title: Kill the Program Counter: Reconstructing Program Behavior in the Processor Cache Hierarchy

abstract: Data prefetching and cache replacement algorithms have been intensively studied in the design of high performance microprocessors. Typically, the data prefetcher operates in the private caches and does not interact with the replacement policy in the shared Last-Level Cache (LLC). Similarly, most replacement policies do not consider demand and prefetch requests as different types of requests. In particular, program counter (PC)-based replacement policies cannot learn from prefetch requests since the data prefetcher does not generate a PC value. PC-based policies can also be negatively affected by compiler optimizations. In this paper, we propose a holistic cache management technique called Kill-the-PC (KPC) that overcomes the weaknesses of traditional prefetching and replacement policy algorithms. KPC cache management has three novel contributions. First, a prefetcher which approximates the future use distance of prefetch requests based on its prediction confidence. Second, a simple replacement policy provides similar or better performance than current state-of-the-art PC-based prediction using global hysteresis. Third, KPC integrates prefetching and replacement policy into a whole system which is greater than the sum of its parts. Information from the prefetcher is used to improve the performance of the replacement policy and vice-versa. Finally, KPC removes the need to propagate the PC through entire on-chip cache hierarchy while providing a holistic cache management approach with better performance than state-of-the-art PC-, and non-PC-based schemes. Our evaluation shows that KPC provides 8% better performance than the best combination of existing prefetcher and replacement policy for multi-core workloads.


title: Extending postgreSQL to handle OLXP workloads

abstract: The importance of database systems to efficiently support OLTP and OLAP mixed workloads, the so-called OLXP workloads, has been recognized recently. Some research projects and also some commercial database systems with focus on the processing of OLXP workloads have appeared in the last few years. In this paper, we present our work to extend the PostgreSQL OSS database system to efficiently handle OLXP workloads. Besides PostgreSQL’s traditional OLTP-oriented row data store, we provide a new OLAP-oriented column data store in the form of a new index. Unlike previous works that support both row and column data stores, we propose a column index with no restrictions concerning data size and/or updatability. Therefore, transactional data inserted to PostgreSQL row data store become immediately available for efficient analytical processing using the proposed column store index.


title: A new NOMA approach for fair power allocation

abstract: A non-orthogonal multiple access (NOMA) approach to user signal power allocation called Fair-NOMA is introduced. Fair-NOMA is the application of NOMA in such a way that two mobile users have the opportunity to always achieve at least the information capacity they can achieve by using orthogonal multiple access (OMA), regardless of the user selection criteria, making it suitable for implementation using any current or future scheduling paradigms. Given this condition, the bounds of the power allocation coefficients are derived as functions of the channel gains of the two mobile users. The NOMA power allocation is analyzed for two scheduled users that are selected randomly with i.i.d. channel gains. The capacity improvements made by each user and the sum capacity improvement are derived.


title: Increasing Response Rates to Email Surveys in MOOCs

abstract: Email is an important and widely used communication medium. However, email is increasingly unreliable as people become unlikely to respond to the growing influx of information they receive. Low response rate to email becomes a problem in situations where closing the feedback loop is critical, such as in education, marketing or research. To investigate ways of increasing email response rate, we designed experiments that manipulated the textual elements of the emails. We conducted experiments in a MOOC setting, with email surveys sent out to over 3,000 learners. The emails were sent to elicit responses as to why learners were not engaging with the course. We found that response rates were significantly increased by varying how closely emails were framed as pertaining to a learner's personal situation, such as by changing introductory message, and the format in which links to a survey were presented. Our results yield useful implications to educational and marketing context.


title: Theory of spread spectrum communications—a tutorial

abstract: Spread-spectrum communications, with its inherent interference attenuation capability, has over the years become an increasingly popular technique for use in many different systems. Applications range from antijam systems, to code division multiple access systems, to systems designed to combat multipath. It is the intention of this paper to provide a tutorial treatment of the theory of spread-spectrum communications, including a discussion on the applications referred to above, on the properties of common spreading sequences, and on techniques that can be used for acquisition and tracking.


title: Fairness in Reinforcement Learning

abstract: We initiate the study of fairness in reinforcement learning, where the actions of a learning algorithm may affect its environment and future rewards. Our fairness constraint requires that an algorithm never prefers one action over another if the long-term (discounted) reward of choosing the latter action is higher. Our first result is negative: despite the fact that fairness is consistent with the optimal policy, any learning algorithm satisfying fairness must take time exponential in the number of states to achieve non-trivial approximation to the optimal policy. We then provide a provably fair polynomial time algorithm under an approximate notion of fairness, thus establishing an exponential gap between exact and approximate fairness


title: A Synchronous Hyperedge Replacement Grammar based approach for AMR parsing

abstract: This paper presents a synchronous-graphgrammar-based approach for string-toAMR parsing. We apply Markov Chain Monte Carlo (MCMC) algorithms to learn Synchronous Hyperedge Replacement Grammar (SHRG) rules from a forest that represents likely derivations consistent with a fixed string-to-graph alignment. We make an analogy of string-toAMR parsing to the task of phrase-based machine translation and come up with an efficient algorithm to learn graph grammars from string-graph pairs. We propose an effective approximation strategy to resolve the complexity issue of graph compositions. We also show some useful strategies to overcome existing problems in an SHRG-based parser and present preliminary results of a graph-grammar-based approach.


title: Automatical Storyline Generation with Help from Twitter

abstract: Storyline detection aims to connect seemly irrelevant single documents into meaningful chains, which provides opportunities for understanding how events evolve over time and what triggers such evolutions. Most previous work generated the storylines through unsupervised methods that can hardly reveal underlying factors driving the evolution process. This paper introduces a Bayesian model to generate storylines from massive documents and infer the corresponding hidden relations and topics. In addition, our model is the first attempt that utilizes Twitter data as human input to ``supervise'' the generation of storylines. Through extensive experiments, we demonstrate our proposed model can achieve significant improvement over baseline methods and can be used to discover interesting patterns for real world cases.


title: Developer onboarding in GitHub: the role of prior social links and language experience

abstract: The team aspects of software engineering have been a subject of great interest since early work by Fred Brooks and others: how well do people work together in teams? why do people join teams? what happens if teams are distributed? Recently, the emergence of project ecosystems such as GitHub have created an entirely new, higher level of organization. GitHub supports numerous teams; they share a common technical platform (for work activities) and a common social platform (via following, commenting, etc). We explore the GitHub evidence for socialization as a precursor to joining a project, and how the technical factors of past experience and social factors of past connections to team members of a project affect productivity both initially and in the long run. We find developers preferentially join projects in GitHub where they have pre-existing relationships; furthermore, we find that the presence of past social connections combined with prior experience in languages dominant in the project leads to higher productivity both initially and cumulatively. Interestingly, we also find that stronger social connections are associated with slightly less productivity initially, but slightly more productivity in the long run.


title: Neural Question Answering at BioASQ 5B

abstract: This paper describes our submission to the 2017 BioASQ challenge. We participated in Task B, Phase B which is concerned with biomedical question answering (QA). We focus on factoid and list question, using an extractive QA model, that is, we restrict our system to output substrings of the provided text snippets. At the core of our system, we use FastQA, a state-of-the-art neural QA system. We extended it with biomedical word embeddings and changed its answer layer to be able to answer list questions in addition to factoid questions. We pre-trained the model on a large-scale open-domain QA dataset, SQuAD, and then fine-tuned the parameters on the BioASQ training set. With our approach, we achieve state-of-the-art results on factoid questions and competitive results on list questions.


title: Threats and Countermeasures in GSM Networks

abstract: Mobile networks not only provide great benefits to their users but they also introduce inherent security issues. With respect to security, the emerging risks of denial of service (DOS) attacks will evolve into a critical danger as the availability of mobile networks becomes more and more important for the modern information society. This paper outlines a critical flaw in GSM networks which opens the avenue for distributed denial of service attacks. We propose a way to mitigate the attacks by adding minimal authentication to the GSM channel assignment protocol.


title: A Review on Distributed Application Processing Frameworks in Smart Mobile Devices for Mobile Cloud Computing

abstract: The latest developments in mobile devices technology have made smartphones as the future computing and service access devices. Users expect to run computational intensive applications on Smart Mobile Devices (SMDs) in the same way as powerful stationary computers. However in spite of all the advancements in recent years, SMDs are still low potential computing devices, which are constrained by CPU potentials, memory capacity and battery life time. Mobile Cloud Computing (MCC) is the latest practical solution for alleviating this incapacitation by extending the services and resources of computational clouds to SMDs on demand basis. In MCC, application offloading is ascertained as a software level solution for augmenting application processing capabilities of SMDs. The current offloading algorithms offload computational intensive applications to remote servers by employing different cloud models. A challenging aspect of such algorithms is the establishment of distributed application processing platform at runtime which requires additional computing resources on SMDs. This paper reviews existing Distributed Application Processing Frameworks (DAPFs) for SMDs in MCC domain. The objective is to highlight issues and challenges to existing DAPFs in developing, implementing, and executing computational intensive mobile applications within MCC domain. It proposes thematic taxonomy of current DAPFs, reviews current offloading frameworks by using thematic taxonomy and analyzes the implications and critical aspects of current offloading frameworks. Further, it investigates commonalities and deviations in such frameworks on the basis significant parameters such as offloading scope, migration granularity, partitioning approach, and migration pattern. Finally, we put forward open research issues in distributed application processing for MCC that remain to be addressed.


title: Learning small-size DNN with output-distribution-based criteria.

abstract: Deep neural network (DNN) obtains significant accuracy improvements on many speech recognition tasks and its power comes from the deep and wide network structure with a very large number of parameters. It becomes challenging when we deploy DNN on devices which have limited computational and storage resources. The common practice is to train a DNN with a small number of hidden nodes and a small senone set using the standard training process, leading to significant accuracy loss. In this study, we propose to better address these issues by utilizing the DNN output distribution. To learn a DNN with small number of hidden nodes, we minimize the Kullback–Leibler divergence between the output distributions of the small-size DNN and a standard large-size DNN by utilizing a large number of un-transcribed data. For better senone set generation, we cluster the senones in the large set into a small one by directly relating the clustering process to DNN parameters, as opposed to decoupling the senone generation and DNN training process in the standard training. Evaluated on a short message dictation task, the proposed two methods get 5.08% and 1.33% relative word error rate reduction from the standard training method, respectively.


title: Dynamic vs. Static Flow-Sensitive Security Analysis

abstract: This paper seeks to answer fundamental questions about trade-offs between static and dynamic security analysis. It has been previously shown that flow-sensitive static information-flow analysis is a natural generalization of flow-insensitive static analysis, which allows accepting more secure programs. It has been also shown that sound purely dynamic information-flow enforcement is more permissive than static analysis in the flow-insensitive case. We argue that the step from flow-insensitive to flow-sensitive is fundamentally limited for purely dynamic information-flow controls. We prove impossibility of a sound purely dynamic information-flow monitor that accepts programs certified by a classical flow-sensitive static analysis. A side implication is impossibility of permissive dynamic instrumented security semantics for information flow, which guides us to uncover an unsound semantics from the literature. We present a general framework for hybrid mechanisms that is parameterized in the static part and in the reaction method of the enforcement (stop, suppress, or rewrite) and give security guarantees with respect to termination-insensitive noninterference for a simple language with output.


title: A Low-Cost Stand-Alone Multichannel Data Acquisition, Monitoring, and Archival System With On-Chip Signal Preprocessing

abstract: The objective of this paper is to design a new generation of affordable sophisticated data acquisition and processing (DAQP) systems. Because of the proposed system hardware reconfigurability, it can be used to meet the need of many real-world applications while keeping the cost of a device low. The hardware implementation of the different processing functions of the device allows for high-speed processing without the need of expensive general-purpose processors, as is the case of computer-based or microcontroller-based data acquisitions (DAQs). The target technology of implementing the proposed design is the system on chip via field-programmable gate array (SoC-FPGA). A four-channel DAQP was designed, developed, and tested in the Embedded Systems Design Laboratory, Tennessee Technological University, Cookeville. Various modules of the conceptual design are implemented and verified. Performance evaluation and cost comparisons are provided. The comparison showed that the results of the proposed instrument are comparable to existing technologies at a fraction of the cost.


title: PRISM : Probabilistic Symbolic Model Checker ?

abstract: In this paper we describe PRISM, a tool being developed at the University of Birmingham for the analysis of probabilistic systems. PRISM supports three probabilistic models: discrete-time Markov chains, Markov decision processes and continuous-time Markov chains. Analysis is performed through model checking such systems against specifications written in the probabilistic temporal logics PCTL and CSL. The tool features three model checking engines: one symbolic, using BDDs (binary decision diagrams) and MTBDDs (multi-terminal BDDs); one based on sparse matrices; and one which combines both symbolic and sparse matrix methods. PRISM has been successfully used to analyse probabilistic termination, performance, and quality of service properties for a range of systems, including randomized distributed algorithms, manufacturing systems and workstation clusters.


title: Tracking Multiple Vehicles using Foreground, Background and Motion Models

abstract: Abstract In this paper a vehicle tracking algorithm is presented based on the combination of a novel per-pixel (Gaussian Mixture Based) background model and a set of foreground models of object size, position, velocity, and colour distribution. Each pixel in the scene is ‘explained’ as either background, belonging to a foreground object, or as noise. A projective ground-plane transform is used within the foreground model to strengthen object size and velocity consistency assumptions. A learned model of typical road travel direction and speed is used to provide a prior estimate of object velocity, which is used to initialise the velocity model for each of the foreground objects. The system runs at near video framerate (>20 fps) on modest hardware and is robust assuming sufficient image resolution is available and vehicle sizes do not greatly exceed the priors on object size used in object initialisation.


title: Image Recognition of 85 Food Categories by Feature Fusion

abstract: Recognition of food images is challenging due to their diversity and practical for health care on foods for people. In this paper, we propose an automatic food image recognition system for 85 food categories by fusing various kinds of image features including bag-of-features~(BoF), color histogram, Gabor features and gradient histogram with Multiple Kernel Learning~(MKL). In addition, we implemented a prototype system to recognize food images taken by cellular-phone cameras. In the experiment, we have achieved the 62.52% classification rate for 85 food categories.


title: Sensing and Force-Feedback Exoskeleton (SAFE) Robotic Glove

abstract: This paper presents the design, implementation and experimental validation of a novel robotic haptic exoskeleton device to measure the user's hand motion and assist hand motion while remaining portable and lightweight. The device consists of a five-finger mechanism actuated with miniature DC motors through antagonistically routed cables at each finger, which act as both active and passive force actuators. The SAFE Glove is a wireless and self-contained mechatronic system that mounts over the dorsum of a bare hand and provides haptic force feedback to each finger. The glove is adaptable to a wide variety of finger sizes without constraining the range of motion. This makes it possible to accurately and comfortably track the complex motion of the finger and thumb joints associated with common movements of hand functions, including grip and release patterns. The glove can be wirelessly linked to a computer for displaying and recording the hand status through 3D Graphical User Interface (GUI) in real-time. The experimental results demonstrate that the SAFE Glove is capable of reliably modeling hand kinematics, measuring finger motion and assisting hand grasping motion. Simulation and experimental results show the potential of the proposed system in rehabilitation therapy and virtual reality applications.


title: 2D diffuse optical imaging using clustered sparsity

abstract: Diffuse optical imaging (DOI) is a non-invasive technique which measures hemodynamic changes in the tissue with near infrared light, and has been increasingly used to study brain functions. Due to the nature of light propagation in the tissue, the reconstruction problem is severely ill-posed. Sparsityregularization has achieved promising results in recent works for linearized DOI problem. In this paper, we exploit more prior information to improve DOI besides sparsity. Based on the functional specialization of the brain, the in vivo absorption changes caused by specific brain function can be clustered in certain region(s) and not randomly distributed. Thus, a new algorithm is proposed to utilize this prior in reconstruction. Results of numerical simulations and phantom experiments have demonstrated the superiority of the proposed method over the state-of-the-art.


title: The Reliability and Validity of a Chair Sit-and-Reach Test as a Measure of Hamstring Flexibility in Older Adults

abstract: Abstract The purpose of this study was to examine the test-retest reliability and the criterion validity of a newly developed chair sit-and-reach (CSR) test as a measure of hamstring flexibility in older adults. CSR performance was also compared to sit-and-reach (SR) and back-saver sit-and-reach (BSR) measures of hamstring flexibility. To estimate reliability, 76 men and women (M age = 70.5 years) performed the CSR on 2 different days, 2–5 days apart. In the validity phase of the study, scores of 80 men and women (M age = 74.2 years) were obtained on three field test measures of hamstring flexibility (CSR, SR, and BSR) and on a criterion test (goniometer measurement of a passive straight-leg raise). Results indicate that the CSR has good intraclass test-retest reliability (R = .92 for men; r = .96 for women), and has a moderate-to-good relationship with the criterion measure (r = .76 for men; r = .81 for women). The criterion validity of the CSR for the male and female participants is comparable to that o...


title: Entropy Optimized Feature-Based Bag-of-Words Representation for Information Retrieval

abstract: In this paper, we present a supervised dictionary learning method for optimizing the feature-based Bag-of-Words (BoW) representation towards Information Retrieval. Following the cluster hypothesis, which states that points in the same cluster are likely to fulfill the same information need, we propose the use of an entropy-based optimization criterion that is better suited for retrieval instead of classification. We demonstrate the ability of the proposed method, abbreviated as EO-BoW, to improve the retrieval performance by providing extensive experiments on two multi-class image datasets. The BoW model can be applied to other domains as well, so we also evaluate our approach using a collection of 45 time-series datasets, a text dataset, and a video dataset. The gains are three-fold since the EO-BoW can improve the mean Average Precision, while reducing the encoding time and the database storage requirements. Finally, we provide evidence that the EO-BoW maintains its representation ability even when used to retrieve objects from classes that were not seen during the training.


title: Efficient inference and learning in a large knowledge base Reasoning with extracted information using a locally groundable first-order probabilistic logic

abstract: One important challenge for probabilistic logics is reasoning with very large knowledge bases (KBs) of imperfect information, such as those produced by modern web-scale information extraction systems. One scalability problem shared by many probabilistic logics is that answering queries involves "grounding" the query--i.e., mapping it to a propositional representation--and the size of a "grounding" grows with database size. To address this bottleneck, we present a first-order probabilistic language called ProPPR in which approximate "local groundings" can be constructed in time independent of database size. Technically, ProPPR is an extension to stochastic logic programs that is biased towards short derivations; it is also closely related to an earlier relational learning algorithm called the path ranking algorithm. We show that the problem of constructing proofs for this logic is related to computation of personalized PageRank on a linearized version of the proof space, and based on this connection, we develop a provably-correct approximate grounding scheme, based on the PageRank---Nibble algorithm. Building on this, we develop a fast and easily-parallelized weight-learning algorithm for ProPPR. In our experiments, we show that learning for ProPPR is orders of magnitude faster than learning for Markov logic networks; that allowing mutual recursion (joint learning) in KB inference leads to improvements in performance; and that ProPPR can learn weights for a mutually recursive program with hundreds of clauses defining scores of interrelated predicates over a KB containing one million entities.


title: A Low-Cost Scalable 32-Element 28-GHz Phased Array Transceiver for 5G Communication Links Based on a $2\times 2$ Beamformer Flip-Chip Unit Cell

abstract: This paper presents a scalable 28-GHz phased-array architecture suitable for fifth-generation (5G) communication links based on four-channel ( $2\times 2$ ) transmit/receive (TRX) quad-core chips in SiGe BiCMOS with flip-chip packaging. Each channel of the quad-core beamformer chip has 4.6-dB noise figure (NF) in the receive (RX) mode and 10.5-dBm output 1-dB compression point (OP1dB) in the transmit (TX) mode with 6-bit phase control and 14-dB gain control. The phase change with gain control is only ±3°, allowing orthogonality between the variable gain amplifier and the phase shifter. The chip has high RX linearity (IP1dB = −22 dBm/channel) and consumes 130 mW in the RX mode and 200 mW in the TX mode at P1dB per channel. Advantages of the scalable all-RF beamforming architecture and circuit design techniques are discussed in detail. 4- and 32-element phased-arrays are demonstrated with detailed data link measurements using a single or eight of the four-channel TRX core chips on a low-cost printed circuit board with microstrip antennas. The 32-element array achieves an effective isotropic radiated power (EIRP) of 43 dBm at P1dB, a 45-dBm saturated EIRP, and a record-level system NF of 5.2 dB when the beamformer loss and transceiver NF are taken into account and can scan to ±50° in azimuth and ±25° in elevation with < −12-dB sidelobes and without any phase or amplitude calibration. A wireless link is demonstrated using two 32-element phased-arrays with a state-of-the-art data rate of 1.0–1.6 Gb/s in a single beam using 16-QAM waveforms over all scan angles at a link distance of 300 m.


title: Critical comparison between time- and frequency-domain relaxation functions

abstract: Considerable work has been performed on providing a theoretical basis for the Kohlrausch-Williams-Watts (KWW) and Havriliak-Negami (HN) relaxation functions. Because of this, several papers have examined the ``interconnection'' of these two functions. In this paper, we demonstrate that, with achievable instrumental sensitivity, these two functions are distinguishable. We further address the issue of the ``universal'' limiting power laws and the ability to obtain the exponents associated with them. Finally, the stability and accuracy of our numerical Laplace transform is demonstrated by comparison between functions with known analytical time and frequency solutions. The stability of our algorithm indicates that the method of Alvarez and co-workers [F. Alvarez, A. Alegr\'{\i}a, and J. Colmenero, Phys. Rev. B 44, 7306 (1991)] is an unnecessary approximation for converting between the time and frequency domain.


title: Spiral Leaky-Wave Antennas Based on Modulated Surface Impedance

abstract: Different kinds of spiral planar circularly polarized (CP) antennas are presented. These antennas are based on an interaction between a cylindrical surface-wave excited by an omnidirectional probe and a inhomogeneous surface impedance with a spiral pattern. The surface impedance interaction transforms a bounded surface wave into a circularly polarized leaky wave with almost broadside radiation. The problem is studied by adiabatically matching the local 2D solution of a modulated surface-impedance problem to the actual surface. Analytical expressions are derived for the far-field radiation pattern; on this basis, universal design curves for antenna gain are given and a design procedure is outlined. Two types of practical solutions are presented, which are relevant to different implementations of the impedance modulation: i) a grounded dielectric slab with a spiral-sinusoidal thickness and ii) a texture of dense printed patches with sizes variable with a spiral-sinusoidal function. Full wave results are compared successfully with the analytical approximations. Both the layouts represent good solutions for millimeter wave CP antennas.


rtificial intelligence framework for simulating clinical decision-making : Markov decision process approach asey


title: Personal Health Record System and Integration Techniques with Various Electronic Medical Record Systems

abstract: This paper discusses the importance of a Patient Centric Health Record system. Such systems can empower patients to participate in improving health care quality. It would also provide an economically viable solution to the need for better healthcare without escalating costs by avoiding duplication. The proposed system is Web-based so patients and healthcare providers can access it from any location. Moreover the architecture is cloud-based so large amount of data can be stored without any restrictions. Also the use of cloud computing architecture will allow consumers to address the challenge of sharing medical data that is overly complex and highly expensive to address with traditional technologies.


Synthesizing Audio with GANs


title: Deep Lambertian Networks

abstract: Visual perception is a challenging problem in part due to illumination variations. A possible solution is to first estimate an illumination invariant representation before using it for recognition. The object albedo and surface normals are examples of such representations. In this paper, we introduce a multilayer generative model where the latent variables include the albedo, surface normals, and the light source. Combining Deep Belief Nets with the Lambertian reectance assumption, our model can learn good priors over the albedo from 2D images. Illumination variations can be explained by changing only the lighting latent variable in our model. By transferring learned knowledge from similar objects, albedo and surface normals estimation from a single image is possible in our model. Experiments demonstrate that our model is able to generalize as well as improve over standard baselines in one-shot face recognition.


title: Optimal Biasing of a Self-Homodyne Optically Coherent RF Receiver

abstract: We present a technique that achieves the most efficient use of the linear dynamic range of available photodiodes without sacrificing the linearity of the radio-frequency (RF) digital receiver and photonic link based on optical phase modulation and coherent in-phase quadrature $(I/Q)$ demodulation. We demonstrate, with a relative intensity noise (RIN)-limited optical system, that under these optimal bias conditions our 1-GHz coherent receiver achieves a link spurious-free dynamic range (SFDR), gain, and noise figure of 126.8 $\hbox{dB-Hz}^{2/3}$ , 8 dB, and 18.6 dB, respectively, and show the capability, with a shot-noise-limited optical source, to achieve an SFDR of 135.3 $\hbox{dB-Hz}^{2/3}$ and a noise figure of 6 dB.


title: Effects of a Year-Long Pedometer-Based Walking Program on Cardiovascular Disease Risk Factors in Active Older People

abstract: We assessed the relationship between the number of daily steps and changes in cardiovascular disease (CVD) risk factors in a year-long pedometer-based walking program for physically active older people. A total of 36 physically active older people (68.3 ± 5.8 years) completed this 59-week program. The CVD risk parameters were measured at baseline and at weeks 21 and 59. The mean number of steps increased by week 21 and was maintained at week 59 (approximately 10 000 steps, increase in 1500 steps from the baseline; P < .05). Following a significant reduction in body mass index at week 21, systolic blood pressure levels were significantly reduced, and high-density lipoprotein cholesterol levels were significantly increased at week 59 (P < .05). In summary, even active older people can achieve further protection against CVD risks by minor, but sustained, physical activity using pedometers for a period of more than 1 year.


title: EnergyNet: Energy-based Adaptive Structural Learning of Artificial Neural Network Architectures

abstract: We present E NERGY N ET , a new framework for analyzing and building artificial neural network architectures. Our approach adaptively learns the structure of the networks in an unsupervised manner. The methodology is based upon the theoretical guarantees of the energy function of restricted Boltzmann machines (RBM) of infinite number of nodes. We present experimental results to show that the final network adapts to the complexity of a given problem.


Intrusion-Tolerant Architectures: Concepts and Design


title: A splicing mutation of the HMGA2 gene is associated with Silver–Russell syndrome phenotype

abstract: Silver-Russell syndrome (SRS) is a heterogeneous disorder characterized by intrauterine and post-natal growth retardation, dysmorphic facial features and body asymmetry. About 50% of the patients carry (epi)genetic alterations involving chromosomes 7 or 11.The high proportion of patients with unidentified molecular etiology suggests the involvement of other genes. Interestingly, SRS patients share clinical features with the 12q14 microdeletion syndrome, characterized by several deletions with a 2.6 Mb region of overlap. Among the genes present in this interval, high mobility AT-hook 2 (HMGA2) appears to be the most likely cause of the growth deficiency, due to its described growth control function. To define the role of HMGA2 in SRS, we looked for 12q14 chromosome imbalances and HMGA2 mutations in a cohort of 45 patients with growth retardation and SRS-like phenotype but no 11p15 (epi)mutations or maternal uniparental disomy of chromosome 7 (matUPD7). We identified a novel 7 bp intronic deletion in HMGA2 present in heterozygosity in the proband and her mother both displaying the typical features of SRS. We demonstrated that the deletion affected normal splicing, indicating that it is a likely cause of HMGA2 deficiency. This study provides the first evidence that a loss-of-function mutation of HMGA2 can be associated with a familial form of SRS. We suggest that HMGA2 mutations leading to haploinsufficiency should be investigated in the SRS patients negative for the typical 11p15 (epi)mutations and matUPD7.


title: Design of UWB Bandpass Filter Using Stepped-Impedance Stub-Loaded Resonator

abstract: In this letter, an ultra-wideband (UWB) bandpass filter (BPF) using stepped-impedance stub-loaded resonator (SISLR) is presented. Characterized by theoretical analysis, the proposed SISLR is found to have the advantage of providing more degrees of freedom to adjust the resonant frequencies. Besides, two transmission zeros can be created at both lower and upper sides of the passband. Benefiting from these features, a UWB BPF is then investigated by incorporating this SISLR and two aperture-backed interdigital coupled-lines. Finally, this filter is built and tested. The simulated and measured results are in good agreement with each other, showing good wideband filtering performance with sharp rejection skirts outside the passband.


title: Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types

abstract: We present a neural model for question generation from knowledge base triples in a "Zero-Shot" setup, that is generating questions for triples containing predicates, subject types or object types that were not seen at training time. Our model leverages triples occurrences in the natural language corpus in an encoder-decoder architecture, paired with an original part-of-speech copy action mechanism to generate questions. Benchmark and human evaluation show that our model sets a new state-of-the-art for zero-shot QG.


title: Characterization of water mobility and distribution in low- and intermediate-moisture food systems.

abstract: The mechanism of water uptake in low moisture cereals and cookies has been studied by NMR relaxometry and solid imaging technology implemented on a low-resolution benchtop NMR spectrometer. A comparison between classical MRI and SPRITE imaging are also presented to highlight the benefits of each technology. The spin lattice (T(1)) and spin spin (T(2)) relaxation times, the 1D and 2D SPRITE imaging, were determined on Smacks, corn flakes, chocolate chips cookies, soft caramel candies with a chocolate crème filler, and corn starch/water systems. The Smacks and corn flakes were studied based on the soaking time in milk, and the results showed that T(1) and T(2) decreased in the first 20 sec of soaking and then increased with the soaking time. For Smacks stored at different relative humidity, T(1) decreased during the first day of storage and then was relatively constant over storage time indicating that the system reached an equilibrium. 1D SPRITE profiles indicated an increase in signal intensity over storage time for cookies in 58% RH. However, the moisture uptake was insignificant indicating that the water mobility (and not the amount of water) changed due to various chemical interactions in the system (hydrogen bonding, starch retrogradation, glassy/rubbery equilibrium). The T(1) and T(2) of corn starch/water systems decreased as the concentration in starch increased and temperature increased from 30 degrees C to 60 degrees C. However, for temperatures higher than 60 degrees C, the relaxation times increased showing more mobility and flexibility of the polymer chains during gelatinization.


title: Altered anterior insula activation during anticipation and experience of painful stimuli in expert meditators

abstract: article i nfo Experientially opening oneself to pain rather than avoiding it is said to reduce the mind's tendency toward avoidance or anxiety which can further exacerbate the experience of pain. This is a central feature of mindfulness-based therapies. Little is known about the neural mechanisms of mindfulness on pain. During a meditation practice similar to mindfulness, functional magnetic resonance imaging was used in expert meditators (>10,000 h of practice) to dissociate neural activation patterns associated with pain, its anticipation, and habituation. Compared to novices, expert meditators reported equal pain intensity, but less unpleasantness. This difference was associated with enhanced activity in the dorsal anterior insula (aI), and the anterior mid-cingulate (aMCC) the so-called 'salience network', for experts during pain. This enhanced activity during pain was associated with reduced baseline activity before pain in these regions and the amygdala for experts only. The reduced baseline activation in left aI correlated with lifetime meditation experience. This pattern of low baseline activity coupled with high response in aIns and aMCC was associated with enhanced neural habituation in amygdala and pain-related regions before painful stimulation and in the pain-related regions during painful stimulation.These findings suggest that cultivating experiential openness down-regulates anticipatory representa- tion of aversive events, and increases the recruitment of attentional resources during pain, which is associated with faster neural habituation.


title: Simulated Penetration Testing and Mitigation Analysis

abstract: Penetration testing is a well-established practical concept for the identification of potentially exploitable security weaknesses and an important component of a security audit. Providing a holistic security assessment for networks consisting of several hundreds hosts is hardly feasible though without some sort of mechanization. Mitigation, prioritizing counter- measures subject to a given budget, currently lacks a solid theoretical understanding and is hence more art than science. In this work, we propose the first approach for conduct- ing comprehensive what-if analyses in order to reason about mitigation in a conceptually well-founded manner. To evaluate and compare mitigation strategies, we use simulated penetration testing, i.e., automated attack-finding, based on a network model to which a subset of a given set of mitigation actions, e.g., changes to the network topology, system updates, configuration changes etc. is applied. We determine optimal combinations that minimize the maximal attacker success (similar to a Stackelberg game), and thus provide a well-founded basis for a holistic mitigation strategy. We show that these what-if analysis models can largely be derived from network scan, public vulnerability databases and manual inspection with various degrees of automation and detail, and we simulate mitigation analysis on networks of different size and vulnerability.


title: Genetic dissection of mammalian fertility pathways

abstract: The world's population is increasing at an alarming rate and is projected to reach nine billion by 2050. Despite this, 15% of couples world-wide remain childless because of infertility. Few genetic causes of infertility have been identified in humans; nevertheless, genetic aetiologies are thought to underlie many cases of idiopathic infertility. Mouse models with reproductive defects as a major phenotype are being rapidly created and discovered and now total over 200. These models are helping to define mechanisms of reproductive function, as well as identify potential new contraceptive targets and genes involved in the pathophysiology of reproductive disorders. With this new information, men and women will continue to be confronted with difficult decisions on whether or not to use state-of-the-art technology and hormonal treatments to propagate their germline, despite the risks of transmitting mutant genes to their offspring.


title: Divide, Denoise, and Defend against Adversarial Attacks

abstract: Deep neural networks, although shown to be a successful class of machine learning algorithms, are known to be extremely unstable to adversarial perturbations. Improving the robustness of neural networks against these attacks is important, especially for security-critical applications. To defend against such attacks, we propose dividing the input image into multiple patches, denoising each patch independently, and reconstructing the image, without losing significant image content. This proposed defense mechanism is non-differentiable which makes it non-trivial for an adversary to apply gradient-based attacks. Moreover, we do not fine-tune the network with adversarial examples, making it more robust against unknown attacks. We present a thorough analysis of the tradeoff between accuracy and robustness against adversarial attacks. We evaluate our method under black-box, grey-box, and white-box settings. The proposed method outperforms the state-of-the-art by a significant margin on the ImageNet dataset under grey-box attacks while maintaining good accuracy on clean images. We also establish a strong baseline for a novel white-box attack.


title: MATLAB/Simulink modelling and experimental results of a PEM electrolyzer powered by a solar panel

abstract: In this paper, the solar panels are used to power an electrolyzer to separate the water into hydrogen and oxygen gas. The electrical equivalent circuit for the proton exchange membrane electrolyzer was developed and implemented in MATLAB/Simulink along with the atmospheric hydrogen storage tank. The voltage (2 volt) and current (1 ampere) were supplied in a similar manner in order to compare the simulated and experimental results. The hydrogen amount is calculated to be 7.345 (ml/min A) from the model as well as the experimental set up. The experimental and simulation results were matched.


title: Relationship between space-vector modulation and three-phase carrier-based PWM: a comprehensive analysis [three-phase inverters]

abstract: This paper comprehensively analyzes the relationship between space-vector modulation and three-phase carrier-based pulse width modulation (PWM). The relationships involved, such as the relationship between modulation signals (including zero-sequence component and fundamental components) and space vectors, the relationship between the modulation signals and the space-vector sectors, the relationship between the switching pattern of space-vector modulation and the type of carrier, and the relationship between the distribution of zero vectors and different zero-sequence signal are systematically established. All the relationships provide a bidirectional bridge for the transformation between carrier-based PWM modulators and space-vector modulation modulators. It is shown that all the drawn conclusions are independent of the load type. Furthermore, the implementations of both space-vector modulation and carrier-based PWM in a closed-loop feedback converter are discussed.


title: Closing the Gap Between Short and Long XORs for Model Counting

abstract: Many recent algorithms for approximate model counting are based on a reduction to combinatorial searches over random subsets of the space defined by parity or XOR constraints. Long parity constraints (involving many variables) provide strong theoretical guarantees but are computationally difficult. Short parity constraints are easier to solve but have weaker statistical properties. It is currently not known how long these parity constraints need to be. We close the gap by providing matching necessary and sufficient conditions on the required asymptotic length of the parity constraints. Further, we provide a new family of lower bounds and the first non-trivial upper bounds on the model count that are valid for arbitrarily short XORs. We empirically demonstrate the effectiveness of these bounds on model counting benchmarks and in a Satisfiability Modulo Theory (SMT) application motivated by the analysis of contingency tables in statistics.


title: Filter Optimization for Carrier-Frequency- and Timing-Offset in Universal Filtered Multi-Carrier Systems

abstract: Universal Filtered Multi-Carrier (UFMC) is a novel multi-carrier modulation technique which can be seen as a generalization of filtered OFDM and filter bank based multi-carrier (FBMC-FMT). Being a candidate waveform technology for 5G wireless systems, it combines the simplicity of OFDM with the advantages of FBMC. The FIR-filter, used in UFMC to filter a group of subcarriers, is a key design parameter to gain more robustness in relaxed synchronization conditions, i.e. time-frequency misalignment. It was shown in previous work that very significant SIR improvement can be achieved for UFMC by optimizing the FIR-filter, taking carrier frequency offset into account. In this paper, we optimize the FIR-filter design in UFMC by taking both carrier frequency and timing offset into account in an uplink multi-user FDMA scenario. From the simulation results, up to 3.6 dB SIR improvement can be achieved with the optimized FIR filter compared to UFMC with non-optimized Dolph-Chebyshev filter and 15.1 dB SIR gain against classical CP-OFDM system respectively, provided that the normalized carrier frequency and timing offset are uniformly distributed in the interval ±5%.


title: Staple: Complementary Learners for Real-Time Tracking

abstract: Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes. However, since the model that they learn depends strongly on the spatial layout of the tracked object, they are notoriously sensitive to deformation. Models based on colour statistics have complementary traits: they cope well with variation in shape, but suffer when illumination is not consistent throughout a sequence. Moreover, colour distributions alone can be insufficiently discriminative. In this paper, we show that a simple tracker combining complementary cues in a ridge regression framework can operate faster than 80 FPS and outperform not only all entries in the popular VOT14 competition, but also recent and far more sophisticated trackers according to multiple benchmarks.


title: The trigeminocardiac reflex – a comparison with the diving reflex in humans

abstract: The trigeminocardiac reflex (TCR) has been described in the literature as a reflexive response of bradycardia, hypotension, and gastric hypermotility seen upon predominantly mechanical, but also thermic stimulation in the distribution of the trigeminal nerve. The diving reflex (DR) in humans is initiated upon holding the breath (breath holding) and is characterized by a slowing of the heart rate, a reduction in blood flow to the limbs, and a gradual rise in mean arterial blood pressure. Although both reflexes share a considerable number of similarities, their relationship—especially their functional purpose in humans—has not yet been clearly defined. In this chapter, we integrate these two phenomena into a unified physiological concept. We believe that the TCR and the DR are closely linked functionally and represent phylogenetically old reflexes that are physiological in humans in the first months of life.


title: Bridging physical and virtual worlds with electronic tags

abstract: The role of computers in the modern office has divided ouractivities between virtual interactions in the realm of thecomputer and physical interactions with real objects within thetraditional office infrastructure. This paper extends previous workthat has attempted to bridge this gap, to connect physical objectswith virtual representations or computational functionality, viavarious types of tags. We discuss a variety of scenarios we haveimplemented using a novel combination of inexpensive, unobtrusiveand easy to use RFID tags, tag readers, portable computers andwireless networking. This novel combination demonstrates theutility of invisibly, seamlessly and portably linking physicalobjects to networked electronic services and actions that arenaturally associated with their form.


title: HaptoClone (Haptic-Optical Clone) for Mutual Tele-Environment by Real-time 3D Image Transfer with Midair Force Feedback

abstract: In this paper, we propose a novel interactive system that mutually copies adjacent 3D environments optically and physically. The system realizes mutual user interactions through haptics without wearing any devices. A realistic volumetric image is displayed using a pair of micro-mirror array plates (MMAPs). The MMAP transmissively reflects the rays from an object, and a pair of them reconstructs the floating aerial image of the object. Our system can optically copy adjacent environments based on this technology. Haptic feedback is also given by using an airborne ultrasound tactile display (AUTD). Converged ultrasound can give force feedback in midair. Based on the optical characteristics of the MMAPs, the cloned image and the user share an identical coordinate system. When a user touches the transferred clone image, the system gives force feedback so that the user can feel the mechanical contact and reality of the floating image.


title: Movie/Script: Alignment and Parsing of Video and Text Transcription

abstract: Movies and TV are a rich source of diverse and complex video of people, objects, actions and locales "in the wild". Harvesting automatically labeled sequences of actions from video would enable creation of large-scale and highly-varied datasets. To enable such collection, we focus on the task of recovering scene structure in movies and TV series for object tracking and action retrieval. We present a weakly supervised algorithm that uses the screenplay and closed captions to parse a movie into a hierarchy of shots and scenes. Scene boundaries in the movie are aligned with screenplay scene labels and shots are reordered into a sequence of long continuous tracks or threads which allow for more accurate tracking of people, actions and objects. Scene segmentation, alignment, and shot threading are formulated as inference in a unified generative model and a novel hierarchical dynamic programming algorithm that can handle alignment and jump-limited reorderings in linear time is presented. We present quantitative and qualitative results on movie alignment and parsing, and use the recovered structure to improve character naming and retrieval of common actions in several episodes of popular TV series.


title: A large-scale sentiment analysis for Yahoo! answers

abstract: Sentiment extraction from online web documents has recently been an active research topic due to its potential use in commercial applications. By sentiment analysis, we refer to the problem of assigning a quantitative positive/negative mood to a short bit of text. Most studies in this area are limited to the identification of sentiments and do not investigate the interplay between sentiments and other factors. In this work, we use a sentiment extraction tool to investigate the influence of factors such as gender, age, education level, the topic at hand, or even the time of the day on sentiments in the context of a large online question answering site. We start our analysis by looking at direct correlations, e.g., we observe more positive sentiments on weekends, very neutral ones in the Science & Mathematics topic, a trend for younger people to express stronger sentiments, or people in military bases to ask the most neutral questions. We then extend this basic analysis by investigating how properties of the (asker, answerer) pair affect the sentiment present in the answer. Among other things, we observe a dependence on the pairing of some inferred attributes estimated by a user's ZIP code. We also show that the best answers differ in their sentiments from other answers, e.g., in the Business & Finance topic, best answers tend to have a more neutral sentiment than other answers. Finally, we report results for the task of predicting the attitude that a question will provoke in answers. We believe that understanding factors influencing the mood of users is not only interesting from a sociological point of view, but also has applications in advertising, recommendation, and search.


title: Differentially private aggregation of distributed time-series with transformation and encryption

abstract: We propose the first differentially private aggregation algorithm for distributed time-series data that offers good practical utility without any trusted server. This addresses two important challenges in participatory data-mining applications where (i) individual users collect temporally correlated time-series data (such as location traces, web history, personal health data), and (ii) an untrusted third-party aggregator wishes to run aggregate queries on the data. To ensure differential privacy for time-series data despite the presence of temporal correlation, we propose the Fourier Perturbation Algorithm (FPAk). Standard differential privacy techniques perform poorly for time-series data. To answer n queries, such techniques can result in a noise of Θ(n) to each query answer, making the answers practically useless if n is large. Our FPAk algorithm perturbs the Discrete Fourier Transform of the query answers. For answering n queries, FPAk improves the expected error from Θ(n) to roughly Θ(k) where k is the number of Fourier coefficients that can (approximately) reconstruct all the n query answers. Our experiments show that k To deal with the absence of a trusted central server, we propose the Distributed Laplace Perturbation Algorithm (DLPA) to add noise in a distributed way in order to guarantee differential privacy. To the best of our knowledge, DLPA is the first distributed differentially private algorithm that can scale with a large number of users: DLPA outperforms the only other distributed solution for differential privacy proposed so far, by reducing the computational load per user from O(U) to O(1) where U is the number of users.


title: Programmable firewall using Software Defined Networking

abstract: Software Defined Networking is an exciting technology that enables innovation and flexibility in designing and managing networks, but it also introduces new security issues. Our Major challenge is to build powerful and flexible firewall applications for protecting software defined based networks. In this paper we focus on designing and developing OpenFlow based firewall application. The implementation shows that most of the firewall functionalities can be built using software, without need of dedicated hardware. We are using open source POX Controller based on python for our experiments. To perform experiment, we have used VMPlayer virtualization solution and installed Mininet emulator for creating network topologies. In this paper, we present the implementation details as well as experimentation results of firewall application.


title: Polymorphisms in FKBP5 are associated with increased recurrence of depressive episodes and rapid response to antidepressant treatment

abstract: The stress hormone-regulating hypothalamic-pituitary-adrenal (HPA) axis has been implicated in the causality as well as the treatment of depression. To investigate a possible association between genes regulating the HPA axis and response to antidepressants and susceptibility for depression, we genotyped single-nucleotide polymorphisms in eight of these genes in depressed individuals and matched controls. We found significant associations of response to antidepressants and the recurrence of depressive episodes with single-nucleotide polymorphisms in FKBP5, a glucocorticoid receptor-regulating cochaperone of hsp-90, in two independent samples. These single-nucleotide polymorphisms were also associated with increased intracellular FKBP5 protein expression, which triggers adaptive changes in glucocorticoid receptor and, thereby, HPA-axis regulation. Individuals carrying the associated genotypes had less HPA-axis hyperactivity during the depressive episode. We propose that the FKBP5 variant-dependent alterations in HPA-axis regulation could be related to the faster response to antidepressant drug treatment and the increased recurrence of depressive episodes observed in this subgroup of depressed individuals. These findings support a central role of genes regulating the HPA axis in the causality of depression and the mechanism of action of antidepressant drugs.


title: Aligning Qualitative, Real-Time, and Probabilistic Property Specification Patterns Using a Structured English Grammar

abstract: Formal methods offer an effective means to assert the correctness of software systems through mathematical reasoning. However, the need to formulate system properties in a purely mathematical fashion can create pragmatic barriers to the application of these techniques. For this reason, Dwyer et al. invented property specification patterns which is a system of recurring solutions to deal with the temporal intricacies that would make the construction of reactive systems very hard otherwise. Today, property specification patterns provide general rules that help practitioners to qualify order and occurrence, to quantify time bounds, and to express probabilities of events. Nevertheless, a comprehensive framework combining qualitative, real-time, and probabilistic property specification patterns has remained elusive. The benefits of such a framework are twofold. First, it would remove the distinction between qualitative and quantitative aspects of events; and second, it would provide a structure to systematically discover new property specification patterns. In this paper, we report on such a framework and present a unified catalogue that combines all known plus 40 newly identified or extended patterns. We also offer a natural language front-end to map patterns to a temporal logic of choice. To demonstrate the virtue of this new framework, we applied it to a variety of industrial requirements, and use PSPWizard , a tool specifically developed to work with our unified pattern catalogue, to automatically render concrete instances of property specification patterns to formulae of an underlying temporal logic of choice.


title: Formalization and analysis of jacobian matrix in screw theory and its application in kinematic singularity

abstract: Accurate specification and rigorous analysis of Jacobian matrix are indispensable to guarantee correct evaluation on the manipulator kinematics performance. In this paper, a formal analysis method of the Jacobian matrix in the screw theory is presented by using the higher-order logic theorem prover HOL4. Formalizations of twists and the forward kinematics are characterized with the product of exponential formula and the theory of functional matrices. To the best of our knowledge, this work is the first to formally reason about the spatial Jacobian using theorem proving. The formal modeling and analysis of a 3-DOF planar manipulator substantiate the effectiveness and applicability of the proposed approach to formally verify the kinematics properties of manipulator.


title: Structure Based Data De-Anonymization of Social Networks and Mobility Traces

abstract: We present a novel de-anonymization attack on mobility trace data and social data. First, we design an Unified Similarity (US) measurement, based on which we present a US based De-Anonymization (DA) framework which iteratively de-anonymizes data with an accuracy guarantee. Then, to de-anonymize data without the knowledge of the overlap size between the anonymized data and the auxiliary data, we generalize DA to an Adaptive De-Anonymization (ADA) framework. Finally, we examine DA/ADA on mobility traces and social data sets.


title: Towards Confidence in the Truth: A Bootstrapping based Truth Discovery Approach

abstract: The demand for automatic extraction of true information (i.e., truths) from conflicting multi-source data has soared recently. A variety of truth discovery methods have witnessed great successes via jointly estimating source reliability and truths. All existing truth discovery methods focus on providing a point estimator for each object's truth, but in many real-world applications, confidence interval estimation of truths is more desirable, since confidence interval contains richer information. To address this challenge, in this paper, we propose a novel truth discovery method (ETCIBoot) to construct confidence interval estimates as well as identify truths, where the bootstrapping techniques are nicely integrated into the truth discovery procedure. Due to the properties of bootstrapping, the estimators obtained by ETCIBoot are more accurate and robust compared with the state-of-the-art truth discovery approaches. Theoretically, we prove the asymptotical consistency of the confidence interval obtained by ETCIBoot. Experimentally, we demonstrate that ETCIBoot is not only effective in constructing confidence intervals but also able to obtain better truth estimates.


title: Robust Energy-Efficient Adder Topologies

abstract: In this paper we explore the relationship between adder topology and energy efficiency. We compare the energy-delay tradeoff curves of selected 32- bit adder topologies, to determine how architectural features and design techniques affect energy efficiency. Optimizing different adders for the supply and threshold voltages, and transistor sizing, we show that topologies with the least number of logic stages having an average fanin of two per stage, and fewest wires are most energy efficient. While a design with fully custom sizes can be extremely tedious to layout, we show that custom sizing can be used as a guide to group different gates in the design, resulting in a manageable layout overhead without significant loss of energy efficiency.


title: A passively safe and gravity-counterbalanced anthropomorphic robot arm

abstract: When designing a robot for human-safety during direct physical interaction, one approach is to size the robot’s actuators to be physically incapable of exerting damaging impulses, even during a controller failure. Merely lifting the arms against their own weight may consume the entire available torque budget, preventing the rapid and expressive movement required for anthropomorphic robots. To mitigate this problem, gravity-counterbalancing of the arms is a common tactic; however, most designs adopt a shoulder singularity configuration which, while favorable for simple counterbalance design, has a range of motion better suited for industrial robot arms. In this paper we present a shoulder design using a novel differential mechanism to counterbalance the arm while preserving an anthropomorphically favorable singularity configuration and natural range-of-motion. Furthermore, because the motors driving the shoulder are completely grounded, counterbalance masses or springs are easily placed away from the shoulder and low in the torso, improving mass distribution and balance. A robot arm using this design is constructed and evaluated for counterbalance efficacy and backdrivability under closed-loop force control.


title: The adequacy of LoRaWAN on smart grids: A comparison with RF mesh technology

abstract: Smart cities initiatives have been subject of discussion in various forums throughout the world. This innovation in urban infrastructure of cities and the impacts of intensive application of technology are still being identified and mapped. A fundamental aspect under technological viewpoint to make this novelty a reality, and most comprehensive as possible is related to communication networks. Concept of Internet of Things (IoT) only materializes through a combination of technologies based on private and public networks and the Internet as the main platform. Utilities companies have already started their participation in this process in sense that they are deploying smart grids using technologies like RF Mesh and PLC, being RF Mesh the most widespread in world markets. But it is necessary to insert smart grid projects in the context of smart cities and in its converging alternatives of communications infrastructure. This work explores an emerging technology named as Long Range Wide Area Network (LoRaWAN) and proposes that this is the current alternative best suited to the big scenario of IoT, in which smart grids are included, and also compares with the RF Mesh technology widely adopted for this application.


title: Visual Business Ecosystem Intelligence: Lessons from the Field

abstract: Macroscopic insight into business ecosystems is becoming increasingly important. With the emergence of new digital business data, opportunities exist to develop rich, interactive visual-analytics tools. Georgia Institute of Technology researchers have been developing and implementing visual business ecosystem intelligence tools in corporate settings. This article discusses the challenges they faced, the lessons learned, and opportunities for future research.


title: Bayesian Reinforcement Learning in Factored POMDPs

abstract: Model-based Bayesian Reinforcement Learning (BRL) provides a principled solution to dealing with the exploration-exploitation trade-off, but such methods typically assume a fully observable environments. The few Bayesian RL methods that are applicable in partially observable domains, such as the Bayes-Adaptive POMDP (BA-POMDP), scale poorly. To address this issue, we introduce the Factored BA-POMDP model (FBA-POMDP), a framework that is able to learn a compact model of the dynamics by exploiting the underlying structure of a POMDP. The FBA-POMDP framework casts the problem as a planning task, for which we adapt the Monte-Carlo Tree Search planning algorithm and develop a belief tracking method to approximate the joint posterior over the state and model variables. Our empirical results show that this method outperforms a number of BRL baselines and is able to learn efficiently when the factorization is known, as well as learn both the factorization and the model parameters simultaneously.


title: From Brand Loyalty to E-Loyalty : A Conceptual Framework

abstract: With the rapid growth of E -commerce and on-line consumer shopping trends, the importance of building and maintaining customer loyalty in electronic marketplaces has come into sharper focus in marketing theory and practice. This paper integrates previous research in the field of brand loyalty to present a conceptial framework of "e-loyalty" and its underlying drivers. Implications for e- marketing practice and future research directions are also presented.


Classifying NBA Offensive Plays Using Neural Networks


title: Nurse–physician communication – An integrated review

abstract: AIM AND OBJECTIVE ::: To present a comprehensive review of current evidence on the factors which impact on nurse-physician communication and interventions developed to improve nurse-physician communication. ::: ::: ::: BACKGROUND ::: The challenges in nurse-physician communication persist since the term 'nurse-doctor game' was first used in 1967, leading to poor patient outcomes such as treatment delays and potential patient harm. Inconsistent evidence was found on the factors and interventions which foster or impair effective nurse-physician communication. ::: ::: ::: DESIGN ::: An integrative review was conducted following a five-stage process: problem identification, literature search, data evaluation, data analysis and presentation. ::: ::: ::: METHODS ::: Five electronic databases were searched from 2005 to April 2016 using key search terms: "improve*," "nurse-physician," "nurse," "physician" and "communication" in five electronic databases including the Cumulative Index to Nursing and Allied Health Literature (CINAHL), MEDLINE, PubMed, Science Direct and Scopus. ::: ::: ::: RESULTS ::: A total of 22 studies were included in the review. Four themes emerged from the data synthesis, namely communication styles; factors that facilitate nurse-physician communication; barriers to effective nurse-physician communication; and interventions to improve nurse-physician communication. ::: ::: ::: CONCLUSION ::: This integrative review suggests that nurse-physician communication still remains ineffective. Current interventions only address information needs of nurses and physicians in limited situations and specific settings but cannot adequately address the interprofessional communication skills that are lacking in practice. The disparate views of nurses and physicians on communication due to differing training backgrounds confound the effectiveness of current interventions or strategies. ::: ::: ::: RELEVANCE TO CLINICAL PRACTICE ::: Cross-training and interprofessional educational from undergraduate to postgraduate programmes will better align the training of nurses and physicians to communicate effectively. Further research is needed to determine the feasibility and generalisability of interventions, such as localising physicians and using communication tools, to improve nurse-physician communication. Organisational and cultural changes are needed to overcome ingrained practices impeding nurse-physician communication.


title: Video Tracking Using Learned Hierarchical Features

abstract: In this paper, we propose an approach to learn hierarchical features for visual object tracking. First, we offline learn features robust to diverse motion patterns from auxiliary video sequences. The hierarchical features are learned via a two-layer convolutional neural network. Embedding the temporal slowness constraint in the stacked architecture makes the learned features robust to complicated motion transformations, which is important for visual object tracking. Then, given a target video sequence, we propose a domain adaptation module to online adapt the pre-learned features according to the specific target object. The adaptation is conducted in both layers of the deep feature learning module so as to include appearance information of the specific target object. As a result, the learned hierarchical features can be robust to both complicated motion transformations and appearance changes of target objects. We integrate our feature learning algorithm into three tracking methods. Experimental results demonstrate that significant improvement can be achieved using our learned hierarchical features, especially on video sequences with complicated motion transformations.


title: Representation Learning with Pair-wise Constraints for Collaborative Ranking

abstract: Last decades have witnessed a vast amount of interest and research in recommendation systems. Collaborative filtering, which uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users, is one of the most successful approaches to build recommendation systems. Most previous collaborative filtering approaches employ the matrix factorization techniques to learn latent user feature profiles and item feature profiles. Also many subsequent works are proposed to incorporate users' social network information and items' attributions to further improve recommendation performance under the matrix factorization framework. However, the matrix factorization based methods may not make full use of the rating information, leading to unsatisfying performance. Recently deep learning has been approved to be able to find good representations in natural language processing, image classification, and so on. Along this line, we propose a collaborative ranking framework via representation learning with pair-wise constraints (REAP for short), in which autoencoder is used to simultaneously learn the latent factors of both users and items and pair-wise ranked loss defined by (user, item) pairs is considered. Extensive experiments are conducted on five data sets to demonstrate the effectiveness of the proposed framework.


Gaia: A Middleware Infrastructure to Enable Active Spaces


title: Emotion Analysis Using Latent Affective Folding and Embedding

abstract: Though data-driven in nature, emotion analysis based on latent semantic analysis still relies on some measure of expert knowledge in order to isolate the emotional keywords or keysets necessary to the construction of affective categories. This makes it vulnerable to any discrepancy between the ensuing taxonomy of affective states and the underlying domain of discourse. This paper proposes a more general strategy which leverages two distincts semantic levels, one that encapsulates the foundations of the domain considered, and one that specifically accounts for the overall affective fabric of the language. Exposing the emergent relationship between these two levels advantageously informs the emotion classification process. Empirical evidence suggests that this is a promising solution for automatic emotion detection in text.


title: e-Sports: Playing just for fun or playing to satisfy life goals?

abstract: Clan leaders significantly differed in life goal power from not members of any clan.e-Sports players differed from casual players in life goals affiliation and diversion.e-Sports - a means of satisfying the need to belong through (teams, LAN parties).e-Sports - a means of satisfying the need for power(position of a game team leader). Purposee-Sports is an area of the game scene, in which computer game players specialize in a specific game, form game teams (clans), compete together in tournaments and meet at so-called LAN (Local Area Network) parties. The objective of this study was to compare the different types of e-Sports players from the perspective of their personality traits and explicit motives and to compare e-Sports players with casual players in selected life goals. MethodsA questionnaire assessing life goals (Pohlmann & Brunstein, 1997) and basic personality traits (Personality inventory KUD, 1986) were administered to 108 e-Sports players and 54 casual computer game players. ResultsIn the group of e-Sports players, only clan leaders significantly differed in life goal power from those who were not members of any clan. Significant differences were also found between e-Sports players and casual players in terms of life goals affiliation and diversion. Conclusionse-Sports seem not only to be about playing computer games, but can also serve as a means of satisfying the need to belong. They do this by creating friendly relationships through membership in game teams and participation in LAN parties, or satisfying the need for power by upholding a position of a game team leader and determining its course of action.


title: Analytical Models for Power Networks: The Case of the Western U.S. and ERCOT Grids

abstract: The topological structure of the power grid plays a key role in the reliable delivery of electricity and price settlement in the electricity market. Incorporation of new energy sources and loads into the grid over time has led to its structural and geographical expansion and can affect its stable operation. This paper presents an intuitive analytical model for the temporal evolution of large grids and uses it to understand common structural features observed in grids across America. In particular, key graph parameters like degree distribution, graph diameter, betweenness centralities, eigen-spread and clustering coefficients, as well as graph processes like infection propagation are used to quantify the model's benefits through comparison with the Western US and ERCOT power grids. The most significant contribution of the developed model is its analytical tractability, that provides a closed form expression for the nodal degree distribution observed in large grids. The discussed model can be used to generate realistic test cases to analyze topological effects on grid functioning and new grid technologies.


title: The Neglected Significance of “Antioxidative Stress”

abstract: Oxidative stress arises when there is a marked imbalance between the production and removal of reactive oxygen species (ROS) in favor of the prooxidant balance, leading to potential oxidative damage. ROSs were considered traditionally to be only a toxic byproduct of aerobic metabolism. However, recently, it has become apparent that ROS might control many different physiological processes such as induction of stress response, pathogen defense, and systemic signaling. Thus, the imbalance of the increased antioxidant potential, the so-called antioxidative stress, should be as dangerous as well. Here, we synthesize increasing evidence on "antioxidative stress-induced" beneficial versus harmful roles on health, disease, and aging processes. Oxidative stress is not necessarily an un-wanted situation, since its consequences may be beneficial for many physiological reactions in cells. On the other hand, there are potentially harmful effects of "antioxidative stress," especially in the cases of overconsumption of synthetic antioxidants. Antioxidants can neutralize ROS and decrease oxidative stress; however, this is not always beneficial in regard to disease formation or progression (of, e.g., cancer) or for delaying aging.


title: Expression: Visualizing Affective Content from Social Streams

abstract: Analysis of social content for understanding people's sentiments towards topics of interest that change over time has become an attractive and challenging research area. Natural Language Processing (NLP) techniques are being adapted to deal with streams of social content. New visualization approaches need also to be proposed to express, in a user friendly and reactive manner, individual as well as collective sentiments. In this paper, we present Expression, an integrated framework that allows users to express their opinions through a social platform and to see others' comments. We introduce the Sentiment Card concept: a live representation of a topic of interest. The Sentiment Card is a space that allows users to express their comments and to understand the trend of selected topics of interest expressed by other users. The design of Expression is presented, describing in particular, the sentiment classification module as well as the sentiment card visualization component. Results of the evaluation of our prototype by a usability study are also discussed and considered for motivating future research.


title: Automatic bone age assessment based on intelligent algorithms and comparison with TW3 method

abstract: Abstract Purpose New algorithms are proposed to improve the validity, accuracy and practicality of automatic bone age assessment (ABAA). Materials and methods The concept of object-based region of interest (ROI) was proposed. Thirteen RUS (including radius, ulna and short finger bones) ROIs and seven carpal ROIs were appointed respectively according to Tanner–Whitehouse (TW3) method. Five features including size, morphologic features and fusional/adjacent stage of each ROI were extracted based on particle swarm optimization (PSO) and input into ANN classifiers. ANNs were built upon feed-forward multilayer networks and trained with back-propagation algorithm rules to process RUS and carpal features respectively. About 1046 digital left hand-wrist radiographs were randomly utilized half for training ANNs and the rest for ABAA after manual reading by TW3 method. Results BA comparison between observers indicated that the S.D. of RUS BA was larger than that of carpal BA (S.D.=4.40, 2.42 respectively), but interestingly, both CVs were 4.0, and both concordance rates were very high (95.5% and 94.2%), and both differences between observers were not significant (both P >0.05). We found by comparison between results of ABAA and manual readings that RUS BA had larger S.D.s than carpal BA between two methods, but the CVs were very similar in the case of carpal BA P >0.05). Conclusions PSO method made image segmentation and feature extraction more valid and accurate, and the ANN models were sophisticated in processing image information. ABAA system based on intelligent algorithms had been successfully applied to all cases from 0 to 18 years of bone age.


title: Recognition of Sarcasms in Tweets Based on Concept Level Sentiment Analysis and Supervised Learning Approaches

abstract: Sarcasm is a form of communication that is intended to mock or harass someone by us- ing words with the opposite of their literal meaning. However, identification of sarcasm is somewhat difficult due to the gap between its literal and intended meaning. Recognition of sarcasm is a task that can potentially pro- vide a lot of benefits to other areas of nat- ural language processing. In this research, we propose a new method to identify sarcasm in tweets that focuses on several approaches: 1) sentiment analysis, 2) concept level and common-sense knowledge 3) coherence and 4) machine learning classification. We will use support vector machine (SVM) to classify sarcastic tweet based on our proposed features as well as ordinary N-grams. Our proposed classifier is an ensemble of two SVMs with two different feature sets. The results of the experiment show our method outperforms the baseline method and achieves 80% accuracy.


title: Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model

abstract: Derived from the theory of reasoned action, the technology acceptance model (TAM) focuses on two specific salient beliefs-- ease of use and usefulness. It has been applied in the study of user adoption of different technologies, and has emerged as a reliable and robust model. However, this has not discouraged researchers from incorporating additional constructs to the original model in their quest for increased predictive power. Here, an attempt is made in the context of explaining consumer intention to use on-line shopping. Besides ease of use and usefulness, compatibility, privacy, security, normative beliefs, and self- efficacy are included in an augmented TAM. A test of this model, with data collected from 281 consumers, show support for seven of nine research hypotheses. Specifically, compatibility, usefulness, ease of use, and security were found to be significant predictors of attitude towards on-line shopping, but privacy was not. Further, intention to use on-line shopping was strongly influenced by attitude toward on-line shopping, normative beliefs, and self-efficacy.


title: Robust Argumentative Zoning for Sensemaking in Scholarly Documents

abstract: We present an automated approach to classify sentences of scholarly work with respect to their rhetorical function. While previous work that achieves this task of argumentative zoning requires richly annotated input, our approach is robust to noise and can process raw text. Even in cases where the input has noise (as it is obtained from optical character recognition or text extraction from PDF files), our robust classifier is largely accurate. We perform an in-depth study of our system both with clean and noisy inputs. We also give preliminary results from in situ acceptability testing when the classifier is embedded within a digital library reading environment.


title: COTS-based systems top 10 list

abstract: Presents a COTS-based system (CBS) software defect-reduction list as hypotheses, rather than results, that also serve as software challenges for enhancing our empirical understanding of CBSs. The hypotheses are: (1) more than 99% of all executing computer instructions come from COTS products (each instruction passed a market test for value); (2) more than half the features in large COTS software products go unused; (3) the average COTS software product undergoes a new release every 8-9 months, with active vendor support for only its latest three releases; (4) CBS development and post-deployment efforts can scale as high as the square of the number of independently developed COTS products targeted for integration; (5) CBS post-deployment costs exceed CBS development costs; (6) although glue-code development usually accounts for less than half the total CBS software development effort, the effort per line of glue code averages about three times the effort per line of developed applications code; (7) non-development costs, such as licensing fees, are significant, and projects must plan for and optimize them; (8) CBS assessment and tailoring efforts vary significantly by COTS product class (operating system, database management system, user interface, device driver, etc.); (9) personnel capability and experience remain the dominant factors influencing CBS development productivity; and (10) CBS is currently a high-risk activity, with effort and schedule overruns exceeding non-CBS software overruns, yet many systems have used COTS successfully for cost reduction and early delivery.


Multi-View Unsupervised User Feature Embedding for Social Media-based Substance Use Prediction.


title: Action-embedded transformational leadership in self-managing global information systems development teams

abstract: While software development teams are becoming more and more distributed around the globe, most software development methodologies used by global teams prescribe self-managing teams. Transformational leadership is the key to successful information systems development and use for competitive advantage. Yet, little is known about transformational leadership in self-managing global information systems development team settings. This study answers the research question of how leaders emerge and strategically influence systems development in self-managing global information systems development teams. This question is answered with a grounded theory study of Apache Open Source Software development teams. A theoretical model of action-embedded transformational leadership is developed to demonstrate how leaders emerge and strategically influence systems development efforts through their leadership, which is embedded in their work-related actions.


title: ANALYZING THE BUSINESS MODEL CONCEPT — A COMPREHENSIVE CLASSIFICATION OF LITERATURE

abstract: The business model concept is characterized by numerous fields of application which are promising in business practice. Consequently, research on business models has attracted increasing attention in the scientific world. However, for a successful utilization, the widely-criticized lack of theoretical consensus in this field of research has to be overcome. Thus, this paper conducted a comprehensive and up-to-date literature analysis examining 30 relevant literature sources focusing mainly on business model research. To achieve this, the analysis was based on a classification framework containing 17 evaluation criteria. Hereby, a systematic and objective penetration of the research area could be achieved. Moreover, existing research gaps as well as the most important fields to be addressed in future research could be revealed.


title: Random k-Labelsets for Multilabel Classification

abstract: A simple yet effective multilabel learning method, called label powerset (LP), considers each distinct combination of labels that exist in the training set as a different class value of a single-label classification task. The computational efficiency and predictive performance of LP is challenged by application domains with large number of labels and training examples. In these cases, the number of classes may become very large and at the same time many classes are associated with very few training examples. To deal with these problems, this paper proposes breaking the initial set of labels into a number of small random subsets, called labelsets and employing LP to train a corresponding classifier. The labelsets can be either disjoint or overlapping depending on which of two strategies is used to construct them. The proposed method is called RAkEL (RAndom k labELsets), where k is a parameter that specifies the size of the subsets. Empirical evidence indicates that RAkEL manages to improve substantially over LP, especially in domains with large number of labels and exhibits competitive performance against other high-performing multilabel learning methods.


title: A face(book) in the crowd: social Searching vs. social browsing

abstract: Large numbers of college students have become avid Facebook users in a short period of time. In this paper, we explore whether these students are using Facebook to find new people in their offline communities or to learn more about people they initially meet offline. Our data suggest that users are largely employing Facebook to learn more about people they meet offline, and are less likely to use the site to initiate new connections.


title: The trilateral filter for high contrast images and meshes

abstract: We present a new, single-pass nonlinear filter for edge-preserving smoothing and visual detail removal for N dimensional signals in computer graphics, image processing and computer vision applications. Built from two modified forms of Tomasi and Manduchi's bilateral filter, the new "trilateral" filter smoothes signals towards a sharply-bounded, piecewise-linear approximation. Unlike bilateral filters or anisotropic diffusion methods that smooth towards piecewise constant solutions, the trilateral filter provides stronger noise reduction and better outlier rejection in high-gradient regions, and it mimics the edge-limited smoothing behavior of shock-forming PDEs by region finding with a fast min-max stack. Yet the trilateral filter requires only one user-set parameter, filters an input signal in a single pass, and does not use an iterative solver as required by most PDE methods. Like the bilateral filter, the trilateral filter easily extends to N-dimensional signals, yet it also offers better performance for many visual applications including appearance-preserving contrast reduction problems for digital photography and denoising polygonal meshes.


title: Automatic Micro-expression Recognition from Long Video Using a Single Spotted Apex

abstract: Recently, micro-expression recognition has seen an increase of interest from psychological and computer vision communities. As micro-expressions are generated involuntarily on a person’s face, and are usually a manifestation of repressed feelings of the person. Most existing works pay attention to either the detection or spotting of micro-expression frames or the categorization of type of micro-expression present in a short video shot. In this paper, we introduced a novel automatic approach to micro-expression recognition from long video that combines both spotting and recognition mechanisms. To achieve this, the apex frame, which provides the instant when the highest intensity of facial movement occurs, is first spotted from the entire video sequence. An automatic eye masking technique is also presented to improve the robustness of apex frame spotting. With the single apex, we describe the spotted micro-expression instant using a state-of-the-art feature extractor before proceeding to classification. This is the first known work that recognizes micro-expressions from a long video sequence without the knowledge of onset and offset frames, which are typically used to determine a cropped sub-sequence containing the micro-expression. We evaluated the spotting and recognition tasks on four spontaneous micro-expression databases comprising only of raw long videos – CASME II-RAW, SMIC-E-HS, SMIC-E-VIS and SMIC-E-NIR. We obtained compelling results that show the effectiveness of the proposed approach, which outperform most methods that rely on human annotated sub-sequences.


Progress in primary prevention: the smoking-health issue.


title: Revisiting hash table design for phase change memory

abstract: Phase Change Memory (PCM) is emerging as an attractive alternative to Dynamic Random Access Memory (DRAM) in building data-intensive computing systems. PCM offers read/write performance asymmetry that makes it necessary to revisit the design of in-memory applications. In this paper, we focus on in-memory hash tables, a family of data structures with wide applicability. We evaluate several popular hash-table designs to understand their performance under PCM. We find that for write-heavy workloads the designs that achieve best performance for PCMdiffer from the ones that are best for DRAM, and that designs achieving a high load factor also cause a high number of memory writes. Finally, we propose PFHT, a PCM-Friendly Hash Table which presents a cuckoo hashing variant that is tailored to PCM characteristics, and offers a better trade-off between performance, the amount of writes generated, and the expected load factor than any of the existing DRAM-based implementations.


title: Planning esthetic treatment after avulsion of maxillary incisors.

abstract: BACKGROUND ::: When a young patient accidentally loses two neighboring maxillary incisors, the choice of treatment plan is difficult. Although implant restorations are a popular option, they cannot be placed until skeletal growth is finished. Furthermore, the use of two neighboring implant crowns represents a considerable challenge from an esthetic point of view. This case report describes an innovative solution that combined autotransplantation of a developing premolar and orthodontic space closure. ::: ::: ::: CASE DESCRIPTION ::: An 11-year-old girl sought treatment for avulsion of both her maxillary right lateral incisor and her central incisor, which occurred as a result of a horseback-riding accident. Replantation by her general dentist was unsuccessful. An oral surgeon transplanted the mandibular right second premolar to the injury site, and the author moved the maxillary right quadrant mesially to close all spaces. The first premolar was intruded, and the canine extruded, to provide normal marginal gingival contours. A prosthodontist, working with his dental technician, restored the "abnormal" crowns with three porcelain veneers. The outcome was almost indistinguishable from a natural dentition. ::: ::: ::: CONCLUSIONS ::: and ::: ::: ::: CLINICAL IMPLICATIONS ::: When faced with treating severe traumatic injuries in growing patients, even after avulsion of incisors, clinicians working in cooperation can optimize the outcome. A combination of transplantation and space closure may represent the best treatment option, particularly when coupled with elements of esthetic dentistry. The advantage of this approach is that a concomitant malocclusion can be treated simultaneously, and that the treatment result is permanent. Interdisciplinary cooperation between orthodontists and other dentists, such as oral surgeons, prosthodontists and general practitioners, appears to be of increasing importance in achieving high-quality treatment results in such cases.


title: A Raspberry Pi and LXC Based Distributed Computing Testbed

abstract: With the development of the teaching and research of big data processing and distributed algorithms, there emerges an increasing need to leverage low-cost hardware to build the distributed computing test environment for the purpose of the algorithm validation and experiment simulation. In this paper, we propose a distributed computing testbed using the affordable Raspberry Pi board as the basic computing unit. We develop a cluster platform by building and assembling the units, and design a containerized service-computing framework by merging LXC and service computing techniques. Three kinds of services are introduced and configured to implement specific workflow orchestration on demand. For convenience, we develop a visualized user interface to manage cluster, deploy service containers, and manage workflows. We present the containerized service workflow that links the components of data source, distributed data processing and visualization, to validate the effectiveness of the proposed testbed.


Accessory Penis and Scrotum in a Male Infant