title: Cloud Computing in Healthcare and Biomedicine

abstract: High throughput platforms available in clinical settings or in research laboratories, such as magnetic resonance imaging, microarray, mass spectrometry and next-generation sequencing, are producing an increasing volume of clinical and omics data that poses new issues in terms of secure data storage, models for data integration and analysis, and high performance computing. Cloud Computing offers large scalable computing and storage, data sharing, on-demand anytime and anywhere access to resources and applications, and it supports easy but powerful distributed computing models, for facing those issues. In fact, in the ::: recent years it has been adopted for the deployment of several applications in healthcare and bioinformatics both in academia and in the industry. However, cloud computing presents several issues regarding the security and privacy of data, that are particularly important when analyzing patients data, such as in personalized medicine. This paper reviews main cloud-based healthcare and biomedicine applications; with a special focus on healthcare, biomedicine and bioinformatics solutions and underlines main issues and problems related to the use of such platforms for the storage and analysis of patients data.


title: Topical Word Importance for Fast Keyphrase Extraction

abstract: We propose an improvement on a state-of-the-art keyphrase extraction algorithm, Topical PageRank (TPR), incorporating topical information from topic models. While the original algorithm requires a random walk for each topic in the topic model being used, ours is independent of the topic model, computing but a single PageRank for each text regardless of the amount of topics in the model. This increases the speed drastically and enables it for use on large collections of text using vast topic models, while not altering performance of the original algorithm.


title: The infinite partially observable Markov decision process

abstract: The Partially Observable Markov Decision Process (POMDP) framework has proven useful in planning domains where agents must balance actions that provide knowledge and actions that provide reward. Unfortunately, most POMDPs are complex structures with a large number of parameters. In many real-world problems, both the structure and the parameters are difficult to specify from domain knowledge alone. Recent work in Bayesian reinforcement learning has made headway in learning POMDP models; however, this work has largely focused on learning the parameters of the POMDP model. We define an infinite POMDP (iPOMDP) model that does not require knowledge of the size of the state space; instead, it assumes that the number of visited states will grow as the agent explores its world and only models visited states explicitly. We demonstrate the iPOMDP on several standard problems.


title: Data Mining in Finance and Accounting: A Review of Current Research Trends

abstract: Data mining tools become important in finance and accounting. Their classification and prediction abilities enable them to be used for the purposes of bankruptcy prediction, going concern status and financial distress prediction, management fraud detection, credit risk estimation, and corporate performance prediction. This study aims to provide a state-of-the-art review of the relative literature and to indicate relevant research opportunities.


title: A survey of robotic motion planning in dynamic environments

abstract: Abstract Robot Motion Planning (RMP) has been a thrust area of research in computing due to its complexity, since RMP in dynamic environments for a point robot with bounded velocity is an NP-hard problem. This paper is a critical review of the major contributions to RMP in dynamic environments. Between 1985 and 2015 the focus has changed from the classical approach to a heuristic approach. For velocity based motion planning in dynamic environments, ICS — AVOID (Fraichard and Asama, 2004, also see Section 2.4.4) is the safest approach which means that this method have the capability of for an autonomous robotic system to avoid collision with the obstacles in the environment. Other important approaches include artificial potential field based, artificial intelligence based, probabilistic based RMP and applications in areas of Agent systems and computer geometry. Classification of the RMP literature on the basis of the techniques and their performance has been attempted.


title: The moderating effect of technology awareness on the relationship between UTAUT constructs and behavioural intention to use technology: A conceptual paper

abstract: The volume and value of cash-based transactions in Nigeria is high and expected to rise. This is liked to non-adoption of new e-payment process introduced by the Central Bank.The adoption of Point of Sale (POS) terminal is reported to be very low.A number of researchers have used several technology adoption theories to answer a similar phenomenon, but their studies were fragmented.Thus the Unified Theory of acceptance and Use of Technology (UTAUT) combined eight among those numerous theories and models of technology adoption, toward a unified view.However, several studies that used UTAUT and investigate behavioural intention to use technology yielded conflicting findings.Therefore this paper, based on review of past literature, conceptualised that ‘technology awareness’ moderates the relationship between performance expectancy, effort expectancy, social influence, facilitating conditions and behavioural intention to use POS.


title: Anti-inflammatory effect of Moringa oleifera Lam. seeds on acetic acid-induced acute colitis in rats

abstract: Anti-inflammatory, immuno-modulatory, and antioxidant properties of Moringa oleifera Lam. suggest that it might have beneficial effects on colitis. The present study was performed to investigate the anticolitis effect of Moringa oleifera seeds hydro-alcoholic extract (MSHE) and its chloroform fraction (MCF) on acetic acid-induced colitis in rats. Materials and Methods: Both MSHE and MCF with three increasing doses (50, 100, and 200 mg/kg) were administered orally to separate groups of male Wistar rats, 2 h before ulcer induction (using acetic acid 4%) and continued for 5 days. Prednisolone (4 mg/kg) and normal saline (1 ml/kg) were used in reference and control groups, respectively. All rats were sacrificed 24 h after the last dose (at day 6) and tissue injuries were assessed macroscopically and pathologically. Results: Extracts with three doses mentioned before were effective to reduce weight of distal colon (8 cm) as a marker for inflammation and tissue edema. Three doses of MSHE and two greater doses of MCF (100 and 200 mg/kg) were effective to reduce ulcer severity, area, and index as well as mucosal inflammation severity and extent, crypt damage, invasion involvement, total colitis index, and MPO activity compared with controls. MCF (50 mg/kg) was not significantly effective in reducing evaluated parameters of colitis compared with controls. Conclusion: It is concluded that MSHE and MCF were both effective to treat experimental colitis and this might be attributed to their similar major components, biophenols and flavonoids. Since the efficacy was evident even in low doses of MSHE, presence of active constituents with high potency in seeds is persuasive.


title: Compact Broadband Dual-Polarized Antenna for Indoor MIMO Wireless Communication Systems

abstract: A compact $({100}\;\text{mm} \times {100}\;\text{mm} \times {40}\;\text{mm})$ dual-polarized antenna with wide impedance bandwidth and high isolation is proposed for an indoor MIMO wireless communication system. The proposed antenna consists of cavity-backed bowtie antennas with parasitic elements. It can cover the DCS/PCS/UMTS/LTE 1, 2, 3, 4, 7, 9, 10, 15, 16, 23, 25 bands (1710–2690 MHz). The wide impedance bandwidth $(\text{VSWR} , of about 44.5%, is achieved by using parasitic elements with optimal location and length and the cavity of an electrical wall. High-isolation $({>}{31}\;\text{dB})$ and low-envelop correlation coefficient $(\text{ECC} between MIMO elements are achieved by orthogonal dual-polarization while suppressing the cross-polarization of each MIMO antenna element. The peak gain is higher than 7 dBi in the entire operating frequency band.


title: Combining deep learning and hand-crafted features for skin lesion classification

abstract: Melanoma is one of the most lethal forms of skin cancer. It occurs on the skin surface and develops from cells known as melanocytes. The same cells are also responsible for benign lesions commonly known as moles, which are visually similar to melanoma in its early stage. If melanoma is treated correctly, it is very often curable. Currently, much research is concentrated on the automated recognition of melanomas. In this paper, we propose an automated melanoma recognition system, which is based on deep learning method combined with so called hand-crafted RSurf features and Local Binary Patterns. The experimental evaluation on a large publicly available dataset demonstrates high classification accuracy, sensitivity, and specificity of our proposed approach when it is compared with other classifiers on the same dataset.


title: Research on Algorithm of Extracting PPG Signal for Detecting Atrial Fibrillation based on Probability Density Function

abstract: The paper introduced a new method based on probability density function (PDF) and phase space diagram method for photoplethysmography (PPG) signal extracting. In the paper, PPG information was generated from human fingertips by smartphones. The pulse wave period was then separated and reconstructed into probability density function (PDF) by the phase space diagram algorithm. The difference between normal sinus rhythm (NSR) and atrial fibrillation (AF) was finally found by skewness of the PDF. The results of the present study demonstrates that the new method is vividly viable for detecting AF on the smartphone.


title: Estimating relatedness via data compression

abstract: We show that it is possible to use data compression on independently obtained hypotheses from various tasks to algorithmically provide guarantees that the tasks are sufficiently related to benefit from multitask learning. We give uniform bounds in terms of the empirical average error for the true average error of the n hypotheses provided by deterministic learning algorithms drawing independent samples from a set of n unknown computable task distributions over finite sets.


title: Quantum Deep Learning

abstract: In recent years, deep learning has had a profound impact on machine learning and artificial intelligence. Here we investigate if quantum algorithms for deep learning lead to an advantage over existing classical deep learning algorithms. We develop two quantum machine learning algorithms that reduce the time required to train a deep Boltzmann machine and allow richer classes of models, namely multi--layer, fully connected networks, to be efficiently trained without the use of contrastive divergence or similar approximations. Our algorithms may be used to efficiently train either full or restricted Boltzmann machines. By using quantum state preparation methods, we avoid the use of contrastive divergence approximation and obtain improved maximization of the underlying objective function.


title: Just-in-Time Adaptive Classifiers—Part I: Detecting Nonstationary Changes

abstract: The stationarity requirement for the process generating the data is a common assumption in classifiers' design. When such hypothesis does not hold, e.g., in applications affected by aging effects, drifts, deviations, and faults, classifiers must react just in time, i.e., exactly when needed, to track the process evolution. The first step in designing effective just-in-time classifiers requires detection of the temporal instant associated with the process change, and the second one needs an update of the knowledge base used by the classification system to track the process evolution. This paper addresses the change detection aspect leaving the design of just-in-time adaptive classification systems to a companion paper. Two completely automatic tests for detecting nonstationarity phenomena are suggested, which neither require a priori information nor assumptions about the process generating the data. In particular, an effective computational intelligence-inspired test is provided to deal with multidimensional situations, a scenario where traditional change detection methods are generally not applicable or scarcely effective.


title: Development of third-generation intelligently Controllable ankle-foot orthosis with compact MR fluid brake

abstract: Ankle-foot orthoses (AFOs) are orthotic devices that support the movement of the ankles of disabled people, for example, those suffering from hemiplegia or peroneal nerve palsy. We have developed an intelligently controllable AFO (i-AFO) in which the ankle torque is controlled by a compact magnetorheological fluid brake. Gait-control tests with the i-AFO were performed for a patient with flaccid paralysis of the ankles, who has difficulty in voluntary movement of the peripheral part of the inferior limb, and physical limitations on his ankles. By using the i-AFO, his gait control was improved by prevention of drop foot in the swing phase and by forward promotion in the stance phase.


title: Enhancing Differential Evolution Utilizing Eigenvector-Based Crossover Operator

abstract: Differential evolution has been shown to be an effective methodology for solving optimization problems over continuous space. In this paper, we propose an eigenvector-based crossover operator. The proposed operator utilizes eigenvectors of covariance matrix of individual solutions, which makes the crossover rotationally invariant. More specifically, the donor vectors during crossover are modified, by projecting each donor vector onto the eigenvector basis that provides an alternative coordinate system. The proposed operator can be applied to any crossover strategy with minimal changes. The experimental results show that the proposed operator significantly improves DE performance on a set of 54 test functions in CEC 2011, BBOB 2012, and CEC 2013 benchmark sets.


title: Influence of outcome valence in the subjective experience of episodic past, future, and counterfactual thinking

abstract: Recent findings suggest that our capacity to imagine the future depends on our capacity to remember the past. However, the extent to which episodic memory is involved in our capacity to think about what could have happened in our past, yet did not occur (i.e., episodic counterfactual thinking), remains largely unexplored. The current experiments investigate the phenomenological characteristics and the influence of outcome valence on the experience of past, future and counterfactual thoughts. Participants were asked to mentally simulate past, future, and counterfactual events with positive or negative outcomes. Features of their subjective experiences during each type of simulation were measured using questionnaires and autobiographical interviews. The results suggest that clarity and vividness were higher for past than future and counterfactual simulations. Additionally, emotional intensity was lower for counterfactual simulations than past and future simulations. Finally, outcome valence influenced participants’ judgment of probability for future and counterfactual simulations.


title: Multimodal Compact Bilinear Pooling for Multimodal Neural Machine Translation

abstract: In state-of-the-art Neural Machine Translation, an attention mechanism is used during decoding to enhance the translation. At every step, the decoder uses this mechanism to focus on different parts of the source sentence to gather the most useful information before outputting its target word. Recently, the effectiveness of the attention mechanism has also been explored for multimodal tasks, where it becomes possible to focus both on sentence parts and image regions. Approaches to pool two modalities usually include element-wise product, sum or concatenation. In this paper, we evaluate the more advanced Multimodal Compact Bilinear pooling method, which takes the outer product of two vectors to combine the attention features for the two modalities. This has been previously investigated for visual question answering. We try out this approach for multimodal image caption translation and show improvements compared to basic combination methods.


title: An integrated closed-form fusion algorithm for TDOA/AOA

abstract: Target positioning in 3D based on TDOA (time difference of arrival) measurements is a challenging problem. This paper will design a novel integrated closed-form (ICF) fusion algorithm by bringing in an ancillary-variable, which could be uniformly applicable to TDOA, AOA, as well as TDOA+AOA. This ICF fusion algorithm is stable and robust for any sensor network distribution and applicable for 3D position estimation with as few as two sensors with both TOA and DOA measurements. The effectiveness and accuracy of the algorithm for different kind of sensor distributions have been illustrated with a number of simulation examples.


title: A Pilot Study on the Effects of Heart Rate Variability Biofeedback in Patients with Depression and in Healthy Subjects

abstract: Decreased vagal activity and increased sympathetic arousal have been proposed as major contributors to the increased risk of cardiovascular mortality in patients with depression. It was aim of the present study to assess the feasibility of using heart rate variability (HRV) biofeedback to treat moderate to severe depression. This was an open-label study in which 14 patients with different degrees of depression (13 f, 1 m) aged 30 years (18–47; median; range) and 12 healthy volunteers attended 6 sessions of HRV biofeedback over two weeks. Another 12 healthy subjects were observed under an active control condition. At follow up BDI was found significantly decreased (BDI 6; 2–20; median 25%–75% quartile) as compared to baseline conditions (BDI 22;15–29) in patients with depression. In addition, depressed patients had reduced anxiety, decreased heart rate and increased HRV after conduction of biofeedback (p < 0.05). By contrast, no changes were noted in healthy subjects receiving biofeedback nor in normal controls. In conclusion, HRV biofeedback appears to be a useful adjunct for the treatment of depression, associated with increases in HRV.


title: Sensus: a cross-platform, general-purpose system for mobile crowdsensing in human-subject studies

abstract: The burden of entry into mobile crowdsensing (MCS) is prohibitively high for human-subject researchers who lack a technical orientation. As a result, the benefits of MCS remain beyond the reach of research communities (e.g., psychologists) whose expertise in the study of human behavior might advance applications and understanding of MCS systems. This paper presents Sensus, a new MCS system for human-subject studies that bridges the gap between human-subject researchers and MCS methods. Sensus alleviates technical burdens with on-device, GUI-based design of sensing plans, simple and efficient distribution of sensing plans to study participants, and uniform participant experience across iOS and Android devices. Sensing plans support many hardware and software sensors, automatic deployment of sensor-triggered surveys, and double-blind assignment of participants within randomized controlled trials. Sensus offers these features to study designers without requiring knowledge of markup and programming languages. We demonstrate the feasibility of using Sensus within two human-subject studies, one in psychology and one in engineering. Feedback from non-technical users indicates that Sensus is an effective and low-burden system for MCS-based data collection and analysis.


title: Ensuring Privacy in Opportunistic Network

abstract: The emanate of extremely powerful mobile communication devices in recent times has elicit the development of many exploitative technologies that attempt at leveraging the ever increasing processing, storage and communicating capacities of these devices. One of the most developing areas of network is opportunistic network that provides communication even in disconnected mode. Nodes are mobile and can change their location and message or packet is forward through many intermediate nodes so identity of users is shown to all. Any intermediate can drop the data packets if the node is malicious and do not wishes to forward the data to a particular destination id. A few privacy preventing algorithms are proposed to maintain it. In this research an algorithm is proposed to maintain the privacy of user if user wants it. Stable node provides a new virtual ID to a user who wishes to communicate without disclosing his privacy. This research ensures the privacy of the data with the use of concept of cluster estimation. In this research the public private cryptography technique is used for data encryption and decryption. Algorithm is implemented on NS2 (Network Simulator 2.35).


title: Companion Versus Comparison Examining Seeking Social Companionship or Social Comparison as Characteristics That Differentiate Happy and Unhappy People

abstract: Which friend do you want to spend time with—a happy friend who performs better than you or an unhappy friend who performs worse than you? The present research demonstrates that in such conflicting situations, when the desires for companionship and comparison are pitted against each other, one’s level of happiness plays an important role in one’s choice. Using hypothetical scenarios, we found that compared with unhappy people, happy people expected that spending time with a happy, superior friend would be more pleasant than spending time with an unhappy, inferior friend (Studies 1B through 2) and were more willing to socialize with a happy, superior friend than with an unhappy, inferior friend (Studies 1B through 2). Moreover, this pattern was not explained by self-esteem (Study 2) or the similarity-attraction hypothesis (Study 3). The present findings suggest that happy people place more value on companionship than on comparison.


title: Learning to share visual appearance for multiclass object detection

abstract: We present a hierarchical classification model that allows rare objects to borrow statistical strength from related objects that have many training examples. Unlike many of the existing object detection and recognition systems that treat different classes as unrelated entities, our model learns both a hierarchy for sharing visual appearance across 200 object categories and hierarchical parameters. Our experimental results on the challenging object localization and detection task demonstrate that the proposed model substantially improves the accuracy of the standard single object detectors that ignore hierarchical structure altogether.


title: Robot Task Commander: A framework and IDE for robot application development

abstract: This paper introduces the Robot Task Commander (RTC) framework for defining, developing, and deploying robot application software for use in different run-time contexts. RTC was created by NASA-JSC in conjunction with General Motors for use with the Robonaut-2 and Valkyrie humanoid robot platforms. RTC provides a robot programming syntax and an IDE appropriate for use by experts and non-experts for implementation and execution. An expert developer can implement a new application with a combination of scripts, called process nodes, and state machines that set the control mode of the robot. A non-expert developer can assemble process nodes and controller state machines into novel hierarchical applications using a visual programming language (VPL). This VPL also allows developers to interface with other RTC applications or with third-party software packages using a variety of network transport mechanisms (ROS, TCP, shared memory, etc.). RTC represents an advantage over other robot programming frameworks by providing multiple levels of flexibility for development. The efficacy of RTC is demonstrated through examples of sophisticated behaviors, such as programming the Valkyrie robot to grab objects and turn a valve.


title: CAR: Clock with Adaptive Replacement

abstract: CLOCK is a classical cache replacement policy dating back to 1968 that was proposed as a low-complexity approximation to LRU. On every cache hit, the policy LRU needs to move the accessed item to the most recently used position, at which point, to ensure consistency and correctness, it serializes cache hits behind a single global lock. CLOCK eliminates this lock contention, and, hence, can support high concurrency and high throughput environments such as virtual memory (for example, Multics, UNIX, BSD, AIX) and databases (for example, DB2). Unfortunately, CLOCK is still plagued by disadvantages of LRU such as disregard for "frequency", susceptibility to scans, and low performance.As our main contribution, we propose a simple and elegant new algorithm, namely, CLOCK with Adaptive Replacement (CAR), that has several advantages over CLOCK: (i) it is scan-resistant; (ii) it is self-tuning and it adaptively and dynamically captures the "recency" and "frequency" features of a workload; (iii) it uses essentially the same primitives as CLOCK, and, hence, is low-complexity and amenable to a high-concurrency implementation; and (iv) it outperforms CLOCK across a wide-range of cache sizes and workloads. The algorithm CAR is inspired by the Adaptive Replacement Cache (ARC) algorithm, and inherits virtually all advantages of ARC including its high performance, but does not serialize cache hits behind a single global lock. As our second contribution, we introduce another novel algorithm, namely, CAR with Temporal filtering (CART), that has all the advantages of CAR, but, in addition, uses a certain temporal filter to distill pages with long-term utility from those with only short-term utility.


title: Data-Driven 3-D Human Body Customization With a Mobile Device

abstract: It is more convincing for users to have their own 3-D body shapes in the virtual fitting room when they shop clothes online. However, existing methods are limited for ordinary users to efficiently and conveniently access their 3-D bodies. We propose an efficient data-driven approach and develop an android application for 3-D body customization. Users stand naturally and their photos are taken from front and side views with a handy phone camera. They can wear casual clothes like a short-sleeved/long-sleeved shirt and short/long pants. First, we develop a user-friendly interface to semi-automatically segment the human body from photos. Then, the segmented human contours are scaled and translated to the ones under our virtual camera configurations. Through this way, we only need one camera to take photos of human in two views and do not need to calibrate the camera, which satisfy the convenience requirement. Finally, we learn body parameters that determine the 3-D body from dressed-human silhouettes with cascaded regressors. The regressors are trained using a database containing 3-D naked and dressed body pairs. Body parameters regression only costs 1.26 s on an android phone, which ensures the efficiency of our method. We invited 12 volunteers for tests, and the mean absolute estimation error for chest/waist/hip size is 2.89/1.93/2.22 centimeters. We additionally use 637 synthetic data to evaluate the main procedures of our approach.


Sampling Informative Training Data for RNN Language Models


title: The imbalance of oculomotor capture in unilateral visual neglect

abstract: Visual neglect has been associated with an imbalance in the level of activity in the saccadic system: activity in the contralesional field is suppressed, which makes target selection unlikely. We recorded eye movements of a patient with hemispatial neglect and a group of healthy participants during an oculomotor distractor paradigm. Results showed that the interfering effects of a distractor were very strong when presented in her ipsilesional visual field. However, when the distractor was presented in her contralesional field, there were no interfering effects when the target was presented in her ipsilesional field. These findings could not be explained by the presence of a visual field defect as revealed by the results of two hemianopic patients. Our results are in line with an imbalance in the level of activity in the saccadic system in visual neglect because visual elements presented in the contralesional field did not compete for saccadic selection.


title: Wrinkled, wavelength-tunable graphene-based surface topographies for directing cell alignment and morphology.

abstract: Abstract Textured surfaces with periodic topographical features and long-range order are highly attractive for directing cell-material interactions. They mimic physiological environments more accurately than planar surfaces and can fundamentally alter cell alignment, shape, gene expression, as well as multicellular organization into hierarchical tissue architectures. Here we demonstrate for the first time that wrinkled graphene-based surfaces are suitable as textured cell attachment substrates, and that engineered wrinkling can dramatically alter cell alignment and morphology. The wrinkled surfaces are fabricated by graphene oxide wet deposition onto pre-stretched elastomers followed by relaxation and mild thermal treatment to stabilize the films in cell culture medium. Multilayer graphene oxide films form periodic, delaminated buckle textures whose wavelengths and amplitudes can be systematically tuned by variation in the wet deposition process. Human and murine fibroblasts attach to these textured films and remain viable, while developing pronounced alignment and elongation relative to those on planar graphene controls. Compared to lithographic patterning of nanogratings, this method has advantages in the simplicity and scalability of fabrication, as well as the opportunity to couple the use of topographic cues with the unique conductive, adsorptive, or barrier properties of graphene materials for functional biomedical devices.


title: A fast diffeomorphic image registration algorithm

abstract: This paper describes DARTEL, which is an algorithm for diffeomorphic image registration. It is implemented for both 2D and 3D image registration and has been formulated to include an option for estimating inverse consistent deformations. Nonlinear registration is considered as a local optimisation problem, which is solved using a Levenberg-Marquardt strategy. The necessary matrix solutions are obtained in reasonable time using a multigrid method. A constant Eulerian velocity framework is used, which allows a rapid scaling and squaring method to be used in the computations. DARTEL has been applied to intersubject registration of 471 whole brain images, and the resulting deformations were evaluated in terms of how well they encode the shape information necessary to separate male and female subjects and to predict the ages of the subjects.


title: 3-D ghost imaging with microwave radar

abstract: In this paper, we demonstrate a new three- dimensional (3-D) radar imaging mechanism borrowing the concept of optical ghost imaging (GI). High range resolution is achieved by transmitting wideband electromagnetic (EM) signal, while high resolution in the orthogonal plane is obtained by correlating the echoes with radar antenna's stochastically fluctuating radiation pattern which can be generated with a 2-D phased array. This new concept of radar imaging takes no advantage of relative motion between the target and radar platform. Moreover, by combining the compressive sensing (CS) theory, our proposed imaging system is capable of realizing super-resolution with fewer data samples. Numerical simulation is given to validate the proposal.


title: Plotting Alloy Corrosion State Diagrams

abstract: Abstract A method of plotting experimental data is described, which reveals the relationships among alloy corrosion state, chemical composition, and external polarization. The diagrams are plotted ...


title: Abstractive Dialogue Summarization with Sentence-Gated Modeling Optimized by Dialogue Acts

abstract: Neural abstractive summarization has been increasingly studied, where the prior work mainly focused on summarizing single-speaker documents (news, scientific publications, etc). In dialogues, there are different interactions between speakers, which are usually defined as dialogue acts. The interactive signals may provide informative cues for better summarizing dialogues. This paper proposes to explicitly leverage dialogue acts in a neural summarization model, where a sentence-gated mechanism is designed for modeling the relationship between dialogue acts and the summary. The experiments show that our proposed model significantly improves the abstractive summarization performance compared to the state-of-the-art baselines on AMI meeting corpus, demonstrating the usefulness of the interactive signal provided by dialogue acts.


title: Learning Universal Sentence Representations with Mean-Max Attention Autoencoder

abstract: In order to learn universal sentence representations, previous methods focus on complex recurrent neural networks or supervised learning. In this paper, we propose a mean-max attention autoencoder (mean-max AAE) within the encoder-decoder framework. Our autoencoder rely entirely on the MultiHead self-attention mechanism to reconstruct the input sequence. In the encoding we propose a mean-max strategy that applies both mean and max pooling operations over the hidden vectors to capture diverse information of the input. To enable the information to steer the reconstruction process dynamically, the decoder performs attention over the mean-max representation. By training our model on a large collection of unlabelled data, we obtain high-quality representations of sentences. Experimental results on a broad range of 10 transfer tasks demonstrate that our model outperforms the state-of-the-art unsupervised single methods, including the classical skip-thoughts and the advanced skip-thoughts+LN model. Furthermore, compared with the traditional recurrent neural network, our mean-max AAE greatly reduce the training time.


title: Local and global regressive mapping for manifold learning with out-of-sample extrapolation

abstract: Over the past few years, a large family of manifold learning algorithms have been proposed, and applied to various applications. While designing new manifold learning algorithms has attracted much research attention, fewer research efforts have been focused on out-of-sample extrapolation of learned manifold. In this paper, we propose a novel algorithm of manifold learning. The proposed algorithm, namely Local and Global Regressive Mapping (LGRM), employs local regression models to grasp the manifold structure. We additionally impose a global regression term as regularization to learn a model for out-of-sample data extrapolation. Based on the algorithm, we propose a new manifold learning framework. Our framework can be applied to any manifold learning algorithms to simultaneously learn the low dimensional embedding of the training data and a model which provides explicit mapping of the out-of-sample data to the learned manifold. Experiments demonstrate that the proposed framework uncover the manifold structure precisely and can be freely applied to unseen data.


title: The GRASP Taxonomy of Human Grasp Types

abstract: In this paper, we analyze and compare existing human grasp taxonomies and synthesize them into a single new taxonomy (dubbed “The GRASP Taxonomy” after the GRASP project funded by the European Commission). We consider only static and stable grasps performed by one hand. The goal is to extract the largest set of different grasps that were referenced in the literature and arrange them in a systematic way. The taxonomy provides a common terminology to define human hand configurations and is important in many domains such as human–computer interaction and tangible user interfaces where an understanding of the human is basis for a proper interface. Overall, 33 different grasp types are found and arranged into the GRASP taxonomy. Within the taxonomy, grasps are arranged according to 1) opposition type, 2) the virtual finger assignments, 3) type in terms of power, precision, or intermediate grasp, and 4) the position of the thumb. The resulting taxonomy incorporates all grasps found in the reviewed taxonomies that complied with the grasp definition. We also show that due to the nature of the classification, the 33 grasp types might be reduced to a set of 17 more general grasps if only the hand configuration is considered without the object shape/size.


Core and equilibria in models of large household economy


title: Characterizing Long-tail SEO Spam on Cloud Web Hosting Services

abstract: The popularity of long-tail search engine optimization (SEO) brings with new security challenges: incidents of long-tail keyword poisoning to lower competition and increase revenue have been reported. The emergence of cloud web hosting services provides a new and effective platform for long-tail SEO spam attacks. There is growing evidence that large-scale long-tail SEO campaigns are being carried out on cloud hosting platforms because they offer low-cost, high-speed hosting services. In this paper, we take the first step toward understanding how long-tail SEO spam is implemented on cloud hosting platforms. After identifying 3,186 cloud directories and 318,470 doorway pages on the leading cloud platforms for long-tail SEO spam, we characterize their abusive behavior. One highlight of our findings is the effectiveness of the cloud-based long-tail SEO spam, with 6% of the doorway pages successfully appearing in the top 10 search results of the poisoned long-tail keywords. ::: ::: Examples of other important discoveries include how such doorway pages monetize traffic and their ability to manage cloud platform's countermeasures. These findings bring such abuse to the spotlight and provide some insights to eliminating this practice.


title: Gamma-spectrometric uranium age-dating using intrinsic efficiency calibration

abstract: A non-destructive, gamma-spectrometric method for uranium age-dating is presented which is applicable to material of any physical form and geometrical shape. It relies on measuring the daughter/parent activity ratio 214Bi/234U by low-background, high-resolution gamma-spectrometry using intrinsic efficiency calibration. The method does not require the use of any reference materials nor the use of an efficiency calibrated geometry.


title: Why is Differential Evolution Better than Grid Search for Tuning Defect Predictors?

abstract: Context: One of the black arts of data mining is learning the magic parameters which control the learners. In software analytics, at least for defect prediction, several methods, like grid search and differential evolution (DE), have been proposed to learn these parameters, which has been proved to be able to improve the performance scores of learners. ::: Objective: We want to evaluate which method can find better parameters in terms of performance score and runtime cost. ::: Methods: This paper compares grid search to differential evolution, which is an evolutionary algorithm that makes extensive use of stochastic jumps around the search space. ::: Results: We find that the seemingly complete approach of grid search does no better, and sometimes worse, than the stochastic search. When repeated 20 times to check for conclusion validity, DE was over 210 times faster than grid search to tune Random Forests on 17 testing data sets with F-Measure ::: Conclusions: These results are puzzling: why does a quick partial search be just as effective as a much slower, and much more, extensive search? To answer that question, we turned to the theoretical optimization literature. Bergstra and Bengio conjecture that grid search is not more effective than more randomized searchers if the underlying search space is inherently low dimensional. This is significant since recent results show that defect prediction exhibits very low intrinsic dimensionality-- an observation that explains why a fast method like DE may work as well as a seemingly more thorough grid search. This suggests, as a future research direction, that it might be possible to peek at data sets before doing any optimization in order to match the optimization algorithm to the problem at hand.


title: Divide and Conquer: Partitioning OSPF networks with SDN

abstract: Software Defined Networking (SDN) is an emerging network control paradigm focused on logical centralization and programmability. At the same time, distributed routing protocols, most notably OSPF and IS-IS, are still prevalent in IP networks, as they provide shortest path routing, fast topological convergence after network failures, and, perhaps most importantly, the confidence based on decades of reliable operation. Therefore, a hybrid SDN/OSPF operation remains a desirable proposition. In this paper, we propose a new method of hybrid SDN/OSPF operation. Our method is different from other hybrid approaches, as it uses SDN nodes to partition an OSPF domain into sub-domains thereby achieving the traffic engineering capabilities comparable to full SDN operation. We place SDN-enabled routers as subdomain border nodes, while the operation of the OSPF protocol continues unaffected. In this way, the SDN controller can tune routing protocol updates for traffic engineering purposes before they are flooded into sub-domains. While local routing inside sub-domains remains stable at all times, inter-sub-domain routes can be optimized by determining the routes in each traversed sub-domain. As the majority of traffic in non-trivial topologies has to traverse multiple subdomains, our simulation results confirm that a few SDN nodes allow traffic engineering up to a degree that renders full SDN deployment unnecessary.


title: Online handwriting recognition: the NPen++ recognizer

abstract: This paper presents the online handwriting recognition system NPen++ developed at the University of Karlsruhe and Carnegie Mellon University. The NPen++ recognition engine is based on a multi-state time delay neural network and yields recognition rates from 96% for a 5,000 word dictionary to 93.4% on a 20,000 word dictionary and 91.2% for a 50,000 word dictionary. The proposed tree search and pruning technique reduces the search space considerably without losing too much recognition performance compared to an exhaustive search. This enables the NPen++ recognizer to be run in real-time with large dictionaries. Initial recognition rates for whole sentences are promising and show that the MS-TDNN architecture is suited to recognizing handwritten data ranging from single characters to whole sentences.


Understanding the Role and Methods of Meta-Analysis in IS Research


title: A Cluster Separation Measure

abstract: A measure is presented which indicates the similarity of clusters which are assumed to have a data density which is a decreasing function of distance from a vector characteristic of the cluster. The measure can be used to infer the appropriateness of data partitions and can therefore be used to compare relative appropriateness of various divisions of the data. The measure does not depend on either the number of clusters analyzed nor the method of partitioning of the data and can be used to guide a cluster seeking algorithm.


title: X-trace: A pervasive network tracing framework

abstract: Modern Internet systems often combine different applications (e.g., DNS, web, and database), span different administrative domains, and function in the context of network mechanisms like tunnels, VPNs, NATs, and overlays. Diagnosing these complex systems is a daunting challenge. Although many diagnostic tools exist, they are typically designed for a specific layer (e.g., traceroute) or application, and there is currently no tool for reconstructing a comprehensive view of service behavior. In this paper we propose X-Trace, a tracing framework that provides such a comprehensive view for systems that adopt it. We have implemented X-Trace in several protocols and software systems, and we discuss how it works in three deployed scenarios: DNS resolution, a three-tiered photo-hosting website, and a service accessed through an overlay network.


title: Glasses frame detection with 3D Hough transform

abstract: This paper describes a method to detect glasses frames for robust facial image processing. This method makes use of the 3D features obtained by a trinocular stereo vision system. The glasses frame detection is based on the fact that the rims of a pair of glasses lie on the same plane in 3D space. We use a 3D Hough transform to obtain a plane in which 3D features are concentrated. Then, based on the obtained 3D plane and with some geometry constraints, we can detect a group of 3D features belonging to the frame of the glasses. Using this approach, we can separate the 3D features of the glasses frame from those of facial features. This approach does not require any prior knowledge about face pose, eye positions, or the shape of the glasses.


title: Effects of Sensory-Enhanced Yoga on Symptoms of Combat Stress in Deployed Military Personnel

abstract: OBJECTIVE. We examined the effects of sensory-enhanced hatha yoga on symptoms of combat stress in deployed military personnel, compared their anxiety and sensory processing with that of stateside civilians, and identified any correlations between the State–Trait Anxiety Inventory scales and the Adolescent/Adult Sensory Profile quadrants. METHOD. Seventy military personnel who were deployed to Iraq participated in a randomized controlled trial. Thirty-five received 3 wk (³9 sessions) of sensory-enhanced hatha yoga, and 35 did not receive any form of yoga. RESULTS. Sensory-enhanced hatha yoga was effective in reducing state and trait anxiety, despite normal pretest scores. Treatment participants showed significantly greater improvement than control participants on 16 of 18 mental health and quality-of-life factors. We found positive correlations between all test measures except sensory seeking. Sensory seeking was negatively correlated with all measures except low registration, which was insignificant. CONCLUSION. The results support using sensory-enhanced hatha yoga for proactive combat stress management.


title: Optimal Parallel Algorithms for Computing the Sum, the Prefix-Sums, and the Summed Area Table on the Memory Machine Models

abstract: The main contribution of this paper is to show optimal parallel algorithms to compute the sum, the prefix-sums, and the summed area table on two memory machine models, the Discrete Memory Machine (DMM) and the Unified Memory Machine (UMM). The DMM and the UMM are theoretical parallel computing models that capture the essence of the shared memory and the global memory of GPUs. These models have three parameters, the number p of threads, and the width w of the memory, and the memory access latency l. We first show that the sum of n numbers can be computed in O( n w + nl p + l log n) time units on the DMM and the UMM. We then go on to show that Ω(n w + nl p + l log n) time units are necessary to compute the sum. We also present a parallel algorithm that computes the prefix-sums of n numbers in O( n w + nl p + l log n) time units on the DMM and the UMM. Finally, we show that the summed area table of size √ n × √n can be computed in O( n w + nl p + l log n) time units on the DMM and the UMM. Since the computation of the prefix-sums and the summed area table is at least as hard as the sum computation, these parallel algorithms are also optimal. key words: Memory machine models, prefix-sums computation, parallel algorithm, GPU, CUDA


title: An edge-set based large scale graph processing system

abstract: Next generation analytics will be all about graphs, though performance has been a fundamental challenge for large scale graph processing. In this paper, we present an industrial graph processing engine for exploring various large scale linked data, which exhibits superior performance due to the several innovations. This engine organizes a graph as a set of edge-sets, compatible with the traditional edge-centric sharding for graphs, but becomes more amenable for large scale processing. Each time only a portion of the sets are needed for computation and the data access patterns can be highly predictable for prefetch for many graph computing algorithms. Due to the sparsity of large scale graph structure, this engine differentiates logical edge-sets from the edge-sets physically stored on the disk, where multiple logical edge-sets can be organized into a same physical edge-set to increase the data locality. Besides, in contrast to existing solution, the data structures utilized for the physical edge-sets can vary from one to another. Such heterogeneous edge-set representation explores the best graph processing performance according to local data access patterns. We conduct experiments on a representative set of property graphs on multiple platforms, where the proposed system outperform the baseline systems consistently.


title: As-You-Type Social Aware Search

abstract: Modern search applications feature real-time as-you-type query search. In its elementary form, the problem consists in retrieving a set of k search results, that is, performing a search with a given prefix, and showing the top-ranked results. In this article, we focus on as-you-type keyword search over social media, that is, data published by users who are interconnected through a social network. We adopt a “network-aware” interpretation for information relevance, by which information produced by users who are closer to the user issuing a request is considered more relevant. This query model raises new challenges for effectiveness and efficiency in online search, even when the intent of the user is fully specified, as a complete query given as input in one keystroke. This is mainly because it requires a joint exploration of the social space and traditional IR indexes, such as inverted lists. We describe a memory-efficient and incremental prefix-based retrieval algorithm, which also exhibits an anytime behavior, allowing output of the most likely answer within any chosen runtime limit. We evaluate our approach through extensive experiments for several applications and search scenarios. We consider searching for posts in microblogging (Twitter and Tumblr), for businesses (Yelp), as well as for movies (Amazon) based on reviews. We also conduct a series of experiments comparing our algorithm with baselines using state-of-the-art techniques and measuring the improvements brought by several key optimizations. They show that our solution is effective in answering real-time as-you-type searches over social media.


Is deinstitutionalization a" failed experiment"? The ethics of re-institutionalization


title: Fast iterative graph computation: a path centric approach

abstract: Large scale graph processing represents an interesting challenge due to the lack of locality. This paper presents Path Graph for improving iterative graph computation on graphs with billions of edges. Our system design has three unique features: First, we model a large graph using a collection of tree-based partitions and use an path-centric computation rather than vertex-centric or edge-centric computation. Our parallel computation model significantly improves the memory and disk locality for performing iterative computation algorithms. Second, we design a compact storage that further maximize sequential access and minimize random access on storage media. Third, we implement the path-centric computation model by using a scatter/gather programming model, which parallels the iterative computation at partition tree level and performs sequential updates for vertices in each partition tree. The experimental results show that the path-centric approach outperforms vertex centric and edge-centric systems on a number of graph algorithms for both in-memory and out-of-core graphs.


title: Design and Application of a 3-DoF Manipulator for Launch and Recovery System

abstract: A novel 3-DoF translational manipulator is designed by constructing the parallel linear motion elements and the single loop 2-SPR. Based on screw theory, the mobility and the singularity are analyzed. Then the simplified kinematic and dynamic equations for this manipulator are derived. This mechanism can be applied for the Launch and Recovery System in the catamaran-style USV, on the purpose of picking and placing the autonomous vehicles in the water. The control model is established and the simulation is also conducted. To save the storage and enlarge the workspace of this manipulator, the scissor-like elements(SLEs) are introduced to improve the design of this mechanism. The improved 3- DoF translational manipulator with SLEs is proposed at last.


title: A relativistic extension of Hopfield neural networks via the mechanical analogy

abstract: We propose a modification of the cost function of the Hopfield model whose salient features shine in its Taylor expansion and result in more than pairwise interactions with alternate signs, suggesting a unified framework for handling both with deep learning and network pruning. In our analysis, we heavily rely on the Hamilton-Jacobi correspondence relating the statistical model with a mechanical system. In this picture, our model is nothing but the relativistic extension of the original Hopfield model (whose cost function is a quadratic form in the Mattis magnetization which mimics the non-relativistic Hamiltonian for a free particle). We focus on the low-storage regime and solve the model analytically by taking advantage of the mechanical analogy, thus obtaining a complete characterization of the free energy and the associated self-consistency equations in the thermodynamic limit. On the numerical side, we test the performances of our proposal with MC simulations, showing that the stability of spurious states (limiting the capabilities of the standard Hebbian construction) is sensibly reduced due to presence of unlearning contributions in this extended framework.


title: Fully Convolutional Instance-Aware Semantic Segmentation

abstract: We present the first fully convolutional end-to-end solution for instance-aware semantic segmentation task. It inherits all the merits of FCNs for semantic segmentation and instance mask proposal. It performs instance mask prediction and classification jointly. The underlying convolutional representation is fully shared between the two sub-tasks, as well as between all regions of interest. The proposed network is highly integrated and achieves state-of-the-art performance in both accuracy and efficiency. It wins the COCO 2016 segmentation competition by a large margin. The code would be released at \url{https://github.com/daijifeng001/TA-FCN}.


title: AN EMPIRICAL COMPARISON OF THE USABILITY OF BPMN AND UML ACTIVITY DIAGRAMS FOR BUSINESS USERS

abstract: The widespread implementation of Business Process Management (BPM) strategies has increased the demand for an integral approach to business process modeling, in which all stakeholders can effectively participate and together shape a company’s business processes. Amongst others, this demand was a basis for the development of the Business Process Modeling Notation (BPMN) as a proposed industry standard. It does not only provide technical advantages such as a support for serviceoriented computing, but also claims to be readily usable for business users. Following this presumption, BPMN is even used by the Object Management Group (OMG). It adopted BPMN instead of the Activity Diagram (UML AD) as the core standard to create a business modeling framework. For companies, however, changing to a new process modeling language is a significant expense factor. Furthermore, consolidated findings on whether BPMN is indeed more usable for business users than UML AD are missing. In this paper, we present results from a comprehensive empirical comparison of both languages, in which we examined the application by business users during a model creation task. Results indicate that the UML AD is at least as usable as BPMN, since BPMN did neither differ significantly in effectiveness, efficiency, nor user satisfaction.


title: Using reinforcement learning to guide the development of self-organised feature maps for visual orienting

abstract: We present a biologically inspired neural network model of visual orienting (using saccadic eye movements) in which targets are preferentially selected according to their reward value. Internal representations of visual features that guide saccades are developed in a self-organised map whose plasticity is modulated under reward. In this way, only those features relevant for acquiring rewarding targets are generated. As well as guiding the formation of feature representations, rewarding stimuli are stored in a working memory and bias future saccade generation. In addition, a reward prediction error is used to initiate retraining of the self-organised map to generate more efficient representations of the features when necessary.


title: Interactive adaptation of real-time object detectors

abstract: In the following paper, we present a framework for quickly training 2D object detectors for robotic perception. Our method can be used by robotics practitioners to quickly (under 30 seconds per object) build a large-scale real-time perception system. In particular, we show how to create new detectors on the fly using large-scale internet image databases, thus allowing a user to choose among thousands of available categories to build a detection system suitable for the particular robotic application. Furthermore, we show how to adapt these models to the current environment with just a few in-situ images. Experiments on existing 2D benchmarks evaluate the speed, accuracy, and flexibility of our system.


title: An improved DC-link voltage fast control scheme for a PWM rectifier-inverter system

abstract: This paper presents an improved dc-link voltage fast control strategy based on energy balance for a pulse width modulation (PWM) rectifier-inverter system to reduce the fluctuation of dc-link voltage. A conclusion is drawn that the energy in dc-link capacitor cannot be kept constant in dynamic process when the system operates in rectifier mode, even in ideal case. Meanwhile, the minimum dc-link voltage deviation is analyzed. Accordingly, a predictive dc-link voltage control scheme based on energy balance is proposed, while the grid current is regulated by deadbeat predictive control. A prediction method for output power is also introduced. Furthermore, a small-signal model with the control delay is adopted to analyze the stability and robustness of the control strategy. The simulation and experimental results demonstrate both good dynamic and steady-state performances of the rectifier-inverter system with the proposed control scheme.


title: Piezoelectric Energy Harvesting Solutions

abstract: This paper reviews the state of the art in piezoelectric energy harvesting. It presents the basics of piezoelectricity and discusses materials choice. The work places emphasis on material operating modes and device configurations, from resonant to non-resonant devices and also to rotational solutions. The reviewed literature is compared based on power density and bandwidth. Lastly, the question of power conversion is addressed by reviewing various circuit solutions.


title: Dance Dance Convolution

abstract: Dance Dance Revolution (DDR) is a popular rhythm-based video game. Players perform steps on a dance platform in synchronization with music as directed by on-screen step charts. While many step charts are available in standardized packs, players may grow tired of existing charts, or wish to dance to a song for which no chart exists. We introduce the task of learning to choreograph. Given a raw audio track, the goal is to produce a new step chart. This task decomposes naturally into two subtasks: deciding when to place steps and deciding which steps to select. For the step placement task, we combine recurrent and convolutional neural networks to ingest spectrograms of low-level audio features to predict steps, conditioned on chart difficulty. For step selection, we present a conditional LSTM generative model that substantially outperforms n-gram and fixed-window approaches.


title: A 77 GHz FM/CW radar frontend with a low-profile, low-loss printed antenna

abstract: Design and results of a 77-GHz frequency-modulation/continuous-wave radar sensor based on a simple waveguide circuitry and a novel type of printed, low-profile, and low-loss antenna are presented. A Gunn voltage-controlled oscillator and a finline mixer act as transmitter and receiver, respectively, connected by two E-plane couplers. The folded reflector-type antenna consists of a printed slot array and another planar substrate, which, at the same time, provides twisting of the polarization and focusing of the incident wave. The performance of the radar is described, together with the initial results of a scanning of the antenna beam.


title: Microstrip Magnetic Monopole and Dipole Antennas With High Directivity and a Horizontally Polarized Omnidirectional Pattern

abstract: Novel microstrip magnetic monopole and dipole antennas are proposed to achieve a high directivity along the elevation plane as well as a horizontally polarized omnidirectional pattern in the azimuth plane. The directivity of this microstrip magnetic monopole antenna is calculated by numerical integration; the results show that the directivity increases with the antenna length when the antenna resonates in the fundamental mode. However, simulation shows that the main beam is tilted away from the azimuth plane when the antenna length exceeds $2\lambda $ due to high-order resonant mode interference. A solution is provided to suppress the second resonant mode and retain the radiation peak in the azimuth direction. The peak gain of the microstrip magnetic monopole is increased to 7.2 dBi when the antenna length is approximately $3\lambda $ . A microstrip magnetic dipole antenna with an antenna length of $6\lambda $ is also designed to obtain a higher gain of 9.7 dBi. The impedance bandwidths of the microstrip magnetic monopole and dipole antennas are 6.5% (2.37–2.52 GHz) and 7.3% (2.35–2.53 GHz), respectively. The proposed antennas produce maximum radiation in the azimuth direction and have horizontally polarized omnidirectional patterns in the azimuth plane.


title: Towards open manufacturing: a cross-enterprises knowledge and services exchange framework based on blockchain and edge computing

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to propose a cross-enterprises framework to achieve a higher level of sharing of knowledge and services in manufacturing ecosystems. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The authors describe the development of the emerging open manufacturing and discuss the model of knowledge creation processes of manufacturers. The authors present a decentralized framework based on blockchain and edge computing technologies, which consists of a customer layer, an enterprise layer, an application layer, an intelligence layer, a data layer, and an infrastructure layer. And a case study is provided to illustrate the effectiveness of the framework. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The authors discuss that the manufacturing ecosystem is changing from integrated and centralized systems to shared and distributed systems. The proposed framework incorporates the recent development in blockchain and edge computing that can meet the secure and distributed requirements for the sharing of knowledge and services in manufacturing ecosystems. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: The proposed framework provides a more secure and controlled way to share knowledge and services, thereby supports the company to develop scalable and flexible business at a lower cost, and ultimately improves the overall quality, efficiency, and effectiveness of manufacturing services. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: The proposed framework incorporates the recent development in edge computing technologies to achieve a flexible and distributed network. With the blockchain technology, it provides standards and protocols for implementing the framework and ensures the security issues. Not only information can be shared, but the framework also supports in the exchange of knowledge and services so that the parties can contribute their parts.


title: ARTIFICIAL INTELLIGENCE 153 Pragmatics and Natural Language Generation*

abstract: This paper addresses the question "why and how is it that we say the same thing differently to different people, or even to the same person in different circumstances?" We vary the content and form of our text in order to convey more information than is contained in the literal meanings of our words. This information expresses the speaker's interpersonal goals toward the hearer and, in general, his or her perception of the pragmatic aspects of the conversation. This paper discusses two insights that arise when one studies this question: the existence of a level of organization that mediates between communicative goals and generator decisions, and the interleaved planningrealization regime and associated monitoring required for generation. To illustrate these ideas, a computer program is described which contains plans and strategies to produce stylistically appropriate texts from a single representation under various settings that model pragmatic circumstances.


title: Exploiting Local and Global Social Context for Recommendation

abstract: With the fast development of social media, the information overload problem becomes increasingly severe and recommender systems play an important role in helping online users find relevant information by suggesting information of potential interests. Social activities for online users produce abundant social relations. Social relations provide an independent source for recommendation, presenting both opportunities and challenges for traditional recommender systems. Users are likely to seek suggestions from both their local friends and users with high global reputations, motivating us to exploit social relations from local and global perspectives for online recommender systems in this paper. We develop approaches to capture local and global social relations, and propose a novel framework LOCABAL taking advantage of both local and global social context for recommendation. Empirical results on real-world datasets demonstrate the effectiveness of our proposed framework and further experiments are conducted to understand how local and global social context work for the proposed framework.


title: Fast Fourier Transforms for Nonequispaced Data

abstract: Two groups of algorithms are presented generalizing the fast Fourier transform (FFT) to the case of non-integer frequencies and non-equispaced nodes on the interval ($-\pi,\pi\rbrack.$ These schemes are based on combinations of certain analytical considerations with the classical fast Fourier transform, and generalize both the forward and backward FFTs. Each of the algorithms requires a number of arithmetic operations proportional to $N{\cdot}\log N + N{\cdot}\log(1/\varepsilon),$ where $\varepsilon$ is the desired precision of computations and N is the number of nodes. Several related algorithms are also presented, each of which utilizes a similar set of techniques from analysis and linear algebra. These include an efficient version of the Fast Multipole Method in one dimension, a fast algorithm for polynomial interpolation, and a fast algorithm for indefinite integration. Several numerical examples are used to illustrate the efficiency of the approach, and to compare the performances of the two sets of non-uniform FFT algorithms.


title: Robot-Aided Neurorehabilitation: A Robot for Wrist Rehabilitation

abstract: In 1991, a novel robot, MIT-MANUS, was introduced to study the potential that robots might assist in and quantify the neuro-rehabilitation of motor function. MIT-MANUS proved an excellent tool for shoulder and elbow rehabilitation in stroke patients, showing in clinical trials a reduction of impairment in movements confined to the exercised joints. This successful proof of principle as to additional targeted and intensive movement treatment prompted a test of robot training examining other limb segments. This paper focuses on a robot for wrist rehabilitation designed to provide three rotational degrees-of-freedom. The first clinical trial of the device will enroll 200 stroke survivors. Ultimately 160 stroke survivors will train with both the proximal shoulder and elbow MIT-MANUS robot, as well as with the novel distal wrist robot, in addition to 40 stroke survivor controls. So far 52 stroke patients have completed the robot training (ongoing protocol). Here, we report on the initial results on 36 of these volunteers. These results demonstrate that further improvement should be expected by adding additional training to other limb segments.


title: Work-Life Balance: The Good and the Bad of Boundary Management

abstract: Work-life balance is an important issue in today’s world and the different strategies used by people to manage their work and their personal life can have a great impact. Two studies were conducted (study 1: n = 117; study 2: n = 293) to examine how boundary segmentation preferences (studies 1 & 2) and boundary integration strategies (study 2) affect work-family conflict and enrichment. Results from structural equation modeling partly confirmed the hypothetical model in both studies. Study 1 showed that work-home segmentation preference negatively predicted work-family enrichment, while home-work segmentation preference negatively predicted family-work enrichment. Study 2 provided similar results, as it showed that work-home segmentation preference negatively predicted work-family enrichment. It also showed that work-home segmentation preference positively predicted work-family conflict and home-work segmentation preference positively predicted work-family enrichment, while work-life integration strategy positively predicted work-family conflict, family-work conflict, work-family enrichment and family-work enrichment. No significant relationship was found between life-work integration strategy and any of the dependent variables. Findings from these studies highlight the importance of using appropriate boundary management strategies in order to promote a better work-life balance. They also enhance current knowledge related to boundary management and work-life balance by examining relationships with work-family enrichment.


title: Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses – a review

abstract: Current methods of in-house plant phenotyping are providing a powerful new tool for plant biology studies. The self-constructed and commercial platforms established in the last few years, employ non-destructive methods and measurements on a large and high-throughput scale. The platforms offer to certain extent, automated measurements, using either simple single sensor analysis, or advanced integrative simultaneous analysis by multiple sensors. However, due to the complexity of the approaches used, it is not always clear what such forms of plant phenotyping can offer the potential end-user, i.e. plant biologist. This review focuses on imaging methods used in the phenotyping of plant shoots including a brief survey of the sensors used. To open up this topic to a broader audience, we provide here a simple introduction to the principles of automated non-destructive analysis, namely RGB, chlorophyll fluorescence, thermal and hyperspectral imaging. We further on present an overview on how and to which extent, the automated integrative in-house phenotyping platforms have been used recently to study the responses of plants to various changing environments.


title: 3D virtual clothing: from garment design to web3d visualization and simulation

abstract: One of the major challenges in Computer Graphics concerns the 3D representation and physically-based simulation of garments. In our research, we are working closely with the textile industry, investigating three different classes of problems. First, we aim at developing techniques and methods for cloth simulation specifically aimed at the Web3D context. Second, we are defining a cross-application data exchange format among the different CAD systems and applications used in the textile industry, including the additional information needed to support 3D simulations. Third, we are implementing a tool that complements traditional textile CAD systems (which are based on 2D graphics), allowing the user to automatically obtain VRML-based 3D previews of the garment (for evaluating garment designs and also easily publishing them on the Web). This paper illustrates the results we have achieved in these three directions.


title: Satellite phased arrays: Use of imaging reflectors with spatial filtering in the focal plane to reduce grating lobes

abstract: The imaging reflector arrangement described in this paper forms a very compact antenna design suitable for generating a scanning fan beam for a 12/14-GHz synchronous satellite communicating with points located in the continental United States. A magnified image of a small array is obtained using a Gregorian arrangement of two paraboloids. A filter, placed in the focal plane of the main reflector, eliminates undesirable field components due to the grating lobes of the small array. Because of the filter, the illumination over the main aperture is a smoothed version of the array illumination. Thus, grating lobes are greatly reduced. By properly adjusting the excitation of the various array elements, an antenna with very low side lobes is obtained.


"With Simular Proof Enough": Modes of Misperception in Cymbeline


title: Crime detection and criminal identification in India using data mining techniques

abstract: In the current paper, we propose an approach for the design and implementation of crime detection and criminal identification for Indian cities using data mining techniques. Our approach is divided into six modules, namely--data extraction (DE), data preprocessing (DP), clustering, Google map representation, classification and WEKA® implementation. First module, DE extracts the unstructured crime dataset from various crime Web sources, during the period of 2000---2012. Second module, DP cleans, integrates and reduces the extracted crime data into structured 5,038 crime instances. We represent these instances using 35 predefined crime attributes. Safeguard measures are taken for the crime database accessibility. Rest four modules are useful for crime detection, criminal identification and prediction, and crime verification, respectively. Crime detection is analyzed using k-means clustering, which iteratively generates two crime clusters that are based on similar crime attributes. Google map improves visualization to k-means. Criminal identification and prediction is analyzed using KNN classification. Crime verification of our results is done using WEKA®. WEKA® verifies an accuracy of 93.62 and 93.99 % in the formation of two crime clusters using selected crime attributes. Our approach contributes in the betterment of the society by helping the investigating agencies in crime detection and criminals' identification, and thus reducing the crime rates.


A Decentralized Publication System for Open Science using Blockchain and IPFS


A Spherical Hidden Markov Model for Semantics-Rich Human Mobility Modeling


title: Robust Multiperson Tracking from a Mobile Platform

abstract: In this paper, we address the problem of multiperson tracking in busy pedestrian zones using a stereo rig mounted on a mobile platform. The complexity of the problem calls for an integrated solution that extracts as much visual information as possible and combines it through cognitive feedback cycles. We propose such an approach, which jointly estimates camera position, stereo depth, object detection, and tracking. The interplay between those components is represented by a graphical model. Since the model has to incorporate object-object interactions and temporal links to past frames, direct inference is intractable. We, therefore, propose a two-stage procedure: for each frame, we first solve a simplified version of the model (disregarding interactions and temporal continuity) to estimate the scene geometry and an overcomplete set of object detections. Conditioned on these results, we then address object interactions, tracking, and prediction in a second step. The approach is experimentally evaluated on several long and difficult video sequences from busy inner-city locations. Our results show that the proposed integration makes it possible to deliver robust tracking performance in scenes of realistic complexity.


title: Live-action Virtual Reality Games

abstract: This paper proposes the concept of "live-action virtual reality games" as a new genre of digital games based on an innovative combination of live-action, mixed-reality, context-awareness, and interaction paradigms that comprise tangible objects, context-aware input devices, and embedded/embodied interactions. Live-action virtual reality games are "live-action games" because a player physically acts out (using his/her real body and senses) his/her "avatar" (his/her virtual representation) in the game stage, which is the mixed-reality environment where the game happens. The game stage is a kind of "augmented virtuality"; a mixed-reality where the virtual world is augmented with real-world information. In live-action virtual reality games, players wear HMD devices and see a virtual world that is constructed using the physical world architecture as the basic geometry and context information. Physical objects that reside in the physical world are also mapped to virtual elements. Live-action virtual reality games keeps the virtual and real-worlds superimposed, requiring players to physically move in the environment and to use different interaction paradigms (such as tangible and embodied interaction) to complete game activities. This setup enables the players to touch physical architectural elements (such as walls) and other objects, "feeling" the game stage. Players have free movement and may interact with physical objects placed in the game stage, implicitly and explicitly. Live-action virtual reality games differ from similar game concepts because they sense and use contextual information to create unpredictable game experiences, giving rise to emergent gameplay.


title: SAR Image Despeckling Using a Convolutional

abstract: Synthetic Aperture Radar (SAR) images are often contaminated by a multiplicative noise known as speckle. Speckle makes the processing and interpretation of SAR images difficult. We propose a deep learning-based approach called, Image Despeckling Convolutional Neural Network (ID-CNN), for automatically removing speckle from the input noisy images. In particular, ID-CNN uses a set of convolutional layers along with batch normalization and rectified linear unit (ReLU) activation function and a component-wise division residual layer to estimate speckle and it is trained in an end-to-end fashion using a combination of Euclidean loss and Total Variation (TV) loss. Extensive experiments on synthetic and real SAR images show that the proposed method achieves significant improvements over the state-of-the-art speckle reduction methods.


title: Hybrid-Type Quantum-Dot Cosensitized ZnO Nanowire Solar Cell with Enhanced Visible-Light Harvesting

abstract: A polymer hybrid quantum-dot-sensitized solar cell was developed using CdSe/CdS/ZnO nanowires as a photoanode and regioregular P3HT as a conjugated polymer. The P3HT polymer was used as a hole transport material to replace the liquid electrolyte in quantum dot sensitized solar cells, CdSe/CdS acts as a cosensitizer, which enhances light harvesting in the visible range, and the ZnO nanowires provide a direct pathway for electron transport. Through an adequate cascade bandgap structure of the photoanode, the photoexcited electrons were effectively separated from the electron/hole pairs and transported under illumination. The remaining holes at the anode were transported by the conjugated polymer P3HT without any intermediate potential loss. The fabrication of the hybrid solar cell was optimized with various experimental conditions, including the length of the ZnO nanowires, quantum sensitizers, P3HT filling conditions, and electrolytes. The optimally obtained hybrid solar cells exhibited 1.5% power-conversi...


title: On Human Intellect and Machine Failures: Troubleshooting Integrative Machine Learning Systems

abstract: We study the problem of troubleshooting machine learning systems that rely on analytical pipelines of distinct components. Understanding and fixing errors that arise in such integrative systems is difficult as failures can occur at multiple points in the execution workflow. Moreover, errors can propagate, become amplified or be suppressed, making blame assignment difficult. We propose a human-in-the-loop methodology which leverages human intellect for troubleshooting system failures. The approach simulates potential component fixes through human computation tasks and measures the expected improvements in the holistic behavior of the system. The method provides guidance to designers about how they can best improve the system. We demonstrate the effectiveness of the approach on an automated image captioning system that has been pressed into real-world use.


title: A New High-Gain Microstrip Yagi Array Antenna With a High Front-to-Back (F/B) Ratio for WLAN and Millimeter-Wave Applications

abstract: A new printed microstrip Yagi array antenna is proposed that can achieve a high gain and low backside radiation for various applications up to the millimeter-wave frequency range. The high front-to-back (F/B) ratio (up to 15 dB) is attributed to the constructive interference that takes place between the individual printed Yagi arrays in the design. Through the spacing of the elements, the directivity (between 9-11.5 dBi) and the F/B ratio can be altered to suit the application of interest. The operational principles of this design are discussed to give insight on the radiation mechanism of the antenna. An initial design at around 32.5 GHz is presented to show the performance capabilities of this configuration. An impedance bandwidth of 8.3% can be achieved around this frequency. Then, a parametric analysis is conducted to estimate the significance of the design parameters that affect the antenna's performance. Finally, measured return loss and radiation pattern performance at 5.2 GHz is displayed to validate the principles and simulated results of the design. The measured impedance bandwidth of 10% is achieved. The F/B ratio is 15 dB which is larger than values previously published by 5-10 dB. Additionally, a gain of 10.7 dBi is observed. To the author's knowledge, this is the first microstrip Yagi array antenna presented that has a high gain and a high F/B ratio designed using simple fabrication techniques


title: Peripheral artery disease. Part 1: clinical evaluation and noninvasive diagnosis

abstract: Peripheral artery disease (PAD) is a marker of systemic atherosclerosis. Most patients with PAD also have concomitant coronary artery disease (CAD), and a large burden of morbidity and mortality in patients with PAD is related to myocardial infarction, ischemic stroke, and cardiovascular death. PAD patients without clinical evidence of CAD have the same relative risk of death from cardiac or cerebrovascular causes as those diagnosed with prior CAD, consistent with the systemic nature of the disease. The same risk factors that contribute to CAD and cerebrovascular disease also lead to the development of PAD. Because of the high prevalence of asymptomatic disease and because only a small percentage of PAD patients present with classic claudication, PAD is frequently underdiagnosed and thus undertreated. Health care providers may have difficulty differentiating PAD from other diseases affecting the limb, such as arthritis, spinal stenosis or venous disease. In Part 1 of this Review, we explain the epidemiology of and risk factors for PAD, and discuss the clinical presentation and diagnostic evaluation of patients with this condition.


title: Exhaustive search algorithms to mine subgroups on Big Data using Apache Spark

abstract: Subgroup discovery is a well-known technique for the extraction of patterns, with respect to a variable of interest in the data. However, the explosion in data gathering has hampered the performance of traditional algorithms to discover interesting relationships between different objects in a set with respect to a specific property which is of interest to the user. In this regard, our goal is to propose a set of efficient techniques to mine subgroups on Big Data by means of Apache Spark. On this matter, AprioriK-SD-OE and PFP-SD-OE are proposed as fast exhaustive search algorithms to discover subgroups on Big Data using Apache Spark. The experimental study includes more than 70 datasets considering search spaces bigger than \(10^{15}\) subgroups. The scalability of our proposals are analyzed by considering datasets with 200 million of instances demonstrating the usefulness of using Spark to tackle Big Data.


title: Impact of organizational structure on distributed requirements engineering processes: lessons learned

abstract: The requirements engineering program at Siemens Corporate Research has been involved with process improvement, training and project execution across many of the Siemens operating companies. We have been able to observe and assist with process improvement in mainly global software development efforts. Other researchers have reported extensively on various aspects of distributed requirements engineering, but issues specific to organizational structure have not been well categorized. Our experience has been that organizational and other management issues can overshadow technical problems caused by globalization. This paper describes some of the different organizational structures we have encountered, the problems introduced into requirements engineering processes by these structures, and techniques that were effective in mitigating some of the negative effects of global software development.


title: Random Graph Models of Social Networks

abstract: We describe some new exactly solvable models of the structure of social networks, based on random graphs with arbitrary degree distributions. We give models both for simple unipartite networks, such as acquaintance networks, and bipartite networks, such as affiliation networks. We compare the predictions of our models to data for a number of real-world social networks and find that in some cases, the models are in remarkable agreement with the data, whereas in others the agreement is poorer, perhaps indicating the presence of additional social structure in the network that is not captured by the random graph.


title: Improved spiking neural networks for EEG classification and epilepsy and seizure detection

abstract: The goal of this research is to develop an efficient SNN model for epilepsy and epileptic seizure detection using electroencephalograms (EEGs), a complicated pattern recognition problem. Three training algorithms are investigated: SpikeProp (using both incremental and batch processing), QuickProp, and RProp. Since the epilepsy and epileptic seizure detection problem requires a large training dataset the efficacy of these algorithms is investigated by first applying them to the XOR and Fisher iris benchmark problems. Three measures of performance are investigated: number of convergence epochs, computational efficiency, and classification accuracy. Extensive parametric analysis is performed to identify heuristic rules and optimum parameter values that increase the computational efficiency and classification accuracy. The result is a remarkable increase in computational efficiency. For the XOR problem, the computational efficiency of SpikeProp, QuickProp, and RProp is increased by a factor of 588, 82, and 75, respectively, compared with the results reported in the literature. EEGs from three different subject groups are analyzed: (a) healthy subjects, (b) epileptic subjects during a seizure-free interval, and (c) epileptic subjects during a seizure. It is concluded that RProp is the best training algorithm because it has the highest classification accuracy among all training algorithms specially for large size training datasets with about the same computational efficiency provided by SpikeProp. The SNN model for EEG classification and epilepsy and seizure detection uses RProp as training algorithm. This model yields a high classification accuracy of 92.5%.


title: LSTM CCG Parsing

abstract: We demonstrate that a state-of-the-art parser can be built using only a lexical tagging model and a deterministic grammar, with no explicit model of bi-lexical dependencies. Instead, all dependencies are implicitly encoded in an LSTM supertagger that assigns CCG lexical categories. The parser significantly outperforms all previously published CCG results, supports efficient and optimal A decoding, and benefits substantially from semisupervised tri-training. We give a detailed analysis, demonstrating that the parser can recover long-range dependencies with high accuracy and that the semi-supervised learning enables significant accuracy gains. By running the LSTM on a GPU, we are able to parse over 2600 sentences per second while improving state-of-the-art accuracy by 1.1 F1 in domain and up to 4.5 F1 out of domain.


title: Collaborative Filtering for Multi-class Data Using Belief Nets Algorithms

abstract: As one of the most successful recommender systems, collaborative filtering (CF) algorithms can deal with high sparsity and high requirement of scalability amongst other challenges. Bayesian belief nets (BNs), one of the most frequently used classifiers, can be used for CF tasks. Previous works of applying BNs to CF tasks were mainly focused on binary-class data, and used simple or basic Bayesian classifiers (Miyahara and Pazzani, 2002; Breese et al., 1998). In this work, we apply advanced BNs models to CF tasks instead of simple ones, and work on real-world multi-class CF data instead of synthetic binary-class data. Empirical results show that with their ability to deal with incomplete data, extended logistic regression on naive Bayes and tree augmented naive Bayes (NB-ELR and TAN-ELR) models (Greiner et al., 2005) consistently perform better than the state-of-the-art Pearson correlation-based CF algorithm. In addition, the ELR-optimized BNs CF models are robust in terms of the ability to make predictions, while the robustness of the Pearson correlation-based CF algorithm degrades as the sparseness of the data increases


title: A Hybrid Intrusion Detection System of Cluster-based Wireless Sensor Networks

abstract: Recent advances in Wireless Sensor Networks (WSNs) have made them extremely useful in various applications. WSNs are susceptible to attack, because they are cheap, small devices and are deployed in open and unprotected environments. In this paper, we propose an Intrusion Detection System (IDS) created in Cluster-based Wireless Sensor Networks (CWSNs). According to the capability of Cluster Head (CH) is better than other Sensor Nodes (SNs) in CWSN. Therefore, a Hybrid Intrusion Detection System (HIDS) is designed in this research. The CH is used to detect intruders that not only decreases the consumption of energy, but also efficiently reduces the amount of information in the entire network. However, the lifetime of network can be prolonged by the proposed HIDS.


title: A complete fuzzy decision tree technique

abstract: In this paper, a new method of fuzzy decision trees called soft decision trees (SDT) is presented. This method combines tree growing and pruning, to determine the structure of the soft decision tree, with refitting and backfitting, to improve its generalization capabilities. The method is explained and motivated and its behavior is first analyzed empirically on 3 large databases in terms of classification error rate, model complexity and CPU time. A comparative study on 11 standard UCI Repository databases then shows that the soft decision trees produced by this method are significantly more accurate than standard decision trees. Moreover, a global model variance study shows a much lower variance for soft decision trees than for standard trees as a direct cause of the improved accuracy.


title: Eye movements in code reading: relaxing the linear order

abstract: Code reading is an important skill in programming. Inspired by the linearity that people exhibit while natural language text reading, we designed local and global gaze-based measures to characterize linearity (left-to-right and top-to-bottom) in reading source code. Unlike natural language text, source code is executable and requires a specific reading approach. To validate these measures, we compared the eye movements of novice and expert programmers who were asked to read and comprehend short snippets of natural language text and Java programs. Our results show that novices read source code less linearly than natural language text. Moreover, experts read code less linearly than novices. These findings indicate that there are specific differences between reading natural language and source code, and suggest that non-linear reading skills increase with expertise. We discuss the implications for practitioners and educators.


title: Parsimonious language models for information retrieval

abstract: We systematically investigate a new approach to estimating the parameters of language models for information retrieval, called parsimonious language models. Parsimonious language models explicitly address the relation between levels of language models that are typically used for smoothing. As such, they need fewer (non-zero) parameters to describe the data. We apply parsimonious models at three stages of the retrieval process: 1) at indexing time; 2) at search time; 3) at feedback time. Experimental results show that we are able to build models that are significantly smaller than standard models, but that still perform at least as well as the standard approaches.


title: RELLY: Inferring Hypernym Relationships Between Relational Phrases

abstract: Relational phrases (e.g., “got married to”) and their hypernyms (e.g., “is a relative of”) are central for many tasks including question answering, open information extraction, paraphrasing, and entailment detection. This has motivated the development of several linguistic resources (e.g. DIRT, PATTY, and WiseNet) which systematically collect and organize relational phrases. These resources have demonstrable practical benefits, but are each limited due to noise, sparsity, or size. We present a new general-purpose method, RELLY, for constructing a large hypernymy graph of relational phrases with high-quality subsumptions using collective probabilistic programming techniques. Our graph induction approach integrates small highprecision knowledge bases together with large automatically curated resources, and reasons collectively to combine these resources into a consistent graph. Using RELLY, we construct a high-coverage, high-precision hypernymy graph consisting of 20K relational phrases and 35K hypernymy links. Our evaluation indicates a hypernymy link precision of 78%, and demonstrates the value of this resource for a document-relevance ranking task.


title: Unified constant-frequency integration control of three-phase standard bridge boost rectifiers with power-factor correction

abstract: In this paper, a three-phase six-switch standard boost rectifier with unity-power-factor correction is investigated. A general equation is derived that relates the input phase voltages, output DC voltage, and duty ratios of the switches in continuous conduction mode. Based on one of the solutions and using one-cycle control, a unified constant-frequency integration controller for PFC is proposed. For the standard bridge boost rectifier, a unity power factor and low total harmonic distortion can be realized in all three phases with a simple circuit that is composed of one integrator with reset along with several flips-flops, comparators, and some logic and linear components. It does not require multipliers and three-phase voltage sensors, which are required in many other control approaches. In addition, it employs constant-switching-frequency modulation that is desirable for industrial applications. The proposed control approach is simple and reliable. All findings are supported by experiments.


title: A Configurable and Low-Power Mixed Signal SoC for Portable ECG Monitoring Applications

abstract: This paper describes a mixed-signal ECG System-on-Chip (SoC) that is capable of implementing configurable functionality with low-power consumption for portable ECG monitoring applications. A low-voltage and high performance analog front-end extracts 3-channel ECG signals and single channel electrode-tissue-impedance (ETI) measurement with high signal quality. This can be used to evaluate the quality of the ECG measurement and to filter motion artifacts. A custom digital signal processor consisting of 4-way SIMD processor provides the configurability and advanced functionality like motion artifact removal and R peak detection. A built-in 12-bit analog-to-digital converter (ADC) is capable of adaptive sampling achieving a compression ratio of up to 7, and loop buffer integration reduces the power consumption for on-chip memory access. The SoC is implemented in 0.18 $\mu$ m CMOS process and consumes 32 $\mu$ W from a 1.2 V while heart beat detection application is running, and integrated in a wireless ECG monitoring system with Bluetooth protocol. Thanks to the ECG SoC, the overall system power consumption can be reduced significantly.