title: Vegetative versus Minimally Conscious States: A Study Using TMS-EEG, Sensory and Event-Related Potentials

abstract: Differential diagnoses between vegetative and minimally conscious states (VS and MCS, respectively) are frequently incorrect. Hence, further research is necessary to improve the diagnostic accuracy at the bedside. The main neuropathological feature of VS is the diffuse damage of cortical and subcortical connections. Starting with this premise, we used electroencephalography (EEG) recordings to evaluate the cortical reactivity and effective connectivity during transcranial magnetic stimulation (TMS) in chronic VS or MCS patients. Moreover, the TMS-EEG data were compared with the results from standard somatosensory-evoked potentials (SEPs) and event-related potentials (ERPs). Thirteen patients with chronic consciousness disorders were examined at their bedsides. A group of healthy volunteers served as the control group. The amplitudes (reactivity) and scalp distributions (connectivity) of the cortical potentials evoked by TMS (TEPs) of the primary motor cortex were measured. Short-latency median nerve SEPs and auditory ERPs were also recorded. Reproducible TEPs were present in all control subjects in both the ipsilateral and the contralateral hemispheres relative to the site of the TMS. The amplitudes of the ipsilateral and contralateral TEPs were reduced in four of the five MCS patients, and the TEPs were bilaterally absent in one MCS patient. Among the VS patients, five did not manifest ipsilateral or contralateral TEPs, and three of the patients exhibited only ipsilateral TEPs with reduced amplitudes. The SEPs were altered in five VS and two MCS patients but did not correlate with the clinical diagnosis. The ERPs were impaired in all patients and did not correlate with the clinical diagnosis. These TEP results suggest that cortical reactivity and connectivity are severely impaired in all VS patients, whereas in most MCS patients, the TEPs are preserved but with abnormal features. Therefore, TEPs may add valuable information to the current clinical and neurophysiological assessment of chronic consciousness disorders.


title: Perceiving temporal regularity in music

abstract: We address how listeners perceive temporal regularity in music performances, which are rich in temporal irregularities. A computational model is described in which a small system of internal self-sustained oscillations, operating at different periods with specific phase and period relations, entrains to the rhythms of music performances. Based on temporal expectancies embodied by the oscillations, the model predicts the categorization of temporally changing event intervals into discrete metrical categories, as well as the perceptual salience of deviations from these categories. The model's predictions are tested in two experiments using piano performances of the same music with different phrase structure interpretations (Experiment 1) or different melodic interpretations (Experiment 2). The model successfully tracked temporal regularity amidst the temporal fluctuations found in the performances. The model's sensitivity to performed deviations from its temporal expectations compared favorably with the performers' structural (phrasal and melodic) intentions. Furthermore, the model tracked normal performances (with increased temporal variability) better than performances in which temporal fluctuations associated with individual voices were removed (with decreased variability). The small, systematic temporal irregularities characteristic of human performances (chord asynchronies) improved tracking, but randomly generated temporal irregularities did not. These findings suggest that perception of temporal regularity in complex musical sequences is based on temporal expectancies that adapt in response to temporally fluctuating input.


title: Parsing the heterogeneity of impulsivity: A meta-analytic review of the behavioral implications of the UPPS for psychopathology.

abstract: The construct of impulsivity is implicated in a wide variety of psychopathology. However, the heterogeneous factors or subcomponents that differentially predict outcomes are still in the process of being parsed. The present review and meta-analysis focuses on the psychopathological correlates of the Negative Urgency, (lack of) Premeditation, (lack of) perseverance, Sensation Seeking, and Positive Urgency (UPPS/UPPS-P; Whiteside & Lynam, 2001). which provides a relatively new model of impulsivity that posits 5 potentially overlapping pathways to impulsive action. The present meta-analysis included 115 studies that used the UPPS, with a total of 40,432 participants. Findings suggested that the Negative Urgency pathway to impulsivity demonstrated the greatest correlational effect sizes across all forms of psychopathology, with the Positive Urgency pathway demonstrating a pattern of correlations similar to that of Negative Urgency. These findings raise questions regarding the conceptual and practical separability of these pathways. Lack of Premeditation and Lack of Perseverance also demonstrated similar correlational patterns, suggesting that further investigation of the distinctiveness of these pathways is warranted.


title: Bayesian Network Classifiers for Identifying the . . .

abstract: Undoubtedly, Customer Relationship Management (CRM) has gained its importance through the statement that acquiring a new customer is several times more costly than retaining and selling additional products to existing customers. Consequently, marketing practitioners are currently often focusing on retaining customers for as long as possible. However, recent findings in relationship marketing literature have shown that large differences exist within the group of long-life customers in terms of spending and spending evolution. Therefore, this paper focuses on introducing a measure of a customer's future spending evolution that might improve relationship marketing decision making. In this study, from a marketing point of view, we focus on predicting whether a newly acquired customer will increase or decrease his/her future spending from initial purchase information. This is essentially a classification task. The main contribution of this study lies in comparing and evaluating several Bayesian network classifiers with statistical and other artificial intelligence techniques for the purpose of classifying customers in the binary classification problem at hand. Certain Bayesian network classifiers have been recently proposed in the artificial intelligence literature as probstudy. We discuss and evaluate several types of Bayesian network classifiers and their corresponding structure learning algorithms. We contribute to the literature by providing experimental evidence that: (1) Bayesian network classifiers offer an interesting and viable alternative for our customer lifecycle slope estimation problem; (2) the Markov Blanket concept allows for a natural form of attribute selection that was very effective for the application at hand; (3) the sign of the slope can be predicted with a powerful and parsimonious general, unrestricted Bayesian network classifier; (4) a set of three variables measuring the volume of initial purchases and the degree to which customers originally buy in different categories, are powerful predictors for estimating the sign of the slope, and might therefore provide desirable additional information for relationship marketing decision making.


title: BOXTREE: A Hierarchical Representation for Surfaces in 3D

abstract: We introduce the boxtree, a versatile data structure for representing triangulated or meshed surfaces in 3D. A boxtree is a hierarchical structure of nested boxes that supports efficient ray tracing and collision detection. It is simple and robust, and requires minimal space. In situations where storage is at a premium, boxtrees are effective alternatives to octrees and BSP trees. They are also more flexible and efficient than R-trees, and nearly as simple to implement.


title: A Unified Generative Adversarial Framework for Image Generation and Person Re-identification

abstract: Person re-identification (re-id) aims to match a certain person across multiple non-overlapping cameras. It is a challenging task because the same person's appearance can be very different across camera views due to the presence of large pose variations. To overcome this issue, in this paper, we propose a novel unified person re-id framework by exploiting person poses and identities jointly for simultaneous person image synthesis under arbitrary poses and pose-invariant person re-identification. The framework is composed of a GAN based network and two Feature Extraction Networks (FEN), and enjoys following merits. First, it is a unified generative adversarial model for person image generation and person re-identification. Second, a pose estimator is utilized into the generator as a supervisor in the training process, which can effectively help pose transfer and guide the image generation with any desired pose. As a result, the proposed model can automatically generate a person image under an arbitrary pose. Third, the identity-sensitive representation is explicitly disentangled from pose variations through the person identity and pose embedding. Fourth, the learned re-id model can have better generalizability on a new person re-id dataset by using the synthesized images as auxiliary samples. Extensive experimental results on four standard benchmarks including Market-1501 [69], DukeMTMC-reID [40], CUHK03 [23], and CUHK01 [22] demonstrate that the proposed model can perform favorably against state-of-the-art methods.


title: High-Quality Exposure Correction of Underexposed Photos

abstract: We address the problem of correcting the exposure of underexposed photos. Previous methods have tackled this problem from many different perspectives and achieved remarkable progress. However, they usually fail to produce natural-looking results due to the existence of visual artifacts such as color distortion, loss of detail, exposure inconsistency, etc. We find that the main reason why existing methods induce these artifacts is because they break a perceptually similarity between the input and output. Based on this observation, an effective criterion, termed as perceptually bidirectional similarity (PBS) is proposed. Based on this criterion and the Retinex theory, we cast the exposure correction problem as an illumination estimation optimization, where PBS is defined as three constraints for estimating illumination that can generate the desired result with even exposure, vivid color and clear textures. Qualitative and quantitative comparisons, and the user study demonstrate the superiority of our method over the state-of-the-art methods.


title: Data Mining of Causal Relations from Text: Analysing Maritime Accident Investigation Reports

abstract: Text mining is a process of extracting information of interest from text. Such a method includes techniques from various areas such as Information Retrieval (IR), Natural Language Processing (NLP), and Information Extraction (IE). In this study, text mining methods are applied to extract causal relations from maritime accident investigation reports collected from the Marine Accident Investigation Branch (MAIB). These causal relations provide information on various mechanisms behind accidents, including human and organizational factors relating to the accident. The objective of this study is to facilitate the analysis of the maritime accident investigation reports, by means of extracting contributory causes with more feasibility. A careful investigation of contributory causes from the reports provide opportunity to improve safety in future. Two methods have been employed in this study to extract the causal relations. They are 1) Pattern classification method and 2) Connectives method. The earlier one uses naive Bayes and Support Vector Machines (SVM) as classifiers. The latter simply searches for the words connecting cause and effect in sentences. The causal patterns extracted using these two methods are compared to the manual (human expert) extraction. The pattern classification method showed a fair and sensible performance with F-measure(average) = 65% when compared to connectives method with F-measure(average) = 58%. This study is an evidence, that text mining methods could be employed in extracting causal relations from marine accident investigation reports.


title: A Framework for an Intrinsic Evaluation of Word Vector Representations

abstract: We present a new framework for an intrinsic evaluation of word vector representations based on the outlier detection task. This task is intended to test the capability of vector space models to create semantic clusters in the space. We carried out a pilot study building a gold standard dataset and the results revealed two important features: human performance on the task is extremely high compared to the standard word similarity task, and stateof-the-art word embedding models, whose current shortcomings were highlighted as part of the evaluation, still have considerable room for improvement.


title: Usability of Forensics Tools: A User Study

abstract: Digital forensics has become a critical part of almost every investigation, and users of digital forensics tools are becoming more diverse in their backgrounds and interests. As a result, usability is an important aspect of these tools. This paper examines the usability aspect of forensics tools through interviews and surveys designed to obtain feedback from professionals using these tools as part of their regularly assigned duties. The study results highlight a number of usability issues that need to be taken into consideration when designing and implementing digital forensics tools.


title: Generalized modeling and optimization of a bidirectional dual active bridge DC-DC converter including frequency variation

abstract: The paper presents a novel modeling approach of the power flow in a bidirectional dual active bridge DC-DC converter. By using basic superposition principles, the mathematical distinction of cases is avoided in the modeling process of high-frequency transformer currents for different types of modulation. The generalized model is used in an optimization of converter losses of a 3.3 kW electric vehicle battery charger with an input voltage of 400 V and a battery voltage range of 280 V to 420 V. Besides the commonly used control variables such as phase-shift and clamping intervals, also the variation of switching frequency is considered in the optimization process. The optimal modulation including frequency variation leads to an increase of converter efficiency up to 8.6% using IGBTs and 17.8% using MOSFETs in the most critical point compared to phase-shift modulation at fixed switching frequency.


title: On robotic trajectory planning using polynomial interpolations

abstract: Trajectory planning is a classic topic in robotics. Although cubic splines are widely used to interpolate the trajectory in the literature, they have drawbacks in satisfying more boundary conditions for high dynamic performance, say, the control of acceleration at the end points. On the other hand, if quartic splines are employed, more conditions are needed to determine the coefficients of the fourth-order polynomials for the trajectory. In this paper, we present a method to interpolate the trajectory by combining third-order and fourth-order polynomials. We use two quartic polynomials for the first and last segments of the trajectory, and cubic ones for other segments. The trajectory can be uniquely determined by a set of path points for boundary conditions including accelerations at end points. We also investigate the planning of optimal trajectories in the case of variant intervals between adjacent path points. In the optimization, we use multiple objectives including accelerations and jerks. How to determine succinctly the polynomial coefficients is described. Examples are provided for illustration, and comparisons with previous planning method are reported. Good results of application in humanoid robot motion planning for obstacle stepping-over are obtained


title: Security Issues in the Internet of Things (IoT): A Comprehensive Study

abstract: Wireless communication networks are highly prone to security threats. The major applications of wireless communication networks are in military, business, healthcare, retail, and transportations. These systems use wired, cellular, or adhoc networks. Wireless sensor networks, actuator networks, and vehicular networks have received a great attention in society and industry. In recent years, the Internet of Things (IoT) has received considerable research attention. The IoT is considered as future of the internet. In future, IoT will play a vital role and will change our living styles, standards, as well as business models. The usage of IoT in different applications is expected to rise rapidly in the coming years. The IoT allows billions of devices, peoples, and services to connect with others and exchange information. Due to the increased usage of IoT devices, the IoT networks are prone to various security attacks. The deployment of efficient security and privacy protocols in IoT networks is extremely needed to ensure confidentiality, authentication, access control, and integrity, among others. In this paper, an extensive comprehensive study on security and privacy issues in IoT networks is provided.


title: General Tensor Discriminant Analysis and Gabor Features for Gait Recognition

abstract: Traditional image representations are not suited to conventional classification methods such as the linear discriminant analysis (LDA) because of the undersample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two-dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA, compared with existing preprocessing methods such as the principal components analysis (PCA) and 2DLDA, include the following: 1) the USP is reduced in subsequent classification by, for example, LDA, 2) the discriminative information in the training tensors is preserved, and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, whereas that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor-function-based image decompositions for image understanding and object recognition, we develop three different Gabor-function-based image representations: 1) GaborD is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS, and GaborSD representations are applied to the problem of recognizing people from their averaged gait images. A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS, or GaborSD image representation, then using GDTA to extract features and, finally, using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the University of South Florida (USF) HumanID Database. Experimental comparisons are made with nine state-of-the-art classification methods in gait recognition.


title: Where to Go on Your Next Trip?: Optimizing Travel Destinations Based on User Preferences

abstract: Recommendation based on user preferences is a common task for e-commerce websites. New recommendation algorithms are often evaluated by offline comparison to baseline algorithms such as recommending random or the most popular items. Here, we investigate how these algorithms themselves perform and compare to the operational production system in large scale online experiments in a real-world application. Specifically, we focus on recommending travel destinations at Booking.com, a major online travel site, to users searching for their preferred vacation activities. To build ranking models we use multi-criteria rating data provided by previous users after their stay at a destination. We implement three methods and compare them to the current baseline in Booking.com: random, most popular, and Naive Bayes. Our general conclusion is that, in an online A/B test with live users, our Naive-Bayes based ranker increased user engagement significantly over the current online system.


title: Joint Matrix-Tensor Factorization for Knowledge Base Inference

abstract: While several matrix factorization (MF) and tensor factorization (TF) models have been proposed for knowledge base (KB) inference, they have rarely been compared across various datasets. Is there a single model that performs well across datasets? If not, what characteristics of a dataset determine the performance of MF and TF models? Is there a joint TF+MF model that performs robustly on all datasets? We perform an extensive evaluation to compare popular KB inference models across popular datasets in the literature. In addition to answering the questions above, we remove a limitation in the standard evaluation protocol for MF models, propose an extension to MF models so that they can better handle out-of-vocabulary (OOV) entity pairs, and develop a novel combination of TF and MF models. We also analyze and explain the results based on models and dataset characteristics. Our best model is robust, and obtains strong results across all datasets.


Binarization with Fully Convolutional Neural Networks


title: An Approach for Service Function Chain Routing and Virtual Function Network Instance Migration in Network Function Virtualization Architectures

abstract: Network function virtualization foresees the virtualization of service functions and their execution on virtual machines. Any service is represented by a service function chain (SFC) that is a set of VNFs to be executed according to a given order. The running of VNFs needs the instantiation of VNF Instances (VNFIs) that in general are software modules executed on virtual machines. The virtualization challenges include: 1) where to instantiate VNFIs; ii) how many resources to allocate to each VNFI; iii) how to route SFC requests to the appropriate VNFIs in the right sequence; and iv) when and how to migrate VNFIs in response to changes to SFC request intensity and location. We develop an approach that uses three algorithms that are used back-to-back resulting in VNFI placement, SFC routing, and VNFI migration in response to changing workload. The objective is to first minimize the rejection of SFC bandwidth and second to consolidate VNFIs in as few servers as possible so as to reduce the energy consumed. The proposed consolidation algorithm is based on a migration policy of VNFIs that considers the revenue loss due to QoS degradation that a user suffers due to information loss occurring during the migrations. The objective is to minimize the total cost given by the energy consumption and the revenue loss due to QoS degradation. We evaluate our suite of algorithms on a test network and show performance gains that can be achieved over using other alternative naive algorithms.


title: From feedforward to recurrent LSTM neural networks for language modeling

abstract: Language models have traditionally been estimated based on relative frequencies, using count statistics that can be extracted from huge amounts of text data. More recently, it has been found that neural networks are particularly powerful at estimating probability distributions over word sequences, giving substantial improvements over state-of-the-art count models. However, the performance of neural network language models strongly depends on their architectural structure. This paper compares count models to feedforward, recurrent, and long short-term memory (LSTM) neural network variants on two large-vocabulary speech recognition tasks. We evaluate the models in terms of perplexity and word error rate, experimentally validating the strong correlation of the two quantities, which we find to hold regardless of the underlying type of the language model. Furthermore, neural networks incur an increased computational complexity compared to count models, and they differently model context dependences, often exceeding the number of words that are taken into account by count based approaches. These differences require efficient search methods for neural networks, and we analyze the potential improvements that can be obtained when applying advanced algorithms to the rescoring of word lattices on large-scale setups.


title: Semi-Supervised Kernel Mean Shift Clustering

abstract: Mean shift clustering is a powerful nonparametric technique that does not require prior knowledge of the number of clusters and does not constrain the shape of the clusters. However, being completely unsupervised, its performance suffers when the original distance metric fails to capture the underlying cluster structure. Despite recent advances in semi-supervised clustering methods, there has been little effort towards incorporating supervision into mean shift. We propose a semi-supervised framework for kernel mean shift clustering (SKMS) that uses only pairwise constraints to guide the clustering procedure. The points are first mapped to a high-dimensional kernel space where the constraints are imposed by a linear transformation of the mapped points. This is achieved by modifying the initial kernel matrix by minimizing a log det divergence-based objective function. We show the advantages of SKMS by evaluating its performance on various synthetic and real datasets while comparing with state-of-the-art semi-supervised clustering algorithms.


title: Neural pre-translation for hybrid machine translation

abstract: Hybrid machine translation (HMT) takes advantage of different types of machine translation ::: (MT) systems to improve translation performance. Neural machine translation (NMT) can ::: produce more fluent translations while phrase-based statistical machine translation (PB-SMT) ::: can produce adequate results primarily due to the contribution of the translation model. In ::: this paper, we propose a cascaded hybrid framework to combine NMT and PB-SMT to improve translation quality. Specifically, we first use the trained NMT system to pre-translate ::: the training data, and then employ the pre-translated training data to build an SMT system and ::: tune parameters using the pre-translated development set. Finally, the SMT system is utilised ::: as a post-processing step to re-decode the pre-translated test set and produce the final result. ::: Experiments conducted on Japanese!English and Chinese!English show that the proposed ::: cascaded hybrid framework can significantly improve performance by 2.38 BLEU points and ::: 4.22 BLEU points, respectively, compared to the baseline NMT system.


title: The Fastest Deformable Part Model for Object Detection

abstract: This paper solves the speed bottleneck of deformable part model (DPM), while maintaining the accuracy in detection on challenging datasets. Three prohibitive steps in cascade version of DPM are accelerated, including 2D correlation between root filter and feature map, cascade part pruning and HOG feature extraction. For 2D correlation, the root filter is constrained to be low rank, so that 2D correlation can be calculated by more efficient linear combination of 1D correlations. A proximal gradient algorithm is adopted to progressively learn the low rank filter in a discriminative manner. For cascade part pruning, neighborhood aware cascade is proposed to capture the dependence in neighborhood regions for aggressive pruning. Instead of explicit computation of part scores, hypotheses can be pruned by scores of neighborhoods under the first order approximation. For HOG feature extraction, look-up tables are constructed to replace expensive calculations of orientation partition and magnitude with simpler matrix index operations. Extensive experiments show that (a) the proposed method is 4 times faster than the current fastest DPM method with similar accuracy on Pascal VOC, (b) the proposed method achieves state-of-the-art accuracy on pedestrian and face detection task with frame-rate speed.


title: Fiber and Prebiotics: Mechanisms and Health Benefits

abstract: The health benefits of dietary fiber have long been appreciated. Higher intakes of dietary fiber are linked to less cardiovascular disease and fiber plays a role in gut health, with many effective laxatives actually isolated fiber sources. Higher intakes of fiber are linked to lower body weights. Only polysaccharides were included in dietary fiber originally, but more recent definitions have included oligosaccharides as dietary fiber, not based on their chemical measurement as dietary fiber by the accepted total dietary fiber (TDF) method, but on their physiological effects. Inulin, fructo-oligosaccharides, and other oligosaccharides are included as fiber in food labels in the US. Additionally, oligosaccharides are the best known “prebiotics”, “a selectively fermented ingredient that allows specific changes, both in the composition and/or activity in the gastrointestinal microflora that confers benefits upon host well-bring and health.” To date, all known and suspected prebiotics are carbohydrate compounds, primarily oligosaccharides, known to resist digestion in the human small intestine and reach the colon where they are fermented by the gut microflora. Studies have provided evidence that inulin and oligofructose (OF), lactulose, and resistant starch (RS) meet all aspects of the definition, including the stimulation of Bifidobacterium, a beneficial bacterial genus. Other isolated carbohydrates and carbohydrate-containing foods, including galactooligosaccharides (GOS), transgalactooligosaccharides (TOS), polydextrose, wheat dextrin, acacia gum, psyllium, banana, whole grain wheat, and whole grain corn also have prebiotic effects.


User Acceptance of Mobile Payments : A Theoretical Model for Mobile Payments


title: Private Pliable Index Coding

abstract: The Pliable Index CODing (PICOD) problem is a variant of the Index Coding (IC) problem, where the desired messages by the users, who are equipped with message side information, are part of the optimization. This paper studies the PICOD problem where users are subject to a privacy constraint. In particular, the following special class of private PICODs is investigated: 1) the side information structure is circular, and 2) each user can decode one and only one message. The first condition is a special case of the “circular-arc network topology hypergraph” class of PICOD studied in [6], for which an optimal solution was given without the privacy constraint. The second condition was first studied in [9] and was motivated by the need to keep content private in some distribution networks. This paper proposes both converse and achievable bounds. The proposed achievable scheme not only strictly outperforms the existing one for some values of the system parameters, but it is also information theoretically optimal in some settings. For the remaining cases, the proposed linear code is shown to require at most one more transmission than the best possible linear code.


title: Consumer Mobile Health Apps: Current State, Barriers, and Future Directions

abstract: This paper discusses the current state, barriers, and future directions of consumer-facing applications (apps). There are currently more than 165,000 mobile health apps publicly available in major app stores, the vast majority of which are designed for patients. The top 2 categories are wellness management and disease management apps, whereas other categories include self-diagnosis, medication reminder, and electronic patient portal apps. Apps specific to physical medicine and rehabilitation also are reviewed. These apps have the potential to provide low-cost, around-the-clock access to high-quality, evidence-based health information to end users on a global scale. However, they have not yet lived up to their potential due to multiple barriers, including lack of regulatory oversight, limited evidence-based literature, and concerns of privacy and security. The future directions may consist of improving data integration into the health care system, an interoperable app platform allowing access to electronic health record data, cloud-based personal health record across health care networks, and increasing app prescription by health care providers. For consumer mobile health apps to fully contribute value to health care delivery and chronic disease management, all stakeholders within the ecosystem must collaborate to overcome the significant barriers.


title: Memory reconsolidation

abstract: The formation, storage and use of memories is critical for normal adaptive functioning, including the execution of goal-directed behavior, thinking, problem solving and decision-making, and is at the center of a variety of cognitive, addictive, mood, anxiety, and developmental disorders. Memory also significantly contributes to the shaping of human personality and character, and to social interactions. Hence, understanding how memories are formed, stored, retrieved, modified, updated and used potentially impacts many areas in human life, including mental health.


title: Algorithms column: the vertex cover problem

abstract: In this column I give a slightly simpler proof of an old result by Nemhauser and Trotter [10]. It would be interesting to see for which other problems such results hold. One of the widely studied problems in Combinatorial Optimization is the Weighted Vertex Cover problem. Given a graph G = (V, E) with a weight function defined on the vertices, a vertex cover is a subset of vertices S such that for each edge e = (u, v) either u E S or v 6 S. The minimum weight vertex cover problem asks for a vertex cover of minimum total weight. This problem has been the subject of many papers, since the problem is NP-hard, but can be solved optimally for bipartite graphs by a reduction to network flows. There are many papers addressing the issue of obtaining polynomial time approximation algorithms [1, 2, 3, 4, 5, 8, 7]. One of the first approaches (in fact this also implies a polynomial time 2 approximation), is by Nemhauser and Trotter [10]. Consider the following simple Integer Program (IP) for the Weighted Vertex Cover Problem. Here Xu refers to a binary indicator variable, and its value is 1 if and only if vertex u is in the cover.


title: Machine Learning versus Knowledge Based Classification of Legal Texts

abstract: This paper presents results of an experiment in which we used machine learning (ML) techniques to classify sentences in Dutch legislation. These results are compared to the results of a pattern-based classifier. Overall, the ML classifier performs as accurate (>90%) as the pattern based one, but seems to generalize worse to new laws. Given these results, the pattern based approach is to be preferred since its reasons for classification are clear and can be used for further modelling of the content of the sentences.


title: Anatomy of a Personalized Livestreaming System

abstract: With smartphones making video recording easier than ever, new apps like Periscope and Meerkat brought personalized interactive video streaming to millions. With a touch, viewers can switch between first person perspectives across the globe, and interact in real-time with broadcasters. Unlike traditional video streaming, these services require low-latency video delivery to support high interactivity between broadcasters and audiences. We perform a detailed analysis into the design and performance of Periscope, the most popular personal livestreaming service with 20 million users. Using detailed measurements of Periscope (3 months, 19M streams, 705M views) and Meerkat (1 month, 164K streams, 3.8M views), we ask the critical question: ``Can personalized livestreams continue to scale, while allowing their audiences to experience desired levels of interactivity?' We analyze the network path of each stream and break down components of its end-to-end delay. We find that much of each stream's delay is the direct result of decisions to improve scalability, from chunking video sequences to selective polling for reduced server load. Our results show a strong link between volume of broadcasts and stream delivery latency. Finally, we discovered a critical security flaw during our study, and shared it along with a scalable solution with Periscope and Meerkat management.


abstract: A broadband low profile microstrip filter-antenna with an omni-directional pattern is presented. A coupled line is used to connect a microstrip antenna and the second resonator of a filter as admittance inverter. In the design, the antenna performs as a radiator; meanwhile it is also the last resonator of the bandpass filter. Therefore, the loss is reduced between the filter and the microstrip antenna. The proposed filter-antenna can provide 15% impedance bandwidth, a low profile of 3.8mm, an omni-directional pattern and high suppression in the stopband. The proposed antenna simulated results are presented.


Multi-Modal Integration of EEG-fNIRS for Brain-Computer Interfaces – Current Limitations and Future Directions


title: AF classification from a short single lead ECG recording: The PhysioNet/computing in cardiology challenge 2017

abstract: The PhysioNet/Computing in Cardiology (CinC) Challenge 2017 focused on differentiating AF from noise, normal or other rhythms in short term (from 9-61 s) ECG recordings performed by patients. A total of 12,186 ECGs were used: 8,528 in the public training set and 3,658 in the private hidden test set. Due to the high degree of inter-expert disagreement between a significant fraction of the expert labels we implemented a mid-competition bootstrap approach to expert relabeling of the data, levering the best performing Challenge entrants' algorithms to identify contentious labels. A total of 75 independent teams entered the Challenge using a variety of traditional and novel methods, ranging from random forests to a deep learning approach applied to the raw data in the spectral domain. Four teams won the Challenge with an equal high F1 score (averaged across all classes) of 0.83, although the top 11 algorithms scored within 2% of this. A combination of 45 algorithms identified using LASSO achieved an F1 of 0.87, indicating that a voting approach can boost performance.


title: Dataset issues in object recognition

abstract: Appropriate datasets are required at all stages of object recognition research, including learning visual models of object and scene categories, detecting and localizing instances of these models in images, and evaluating the performance of recognition algorithms. Current datasets are lacking in several respects, and this paper discusses some of the lessons learned from existing efforts, as well as innovative ways to obtain very large and diverse annotated datasets. It also suggests a few criteria for gathering future datasets.


title: Predictive value of the exercise tolerance test for mortality in North American men: the Lipid Research Clinics Mortality Follow-up Study

abstract: More than 3600 white men, from 30 to 79 years old and without a history of myocardial infarction, underwent submaximal treadmill exercise tolerance tests as part of their baseline evaluation for the Lipid Research Clinics Mortality Follow-up Study. The exercise test was conducted according to a common protocol and coded centrally; depression of the ST segment by at least 1 mm (visual coding) and/or 10 microV-sec (ST integral, computer coding) signified a positive test. Concurrent measurements of age, blood pressure, history of cigarette smoking, and plasma levels of lipids, lipoproteins, and glucose, as well as other coronary risk factors, were obtained. Cumulative mortality from cardiovascular disease was 11.9% (22/185) over 8.1 years mean follow-up among men with a positive exercise test vs 1.2% (36/2993) over 8.6 years mean follow-up among men with a negative test. Three-quarters (43) of these deaths were due to coronary heart disease. The relative risk for cardiovascular mortality associated with a po...


title: Automatic Extraction of Linguistic Metaphors with LDA Topic Modeling

abstract: We aim to investigate cross-cultural patterns of thought through cross-linguistic investigation of the use of metaphor. As a first step, we produce a system for locating instances of metaphor in English and Spanish text. In contrast to previous work which relies on resources like syntactic parsing and WordNet, our system is based on LDA topic modeling, enabling its application even to low-resource languages, and requires no labeled data. We achieve an F-score of 59% for English.


FOR MUSIC AUTO-TAGGING USING RAW WAVEFORMS


title: Analysis of Broadband Variations of U-slot cut Rectangular Microstrip Antennas

abstract: i»?A very popularly used technique to realize broadband and compact microstrip antenna (MSA) is by cutting a slot at an appropriate position inside the patch. More frequently, U-slot and its variation, a half U-slot, have been used to increase the bandwidth (BW). In most of the available literature on U-slot cut antennas, a detailed explanation for broadband response due to U-slot has not been reported. In this paper, an extensive study for the broadband behavior for U-slot, half U-slot and double U-slot cut rectangular MSAs (RMSAs) is presented. The U-slot configurations with different aspect ratios (i.e., length-to-width ratio) for patch and slot dimensions have been studied. In U-slot and half U-slot cut patches, it has been observed that slot does not introduce any additional mode, but it modifies the fundamental and higher order mode resonance frequencies of the patch to yield broadband response. The slot also modifies surface current distribution at higher order mode to yield broadside radiation pattern characteristics over the complete BW, without any variations in the directions of principle planes. Further, in double U-slot cut RMSA, the second U-slot optimizes the impedance and frequency at modified higher order TM21 mode and, along with modified TM01 and TM20 modes, yields increase in the BW. This paper will give an insight into the functioning of U-slot cut antennas.


title: NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications

abstract: This work proposes an algorithm, called NetAdapt, that automatically adapts a pre-trained deep neural network to a mobile platform given a resource budget. While many existing algorithms simplify networks based on the number of MACs or weights, optimizing those indirect metrics may not necessarily reduce the direct metrics, such as latency and energy consumption. To solve this problem, NetAdapt incorporates direct metrics into its adaptation algorithm. These direct metrics are evaluated using empirical measurements, so that detailed knowledge of the platform and toolchain is not required. NetAdapt automatically and progressively simplifies a pre-trained network until the resource budget is met while maximizing the accuracy. Experiment results show that NetAdapt achieves better accuracy versus latency trade-offs on both mobile CPU and mobile GPU, compared with the state-of-the-art automated network simplification algorithms. For image classification on the ImageNet dataset, NetAdapt achieves up to a 1.7$\times$ speedup in measured inference latency with equal or higher accuracy on MobileNets (V1&V2).


title: Emotional brain-computer interfaces

abstract: Research in brain-computer interface (BCI) has significantly increased during the last few years. Additionally to their initial role as assisting devices for the physically challenged, BCIs are now proposed for a wider range of applications. As any human-machine interaction system, BCIs can benefit from adapting their operation to the emotional state of the user. BCIs already have access to the brain activity, which provides significant insight into the user's emotional state. This information can be utilised in two manners. (1) Knowledge of the influence of the emotional state on brain activity patterns can allow the BCI to adapt its recognition algorithms, so that the intention of the user is correctly interpreted in spite of signal deviations induced by the subject's emotional state. (2) The ability to recognise emotions can be used to provide the user with more natural ways of controlling the BCI through affective modulation and can potentially lead to higher communication throughput.


title: What would it mean to blog on the semantic web

abstract: The phenomenon known as Web logging (''blogging'') has helped realize an initial goal of the Web: to turn Web content consumers (i.e., end users) into Web content producers. As the Semantic Web unfolds, we feel there are two questions worth posing: (1) do blog entries have semantic structure that can be usefully captured and exploited? (2) Is blogging a natural way to encourage growth of the Semantic Web? We explore empirical evidence for answering these questions in the affirmative and propose means to bring blogging into the mainstream of the Semantic Web, including ontologies that extend the RSS 1.0 specification and an XSL transform for handling RSS 0.9x/2.0 files. To demonstrate the validity of our approach we have constructed a semantic blogging environment based on Haystack. We argue that with tools such as Haystack, semantic blogging will be an important paradigm by which metadata authoring will occur in the future.


title: Multifield visualization using local statistical complexity

abstract: Modern unsteady (multi-)field visualizations require an effective reduction of the data to be displayed. From a huge amount of information the most informative parts have to be extracted. Instead of the fuzzy application dependent notion of feature, a new approach based on information theoretic concepts is introduced in this paper to detect important regions. This is accomplished by extending the concept of local statistical complexity from finite state cellular automata to discretized (multi-)fields. Thus, informative parts of the data can be highlighted in an application-independent, purely mathematical sense. The new measure can be applied to unsteady multifields on regular grids in any application domain. The ability to detect and visualize important parts is demonstrated using diffusion, flow, and weather simulations.


title: A Linear InGaP/GaAs HBT Power Amplifier Using Parallel-Combined Transistors With IMD3 Cancellation

abstract: In this letter, we present a linear InGaP/GaAs HBT power amplifier (PA) with parallel-combined transistors for small-cell applications. Ballast resistors with bypass resistors and capacitors are employed in parallel-combined transistors. When the resistors and capacitors are set to appropriate values, IMD3 components of the parallel-combined transistors are found to be out of phase with each other by 180° and are canceled out at the output. The experimental results show that the proposed HBT PA with parallel-combined transistors produces a saturated output power of 33.5 dBm at 0.88 GHz, with a power-added efficiency (PAE) of 46.1% at a 5-V supply voltage. To validate the effectiveness of the proposed HBT PA for linearity improvement, the implemented PA is also tested with a long-term evolution (LTE) signal (8.1-dB PAPR with 10-MHz bandwidth). The proposed PA achieves an adjacent channel leakage ratio (ACLR) below −45 dBc at an average power of 25.6 dBm with a PAE of 18.8% without applying predistortion.


title: Determining Term Subjectivity And Term Orientation For Opinion Mining

abstract: Opinion mining is a recent subdiscipline of computational linguistics which is concerned not with the topic a document is about, but with the opinion it expresses. To aid the extraction of opinions from text, recent work has tackled the issue of determining the orientation of “subjective” terms contained in text, i.e. deciding whether a term that carries opinionated content has a positive or a negative connotation. This is believed to be of key importance for identifying the orientation of documents, i.e. determining whether a document expresses a positive or negative opinion about its subject matter. We contend that the plain determination of the orientation of terms is not a realistic problem, since it starts from the nonrealistic assumption that we already know whether a term is subjective or not; this would imply that a linguistic resource that marks terms as “subjective” or “objective” is available, which is usually not the case. In this paper we confront the task of deciding whether a given term has a positive connotation, or a negative connotation, or has no subjective connotation at all; this problem thus subsumes the problem of determining subjectivity and the problem of determining orientation. We tackle this problem by testing three different variants of a semi-supervised method previously proposed for orientation detection. Our results show that determining subjectivity and orientation is a much harder problem than determining orientation alone.


BIG DATA ANALYTICS IN HEALTHCARE : A SURVEY Gemson


title: Topologies and performance of intelligent algorithms: a comprehensive review

abstract: Recently, optimization makes an important role in our day-to-day life. Evolutionary and population-based optimization algorithms are widely employed in several of engineering areas. The design of an optimization algorithm is a challenging endeavor caused of physical phenomena in order to obtain appropriate local and global search operators. Generally, local operators are fast. In contrast, global operators are used to find best solution in the search space; therefore they are slower compare to the local ones. The best review-knowledge of papers show that there are many optimization algorithms such as genetic algorithm, particle swarm optimization, artificial bee colony and etc in the engineering as a powerful tools. However, there is not a comprehensive review for theirs topologies and performance; therefore, the main goal of this paper is filling of this scientific gap. Moreover, several aspects of optimization heuristic designs and analysis are discussed in this paper. As a result, detailed explanation, comparison, and discussion on AI are achieved. Furthermore, some future research fields on AI are well summarized.


title: Strong Baselines for Neural Semi-supervised Learning under Domain Shift

abstract: Novel neural models have been proposed in recent years for learning under domain shift. Most models, however, only evaluate on a single task, on proprietary datasets, or compare to weak baselines, which makes comparison of models difficult. In this paper, we re-evaluate classic general-purpose bootstrapping approaches in the context of neural networks under domain shifts vs. recent neural approaches and propose a novel multi-task tri-training method that reduces the time and space complexity of classic tri-training. Extensive experiments on two benchmarks are negative: while our novel method establishes a new state-of-the-art for sentiment analysis, it does not fare consistently the best. More importantly, we arrive at the somewhat surprising conclusion that classic tri-training, with some additions, outperforms the state of the art. We conclude that classic approaches constitute an important and strong baseline.


title: Enhancing Freebase Question Answering Using Textual Evidence

abstract: Existing knowledge-based question answering systems often rely on small annotated training data. While shallow methods like information extraction techniques are robust to data scarcity, they are less expressive than deep understanding methods, thereby failing at answering questions involving multiple constraints. Here we alleviate this problem by empowering a relation extraction method with additional evidence from Wikipedia. We first present a novel neural network based relation extractor to retrieve the candidate answers from Freebase, and then develop a refinement model to validate answers using Wikipedia. We achieve 53.3 F1 on WebQuestions, a substantial improvement over the state-of-the-art.


title: Mask-CNN: Localizing Parts and Selecting Descriptors for Fine-Grained Image Recognition

abstract: Fine-grained image recognition is a challenging computer vision problem, due to the small inter-class variations caused by highly similar subordinate categories, and the large intra-class variations in poses, scales and rotations. In this paper, we propose a novel end-to-end Mask-CNN model without the fully connected layers for fine-grained recognition. Based on the part annotations of fine-grained images, the proposed model consists of a fully convolutional network to both locate the discriminative parts (e.g., head and torso), and more importantly generate object/part masks for selecting useful and meaningful convolutional descriptors. After that, a four-stream Mask-CNN model is built for aggregating the selected object- and part-level descriptors simultaneously. The proposed Mask-CNN model has the smallest number of parameters, lowest feature dimensionality and highest recognition accuracy when compared with state-of-the-arts fine-grained approaches.


title: Affective Visual Perception Using Machine Pareidolia of Facial Expressions

abstract: This article presents a computer vision approach that can detect and classify abstract face-like patterns, including subliminal faces within a scene. This can be regarded as a way of simulating the phenomenon of pareidolia, that is, the tendency of humans to ‘see faces’ in random structures such as clouds or rocks. The paper describes the system consisting of a component-based face detector and an expression classifier. The face detector creates a number of component images from the original image at different resolutions. A component image is a binary edge image where the edges are segmented into components using a labelling method with a border-following technique. The component images are then overlaid to produce a component height map where large and notable components across all resolutions have high values, while specular and noisy components have low values. The method retains three-shape components, representing two eyes and a mouth, that have height map values that are larger than the noise cut-off value. Support vector machines using scale-invariant feature vectors are applied for ranking these three-shape components by their geometry and size, and their shape semblance to human faces in the training data. The outcome is a facial expression analysis system that uses face components, with the potential to estimate an emotional expression value for a scene by producing an array of emotion scores corresponding to Ekman’s seven Universal Facial Expressions of Emotion. An advantage of this technique, when compared to a holistic method, is that the face components are explicitly isolated. This supports a process of abstraction that can facilitate the detection of distorted and minimal face-like patterns.


title: From Fly-by-Wire to Drive-by-Wire : Safety implications of automation in vehicles

abstract: The purpose of this paper is to critically review the current trend in automobile engineering toward automation of many of the functions previously performed by the driver. Working on the assumption that automation in aviation represents the basic model for driver automation, the costs and benefits of automation in aviation are explored as a means of establishing where automation of drivers' tasks are likely to yield benefits. It is concluded that there are areas where automation can provide benefits to the driver, but there are other areas where this is unlikely to be the case. Automation per se does not guarantee success, and therefore it becomes vital to involve Human Factors into design to identify where automation of driver functions can be allocated with a beneficial outcome for driving performance.


title: TCAD simulation of Ge-GaAs hetrojunction dopingless tunnel field effect transistor

abstract: This paper presents a TCAD study on the performance of Si and Ge-GaAs hetrojunction dopingless tunnel field effect transistor (TFETs). Si tunnel device have low on current. So hetrojunction architecture is combined with dopingless structure for improving on current with smaller off current. Dopingless architecture uses low thermal budget for fabrication so hetrojunction dopingless TFET are suitable for low power low cost application. By comparison it is found that Ge-GaAs and Ge-Si devices have increased on current in compare to Si dopingless TFET but off current in Ge-GaAs devices is lower than Ge-Si device. In further steps capacitance of Ge-GaAs are also compared to Si Dopingless TFET. All simulation are done using Sentaurus TCAD simulator.


title: Evaluating Classification Techniques for Mapping Vertical Geology Using Field-Based Hyperspectral Sensors

abstract: Hyperspectral data acquired from field-based platforms present new challenges for their analysis, particularly for complex vertical surfaces exposed to large changes in the geometry and intensity of illumination. The use of hyperspectral data to map rock types on a vertical mine face is demonstrated, with a view to providing real-time information for automated mining applications. The performance of two classification techniques, namely, spectral angle mapper (SAM) and support vector machines (SVMs), is compared rigorously using a spectral library acquired under various conditions of illumination. SAM and SVM are then applied to a mine face, and results are compared with geological boundaries mapped in the field. Effects of changing conditions of illumination, including shadow, were investigated by applying SAM and SVM to imagery acquired at different times of the day. As expected, classification of the spectral libraries showed that, on average, SVM gave superior results for SAM, although SAM performed better where spectra were acquired under conditions of shadow. In contrast, when applied to hypserspectral imagery of a mine face, SVM did not perform as well as SAM. Shadow, through its impact upon spectral curve shape and albedo, had a profound impact on classification using SAM and SVM.


title: Data-Driven Hint Generation in Vast Solution Spaces: a Self-Improving Python Programming Tutor

abstract: To provide personalized help to students who are working on code-writing problems, we introduce a data-driven tutoring system, ITAP (Intelligent Teaching Assistant for Programming). ITAP uses state abstraction, path construction, and state reification to automatically generate personalized hints for students, even when given states that have not occurred in the data before. We provide a detailed description of the system’s implementation and perform a technical evaluation on a small set of data to determine the effectiveness of the component algorithms and ITAP’s potential for self-improvement. The results show that ITAP is capable of producing hints for almost any given state after being given only a single reference solution, and that it can improve its performance by collecting data over time.


title: TOSS: Traffic offloading by social network service-based opportunistic sharing in mobile social networks

abstract: The ever increasing traffic demand becomes a serious concern of mobile network operators. To solve this traffic explosion problem, there have been many efforts to offload the traffic from cellular links to direct communications among users. In this paper, we propose the framework of Traffic Offloading assisted by Social network services (SNS) via opportunistic Sharing in mobile social networks, TOSS, to offload SNS-based cellular traffic by user-to-user sharing. First we select a subset of users who are to receive the same content as initial seeds depending on their content spreading impacts in online SNSs and their mobility patterns in offline mobile social networks (MSNs). Then users share the content via opportunistic local connectivity (e.g., Bluetooth, Wi-Fi Direct, Device-to-device in LTE) with each other. The observation of SNS user activities reveals that individual users have distinct access patterns, which allows TOSS to exploit the user-dependent access delay between the content generation time and each user's access time for traffic offloading purposes. We model and analyze the traffic offloading and content spreading among users by taking into account various options in linking SNS and MSN trace data. The trace-driven evaluation demonstrates that TOSS can reduce up to 86.5% of the cellular traffic while satisfying the access delay requirements of all users.


title: Moving Up the Food Chain : Motivation and Emotion in Behavior-Based Robots

abstract: This article investigates the commonalities between motivations and emotions as evidenced in a broad range of animal models, including humans. In particular, a focus is placed on how these models can have utility within the context of working robotic systems. Behavior-based control serves as the primary vehicle through which emotions and motivations are integrated into robots ranging from hexapods to wheeled robots to humanoids. Starting from relatively low-level organisms, such as the sowbug and praying mantis, and then moving upward to human interactions, a progression of motivational/emotional models and robotic experiments is reported. These capture a wide set of affective phenomena including social attachment, emotional behavior in support of interspecies interaction, multiscale temporal affect, and various motivational drives such as hunger and fear.


Sentimentor: Sentiment Analysis of Twitter Data


HypeBIOS : Enforcing VM Isolation with Minimized and Decomposed Cloud TCB


title: Document analysis of PDF files : methods , results and implications

abstract: SUMMARY A strategy for document analysis is presented which uses Portable Document Format (PDF — the underlying file structure for Adobe Acrobat software) as its starting point. This strategy examines the appearance and geometric position of text and image blocks distributed over an entire document. A blackboard system is used to tag the blocks as a first stage in deducing the fundamental relationships existing between them. PDF is shown to be a useful intermediate stage in the bottom-up analysis of document structure. Its information on line spacing and font usage gives important clues in bridging the ‘semantic gap’ between the scanned bitmap page and its fully analysed, block-structured form. Analysis of PDF can yield not only accurate page decomposition but also sufficient document information for the later stages of structural analysis and document understanding.


title: Mirror pairs of Calabi-Yau threefolds from mirror pairs of quasi-Fano threefolds

abstract: Abstract We present a new construction of mirror pairs of Calabi–Yau manifolds by smoothing normal crossing varieties, consisting of two quasi-Fano manifolds. We introduce a notion of mirror pairs of quasi-Fano manifolds with anticanonical Calabi–Yau fibrations using recent conjectures about Landau–Ginzburg models. Utilizing this notion, we give pairs of normal crossing varieties and show that the pairs of smoothed Calabi–Yau manifolds satisfy the Hodge number relations of mirror symmetry. We consider quasi-Fano threefolds that are some blow-ups of Gorenstein toric Fano threefolds and build 6518 mirror pairs of Calabi–Yau threefolds, including 79 self-mirrors.


title: LibRec: A Java Library for Recommender Systems

abstract: The large array of recommendation algorithms proposed over the years brings a challenge in reproducing and comparing their performance. This paper introduces an open-source Java library that implements a suite of state-of-the-art algorithms as well as a series of evaluation metrics. We empirically find that LibRec performs faster than other such libraries, while achieving competitive evaluative performance.


title: Single- and Dual-Polarized Tunable Slot-Ring Antennas

abstract: Single-and dual-polarized slot-ring antennas with wideband tuning using varactor diodes have been demonstrated. The single-polarized antenna tunes from 0.95 to 1.8 GHz with better than -13 dB return loss. Both polarizations of the dual-polarized antenna tune from 0.93 to 1.6 GHz independently with better than -10 dB return loss and > 20 dB port-to-port isolation over most of the tuning range. The capacitance of the varactor diodes varies from 0.45 to 2.5 pF, and the antennas are printed on 70times70 times0.787 mm3 substrates with epsivr = 2.2. The dual-polarized slot-ring antenna can either be made both frequency- and polarization-agile simultaneously, or can operate at two independent frequencies on two orthogonal polarizations. To our knowledge, this is the first dual-polarized tunable antenna with independent control of both polarizations over a 1.7:1 frequency range.


title: Identification and expansion of the tumorigenic lung cancer stem cell population

abstract: Lung carcinoma is often incurable and remains the leading cancer killer in both men and women. Recent evidence indicates that tumors contain a small population of cancer stem cells that are responsible for tumor maintenance and spreading. The identification of the tumorigenic population that sustains lung cancer may contribute significantly to the development of effective therapies. Here, we found that the tumorigenic cells in small cell and non-small cell lung cancer are a rare population of undifferentiated cells expressing CD133, an antigen present in the cell membrane of normal and cancer-primitive cells of the hematopoietic, neural, endothelial and epithelial lineages. Lung cancer CD133+ cells were able to grow indefinitely as tumor spheres in serum-free medium containing epidermal growth factor and basic fibroblast growth factor. The injection of 104 lung cancer CD133+ cells in immunocompromised mice readily generated tumor xenografts phenotypically identical to the original tumor. Upon differentiation, lung cancer CD133+ cells acquired the specific lineage markers, while loosing the tumorigenic potential together with CD133 expression. Thus, lung cancer contains a rare population of CD133+ cancer stem-like cells able to self-renew and generates an unlimited progeny of non-tumorigenic cells. Molecular and functional characterization of such a tumorigenic population may provide valuable information to be exploited in the clinical setting.


title: Robust algorithm for real-time route planning

abstract: Route planning for intelligent guidance and navigation systems is an extremely complex problem with both military and commercial applications. Standard route planning algorithms usually generate a minimum cost route based on a predetermined cost function. Unfortunately, such a solution may not represent a desirable route for various mission scenarios. We present a novel route planning approach to generate mission-adaptable routes in an accurate and efficient manner. The routes are computed in real-time and are able to take into account various mission constraints including: minimum route leg length, maximum turning angle, route distance constraint, and fixed approach vector to goal position.


title: Bayesian Learning of Recursively Factored Environments

abstract: Model-based reinforcement learning techniques have historically encountered a number of difficulties scaling up to large observation spaces. One promising approach has been to decompose the model learning task into a number of smaller, more manageable sub-problems by factoring the observation space. Typically, many different factorizations are possible, which can make it difficult to select an appropriate factorization without extensive testing. In this paper we introduce the class of recursively decomposable factorizations, and show how exact Bayesian inference can be used to efficiently guarantee predictive performance close to the best factorization in this class. We demonstrate the strength of this approach by presenting a collection of empirical results for 20 different Atari 2600 games.


title: Microservices Architecture Enables DevOps: Migration to a Cloud-Native Architecture

abstract: When DevOps started gaining momentum in the software industry, one of the first service-based architectural styles to be introduced, be applied in practice, and become popular was microservices. Migrating monolithic architectures to cloud-native architectures such as microservices reaps many benefits, such as adaptability to technological changes and independent resource management for different system components. This article reports on experiences and lessons learned during incremental migration and architectural refactoring of a commercial MBaaS (mobile back end as a service) to microservices. It explains how adopting DevOps facilitated a smooth migration. Furthermore, the researchers transformed their experiences in different projects into reusable migration practices, resulting in microservices migration patterns. This article is part of a theme issue on DevOps. The Web extra at https://youtu.be/MF3-dKTCQ88 is an audio recording of Brian Brannon speaking with author Pooyan Jamshidi and James Lewis, principal engineer at ThoughtWorks, about DevOps and microservices architecture.


title: DeepTrend: A Deep Hierarchical Neural Network for Traffic Flow Prediction

abstract: In this paper, we consider the temporal pattern in traffic flow time series, and implement a deep learning model for traffic flow prediction. Detrending based methods decompose original flow series into trend and residual series, in which trend describes the fixed temporal pattern in traffic flow and residual series is used for prediction. Inspired by the detrending method, we propose DeepTrend, a deep hierarchical neural network used for traffic flow prediction which considers and extracts the time-variant trend. DeepTrend has two stacked layers: extraction layer and prediction layer. Extraction layer, a fully connected layer, is used to extract the time-variant trend in traffic flow by feeding the original flow series concatenated with corresponding simple average trend series. Prediction layer, an LSTM layer, is used to make flow prediction by feeding the obtained trend from the output of extraction layer and calculated residual series. To make the model more effective, DeepTrend needs first pre-trained layer-by-layer and then fine-tuned in the entire network. Experiments show that DeepTrend can noticeably boost the prediction performance compared with some traditional prediction models and LSTM with detrending based methods.


title: Interventive Interviewing: Part II. Reflexive Questioning as a Means to Enable Self-Healing

abstract: Reflexive questioning is an aspect of interventive interviewing oriented toward enabling clients or families to generate new patterns of cognition and behavior on their own. The therapist adopts a facilitative posture and deliberately asks those kinds of questions that are liable to open up new possibilities for self-healing. The mechanism for the resultant therapeutic change in clients is postulated to be reflexivity between levels of meaning within their own belief systems. By adopting this mode of enquiry and taking advantage of opportunities to ask a variety of reflexive questions, a therapist may be able to augment the clinical effectiveness of his or her interviews.


Animated People Textures


title: Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors

abstract: Visual features are of vital importance for human action understanding in videos. This paper presents a new video representation, called trajectory-pooled deep-convolutional descriptor (TDD), which shares the merits of both hand-crafted features [31] and deep-learned features [24]. Specifically, we utilize deep architectures to learn discriminative convolutional feature maps, and conduct trajectory-constrained pooling to aggregate these convolutional features into effective descriptors. To enhance the robustness of TDDs, we design two normalization methods to transform convolutional feature maps, namely spatiotemporal normalization and channel normalization. The advantages of our features come from (i) TDDs are automatically learned and contain high discriminative capacity compared with those hand-crafted features; (ii) TDDs take account of the intrinsic characteristics of temporal dimension and introduce the strategies of trajectory-constrained sampling and pooling for aggregating deep-learned features. We conduct experiments on two challenging datasets: HMD-B51 and UCF101. Experimental results show that TDDs outperform previous hand-crafted features [31] and deep-learned features [24]. Our method also achieves superior performance to the state of the art on these datasets.


title: Fast moving object detection with non-stationary background

abstract: The detection of moving objects under a free-moving camera is a difficult problem because the camera and object motions are mixed together and the objects are often detected into the separated components. To tackle this problem, we propose a fast moving object detection method using optical flow clustering and Delaunay triangulation as follows. First, we extract the corner feature points using Harris corner detector and compute optical flow vectors at the extracted corner feature points. Second, we cluster the optical flow vectors using K-means clustering method and reject the outlier feature points using Random Sample Consensus algorithm. Third, we classify each cluster into the camera and object motion using its scatteredness of optical flow vectors. Fourth, we compensate the camera motion using the multi-resolution block-based motion propagation method and detect the objects using the background subtraction between the previous frame and the motion compensated current frame. Finally, we merge the separately detected objects using Delaunay triangulation. The experimental results using Carnegie Mellon University database show that the proposed moving object detection method outperforms the existing other methods in terms of detection accuracy and processing time.


title: Analysing emerging memory technologies for big data and signal processing applications

abstract: In this paper, we investigate and compare different emerging memory technologies as on-chip cache for big data and signal processing applications. Static Random Access Memory (SRAM) has been widely used as level 1 and last level caches for multicore processors. Server chips integrate Dynamic Random Access Memory (DRAM) as an additional cache for better server-level applications that process more data. Both SRAM and DRAM have advantages and disadvantages. Therefore new types of RAMs are proposed and prototyped. For big data and signal processing applications nowadays, enormous amount of data are processed, usually with time limitations. We analyse novel RAMs, including Phase-change RAM (PRAM), Magnetoresistive RAM (MRAM), Ferroelectric RAM (FRAM) and Resistive RAM (RRAM). The conventional and new memories are analysed in terms of size, area, access latency and power consumption. We present benchmark results using a full system simulator. Workloads are selected from several big data, server, signal processing and video processing applications. Experiments show that, in consideration of these applications, it is crucial to replace SRAM and DRAM caches with MRAM and RRAM.


title: Gradient-free Policy Architecture Search and Adaptation

abstract: We develop a method for policy architecture search and adaptation via gradient-free optimization which can learn to perform autonomous driving tasks. By learning from both demonstration and environmental reward we develop a model that can learn with relatively few early catastrophic failures. We first learn an architecture of appropriate complexity to perceive aspects of world state relevant to the expert demonstration, and then mitigate the effect of domain-shift during deployment by adapting a policy demonstrated in a source domain to rewards obtained in a target environment. We show that our approach allows safer learning than baseline methods, offering a reduced cumulative crash metric over the agent's lifetime as it learns to drive in a realistic simulated environment.


title: Crowd anomaly detection for automated video surveillance

abstract: Video-based crowd behaviour detection aims at tackling challenging problems such as automating and identifying changing crowd behaviours under complex real life situations. In this paper, real-time crowd anomaly detection algorithms have been investigated. Based on the spatio-temporal video volume concept, an innovative spatio-temporal texture model has been proposed in this research for its rich crowd pattern characteristics. Through extracting and integrating those crowd textures from surveillance recordings, a redundancy wavelet transformation-based feature space can be deployed for behavioural template matching. Experiment shows that the abnormality appearing in crowd scenes can be identified in a real-time fashion by the devised method. This new approach is envisaged to facilitate a wide spectrum of crowd analysis applications through automating current Closed-Circuit Television (CCTV)-based surveillance systems.


title: Depth Image Based View Synthesis: New Insights and Perspectives on Hole Generation and Filling

abstract: View synthesis with depth-image-based rendering (DIBR) has attracted great interest in that it can provide a virtual image at any arbitrary viewpoint in 3-D video and free-viewpoint TV. An inherent problem in the DIBR view synthesis is occurrence of holes in a synthesized image, which is also known as disocclusion problem. The disoccluded regions need to be handled properly in order to generate a synthesized view of good quality. This paper provides a fundamental examination of hole generation mechanism in the DIBR oriented view synthesis process. A necessary and sufficient condition of hole generation is first shown, and the corresponding hole location and length is obtained analytically. Furthermore, in view that the conventional hole filling algorithms may fail to fill up a hole correctly when lacking (adequate) visible background information, we propose utilizing the occluded (invisible) information to identify and locate the relevant background pixels around a hole. We then make use of the visible and invisible background information together to perform hole filling. Experimental results validate our hole generation model demonstrating agreement to our analytical results, while our proposed hole filling approach shows superior performance in terms of visual quality of synthesized views.


title: VComputeBench: A Vulkan Benchmark Suite for GPGPU on Mobile and Embedded GPUs

abstract: GPUs have become immensely important computational units on embedded and mobile devices. However, GPGPU developers are often not able to exploit the compute power offered by GPUs on these devices mainly due to the lack of support of traditional programming models such as CUDA and OpenCL. The recent introduction of the Vulkan API provides a new programming model that could be explored for GPGPU computing on these devices, as it supports compute and promises to be portable across different architectures. In this paper we propose VComputeBench, a set of benchmarks that help developers understand the differences in performance and portability of Vulkan. We also evaluate the suitability of Vulkan as an emerging cross-platform GPGPU framework by conducting a thorough analysis of its performance compared to CUDA and OpenCL on mobile as well as on desktop platforms. Our experiments show that Vulkan provides better platform support on mobile devices and can be regarded as a good cross-platform GPGPU framework. It offers comparable performance and with some low-level optimizations it can offer average speedups of 1.53x and 1.66x compared to CUDA and OpenCL respectively on desktop platforms and 1.59x average speedup compared to OpenCL on mobile platforms. However, while Vulkan’s low-level control can enhance performance, it requires a significantly higher programming effort.


title: Salt stress and phyto-biochemical responses of plants – a review

abstract: The ability of plants to tolerate salts is determined by multiple biochemical pathways that facilitate retention and/or acquisition of water, protect chloroplast functions and maintain ion homeostasis. Essential pathways include those that lead to synthesis of osmotically active metabolites, specific proteins and certain free radical enzymes to control ion and water flux and support scavenging of oxygen radicals. No well-defined indicators are available to facilitate the improvement in salinity tolerance of agricultural crops through breeding. If the crop shows distinctive indicators of salt tolerance at the whole plant, tissue or cellular level, selection is the most convenient and practical method. There is therefore a need to determine the underlying biochemical mechanisms of salinity tolerance so as to provide plant breeders with appropriate indicators. In this review, the possibility of using these biochemical characteristics as selection criteria for salt tolerance is discussed.


Using of Jaccard Coefficient for Keywords Similarity


title: Unsupervised Histopathology Image Synthesis

abstract: Hematoxylin and Eosin stained histopathology image analysis is essential for the diagnosis and study of complicated diseases such as cancer. Existing state-of-the-art approaches demand extensive amount of supervised training data from trained pathologists. In this work we synthesize in an unsupervised manner, large histopathology image datasets, suitable for supervised training tasks. We propose a unified pipeline that: a) generates a set of initial synthetic histopathology images with paired information about the nuclei such as segmentation masks; b) refines the initial synthetic images through a Generative Adversarial Network (GAN) to reference styles; c) trains a task-specific CNN and boosts the performance of the task-specific CNN with on-the-fly generated adversarial examples. Our main contribution is that the synthetic images are not only realistic, but also representative (in reference styles) and relatively challenging for training task-specific CNNs. We test our method for nucleus segmentation using images from four cancer types. When no supervised data exists for a cancer type, our method without supervision cost significantly outperforms supervised methods which perform across-cancer generalization. Even when supervised data exists for all cancer types, our approach without supervision cost performs better than supervised methods.


title: Firewall design: consistency, completeness, and compactness

abstract: A firewall is often placed at the entrance of each private network in the Internet. The function of a firewall is to examine each packet that passes through the entrance and decide whether to accept the packet and allow it to proceed or to discard the packet. A firewall is usually designed as a sequence of rules. To make a decision concerning some packets, the firewall rules are compared, one by one, with the packet until one rule is found to be satisfied by the packet: this rule determines the fate of the packet. We present the first ever method for designing the sequence of rules in a firewall to be consistent, complete, and compact. Consistency means that the rules are ordered correctly, completeness means that every packet satisfies at least one rule in the firewall, and compactness means that the firewall has no redundant rules. Our method starts by designing a firewall decision diagram (FDD, for short) whose consistency and completeness can be checked systematically (by an algorithm). We then apply a sequence of five algorithms to this FDD to generate, reduce and simplify the target firewall rules while maintaining the consistency and completeness of the original FDD.


title: Red-capped species of Russula sect. Xerampelinae associated with dwarf scrub

abstract: The microscopic structure of herbarium material of alpine and arctic species of Russula sect. Xerampelinae from Europe and Greenland was studied. Based on our observations two taxa with prevailingly red pilei are accepted from the area: R. subrubens and R. pascua. Other related species occurring in arctic areas are R. cicatricata with a coppery coloured pileus and R. clavipes with a prevailingly greenish olivaceous pileus. As a consequence of the study of type material, R. chamiteae var. chamiteae and R. chamiteae var. microsperma are synonymized with R. subrubens. No European collections could be referred to R. oreina , and accordingly we suggest that this species is absent from Europe.


title: A Novel Design Method for Resolver-to-Digital Conversion

abstract: A simple and novel design method for resolver-to-digital conversion is presented. The proposed method takes advantage of the auxiliary sinusoidal signals generated by addition and subtraction operations of the demodulated signals of the resolver's outputs. A pseudolinear signal is obtained through appropriate mathematical manipulation between the auxiliary sinusoidal signals and the demodulated ones. A calibration strategy is then provided to achieve the highest possible precision. It is shown that the error between the calibrated angular and the true angular position is less than 0.00235 $^{\circ}$ over $0^{\circ}\sim 360^{\circ} $ . The proposed method enables the determination of the angular position without utilizing a lookup table or additional reference signals. Furthermore, the presented strategy is implemented using a field-programmable gate array. Finally, our method is tested on a tracking radar antenna servo system where resolvers are installed on it. Several experiments under different conditions are performed to further verify the effectiveness of the proposed method.


title: A 105GHz VCO with 9.5% tuning range and 2.8mW peak output power using coupled colpitts oscillators in 65nm bulk CMOS

abstract: In this work, a loop of unidirectionally coupled oscillators to demonstrate high tuning range and output power is proposed. To achieve large tuning range, two different tuning mechanisms are simultaneously exploited. First each core oscillator is tuned using a variable capacitor. Next, by controlling the phase/delay between the coupled oscillators, the entire loop dynamics and hence its frequency is tuned. In this paper, we analyze a loop of “n” coupled oscillators using Adler's equation and derive the expression for the maximum tuning range. The proposed system is designed and implemented using four coupled Colpitts VCOs in a 65nm bulk CMOS process. The VCO achieves continuous tuning range of 9.5% at the center frequency of 105GHz with the peak output power of 2.8mW. The circuit consumes 54mW from a 1.2V supply. To the best of our knowledge, this VCO has the highest output power and tuning range among all the CMOS oscillators at or above 100GHz.


title: A comparison of a graph database and a relational database: a data provenance perspective

abstract: Relational databases have been around for many decades and are the database technology of choice for most traditional data-intensive storage and retrieval applications. Retrievals are usually accomplished using SQL, a declarative query language. Relational database systems are generally efficient unless the data contains many relationships requiring joins of large tables. Recently there has been much interest in data stores that do not use SQL exclusively, the socalled NoSQL movement. Examples are Google’s BigTable and Facebook’s Cassandra. This paper reports on a comparison of one such NoSQL graph database called Neo4j with a common relational database system, MySQL, for use as the underlying technology in the development of a software system to record and query data provenance information.


title: Fuzzy Enhanced Control of an Underactuated Finger Using Tactile and Position Sensors

abstract: This paper proposes a control scheme dedicated to underactuated fingers with the intention of maximizing the capabilities of the latter using tactile and position information at a minimum cost. Tactile sensors are implemented on one prototype of underactuated finger and used to enhance the behaviour of the hand despite its limited number of control signals. First, tactile technology is briefly recalled and discussed. Second, the electronic design of the sensors' controller is presented. Third, a real-time control scheme is introduced, based on a fuzzy force control method. Finally, a slippage prevention technique is presented. Results are discussed based on experimental observations and indicate that the behaviour of underactuated fingers can be substantially enhanced with tactile information and a classic fuzzy control approach.


title: Managing Internal Control: Designing a Wiki-based Information System for Continuous Process Assurance

abstract: The assessment of a client’s internal control environment is a recognized and crucial part of financial statements auditing. Appropriate tools and techniques are required in order to enable the auditor to understand the organizational environment and to evaluate the compliance of controls along accounting-relevant processes. This researchin-progress paper provides initial research findings in the area of continuous assurance in terms of collecting requirements for the support of process auditing, and the extension of core functionalities of an existing prototype. This prototype is realized as a web application combining complete wiki functionality with a modeling component, and enables the automated synchronization between workflow procedures and business process models. This empowers the auditor to monitor and assess changes of client’s business environment and internal control constantly. In conclusion, the paper introduces future work encompassing the strengthening of analysis and design as well as evaluation by implementing the prototype in multiple organizations.


title: Anthropomorphic Soft Robotics – From Torque Control to Variable Intrinsic Compliance

abstract: The paper gives an overview on the developments at the German Aerospace Center DLR towards anthropomorphic robots which not only try to approach the force and velocity performance of humans, but also have similar safety and robustness features based on a compliant behaviour.We achieve this compliance either by joint torque sensing and impedance control, or, in our newest systems, by compliant mechanisms (so called VIA - variable impedance actuators), whose intrinsic compliance can be adjusted by an additional actuator. Both approaches required highly integrated mechatronic design and advanced, nonlinear control and planning strategies, which are presented in this paper.


Heteroplastic transplantation of somites


title: Endothelin-1-induced PMN infiltration and mucosal dysfunction in the rat small intestine

abstract: The objectives of this study were to characterize the effects of endothelin (ET)-1 on intestinal mucosal parameters and to assess the contribution of polymorphonuclear leukocytes (PMNs), intercellular adhesion molecule-1 (ICAM-1), and a platelet-activating factor (PAF) to the mucosal dysfunction induced by ET-1. Different concentrations of ET-1 (100, 200, and 400 pmol/kg) were infused into the superior mesenteric artery for 10 min, and tissue samples were obtained 30 min after terminating the infusion. ET-1 administration significantly elevated tissue myeloperoxidase activity, plasma carbonyl content, and tissue chemiluminescence intensity, indicating that ET-1 produces PMN infiltration and oxidant stress. Blood-to-lumen clearance of 51Cr-EDTA significantly increased after ET-1 infusion (400 pmol/kg). Monoclonal antibodies against ICAM-1 (1A29, 2 mg/kg), antineutrophil serum, and PAF antagonist (WEB-2086, 10 mg/kg) attenuated the mucosal barrier dysfunction induced by ET-1. Overall, our data indicate that ET-1 causes PMN accumulation, oxidant stress, and mucosal dysfunction in the rat small intestine and that ET-1-induced mucosal dysfunction involves a mechanism that includes a role for PMNs, ICAM-1, and PAF.


title: Reinforcement Learning for Predictive Analytics in Smart Cities

abstract: The digitization of our lives cause a shift in the data production as well as in the required data management. Numerous nodes are capable of producing huge volumes of data in our everyday activities. Sensors, personal smart devices as well as the Internet of Things (IoT) paradigm lead to a vast infrastructure that covers all the aspects of activities in modern societies. In the most of the cases, the critical issue for public authorities (usually, local, like municipalities) is the efficient management of data towards the support of novel services. The reason is that analytics provided on top of the collected data could help in the delivery of new applications that will facilitate citizens’ lives. However, the provision of analytics demands intelligent techniques for the underlying data management. The most known technique is the separation of huge volumes of data into a number of parts and their parallel management to limit the required time for the delivery of analytics. Afterwards, analytics requests in the form of queries could be realized and derive the necessary knowledge for supporting intelligent applications. In this paper, we define the concept of a Query Controller ( Q C ) that receives queries for analytics and assigns each of them to a processor placed in front of each data partition. We discuss an intelligent process for query assignments that adopts Machine Learning (ML). We adopt two learning schemes, i.e., Reinforcement Learning (RL) and clustering. We report on the comparison of the two schemes and elaborate on their combination. Our aim is to provide an efficient framework to support the decision making of the QC that should swiftly select the appropriate processor for each query. We provide mathematical formulations for the discussed problem and present simulation results. Through a comprehensive experimental evaluation, we reveal the advantages of the proposed models and describe the outcomes results while comparing them with a deterministic framework.


title: DESIGN OF A 2-FINGER HAND EXOSKELETON FOR VR GRASPING SIMULATION

abstract: There are numerous applications of VR simulation requiring the grasping and manipulation of virtual objects. Standard-use haptic interfaces (e.g. Virtuose 6D, PHANToM) allow only a limited level of realism, as grasping is approximated through a metaphor (e.g. pressing a button for grasping an object). Existing hand exoskeletons have also certain drawbacks (e.g. feedback only for finger flexion, limited finger workspace etc.). The work presented in this paper introduces a hand force exoskeleton that allows full finger flexion and extension and applies bi-directional feedback. It has 3dof at the index finger and 4 at the thumb. The system is actuated by DC motors and cable transmissions are used. It has been designed for use in conjunction with Virtuose 6D, a commercial 6dof haptic arm, in order to allow the simulation of external forces (gravity, contact reaction forces, etc.).


abstract: A fast algorithm for finding dominators in a flowgraph is presented. The algorithm uses depth-first search and an efficient method of computing functions defined on paths in trees. A simple implementation of the algorithm runs in O ( m log n ) time, where m is the number of edges and n is the number of vertices in the problem graph. A more sophisticated implementation runs in O ( m α( m , n )) time, where α( m , n ) is a functional inverse of Ackermann's function. Both versions of the algorithm were implemented in Algol W, a Stanford University version of Algol, and tested on an IBM 370/168. The programs were compared with an implementation by Purdom and Moore of a straightforward O ( mn )-time algorithm, and with a bit vector algorithm described by Aho and Ullman. The fast algorithm beat the straightforward algorithm and the bit vector algorithm on all but the smallest graphs tested.


title: Herding Behavior In Online P2P Lending: An Empirical Investigation.

abstract: We study lender behavior in the peer-to-peer (P2P) lending market, where individuals bid on unsecured microloans requested by other individual borrowers. Online P2P exchanges are growing, but lenders in this market are not professional investors. In addition, lenders have to take big risks because loans in P2P lending are granted without collateral. While the P2P lending market shares some characteristics of online markets with respect to herding behavior, it also has characteristics that may discourage it. This study empirically investigates herding behavior in the P2P lending market where seemingly conflicting conditions and features of herding are present. Using a large sample of daily data from one of the largest P2P lending platforms in Korea, we find strong evidence of herding and its diminishing marginal effect as bidding advances. We employ a multinomial logit market-share model in which relevant variables from prior studies on P2P lending are assessed.


title: Mechatronic design of PRH: A NEW 3D printed and anthropometric humanoid robotic platform

abstract: Bipedal locomotion is one of the main research focuses on humanoid robotics that has evolved towards new methods and prototypes, to successfully emulate the human gait. However, much of the research is done with low-cost robots with serious deficiencies in anthropometrics and biomechanics referred to the human body, that constraint the applicability of the research to further human scale prototypes or its use in new orthoses or prosthesis. This paper presents the design methodology of a 90-cm tall humanoid robotic platform, completely anthropometric, easy to assemble, modular, lightweight, inexpensive and produced mainly with 3D printing. The design comprises 56 degrees of freedom, being described primarily the development of the hardware of legs and some parts of the torso implied in bipedal locomotion. It is also explained the electronic architecture, sensors and power supply of the robot in order to execute stable gait patterns with the use of toe-off motion.