title: Wavelet support vector machine

abstract: An admissible support vector (SV) kernel (the wavelet kernel), by which we can construct a wavelet support vector machine (SVM), is presented. The wavelet kernel is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. The existence of wavelet kernels is proven by results of theoretic analysis. Computer simulations show the feasibility and validity of wavelet support vector machines (WSVMs) in regression and pattern recognition.


title: Attachment Security in Couple Relationships: A Systemic Model and Its Implications for Family Dynamics

abstract: Theory and research on adult attachment style emphasize the crucial role that the sense of attachment security plays in the formation and maintenance of couple relationships. In the present article, we review studies that have examined the contribution of adult attachment style to relational cognitions, emotions, and behaviors as well as to the formation, quality, and stability of dating and marital relationships. We discuss some of the measurement and design issues raised by this research. Based on the reviewed findings, we provide an integrative, systemic theoretical model delineating how the links between partners' attachment security and the quality of their couple relationship occurs. Finally, we discuss the implications of this model for the understanding of how attachment style and couple relationships combine to affect the family system in general, and parent-child relationships and children's developmental outcomes, in particular.


title: Indoor scene structure analysis for single image depth estimation

abstract: We tackle the problem of single image depth estimation, which, without additional knowledge, suffers from many ambiguities. Unlike previous approaches that only reason locally, we propose to exploit the global structure of the scene to estimate its depth. To this end, we introduce a hierarchical representation of the scene, which models local depth jointly with mid-level and global scene structures. We formulate single image depth estimation as inference in a graphical model whose edges let us encode the interactions within and across the different layers of our hierarchy. Our method therefore still produces detailed depth estimates, but also leverages higher-level information about the scene. We demonstrate the benefits of our approach over local depth estimation methods on standard indoor datasets.


title: Comparison between android and iOS Operating System in terms of security

abstract: This paper compares between android and iPhone Operating System (iOS) mobile operating systems (MOS) that available in the market which is more specific on the security issue. These issues are reportedly the concern of not only the mobile customers but also the software developers. In achieving security requirements, the MOS developers need to know how to achieve the criteria. The security requirements for MOS are Application Sandboxing, Memory Randomization, Encryption, Data Storage Format and Built-in Antivirus. Application sandboxing enforces permissions, privileges, directories, entitlements and kernel access for a mobile app. Memory randomization ensures that the memory regions of mobile application as well as system shared libraries are all randomized at device and application start-up. Encryption is performed on disk or filer/folder level and also at the interprocess communication level. It is difficult to speak in favor or against the android or the iOS operating system in terms of better security. The way of using the device plays a major role in determining the security level. In terms of storage, all data are stored in Data Storage Format. Data can be stored at internal storage or external storage. To protect the MOS from virus attacks, antivirus need to be installed for increasing security areas.


title: Information Transfer in Social Media

abstract: Recent research has explored the increasingly important role of social media by examining the dynamics of individual and group behavior, characterizing patterns of information diffusion, and identifying influential individuals. In this paper we suggest a measure of causal relationships between nodes based on the information--theoretic notion of transfer entropy, or information transfer. This theoretically grounded measure is based on dynamic information, captures fine--grain notions of influence, and admits a natural, predictive interpretation. Networks inferred by transfer entropy can differ significantly from static friendship networks because most friendship links are not useful for predicting future dynamics. We demonstrate through analysis of synthetic and real-world data that transfer entropy reveals meaningful hidden network structures. In addition to altering our notion of who is influential, transfer entropy allows us to differentiate between weak influence over large groups and strong influence over small groups.


title: Estimating driving behavior by a smartphone

abstract: In this paper, we propose an approach to understand the driver behavior using smartphone sensors. The aim for analyzing the sensory data acquired using a smartphone is to design a car-independent system which does not need vehicle mounted sensors measuring turn rates, gas consumption or tire pressure. The sensory data utilized in this paper includes the accelerometer, gyroscope and the magnetometer. Using these sensors we obtain position, speed, acceleration, deceleration and deflection angle sensory information and estimate commuting safety by statistically analyzing driver behavior. In contrast to state of the art, this work uses no external sensors, resulting in a cost efficient, simplistic and user-friendly system.


title: Malaysian Vehicle License Plate Recognition

abstract: Vehicle license plate recognition is an image-processing technology used to identify vehicles by their license plates. This technology can be used in various security and traffic applications, such as finding stolen cars, controlling access to car parks and gathering traffic flow statistics. In this paper an approach to license plate localization and recognition is presented. A proposed method to perform recognition of license plates under any environmental conditions, with no assumptions about the orientation of the plate or its distance from the camera is designed. To solve the problem of localization of a license plate, a simple texture-based approach based on edge information is used. Segmentation of characters is performed by using connected components analysis on license plate’s image, and a simple multi-layer Perceptron neural network is used to recognize them. Simulation results were shown to be an efficient method for real time plate recognition.


title: An Example-Based Face Hallucination Method for Single-Frame, Low-Resolution Facial Images

abstract: This paper proposes a face hallucination method for the reconstruction of high-resolution facial images from single-frame, low-resolution facial images. The proposed method has been derived from example-based hallucination methods and morphable face models. First, we propose a recursive error back-projection method to compensate for residual errors, and a region-based reconstruction method to preserve characteristics of local facial regions. Then, we define an extended morphable face model, in which an extended face is composed of the interpolated high-resolution face from a given low-resolution face, and its original high-resolution equivalent. Then, the extended face is separated into an extended shape and an extended texture. We performed various hallucination experiments using the MPI, XM2VTS, and KF databases, compared the reconstruction errors, structural similarity index, and recognition rates, and showed the effects of face detection errors and shape estimation errors. The encouraging results demonstrate that the proposed methods can improve the performance of face recognition systems. Especially the proposed method can enhance the resolution of single-frame, low-resolution facial images.


title: Design Challenges and Misconceptions in Named Entity Recognition

abstract: We analyze some of the fundamental design challenges and misconceptions that underlie the development of an efficient and robust NER system. In particular, we address issues such as the representation of text chunks, the inference approach needed to combine local NER decisions, the sources of prior knowledge and how to use them within an NER system. In the process of comparing several solutions to these challenges we reach some surprising conclusions, as well as develop an NER system that achieves 90.8 F1 score on the CoNLL-2003 NER shared task, the best reported result for this dataset.


title: Preadolescents' and adolescents' online communication and their closeness to friends

abstract: The 1st goal of this study was to investigate how online communication is related to the closeness of existing friendships. Drawing from a sample of 794 preadolescents and adolescents, the authors found that online communication was positively related to the closeness of friendships. However, this effect held only for respondents who primarily communicated online with existing friends and not for those who mainly talked with strangers. The 2nd goal was to refine 2 opposing hypotheses, the rich-get-richer and the social compensation hypotheses. Consistent with the rich-get-richer hypothesis, socially anxious respondents communicated online less often than did nonsocially anxious respondents. However, socially anxious respondents perceived the Internet as more valuable for intimate self-disclosure than did nonsocially anxious respondents, and this perception in turn led to more online communication. This result is consistent with the social compensation hypothesis. Online communication and closeness to friends increased with age. There was a curvilinear relationship between age and perceived value of the Internet for intimate self-disclosure, such that 15-year-olds were at the epitome of online self-disclosure. Girls were closer to friends and more socially anxious than were boys.


title: Sequential Projection Learning for Hashing with Compact Codes

abstract: Hashing based Approximate Nearest Neighbor (ANN) search has attracted much attention due to its fast query time and drastically reduced storage. However, most of the hashing methods either use random projections or extract principal directions from the data to derive hash functions. The resulting embedding suffers from poor discrimination when compact codes are used. In this paper, we propose a novel data-dependent projection learning method such that each hash function is designed to correct the errors made by the previous one sequentially. The proposed method easily adapts to both unsupervised and semi-supervised scenarios and shows significant performance gains over the state-of-the-art methods on two large datasets containing up to 1 million points.


title: A unified framework for capacitive series-parallel DC-DC converter design

abstract: This paper presents a design framework to select the best topology and conversion ratio of operation for series-parallel capacitive buck/boost DC-DC converters. The framework models all converter families providing design insights and tradeoffs among the various topologies. We select the 10 optimal topologies that always performs better than any of the 96 permutations that are possible for series-parallel converters. A logical extension of the framework includes the impact of parasitics and other second order effects including partial charging of bucket capacitors and finite tank capacitor size. The paper includes a step by step design methodology. Finally, we analyze a number of prior designs and one new design and show how they fit into our framework.


title: Strategic innovation through outsourcing: The role of relational and contractual governance

abstract: There is growing evidence that client firms expect outsourcing suppliers to transform their business. Indeed, most outsourcing suppliers have delivered IT operational and business process innovation to client firms; however, achieving strategic innovation through outsourcing has been perceived to be far more challenging. Building on the growing interest in the IS outsourcing literature, this paper seeks to advance our understanding of the role that relational and contractual governance plays in achieving strategic innovation through outsourcing. We hypothesized and tested empirically the relationship between the quality of client-supplier relationships and the likelihood of achieving strategic innovation, and the interaction effect of different contract types, such as fixed-price, time and materials, partnership and their combinations. Results from a pan-European survey of 248 large firms suggest that high-quality relationships between clients and suppliers may indeed help achieve strategic innovation through outsourcing. However, within the spectrum of various outsourcing contracts, only the partnership contract, when included in the client contract portfolio alongside either fixed-price, time and materials or their combination, presents a significant positive effect on relational governance and is likely to strengthen the positive effect of the quality of client-supplier relationships on strategic innovation.


title: Recommendations of the Sardinian public for the treatment of depression

abstract: Background: Public beliefs about appropriate treatment impact, help-seeking and treatment adherence. Aim: To determine the recommendations of the Sardinian public for the treatment of depression. Methods: In 2012, a population-based survey was conducted by phone in Sardinia ( N = 1,200). In the context of a fully structured interview, respondents were presented with a vignette depicting a case of depression. Subsequently, they were asked about their treatment recommendations. The results are contrasted with findings from a similar survey which had been conducted in Vienna 3 years before. Results: In Sardinia as in Vienna, psychotherapy was the uncontested favorite, while antidepressant medication was recommended by relatively few respondents. In Sardinia, there were also no marked differences between urban and rural areas with regard to these two treatments. However, between Sardinia and Vienna, as well as within Sardinia, great differences were found with regard to autogenic training and â€˜alternativeâ€™ methods like homeopathic medicines and acupuncture. Conclusion: Cross-cultural comparisons may help better understand treatment preferences of the public. In Sardinia, as in Vienna, there seems to be a need for improving the publicâ€™s knowledge about the appropriate treatment of depression.


title: Do formulated feeds for juvenile Panulirus ornatus lobsters require dietary cholesterol supplementation

abstract: Abstract Mortality rates of wild spiny lobster seed Panulirus ornatus are typically high, with losses of 40–60% occurring within 30 days of capture. Mortality appears symptomatic of a micro-nutrient deficiency or captivity related stressors. Cholesterol is a micro-nutrient essential for healthy ecdysis, growth and survival in crustaceans. This paper reports a 7-week study examining the growth, survival and tissue cholesterol responses of juvenile P. ornatus lobsters fed a low cholesterol basal diet that was serially supplemented with cholesterol at 0.75 g kg − 1 increments from 1.25 to 4.25 g kg − 1 dry matter (DM). The basal diet contained 45 g kg − 1 phospholipid (from natural ingredients and supplemented soybean lecithin) and this was fed to all lobsters for 2 weeks prior to commencement of the experiment to reduce cholesterol reserves in the lobsters. A sixth diet comprising equal amounts of green-lipped mussel ( Perna canaliculus ) and whiting fillet ( Silago ciliate ) was included in the treatment array as a reference diet. One hundred and fifty lobsters were blocked by initial weight into five groups and then from within these groups they were equally distributed to 30 tanks (n = 5 tanks per treatment). There was no significant difference in survival, daily growth coefficient or tissue dry matter cholesterol content between lobsters fed the lowest and highest cholesterol diets: 55%, 349%, 243 mg 100 g − 1 and 70%, 456%, 253 mg 100 g − 1 respectively. However, there was a trend (P = 0.065) for lobster percentage weight gain to increase linearly with dietary cholesterol. There was a significant relationship between the starting weight of the lobsters and subsequent survival, with only 43% of the smallest lobsters (0.2 to 0.5 g block) surviving compared to 90% for the largest lobsters (2.71 to 3.40 g block). No specific dietary cholesterol requirement for small P. ornatus was determined. Feeds which contain practical marine proteins require no supplementary cholesterol, a significant cost saving in feed formulation. Feeds which contain high levels of plant proteins may require supplementary cholesterol to provide at least 4.0 g kg − 1 DM dietary cholesterol.


title: 3D Body Reconstruction from Photos Based on Range Scan

abstract: We present a data-driven shape model for reconstructing human body models from one or more 2D photos. One of the key tasks in reconstructing the 3D model from image data is shape recovery, a task done until now in utterly geometric way, in the domain of human body modeling. In contrast, we adopt a data-driven, parameterized deformable model that is acquired from a collection of range scans of real human body. The key idea is to complement the image-based reconstruction method by leveraging the quality shape and statistic information accumulated from multiple shapes of range-scanned people. In the presence of ambiguity either from the noise or missing views, our technique has a bias towards representing as much as possible the previously acquired ‘knowledge' on the shape geometry. Texture coordinates are then generated by projecting the modified deformable model onto the front and back images. Our technique has shown to reconstruct successfully human body models from minimum number images, even from a single image input.


title: PHEV control strategy including vehicle to home (V2H) and home to vehicle (H2V) functionalities

abstract: Plug-in Hybrid Electric Vehicles (PHEVs) are seen to be a step forward in vehicle electrification, to replace ICE-based conventional vehicles. On the one hand, using a PHEV means that a part of the vehicle energy comes from the grid or other sources, such as renewable energy, to charge the battery. On the other hand, increasing the number of nuclear and coal power plants to supply these new needs would shift the problem to another place, and will not permit solving the problem of pollution or fossil fuel depletion. The main idea presented in this paper consists in sharing the production between the home and the vehicle and use at the same time the vehicle to supply power to the home appliances during the electricity peak demand (i.e., when the prices are the highest). In this study, a general control strategy is designed, taking into account the overall system (grid, local production from renewable, and vehicle, including its battery). Furthermore, a simulation, including the household daily power profile and the PHEV drive cycle is presented. The home power profile takes into account photovoltaic (PV) and wind production as well as home load profile based on real data. The objective of the control strategy is to minimize the total energy cost.


title: Person reidentification by support vector ranking

abstract: Solving the person re-identification problem involves matching observation s of individuals across disjoint camera views. The problem becomes particularly hard in a busy public scene as the number of possible matches is very high. This is further compounded by significant appearance changes due to varying lighting conditions, vie wing angles and body poses across camera views. To address this problem, existing approaches focus on extracting or learning discriminative features followed by template matching using a distance measure. The novelty of this work is that we reformulate the person reidentification problem as a ranking problem and learn a subspace where th e potential true match is given highest ranking rather than any direct distance measure. By doing so, we convert the person re-identification problem from an absolute scoring p roblem to a relative ranking problem. We further develop an novel Ensemble RankSVMto overcome the scalability limitation problem suffered by existing SVM-based ranking methods. This new model reduces significantly memory usage therefore is much more scalable, whilst maintaining high-level performance. We present extensive experiments to demonstrate the performance gain of the proposed ranking approach over existing template matching and classification models.


title: Software reuse research: status and future

abstract: This paper briefly summarizes software reuse research, discusses major research contributions and unsolved problems, provides pointers to key publications, and introduces four papers selected from The Eighth International Conference on Software Reuse (ICSR8).


title: BRAND EFFICIENCY AND BRAND RELEVANCE: INTRODUCING AND LINKING BOTH CONCEPTS

abstract: Brand managers are under increased pressure to illustrate the performance of their multimillion dollar expenditures. This paper provides three contributions in the context of brand management. First, we introduce the concept of brand efficiency as a measure for the performance of the brand management process. Second, we develop a measure for the industry-specific brand relevance (influence of branding on purchase decisions). Third, we link these concepts and examine if brand management efficiency in an industry is influenced by brand relevance.


title: Cross-language plagiarism detection based on WordNet

abstract: In this paper, we propose a Cross-language Plagiarism Detection (CLPD) methodology based on the WordNet with the summary and analysis of current researches on both the monolingual and cross-linguistic plagiarism detection. Firstly, we build a language-independent semantic mid-layer based on the nouns of WordNet, and implement a semantic hash encoding on that mid-layer. Secondly, we propose a novel approach for nouns' sense disambiguation based on concept correlation, which is applied in the process of obtaining feature sequences of texts. Through analyzing and comparing the results of experiments, it can be concluded that the methodology is indeed effective.


title: A Comparison of the Triangle Algorithm and SMO for Solving the Hard Margin Problem

abstract: In this article we consider the problem of testing, for two finite sets of points in the Euclidean space, if their convex hulls are disjoint and computing an optimal supporting hyperplane if so. This is a fundamental problem of classification in machine learning known as the hard-margin SVM. The problem can be formulated as a quadratic programming problem. The SMO algorithm is the current state of art algorithm for solving it, but it does not answer the question of separability. An alternative to solving both problems is the Triangle Algorithm, a geometrically inspired algorithm, initially described for the convex hull membership problem, a fundamental problem in linear programming. First, we describe the experimental performance of the Triangle Algorithm for testing the intersection of two convex hulls. Next, we compare the performance of Triangle Algorithm with SMO for finding the optimal supporting hyperplane. Based on experimental results ranging up to 5000 points in each set in dimensions up to 10000, the Triangle Algorithm outperforms SMO.


title: Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques

abstract: This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete wavelet transform is applied on EEG signals and the relative wavelet energy is calculated in terms of detailed coefficients and the approximation coefficients of the last decomposition level. The extracted relative wavelet energy features are passed to classifiers for the classification purpose. The EEG dataset employed for the validation of the proposed method consisted of two classes: (1) the EEG signals recorded during the complex cognitive task—Raven’s advance progressive metric test and (2) the EEG signals recorded in rest condition—eyes open. The performance of four different classifiers was evaluated with four performance measures, i.e., accuracy, sensitivity, specificity and precision values. The accuracy was achieved above 98 % by the support vector machine, multi-layer perceptron and the K-nearest neighbor classifiers with approximation (A4) and detailed coefficients (D4), which represent the frequency range of 0.53–3.06 and 3.06–6.12 Hz, respectively. The findings of this study demonstrated that the proposed feature extraction approach has the potential to classify the EEG signals recorded during a complex cognitive task by achieving a high accuracy rate.


title: A Survey of ZigBee Wireless Sensor Network Technology: Topology, Applications and Challenges

abstract: ZigBee technology as a wireless sensor and control network is one of the most popularly deployed wireless technologies in recent years. This is because ZigBee is an open standard lightweight, low-cost, low-speed, low-power protocol that allows true operability between systems. It is built on existing IEEE 802.15.4 protocol and therefore combines the IEEE 802.15.4 features and newly added features to meet required functionalities thereby finding applications in wide variety of wireless personal area networked systems such as home/industrial automation and monitoring systems. Although the ZigBee design specification includes security features to protect data communication confidentiality and integrity, however, when simplicity and low-cost are the major goals, security suffers. This paper gives the general survey of the ZigBee as a wireless sensor network based technology which provides the readers with the general overview of ZigBee network technology including its topology, applications and challenges.


title: Assert Use in GitHub Projects

abstract: Abstract -- Asserts have long been a strongly recommended (if non-functional) adjunct to programs. They certainly don't add any user-evident feature value; and it can take quite some skill and effort to devise and add useful asserts. However, they are believed to add considerable value to the developer. Certainly, they can help with automated verification; but even in the absence of that, claimed advantages include improved understandability, maintainability, easier fault localization and diagnosis, all eventually leading to better software quality. We focus on this latter claim, and use a large dataset of asserts in C and C++ programs to explore the connection between asserts and defect occurrence. Our data suggests a connection: functions with asserts do have significantly fewer defects. This indicates that asserts do play an important role in software quality; we therefore explored further the factors that play a role in assertion placement: specifically, process factors (such as developer experience and ownership) and product factors, particularly interprocedural factors, exploring how the placement of assertions in functions are influenced by local and global network properties of the callgraph. Finally, we also conduct a differential analysis of assertion use across different application domains.


title: A grouser spacing equation for determining appropriate geometry of planetary rover wheels

abstract: Grousers, sometimes called lugs, are recognized as a way to improve wheel performance and traction, but there have been, to date, no comprehensive guidelines for choosing grouser patterns. This work presents a quantitative expression for determining appropriate grouser spacing for rigid wheels. Past empirical studies have shown that increasing grouser height and number can improve performance, to a point. The newly proposed grouser spacing equation is based on observations that wheels with an inadequate number of grousers induce forward soil flow ahead of the wheel, and thus rolling resistance. The equation relates geometric wheel parameters (wheel radius, grouser height and spacing) and operating parameters (slip and sinkage), and predicts a maximum allowable grouser spacing (or, equivalently, a minimum number of grousers). Experiments with various grouser heights and numbers demonstrate good correspondence to the proposed equation, as increases in number of grousers beyond the predicted minimum number stop improving performance. A grouser spacing equation is particularly useful for designing efficient wheels. The proposed relation includes slip and sinkage, parameters that cannot be assumed constant or known a priori, but this work shows that wheels designed using the proposed equation are robust to changing operating scenarios even if they degrade beyond estimated nominal conditions.


title: Culture Care Theory, Research, and Practice

abstract: Today nurses are facing a world in which they are almost forced to use transculturally-based nursing theories and practices in order to care for people of diverse cultures. The author, who in the mid-'50s pioneered the development of the first transcultural nursing theory with a care focus, discusses the relevance, assumptions, and predictions of the culture care theory along with the ethnonursing research method. The author contends that transcultural nursing findings are gradually transforming nursing practice and are providing a new paradigm shift from traditional medical and unicultural practice to multiculturally congruent and specific care modalities. A few research findings are presented to show the importance of being attentive to cultural care diversities and universalities as the major tenets of the theory. In addition, some major contributions of the theory are cited along with major challenges for the immediate future.


title: A Case Study and Analysis of E-Tourism Curriculum Development

abstract: With the rapid expansion of information technology throughout almost all aspects of tourism and hospitality industries, the task of integrating technology into educational curriculum has challenged many institutions. This article presents a practical process of developing and integrating e-tourism modules in an existing educational program through a case study at the University of Guelph. The academic literature relevant to e-tourism is reviewed and synthesized, and industry experts are consulted to identify key competencies required for students to manage in the e-tourism environment. The process of new curriculum development, with industry partnership, is presented as a proposed best practice for the consideration of interested institutions.


title: Exploring determinants of voting for the helpfulness of online user reviews: A text mining approach

abstract: The ''helpfulness'' feature of online user reviews helps consumers cope with information overloads and facilitates decision-making. However, many online user reviews lack sufficient helpfulness votes for other users to evaluate their true helpfulness level. This study empirically examines the impact of the various features, that is, basic, stylistic, and semantic characteristics of online user reviews on the number of helpfulness votes those reviews receive. Text mining techniques are employed to extract semantic characteristics from review texts. Our findings show that the semantic characteristics are more influential than other characteristics in affecting how many helpfulness votes reviews receive. Our findings also suggest that reviews with extreme opinions receive more helpfulness votes than those with mixed or neutral opinions. This paper sheds light on the understanding of online users' helpfulness voting behavior and the design of a better helpfulness voting mechanism for online user review systems.


title: Harbormaster: Policy Enforcement for Containers

abstract: Lightweight virtualization, as implemented by application container solutions such as Docker, have the potential to revolutionize the way multi-tier applications are developed and deployed, especially in the cloud. The success of application containers can be partly attributed to their ability to share resources with the underlying platform that hosts them. As such, the isolation provided by such containers is not as strict as with traditional VMs. These very characteristics that have contributed to the success of application containers can also be seen as factors that limit their widespread commercial adoption, since enterprise IT administrators cannot implement the various -- and often fine-grained -- security policies they are required to abide by. This problem is of limited consequence when a host is running a single user's application containers. But sharing compute resources among multiple users is an important benefit of containers and cloud-based deployment. In this paper we present a preliminary discussion of the challenges associated with enterprise security policy management for application containers deployed in multi-user environments. Furthermore, we present Harbormaster, a system that addresses some of these challenges by enforcing policy checks on Docker container management operations and allowing administrators to implement the principle of least privilege.


title: Agricultural robot for radicchio harvesting

abstract: In the last few years, robotics has been increasingly adopted in agriculture to improve productivity and efficiency. This paper presents recent and current work at the Politecnico of Bari, in collaboration with the University of Lecce, in the field of agriculture robotics. A cost effective robotic arm is introduced for the harvesting of radicchio, which employs visual localization of the plants in the field. The proposed harvester is composed of a double four-bar linkage manipulator and a special gripper, which fulfills the requirement for a plant cut approximately 10 mm underground. Both manipulator and end-effector are pneumatically actuated, and the gripper works with flexible pneumatic muscles. The system employs computer vision to localize the plants in the field based on intelligent color filtering and morphological operations; we call this algorithm the radicchio visual localization RVL. Details are provided for the functional and executive design of the robotic arm and its control system. Experimental results are discussed; obtained with a prototype operating in a laboratory testbed showing the feasibility of the system in localizing and harvesting radicchio plants. The performance of the RVL is analyzed in terms of accuracy, robustness to noises, and variations in lighting, and is also validated in field


title: A Knowledge Processing Service for Robots and Robotics/AI Researchers

abstract: Making future autonomous robots capable of accomplishing human-scale manipulation tasks requires us to equip them with knowledge and reasoning mechanisms. We propose OPEN-EASE, a remote knowledge representation and processing service that aims at facilitating these capabilities. OPEN-EASE gives its users unprecedented access to the knowl- edge of leading-edge autonomous robotic agents. It also provides the representational infrastructure to make inhomogeneous experience data from robots and human manipulation episodes semantically accessible, and is complemented by a suite of software tools that enable researchers and robots to interpret, analyze, visualize, and learn from the experience data. Using OPEN-EASE users can retrieve the memorized experiences of manipulation episodes and ask queries regarding to what the robot saw, reasoned, and did as well as how the robot did it, why, and what effects it caused.


title: Visual Perception of Procedural Textures: Identifying Perceptual Dimensions and Predicting Generation Models

abstract: Procedural models are widely used in computer graphics for generating realistic, natural-looking textures. However, these mathematical models are not perceptually meaningful, whereas the users, such as artists and designers, would prefer to make descriptions using intuitive and perceptual characteristics like "repetitive," "directional," "structured," and so on. To make up for this gap, we investigated the perceptual dimensions of textures generated by a collection of procedural models. Two psychophysical experiments were conducted: free-grouping and rating. We applied Hierarchical Cluster Analysis (HCA) and Singular Value Decomposition (SVD) to discover the perceptual features used by the observers in grouping similar textures. The results suggested that existing dimensions in literature cannot accommodate random textures. We therefore utilized isometric feature mapping (Isomap) to establish a three-dimensional perceptual texture space which better explains the features used by humans in texture similarity judgment. Finally, we proposed computational models to map perceptual features to the perceptual texture space, which can suggest a procedural model to produce textures according to user-defined perceptual scales.


title: P5: Policy-driven optimization of P4 pipeline

abstract: The physical pipeline of flexible network switches is usually programmed using packet-level programs, such as P4 programs. However, those programs are low level and leave room for further optimization. We propose P5 (Policy-driven optimization of P4 Pipeline), a system that exploits knowledge of application deployments embedded in a high-level policy abstraction to: 1) detect features that are used by applications in a mutually-exclusive way and thereby remove inter-feature dependencies between the tables implementing these features in a network switch. This improves the pipeline concurrency of switches and hence its pipeline efficiency. 2) detect and remove the features that are not used by any application/traffic on the switch in a given topology. This reduces the number of tables and the resource consumed by switches, which also improves its pipeline efficiency. Our experiments on real P4 switch programs show that the resulting switch pipelines are up to 50% more efficient as compared to the cases that do not exploit this information.


title: Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-Scale Lesion Database

abstract: Radiologists in their daily work routinely find and annotate significant abnormalities on a large number of radiology images. Such abnormalities, or lesions, have collected over years and stored in hospitals' picture archiving and communication systems. However, they are basically unsorted and lack semantic annotations like type and location. In this paper, we aim to organize and explore them by learning a deep feature representation for each lesion. A large-scale and comprehensive dataset, DeepLesion, is introduced for this task. DeepLesion contains bounding boxes and size measurements of over 32K lesions. To model their similarity relationship, we leverage multiple supervision information including types, self-supervised location coordinates and sizes. They require little manual annotation effort but describe useful attributes of the lesions. Then, a triplet network is utilized to learn lesion embeddings with a sequential sampling strategy to depict their hierarchical similarity structure. Experiments show promising qualitative and quantitative results on lesion retrieval, clustering, and classification. The learned embeddings can be further employed to build a lesion graph for various clinically useful applications. We propose algorithms for intra-patient lesion matching and missing annotation mining. Experimental results validate their effectiveness.


title: Waste Bin Monitoring System UsingIntegrated Technologies

abstract: Now a days, there are a number of techniques which are purposefully used and are being build up for well management of garbage or solid waste . Zigbee and Global System for Mobile Communication (GSM) are the latest trends and are one of the best combination to be used in the project. Hence,a combination of both of these technologies is used in the project . To give a brief description of the project , the sensors are placed in the common garbage bins placed at the public places. When the garbage reaches the level of the sensor, then that indication will be given to ARM 7 Controller. The controller will give indication to the driver of garbage collection truck as to which garbage bin is completely filled and needs urgent attention. ARM 7 will give indication by sending SMS using GSM technology.


title: Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting

abstract: Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We formalize this task as a multiarmed bandit problem, where the payoff function is either sampled from a Gaussian process (GP) or has low norm in a reproducing kernel Hilbert space. We resolve the important open problem of deriving regret bounds for this setting, which imply novel convergence rates for GP optimization. We analyze an intuitive Gaussian process upper confidence bound (GP-UCB) algorithm, and bound its cumulative regret in terms of maximal in- formation gain, establishing a novel connection between GP optimization and experimental design. Moreover, by bounding the latter in terms of operator spectra, we obtain explicit sublinear regret bounds for many commonly used covariance functions. In some important cases, our bounds have surprisingly weak dependence on the dimensionality. In our experiments on real sensor data, GP-UCB compares favorably with other heuristical GP optimization approaches.


title: Compensating for intraoperative soft-tissue deformations using incomplete surface data and finite elements

abstract: Image-guided liver surgery requires the ability to identify and compensate for soft tissue deformation in the organ. The predeformed state is represented as a complete three-dimensional surface of the organ, while the intraoperative data is a range scan point cloud acquired from the exposed liver surface. The first step is to rigidly align the coordinate systems of the intraoperative and preoperative data. Most traditional rigid registration methods minimize an error metric over the entire data set. In this paper, a new deformation-identifying rigid registration (DIRR) is reported that identifies and aligns minimally deformed regions of the data using a modified closest point distance cost function. Once a rigid alignment has been established, deformation is accounted for using a linearly elastic finite element model (FEM) and implemented using an incremental framework to resolve geometric nonlinearities. Boundary conditions for the incremental formulation are generated from intraoperatively acquired range scan surfaces of the exposed liver surface. A series of phantom experiments is presented to assess the fidelity of the DIRR and the combined DIRR/FEM approaches separately. The DIRR approach identified deforming regions in 90% of cases under conditions of realistic surgical exposure. With respect to the DIRR/FEM algorithm, subsurface target errors were correctly located to within 4 mm in phantom experiments.


title: Are web applications ready for parallelism?

abstract: In recent years, web applications have become pervasive. Their backbone is JavaScript, the only programming language supported by all major web browsers. Most browsers run on desktop or mobile devices with parallel hardware. However, JavaScript is by design sequential, and current web applications make little use of hardware parallelism. Are web applications ready to exploit parallel hardware? We answer the question in two steps: First, we survey 174 web developers about the potential and challenges of using parallelism. Then, we study the performance and computation shape of a set of web applications that are representative for the emerging web. Our findings indicate that emerging web applications do have latent data parallelism, and JavaScript developers' programming style is not a significant impediment to exploiting this parallelism.


title: Study of 3D SiP (system-in-package) module for package-on-package application using multi-layer PCB manufacturing process

abstract: A 3D system-in-package (SiP) module for pakage-on-package (PoP) application base on the commercial multi-layer printed circuit board (PCB) using BT laminated substrate is presented in this paper. To achieve the miniaturized package-level 3D SiP, double-sided SMT process and a stacked interposer as the interconnection of IO signals were selected. One ASIC and three ADC were mounted on the top of the substrate, two ADC were mounted on the middle portion of the substrate below and several SMD capacitors were attached on the backside of the substrate. Besides, the interposer around was as the electrical interconnection of the functional substrate and the system board. The package size of the 3D SiP was 44 × 44 × 5.3 m3, with 540 solder bumps. The electrical performance and reliability of the proposed stacked package were analyzed by experimental and numerical modeling methods. In addition, process development was studied to address and overcome the assembly challenges. Furthermore, X-ray inspection was used to check the quality of the solder joints after reflow.


title: Estimating Energy Expenditure Using Body-Worn Accelerometers: A Comparison of Methods, Sensors Number and Positioning

abstract: Several methods to estimate energy expenditure (EE) using body-worn sensors exist; however, quantifications of the differences in estimation error are missing. In this paper, we compare three prevalent EE estimation methods and five body locations to provide a basis for selecting among methods, sensors number, and positioning. We considered 1) counts-based estimation methods, 2) activity-specific estimation methods using METs lookup, and 3) activity-specific estimation methods using accelerometer features. The latter two estimation methods utilize subsequent activity classification and EE estimation steps. Furthermore, we analyzed accelerometer sensors number and on-body positioning to derive optimal EE estimation results during various daily activities. To evaluate our approach, we implemented a study with 15 participants that wore five accelerometer sensors while performing a wide range of sedentary, household, lifestyle, and gym activities at different intensities. Indirect calorimetry was used in parallel to obtain EE reference data. Results show that activity-specific estimation methods using accelerometer features can outperform counts-based methods by $\hbox{88}\%$ and activity-specific methods using METs lookup for active clusters by $\hbox{23}\%$ . No differences were found between activity-specific methods using METs lookup and using accelerometer features for sedentary clusters. For activity-specific estimation methods using accelerometer features, differences in EE estimation error between the best combinations of each number of sensors (1 to 5), analyzed with repeated measures ANOVA, were not significant. Thus, we conclude that choosing the best performing single sensor does not reduce EE estimation accuracy compared to a five sensors system and can reliably be used. However, EE estimation errors can increase up to $\hbox{80}\%$ if a nonoptimal sensor location is chosen.


title: Improved cost-based algorithm for task scheduling in cloud computing

abstract: Cloud computing has been build upon the development of distributed computing, grid computing and virtualization. Since cost of each task in cloud resources is different with one another, scheduling of user tasks in cloud is not the same as in traditional scheduling methods. The objective of this paper is to schedule task groups in cloud computing platform, where resources have different resource costs and computation performance. Due to job grouping, communication of coarse-grained jobs and resources optimizes computation/communication ratio. For this purpose, an algorithm based on both costs with user task grouping is proposed. The proposed scheduling approach in cloud employs an improved cost-based scheduling algorithm for making efficient mapping of tasks to available resources in cloud. This scheduling algorithm measures both resource cost and computation performance, it also improves the computation/communication ratio by grouping the user tasks according to a particular cloud resource's processing capability and sends the grouped jobs to the resource.


title: Mazda - a software for texture analysis

abstract: This paper presents MaZda software for quantitative image texture analysis. This software, primarily developed for classification of magnetic resonance images, can be applied for wide class of textured images including color ones and 3D data. It enables estimation of almost 300 texture features; includes procedures for their reduction and classification. Feature clustering is also provided. The software has been developed since 1998. Currently it is a reliable and efficient tool used by many research institutes for different image analysis tasks.


title: Network orchestrator for QoS-enabled service function chaining in reliable NFV/SDN infrastructure

abstract: The promise of SDN/NFV to offer enhanced functionalities to the service provider networks is now tangible as both technologies have started being steadily investigated and experimentally demonstrated. However, a number of research challenges have not been fully addressed yet. More specifically, a limited interest has been devoted on how to assure reliability and QoS performance while data traverse service chain paths. In this work, we present an SDN orchestrator that periodically evaluate the availability status of the network and, accordingly, adapt service chain paths to recover from congestion events and to preserve QoS performance thus avoiding SLA violations. A prototype is presented along with a performance evaluation of the orchestrator in terms of improved data transfer capabilities against the cost of path adaptations and the controller overhead.


title: Adversarial learning games with deep learning models

abstract: Deep learning has been found to be vulnerable to changes in the data distribution. This means that inputs that have an imperceptibly and immeasurably small difference from training data correspond to a completely different class label in deep learning. Thus an existing deep learning network like a Convolutional Neural Network (CNN) is vulnerable to adversarial examples. We design an adversarial learning algorithm for supervised learning in general and CNNs in particular. Adversarial examples are generated by a game theoretic formulation on the performance of deep learning. In the game, the interaction between an intelligent adversary and deep learning model is a two-person sequential noncooperative Stackelberg game with stochastic payoff functions. The Stackelberg game is solved by the Nash equilibrium which is a pair of strategies (learner weights and genetic operations) from which there is no incentive for either learner or adversary to deviate. The algorithm performance is evaluated under different strategy spaces on MNIST handwritten digits data. We show that the Nash equilibrium leads to solutions robust to subsequent adversarial data manipulations. Results suggest that game theory and stochastic optimization algorithms can be used to study performance vulnerabilities in deep learning models.


title: Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network

abstract: Twitter messages are increasingly used to determine consumer sentiment towards a brand. The existing literature on Twitter sentiment analysis uses various feature sets and methods, many of which are adapted from more traditional text classification problems. In this research, we introduce an approach to supervised feature reduction using n-grams and statistical analysis to develop a Twitter-specific lexicon for sentiment analysis. We augment this reduced Twitter-specific lexicon with brand-specific terms for brand-related tweets. We show that the reduced lexicon set, while significantly smaller (only 187 features), reduces modeling complexity, maintains a high degree of coverage over our Twitter corpus, and yields improved sentiment classification accuracy. To demonstrate the effectiveness of the devised Twitter-specific lexicon compared to a traditional sentiment lexicon, we develop comparable sentiment classification models using SVM. We show that the Twitter-specific lexicon is significantly more effective in terms of classification recall and accuracy metrics. We then develop sentiment classification models using the Twitter-specific lexicon and the DAN2 machine learning approach, which has demonstrated success in other text classification problems. We show that DAN2 produces more accurate sentiment classification results than SVM while using the same Twitter-specific lexicon.


title: Default reasoning, nonmonotonic logics, and the frame problem

abstract: Nonmonotonic formal systems have been proposed as an extension to classical first-order logic that will capture the process of human "default reasoning" or "plausible inference" through their inference mechanisms just as modus ponens provides a model for deductive reasoning. But although the technical properties of these logics have been studied in detail and many examples of human default reasoning have been identified, for the most part these logics have not actually been applied to practical problems to see whether they produce the expected results. ::: ::: We provide axioms for a simple problem in temporal reasoning which has long been identified as a case of default reasoning, thus presumably amenable to representation in nonmonotonic logic. Upon examining the resulting nonmonotonic theories, however, we find that the inferences permitted by the logics are not those we had intended when we wrote the axioms, and in fact are much weaker. This problem is shown to be independent of the logic used; nor does it depend on any particular temporal representation. Upon analyzing the failure we find that the nonmonotonic logics we considered are inherently incapable of representing this kind of default reasoning. Finally we discuss two recent proposals for solving this problem.


title: A survey of black hole attacks in wireless mobile ad hoc networks

abstract: The black hole attack is one of the well-known security threats in wireless mobile ad hoc networks. The intruders utilize the loophole to carry out their malicious behaviors because the route discovery process is necessary and inevitable. Many researchers have conducted different detection techniques to propose different types of detection schemes. In this paper, we survey the existing solutions and discuss the state-of-the-art routing methods. We not only classify these proposals into single black hole attack and collaborative black hole attack but also analyze the categories of these solutions and provide a comparison table. We expect to furnish more researchers with a detailed work in anticipation.


A Deep Representation for Volumetric Shape Modeling


title: Lignin peroxidase functionalities and prospective applications

abstract: Ligninolytic extracellular enzymes, including lignin peroxidase, are topical owing to their high redox potential and prospective industrial applications. The prospective applications of lignin peroxidase span through sectors such as biorefinery, textile, energy, bioremediation, cosmetology, and dermatology industries. The litany of potentials attributed to lignin peroxidase is occasioned by its versatility in the degradation of xenobiotics and compounds with both phenolic and non-phenolic constituents. Over the years, ligninolytic enzymes have been studied however; research on lignin peroxidase seems to have been lagging when compared to other ligninolytic enzymes which are extracellular in nature including laccase and manganese peroxidase. This assertion becomes more pronounced when the application of lignin peroxidase is put into perspective. Consequently, a succinct documentation of the contemporary functionalities of lignin peroxidase and, some prospective applications of futuristic relevance has been advanced in this review. Some articulated applications include delignification of feedstock for ethanol production, textile effluent treatment and dye decolourization, coal depolymerization, treatment of hyperpigmentation, and skin-lightening through melanin oxidation. Prospective application of lignin peroxidase in skin-lightening functions through novel mechanisms, hence, it holds high value for the cosmetics sector where it may serve as suitable alternative to hydroquinone; a potent skin-lightening agent whose safety has generated lots of controversy and concern.


title: Planar Translational Cable-Direct-Driven Robots

abstract: We present the simulated dynamics and control of a planar, translational cable-directdriven robot (CDDR). The motivation behind this work is to improve the serious cable interference problem with existing CDDRs and to avoid configurations where negative cable tensions are required to exert general forces and moments on the environment and during dynamic motions. Generally for CDDRs the commanded rotations are more demanding than commanded translations in terms of slack cable conditions. Therefore we propose a translational CDDR whose end-effector may be fitted with a traditional serial wrist mechanism to provide rotational freedom (assuming proper design to resist the moments). Only the translational CDDR is considered in this article, including kinematics and statics modeling, statics workspace (wherein all possible Cartesian forces and moments may be exerted with only positive cable tensions), plus a dynamics model and simulated control for planar CDDRs. Here we focus only on planar CDDRs, to clearly demonstrate our dynamics and control work; we will extend this work to spatial CDDRs in the future. Examples are presented to demonstrate simulated control including feedback linearization of the four-cable CDDR (with one degree of actuation redundancy) performing a Cartesian task. An on-line dynamic minimum torque estimation algorithm is introduced to ensure all cable tensions remain positive for all motion; otherwise slack cables can result from CDDR dynamics and control is lost. © 2003 Wiley Periodicals, Inc.


title: Micromachined thermal shear-stress sensor for underwater applications

abstract: This paper reports the development of micromachined thermal shear-stress sensors for underwater applications. The thermal shear-stress sensor is a polysilicon resistor sitting atop a vacuum-insulated nitride diaphragm. Special challenges for underwater measurements, such as the waterproof coating and minimization of pressure crosstalk, have been addressed. More rigid diaphragms than the aerial sensors are implemented to increase the operating range and reduce pressure crosstalk, with the cost of larger power consumption and lower sensitivity. Sensors with different diaphragm dimensions and resistor lengths have been fabricated and tested. Nearly zero pressure sensitivity has been achieved by either reducing the diaphragm width or adjusting the sensing element length. The effects of overheat ratio and operating mode on the sensor's pressure crosstalk have been discussed. Parylene C is chosen as the waterproof material for the underwater shear-stress sensors. The primary failure mode is identified as the corrosion of the soldering pads.


title: Evaluating Quality of Chatbots and Intelligent Conversational Agents

abstract: Chatbots are one class of intelligent, conversational software agents activated by natural language input (which can be in the form of text, voice, or both). They provide conversational output in response, and if commanded, can sometimes also execute tasks. Although chatbot technologies have existed since the 1960s and have influenced user interface development in games since the early 1980s, chatbots are now easier to train and implement. This is due to plentiful open source code, widely available development platforms, and implementation options via Software as a Service (SaaS). In addition to enhancing customer experiences and supporting learning, chatbots can also be used to engineer social harm - that is, to spread rumors and misinformation, or attack people for posting their thoughts and opinions online. This paper presents a literature review of quality issues and attributes as they relate to the contemporary issue of chatbot development and implementation. Finally, quality assessment approaches are reviewed, and a quality assessment method based on these attributes and the Analytic Hierarchy Process (AHP) is proposed and examined.


title: How many people would benefit from a smart wheelchair?

abstract: Independent mobility is important, but some wheelchair users find operating existing manual or powered wheelchairs difficult or impossible. Challenges to safe, independent wheelchair use can result from various overlapping physical, perceptual, or cognitive symptoms of diagnoses such as spinal cord injury, cerebrovascular accident, multiple sclerosis, amyotrophic lateral sclerosis, and cerebral palsy. Persons with different symptom combinations can benefit from different types of assistance from a smart wheelchair and different wheelchair form factors. The sizes of these user populations have been estimated based on published estimates of the number of individuals with each of several diseases who (1) also need a wheeled mobility device and (2) have specific symptoms that could interfere with mobility device use. Language: en


title: Mapping customer journeys in multichannel decision-making

abstract: This study is focused on multi-channel shopping, which refers to the integration of various channels in the consumer decision-making process. The term was coined in the early 2000s to signify the integration of offline and online shopping channels. It has since evolved to encompass the proliferating number of channels and media used to formulate, evaluate and execute buying decisions. With the explosion of mobile technologies and social media, multi-channel shopping has indeed become a journey in which customers choose the route they take and which, arguably, needs to be mapped to be understood. Existing consumer decision-making models were developed in pre-internet days and have remained for the most part unquestioned in the digital marketing discourse. Darley, Blankson and Luethge concluded that there is a ‘paucity of research on the impact of online environments on decision making’, which has also been observed in the multi-channel context. Our study adopts an inductive approach allowing for realistic patterns to emerge of how consumers use and react to different media and channels in their shopping journeys for cosmetics. It therefore provides a threefold contribution: (1) it systematizes what are widely used yet largely misunderstood practices (ZMOT, webrooming and showrooming); (2) it defines the key multi-channel influences across different stages of decision making; and (3) it segments actual customer journeys into three distinct patterns that brands can use to optimize their multi-channel strategies.


title: Forest Fire Detection Algorithm Based on Digital Image

abstract: A series of computer vision-based fire detection algorithms is proposed in this paper. These algorithms can be used in parallel with conventional fire detection systems to reduce false alarms. They can also be deployed as a stand- alone system to detect fire combined with a video acquisition device. This paper chiefly discussed the segmentation problem. It adopted the L*a*b*, YCbCr color space and k- means clustering algorithm to isolate the forest fire detection. This algorithm is characterized by high stabilization and versatility, improves fire detection in real time and accurate, and has been used in the forest fires detecting. And this performed well in experiments.


title: Planning and Control of a Humanoid Robot for Navigation on Uneven Multi-scale Terrain

abstract: This paper presents a humanoid navigation system on uneven terrains that include unknown roughness on the order of a few centimeters. A footstep planner decides where to step by using the known terrain shape. A walking balance controller that consists of a 20[ms] cycle dynamically stable motion pattern generation loop and a 1[ms] cycle sensor feedback loop allows the robot to step to the planned footprints and is able to manage a few centimeters of uncertainty. The free leg trajectories and the torso height trajectory are also designed automatically according to the given terrain shape and the planned footprints. We introduced an interactive navigation system that uses mixed reality technology. An outline of the path can be drawn on the real environment to give the commands to the robot in the system. Each developed technology is implemented and integrated on the full-size humanoid HRP-2. Experimental results walking over multi-level platform with unknown small obstacles show the performance of the proposed system.


Da Vinci research kit : PSM and MTM dynamic modelling


title: A markov prediction model for data-driven semi-structured business processes

abstract: In semi-structured case-oriented business processes, the sequence of process steps is determined by case workers based on available document content associated with a case. Transitions between process execution steps are therefore case specific and depend on independent judgment of case workers. In this paper, we propose an instance-specific probabilistic process model (PPM) whose transition probabilities are customized to the semi-structured business process instance it represents. An instance-specific PPM serves as a powerful representation to predict the likelihood of different outcomes. We also show that certain instance-specific PPMs can be transformed into a Markov chain under some non-restrictive assumptions. For instance-specific PPMs that contain parallel execution of tasks, we provide an algorithm to map them to an extended space Markov chain. This way existing Markov techniques can be leveraged to make predictions about the likelihood of executing future tasks. Predictions provided by our technique could generate early alerts for case workers about the likelihood of important or undesired outcomes in an executing case instance. We have implemented and validated our approach on a simulated automobile insurance claims handling semi-structured business process. Results indicate that an instance-specific PPM provides more accurate predictions than other methods such as conditional probability. We also show that as more document data become available, the prediction accuracy of an instance-specific PPM increases.


title: A Laser Point Interaction System Integrating Mouse Functions

abstract: The computer has become an essential tool in modern life, and the combined use of a computer with a projector is very common in teaching and presentations. However, as typical computer operating devices involve a mouse or keyboard, when making presentations, users often need to stay near the computer to execute functions such as changing pages, writing, and drawing, thus, making the operation time-consuming, and reducing interactions with the audience. This paper proposes a laser pointer interaction system able to simulate mouse functions in order that users need not remain near the computer, but can directly use laser pointer operations from at a distance. It can effectively reduce the users’ time spent by the computer, allowing for greater interactions with the audience. Keywords—laser pointer, presentation, interaction, mouse function


title: Pattern Recognition in a Bucket

abstract: This paper demonstrates that the waves produced on the surface of water can be used as the medium for a “Liquid State Machine” that pre-processes inputs so allowing a simple perceptron to solve the XOR problem and undertake speech recognition. Interference between waves allows non-linear parallel computation upon simultaneous sensory inputs. Temporal patterns of stimulation are converted to spatial patterns of water waves upon which a linear discrimination can be made. Whereas Wolfgang Maass’ Liquid State Machine requires fine tuning of the spiking neural network parameters, water has inherent self-organising properties such as strong local interactions, time-dependent spread of activation to distant areas, inherent stability to a wide variety of inputs, and high complexity. Water achieves this “for free”, and does so without the time-consuming computation required by realistic neural models. An analogy is made between water molecules and neurons in a recurrent neural network.


title: Solving timetable scheduling problem using genetic algorithms

abstract: A genetic algorithm for solving a timetable scheduling problem is described. The algorithm was tested on small and large instances of the problem. Algorithm performance was significantly enhanced with modification of basic genetic operators. Intelligent operators restrain the creation of new conflicts in the individual and improve the overall algorithm 's behavior.


Remotely Teleoperating a Humanoid Robot to Perform Fine Motor Tasks with Virtual Reality – 18446


title: Cloud based intelligent attendance system through video streaming

abstract: This paper presents Cloud Based Intelligent Attendance System through Video Streaming. The architecture and algorithm used in each stage of this system is described in this paper. The system is based on the concept of face recognition relatively on a larger scale [9]. The system identifies and authenticates each student present in the class. Through this system, the attendance of group of students is carried out at a single point of time. The objective of the system is to automate the traditional way of taking the attendance on registers and to integrate the system with the cloud so as to make all the records readily available that are maintained by the system by reducing the errors. Thus, when compared with the other alternatives of marking the attendance, this system proves to be more reliable and accurate. Keeping in mind the future scope of making it a commercial product, the system is integrated on a private cloud which can be accessed easily and makes the system more reliable.


title: Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations

abstract: Much work in Natural Language Processing (NLP) has been for resource-rich languages, making generalization to new, less-resourced languages challenging. We present two approaches for improving generalization to low-resourced languages by adapting continuous word representations using linguistically motivated subword units: phonemes, morphemes and graphemes. Our method requires neither parallel corpora nor bilingual dictionaries and provides a significant gain in performance over previous methods relying on these resources. We demonstrate the effectiveness of our approaches on Named Entity Recognition for four languages, namely Uyghur, Turkish, Bengali and Hindi, of which Uyghur and Bengali are low resource languages, and also perform experiments on Machine Translation. Exploiting subwords with transfer learning gives us a boost of +15.2 NER F1 for Uyghur and +9.7 F1 for Bengali. We also show improvements in the monolingual setting where we achieve (avg.) +3 F1 and (avg.) +1.35 BLEU.


title: Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking

abstract: Online multi-object tracking aims at estimating the tracks of multiple objects instantly with each incoming frame and the information provided up to the moment. It still remains a difficult problem in complex scenes, because of the large ambiguity in associating multiple objects in consecutive frames and the low discriminability between objects appearances. In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively. We first define the tracklet confidence using the detectability and continuity of a tracklet, and decompose a multi-object tracking problem into small subproblems based on the tracklet confidence. We then solve the online multi-object tracking problem by associating tracklets and detections in different ways according to their confidence values. Based on this strategy, tracklets sequentially grow with online-provided detections, and fragmented tracklets are linked up with others without any iterative and expensive association steps. For more reliable association between tracklets and detections, we also propose a deep appearance learning method to learn a discriminative appearance model from large training datasets, since the conventional appearance learning methods do not provide rich representation that can distinguish multiple objects with large appearance variations. In addition, we combine online transfer learning for improving appearance discriminability by adapting the pre-trained deep model during online tracking. Experiments with challenging public datasets show distinct performance improvement over other state-of-the-arts batch and online tracking methods, and prove the effect and usefulness of the proposed methods for online multi-object tracking.


title: Driver Fatigue Classification With Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-Based System

abstract: This paper presents a two-class electroencephal-ography-based classification for classifying of driver fatigue (fatigue state versus alert state) from 43 healthy participants. The system uses independent component by entropy rate bound minimization analysis (ERBM-ICA) for the source separation, autoregressive (AR) modeling for the features extraction, and Bayesian neural network for the classification algorithm. The classification results demonstrate a sensitivity of 89.7%, a specificity of 86.8%, and an accuracy of 88.2%. The combination of ERBM-ICA (source separator), AR (feature extractor), and Bayesian neural network (classifier) provides the best outcome with a p -value < 0.05 with the highest value of area under the receiver operating curve (AUC-ROC = 0.93) against other methods such as power spectral density as feature extractor (AUC-ROC = 0.81). The results of this study suggest the method could be utilized effectively for a countermeasure device for driver fatigue identification and other adverse event applications.


title: Control strategies for active lower extremity prosthetics and orthotics: a review

abstract: Technological advancements have led to the development of numerous wearable robotic devices for the physical assistance and restoration of human locomotion. While many challenges remain with respect to the mechanical design of such devices, it is at least equally challenging and important to develop strategies to control them in concert with the intentions of the user.This work reviews the state-of-the-art techniques for controlling portable active lower limb prosthetic and orthotic (P/O) devices in the context of locomotive activities of daily living (ADL), and considers how these can be interfaced with the user’s sensory-motor control system. This review underscores the practical challenges and opportunities associated with P/O control, which can be used to accelerate future developments in this field. Furthermore, this work provides a classification scheme for the comparison of the various control strategies.As a novel contribution, a general framework for the control of portable gait-assistance devices is proposed. This framework accounts for the physical and informatic interactions between the controller, the user, the environment, and the mechanical device itself. Such a treatment of P/Os – not as independent devices, but as actors within an ecosystem – is suggested to be necessary to structure the next generation of intelligent and multifunctional controllers.Each element of the proposed framework is discussed with respect to the role that it plays in the assistance of locomotion, along with how its states can be sensed as inputs to the controller. The reviewed controllers are shown to fit within different levels of a hierarchical scheme, which loosely resembles the structure and functionality of the nominal human central nervous system (CNS). Active and passive safety mechanisms are considered to be central aspects underlying all of P/O design and control, and are shown to be critical for regulatory approval of such devices for real-world use.The works discussed herein provide evidence that, while we are getting ever closer, significant challenges still exist for the development of controllers for portable powered P/O devices that can seamlessly integrate with the user’s neuromusculoskeletal system and are practical for use in locomotive ADL.


title: Manual And Automatic Evaluation Of Machine Translation Between European Languages

abstract: We evaluated machine translation performance for six European language pairs that participated in a shared task: translating French, German, Spanish texts to English and back. Evaluation was done automatically using the Bleu score and manually on fluency and adequacy.


title: Visually-Guided Landing of an Unmanned Aerial Vehicle

abstract: We present the design and implementation of a real-time, vision-based landing algorithm for an autonomous helicopter. The landing algorithm is integrated with algorithms for visual acquisition of the target (a helipad) and navigation to the target, from an arbitrary initial position and orientation. We use vision for precise target detection and recognition, and a combination of vision and Global Positioning System for navigation. The helicopter updates its landing target parameters based on vision and uses an onboard behavior-based controller to follow a path to the landing site. We present significant results from flight trials in the field which demonstrate that our detection, recognition, and control algorithms are accurate, robust, and repeatable.


title: Securing web applications with static and dynamic information flow tracking

abstract: SQL injection and cross-site scripting are two of the most common security vulnerabilities that plague web applications today. These and many others result from having unchecked data input reach security-sensitive operations. This paper describes a language called PQL (Program Query Language) that allows users to declare to specify information flow patterns succinctly and declaratively. We have developed a static context-sensitive, but flow-insensitive information flow tracking analysis that can be used to find all the vulnerabilities in a program. In the event that the analysis generates too many warnings, the result can be used to drive a model-checking system to analyze more precisely. Model checking is also used to automatically generate the input vectors that expose the vulnerability. Any remaining behavior these static analyses have not isolated may be checked dynamically. The results of the static analyses may be used to optimize these dynamic checks. Our experimental results indicate the language is expressive enough for describing a large number of vulnerabilities succinctly. We have analyzed over nine applications, detecting 30 serious security vulnerabilities. We were also able to automatically recover from attacks as they occurred using the dynamic checker.


title: Product Aspect Ranking and Its Applications

abstract: Numerous consumer reviews of products are now available on the Internet. Consumer reviews contain rich and valuable knowledge for both firms and users. However, the reviews are often disorganized, leading to difficulties in information navigation and knowledge acquisition. This article proposes a product aspect ranking framework, which automatically identifies the important aspects of products from online consumer reviews, aiming at improving the usability of the numerous reviews. The important product aspects are identified based on two observations: 1) the important aspects are usually commented on by a large number of consumers and 2) consumer opinions on the important aspects greatly influence their overall opinions on the product. In particular, given the consumer reviews of a product, we first identify product aspects by a shallow dependency parser and determine consumer opinions on these aspects via a sentiment classifier. We then develop a probabilistic aspect ranking algorithm to infer the importance of aspects by simultaneously considering aspect frequency and the influence of consumer opinions given to each aspect over their overall opinions. The experimental results on a review corpus of 21 popular products in eight domains demonstrate the effectiveness of the proposed approach. Moreover, we apply product aspect ranking to two real-world applications, i.e., document-level sentiment classification and extractive review summarization, and achieve significant performance improvements, which demonstrate the capacity of product aspect ranking in facilitating real-world applications.


title: Real time cooperative localization for autonomous vehicles

abstract: This paper describes a new strategy for real-time cooperative localization of autonomous vehicles. The strategy aims to improve the vehicles localization accuracy and reduce the impact of computing time of multi-sensor data fusion algorithms and vehicle-to-vehicle communication on parallel architectures. The method aims to solve localization issues in a cluster of autonomous vehicles, equipped with low-cost navigation systems in an unknown environment. It stands on multiple forms of the Kalman filter derivatives to estimate the vehicles' nonlinear model vector state, named local fusion node. The vehicles exchange their local state estimate and Covariance Intersection algorithm for merging the local vehicles' state estimate in the second node (named global data fusion node). This strategy simultaneously exploits the proprioceptive and sensors —a Global Positioning System, and a vehicle-to-vehicle transmitter and receiver— and an exteroceptive sensor, range finder, to sense their surroundings for more accurate and reliable collaborative localization.


title: More spoken or more translated ?: Exploring a known unknown of simultaneous interpreting

abstract: Since the early 1990s, with the advance of computerized corpora, translation scholars have been using corpus-based methodologies to look into the possible existence of overriding patterns (tentatively described as universals or as laws) in translated texts. The application of such methodologies to interpreted texts has been much slower in developing than in the case of translated ones, but significant progress has been made in recent years. After presenting the fundamental methodological hurdles � and advantages � of working on machine-readable (transcribed) oral corpora, we present and discuss several recent studies using cross-modal comparisons, and examine the viability of using interpreted outputs to explore the features that set simultaneous interpreting apart from other forms of translation. We then set out to test the hypothesis that modality may exert a stronger effect than ontology � i.e. that being oral (vs. written) is a more powerful influence than being translated (vs. original).


title: Privacy-Preserving Distributed Linear Regression on High-Dimensional Data

abstract: We propose privacy-preserving protocols for computing linear regression models, in the setting where the training dataset is vertically distributed among several parties. Our main contribution is a hybrid multi-party computation protocol that combines Yao’s garbled circuits with tailored protocols for computing inner products. Like many machine learning tasks, building a linear regression model involves solving a system of linear equations. We conduct a comprehensive evaluation and comparison of different techniques for securely performing this task, including a new Conjugate Gradient Descent (CGD) algorithm. This algorithm is suitable for secure computation because it uses an efficient fixed-point representation of real numbers while maintaining accuracy and convergence rates comparable to what can be obtained with a classical solution using floating point numbers. Our technique improves on Nikolaenko et al.’s method for privacy-preserving ridge regression (S&P 2013), and can be used as a building block in other analyses. We implement a complete system and demonstrate that our approach is highly scalable, solving data analysis problems with one million records and one hundred features in less than one hour of total running time.


title: The German Version of the Humor Styles Questionnaire: Psychometric Properties and Overlap With Other Styles of Humor

abstract: The Humor Styles Questionnaire (HSQ; Martin et al., 2003) is one of the most frequently used questionnaires in humor research and has been adapted to several languages. The HSQ measures four humor styles (affiliative, self-enhancing, aggressive, and self-defeating), which should be adaptive or potentially maladaptive to psychosocial well-being. The present study analyzes the internal consistency, factorial validity, and factorial invariance of the HSQ on the basis of several German-speaking samples combined (total N = 1,101). Separate analyses were conducted for gender (male/female), age groups (16–24, 25–35, >36 years old), and countries (Germany/Switzerland). Internal consistencies were good for the overall sample and the demographic subgroups (.80–.89), with lower values obtained for the aggressive scale (.66–.73). Principal components and confirmatory factor analyses mostly supported the four-factor structure of the HSQ. Weak factorial invariance was found across gender and age groups, while strong factorial invariance was supported across countries. Two subsamples also provided self-ratings on ten styles of humorous conduct (n = 344) and of eight comic styles (n = 285). The four HSQ scales showed small to large correlations to the styles of humorous conduct (-.54 to .65) and small to medium correlations to the comic styles (-.27 to .42). The HSQ shared on average 27.5–35.0% of the variance with the styles of humorous conduct and 13.0–15.0% of the variance with the comic styles. Thus–despite similar labels–these styles of humorous conduct and comic styles differed from the HSQ humor styles.


title: Learning the consensus on visual quality for next-generation image management

abstract: While personal and community-based image collections grow by the day, the demand for novel photo management capabilities grows with it. Recent research has shown that it is possible to learn the consensus on visual quality measures such as aesthetics with a moderate degree of success. Here, we seek to push this performance to more realistic levels and use it to (a) help select high-quality pictures from collections, and (b) eliminate low-quality ones, introducing appropriate performance metrics in each case. To achieve this, we propose a sequential arrangement of a weighted linear least squares regressor and a naive Bayes' classifier, applied to a set of visual features previously found useful for quality prediction. Experiments on real-world data for these tasks show promising performance, with significant improvements over a previously proposed SVM-based method.


title: Theory and practice of flash memory mobile forensics

abstract: This paper is an introduction to flash memory forensics with a special focus on completeness of evidence acquired from mobile phones. Moving through academic papers and industrial documents will be introduced the particular nature of non-volatile memories present in nowadays mobile phones; how they really work and which challenges they pose to forensic investigators. Then will be presented an advanced test in which some brand new flash memories have been used to hide data in man-made bad blocks: the aim is to verify if forensic software tools are able to acquire data from such blocks, and to evaluate the possibility to hide data at analysts’ eyes.


title: Augmented reality 3D pop-up book: an educational research study

abstract: Augmented Reality technology in education is in high demand for its innovative potential since its fascination grows ubiquitously across the world. This research article reports on an educational innovation which utilizes, as teaching material, the Augmented Reality 3D popup book in the hybrid mode of learning for Grade Three students in Bangkok, Thailand. The story contained in this children’s book is “the Seed Shooting Game”. The Augmented Reality 3D popup book can be used in both online and off line modes. The developed media is an edutainment in multimedia format consisting of: text, voice, graphics, animation and interaction. This research was funded by an educational innovation grant in 2011 from the Office of Education, Bangkok Metropolitan Administration. This paper reports on the major findings which presents students’ attitudes towards two representations of educational innovation: Augmented Reality and 3D pop-up book. Keywords— 3D, Assumption University of Thailand, Augmented Reality, Children story, elementary school, English as a second language


title: Active Self-Paced Learning for Cost-Effective and Progressive Face Identification

abstract: This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert recertification. We first initialize the classifier using a few annotated samples for each individual, and extract image features using the convolutional neural nets. Then, a number of candidates are selected from the unannotated samples for classifier updating, in which we apply the current classifiers ranking the samples by the prediction confidence. In particular, our approach utilizes the high-confidence and low-confidence samples in the self-paced and the active user-query way, respectively. The neural nets are later fine-tuned based on the updated classifiers. Such heuristic implementation is formulated as solving a concise active SPL optimization problem, which also advances the SPL development by supplementing a rational dynamic curriculum constraint. The new model finely accords with the “instructor-student-collaborative” learning mode in human education. The advantages of this proposed framework are two-folds: i) The required number of annotated samples is significantly decreased while the comparable performance is guaranteed. A dramatic reduction of user effort is also achieved over other state-of-the-art active learning techniques. ii) The mixture of SPL and AL effectively improves not only the classifier accuracy compared to existing AL/SPL methods but also the robustness against noisy data. We evaluate our framework on two challenging datasets, which include hundreds of persons under diverse conditions, and demonstrate very promising results. Please find the code of this project at: http://hcp.sysu.edu.cn/projects/aspl/ .


title: LoTAD: long-term traffic anomaly detection based on crowdsourced bus trajectory data

abstract: As the development of crowdsourcing technique, acquiring amounts of data in urban cities becomes possible and reliable, which makes it possible to mine useful and significant information from data. Traffic anomaly detection is to find the traffic patterns which are not expected and it can be used to explore traffic problems accurately and efficiently. In this paper, we propose LoTAD to explore anomalous regions with long-term poor traffic situations. Specifically, we process crowdsourced bus data into TS-segments (Temporal and Spatial segments) to model the traffic condition. Later, we explore anomalous TS-segments in each bus line by calculating their AI (Anomaly Index). Then, we combine anomalous TS-segments detected in different lines to mine anomalous regions. The information of anomalous regions provides suggestions for future traffic planning. We conduct experiments with real crowdsourced bus trajectory datasets of October in 2014 and March in 2015 in Hangzhou. We analyze the varieties of the results and explain how they are consistent with the real urban traffic planning or social events happened between the time interval of the two datasets. At last we do a contrast experiment with the most ten congested roads in Hangzhou, which verifies the effectiveness of LoTAD.


title: Increasingly complex representations of natural movies across the dorsal stream are shared between subjects

abstract: Recently, deep neural networks (DNNs) have been shown to provide accurate predictions of neural responses across the ventral visual pathway. We here explore whether they also provide accurate predictions of neural responses across the dorsal visual pathway, which is thought to be devoted to motion processing and action recognition. This is achieved by training deep neural networks to recognize actions in videos and subsequently using them to predict neural responses while subjects are watching natural movies. Moreover, we explore whether dorsal stream representations are shared between subjects. In order to address this question, we examine if individual subject predictions can be made in a common representational space estimated via hyperalignment. Results show that a DNN trained for action recognition can be used to accurately predict how dorsal stream responds to natural movies, revealing a correspondence in representations of DNN layers and dorsal stream areas. It is also demonstrated that models operating in a common representational space can generalize to responses of multiple or even unseen individual subjects to novel spatio-temporal stimuli in both encoding and decoding settings, suggesting that a common representational space underlies dorsal stream responses across multiple subjects.


title: Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods

abstract: Many climate impact applications require the equivalent of point climate observations and are highly sensitive to fine-scale climate variations that are parameterised in coarse-scale models. This is especially true for regions of complex topography, coastal or island locations, and in regions of highly heterogeneous land-cover. This guidance documents reviews statistical methods of estimating point climate from coarse scale climate projections.


title: Bhattacherjee/Information Systems Continuance RESEARCH ARTICLE UNDERSTANDING INFORMATION SYSTEMS CONTINUANCE: AN EXPECTATION- CONFIRMATION MODEL 1 By:

abstract: This paper examines cognitive beliefs and affect influencing one's intention to continue using (continuance) information systems (IS). Expectation-confirmation theory is adapted from the consumer behavior literature and integrated with theoretical and empirical findings from prior IS usage research to theorize a model of IS continuance. Five research hypotheses derived from this model are empirically validated using a field survey of online banking users. The results suggest that users' continuance intention is determined by their satisfaction with IS use and perceived usefulness of continued IS use. User satisfaction, in turn, is influenced by their confirmation of expectation from prior IS use and perceived usefulness. Post-acceptance perceived usefulness is influenced by users' confirmation level. This study draws attention to the substantive differences between acceptance and continuance behaviors, theorizes and validates one of the earliest theoretical models of IS continuance, integrates confirmation and user satisfaction constructs within our current understanding of IS use, conceptualizes and creates an initial scale for measuring IS continuance, and offers an initial explanation for the acceptance-discontinuance anomaly.


title: Crown lengthening: basic principles, indications, techniques and clinical case reports.

abstract: Sometimes, in order to properly restore teeth, surgical intervention in the form of a crown-lengthening procedure is required. Crown lengthening is a periodontal resective procedure, aimed at removing supporting periodontal structures to gain sound tooth structure above the alveolar crest level. Periodontal health is of paramount importance for all teeth, both sound and restored. For the restorative dentist to utilize crown lengthening, it is important to understand the concept of biologic width, indications, techniques and other principles. This article reviews these basic concepts of clinical crown lengthening and presents four clinical cases utilizing crown lengthening as an integral part of treatments, to restore teeth and their surrounding tissues to health.


title: Absent data generating classifier for imbalanced class sizes

abstract: We propose an algorithm for two-class classification problems when the training data are imbalanced. This means the number of training instances in one of the classes is so low that the conventional classification algorithms become ineffective in detecting the minority class. We present a modification of the kernel Fisher discriminant analysis such that the imbalanced nature of the problem is explicitly addressed in the new algorithm formulation. The new algorithm exploits the properties of the existing minority points to learn the effects of other minority data points, had they actually existed. The algorithm proceeds iteratively by employing the learned properties and conditional sampling in such a way that it generates sufficient artificial data points for the minority set, thus enhancing the detection probability of the minority class. Implementing the proposed method on a number of simulated and real data sets, we show that our proposed method performs competitively compared to a set of alternative state-of-the-art imbalanced classification algorithms.