title: How Many Folders Do You Really Need?: Classifying Email into a Handful of Categories

abstract: Email classification is still a mostly manual task. Consequently, most Web mail users never define a single folder. Recently however, automatic classification offering the same categories to all users has started to appear in some Web mail clients, such as AOL or Gmail. We adopt this approach, rather than previous (unsuccessful) personalized approaches because of the change in the nature of consumer email traffic, which is now dominated by (non-spam) machine-generated email. We propose here a novel approach for (1) automatically distinguishing between personal and machine-generated email and (2) classifying messages into latent categories, without requiring users to have defined any folder. We report how we have discovered that a set of 6"latent"categories (one for human- and the others for machine-generated messages) can explain a significant portion of email traffic. We describe in details the steps involved in building a Web-scale email categorization system, from the collection of ground-truth labels, the selection of features to the training of models. Experimental evaluation was performed on more than 500 billion messages received during a period of six months by users of Yahoo mail service, who elected to be part of such research studies. Our system achieved precision and recall rates close to 90% and the latent categories we discovered were shown to cover 70% of both email traffic and email search queries. We believe that these results pave the way for a change of approach in the Web mail industry, and could support the invention of new large-scale email discovery paradigms that had not been possible before.


title: Simulation-based evaluation of MIMO antenna systems in car-to-car communication

abstract: The multipath propagation channel strongly influences the performance of a wireless system. For car-to-car communication systems, reliable communication for safety-related applications is essential. This paper elaborates on different antenna configurations consisting of six antennas mounted at different positions on the car. With this, also MIMO communication links can be established in order to increase data rate or communication reliability. These configurations will then be evaluated in terms of channel capacity and bit error rate in a typical car-to-car scenario at an urban intersection. From this, the achievable communication distance will be derived and compared with required stopping distances of the cars involved.


title: Joint virtual machine assignment and traffic engineering for green data center networks

abstract: The popularization of cloud computing brings emergency concern to the energy consumption in big data centers. Besides the servers, the energy consumed by the network in a data center is also considerable. Existing works for improving the network energy efficiency are mainly focused on traffic engineering, i.e., consolidating flows and switching off unnecessary devices, which fails to comprehensively consider the unique features in data centers. In this paper, we advocate a joint optimization for achieving energy efficiency of data center networks by proposing a unified optimization framework. In this framework, we consider to take advantage of the application characteristics and topology features, and to integrate virtual machine assignment and traffic engineering. Under this framework, we then devise two efficient algorithms, TE VMA and TER, for assigning virtual machines and routing traffic flows respectively. Knowing the ncommunication patterns of the applications, the TE VMA algorithm is purposeful and can generate desirable traffic conditions for the next-step routing optimization. The TER algorithm makes full use of the hierarchical feature of the topology and is conducted on the multipath routing protocol. The performance of the overall framework is confirmed by both theoretical analysis and simulation results, where up to 50% total energy savings can be achieved, 20% more compared with traffic engineering only approaches.


title: Voluntary and Involuntary Attention Have Different Consequences: The Effect of Perceptual Difficult. Paper presented at the Annual Meeting of the Psychonomic Society

abstract: We propose that voluntary and involuntary attention affect different mechanisms and have different consequences for performance measured in reaction time. Voluntary attention enhances the perceptual representation whereas involuntary attention affects the tendency to respond to stimuli in one location or another. In a spatial-cueing paradigm, we manipulated perceptual difficulty and compared voluntary and involuntary attention. For the voluntary-attention condition, the spatial cue was predictive of the target location, whereas in the involuntary-attention condition it was not. Increasing perceptual difficulty increased the attention effect with voluntary attention, but decreased it with involuntary attention. Thus voluntary and involuntary attention have different consequences when perceptual difficulty is manipulated and hence are probably caused by different mechanisms.


title: Study and Development of Novel Feature Selection Framework for Heart Disease Prediction

abstract: Heart disease prediction is designed to support clinicians in their diagnosis. We proposed a method for classifying the heart disease data. The patient's record is predicted to find if they have symptoms of heart disease through Data mining. It is essential to find the best fit classification algorithm that has greater accuracy on classification in the case of heart disease prediction. Since the data is huge attribute selection method used for reducing the dataset. Then the reduced data is given to the classification .In the Investigation, the hybrid attribute selection method combining CFS and Filter Subset Evaluation gives better accuracy for classification. We also propose a new feature selection method algorithm which is the hybrid method combining CFS and Bayes Theorem. The proposed algorithm provides better accuracy compared to the traditional algorithm and the hybrid Algorithm CFS+FilterSubsetEval.


title: Gaze3D: framework for gaze analysis on 3D reconstructed scenes

abstract: An ongoing challenge with head-mounted eye-trackers is how to analyze the data from multiple individuals looking at the same scene. Our work focuses on static scenes. Previous approaches involve capturing a high resolution panorama of the scene and then mapping the fixations from all viewers onto this panorama. However such approaches are limited as they typically restrict all viewers to observe the scene from the same stationary vantage point. We present a system which incorporates user-perspective gaze data with a 3D reconstruction of the scene. The system enables the visualization of gaze data from multiple viewers on a single 3D model of the scene instead of multiple 2D panoramas. The subjects are free to move about the scene as they see fit which leads to more natural task performance. Furthermore since it is not necessary to warp the scene camera video into a flat panorama, our system preserves the relative positions of the objects in the scene during the visualization process. This gives better insight into the viewer's problem solving and search task strategies. Our system has high applicability in complex static environments such as crime scenes and marketing studies in retail stores.


title: A Bivariate Chebyshev Spectral Collocation Quasilinearization Method for Nonlinear Evolution Parabolic Equations

abstract: This paper presents a new method for solving higher order nonlinear evolution partial differential equations (NPDEs). The method combines quasilinearisation, the Chebyshev spectral collocation method, and bivariate Lagrange interpolation. In this paper, we use the method to solve several nonlinear evolution equations, such as the modified KdV-Burgers equation, highly nonlinear modified KdV equation, Fisher's equation, Burgers-Fisher equation, Burgers-Huxley equation, and the Fitzhugh-Nagumo equation. The results are compared with known exact analytical solutions from literature to confirm accuracy, convergence, and effectiveness of the method. There is congruence between the numerical results and the exact solutions to a high order of accuracy. Tables were generated to present the order of accuracy of the method; convergence graphs to verify convergence of the method and error graphs are presented to show the excellent agreement between the results from this study and the known results from literature.


2007 DARPA Urban Challenge The


title: Accelerating Apache Spark Big Data Analysis with FPGAs

abstract: Apache Spark has become one of the most popular engines for big data processing. Spark provides a platform-independent, high-abstraction programming paradigm for large-scale data processing by leveraging the Java framework. Though it provides software portability across various machines, Java also limits the performance of distributed environments, such as Spark. While it may be unrealistic to rewrite platforms like Spark in a faster language, a more viable approach to mitigate its poor performance is to accelerate the computations while still working within the Java-based framework. This paper demonstrates the feasibility of incorporating FPGA acceleration into Spark,, uses a MapReduce implementation of the k-means clustering algorithm to show that acceleration is possible even when using a hardware platform that is not well-optimized for performance. An important feature of our approach is that the use of FPGAs is completely transparent to the user through the use of library functions, which is a common way by which users access functions provided by Spark. Power users can further develop other computations using high-level synthesis.


title: High performance ultra-low-precision convolutions on mobile devices

abstract: Many applications of mobile deep learning, especially real-time computer vision workloads, are constrained by computation power. This is particularly true for workloads running on older consumer phones, where a typical device might be powered by a single- or dual-core ARMv7 CPU. We provide an open-source implementation and a comprehensive analysis of (to our knowledge) the state of the art ultra-low-precision (<4 bit precision) implementation of the core primitives required for modern deep learning workloads on ARMv7 devices, and demonstrate speedups of 4x-20x over our additional state-of-the-art float32 and int8 baselines.


title: Transferring Visual Prior for Online Object Tracking

abstract: Visual prior from generic real-world images can be learned and transferred for representing objects in a scene. Motivated by this, we propose an algorithm that transfers visual prior learned offline for online object tracking. From a collection of real-world images, we learn an overcomplete dictionary to represent visual prior. The prior knowledge of objects is generic, and the training image set does not necessarily contain any observation of the target object. During the tracking process, the learned visual prior is transferred to construct an object representation by sparse coding and multiscale max pooling. With this representation, a linear classifier is learned online to distinguish the target from the background and to account for the target and background appearance variations over time. Tracking is then carried out within a Bayesian inference framework, in which the learned classifier is used to construct the observation model and a particle filter is used to estimate the tracking result sequentially. Experiments on a variety of challenging sequences with comparisons to several state-of-the-art methods demonstrate that more robust object tracking can be achieved by transferring visual prior.


Improving Mask Learning Based Speech Enhancement System with Restoration Layers and Residual Connection.


title: Categorical Data Clustering using Cosine based similarity for Enhancing the Accuracy of Squeezer Algorithm

abstract: Clustering categorical data is the major challenge in data mining. Direct comparison of categorical data is not possible as in numerical data, understanding the province of categorical data help to form good quality clusters. This paper presents an algorithm Enhanced Squeezer, which incorporates Data Intensive Similarity Measure for Categorical data (DISC) in Squeezer algorithm. DISC measure, cluster data by understanding domain of the dataset, thus clusters formed are not purely based on frequency distribution as many similarity measures do. Clusters formed using Enhanced Squeezer algorithms are intensive and accurate.


title: AC voltage regulation by switch mode PWM voltage controller

abstract: Voltage sag is an important power quality problem. It may affect domestic, industrial and commercial customers. Voltage sags may either be decreasing or increasing due to faults or change in loads. In this paper a switch mode AC to AC regulator is investigated to maintain constant voltage across the domestic appliance during the voltage deviation from the rated value. Such deviation may occur due to change in load or change in input voltage due to voltage sag of the system itself. The proposed system incorporates insulated gate bipolar transistor (IGBT) with high frequency switching technology. The Pulse Width Modulation (PWM) controls the ON/OFF time (Duty cycle) of switching devices (IGBTs) of this regulator. By regulating duty cycle of the control signal, output voltage can be maintained almost constant for wide range of input voltage variation. Simulation results show constant voltage can be achieved in either cases of increasing or decreasing input voltage as long as it is within specified limit.


title: Real-time visual tracking using compressive sensing

abstract: The l 1 tracker obtains robustness by seeking a sparse representation of the tracking object via l 1 norm minimization. However, the high computational complexity involved in the l 1 tracker may hamper its applications in real-time processing scenarios. Here we propose Real-time Com-pressive Sensing Tracking (RTCST) by exploiting the signal recovery power of Compressive Sensing (CS). Dimensionality reduction and a customized Orthogonal Matching Pursuit (OMP) algorithm are adopted to accelerate the CS tracking. As a result, our algorithm achieves a realtime speed that is up to 5,000 times faster than that of the l 1 tracker. Meanwhile, RTCST still produces competitive (sometimes even superior) tracking accuracy compared to the l 1 tracker. Furthermore, for a stationary camera, a refined tracker is designed by integrating a CS-based background model (CSBM) into tracking. This CSBM-equipped tracker, termed RTCST-B, outperforms most state-of-the-art trackers in terms of both accuracy and robustness. Finally, our experimental results on various video sequences, which are verified by a new metric — Tracking Success Probability (TSP), demonstrate the excellence of the proposed algorithms.


Effect of processing of cereals on rumen fermentation, digestibility, rumination time, and firmness of subcutaneous fat in lambs


Does the Directivity of a Virtual Agent's Speech Influence the Perceived Social Presence?


title: A Decentralized Coordination Control Method for Parallel Bidirectional Power Converters in a Hybrid AC–DC Microgrid

abstract: In the hybrid ac–dc microgrid, the ac and dc subgrids are connected by bidirectional power converters (BPCs) that play an important role in the load power sharing and power interaction between the ac and dc subgrids. The coordination control and circulating current suppression for the parallel BPC system are very challenging. In this paper, a decentralized coordination control method is proposed for parallel three-phase BPCs, which can suppress the circulating currents, realize proper power interaction, and achieve overall load power sharing in both the grid-connected mode and the islanded mode. The performance of the proposed control methods is verified by the real-time hardware-in-loop tests.


title: Energy Management for Renewable Microgrid in Reducing Diesel Generators Usage With Multiple Types of Battery

abstract: Using solar photovoltaics (PV) to help a microgrid (MG) operator for cost reduction may not be a straightforward problem due to the intermittent nature of PV power generation and unpredictable load demands. One potential way to address this challenge is to use batteries that can store the surplus PV energy whenever possible and supply the energy back to the MG when needed. In this context, this paper proposes a battery energy management system (BEMS) for an MG, in which PVs and diesel generators (DGs) are the primary sources of electricity. The novelty of the proposed BEMS lies within the energy management of multiple types of batteries’ characteristics and the reduction of DGs’ operating hours simultaneously. Furthermore, the proposed BEMS also takes into account different characteristics of the batteries when controlling the charging and discharging decision to extend the battery lifetime. Real-world data of MG load and PV power generations are used to verify the effectiveness of the proposed BEMS. Through numerical simulation case studies, it is demonstrated that the proposed BEMS is capable of achieving the following: reduction in DGs’ operating hours, reduction in PV power fluctuations, and concurrent management of multiple batteries of different characteristics and extension of battery lifetime by controlling battery charge and discharge rate.


title: Low Cost Convolutional Neural Network Accelerator Based on Bi-Directional Filtering and Bit-Width Reduction

abstract: This paper presents a low-area and energy-efficient hardware accelerator for the convolutional neural networks (CNNs). Based on the multiply-accumulate-based architecture, three design techniques are proposed to reduce the hardware cost of the convolutional computations. First, to reduce the computational cost of convolutions, an adaptive bit-width reduction combined with near-zero skipping is proposed based on differential input method (DIM). The DIM-based design technique can reduce 62.5% of operation bit-width and improve 17.0% of activation sparsity with almost ignorable CNN accuracy degradation. Second, it has been found that adopting a bi-directional filtering window in a CNN accelerator can considerably reduce the energy for data movement with a much smaller number of memory accesses. To expedite the bi-directional filtering operations, we also propose a bi-directional first-input-first-output (bi-FIFO). With SRAM bit-cell layout manner, the proposed bi-FIFO facilitates fast data re-distribution with area and energy efficiency. To verify the effectiveness of the proposed techniques, the AlexNet accelerator has been designed. The numerical results show that the proposed adaptive bit-width reduction scheme achieves 34.6% and 58.2% of area and energy savings, respectively. The bi-FIFO-based accelerator also achieves 32.8% improved processing time.


title: Adam: A Method for Stochastic Optimization

abstract: We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.


title: Intrusion detection and Big Heterogeneous Data: a Survey

abstract: Intrusion Detection has been heavily studied in both industry and academia, but cybersecurity analysts still desire much more alert accuracy and overall threat analysis in order to secure their systems within cyberspace. Improvements to Intrusion Detection could be achieved by embracing a more comprehensive approach in monitoring security events from many different heterogeneous sources. Correlating security events from heterogeneous sources can grant a more holistic view and greater situational awareness of cyber threats. One problem with this approach is that currently, even a single event source (e.g., network traffic) can experience Big Data challenges when considered alone. Attempts to use more heterogeneous data sources pose an even greater Big Data challenge. Big Data technologies for Intrusion Detection can help solve these Big Heterogeneous Data challenges. In this paper, we review the scope of works considering the problem of heterogeneous data and in particular Big Heterogeneous Data. We discuss the specific issues of Data Fusion, Heterogeneous Intrusion Detection Architectures, and Security Information and Event Management (SIEM) systems, as well as presenting areas where more research opportunities exist. Overall, both cyber threat analysis and cyber intelligence could be enhanced by correlating security events across many diverse heterogeneous sources.


Process Harmonization Phase Model in Post Merger Integration


title: Promoting Positive Affect through Smartphone Photography

abstract: AbstractBackgroundWith the increasing quality of smartphone cameras, taking photos has become ubiquitous. This paper investigates how smartphone photography can be leveraged to help individuals increase their positive affect.MethodsApplying findings from positive psychology, we designed and conducted a 4-week study with 41 participants. Participants were instructed to take one photo every day in one of the following three conditions: a selfie photo with a smiling expression, a photo of something that would make oneself happy and a photo of something that would make another person happy. ::: FindingsAfter 3 weeks, participants’ positive affect in all conditions increased. Those who took photos to make others happy became much less aroused. Qualitative results showed that those in the selfie group observed changes in their smile over time; the group taking photos to improve their own affect became more reflective and those taking photos for others found that connecting with family members and friends helped to relieve stress.ConclusionsThe findings can offer insights for designers to create systems that enhance emotional well-being.


title: Toward Open-Set Face Recognition

abstract: Much research has been conducted on both face identification and face verification, with greater focus on the latter. Research on face identification has mostly focused on using closed-set protocols, which assume that all probe images used in evaluation contain identities of subjects that are enrolled in the gallery. Real systems, however, where only a fraction of probe sample identities are enrolled in the gallery, cannot make this closed-set assumption. Instead, they must assume an open set of probe samples and be able to reject/ignore those that correspond to unknown identities. In this paper, we address the widespread misconception that thresholding verification-like scores is a good way to solve the open-set face identification problem, by formulating an open-set face identification protocol and evaluating different strategies for assessing similarity. Our open-set identification protocol is based on the canonical labeled faces in the wild (LFW) dataset. Additionally to the known identities, we introduce the concepts of known unknowns (known, but uninteresting persons) and unknown unknowns (people never seen before) to the biometric community. We compare three algorithms for assessing similarity in a deep feature space under an open-set protocol: thresholded verification-like scores, linear discriminant analysis (LDA) scores, and an extreme value machine (EVM) probabilities. Our findings suggest that thresholding EVM probabilities, which are open-set by design, outperforms thresholding verification-like scores.


title: To Err Is Robot: How Humans Assess and Act toward an Erroneous Social Robot

abstract: We conducted a user study for which we purposefully programmed faulty behavior into a robot's routine. It was our aim to explore if participants rate the faulty robot different from an error-free robot and which reactions people show in interaction with a faulty robot. The study was based on our previous research on robot errors where we detected typical error situations and the resulting social signals of our participants during social human-robot interaction. In contrast to our previous work, where we studied video material in which robot errors occurred unintentionally, in the herein reported user study, we purposefully elicited robot errors to further explore the human interaction partners' social signals following a robot error. Our participants interacted with a human-like NAO, and the robot either performed faulty or free from error. First, the robot asked the participants a set of predefined questions and then it asked them to complete a couple of LEGO building tasks. After the interaction, we asked the participants to rate the robot's anthropomorphism, likability, and perceived intelligence. We also interviewed the participants on their opinion about the interaction. Additionally, we video-coded the social signals participants showed during their interaction with the robot as well as the answers they provided the robot with. Our results show that participants liked the faulty robot significantly better than the robot that interacted flawlessly. We did not find significant differences in people's rating of the robot's anthropomorphism and perceived intelligence. The qualitative data confirmed the questionnaire results in showing that although the participants recognized the robot's mistakes, they did not necessarily reject the erroneous robot. The annotations of the video data further showed that gaze shifts (e.g., from and object to the robot or vice versa) and laughter are typical reactions to unexpected robot behavior. In contrast to existing research, we assess dimensions of user experience that have not been considered so far and we analyze the reactions users express when a robot makes a mistake. Our results show that decoding a human's social signals can help the robot understand that there is an error and subsequently react accordingly.


title: One Single Deep Bidirectional LSTM Network for Word Sense Disambiguation of Text Data

abstract: Due to recent technical and scientific advances, we have a wealth of information hidden in unstructured text data such as offline/online narratives, research articles, and clinical reports. To mine these data properly, attributable to their innate ambiguity, a Word Sense Disambiguation (WSD) algorithm can avoid numbers of difficulties in Natural Language Processing (NLP) pipeline. However, considering a large number of ambiguous words in one language or technical domain, we may encounter limiting constraints for proper deployment of existing WSD models. This paper attempts to address the problem of one-classifier-per-one-word WSD algorithms by proposing a single Bidirectional Long Short-Term Memory (BLSTM) network which by considering senses and context sequences works on all ambiguous words collectively. Evaluated on SensEval-3 benchmark, we show the result of our model is comparable with top-performing WSD algorithms. We also discuss how applying additional modifications alleviates the model fault and the need for more training data.


title: Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis

abstract: An increasing number of sensors on mobile, Internet of things (IoT), and wearable devices generate time-series measurements of physical activities. Though access to the sensory data is critical to the success of many beneficial applications such as health monitoring or activity recognition, a wide range of potentially sensitive information about the individuals can also be discovered through access to sensory data and this cannot easily be protected using traditional privacy approaches. In this paper, we propose a privacy-preserving sensing framework for managing access to time-series data in order to provide utility while protecting individuals' privacy. We introduce Replacement AutoEncoder, a novel feature-learning algorithm which learns how to transform discriminative features of multi-variate time-series that correspond to sensitive inferences, into some features that have been more observed in non-sensitive inferences, to protect users' privacy. This efficiency is achieved by defining a user-customized objective function for deep autoencoders. Replacement will not only eliminate the possibility of recognition sensitive inferences, it also eliminates the possibility of detecting the occurrence of them, that is the main weakness of other approaches such as filtering or randomization. We evaluate the efficacy of the algorithm with an activity recognition task in a multi-sensing environment using extensive experiments on three benchmark datasets. We show that it can retain the recognition accuracy of state-of-the-art techniques while simultaneously preserving the privacy of sensitive information. Finally, we utilize the GANs for detecting the occurrence of replacement, after releasing data, and show that this can be done only if the adversarial network is trained on the users' original data.


title: Learning hierarchical representations for face verification with convolutional deep belief networks

abstract: Most modern face recognition systems rely on a feature representation given by a hand-crafted image descriptor, such as Local Binary Patterns (LBP), and achieve improved performance by combining several such representations. In this paper, we propose deep learning as a natural source for obtaining additional, complementary representations. To learn features in high-resolution images, we make use of convolutional deep belief networks. Moreover, to take advantage of global structure in an object class, we develop local convolutional restricted Boltzmann machines, a novel convolutional learning model that exploits the global structure by not assuming stationarity of features across the image, while maintaining scalability and robustness to small misalignments. We also present a novel application of deep learning to descriptors other than pixel intensity values, such as LBP. In addition, we compare performance of networks trained using unsupervised learning against networks with random filters, and empirically show that learning weights not only is necessary for obtaining good multilayer representations, but also provides robustness to the choice of the network architecture parameters. Finally, we show that a recognition system using only representations obtained from deep learning can achieve comparable accuracy with a system using a combination of hand-crafted image descriptors. Moreover, by combining these representations, we achieve state-of-the-art results on a real-world face verification database.


title: Clothing Co-Parsing by Joint Image Segmentation and Labeling

abstract: This paper aims at developing an integrated system of clothing co-parsing, in order to jointly parse a set of clothing images (unsegmented but annotated with tags) into semantic configurations. We propose a data-driven framework consisting of two phases of inference. The first phase, referred as "image co-segmentation", iterates to extract consistent regions on images and jointly refines the regions over all images by employing the exemplar-SVM (ESVM) technique [23]. In the second phase (i.e. "region colabeling"), we construct a multi-image graphical model by taking the segmented regions as vertices, and incorporate several contexts of clothing configuration (e.g., item location and mutual interactions). The joint label assignment can be solved using the efficient Graph Cuts algorithm. In addition to evaluate our framework on the Fashionista dataset [30], we construct a dataset called CCP consisting of 2098 high-resolution street fashion photos to demonstrate the performance of our system. We achieve 90.29% / 88.23% segmentation accuracy and 65.52% / 63.89% recognition rate on the Fashionista and the CCP datasets, respectively, which are superior compared with state-of-the-art methods.


title: A SiGe-based Ku-band digital beamforming array for high speed on-the-move comm/radar system

abstract: A digital beamforming (DBF) prototype that uses compact and low cost Balanced Antipodal Vivaldi Antenna (BAVA) array and SiGe based-receiver is presented. An example is given for a Ku-band DBF system that demonstrates simultaneous reception of two or more full motion videos in a portable device.


title: Integration of variable generation: Capacity value and evaluation of flexibility

abstract: As integration of variable generation continues to grow rapidly in power systems globally, system planners are seeking new tools to understand the role of variable output generators and the challenges experienced with their integration. The North American Electric Reliability Corporation (NERC) has established a task force to examine the integration of variable generation. This paper details the achievements to date and outlines ongoing efforts from Task 1.2 on the capacity value of variable generation and from Task 1.4 on the concept of flexibility in power systems and options for its definition. Arising from international collaboration with the International Energy Agency, a discussion on the definition of flexible resources is presented. A potential metric for flexibility offered by conventional plant is developed and applied to a test system.


title: Uneasy lies the head that wears the crown: the link between guilt proneness and leadership.

abstract: We propose that guilt proneness is a critical characteristic of leaders and find support for this hypothesis across 3 studies. Participants in the first study rated a set of guilt-prone behaviors as more indicative of leadership potential than a set of less guilt-prone behaviors. In a follow-up study, guilt-prone participants in a leaderless group task engaged in more leadership behaviors than did less guilt-prone participants. In a third, and final, study, we move to the field and analyze 360° feedback from a group of young managers working in a range of industries. The results indicate that highly guilt-prone individuals were rated as more capable leaders than less guilt-prone individuals and that a sense of responsibility for others underlies the positive relationship between guilt proneness and leadership evaluations.


title: Aggregating OPC UA servers for monitoring manufacturing systems and mobile work machines

abstract: An aggregating server providing data access to multiple data sources is a software architecture that is often applied in industrial applications. This paper presents a design of an aggregating server based on OPC UA technology and tests it with two different experimental applications, one about production control of a flexible manufacturing system and another about remote monitoring of mobile work machines. The presented design enables transparent access to the data in the address space of the aggregating server through utilization of address space transformations. The transformations can be either configured with rules or be programmed. Feasibility of the design of the aggregating server and the applications was evaluated with experimental implementations containing both simulated devices and real work machines.


title: Optimization of Belt-Type Electrostatic Separation of Triboaerodynamically Charged Granular Plastic Mixtures

abstract: Electrostatic separation is frequently employed for the selective sorting of conducting and insulating materials for waste electric and electronic equipment. In a series of recent papers, the authors have presented several novel triboelectrostatic separation installations for the recycling of the main categories of plastics contained in information technology wastes. The aim of the present work is to optimize the design and the operation of such an installation, composed of a belt-type electrostatic separator associated to a propeller-type tribocharging device. The experimental design method is employed for modeling and optimizing the separation of a mixture of high-impact polystyrene and acrylonitrile butadiene styrene granules originating from shredded out-of-use computer cases. A distinct experiment is carried out on three synthetic mixtures of virgin plastic granules: 75% polyamide (PA)+ 25% polycarbonate (PC), 50% PA+ 50% PC, and 25% PA + 75% PC. The best results are obtained for the mixtures containing equal quantities of the two constituents.


title: Electricity Price Forecasting With Extreme Learning Machine and Bootstrapping

abstract: Artificial neural networks (ANNs) have been widely applied in electricity price forecasts due to their nonlinear modeling capabilities. However, it is well known that in general, traditional training methods for ANNs such as back-propagation (BP) approach are normally slow and it could be trapped into local optima. In this paper, a fast electricity market price forecast method is proposed based on a recently emerged learning method for single hidden layer feed-forward neural networks, the extreme learning machine (ELM), to overcome these drawbacks. The new approach also has improved price intervals forecast accuracy by incorporating bootstrapping method for uncertainty estimations. Case studies based on chaos time series and Australian National Electricity Market price series show that the proposed method can effectively capture the nonlinearity from the highly volatile price data series with much less computation time compared with other methods. The results show the great potential of this proposed approach for online accurate price forecasting for the spot market prices analysis.


title: Assembly of the endoplasmic reticulum phospholipid bilayer: the phosphatidylcholine transporter

abstract: Phospholipids are synthesized and concomitantly inserted on the cytoplasmic surface of the endoplasmic reticulum. Assembly of the phospholipid bilayer requires translocation to the lumenal monolayer. The hypothesis that rat liver microsomes contain a protein transporter, or "flippase," for phosphatidylcholine was tested by measuring the transport of sn-1,2,-dibutyroylphosphatidylcholine (diC4PC). This homolog retains the polar head group, the portion of the phospholipid unable to undergo spontaneous transmembrane movement in vesicles, and its water solubility permits application of standard transport methods. DiC4PC entered the lumenal compartment of microsomal vesicles. Transport was saturable and was dependent on time, amount of microsomes, and an intact permeability barrier. DiC4PC transport was inhibited by structural analogs (but not by sn-2,3-diC4PC) and by treatment of microsomes with proteases, N-ethylmaleimide, and trinitrobenzenesulfonic acid. These data suggest that the transmicrosomal movement of diC4PC is protein mediated. DiC4PC was not transported across PC vesicles or red cell membranes, where PC translocation is slow.


title: On the Extended Relationships Among EVM, BER and SNR as Performance Metrics

abstract: In this paper, we relate the error vector magnitude (EVM) bit error rate (BER) and signal to noise ratio (SNR). We also present the fact that with such relationship it would be possible to predict or in cases substitute EVM in places of BER or even SNR. In doing so, we first define EVM with normalization so that the definition stands for multi-modulation systems, viz. binary phas shift keying (BPSK), quadrature phase shift keying (QPSK) etc. We also compare among the different performance metrics and show that EVM can be equivalently useful as signal to noise ratio and bit error rate. The relationships are based on stream based communication systems. A few Monte Carlo simulations are carried out to illustrate the performance of EVM based on these relationships.


title: Honeypot Scheme for Distributed Denial-of-Service

abstract: Honeypots are physical or virtual machines successfully used as intrusion detection tools to detect worm-infected hosts. Denial of service (DoS) attack consumes the resources of a remote client or network itself, there by denying or degrading the service to the legitimate users. In a DoS defense mechanism, a honeypot acts as a detective server among the pool of servers in a specific network; where any packet received by the honeypot is most likely a packet from an attacker. This paper points out a number of drawbacks such as Legitimate Attacker and Link Unreachable problem in the existing honeypot schemes. This paper proposes a new efficient honeypot model to solve all the existing problems by opening a virtual communication port for any specific communication between an authorized client and server; and by providing facility to act as an Active Server (AS) for any honeypot.


title: Deep Ranking for Person Re-Identification via Joint Representation Learning

abstract: This paper proposes a novel approach to person re-identification, a fundamental task in distributed multi-camera surveillance systems. Although a variety of powerful algorithms have been presented in the past few years, most of them usually focus on designing hand-crafted features and learning metrics either individually or sequentially. Different from previous works, we formulate a unified deep ranking framework that jointly tackles both of these key components to maximize their strengths. We start from the principle that the correct match of the probe image should be positioned in the top rank within the whole gallery set. An effective learning-to-rank algorithm is proposed to minimize the cost corresponding to the ranking disorders of the gallery. The ranking model is solved with a deep convolutional neural network (CNN) that builds the relation between input image pairs and their similarity scores through joint representation learning directly from raw image pixels. The proposed framework allows us to get rid of feature engineering and does not rely on any assumption. An extensive comparative evaluation is given, demonstrating that our approach significantly outperforms all the state-of-the-art approaches, including both traditional and CNN-based methods on the challenging VIPeR, CUHK-01, and CAVIAR4REID datasets. In addition, our approach has better ability to generalize across datasets without fine-tuning.


title: A relaxed ranking-based factor model for recommender system from implicit feedback

abstract: Implicit feedback based recommendation has recently been an important task with the accumulated user-item interaction data. However, it is very challenging to produce recommendations from implicit feedback due to the sparseness of data and the lack of negative feedback/rating. Although various factor models have been proposed to tackle this problem, they either focus on rating prediction that may lead to inaccurate top-k recommendations or are dependent on the sampling of negative feedback that often results in bias. To this end, we propose a Relaxed Ranking-based Factor Model, RRFM, to relax pairwise ranking into a SVM-like task, where positive and negative feedbacks are separated by the soft boundaries, and their non-separate property is employed to capture the characteristic of unobserved data. A smooth and scalable algorithm is developed to solve group- and instance- level's optimization and parameter estimation. Extensive experiments based on real-world datasets demonstrate the effectiveness and advantage of our approach.


title: A Space Vector PWM Scheme for Multilevel Inverters Based on Two-Level Space Vector PWM

abstract: Multilevel inverters are increasingly being used in high-power medium voltage applications due to their superior performance compared to two-level inverters. Among various modulation techniques for a multilevel inverter, the space vector pulsewidth modulation (SVPWM) is widely used. However, the implementation of the SVPWM for a multilevel inverter is complicated. The complexity is due to the difficulty in determining the location of the reference vector, the calculation of on-times, and the determination and selection of switching states. This paper proposes a general SVPWM algorithm for multilevel inverters based on standard two-level SVPWM. Since the proposed multilevel SVPWM method uses two-level modulation to calculate the on-times, the computation of on-times for an n-level inverter becomes easier. The proposed method uses a simple mapping to achieve the SVPWM for a multilevel inverter. A general n-level implementation is explained, and experimental results are given for three-level and five-level inverters


title: 3D Human Action Recognition using Hu Moment Invariants and Euclidean Distance Classifier

abstract: This paper presents a new model of scale, rotation, and translations invariant interest point descriptor for human actions recognition. The descriptor, HMIV (Hu Moment Invariants on Videos) is used for solving surveillance camera recording problems under different conditions of side, position, direction and illumination. The proposed approach deals with raw input human action video sequences. Seven Hu moments are computed for extracting human action features and for storing them in a 1D vector which is constringed as one mean value for all the frames’ moments. The moments are invariant to scale, translation, or rotation, which is the robustness point of Hu moments algorithm. The experiments are evaluated using two different datasets; KTH and UCF101. The classification process is executed by calculating the Euclidean distance between the training and testing datasets. Human action with minimum distance will be selected as the winner matching action. The maximum classification accuracy in this work is 93.4% for KTH dataset and 92.11% for UCF101.


title: Dynamic consensus for secured vehicular ad hoc networks

abstract: Vehicular ad hoc networks provide vehicle-to-vehicle communications and safety-related applications to enhance the road safety. However, safety-related applications, like Local Danger Warning, need a high trust level in received messages. Indeed, decisions are made depending on these messages. To increase the trustworthiness, a consensus mechanism is used. With consensus, vehicles need to receive at least X times the same warning before making a decision. Because the consensus should meet real-time constraints of safety applications, a main issue is to set parameter X. In this paper, we investigate the problem of consensus and propose a generic model to define decision method involved in consensus. Then, we propose to dynamically set the consensus parameter according to the neighborhood density and the warning criticalness. The proposed mechanism enhances the “majority of freshest X with threshold” decision method [1] and is analytically modeled. This context-aware and data-centric security mechanism ensures a quick and correct decision. We present some simulation results that validate our model.


InvisiMem: Smart Memory for Trusted Computing


title: Robust Cross-Domain Sentiment Analysis for Low-Resource Languages

abstract: While various approaches to domain adaptation exist, the majority of them requires knowledge of the target domain, and additional data, preferably labeled. For a language like English, it is often feasible to match most of those conditions, but in low-resource languages, it presents a problem. We explore the situation when neither data nor other information about the target domain is available. We use two samples of Danish, a low-resource language, from the consumer review domain (film vs. company reviews) in a sentiment analysis task. We observe dramatic performance drops when moving from one domain to the other. We then introduce a simple offline method that makes models more robust towards unseen domains, and observe relative improvements of more than 50%.


Analyzing the Correlation of Game Worlds and Built Reality : Depiction , Function and Mediality of Architecture and Urban Landscapes


title: Adopting the Internet of Things technologies in health care systems

abstract: Internet of Things based health care systems play a significant role in Information and Communication Technologies and has contribution in development of medical information systems. The developing of IoT-based health care systems must ensure and increase the safety of patients, the quality of life and other health care activities. The tracking, tracing and monitoring of patients and health care actors activities are challenging research directions. In this paper we propose a general architecture of a health care system for monitoring of patients at risk in smart Intensive Care Units. The system advices and alerts in real time the doctors/medical assistants about the changing of vital parameters or the movement of the patients and also about important changes in environmental parameters, in order to take preventive measures.


ORGANIZATIONAL CULTURE AND ITS IMPACT ON THE QUALITY OF ACCOUNTING INFORMATION SYSTEMS


Ant Colonies for the Traveling Salesman Problem


title: Explicit and implicit affiliation motives predict verbal and nonverbal social behavior in sports competition

abstract: Abstract Objectives The present study investigated the predictive value of the explicit and implicit affiliation motive for social behavior in sport competitions. From an information processing perspective, an explicit motive is linked to verbal cues and respondent behavior. The implicit motive in turn is linked to nonverbal stimuli and operant behavior (McClelland, Koestner, & Weinberger, 1989; Schultheiss, 2008). Both respondent affiliative behavior (e.g., verbal interactions with teammates) and operant nonverbal social behavior (e.g., pleasant to opponents) can be observed in racquet sports team competitions. Design & Methods Fifty-two male racquet sportsmen completed the Personality Research Form (explicit affiliation motive) and the Operant Motive Test (implicit affiliation motive). Motive measures were used to predict social behavior during competitions using multiple regression analyses. To this aim real competitive matches were videotaped and analyzed. Results Results show that the explicit affiliation motive is associated with time spent in verbal team contact. The implicit affiliation motive, by contrast, is linked to pleasant nonverbal behavior shown toward opponents. Conclusions Findings suggest that implicit and explicit affiliation motives predict different kinds of social behavior in sports competition respectively. Indirect motive measures may be of additional predictive value for different behavior in real sports settings.


title: VFH+: reliable obstacle avoidance for fast mobile robots

abstract: This paper presents further improvements on the earlier vector field histogram (VFH) method developed by Borenstein-Koren (1991) for real-time mobile robot obstacle avoidance. The enhanced method, called VFH+, offers several improvements that result in smoother robot trajectories and greater reliability. VFH+ reduces some of the parameter tuning of the original VFH method by explicitly compensating for the robot width. Also added in VFH+ is a better approximation of the mobile robot trajectory, which results in higher reliability.


title: A Comparative Study of Different Segmentation Techniques for Brain Tumour Detection

abstract: Brain tumour detection is one of the challenging tasks in medical image processing. The present study discusses in detail the segmentation process by means of histogram clustering, Global thresholding, Watershed segmentation and edge based segmentation. Six MRI images from radiologists were collected and the experiments were conducted for statistical analysis also. A comparative study is made and the results are of great interest and practical utility.


title: Rape – myth and reality: a clinician's perspective

abstract: I am not an academic, policy-maker or criminal justice professional, but a clinician. I do not have an interest in this field, I have an expertise, particularly when considering the psychological consequences of rape and the potential implications of these for both legal proceedings and for the individual affected. I am grateful to Mr Heaton-Armstrong for outlining so eloquently the key issues from his perspective on this subject area. He questions the impartiality of politicians and others involved in this field; it is difficult to be impartial in an area where views expressed are so polarized, and at times extreme. Interestingly, in discussing ‘myths versus reality’ what emerges is how very different his reality is from my own; perhaps this goes some way to explaining our differing viewpoints. Before proceeding further, I would like to touch on issues of language. Firstly, I will speak from the perspective of women, but acknowledge that anyone can be a victim of rape. In the time allowed, however, it would be impossible to consider the differences between men and women, or adults and children. Victims (or survivors as many would prefer to be known) are just that; people who have experienced an adverse circumstance (and have lived through in spite of the adversity). Some may choose also to become complainants, but many do not. Results from the British Crime Survey in 2000 showed that for women aged between 16 and 59, 0.4% reported rape in the preceding year, which would equate to 61,000 victims. In the same survey 18% of sexual victimization incidents came to the attention of the police. Others suggest that between 5% and 25% of rapes are reported to the police. So is there a problem? My reality suggests that there are far more victims (say 61,000), than there are complaints made (roughly 13,000 a year); and many more victims than there are convictions (around 800 a year). Simple mathematics (and the sexual-offending literature) indicates that even the most prolific serial sex offenders are unlikely to account for these discrepancies, and so in my view there is a clear need for ‘something to be done’. Mr Heaton-Armstrong has raised issues of the falsely accused. How do we reliably judge who is falsely accused? Are false accusations of relevance? In my experience, whenever I attempt to discuss rape and its effects on the individual, the issue of false complaints arises – a different issue as, by the very nature of the complaint being false, the complainant is not a victim of the issue at hand. And, in attempting to quantify false complaints, we cannot rely on the figures relating to rape complaints or ‘not guilty’ verdicts; the judicial process is concerned with what can be proven, a different issue altogether. Given these factors, I do not intend to comment further on this separate area of research. In speaking to you tonight, I amgoing to concentrate on thematter in hand which is ‘Rape – myth and reality’. I am going to concentrate on what we know, rather than what we do not. So, myths and reality in rape; why is this important? When a rape victim turns to anyone and discloses what has happened, that individual (and society’s) response is important, and one determinant of the victim’s future psychological wellbeing. If the victim has chosen to disclose to the police and thus become a complainant, they are often aware that a series of events and interactions will follow. Those who they subsequently encounter through the judicial process will make judgements based on their own belief systems. Attitudes might affect questioning, evidential acquisition, victim response, decisions about proceeding and jurors’ deliberations. So beliefs held, when myth rather than reality, may have a crucial role to play in achieving justice; it is however the law that must be adhered to, not moral judgement. When hearing the word ‘rape’ many people will conjure up an image of a stranger attacking a woman at knife-point in a dark public place. In reality, this situation is extremely rare. Most rapes are committed by someone known to the victim. Common categories of perpetrators include current and ex husbands and partners, recent acquaintances, colleagues and people in positions of trust. This in turn means that many rapes take place in private – including in the victim’s home, in the perpetrator’s home, in workplaces – and in the context of daily lives. And yet, for some, including the victims themselves, such encounters are not always seen as rape, and indeed, it was not until 1991 that rape within marriage was criminalized. The issue of force is another area around which myths abound. Force is not a legal requirement in the crime of rape and many rapes do not involve additional physical assault or threat. Many victims do not resist and many are This presentation was given at the Friends’ Dinner of the British Academy of Forensic Sciences held at Apothecaries’ Hall, London, UK, on Thursday 11 March 2010.


title: Towards optimal binary code learning via ordinal embedding

abstract: Binary code learning, a.k.a., hashing, has been recently popular due to its high efficiency in large-scale similarity search and recognition. It typically maps high-dimensional data points to binary codes, where data similarity can be efficiently computed via rapid Hamming distance. Most existing unsupervised hashing schemes pursue binary codes by reducing the quantization error from an original real-valued data space to a resulting Hamming space. On the other hand, most existing supervised hashing schemes constrain binary code learning to correlate with pairwise similarity labels. However, few methods consider ordinal relations in the binary code learning process, which serve as a very significant cue to learn the optimal binary codes for similarity search. In this paper, we propose a novel hashing scheme, dubbed Ordinal Embedding Hashing (OEH), which embeds given ordinal relations among data points to learn the ranking-preserving binary codes. The core idea is to construct a directed unweighted graph to capture the ordinal relations, and then train the hash functions using this ordinal graph to preserve the permutation relations in the Hamming space. To learn such hash functions effectively, we further relax the discrete constraints and design a stochastic gradient decent algorithm to obtain the optimal solution. Experimental results on two large-scale benchmark datasets demonstrate that the proposed OEH method can achieve superior performance over the state-of-the-arts approaches. At last, the evaluation on query by humming dataset demonstrates the OEH also has good performance for music retrieval by using user's humming or singing.


title: Speed Sensorless Control of PMSM using Model Reference Adaptive System and RBFN

abstract: In the speed sensorless vector control system, the amended method of estimating the rotor speed about model reference adaptive system (MRAS) based on radial basis function neural network (RBFN) for PMSM sensorless vector control system was presented. Based on the PI regulator, the radial basis function neural network which is more prominent learning efficiency and performance is combined with MRAS. The reference model and the adjust model are the PMSM itself and the PMSM current, respectively. The proposed scheme only needs the error signal between q axis estimated current and q axis actual current. Then estimated speed is gained by using RBFN regulator which adjusted error signal. Comparing study of simulation and experimental results between this novel sensorless scheme and the scheme in reference literature, the results show that this novel method is capable of precise estimating the rotor position and speed under the condition of high or low speed. It also possesses good performance of static and dynamic.


title: The role of mitochondria in osteoarthritis

abstract: Recent experimental evidence has indicated that mitochondrial function is impaired in chondrocytes from patients with osteoarthritis (OA). In this Review, the authors discuss how this mitochondrial dysfunction might affect pathogenetic pathways in OA, such as cartilage degradation, and whether the mitochondria might provide useful prognostic or diagnostic biomarkers or act as potential targets for therapy in patients with OA.


title: The Big Five Personality Traits: Psychological Entities or Statistical Constructs?

abstract: The present study employed multivariate genetic item-level analyses to examine the ontology and the genetic and environmental etiology of the Big Five personality dimensions, as measured by the NEO Five Factor Inventory (NEO-FFI) [Costa and McCrae, Revised NEO personality inventory (NEO PI-R) and NEO five-factor inventory (NEO-FFI) professional manual, 1992; Hoekstra et al., NEO personality questionnaires NEO-PI-R, NEO-FFI: manual, 1996]. Common and independent pathway model comparison was used to test whether the five personality dimensions fully mediate the genetic and environmental effects on the items, as would be expected under the realist interpretation of the Big Five. In addition, the dimensionalities of the latent genetic and environmental structures were examined. Item scores of a population-based sample of 7,900 adult twins (including 2,805 complete twin pairs; 1,528 MZ and 1,277 DZ) on the Dutch version of the NEO-FFI were analyzed. Although both the genetic and the environmental covariance components display a 5-factor structure, applications of common and independent pathway modeling showed that they do not comply with the collinearity constraints entailed in the common pathway model. Implications for the substantive interpretation of the Big Five are discussed.


Crime and Punishment : An Economic Approach


title: Double 'pink tooth' associated with extensive internal root resorption after orthodontic treatment: a case report.

abstract: Advanced internal resorption affecting the crown of teeth may result in the appearance of a 'pink tooth', which, when located in the root canal, can perforate the external root surface. Therapeutically, this condition represents a clinical challenge and normally requires a combined endodontic and surgical focus. Presented herein are cases of double 'pink tooth' which appeared at different times after orthodontic treatment. In the most severe case, upon radiographic examination and computed tomography, the maxillary right central incisor presented an internal resorption, extending from the pulp chamber to the root middle third. After pulp removal, the debridement of the defect was performed using a 2.5% sodium hypochlorite solution, a #80 Kerr file, and an ultrasonic tip ST-17, aided by a surgical microscope. During the cleaning process, various sites of periodontal communication were identified. Upon controlling the hemorrhaging, the root canal was completely filled with White mineral trioxide aggregate. Within the 3-month follow-up treatment, a pink spot appeared on the maxillary left central incisor, which received conventional root canal therapy. Clinically and radiographically, over 18 months of follow up, both cases responded favorably to the proposed treatments. Therefore, it is important to monitor the patient due to the fact that pulp and periodontal sequelae can develop at varied moments after orthodontic treatment. Furthermore, with the current technology and biomaterials, it is possible to resolve cases with extensive internal perforating resorption through endodontic treatment.


title: Daylight impact on energy performance of internal lighting

abstract: Daylight in interior offers a considerable contribution of energy saving, the key is to design an integrated lighting system between the electric lighting system configuration and its control strategy. An optimal lighting system has to consider the different presence of daylight in the zones to arrange the most favorable luminaires layout. An automatic control system could be employed to switch or dim selected groups of luminaires. The paper suggests a criterion to evaluate the yearly daylight impact on energy performance of internal lighting according to daylight availability, the lighting system layout, and the control system arranged. A simplified evaluation by the daylight lumen method is useful for designers to estimate the operation costs in reference to the initial costs in preliminary design.


title: Reliable privacy-preserving communications for wireless ad hoc networks

abstract: We present a phantom-receiver-based routing scheme to enhance the anonymity of each source-destination pair (or contextual privacy) while using an adjustable amount of overhead. We also study how traditional network coding and opportunistic routing can leak contextual privacy. We then incorporated both network coding and opportunistic routing into our scheme for better network performance and show how we mitigate its vulnerability. Contrary to prior works, we allow the destination to anonymously submit an acknowledgment to the source for enhanced reliability. Performance analysis and simulations are used to demonstrate the efficacy of the proposed scheme against commonly considered traffic analysis attacks.


title: Copy Number Heterogeneity, Large Origin Tandem Repeats, and Interspecies Recombination in Human Herpesvirus 6A (HHV-6A) and HHV-6B Reference Strains

abstract: Quantitative PCR is a diagnostic pillar for clinical virology testing, and reference materials are necessary for accurate, comparable quantitation between clinical laboratories. Accurate quantitation of human herpesvirus 6A/B (HHV-6A/B) is important for detection of viral reactivation and inherited chromosomally integrated HHV-6A/B in immunocompromised patients. Reference materials in clinical virology commonly consist of laboratory-adapted viral strains that may be affected by the culture process. We performed next-generation sequencing to make relative copy number measurements at single nucleotide resolution of eight candidate HHV-6A and seven HHV-6B reference strains and DNA materials from the HHV-6 Foundation and Advanced Biotechnologies Inc. Eleven of 17 (65%) HHV-6A/B candidate reference materials showed multiple copies of the origin of replication upstream of the U41 gene by next-generation sequencing. These large tandem repeats arose independently in culture-adapted HHV-6A and HHV-6B strains, measuring 1,254 bp and 983 bp, respectively. The average copy number measured was between 5 and 10 times the number of copies of the rest of the genome. We also report the first interspecies recombinant HHV-6A/B strain with a HHV-6A backbone and a >5.5-kb region from HHV-6B, from U41 to U43, that covered the origin tandem repeat. Specific HHV-6A reference strains demonstrated duplication of regions at U1/U2, U87, and U89, as well as deletion in the U12-to-U24 region and the U94/U95 genes. HHV-6A/B strains derived from cord blood mononuclear cells from different laboratories on different continents with fewer passages revealed no copy number differences throughout the viral genome. These data indicate that large origin tandem duplications are an adaptation of both HHV-6A and HHV-6B in culture and show interspecies recombination is possible within the Betaherpesvirinae.IMPORTANCE Anything in science that needs to be quantitated requires a standard unit of measurement. This includes viruses, for which quantitation increasingly determines definitions of pathology and guidelines for treatment. However, the act of making standard or reference material in virology can alter its very accuracy through genomic duplications, insertions, and rearrangements. We used deep sequencing to examine candidate reference strains for HHV-6, a ubiquitous human virus that can reactivate in the immunocompromised population and is integrated into the human genome in every cell of the body for 1% of people worldwide. We found large tandem repeats in the origin of replication for both HHV-6A and HHV-6B that are selected for in culture. We also found the first interspecies recombinant between HHV-6A and HHV-6B, a phenomenon that is well known in alphaherpesviruses but to date has not been seen in betaherpesviruses. These data critically inform HHV-6A/B biology and the standard selection process.


LoopProf : Dynamic Techniques for Loop Detection and Profiling


title: Discrete Graph Hashing

abstract: Hashing has emerged as a popular technique for fast nearest neighbor search in gigantic databases. In particular, learning based hashing has received considerable attention due to its appealing storage and search efficiency. However, the performance of most unsupervised learning based hashing methods deteriorates rapidly as the hash code length increases. We argue that the degraded performance is due to inferior optimization procedures used to achieve discrete binary codes. This paper presents a graph-based unsupervised hashing model to preserve the neighborhood structure of massive data in a discrete code space. We cast the graph hashing problem into a discrete optimization framework which directly learns the binary codes. A tractable alternating maximization algorithm is then proposed to explicitly deal with the discrete constraints, yielding high-quality codes to well capture the local neighborhoods. Extensive experiments performed on four large datasets with up to one million samples show that our discrete optimization based graph hashing method obtains superior search accuracy over state-of-the-art un-supervised hashing methods, especially for longer codes.


title: An approach to reachability analysis for feed-forward ReLU neural networks

abstract: We study the reachability problem for systems implemented as feed-forward neural networks whose activation function is implemented via ReLU functions. We draw a correspondence between establishing whether some arbitrary output can ever be outputed by a neural system and linear problems characterising a neural system of interest. We present a methodology to solve cases of practical interest by means of a state-of-the-art linear programs solver. We evaluate the technique presented by discussing the experimental results obtained by analysing reachability properties for a number of benchmarks in the literature.


title: A Compact Dual-Band CPW-Fed Planar Monopole Antenna for 2.62–2.73 GHz Frequency Band, WiMAX and WLAN Applications

abstract: In this paper, we present a compact and low-profile monopole antenna with a simple structure for the 2.6-2.73 GHz frequency band, the Worldwide Interoperability for Microwave Access (WiMAX) and the Wireless Local Area Network (WLAN) applications. The first configuration of our antenna mainly consists by three radiating elements: inverted L-shaped Stub1, L-shaped Stub2 and a rectangle Stub3. By adjusting the lengths of the three Stubs, three resonant frequencies can be achieved and adjusted separately. Then, the assembled between Stub2 and Stub3 gives the final design of our proposed antenna with a small overall size of 20 mm A— 37 mm A— 1.56 mm. From the experimental results it is observed that, the antenna prototype has achieved two operating bandwidths (S 11 â‰¤ -10 dB): the first band from 2.62 to 2.73 GHz (110 MHz) and a second broadband from 3.02 to 7.30 GHz (4280 MHz) which combines WiMAX and WLAN applications. The antenna also exhibits an almost omnidirectional radiation patterns over the operating bands. The parameters which affect the performance of the antenna in terms of its frequency domain characteristics are studied in this paper. The details of the monopole antenna design along with simulated and experimental results are presented and discussed.


title: A Doherty Power Amplifier With a GaN MMIC for Femtocell Base Stations

abstract: A Doherty Power Amplifier (PA) with the proposed GaN MMIC for femtocell base stations is presented. The MMIC was fabricated using a 0.25 μm TriQuint GaN process. An on-chip quadrature coupler based on a lumped magnetic coupler with capacitors was used to reduce the chip size. The MMIC with an area of 2.5 × 2.7 mm2 includes an input matching circuit, a driver transistor, carrier and peak transistors at power stage, and an inter-stage matching network with the quadrature coupler. The Doherty PA with MMIC shows 56.2% power added efficiency (PAE) with a gain of 19.7 dB when the output power is 41.2 dBm for a 2.14 GHz CW signal. For a down link WCDMA signal, the PAE of 39.6% is achieved at an output power of 35.3 dBm. The Doherty PA with the MMIC presented here has a small effective module area of 7 × 15 mm2, which can be readily reduced further.


title: Acute and Chronic Effects of Cannabinoids on Human Cognition—A Systematic Review

abstract: Cannabis use has been associated with impaired cognition during acute intoxication as well as in the unintoxicated state in long-term users. However, the evidence has been mixed and contested, and no systematic reviews of the literature on neuropsychological task-based measures of cognition have been conducted in an attempt to synthesize the findings. We systematically review the empirical research published in the past decade (from January 2004 to February 2015) on acute and chronic effects of cannabis and cannabinoids and on persistence or recovery after abstinence. We summarize the findings into the major categories of the cognitive domains investigated, considering sample characteristics and associations with various cannabis use parameters. Verbal learning and memory and attention are most consistently impaired by acute and chronic exposure to cannabis. Psychomotor function is most affected during acute intoxication, with some evidence for persistence in chronic users and after cessation of use. Impaired verbal memory, attention, and some executive functions may persist after prolonged abstinence, but persistence or recovery across all cognitive domains remains underresearched. Associations between poorer performance and a range of cannabis use parameters, including a younger age of onset, are frequently reported. Little further evidence has emerged for the development of tolerance to the acutely impairing effects of cannabis. Evidence for potential protection from harmful effects by cannabidiol continues to increase but is not definitive. In light of increasing trends toward legalization of cannabis, the knowledge gained from this body of research needs to be incorporated into strategies to minimize harm.


title: Morphologic diversity of malignant neoplasms arising in preexisting spiradenoma, cylindroma, and spiradenocylindroma based on the study of 24 cases, sporadic or occurring in the setting of Brooke-Spiegler syndrome.

abstract: The authors present a series of 24 malignant neoplasms arising in preexisting benign spiradenoma (20), cylindroma (2), and spiradenocylindroma (2). Nineteen patients (12 females, 7 males; age range, 41 to 92 y) had a solitary neoplasm (size range, 2.2 to 17.5 cm; median 4 cm), whereas the remaining


title: Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation

abstract: Accurate modeling of geographic distributions of species is crucial to various applications in ecology and conservation. The best performing techniques often require some parameter tuning, which may be prohibitively time-consuming to do separately for each species, or unreliable for small or biased datasets. Additionally, even with the abundance of good quality data, users interested in the application of species models need not have the statistical knowledge required for detailed tuning. In such cases, it is desirable to use “default settings”, tuned and validated on diverse datasets. Maxent is a recently introduced modeling technique, achieving high predictive accuracy and enjoying several additional attractive properties. The performance of Maxent is influenced by a moderate number of parameters. The first contribution of this paper is the empirical tuning of these parameters. Since many datasets lack information about species absence, we present a tuning method that uses presence-only data. We evaluate our method on independently collected high-quality presence-absence data. In addition to tuning, we introduce several concepts that improve the predictive accuracy and running time of Maxent. We introduce “hinge features” that model more complex relationships in the training data; we describe a new logistic output format that gives an estimate of probability of presence; finally we explore “background sampling” strategies that cope with sample selection bias and decrease model-building time. Our evaluation, based on a diverse dataset of 226 species from 6 regions, shows: 1) default settings tuned on presence-only data achieve performance which is almost as good as if they had been tuned on the evaluation data itself; 2) hinge features substantially improve model performance; 3) logistic output improves model calibration, so that large differences in output values correspond better to large differences in suitability; 4) “target-group” background sampling can give much better predictive performance than random background sampling; 5) random background sampling results in a dramatic decrease in running time, with no decrease in model performance.


title: Gendered Challenge, Gendered Response Confronting the Ideal Worker Norm in a White-Collar Organization

abstract: This article integrates research on gendered organizations and the work-family interface to investigate an innovative workplace initiative, the Results-Only Work Environment (ROWE), implemented in the corporate headquarters of Best Buy, Inc. While flexible work policies common in other organizations “accommodate” individuals, this initiative attempts a broader and deeper critique of the organizational culture. We address two research questions: How does this initiative attempt to change the masculinized ideal worker norm? And what do women’s and men’s responses reveal about the persistent ways that gender structures work and family life? Data demonstrate the ideal worker norm is pervasive and powerful, even as employees begin critically examining expectations regarding work time that have historically privileged men. Employees’ responses to ROWE are also gendered. Women (especially mothers) are more enthusiastic, while men are more cautious. Ambivalence about and resistance to change is expressed in diffe...


title: An integrated 122GHz differential frequency doubler with 37GHz bandwidth in 130 nm SiGe BiCMOS technology

abstract: This paper describes an integrated frequency multiplier, implemented as a Gilbert cell based frequency doubler in a 130 nm SiGe BiCMOS technology. The circuit demonstrates a 3 dB bandwidth of 97–134GHz with peak output power of 1 dBm for 1 dBm input power. The fundamental suppression, measured at the single-ended output, is better than 21 dBc while the frequency doubler consumes 69mW from a 3.3V supply. The doubler is preceded by a differential amplifier functioning as an active balun to generate a differential signal for the Gilbert cell.


title: A single feed dual-band circularly polarized millimeter-wave antenna for 5G communication

abstract: In this paper, one simple dual-band (28 and 38 GHz) circularly polarized slotted patch antenna, suitable for future millimeter wave mobile communication, is presented. The proposed monolayer circularly polarized patch antenna is considered without any 90° phase shifter and is excited by a single-feed microstrip line, which is very desirable for high gain antenna array implementation in millimeter wave band in order to compensate the link loss. From EM simulation, it has been found that the dimensions of the L-shaped slots significantly affect the performance of the antenna. The antenna has been prototyped and the measured results show that the antenna can simultaneously provide circular polarization in the two suggested millimeter-wave bands for the upcoming 5G mobile communications.


title: Fuzzy Recommendations in Marketing Campaigns

abstract: The population in Sweden is growing rapidly due to immigration. In this light, the issue of infrastructure upgrades to provide telecommunication services is of importance. New antennas can be installed at hot spots of user demand, which will require an investment, and/or the clientele expansion can be carried out in a planned manner to promote the exploitation of the infrastructure in the less loaded geographical zones. In this paper, we explore the second alternative. Informally speaking, the term Infrastructure-Stressing describes a user who stays in the zones of high demand, which are prone to produce service failures, if further loaded. We have studied the Infrastructure-Stressing population in the light of their correlation with geo-demographic segments. This is motivated by the fact that specific geo-demographic segments can be targeted via marketing campaigns. Fuzzy logic is applied to create an interface between big data, numeric methods for its processing, and a manager who wants a comprehensible summary.


title: Wideband differential antenna in package with superstrate structure at 77GHz

abstract: This paper presents a novel antenna in package solution with superstrate structure at 77 GHz for automotive radar applications. Two edge-coupled patches are driven by a differential signal which supports seamless integration with differential output MMICs in a single package. The antenna has a wide bandwidth (17 GHz) and stable gain. The antenna prototypes are fabricated using Embedded Wafer Level Ball Grid Array (eWLB) packaging technology. The measurement results show that the EIRP of the package (chip + antenna) reaches 11 dBm from 75 GHz to 80 GHz. This concept is suitable for a wide area of millimeter-wave front-end applications.


title: A Theoretic Framework Integrating Text Mining and Energy Demand Forecasting

abstract: News articles are an important source providing information about society. The analysis of news articles helps to measure the social importance of many events and to give an understanding about current interests. In this research, a theoretical framework of text mining enhanced approach is proposed to accommodate short-term variations caused by special events, such as severe weather conditions. A sentiment analysis approach for extracting sentiments associated with positive or negative polarities from a series of news reports is utilized to illustrate impact on energy demand from a special event. The magnitudes of the sentiments from the series of news articles are used to compose a timeseries pattern to represent the event that translated into the causes of short-term demand or price variation. The proposed approach for sentiment analysis is demonstrated with experimental results. In particular, the development of an event pattern using text mining is illustrated. The implications of the proposed framework and the future research direction are discussed.


title: Beyond Independence: Probabilistic Models for Query Approximation on Binary Transaction Data

abstract: We investigate the problem of generating fast approximate answers to queries posed to large sparse binary data sets. We focus in particular on probabilistic model-based approaches to this problem and develop a number of techniques that are significantly more accurate than a baseline independence model. In particular, we introduce two techniques for building probabilistic models from frequent itemsets: the itemset maximum entropy model and the itemset inclusion-exclusion model. In the maximum entropy model, we treat itemsets as constraints on the distribution of the query variables and use the maximum entropy principle to build a joint probability model for the query attributes online. In the inclusion-exclusion model, itemsets and their frequencies are stored in a data structure, called an ADtree, that supports an efficient implementation of the inclusion-exclusion principle in order to answer the query. We empirically compare these two itemset-based models to direct querying of the original data, querying of samples of the original data, as well as other probabilistic models such as the independence model, the Chow-Liu tree model, and the Bernoulli mixture model. These models are able to handle high-dimensionality (hundreds or thousands of attributes), whereas most other work on this topic has focused on relatively low-dimensional OLAP problems. Experimental results on both simulated and real-world transaction data sets illustrate various fundamental trade offs between approximation error, model complexity, and the online time required to compute a query answer.


title: A Review of Current Studies on Internet Finance in China

abstract: The Internet and finance are accelerating into integration in the 21 Century. Internet Finance was firstly proposed by Ma Weihua, the former president of China Merchants Bank in July 2012. On the basis of 74 latest research literatures selected from CSSCI Journals, Chinese Core Journals, authoritative magazines and related newspapers, this paper summarizes the current domestic research progress and trend about Internet Finance according to three dimensions, such as the sources of journals, research subjects and research contents. This research shows that the current domestic researches are not only shallow and superficial, but also lack the theoretical analyses and model applications; and the wealth-based and bank-based Internet Finance will be the research focus in the future.


title: Consumer trust in B2C e-commerce and the importance of social presence: experiments in e-Products and e-Services

abstract: Reducing social uncertainty--understanding, predicting, and controlling the behavior of other people--is a central motivating force of human behavior. When rules and customs are not sufficient, people rely on trust and familiarity as primary mechanisms to reduce social uncertainty. The relative paucity of regulations and customs on the Internet makes consumer familiarity and trust especially important in the case of e-Commerce. Yet the lack of an interpersonal exchange and the one-time nature of the typical business transaction on the Internet make this kind of consumer trust unique, because trust relates to other people and is nourished through interactions with them. This study validates a four-dimensional scale of trust in the context of e-Products and revalidates it in the context of e-Services. The study then shows the influence of social presence on these dimensions of this trust, especially benevolence, and its ultimate contribution to online purchase intentions.


title: Multi-agent planning: An introduction to planning and coordination

abstract: Many day-to-day situations involve decision making: for example, a taxi company has some transportation tasks to be carried out, a large firm has to distribute a lot of complicated tasks among its subdivisions or subcontractors, and an air-traffic controller has to assign time slots to planes that are landing or taking off. Intelligent agents can aid in this decision-making process. Agents are often classified into two categories according to the techniques they employ in their decision making: reactive agents (cf. (Ferber and Drogoul, 1992)) base their next decision solely on their current sensory input; planning agents, on the other hand, take into account anticipated future developments — for instance as a result of their own actions — to decide on the most favourable course of action. When an agent should plan and when it should be reactive depends on the particular situation it finds itself in. Consider the example where an agent has to plan a route from one place to another. A reactive agent might use a compass to plot its course, whereas a planning agent would consult a map. Clearly, the planning agent will come up with the shortest route in most cases, as it won’t be confounded by uncrossable rivers, one-way streets, and labyrinthine city layouts. On the other hand, there are also situations where a reactive agent can at least be equally effective, for instance if there are no maps to consult, for instance in a domain of (Mars) exploration rovers. Nevertheless, the ability to plan ahead is invaluable in many domains, so in this paper we will focus on planning agents. The general structure of a planning problem is easy to explain: (the relevant part of) the world is in a certain state, but managers or directors would like it to be in another state. The (abstract) problem of how one should get from the current state of the world through a sequence of actions to the desired goal state is a planning problem. Ideally, to solve such planning problems, we would like to have a general planning-problem solver. However, such an algorithm solving all planning problems can be proven to be non-existing.1 We therefore start to concentrate on a simplification of the general planning problem called ‘the classical planning problem’. Although not all realistic problems can be modeled as a classical planning problem, they can help to solve more


title: Superior Colliculus and Visual Spatial Attention

abstract: The superior colliculus (SC) has long been known to be part of the network of brain areas involved in spatial attention, but recent findings have dramatically refined our understanding of its functional role. The SC both implements the motor consequences of attention and plays a crucial role in the process of target selection that precedes movement. Moreover, even in the absence of overt orienting movements, SC activity is related to shifts of covert attention and is necessary for the normal control of spatial attention during perceptual judgments. The neuronal circuits that link the SC to spatial attention may include attention-related areas of the cerebral cortex, but recent results show that the SC's contribution involves mechanisms that operate independently of the established signatures of attention in visual cortex. These findings raise new issues and suggest novel possibilities for understanding the brain mechanisms that enable spatial attention.


title: Deep Adversarial Context-Aware Landmark Detection for Ultrasound Imaging

abstract: Real-time prostate gland localization in trans-rectal ultrasound images is required for automated ultrasound guided prostate biopsy procedures. We propose a new deep learning based approach aimed at localizing several prostate landmarks efficiently and robustly. Our multitask learning approach primarily makes the overall algorithm more contextually aware. In this approach, we not only consider the explicit learning of landmark locations, but also build-in a mechanism to learn the contour of the prostate. This multitask learning is further coupled with an adversarial arm to promote the generation of feasible structures. We have trained this network using \(\sim \)4000 labeled trans-rectal ultrasound images and tested on an independent set of images with ground truth landmark locations. We have achieved an overall Dice score of 92.6% for the adversarially trained multitask approach, which is significantly better than the Dice score of 88.3% obtained by only learning of landmark locations. The overall mean distance error using the adversarial multitask approach has also improved by 20% while reducing the standard deviation of the error compared to learning landmark locations only. In terms of computational complexity both approaches can process the images in real-time using a standard computer with a CUDA enabled GPU.


title: Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database

abstract: Background The widespread adoption of electronic health records allows us to ask evidence-based questions about the need for and benefits of specific clinical interventions in critical-care settings across large populations. ::: ::: Objective We investigated the prediction of vasopressor administration and weaning in the intensive care unit. Vasopressors are commonly used to control hypotension, and changes in timing and dosage can have a large impact on patient outcomes. ::: ::: Materials and Methods We considered a cohort of 15 695 intensive care unit patients without orders for reduced care who were alive 30 days post-discharge. A switching-state autoregressive model (SSAM) was trained to predict the multidimensional physiological time series of patients before, during, and after vasopressor administration. The latent states from the SSAM were used as predictors of vasopressor administration and weaning. ::: ::: Results The unsupervised SSAM features were able to predict patient vasopressor administration and successful patient weaning. Features derived from the SSAM achieved areas under the receiver operating curve of 0.92, 0.88, and 0.71 for predicting ungapped vasopressor administration, gapped vasopressor administration, and vasopressor weaning, respectively. We also demonstrated many cases where our model predicted weaning well in advance of a successful wean. ::: ::: Conclusion Models that used SSAM features increased performance on both predictive tasks. These improvements may reflect an underlying, and ultimately predictive, latent state detectable from the physiological time series.


title: ArguMed - a template-based argument mediation system for lawyers

abstract: This paper introduces the ArguMed-system. It is an example of a system for computer-mediated defeasible argumentation, a new trend in the field of defeasible argumentation. In this research, computer systems are developed that mediate the process of argumentation by one or more users. Argumentmediation systems should be contrasted with systems for automated reasoning: the latter perform reasoning tasks for users, while the former play the more passive role of a mediator. E.g., mediation systems keep track of the arguments raised and of the justification status of statements. The argumentation theory of the ArguMed-system is an adaptation of Verheij’s CumulA-model, a procedural model of argumentation with arguments and counterarguments. In the CumulA-model, the defeat of arguments is determined by the structure of arguments and the attack relation between arguments. It is completely independent of the underlying language. The process-model is free, in the sense that it allows not only inference (i.e., ‘forward’ argumentation, drawing conclusions from premises), but also justification (i.e., ‘backward’ argumentation, adducing reasons for issues). The ArguMed-system has been designed in an attempt to enhance the familiarity of the interface and the transparency of the underlying argumentation theory of its precursor, the Argue!-system. The ArguMed-system’s user interface is template-based, as is currently common in window-style user interfaces. The user gradually constructs arguments, by filling in templates that correspond to common argument patterns. An innovation of the ArguMed-system is that it uses dedicated templates for different types of argument moves. Whereas existing mediation systems are issue-based (in the style of Rittel’s well-known Issue-Based Information System), the ArguMed-system allows free argumentation, as in the CumulA-model. In contrast with the CumulA-model, which has a very general notion of defeat, defeat in the ArguMed-system is only of Pollock’s undercutter-type. The system allows three types of argument moves, viz. making a statement, adding a reason and its conclusion, and providing an (undercutter-type) exception blocking the connection between a reason and a conclusion. To put the ArguMed-system in context, it is compared with selected existing systems for argument mediation. The differences between the underlying argumentation theories and user interfaces are striking, which is suggested to be a symptom of the early stages of development of argument mediation systems. Given the lack of system evaluation by users in the field, JURIX 1998: Bart Verheij 114 the paper concludes with a discussion of the relevance of current research on computer-mediated defeasible argumentation. It is claimed that the shift of argument mediation systems from theoretical to practical tools is feasible, but can as yet not be made by system developers alone: a strong input from the research community is required.


title: Blockchain: Distributed Event-based Processing in a Data-Centric World: Extended Abstract

abstract: Usage of Blockchain is expanding from the initial focus on crypto-currencies towards applications to support a broad range of collaborative activies amongst businesses, organizations, and individuals. There are two broad levels of Blockchain: the foundation level relates to encryption, consensus algorithms, and support for a single (logical) data store that is shared by all participants; and the "smart contract" level that enables developers and business-level users to specify the data, logic, and behavior that collaborations should follow. The smart contracts are programs that are fundamentally event driven, data-centric, and support the activity of a distributed set of stakeholders situated across multiple organizations. This raises an array of research challenges in areas including language and solution design, interoperation across smart contracts, and verification.


title: Design, Kinematics, and Control of a Multijoint Soft Inflatable Arm for Human-Safe Interaction

abstract: In this paper, a novel soft inflatable arm is proposed for telepresence robots. The new proposed structure of the arm is achieved by a very common and low-cost inflatable material and it is very light, weighing only about 50 g. However, it can realize agile movement by driving six tiny cables installed in the shoulder and elbow joints. The soft inflatable arm can work by pumping air at a very low pressure ( $7.32 \pm 3.45$ kPa) and allows direct and soft human contact without any external force sensors. This paper proposes joint compressed models, joint kinematic models, and kinematic models for the whole inflatable robot arm. These models can be easily applied to multijoint arms. In addition, a new redundancy resolution method is also developed for the inflatable arm, which makes it easier to control resolution and is less complex than other traditional approaches. Numerous experiments have been conducted, including performances of accuracy, repeatability, motion trajectories, human-safe interaction, and remote interaction. Results are satisfactory and validate the expected performance of the proposed robotic arm.