title: A hybrid multilevel inverter employing series-parallel switched-capacitor unit

abstract: This paper presents a new hybrid multilevel inverter (MLI) which adopts switched-capacitor (SC) to step-up the voltage and improve the output power quality. With a simple series-parallel unit, the voltage magnitude and number of output levels of the inverter are doubled. The proposed topology can be applied to arbitrary number of series-connected voltage sources. This paper also suggests the design of gate drive voltage supplies and a scheme to substantially reduce the number of active components for high power factor applications which further simplifies the design and system cost. Due to the considerable increase in number of output levels, the proposed SCMLI has satisfactory output power quality with simple staircase modulation techniques switching at fundamental frequency. The working principle of the proposed SCMLI and the associated staircase modulation technique presented here are studied and verified by simulation and experiment.


Progressive Upper Limb Prosthetics Chris Lake , CPO , FAAOP * ,


title: A Dialectic Perspective on Innovation: Conflicting Demands, Multiple Pathways, and Ambidexterity

abstract: Innovation, the development and intentional introduction of new and useful ideas by individuals, teams, and organizations, lies at the heart of human adaptation. Decades of research in different disciplines and at different organizational levels have produced a wealth of knowledge about how innovation emerges and the factors that facilitate and inhibit innovation. We propose that this knowledge needs integration. In an initial step toward this goal, we apply a dialectic perspective on innovation to overcome limitations of dichotomous reasoning and to gain a more valid account of innovation. We point out that individuals, teams, and organizations need to self-regulate and manage conflicting demands of innovation and that multiple pathways can lead to idea generation and innovation. By scrutinizing the current use of the concept of organizational ambidexterity and extending it to individuals and teams, we develop a framework to help guide and facilitate future research and practice. Readers expecting specific and universal prescriptions of how to innovate will be disappointed as current research does not allow such inferences. Rather, we think innovation research should focus on developing and testing principles of innovation management in addition to developing decision aids for organizational practice. To this end, we put forward key propositions and action principles of innovation management.


title: IT Governance Practices in Small and Medium-Sized Enterprises: Recommendations from an Empirical Study

abstract: Much has been learned through IT governance research about the nature of IT-related decisions, the location of decision rights for these decisions, and governance mechanisms applied to facilitate associated decision processes in large organisations. Our knowledge about IT governance structures in small and medium-sized enterprises (SME), on the other hand, is quite limited. Adopting a qualitative and inductive approach, this study examines the nature and influence of IT governance in SMEs through interviews with executives from three SMEs. Our results demonstrate that IT decision authority was centralized in all three SMEs but that senior management involvement in governance procedures and communication practices about governance policies were observed to explain differences in these organisations’ IT use. We propose recommendations based on the findings of this study.


title: A Survey on Deep Learning in Big Data

abstract: Big Data means extremely huge large data sets that can be analyzed to find patterns, trends. One technique that can be used for data analysis so that able to help us find abstract patterns in Big Data is Deep Learning. If we apply Deep Learning to Big Data, we can find unknown and useful patterns that were impossible so far. With the help of Deep Learning, AI is getting smart. There is a hypothesis in this regard, the more data, the more abstract knowledge. So a handy survey of Big Data, Deep Learning and its application in Big Data is necessary. In this paper, we provide a comprehensive survey on what is Big Data, comparing methods, its research problems, and trends. Then a survey of Deep Learning, its methods, comparison of frameworks, and algorithms is presented. And at last, application of Deep Learning in Big Data, its challenges, open research problems and future trends are presented.


title: Investigations on design technologies for SAW quadplexer with narrow duplex gap

abstract: Carrier aggregation (CA) is an inevitable technology to improve the data transfer rate with widening operation bandwidths, while the current frequency assignment of cellular bands is dispersed over. In the frequency division duplex (FDD) CA, acoustic multiplexers are one of the most important key devices. This paper describes the design technologies for the surface acoustic wave (SAW) multiplexers, such as filter topologies, matching network configurations, SAW characteristics and so on. In the case of narrow duplex gap bandwidth such as Band4 and Band25, the characteristics of SAW resonators such as unloaded quality factor (Q) and out-of band impedances act as extremely important role to realize the low insertion loss and the steep skirt characteristics. In order to solve these challenges, a new type high Q SAW resonator that is named IHP-SAW is introduced. The results of a novel quadplexer of Band4-Band25 using those technologies show enhanced performances


title: Cloud-based emerging services systems

abstract: The emerging services and analytics advocate the service delivery in a polymorphic view that successfully serves a variety of audience. The amalgamation of numerous modern technologies such as cloud computing, Internet of Things (IoT) and Big Data is the potential support behind the emerging services Systems. Today, IoT, also dubbed as ubiquitous sensing is taking the center stage over the traditional paradigm. The evolution of IoT necessitates the expansion of cloud horizon to deal with emerging challenges. In this paper, we study the cloud-based emerging services, useful in IoT paradigm, that support the effective data analytics. Also, we conceive a new classification called CNNC {Clouda, NNClouda) for cloud data models; further, some important case studies are also discussed to further strengthen the classification.


title: Networks Using Probabilistic Attack Graphs

abstract: Today’s information systems face sophisticated attackers who combine multiple vulnerabilities to penetrate networks with devastating impact. The overall security of an enterprise network cannot be determined by simply counting the number of vulnerabilities. To more accurately assess the security of enterprise systems, one must understand how vulnerabilities can be combined and exploited to stage an attack. Composition of vulnerabilities can be modeled using probabilistic attack graphs, which show all paths of attacks that allow incremental network penetration. Attack likelihoods are propagated through the attack graph, yielding a novel way to measure the security risk of enterprise systems. This metric for risk mitigation analysis is used to maximize the security of enterprise systems. This methodology based on probabilistic attack graphs can be used to evaluate and strengthen the overall security of enterprise networks.


Avoiding the Peril of Publishing Qualitative Scholarship in Predatory Journals.


title: Red- and yellow-footed tortoises, Chelonoidis carbonaria and C. denticulata (Reptilia: Testudines: Testudinidae), in South American savannahs and forests: do their phylogeographies reflect distinct habitats?

abstract: Using sequence data of the mitochondrial cytochrome b gene, we investigated phylogeographic differentiation of the Amazonian tortoise species Chelonoidis carbonaria and C. denticulata. While C. carbonaria is generally restricted to savannah habitats and adjacent forests, C. denticulata is associated with wet tropical and subtropical forests. Our study suggests a correlation between distinct habitat preferences and phylogeography of the two species. In Maximum Parsimony, Maximum Likelihood and Bayesian analyses, haplotypes of C. carbonaria cluster in several distinct clades reflecting the species’ patchy distribution in savannah habitats. By contrast, haplotypes of C. denticulata are only weakly differentiated; a finding also confirmed by parsimony network analysis. This suggests that the contiguous Amazonian rainforest allows gene flow between populations of the forest-dwelling C. denticulata throughout the range, but significantly impedes gene flow in C. carbonaria. The phylogeographic structure and extant distribution pattern of C. carbonaria is supportive of former Amazonian rainforest fragmentation, enabling the dispersal of savannah species. Based on fossil calibration, we dated divergence times for the C. carbonaria clades using a relaxed molecular clock, resulting in average estimates ranging from 4.0–2.2 mya. This implies that the onset of rainforest fragmentation could predate the Pleistocene considerably. Furthermore, our findings call for further research on geographic and taxonomic variation in C. carbonaria and for a reassessment of the conservation status of the distinct genetic units.


title: Ground plane effects on planar inverted-F antennas

abstract: The planar inverted-F antenna has been the subject of many different parametric studies to optimize the design. Also, many different researchers have studied the effects of ground plane size on the bandwidth and radiation patterns. This paper looks further at the results and attempts to explain why the bandwidth and radiation patterns change significantly with changes in ground plane structure.


Fitness Costs of Sexual Harassment–The Price of Persuasion


title: Music and Emotions in the Brain: Familiarity Matters

abstract: The importance of music in our daily life has given rise to an increased number of studies addressing the brain regions involved in its appreciation. Some of these studies controlled only for the familiarity of the stimuli, while others relied on pleasantness ratings, and others still on musical preferences. With a listening test and a functional magnetic resonance imaging (fMRI) experiment, we wished to clarify the role of familiarity in the brain correlates of music appreciation by controlling, in the same study, for both familiarity and musical preferences. First, we conducted a listening test, in which participants rated the familiarity and liking of song excerpts from the pop/rock repertoire, allowing us to select a personalized set of stimuli per subject. Then, we used a passive listening paradigm in fMRI to study music appreciation in a naturalistic condition with increased ecological value. Brain activation data revealed that broad emotion-related limbic and paralimbic regions as well as the reward circuitry were significantly more active for familiar relative to unfamiliar music. Smaller regions in the cingulate cortex and frontal lobe, including the motor cortex and Broca's area, were found to be more active in response to liked music when compared to disliked one. Hence, familiarity seems to be a crucial factor in making the listeners emotionally engaged with music, as revealed by fMRI data.


title: Estimation of power system inertia

abstract: Information about power system inertia is of utmost importance for the Transmission System Operators (TSOs), so that it can be guaranteed that the stability of the system is not under risk. A new offline inertia estimation method is proposed in this paper. The method includes the calculation of the total power change after a disturbance in the system, based on selection of appropriate time ranges of the Rate Of Change Of Frequency (ROCOF) curve. Nordic32 test system is used for the simulations, where different disturbances, load dynamics and data processing tools are considered. Finally, a comparison of the proposed method to two methods proposed in the past takes place.


title: Analysis and testing of Web applications

abstract: The economic relevance of Web applications increases the importance of controlling and improving their quality. Moreover, the newly available technologies for their development allow the insertion of sophisticated functions, but often leave the developers responsible for their organization and evolution. As a consequence, a high demand is emerging for methodologies and tools for the quality assurance of Web-based systems. In this paper, a UML model of Web applications is proposed for their high-level representation. Such a model is the starting point for several analyses, which can help in the assessment of the static site structure. Moreover, it drives Web application testing, in that it can be exploited to define white-box testing criteria and to semi-automatically generate the associated test cases. The proposed techniques were applied to several real-world Web applications. The results suggest that automatic support for verification and validation activities can be extremely beneficial. In fact, it guarantees that all paths in the site which satisfy a selected criterion are properly exercised before delivery. The high level of automation that is achieved in test case generation and execution increases the number of tests that are conducted and simplifies the regression checks.


1 Adversarial Perturbations of Deep Neural Networks


title: Effects of Hydrocarbon-Protein Conjugates on Frog Embryos II. Induction and Persistence of Polyploidy in Tail Epithelium"

abstract: Summary Embryos of Rana pipiens immersed in solutions of protein conjugates of a carcinogenic hydrocarbon, 9,10-dimethyl-1,2-benzanthracene, from the tail bud stage to the time of initial feeding (4th–11th day of development) produced, during the period of exposure, means of up to 75 per cent of epidermal cells in the tail with nuclei above the size range found in normal diploid animals. Chromosome counts in the same regions and sizes and numbers of nucleoli indicate that the large cells are polyploid and, in many cases, aneuploid. An unoxidized conjugate of 9,10-dime was more effective than one in which the hydrocarbon was partially oxidized. Furthermore, exposure to an unoxidized conjugate in the light yielded higher percentages of polyploid cells than did exposure at the same concentration of hydrocarbon in the dark. A protein conjugate of anthracene at a similar concentration produced only about 5 per cent of polyploid cells. Colchicine at 10 times the concentration produced 5–25 per cent polyploidy, but the animals did not survive. After removal of embryos from solutions of conjugates of 9,10-dime, there was an increase in mean percentage of large nuclei in successive regenerated tail tips in the groups as a whole for 2 weeks. In one survivor, there were increases in percentage in some regions during the 4th and 5th weeks of regeneration. Simultaneous parallel changes in percentage in regenerated and non-regenerated portions of the same tail tip indicate that variability within the cell populations was systematic. Increases in percentage due to continuing production of polyploid cells were offset by decreases which were due in part to fragmentation of the larger nuclei and in part, possibly, to differential growth rates among the altered cells.


title: Designing for collaborative creative problem solving

abstract: Collaborative creativity is traditionally supported by formal techniques, such as brainstorming. These techniques improve the idea-generation process by creating group synergies, but also suffer from a number of negative effects. Current electronic tools to support collaborative creativity overcome some of these problems, but introduce new ones, by either losing the benefits of face-to-face communication or the immediacy of simultaneous contribution. Using an interactive environment as a test bed, we are investigating how collaborative creativity can be supported electronically while maintaining face-to-face communication. What are the design-factors influencing such a system? We have designed a brainstorming application that uses an interactive table and a large wall display, and compared the results of using it to traditional paper-based brainstorming in a user study with 30 participants. From the considerations that went into the design and the observations during the study we derive a number of design guidelines for collaborative systems in interactive environments.


title: Lightweight Hardware Architectures for the Present Cipher in FPGA

abstract: In recent years, the study of lightweight symmetric ciphers has gained interest due to the increasing demand for security services in constrained computing environments, such as in the Internet of Things. However, when there are several algorithms to choose from and different implementation criteria and conditions, it becomes hard to select the most adequate security primitive for a specific application. This paper discusses the hardware implementations of Present, a standardized lightweight cipher called to overcome part of the security issues in extremely constrained environments. The most representative realizations of this cipher are reviewed and two novel designs are presented. Using the same implementation conditions, the two new proposals and three state-of-the-art designs are evaluated and compared, using area, performance, energy, and efficiency as metrics. From this wide experimental evaluation, to the best of our knowledge, new records are obtained in terms of implementation size and energy consumption. In particular, our designs result to be adequate in regards to energy-per-bit and throughput-per-slice.


title: Coverage Embedding Models for Neural Machine Translation

abstract: In this paper, we enhance the attention-based neural machine translation (NMT) by adding explicit coverage embedding models to alleviate issues of repeating and dropping translations in NMT. For each source word, our model starts with a full coverage embedding vector to track the coverage status, and then keeps updating it with neural networks as the translation goes. Experiments on the large-scale Chinese-to-English task show that our enhanced model improves the translation quality significantly on various test sets over the strong large vocabulary NMT system.


title: Pushing the envelope of modern methods for bundle adjustment

abstract: In this paper, we present results and experiments with several methods for bundle adjustment, producing the fastest bundle adjuster ever published. The fastest methods work with the well known reduced camera system and handle the block-sparse pattern arising in the reduced camera system in a natural way. Adapting to the naturally arising block-sparsity allows the use of BLAS3, efficient memory handling, fast variable ordering, and customized sparse solving all at the same time. We present two methods, one using exact minimum degree ordering and block-based LDL solving, and one using block-based preconditioned conjugate gradient, both on the reduced camera system. We show experimentally that the adaptation to the natural block sparsity allows both these methods to perform better than previous ones. Further speed improvements are achieved by the novel use of embedded point iterations. The embedded point iterations take place inside each camera update step, yielding a higher cost decrease from each camera update step. This is especially true for points projecting far out on the flatter region of the robustifier.


title: Vision therapy for oculomotor dysfunctions in acquired brain injury: a retrospective analysis.

abstract: BACKGROUND ::: Oculomotor dysfunctions are among the most common abnormalities found in the brain-injured population. The purpose of the current study was to determine retrospectively the effectiveness of conventional optometric vision therapy for oculomotor disorders of vergence and version in a sample of ambulatory, visually symptomatic, predominantly adult outpatients who had either mild traumatic brain injury (TBI) or cerebrovascular accident (CVA). ::: ::: ::: METHODS ::: A computer-based query for acquired brain injury patients examined between the years of 2000 and 2003 was conducted in our clinic. This yielded 160 individuals with mild TBI and 60 with CVA. Of these patients, only those for whom vision therapy was prescribed and who completed an optometric vision therapy program for remediation of their oculomotor dysfunctions were selected. This included 33 with TBI and 7 with CVA. The criterion for treatment success was denoted by marked/total improvement in at least 1 primary symptom and at least 1 primary sign. ::: ::: ::: RESULTS ::: Ninety percent of those with TBI and 100% of those with CVA were deemed to have treatment success. These improvements remained stable at retesting 2 to 3 months later. ::: ::: ::: CONCLUSION ::: Nearly all patients in the current clinic sample exhibited either complete or marked reduction in their oculomotor-based symptoms and improvement in related clinical signs, with maintenance of the symptom reduction and sign improvements at the 2- to 3-month follow-up. These findings show the efficacy of optometric vision therapy for a range of oculomotor abnormalities in the primarily adult, mild brain-injured population. Furthermore, it shows considerable residual neural plasticity despite the presence of documented brain injury.


title: Neural Enquirer: Learning to Query Tables with Natural Language

abstract: We proposed Neural Enquirer as a neural network architecture to execute a natural language (NL) query on a knowledge-base (KB) for answers. Basically, Neural Enquirer finds the distributed representation of a query and then executes it on knowledge-base tables to obtain the answer as one of the values in the tables. Unlike similar efforts in end-to-end training of semantic parsers, Neural Enquirer is fully "neuralized": it not only gives distributional representation of the query and the knowledge-base, but also realizes the execution of compositional queries as a series of differentiable operations, with intermediate results (consisting of annotations of the tables at different levels) saved on multiple layers of memory. Neural Enquirer can be trained with gradient descent, with which not only the parameters of the controlling components and semantic parsing component, but also the embeddings of the tables and query words can be learned from scratch. The training can be done in an end-to-end fashion, but it can take stronger guidance, e.g., the step-by-step supervision for complicated queries, and benefit from it. Neural Enquirer is one step towards building neural network systems which seek to understand language by executing it on real-world. Our experiments show that Neural Enquirer can learn to execute fairly complicated NL queries on tables with rich structures.


title: Decision Trees for Mining Data Streams Based on the Gaussian Approximation

abstract: Since the Hoeffding tree algorithm was proposed in the literature, decision trees became one of the most popular tools for mining data streams. The key point of constructing the decision tree is to determine the best attribute to split the considered node. Several methods to solve this problem were presented so far. However, they are either wrongly mathematically justified (e.g., in the Hoeffding tree algorithm) or time-consuming (e.g., in the McDiarmid tree algorithm). In this paper, we propose a new method which significantly outperforms the McDiarmid tree algorithm and has a solid mathematical basis. Our method ensures, with a high probability set by the user, that the best attribute chosen in the considered node using a finite data sample is the same as it would be in the case of the whole data stream.


title: Spam Detection Framework for Online Reviews Using Hadoop’ s Computational Capability

abstract: Nowadays, online reviews have become one of the vital elements for customers to do online shopping. Organizations and individuals use this information to buy the right products and make business decisions. This has influenced the spammers or unethical business people to create false reviews and promote their products to out-beat competitions. Sophisticated systems are developed by spammers to create bulk of spam reviews in any websites within hours. To tackle this problem, studies have been conducted to formulate effective ways to detect the spam reviews. Various spam detection methods have been introduced in which most of them extracts meaningful features from the text or used machine learning techniques. These approaches gave little importance on extracted feature type and processing rate. NetSpam[1] defines a framework which can classify the review dataset based on spam features and maps them to a spam detection procedure which performs better than previous works in predictive accuracy. In this work, a method is proposed that can improve the processing rate by applying a distributed approach on review dataset using MapReduce feature. Parallel programming concept using MapReduce is used for processing big data in Hadoop. The solution involves parallelising the algorithm defined in NetSpam and it defines a spam detection procedure with better predictive accuracy and processing rate.


title: Feature Selection in Time Aware Authorship Attribution

abstract: Authorship Identification is task to identify author of an article or document whose author is not known. This can be possible by comparing set of articles or documents whose authorship is known to this unknown article. This paper presents comparative approach based on similarity of unknown documents against the known by use of various features. The main focus of the paper is to show the difference in articles which were written in different time frames. And also observe, how the feature gets addressed in this different time frame of the same author. All method shows the comparison over several features.


title: Personalized Food Recommendation Using Deep Neural Network

abstract: {Making decision about what and where to eat is a major problem in our everyday lives due to a wide variety of ingredients, culinary styles, ethnicities, cultures, and personal tastes. Choosing right dish at the right time seems to be a very difficult task. Thus, this research proposes an integration of a deep neural network with a recommendation system with Thai food as our test domain. The proposed model extracts interested ingredients from the set of recipes of user’s favorite dishes that is given before using the system. The features are extracted from the analysis of favorite ingredients then a user profile is evaluated by a model of deep neural network(DNN). The system also collects history of selected dishes along with user profile in a database. The model will predict the next dishes using a temporal prediction model on the profile and eating history. The satisfaction is analyzed through experiment based on whether user selected the recommended dishes or not which is calculated as hit ratio. In addition, the accuracy and variety of recommendation are also analyzed. From the experimental results, DNN model can predict the user profile with precision up to 90% and the accuracy of hit ratio is up to 89%.


title: Context-based detection of pedestrian crossing intention for autonomous driving in urban environments

abstract: This paper focuses on the detection of pedestrian crossing intention to improve the situation awareness for autonomous driving in urban environments. A new definition of pedestrian crossing intention is discussed, which allows self-driving vehicles to identify pedestrians, whose intended actions are relevant for the own behavior planning, at an early stage. We propose a context-based feature descriptor in combination with a SVM classifier for detecting this. The descriptor captures the movement of a pedestrian relative to the road and the spatial layout of other scene elements in a generic manner. The performance of the feature descriptor is evaluated in relation to various SVM setups. Feasibility of the approach is demonstrated with data captured on-board of a vehicle in real inner-city traffic. The evaluation of the classification results confirms, that context-based data is a promising indicator for pedestrian crossing intention and that the proposed feature descriptor is capable of representing this. It is further shown that a lack of information about the pedestrian's posture and body movement results in a delayed detection of the pedestrians changing their crossing intention when compared to a human observer.


title: A privacy-preserving protocol for neural-network-based computation

abstract: The problem of secure data processing by means of a neural network (NN) is addressed. Secure processing refers to the possibility that the NN owner does not get any knowledge about the processed data since they are provided to him in encrypted format. At the same time, the NN itself is protected, given that its owner may not be willing to disclose the knowledge embedded within it. Two different levels of protection are considered: according to the first one only the NN weights are protected, whereas the second level also permits to protect the node activation functions. An efficient way of implementing the proposed protocol by means of some recently proposed multi-party computation techniques is described.


title: Evaluation of Keypoint Extraction and Matching for Pose Estimation Using Pairs of Spherical Images

abstract: Keypoint extraction and matching has been widely studied by the computer vision community, mostly focused on pinhole camera models. In this paper we perform a comparative analysis of four keypoint extraction algorithms applied to full spherical images, particularly in the context of pose estimation. Two of the methods chosen for the comparative study, namely A-KAZE and ASIFT, have been designed considering a perspective camera model, but were already applied in an omnidirectional structure from motion pipeline, generating successful results in the literature. The other two algorithms are properly adapted versions of the traditional descriptors SIFT and ORB to the spherical domain, subbed SSFIT and SPHORB. We conduct our tests on captures of omnidirectional cameras, both synthetic and real, arbitrarily translated and rotated with known ground-truth transformations. The extracted keypoints are fed to the well-known 8-point algorithm with RANSAC, allowing to estimate the relative camera poses. These poses (translation vector and rotation matrix) are then compared to the ground-truth transformation parameters, generating the error metrics used in our analysis. Our results indicated that spherical descriptors SSIFT and SPHORB did not produce better results than planar descriptors A-KAZE and ASIFT in the context of pose estimation, particularly in the evaluation with real image pairs.


title: Technology adoption decisions in the household: A seven-model comparison

abstract: We identified 7 theoretical models that have been used to explain technology adoption and use. We then examined the boundary conditions of these models of technology adoption when applied to the household context using longitudinal empirical data from households regarding their purchase and use decisions related to household technologies. We conducted 2 studies and collected 1,247 responses from U.S. households for the first study and 2,064 responses from U.S. households for the second study. Those households that had adopted household technologies were surveyed regarding their use behavior. Potential adopters i.e., those who had currently not adopted were surveyed regarding their purchase intentions. This allowed us to identify the most influential factors affecting a household's decision to adopt and use technologies. The results show that the model of adoption of technology in the household provided the richest explanation and explained best why households purchase and use technologies.


title: Exploring the combination of software visualization and data clustering in the software architecture recovery process

abstract: Modernizing a legacy system is a costly process that requires deep understanding of the system architecture and its components. Without an understanding of the software architecture that will be rewritten, the entire process of reengineering can fail. For this reason, semi-automatic and automatic techniques for architecture recovery have been active focuses of research. However, there are still important improvements that need to be addressed on this field of research w.r.t. achieving a more accurate architecture recovery process. In this work, we propose to explore if an approach where visualization and clustering applied together can provide a higher accuracy on the software architecture recovery process. An experimental study was conducted in a industrial environment to empirically evaluate our investigation in four commercial systems. Our results indicated a statistically significant increase in the accuracy of the recovered architectures in all cases.


title: An Extended Perspective on Individual Security Behaviors: Protection Motivation Theory and a Unified Security Practices (USP) Instrument

abstract: Security threats regularly affect users of home computers. As such, it is important to understand the practices of users for protecting their computers and networks, and to identify determinants of these practices. Several recent studies utilize Protection Motivation Theory (PMT) to explore these practices. However, these studies focus on one specific security protection behavior or on intentions to use a generic measure of security protection tools or techniques (practices). In contrast, this study empirically tests the effectiveness of PMT to explain a newly developed measure for collectively capturing several individual security practices. The results show that PMT explains an important portion of the variance in the unified security practices measure, and demonstrates the importance of explaining individual security practices as a whole as opposed to one particular behavior individually. Implications of the study for research and practice are discussed.


title: Skeleton growing and pruning with bending potential ratio

abstract: We propose a novel significance measure for skeleton pruning, called bending potential ratio (BPR), in which the decision regarding whether a skeletal branch should be pruned or not is based on the context of the boundary segment that corresponds to the branch. By considering this contextual information, we can better evaluate the contribution of the boundary segment to the overall shape, which generally depends on its particular location within the whole contour (i.e., a segment may be considered to be insignificant in one place while it may be considered as a feature elsewhere). The BPR is a measure of the significance of contour segments in such context, and depicts the bending potential of a contour segment. Unlike other significance measures that only contain local shape information, the BPR evaluates both local and global shape information. Thus, it is insensitive to local boundary deformation. In addition, we also present a scheme for skeleton growing, which integrates pruning based on the BPR measurement. Our experiments demonstrate that the skeletons obtained by the proposed algorithm are medially placed and connected. We also demonstrate that shapes reconstructed from these skeletons are very close to the original shapes. Moreover, the BPR measure yields a natural multi-scale skeletal representation.


Fun with Asynchronous Vision Sensors and Processing


title: Context theory of classification learning.

abstract: Most theories dealing with ill-defined concepts assume that performance is based on category level information or a mixture of category level and specific item information. A context theory of classificatio n is described in which judgments are assumed to derive exclusively from stored exemplar information. The main idea is that a probe item acts as a retrieval cue to access information associated with stimuli similar to the probe. The predictions of the context theory are contrasted with those of a class of theories (including prototype theory) that assume that the information entering into judgments can be derived from an additive combination of information from component cue dimensions. Across four experiments using both geometric forms and schematic faces as stimuli, the context theory consistently gave a better account of the data. The relation of the context theory to other theories and phenomena associated with ill-defined concepts is discussed in detail. One of the major components of cognitive behavior concerns abstracting rules and forming concepts. Our entire system of naming objects and events, talking about them, and interacting with them presupposes the ability to group experiences into appropriate classes. Young children learn to tell the difference between dogs and cats, between clocks and fans, and between stars and street lights. Since few concepts are formally taught, the evolution of concepts from experience with exemplars must be a fundamental learning phenomenon. The focus of the present article is to explore how such conceptual achievements emerge from individual instances.


title: Answering Location-Aware Graph Reachability Queries on GeoSocial Data

abstract: Thanks to the wide spread use of mobile and wearable devices, popular social networks, e.g., Facebook, prompts users to add spatial attributes to social entities, e.g., check-ins, traveling posts, and geotagged photos, leading to what is known as, The GeoSocial Graph. In such graph, usersmay issue a Reachability Query with Spatial Range Predicate (abbr. RangeReach). RangeReach finds whether an input vertex can reach any spatial vertex that lies within an input spatial range. The paper proposes GEOREACH, an approach that adds spatial data awareness to a graph database management system. GEOREACH allows efficient execution of RangeReach queries, yet without compromising a lot on the overall system scalability. Experiments based on system implementation inside Neo4j prove that GEOREACH exhibits up to two orders of magnitude better query performance and up to four times less storage than the state-of-the-art spatial and reachability indexing approaches.


title: Septal considerations in revision rhinoplasty.

abstract: Abnormalities of the nasal septum subsequent to septorhinoplasty include structural deficits connected with incorrect excision of the cartilaginous portion, the persistence of deviation to varying degrees, and deformity of the supratip region. In the course of revision, the correction of septal anomalies constitutes an indispensable preliminary stage upon which the end result depends. A straight, sturdy, and flexible supporting septal structure is, in fact, an essential prerequisite if satisfactory results are to be obtained. The authors describe some techniques used to achieve this objective and provide guidelines for their selection. The septum is also involved in the repair strategy of revision operations. It constitutes a primary source of material for structural grafts to reconstruct numerous components of the nasal pyramid.


title: UNMANNED VEHICLE OBSTACLE DETECTION AND AVOIDANCE USING DANGER ZONE APPROACH

abstract: This paper proposes an obstacle avoidance algorithm for unmanned vehicles in unknown environment. The vehicle uses an ultrasonic sensor and a servo motor which rotates from 0 to 180 degrees to obta...


title: Nailfold capillaroscopy image processing for morphological parameters measurement

abstract: Nailfold capillaroscopy is a non-invasive imaging technique employed to assess the microcirculation of blood capillaries. In this paper, image processing will be designed to measure morphological parameters for each capillary. The processes are pre-processing, binarization, skeleton extraction and skeleton segmentation. The morphological parameters are width of capillary, height of capillary, loop morphology, width of terminal loop, caliber and tortuosity. After the implementation, all the parameters can be measured automatically and the binarization process has accuracy up to 96% based on groundtruth image.


title: Eye-Wearable Technology for Machine Maintenance: Effects of Display Position and Hands-free Operation

abstract: Exciting developments in eye-wearable technology and its potential industrial applications warrant a thorough understanding of its advantages and drawbacks through empirical evidence. We conducted an experiment to investigate what characteristics of eye-wearable technology impact user performance in machine maintenance, which included a representative set of car maintenance tasks involving Locate, Manipulate, and Compare actions. Participants were asked to follow instructions displayed on one of four technologies: a peripheral eye-wearable display, a central eye-wearable display, a tablet, or a paper manual. We found a significant effect of display position: the peripheral eye-wearable display resulted in longer completion time than the central display; but no effect for hands-free operation. The technology effects were also modulated by different Tasks and Action types. We discuss the human factors implications for designing more effective eye-wearable technology, including display position, issues of monocular display, and how the physical proximity of the technology affects users' reliance level.


title: Visualizing and Understanding Atari Agents

abstract: Deep reinforcement learning (deep RL) agents have achieved remarkable success in a broad range of game-playing and continuous control tasks. While these agents are effective at maximizing rewards, it is often unclear what strategies they use to do so. In this paper, we take a step toward explaining deep RL agents through a case study in three Atari 2600 environments. In particular, we focus on understanding agents in terms of their visual attentional patterns during decision making. To this end, we introduce a method for generating rich saliency maps and use it to explain 1) what strong agents attend to 2) whether agents are making decisions for the right or wrong reasons, and 3) how agents evolve during the learning phase. We also test our method on non-expert human subjects and find that it improves their ability to reason about these agents. Our techniques are general and, though we focus on Atari, our long-term objective is to produce tools that explain any deep RL policy.


title: An L1 image transform for edge-preserving smoothing and scene-level intrinsic decomposition

abstract: Identifying sparse salient structures from dense pixels is a longstanding problem in visual computing. Solutions to this problem can benefit both image manipulation and understanding. In this paper, we introduce an image transform based on the L1 norm for piecewise image flattening. This transform can effectively preserve and sharpen salient edges and contours while eliminating insignificant details, producing a nearly piecewise constant image with sparse structures. A variant of this image transform can perform edge-preserving smoothing more effectively than existing state-of-the-art algorithms. We further present a new method for complex scene-level intrinsic image decomposition. Our method relies on the above image transform to suppress surface shading variations, and perform probabilistic reflectance clustering on the flattened image instead of the original input image to achieve higher accuracy. Extensive testing on the Intrinsic-Images-in-the-Wild database indicates our method can perform significantly better than existing techniques both visually and numerically. The obtained intrinsic images have been successfully used in two applications, surface retexturing and 3D object compositing in photographs.


title: Towards digital fundamental mode orthogonal fluxgate

abstract: Noise of orthogonal fluxgate in fundamental mode has been strongly reduced in the last years, making it a very competitive vectorial sensor of magnetic field at room temperature. The most important results have been achieved reducing the 1/f noise, which can reach about 1 pT/√Hz at 1 Hz. However, the noise floor is still an issue, which cannot be ignored anymore. The problem of the noise floor in fundamental mode orthogonal fluxgate became evident when sensors based on annealed wire-cores have been proposed. By annealing the wire with proper current flowing through it, the circumferential anisotropy is increased and therefore the 1/f noise reduced. However, the sensitivity is also reduced and that affects the noise floor, which is typically due to the white noise of analog demodulator. The challenge is to produce sensors with simultaneously low 1/f noise and low noise floor. For this reason we propose a transition to digital demodulation of the output voltage of the sensor. The voltage is digitized by high-speed high-resolution digitizer and the first harmonic extracted numerically. In this way we get rid of the noise of the analog demodulator. In this paper we prove that using this method we reduce the noise floor from 650 fT/√Hz to 400 fT/√Hz, a value which could never be achieved with analog demodulator, without increasing the 1/f noise. Moreover, we show how a digital demodulation allows us to efficiently compensate the noise in the output of the sensors due to excitation current. This result is achieved by simultaneous sampling of the excitation current and applying an algorithm based on its correlation to the output voltage of the sensor.


title: The 1947 Soviet Famine and the Entitlement Approach to Famines

abstract: This paper presents an analysis of the economics of the 1947 Soviet famine, using data from recently declassified archives. It is argued that the best estimate that can currently be given of the number of excess deaths in the range 1.0-1.5 million. The demographic loss was greater. During the famine, surplus stocks in the hands of the state seem to have been sufficient to have fed all those who died of starvation. The famine was a FAD2 (preventable food availability decline) famine, which occurred because a drought caused a bad harvest and hence reduced food availability, but, had the priorities of the government been different, there might have been no famine (or a much smaller one) despite the drought. The selection of victims can be understood in terms of the entitlement approach. Copyright 2000 by Oxford University Press.


title: Blockchain-based mobile fingerprint verification and automatic log-in platform for future computing

abstract: Door locks and user authentication are the major issues in the current banking industry. There are a number of user future computing techniques, and security is especially essential to these methods. Existing digital door locks have the problem of opening easily with a stolen PIN number or by electrical shock. Thus, this study proposed and implemented an integrated automatic log-in platform based on mobile fingerprint recognition by applying the blockchain theory. As a result of this research, a convenient, integrated automatic log-in platform with powerful security has been constructed using a smartphone-based fingerprint recognition function. There are three major functions of the platform. First, it is possible to authenticate the user in PC, mobile device, and IoT environments through fingerprint recognition. Second, the platform includes SDK to develop application software for user authentication and IoT services. Last is its strengthened security using the blockchain theory to prepare against tampering/forging/leaking of a user’s fingerprint information by hackers.


title: Scale equivariance in CNNs with vector fields

abstract: We study the effect of injecting local scale equivariance into Convolutional Neural Networks. This is done by applying each convolutional filter at multiple scales. The output is a vector field encoding for the maximally activating scale and the scale itself, which is further processed by the following convolutional layers. This allows all the intermediate representations to be locally scale equivariant. We show that this improves the performance of the model by over $20\%$ in the scale equivariant task of regressing the scaling factor applied to randomly scaled MNIST digits. Furthermore, we find it also useful for scale invariant tasks, such as the actual classification of randomly scaled digits. This highlights the usefulness of allowing for a compact representation that can also learn relationships between different local scales by keeping internal scale equivariance.


title: 3D reconstruction of indoor and outdoor scenes using a mobile range scanner

abstract: Describes 3D mapping of indoor and outdoor environments using a mobile range scanner. In the raw range data preprocessing stage, we propose to use area decreasing flow for surface mesh smoothing instead of the mean curvature flow. Although the proposed area decreasing flow approach is mathematically equivalent to the mean curvature flow, it can avoid the difficulty in curvature estimation and provide an optimal flowing step size. Geometric details are preserved by adaptive smoothing on crease edges. In the multi-view 3D reconstruction stage, we combine the space carving with Hilton's implicit surface-based method (Hilton et al., 1998) to generate watertight 3D models from limited number of scans. Volumetric deformation by mean curvature flow using the level set method is applied in the post-processing stage to remove outliers. We present results of 3D reconstruction of real indoor and outdoor environments from multi-view noisy range data.


title: Glia Keep Synapse Distribution under Wraps

abstract: The wiring of the nervous system requires that axons navigate to the correct targets and maintain their correct positions during developmental growth. In this issue, Shao et al. (2013) now reveal a crucial new role for glia in preserving correct synaptic connectivity during developmental growth.


Title: Stock Market Prediction Using Artificial Neural Networks Authors:


title: Learning Transferable Features for Speech Emotion Recognition

abstract: Emotion recognition from speech is one of the key steps towards emotional intelligence in advanced human-machine interaction. Identifying emotions in human speech requires learning features that are robust and discriminative across diverse domains that differ in terms of language, spontaneity of speech, recording conditions, and types of emotions. This corresponds to a learning scenario in which the joint distributions of features and labels may change substantially across domains. In this paper, we propose a deep architecture that jointly exploits a convolutional network for extracting domain-shared features and a long short-term memory network for classifying emotions using domain-specific features. We use transferable features to enable model adaptation from multiple source domains, given the sparseness of speech emotion data and the fact that target domains are short of labeled data. A comprehensive cross-corpora experiment with diverse speech emotion domains reveals that transferable features provide gains ranging from 4.3% to 18.4% in speech emotion recognition. We evaluate several domain adaptation approaches, and we perform an ablation study to understand which source domains add the most to the overall recognition effectiveness for a given target domain.


3D Printed Antibacterial Prostheses


title: The evolution of substructure II: linking dynamics to environment

abstract: We present results from a series of high-resolution N-body simulations that focus on the formation and evolution of eight dark matter haloes, each of the order of a million particles within the virial radius. We follow the time evolution of hundreds of satellite galaxies with unprecedented time resolution, relating their physical properties to the differing halo environmental conditions. The self-consistent cosmological framework in which our analysis was undertaken allows us to explore satellite disruption within live host potentials, a natural complement to earlier work conducted within static potentials. Our host haloes were chosen to sample a variety of formation histories, ages and triaxialities; despite their obvious differences, we find striking similarities within the associated substructure populations. Namely, all satellite orbits follow nearly the same eccentricity distribution with a correlation between eccentricity and pericentre. We also find that the destruction rate of the substructure population is nearly independent of the mass, age and triaxiality of the host halo. There are, however, subtle differences in the velocity anisotropy of the satellite distribution. We find that the local velocity bias at all radii is greater than unity for all haloes and this increases as we move closer to the halo centre, where it varies from 1.1 to 1.4. For the global velocity bias, we find a small but slightly positive bias, although when we restrict the global velocity bias calculation to satellites that have had at least one orbit, the bias is essentially removed.


title: Normalization of Non-Standard Words

abstract: In addition to ordinary words and names, real text contains non-standard “words" (NSWs), including numbers, abbreviations, dates, currency amounts and acronyms. Typically, one cannot find NSWs in a dictionary, nor can one find their pronunciation by an application of ordinary “letter-to-sound" rules. Non-standard words also have a greater propensity than ordinary words to be ambiguous with respect to their interpretation or pronunciation. In many applications, it is desirable to “normalize" text by replacing the NSWs with the contextually appropriate ordinary word or sequence of words. Typical technology for text normalization involves sets of ad hoc rules tuned to handle one or two genres of text (often newspaper-style text) with the expected result that the techniques do not usually generalize well to new domains. The purpose of the work reported here is to take some initial steps towards addressing deficiencies in previous approaches to text normalization. We developed a taxonomy of NSWs on the basis of four rather distinct text types?news text, a recipes newsgroup, a hardware-product-specific newsgroup, and real-estate classified ads. We then investigated the application of several general techniques including n-gram language models, decision trees and weighted finite-state transducers to the range of NSW types, and demonstrated that a systematic treatment can lead to better results than have been obtained by the ad hoc treatments that have typically been used in the past. For abbreviation expansion in particular, we investigated both supervised and unsupervised approaches. We report results in terms of word-error rate, which is standard in speech recognition evaluations, but which has only occasionally been used as an overall measure in evaluating text normalization systems.


title: Unsupervised Disaggregation for Non-intrusive Load Monitoring

abstract: A method for unsupervised disaggregation of appliance signatures from smart meter data is presented. The primary feature used for unsupervised learning relates to abrupt transitions or magnitude changes in the power waveform. The method consists of a sequence of procedures for appliance signature identification, and disaggregation using hidden Markov modeling (HMM), and residual analysis. The key contributions are (a) a novel 'segmented' application of the Viterbi algorithm for sequence decoding with the HMM, (b) details of establishing observation and state transition probabilities for the HMM, and (c) procedures for careful handling of low power signatures. Results show that the method is effective for magnitude-based disaggregation, and provide insights for a more complete solution.


title: Rodents And Cover Crops—A Review

abstract: Inter-row plantings of herbaceous cover crops has become a widely accepted practice by orchard and vineyard managers. Cover crops, used as part of a production management system, are not considered a cash crop and are therefore selected by individual growers for various reasons. Little is written regarding the relationship of cover crop management and the impact on rodent populations. This paper reviews the recent literature and examines how cover crop species and cultivar selection along with management procedures may be influential in limiting rodent populations and their damage to cropping systems.


title: SuBiC: A Supervised, Structured Binary Code for Image Search

abstract: For large-scale visual search, highly compressed yet meaningful representations of images are essential. Structured vector quantizers based on product quantization and its variants are usually employed to achieve such compression while minimizing the loss of accuracy. Yet, unlike binary hashing schemes, these unsupervised methods have not yet benefited from the supervision, end-to-end learning and novel architectures ushered in by the deep learning revolution. We hence propose herein a novel method to make deep convolutional neural networks produce supervised, compact, structured binary codes for visual search. Our method makes use of a novel block-softmax non-linearity and of batch-based entropy losses that together induce structure in the learned encodings. We show that our method outperforms state-of-the-art compact representations based on deep hashing or structured quantization in single and cross-domain category retrieval, instance retrieval and classification. We make our code and models publicly available online.


title: Adjustable Flux Three-Phase AC Machines With Combined Multiple-Step Star-Delta Winding Connections

abstract: In this paper, a complete theory and analysis method for combined star-delta three-phase windings from the point of view of magnetomotive force spatial harmonics content and equivalent winding factors calculation is developed. The method allows for any spatial angle and any coils percentage distribution between star and delta balanced winding systems. It is proven that connecting the inner delta in the clockwise or counterclockwise direction leads to two different space angles between the star and delta systems and two different steps in the flux level control. It means that, compared to what is known and applied up to now, using n winding sections in each phase, it is possible to obtain 2 n steps (instead of n + 1) in the flux level. To check the analysis validity, two experimental tests are reported on both squirrel-cage, integer-winding, induction motor, and permanent magnet, low-speed, fractional-winding, synchronous generator with a specially designed stator winding, allowing multiple-step air-gap flux regulation.


title: IUI workshop on interactive machine learning

abstract: Many applications of Machine Learning (ML) involve interactions with humans. Humans may provide input to a learning algorithm (in the form of labels, demonstrations, corrections, rankings or evaluations) while observing its outputs (in the form of feedback, predictions or executions). Although humans are an integral part of the learning process, traditional ML systems used in these applications are agnostic to the fact that inputs/outputs are from/for humans. However, a growing community of researchers at the intersection of ML and human-computer interaction are making interaction with humans a central part of developing ML systems. These efforts include applying interaction design principles to ML systems, using human-subject testing to evaluate ML systems and inspire new methods, and changing the input and output channels of ML systems to better leverage human capabilities. With this Interactive Machine Learning (IML) workshop at IUI 2013 we aim to bring this community together to share ideas, get up-to-date on recent advances, progress towards a common framework and terminology for the field, and discuss the open questions and challenges of IML.


title: ERP evidence for conceptual mappings and comparison processes during the comprehension of conventional and novel metaphors

abstract: Cognitive linguists suggest that understanding metaphors requires activation of conceptual mappings between the involved concepts. We tested whether mappings are indeed in use during metaphor comprehension, and what mapping means as a cognitive process with Event-Related Potentials. Participants read literal, conventional metaphorical, novel metaphorical, and anomalous target sentences preceded by primes with related or unrelated mappings. Experiment 1 used sentence-primes to activate related mappings, and Experiment 2 used simile-primes to induce comparison thinking. In the unprimed conditions of both experiments, metaphors elicited N400s more negative than the literals. In Experiment 1, related sentence-primes reduced the metaphor-literal N400 difference in conventional, but not in novel metaphors. In Experiment 2, related simile-primes reduced the metaphor-literal N400 difference in novel, but not clearly in conventional metaphors. We suggest that mapping as a process occurs in metaphors, and the ways in which it can be facilitated by comparison differ between conventional and novel metaphors.


On the importance and implications of studying technology non-use


title: Serendipity: Finger Gesture Recognition using an Off-the-Shelf Smartwatch

abstract: Previous work on muscle activity sensing has leveraged specialized sensors such as electromyography and force sensitive resistors. While these sensors show great potential for detecting finger/hand gestures, they require additional hardware that adds to the cost and user discomfort. Past research has utilized sensors on commercial devices, focusing on recognizing gross hand gestures. In this work we present Serendipity, a new technique for recognizing unremarkable and fine-motor finger gestures using integrated motion sensors (accelerometer and gyroscope) in off-the-shelf smartwatches. Our system demonstrates the potential to distinguish 5 fine-motor gestures like pinching, tapping and rubbing fingers with an average f1-score of 87%. Our work is the first to explore the feasibility of using solely motion sensors on everyday wearable devices to detect fine-grained gestures. This promising technology can be deployed today on current smartwatches and has the potential to be applied to cross-device interactions, or as a tool for research in fields involving finger and hand motion.


title: Feature-adaptive GPU rendering of Catmull-Clark subdivision surfaces

abstract: We present a novel method for high-performance GPU-based rendering of Catmull-Clark subdivision surfaces. Unlike previous methods, our algorithm computes the true limit surface up to machine precision, and is capable of rendering surfaces that conform to the full RenderMan specification for Catmull-Clark surfaces. Specifically, our algorithm can accommodate base meshes consisting of arbitrary valence vertices and faces, and the surface can contain any number and arrangement of semisharp creases and hierarchically defined detail. We also present a variant of the algorithm which guarantees watertight positions and normals, meaning that even displaced surfaces can be rendered in a crack-free manner. Finally, we describe a view-dependent level-of-detail scheme which adapts to both the depth of subdivision and the patch tessellation density. Though considerably more general, the performance of our algorithm is comparable to the best approximating method, and is considerably faster than Stam's exact method.


title: A fabric-regulated soft robotic glove with user intent detection using EMG and RFID for hand assistive application

abstract: This paper presents a soft robotic glove designed to assist individuals with functional grasp pathologies in performing activities of daily living. The glove utilizes soft fabric-regulated pneumatic actuators that are low-profile and require lower pressure than previously developed actuators. They are able to support fingers and thumb motions during hand closure. Upon pressurization, the actuators are able to generate sufficient force to assist in hand closing and grasping during different manipulation tasks. In this work, experiments were conducted to evaluate the performances of the actuators as well as the glove in terms of its kinetic and kinematic assistance on a healthy participant. Additionally, surface electromyography and radio-frequency identification techniques were adopted to detect user intent to activate or deactivate the glove. Lastly, we present preliminary results of a healthy participant performing different manipulation tasks with the soft robotic glove controlled by surface electromyography and radio-frequency identification techniques.


title: The Effect of Compensation on Repurchase Intentions in Service Recovery

abstract: To explore when the presence of compensation enhances repurchase intentions after a service failure, the authors use an experimental procedure and evaluate the impact of compensation in different stability and locus of responsibility conditions. Findings from three studies using scenarios from different service industries indicate that compensation is necessary only when the company is responsible for the failure and the failure occurs frequently. If the failure occurs infrequently or the company is not responsible, compensation does not affect repurchase intentions. The results further demonstrate that stability and locus of responsibility attributions influence the perceived equity of the exchange, which mediates the effectiveness of compensation as a recovery effort. The authors discuss the theoretical and managerial implications.


title: An application research of rainfall-water level forecast model based on BP network

abstract: In this paper,the adaptive adjustment algorithm of the momentum and learning rate of BP Network is improved. The structure of BP network and the data processing method of flood forecast model are discussed. In order to predict the flood water level directly,the rainfall-water level forecast model is built. Furthermore,a new modeling scheme,in which the point rainfall data are used as the model input,is proposed,and the accuracy of BP network is improved.


Misplacing Trust in Bitcoin Information Sources Extended


title: Gilbreth: A Conveyor-Belt Based Pick-and-Sort Industrial Robotics Application

abstract: This paper describes an industrial robotics application, named Gilbreth, for picking up objects of different types from a moving conveyor belt and sorting the objects into bins according to type. The environment, which consists of a moving conveyor belt, a break beam sensor, a 3D camera Kinect sensor, a UR10 industrial robot arm with a vacuum gripper, and different object types such as gears, pulleys, piston rods, was inspired by the NIST ARIAC competition. A first version of the Gilbreth application was implemented leveraging many ROS and ROS-I packages. Gazebo was used to simulate the environment, and six external ROS nodes were implemented to execute the required functions. Experimental measurements of CPU usage and processing times of ROS nodes were obtained. Object recognition required the highest processing times that were on par with the time required for the robot arm to execute its movement between four poses: pick approach, pick, pick retreat and place. A need for enhancing the performance of object recognition and Gazebo simulation was identified.


title: Hybridization of RSA and AES algorithms for authentication and confidentiality of medical images

abstract: Image security is an essential part of communication through network. Nowadays the use of network and transmission of images has been widely increased. These images can be attacked by intruders and destroy the quality of the images or modified the images for illegal use. Considering these issues the proposed system ensuring the confidentiality, integrity and authentication of images using symmetric and asymmetric key cryptographic algorithm. Which make use of hybridization of two techniques i.e., RSA and AES for authentication and confidentiality. For improving the image integrity proposed system implemented image watermarking using LSB hiding algorithm. Watermarking is the process of hiding one image into another for copyright protection. RSA will be used for key generation and once key get generated, using AES algorithm watermarked image will be encrypted and send through a network. The receiving end the image will be decrypted and retrieved original watermark image.


title: Variation tolerant 9T SRAM cell design

abstract: Nanoscale SRAM memory design has become increasingly challenging due to the reducing noise margins and increased sensitivity to threshold voltage variations. These issues oppose our ability to achieve stable bitcells and acceptable performance while maintaining density using the standard six-transistor(6T) circuit. To overcome these challenges, researchers have proposed different topologies for SRAMs with single-ended 8T, 9T, 10T bitcell designs. These designs improve the cell stability in the subthreshold regime but suffer from bit-line leakage noise, placing constraints on the number of cells shared by each bitline. In this paper, we propose a novel 9T SRAM cell topology which achieves both cell stability as well as prevents bit-line leakage. With the proposed 9T SRAM circuit, the read static noise margin is nearly twice that of conventional 6T SRAM circuit. Furthermore, the bitline leakage power consumption of the proposed 9T SRAM cell is reduced by up to 79\%, 76\% and 39\% when compared to the previously published 8T, 10T and 9T SRAM cells, respectively.


title: Enriching behavioral ecology with reinforcement learning methods

abstract: Abstract This article focuses on the division of labor between evolution and development in solving sequential, state-dependent decision problems. Currently, behavioral ecologists tend to use dynamic programming methods to study such problems. These methods are successful at predicting animal behavior in a variety of contexts. However, they depend on a distinct set of assumptions. Here, we argue that behavioral ecology will benefit from drawing more than it currently does on a complementary collection of tools, called reinforcement learning methods. These methods allow for the study of behavior in highly complex environments, which conventional dynamic programming methods do not feasibly address. In addition, reinforcement learning methods are well-suited to studying how biological mechanisms solve developmental and learning problems. For instance, we can use them to study simple rules that perform well in complex environments. Or to investigate under what conditions natural selection favors fixed, non-plastic traits (which do not vary across individuals), cue-driven-switch plasticity (innate instructions for adaptive behavioral development based on experience), or developmental selection (the incremental acquisition of adaptive behavior based on experience). If natural selection favors developmental selection, which includes learning from environmental feedback, we can also make predictions about the design of reward systems. Our paper is written in an accessible manner and for a broad audience, though we believe some novel insights can be drawn from our discussion. We hope our paper will help advance the emerging bridge connecting the fields of behavioral ecology and reinforcement learning.


title: Differences in peripartal plasma parameters related to calcium homeostasis of dairy sheep and goats in comparison with cows.

abstract: Recently it has been demonstrated that there are differences between sheep and goats in respect to adaptation to a calcium-restricted diet. It was the aim of the present study to evaluate whether species-specific peculiarities also occur when calcium homoeostasis is challenged by lactation. Therefore, we investigated the time courses of plasma parameters related to calcium homoeostasis (calcium, phosphate, calcitriol, the bone resorption marker CrossLaps and the bone formation marker osteocalcin) during the transition period in multiparous animals of both species and compared the results to data from a former study carried out with dairy cows. As in cows, plasma calcium and the ratio of bone formation to bone resorption decreased at parturition in goats while plasma calcitriol increased. On day 10 post partum the bone parameters of goats reached prepartum values again, which was not the case in cows. Sheep were found to experience a challenge of calcium homoeostasis already 10 d before parturition, reflected by a very low ratio of bone formation to bone resorption, which was not accompanied by an increase in plasma calcitriol. Additionally, sheep and goats which had been in milk for 3 months were sampled, dried-off and sampled again 6 weeks later. In dried-off animals there were no detectable differences in parameters of bone metabolism. In conclusion we could show that the contribution of bone mobilisation to the compensation for the enhanced calcium demand due to lactation differs between the three ruminant species.


title: SetExpan: Corpus-Based Set Expansion via Context Feature Selection and Rank Ensemble

abstract: Corpus-based set expansion (i.e., finding the “complete” set of entities belonging to the same semantic class, based on a given corpus and a tiny set of seeds) is a critical task in knowledge discovery. It may facilitate numerous downstream applications, such as information extraction, taxonomy induction, question answering, and web search.


title: A case for bufferless routing in on-chip networks

abstract: Buffers in on-chip networks consume significant energy, occupy chip area, and increase design complexity. In this paper, we make a case for a new approach to designing on-chip interconnection networks that eliminates the need for buffers for routing or flow control. We describe new algorithms for routing without using buffers in router input/output ports. We analyze the advantages and disadvantages of bufferless routing and discuss how router latency can be reduced by taking advantage of the fact that input/output buffers do not exist. Our evaluations show that routing without buffers significantly reduces the energy consumption of the on-chip cache/processor-to-cache network, while providing similar performance to that of existing buffered routing algorithms at low network utilization (i.e., on most real applications). We conclude that bufferless routing can be an attractive and energy-efficient design option for on-chip cache/processor-to-cache networks where network utilization is low.


title: Fault tolerancy in Cooperative Adaptive Cruise Control

abstract: Future mobility requires sound solutions in the field of fault tolerance in real-time applications amongst which Cooperative Adaptive Cruise Control (CACC). This control system cannot rely on the driver as a backup and is constantly active and therefore more prominent to the occurrences of faults (such as packet loss in the wireless communication). This paper presents an algorithm which uses the availability of sensor-data in each moment in time to calculate in real-time a safe distance for the CACC system. This safe distance consists of a velocity dependent part and a constant part, both important settings in the spacing policy for the CACC. A critical scenario for which CACC should still be functional is chosen, mathematical models of the system response are derived and errors are modeled. The model enables the calculation of differences in braking distances, which leads to a required safe distance. This approach makes the dynamic algorithm useful for sensor faults. The algorithm is implemented in a test vehicle and test results are presented.


title: Control of Harmonic Drive Motor Actuated Flexible Linkages

abstract: Friction models and compensation methods are applied to harmonic drive motors used with flexible robotic linkages. In the absence of output torque measurements and output shaft encoder data nearly complete friction compensation is achieved allowing the application of a passivity based controller. Simulation and experimental results are given.


Extensive Analysis and Prediction of Optimal Inventory levels in supply chain management based on Particle Swarm Optimization Algorithm


title: A Supply Chain Network Equilibrium Model with Random Demands

abstract: Abstract In this paper, we develop a supply chain network model consisting of manufacturers and retailers in which the demands associated with the retail outlets are random. We model the optimizing behavior of the various decision-makers, derive the equilibrium conditions, and establish the finite-dimensional variational inequality formulation. We provide qualitative properties of the equilibrium pattern in terms of existence and uniqueness results and also establish conditions under which the proposed computational procedure is guaranteed to converge. Finally, we illustrate the model through several numerical examples for which the equilibrium prices and product shipments are computed. This is the first supply chain network equilibrium model with random demands for which modeling, qualitative analysis, and computational results have been obtained.


title: 3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic Parsing of Large-Scale 3D Point Clouds

abstract: Semantic parsing of large-scale 3D point clouds is an important research topic in computer vision and remote sensing fields. Most existing approaches utilize hand-crafted features for each modality independently and combine them in a heuristic manner. They often fail to consider the consistency and complementary information among features adequately, which makes them difficult to capture high-level semantic structures. The features learned by most of the current deep learning methods can obtain high-quality image classification results. However, these methods are hard to be applied to recognize 3D point clouds due to unorganized distribution and various point density of data. In this paper, we propose a 3DCNN-DQN-RNN method which fuses the 3D convolutional neural network (CNN), Deep Q-Network (DQN) and Residual recurrent neural network (RNN) for an efficient semantic parsing of large-scale 3D point clouds. In our method, an eye window under control of the 3D CNN and DQN can localize and segment the points of the object class efficiently. The 3D CNN and Residual RNN further extract robust and discriminative features of the points in the eye window, and thus greatly enhance the parsing accuracy of large-scale point clouds. Our method provides an automatic process that maps the raw data to the classification results. It also integrates object localization, segmentation and classification into one framework. Experimental results demonstrate that the proposed method outperforms the state-of-the-art point cloud classification methods.


Explorer Phase-Functioned Neural Networks for Character Control


title: Bayesian network model for diagnosis of psychiatric diseases

abstract: Because of numerous possible causes involved, it isn't easy for general physicians to identify the precise reason of the psychiatric diseases and to decide the correct treatment. Bayesian networks are recognized as efficient graphical models with significant capabilities for investigating biomedical data either to obtain relationships between variables, either for medical predictions. Our paper provides a Bayesian network-based analysis of psychiatric patient data, which have been gathered from a Romanian specialized clinic during a couple of years. The development of this Bayesian network led us to the identification of most significant factors that affect some important diseases and their correlations.


title: MPPT performance improvement of PV system using hybrid-PI controller

abstract: This paper proposes the hybrid-PI hybrid-PI control for MPPT control of photovoltaic system. The conventional constant voltage(CV), perturbation and observation(PO) and incremental conductance(IC) are the method which finding maximum power point(MPP) by the continued self-excitation vibration, and uses the fixed step size. If the fixed step size is a large, the tracking speed of maximum power point is faster, but the tracking accuracy in the steady state is decreased. On the contrary, when the fixed step size is a small, the tracking accuracy is increased and the tracking speed is slower. Therefore, in order to solve these problems, this paper proposes hybrid-PI controller that is adjusted gain of conventional PI control using fuzzy control, and the maximum power point tracks using this controller. The validity of the controller proposed in this paper proves through the results of the comparisons.


title: Concept and architecture for programming industrial robots using augmented reality with mobile devices like microsoft HoloLens

abstract: This paper proposes a concept for human-robot interaction using techniques of virtual and augmented reality on mobile devices like cell phones and tablets or mixed reality devices like the HoloLens. By combining data received from real robots together with the perception and abilities of a human operator innovative applications are imaginable. Visualizing not only the current robot state but also the robots environment captured with different sensors and processed with both machine and human vision can lead to a rising percentage of robot assisted workplaces or robot installations. Since the visualization of and the interaction with the robotic application is not locally restricted new or improved use cases like remote maintenance or faster startup of industrial robots can be realized. Therefore, an architecture split into three functional units and an overview of our implementation is presented.


title: What is DevOps?: A Systematic Mapping Study on Definitions and Practices

abstract: Context: DevOps, the combination of Development and Operations, is a new way of thinking in the software engineering domain that recently received much attention. Given that DevOps is a new term and novel concept recently introduced, no common understanding of what it entails has been achieved yet. Consequently, definitions of DevOps often only represent a part that is relevant to the concept. Objective:This study aims to characterize DevOps by exploring central components of DevOps definitions reported in the literature, specifying practices explicitly proposed for DevOps and investigating the similarities and differences between DevOps and other existing methods in software engineering. Method: A systematic mapping study was conducted that used six electronic databases: IEEE, ACM, Inspec, Scopus, Wiley Online Library and Web of Science. Result: 44 studies have been selected that report a definition of DevOps, 15 studies explicitly stating DevOps practices, and 15 studies stating how DevOps is related to other existing methods. Papers in some cases stated a combination of a definition, practices, and relations to other methods, the total number of primary studies was 49. Conclusion: We proposed a definition for DevOps which may overcome inconsistencies over the various existing definitions of individual research studies. In addition, the practices explicitly proposed for DevOps have been presented as well as the relation to other software development methods.


title: Overlapped community detection in complex networks

abstract: Extracting and understanding community structure in complex networks is one of the most intensively investigated problems in recent years. In this paper we propose a genetic based approach to discover overlapping communities. The algorithm optimizes a fitness function able to identify densely connected groups of nodes by employing it on the line graph corresponding to the graph modelling the network. The method generates a division of the network in a number of groups in an unsupervised way. This number is automatically determined by the optimal value of the fitness function. Experiments on synthetic and real life networks show the capability of the method to successfully detect the network structure.


Simultaneous Recognition and Pose Estimation of Multiple 3 D Objects : Real-Time Probabilistic Methods using Kinect Data


title: Bias-Variance Error Bounds for Temporal Difference Updates

abstract: Temporal difference (td) algorithms are used in reinforcement learning to compute estimates of the value of a given policy in an unknown Markov decision process (policy evaluation). We give rigorous upper bounds on the error of the closely related phased td algorithms (which differ from the standard updates in their treatment of the learning rate) as a function of the amount of experience. These upper bounds prove exponentially fast convergence, with both the rate of convergence and the asymptote strongly dependent on the length of the backups k or the parameter . Our bounds give formal verification to the well-known intuition that td methods are subject to a bias-variance tradeoff, and they lead to schedules for k and that are predicted to be better than any fixed values for these parameters. We give preliminary experimental confirmation of our theory for a version of the random walk problem.


title: Measurement of the acoustic-to-optical phonon coupling in multicomponent systems

abstract: In this paper we investigate the acoustic-to-optical up-conversion phonon processes in a multicomponent system. These processes take place during heat transport and limit the efficiency of heat flow. By combining time-resolved optical and heat capacity experiments we quantify the thermal coupling constant to be g similar to 0.4 10(17) W/Km(3). The method is based on selective excitation of a part of a multicomponent system, and the measurement of the thermalization dynamics by probing the linear birefringence of the sample with femtosecond resolution. In particular, we study a layered multiferroic organic-inorganic hybrid, in the vicinity of the ferroelectric phase transition. A diverging term of the heat capacity is associated to soft-mode dynamics, in agreement with previous spectroscopy measurements.


title: Continuous Manifold Based Adaptation for Evolving Visual Domains

abstract: We pose the following question: what happens when test data not only differs from training data, but differs from it in a continually evolving way? The classic domain adaptation paradigm considers the world to be separated into stationary domains with clear boundaries between them. However, in many real-world applications, examples cannot be naturally separated into discrete domains, but arise from a continuously evolving underlying process. Examples include video with gradually changing lighting and spam email with evolving spammer tactics. We formulate a novel problem of adapting to such continuous domains, and present a solution based on smoothly varying embeddings. Recent work has shown the utility of considering discrete visual domains as fixed points embedded in a manifold of lower-dimensional subspaces. Adaptation can be achieved via transforms or kernels learned between such stationary source and target subspaces. We propose a method to consider non-stationary domains, which we refer to as Continuous Manifold Adaptation (CMA). We treat each target sample as potentially being drawn from a different subspace on the domain manifold, and present a novel technique for continuous transform-based adaptation. Our approach can learn to distinguish categories using training data collected at some point in the past, and continue to update its model of the categories for some time into the future, without receiving any additional labels. Experiments on two visual datasets demonstrate the value of our approach for several popular feature representations.


title: A New Feedback Method for PR Current Control of LCL-Filter-Based Grid-Connected Inverter

abstract: For a grid-connected converter with an LCL filter, the harmonic compensators of a proportional-resonant (PR) controller are usually limited to several low-order current harmonics due to system instability when the compensated frequency is out of the bandwidth of the system control loop. In this paper, a new current feedback method for PR current control is proposed. The weighted average value of the currents flowing through the two inductors of the LCL filter is used as the feedback to the current PR regulator. Consequently, the control system with the LCL filter is degraded from a third-order function to a first-order one. A large proportional control-loop gain can be chosen to obtain a wide control-loop bandwidth, and the system can be optimized easily for minimum current harmonic distortions, as well as system stability. The inverter system with the proposed controller is investigated and compared with those using traditional control methods. Experimental results on a 5-kW fuel-cell inverter are provided, and the new current control strategy has been verified.


title: Barriers to providing effective mental health services to Asian Americans.

abstract: Using the research framework recommended by L. Rogler, R. Malgady, and D. Rodriguez (1989), the current paper examines the barriers to providing effective mental health services to Asian Americans. Beginning with the recognition that Asian Americans consists of numerous heterogeneous subgroups, the issue of the stereotype of Asian Americans as the “model minority” was also discussed. The primary focus of the paper is on Stages 2, 3, and 4 within the Rogler et al. (1989) model and the identification and discussion of cultural factors that hinder the delivery of mental health services to Asian Americans. The paper is therefore organized into these three sections: (a) help-seeking or mental health service utilization, (b) evaluation of mental health problems, and (c) psychotherapeutic services. In each of the sections, not only are the barriers to delivery of effective mental health services discussed but so are the research and methodological problems as well as some directions for future research. This critical review of the literature has been prepared with the goal of serving as a “blueprint” for us to pursue rigorous but relevant research to identify and reduce these cultural barriers to providing effective mental health services to Asian Americans.


Heterogeneous Teams of Modular Robots for Mapping and Exploration


title: Compressive sensing based channel feedback protocols for spatially-correlated massive antenna arrays

abstract: Incorporating wireless transceivers with numerous antennas (such as Massive-MIMO) is a prospective way to increase the link capacity or enhance the energy efficiency of future communication systems. However, the benefits of such approach can be realized only when proper channel information is available at the transmitter. Since the amount of the channel information required by the transmitter is large with so many antennas, the feedback is arduous in practice, especially for frequency division duplexing (FDD) systems. This paper proposes channel feedback reduction techniques based on the theory of compressive sensing, which permits the transmitter to obtain channel information with acceptable accuracy under substantially reduced feedback load. Furthermore, by leveraging properties of compressive sensing, we present two adaptive feedback protocols, in which the feedback content can be dynamically configured based on channel conditions to improve the efficiency.


title: Vehicular WiFi Offloading : Challenges and Solutions

abstract: WiFi offloading is envisioned as a promising solution to the mobile data explosion problem in cellular networks. WiFi offloading for moving vehicles, however, poses unique characteristics and challenges, due to high mobility, fluctuating mobile channels, etc. In this paper, we focus on the problem of WiFi offloading in vehicular communication environments. Specifically, we discuss the challenges and identify the research issues related to drive-thru Internet access and effectiveness of vehicular WiFi offloading. Moreover, we review the state-of-the-art offloading solutions, in which advanced vehicular communications can be employed. We also shed some lights on the path for future research on this topic.


title: Experimental characterization of components for active soft orthotics

abstract: In this paper, we present the characterization of soft pneumatic actuators currently under development for active soft orthotics. The actuators are tested statically and dynamically to characterize force and displacement properties needed for use in system design. They are shown to demonstrate remarkably repeatable performance, and very predictable behavior as actuator size (initial length) is varied to adjust total actuation. The results of the characterization are then used to inform design rules for selecting the size and number of actuators for a soft leg orthotic that can produce the force/moment/velocity properties required for a target wearer. The device was tested on a silicone model with inner frame and articulated ankle. Additionally, a portable pneumatic system was developed and characterized, that can actuate the pneumatic actuator over 800 times before refilling the gas supply.


title: An algorithm of moving objects detection for indoor environment based on laser and vision

abstract: In this paper, a new algorithm of moving objects detection is proposed for indoor environment, which can effectively overcome light changing and can better detect and extract moving objects in indoor environment. The algorithm first transforms images in the form of RGB into the form of HSI for better image processing, and then detects and extracts moving objects based on image and distance information. One core of the algorithm is effectively combining information of image and distance during the process of motion detection, which can better overcome light changing in indoor environment; the other is reasonable using of the background subtraction and the frame difference during the process of moving objects extraction, so that the moving objects can be extracted more accurately and completely. Experimental results have validated the feasibility and reliability of the algorithm.


title: Three-dimensional CFD study of liquid–solid flow behaviors in tubular loop polymerization reactors: The effect of guide vane

abstract: National Natural Science Foundation of China[21076171]; China National Petroleum Corporation


title: Locally Weighted Ensemble Clustering

abstract: Due to its ability to combine multiple base clusterings into a probably better and more robust clustering, the ensemble clustering technique has been attracting increasing attention in recent years. Despite the significant success, one limitation to most of the existing ensemble clustering methods is that they generally treat all base clusterings equally regardless of their reliability, which makes them vulnerable to low-quality base clusterings. Although some efforts have been made to (globally) evaluate and weight the base clusterings, yet these methods tend to view each base clustering as an individual and neglect the local diversity of clusters inside the same base clustering. It remains an open problem how to evaluate the reliability of clusters and exploit the local diversity in the ensemble to enhance the consensus performance, especially in the case when there is no access to data features or specific assumptions on data distribution. To address this, in this paper, we propose a novel ensemble clustering approach based on ensemble-driven cluster uncertainty estimation and local weighting strategy. In particular, the uncertainty of each cluster is estimated by considering the cluster labels in the entire ensemble via an entropic criterion. A novel ensemble-driven cluster validity measure is introduced, and a locally weighted co-association matrix is presented to serve as a summary for the ensemble of diverse clusters. With the local diversity in ensembles exploited, two novel consensus functions are further proposed. Extensive experiments on a variety of real-world datasets demonstrate the superiority of the proposed approach over the state-of-the-art.


title: A Handful of Heuristics and Some Propositions for Understanding Resilience in Social-Ecological Systems

abstract: This paper is a work-in-progress account of ideas and propositions about resilience in socialecological systems. It articulates our understanding of how these complex systems change and what determines their ability to absorb disturbances in either their ecological or their social domains. We call them “propositions” because, although they are useful in helping us understand and compare different social-ecological systems, they are not sufficiently well defined to be considered formal hypotheses. These propositions were developed in two workshops, in 2003 and 2004, in which participants compared the dynamics of 15 case studies in a wide range of regions around the world. The propositions raise many questions, and we present a list of some that could help define the next phase of resilience-related research.


title: Biologically Inspired Miniature Water Strider Robot

abstract: Recent biological studies on water strider insects revealed the detailed mechanism of their staying and walking on water. While macro scale bodies use buoyancy to stay on water, these very light and small insects balance their weight using repulsive surface tension forces where the insect legs are covered with hydrophobic micro-hairs. Utilizing the unique scaling advantage of these insects, this paper proposes a biologically inspired miniature micro-robot walking on water with a similar principle. The paper focuses on understanding physical characteristics of the insect and designing a robot that mimics the insect's movement. Highly hydrophobic Teflon ® coated wires are used for the legs to take advantage of surface tension force, and the robot body is made of carbon fibers for minimal weight. A T-shaped actuation mechanism with three PZT-5H based unimorph actuators is utilized to move the side legs of the robot independently for controlled locomotion. Kinematics and dynamic properties of the robot prototype are analyzed and compared with the experimental results. The tethered robot can successfully move forward, backward and can also make turns. Maximum speed of the robot in forward motion is 2.3 cm/s. In the future, environmental monitoring applications on dams, lakes, sea, etc. would become possible using a network of these robots with miniature sensors, an on-board power source and electronics.