° phase variation range is achieved at the center frequency. This characteristic makes the proposed configuration unique, as most of the reported subwavelength reflectarray elements can only realize a phase range far less than 360°. In addition, the slope of the phase response remains almost constant from 9 to 11 GHz, demonstrating a broadband property. A $48 \times 48$-element reflectarray antenna is simulated, fabricated, and measured. Good agreement is obtained between simulated and measured results. A measured 1.5-dB gain bandwidth of 18% and 56.5% aperture efficiency is achieved.


Does Computer-Assisted Learning Improve Learning Outcomes ? Evidence from a Randomized Experiment in Migrant Schools in Beijing

The
  education
  of
  the
  poor
  and
  disadvantaged
  population
  has
  been
  a
  long-‐standing challenge
  to
  the
  education
  system
  in
  both
  developed
  and
  developing
  countries. Although
  computer-‐assisted
  learning
  (CAL)
  has
  been
  considered
  one
  alternative
  to improve
  learning
  outcomes
  in
  a
  cost-‐effective
  way,
  the
  empirical
  evidence
  of
  its
  impacts on
  improving
  learning
  outcomes
  is
  mixed.
  This
  paper
  intends
  to
  explore
  the
  nature
  of the
  effects
  of
  CAL
  on
  student
  academic
  and
  non-‐academic
  outcomes
  for
  underserved populations
  in
  a
  developing
  country.
  To
  meet
  this
  goal,
  we
  exploit
  a
  randomized
  field experiment
  of
  a
  CAL
  program
  involving
  over
  4000
  third-‐grade
  students,
  mostly
  from poor
  migrant
  families,
  in
  43
  migrant
  schools
  in
  Beijing.
  The
  main
  intervention
  is
  a
  math CAL
  program
  that
  is
  held
  out
  of
  regular
  school
  hours.
  The
  program
  is
  tailored
  to
  the regular
  school
  math
  curriculum
  and
  is
  remedial
  in
  nature.
  Our
  results
  show
  that,
  the
  CAL program
  improved
  the
  student
  standardized
  math
  scores
  by
  0.14
  standard
  deviations and
  most
  of
  the
  program
  effect
  took
  place
  within
  two
  months
  after
  the
  start
  of
  the program.
  Low-‐performing
  students
  and
  those
  with
  less-‐educated
  parents
  benefited more
  from
  the
  program.
  Moreover,
  CAL
  also
  significantly
  increased
  the
  levels
  of
  self-‐ efficacy
  of
  the
  students
  and
  their
  interest
  in
  learning.
  We
  observed
  at
  most
  a
  moderate program
  spillover
  in
  Chinese
  test
  scores.
  Our
  findings
  are
  robust
  to
  the
  Hawthorne effect
  and
  CAL
  program
  spillovers
  that
  might
  potentially
  bias
  the
  estimates
  of
  the program
  effects. Key
  Words:

  Education;
  Development;
  Computer
  Assisted
  Learning;
  Random Assignment;
  Test
  Scores;
  China;
  Migration JEL
  Codes:
  I20;
  I21;
  I28;
  O15


A network forensics tool for precise data packet capture and replay in cyber-physical systems

Network data packet capture and replay capabilities are basic requirements for forensic analysis of faults and securityrelated anomalies, as well as for testing and development. Cyber-physical networks, in which data packets are used to monitor and control physical devices, must operate within strict timing constraints, in order to match the hardware devices' characteristics. Standard network monitoring tools are unsuitable for such systems because they cannot guarantee to capture all data packets, may introduce their own traffic into the network, and cannot reliably reproduce the original timing of data packets. Here we present a high-speed network forensics tool specifically designed for capturing and replaying data traffic in Supervisory Control and Data Acquisition systems. Unlike general-purpose "packet capture" tools it does not affect the observed network's data traffic and guarantees that the original packet ordering is preserved. Most importantly, it allows replay of network traffic precisely matching its original timing. The tool was implemented by developing novel user interface and back-end software for a special-purpose network interface card. Experimental results show a clear improvement in data capture and replay capabilities over standard network monitoring methods and general-purpose forensics solutions.


Meta-learning with backpropagation

This paper introduces gradient descent methods applied to meta-leaming (leaming how to leam) in Neural Networks. Meta-leaning has been of interest in the machine leaming field for decades because of its appealing applications to intelligent agents, non-stationary time series, autonomous robots, and improved leaming algorithms. Many previous neural network-based approaches toward meta-leaming have been based on evolutionary methods. We show how to use gradient descent for meta-leaming in recurrent neural networks. Based on previous work on FixedWeight Leaming Neural Networks, we hypothesize that any recurrent network topology and its corresponding leaming algorithm(s) is a potential meta-leaming system. We tested several recurrent neural network topologies and their corresponding forms of Backpropagation for their ability to meta-leam. One of our systems, based on the Long Short-Term Memory neural network developed a leaming algorithm that could leam any two-dimensional quadratic function (from a set of such functions} after only 30 training examples.


Swarm-Based Spatial Sorting

Purpose – To present an algorithm for spatially sorting objects into an annular structure. Design/Methodology/Approach – A swarm-based model that requires only stochastic agent behaviour coupled with a pheromone-inspired “attraction-repulsion” mechanism. Findings – The algorithm consistently generates high-quality annular structures, and is particularly powerful in situations where the initial configuration of objects is similar to those observed in nature. Research limitations/implications – Experimental evidence supports previous theoretical arguments about the nature and mechanism of spatial sorting by insects. Practical implications – The algorithm may find applications in distributed robotics. Originality/value – The model offers a powerful minimal algorithmic framework, and also sheds further light on the nature of attraction-repulsion algorithms and underlying natural processes.


A High Efficiency MAC Protocol for WLANs: Providing Fairness in Dense Scenarios

Collisions are a main cause of throughput degradation in wireless local area networks. The current contention mechanism used in the IEEE 802.11 networks is called carrier sense multiple access with collision avoidance CSMA/CA. It uses a binary exponential backoff technique to randomize each contender attempt of transmitting, effectively reducing the collision probability. Nevertheless, CSMA/CA relies on a random backoff that while effective and fully decentralized, in principle is unable to completely eliminate collisions, therefore degrading the network throughput as more contenders attempt to share the channel. To overcome these situations, carrier sense multiple access with enhanced collision avoidance CSMA/ECA is able to create a collision-free schedule in a fully decentralized manner using a deterministic backoff after successful transmissions. Hysteresis and fair share are two extensions of CSMA/ECA to support a large number of contenders in a collision-free schedule. CSMA/ECA offers better throughput than CSMA/CA and short-term throughput fairness. This paper describes CSMA/ECA and its extensions. In addition, it provides the first evaluation results of CSMA/ECA with non-saturated traffic, channel errors, and its performance when coexisting with CSMA/CA nodes. Furthermore, it describes the effects of imperfect clocks over CSMA/ECA and presents a mechanism to leverage the impact of channel errors and the addition/withdrawal of nodes over collision-free schedules. Finally, the experimental results on throughput and lost frames from a CSMA/ECA implementation using commercial hardware and open-source firmware are presented.


Face recognition with one training image per person

At present there are many methods that could deal well with frontal view face recognition. However, most of them cannot work well when there is only one training image per person. In this paper, an extension of the eigenface technique, i.e. (PC)A, is proposed. (PC)A combines the original face image with its horizontal and vertical projections and then performs principal component analysis on the enriched version of the image. It requires less computational cost than the standard eigenface technique and experimental results show that on a gray-level frontal view face database where each person has only one training image, (PC)A achieves 3%-5% higher accuracy than the standard eigenface technique through using 10%-15% fewer eigenfaces.


Travel time estimation of a path using sparse trajectories

In this paper, we propose a citywide and real-time model for estimating the travel time of any path (represented as a sequence of connected road segments) in real time in a city, based on the GPS trajectories of vehicles received in current time slots and over a period of history as well as map data sources. Though this is a strategically important task in many traffic monitoring and routing systems, the problem has not been well solved yet given the following three challenges. The first is the data sparsity problem, i.e., many road segments may not be traveled by any GPS-equipped vehicles in present time slot. In most cases, we cannot find a trajectory exactly traversing a query path either. Second, for the fragment of a path with trajectories, they are multiple ways of using (or combining) the trajectories to estimate the corresponding travel time. Finding an optimal combination is a challenging problem, subject to a tradeoff between the length of a path and the number of trajectories traversing the path (i.e., support). Third, we need to instantly answer users' queries which may occur in any part of a given city. This calls for an efficient, scalable and effective solution that can enable a citywide and real-time travel time estimation. To address these challenges, we model different drivers' travel times on different road segments in different time slots with a three dimension tensor. Combined with geospatial, temporal and historical contexts learned from trajectories and map data, we fill in the tensor's missing values through a context-aware tensor decomposition approach. We then devise and prove an object function to model the aforementioned tradeoff, with which we find the most optimal concatenation of trajectories for an estimate through a dynamic programming solution. In addition, we propose using frequent trajectory patterns (mined from historical trajectories) to scale down the candidates of concatenation and a suffix-tree-based index to manage the trajectories received in the present time slot. We evaluate our method based on extensive experiments, using GPS trajectories generated by more than 32,000 taxis over a period of two months. The results demonstrate the effectiveness, efficiency and scalability of our method beyond baseline approaches.


DeepID3: Face Recognition with Very Deep Neural Networks

The state-of-the-art of face recognition has been significantly advanced by the emergence of deep learning. Very deep neural networks recently achieved great success on general object recognition because of their superb learning capacity. This motivates us to investigate their effectiveness on face recognition. This paper proposes two very deep neural network architectures, referred to as DeepID3, for face recognition. These two architectures are rebuilt from stacked convolution and inception layers proposed in VGG net [10] and GoogLeNet [16] to make them suitable to face recognition. Joint face identification-verification supervisory signals are added to both intermediate and final feature extraction layers during training. An ensemble of the proposed two architectures achieves 99.53% LFW face verification accuracy and 96.0% LFW rank-1 face identification accuracy, respectively. A further discussion of LFW face verification result is given in the end.


Incredible high performance SAW resonator on novel multi-layerd substrate

High Q and low TCF are elemental characteristics for SAW resonator, in order to realize high performance filters and duplexers coping with new LTE frequency bands. Authors focus on the acoustic energy confinement in SAW propagation surface and electrical characteristics have been investigated theoretically and experimentally. A new multi-layered structure substrate have realized incredible high performances such as both three times Q and one fifth TCF compared with conventional structure. Band25 duplexer using the new structure has been developed successfully with low loss and good temperature stability.


Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth

When integrating computational tools, such as automatic segmentation, into clinical practice, it is of utmost importance to be able to assess the level of accuracy on new data and, in particular, to detect when an automatic method fails. However, this is difficult to achieve due to the absence of ground truth. Segmentation accuracy on clinical data might be different from what is found through cross validation, because validation data are often used during incremental method development, which can lead to overfitting and unrealistic performance expectations. Before deployment, performance is quantified using different metrics, for which the predicted segmentation is compared with a reference segmentation, often obtained manually by an expert. But little is known about the real performance after deployment when a reference is unavailable. In this paper, we introduce the concept of reverse classification accuracy (RCA) as a framework for predicting the performance of a segmentation method on new data. In RCA, we take the predicted segmentation from a new image to train a reverse classifier, which is evaluated on a set of reference images with available ground truth. The hypothesis is that if the predicted segmentation is of good quality, then the reverse classifier will perform well on at least some of the reference images. We validate our approach on multi-organ segmentation with different classifiers and segmentation methods. Our results indicate that it is indeed possible to predict the quality of individual segmentations, in the absence of ground truth. Thus, RCA is ideal for integration into automatic processing pipelines in clinical routine and as a part of large-scale image analysis studies.


Understanding Individual Neuron Importance Using Information Theory

In this work, we characterize the outputs of individual neurons in a trained feed-forward neural network by entropy, mutual information with the class variable, and a class selectivity measure based on Kullback-Leibler divergence. By cumulatively ablating neurons in the network, we connect these information-theoretic measures to the impact their removal has on classification performance on the test set. We observe that, looking at the neural network as a whole, none of these measures is a good indicator for classification performance, thus confirming recent results by Morcos et al. However, looking at specific layers separately, both mutual information and class selectivity are positively correlated with classification performance. We thus conclude that it is ill-advised to compare these measures across layers, and that different layers may be most appropriately characterized by different measures. We then discuss pruning neurons from neural networks to reduce computational complexity of inference. Drawing from our results, we perform pruning based on information-theoretic measures on a fully connected feed-forward neural network with two hidden layers trained on MNIST dataset and compare the results to a recently proposed pruning method. We furthermore show that the common practice of re-training after pruning can partly be obviated by a surgery step called bias balancing, without incurring significant performance degradation.


FLEXIBLE MANUFACTURING SYSTEMS MODELLING AND PERFORMANCE EVALUATION USING AUTOMOD

In recent times flexible manufacturing systems emerged as a powerful technology to meet the continuous changing customer demands. Increase in the performance of flexible manufacturing systems is expected as a result of integration of the shop floor activities such as machine and vehicle scheduling. The authors made an attempt to integrate machine and vehicle scheduling with an objective to minimize the makespan using Automod. Automod is a discrete event simulation package used to model and simulate a wide variety of issues in automated manufacturing systems. The key issues related to the design and operation of automated guided vehicles such as flow path layout, number of vehicles and traffic control problems are considered in the study. The performance measures like throughput, machine and vehicle utilization are studied for different job dispatching and vehicle assignment rules in different flexible manufacturing system configurations. (Received in August 2010, accepted in April 2011. This paper was with the authors 1 month for 2 revisions.)


Fusing Social Media Cues: Personality Prediction from Twitter and Instagram

Incorporating users’ personality traits has shown to be instrumental in many personalized retrieval and recommender systems. Analysis of users’ digital traces has become an important resource for inferring personality traits. To date, the analysis of users’ explicit and latent characteristics is typically restricted to a single social networking site (SNS). In this work, we propose a novel method that integrates text, image, and users’ meta features from two different SNSs: Twitter and Instagram. Our preliminary results indicate that the joint analysis of users’ simultaneous activities in two popular SNSs seems to lead to a consistent decrease of the prediction errors for each personality trait.


Current sharing in three-phase LLC interleaved resonant converter

In this paper, a novel approach for multi-phase interleaved LLC resonant converter is presented. The proposed solution, based on the use of three LLC modules with star connection of transformer primary windings, allows a drastic reduction of the output current ripple and consequently of the output filter capacitor size. Differently from other multi-phase solutions, that are greatly susceptible to resonant components' tolerance causing current imbalance, the proposed topology exhibits an inherent current sharing capability. Moreover, a closed-loop phase-shift control is introduced to additionally compensate for current mismatch and completely balance the current supplied by each module. The benefit of such solution on the reduction of output current ripple and the phase-shift control interaction and effect on load-step variations are also investigated. Measurements on a prototype are added to simulations as validation of the assertions and proposals.


Ratiometric Artifact Reduction in Low Power Reflective Photoplethysmography

This paper presents effective signal-processing techniques for the compensation of motion artifacts and ambient light offsets in a reflective photoplethysmography sensor suitable for wearable applications. A ratiometric comparison of infrared (IR) and red absorption characteristics cancels out noise that is multiplicative in nature and amplitude modulation of pulsatile absorption signals enables rejection of additive noise. A low-power, discrete-time pulse-oximeter platform is used to capture IR and red photoplethysmograms so that the data used for analysis have noise levels representative of what a true body sensor network device would experience. The proposed artifact rejection algorithm is designed for real-time implementation with a low-power microcontroller while being robust enough to compensate for varying levels in ambient light as well as reducing the effects of motion-induced artifacts. The performance of the system is illustrated by its ability to extract a typical plethysmogram heart-rate waveform since the sensor is subjected to a range of physical disturbances.