title: An Analysis of the Influence of Flight Parameters in the Generation of Unmanned Aerial Vehicle (UAV) Orthomosaicks to Survey Archaeological Areas

abstract: This article describes the configuration and technical specifications of a multi-rotor unmanned aerial vehicle (UAV) using a red–green–blue (RGB) sensor for the acquisition of images needed for the production of orthomosaics to be used in archaeological applications. Several flight missions were programmed as follows: flight altitudes at 30, 40, 50, 60, 70 and 80 m above ground level; two forward and side overlap settings (80%–50% and 70%–40%); and the use, or lack thereof, of ground control points. These settings were chosen to analyze their influence on the spatial quality of orthomosaicked images processed by Inpho UASMaster (Trimble, CA, USA). Changes in illumination over the study area, its impact on flight duration, and how it relates to these settings is also considered. The combined effect of these parameters on spatial quality is presented as well, defining a ratio between ground sample distance of UAV images and expected root mean square of a UAV orthomosaick. The results indicate that a balance between all the proposed parameters is useful for optimizing mission planning and image processing, altitude above ground level (AGL) being main parameter because of its influence on root mean square error (RMSE).


title: CloudVisor: retrofitting protection of virtual machines in multi-tenant cloud with nested virtualization

abstract: Multi-tenant cloud, which usually leases resources in the form of virtual machines, has been commercially available for years. Unfortunately, with the adoption of commodity virtualized infrastructures, software stacks in typical multi-tenant clouds are non-trivially large and complex, and thus are prone to compromise or abuse from adversaries including the cloud operators, which may lead to leakage of security-sensitive data. In this paper, we propose a transparent, backward-compatible approach that protects the privacy and integrity of customers' virtual machines on commodity virtualized infrastructures, even facing a total compromise of the virtual machine monitor (VMM) and the management VM. The key of our approach is the separation of the resource management from security protection in the virtualization layer. A tiny security monitor is introduced underneath the commodity VMM using nested virtualization and provides protection to the hosted VMs. As a result, our approach allows virtualization software (e.g., VMM, management VM and tools) to handle complex tasks of managing leased VMs for the cloud, without breaking security of users' data inside the VMs. We have implemented a prototype by leveraging commercially-available hardware support for virtualization. The prototype system, called CloudVisor, comprises only 5.5K LOCs and supports the Xen VMM with multiple Linux and Windows as the guest OSes. Performance evaluation shows that CloudVisor incurs moderate slow-down for I/O intensive applications and very small slowdown for other applications.


The Advanced Distributed Region of Interest Tool


title: KairosVM: Deterministic introspection for real-time virtual machine hierarchical scheduling

abstract: Consolidation and isolation are key technologies that promoted the undisputed popularity of virtualization in most of the computer industry. This popularity has recently led to a growing interest in real-time virtualization, making this technology enter the real-time system market. However, it has several issues due to the strict timing guarantees contracted. Moreover supporting legacy software stacks adds another level of complexity when the software is a black box. We present KairosVM, a latency-bounded, real-time extension to Linux's KVM module. It aims to bridge the lack of communication of the real-time requirements between the guest scheduler and the host scheduler, exploiting virtual machine introspection. The hypervisor captures the real-time requirements of the guest by catching previously added undefined instructions, without the need to do any modification to the guests. Our evaluations show that KairosVM's overhead is negligible when compared to existing introspection solutions thus can be used in real-time.


Feature Selection Based on Joint Mutual Information


title: Computer-aided detection of pulmonary nodules: a comparative study using the public LIDC/IDRI database

abstract: AbstractObjectivesTo benchmark the performance of state-of-the-art computer-aided detection (CAD) of pulmonary nodules using the largest publicly available annotated CT database (LIDC/IDRI), and to show that CAD finds lesions not identified by the LIDC’s four-fold double reading process.MethodsThe LIDC/IDRI database contains 888 thoracic CT scans with a section thickness of 2.5 mm or lower. We report performance of two commercial and one academic CAD system. The influence of presence of contrast, section thickness, and reconstruction kernel on CAD performance was assessed. Four radiologists independently analyzed the false positive CAD marks of the best CAD system.ResultsThe updated commercial CAD system showed the best performance with a sensitivity of 82 % at an average of 3.1 false positive detections per scan. Forty-five false positive CAD marks were scored as nodules by all four radiologists in our study.ConclusionsOn the largest publicly available reference database for lung nodule detection in chest CT, the updated commercial CAD system locates the vast majority of pulmonary nodules at a low false positive rate. Potential for CAD is substantiated by the fact that it identifies pulmonary nodules that were not marked during the extensive four-fold LIDC annotation process.Key Points• CAD systems should be validated on public, heterogeneous databases. ::: • The LIDC/IDRI database is an excellent database for benchmarking nodule CAD. ::: • CAD can identify the majority of pulmonary nodules at a low false positive rate. ::: • CAD can identify nodules missed by an extensive two-stage annotation process.


title: Runtime Verification for Business Processes Utilizing the Bitcoin Blockchain

abstract: Abstract The usage of process choreographies and decentralized Business Process Management Systems has been named as an alternative to centralized business process orchestration. In choreographies, control over a process instance is shared between independent parties, and no party has full control or knowledge during process runtime. Nevertheless, it is necessary to monitor and verify process instances during runtime for purposes of documentation, accounting, or compensation. To achieve business process runtime verification, this work explores the suitability of the Bitcoin blockchain to create a novel solution for choreographies. The resulting approach is realized in a fully-functional software prototype. This software solution is evaluated in a qualitative comparison. Findings show that our blockchain-based approach enables a seamless execution monitoring and verification of choreographies, while at the same time preserving anonymity and independence of the process participants. Furthermore, the prototype is evaluated in a performance analysis.


title: From laser-scanned data to feature human model : a system based on fuzzy logic concept

abstract: This paper describes the development of a prototype system using fuzzy logic concept for constructing a feature human model, which is to be stored in a 3D digital human model database. In our approach, the feature human model is constructed by unorganized cloud points obtained from 3D laser scanners. Firstly, noisy points are removed, and the orientation of the human model is adjusted; secondly, a feature based mesh generation algorithm is applied on the cloud points to construct the mesh surface of a human model; lastly, semantic features of the human model are extracted from the mesh surface. Compared with earlier approach, our method strongly preserves the topology of a human model; more details can be constructed; and both the robustness and the efficiency of the algorithm are improved. At the end of the paper, in order to demonstrate the functionality of feature human models, potential applications are given.


Towards a Human-in-the-Loop Library for Tracking Hyperparameter Tuning in Deep Learning Development


title: The Online Citizen: Is Social Media Changing Citizens’ Beliefs About Democratic Values?

abstract: Social media websites are rapidly changing the way that Americans live and communicate with one another. Social media sites encourage individuals to constantly share information about one’s self (and constantly seek information about others) that would have been private in the past. This experience can alter how an individual views the world in ways that political scientists have not been able to fully capture. In a cross-sectional survey of the American public I find a strong correlation between the use of Facebook and personal blogs and support for civil liberties. Individuals who spend more time self-publicizing on the Internet seem to value freedom of expression more, but also value the right to privacy less than individuals who use social media less often. This pattern suggests that technology may be altering American attitudes on basic democratic values and highlights the need for dynamic research designs that account for the causal effect Internet use may have on individual political development.


title: Stream Bench: Towards Benchmarking Modern Distributed Stream Computing Frameworks

abstract: While big data is becoming ubiquitous, interest in handling data stream at scale is also gaining popularity, which leads to the sprout of many distributed stream computing systems. However, complexity of stream computing and diversity of workloads expose great challenges to benchmark these systems. Due to lack of standard criteria, evaluations and comparisons of these systems tend to be difficult. This paper takes an early step towards benchmarking modern distributed stream computing frameworks. After identifying the challenges and requirements in the field, we raise our benchmark definition Stream Bench regarding the requirements. Stream Bench proposes a message system functioning as a mediator between stream data generation and consumption. It also covers 7 benchmark programs that intend to address typical stream computing scenarios and core operations. Not only does it care about performance of systems under different data scales, but also takes fault tolerance ability and durability into account, which drives to incorporate four workload suites targeting at these various aspects of systems. Finally, we illustrate the feasibility of Stream Bench by applying it to two popular frameworks, Apache Storm and Apache Spark Streaming. We draw comparisons from various perspectives between the two platforms with workload suites of Stream Bench. In addition, we also demonstrate performance improvement of Storm's latest version with the benchmark.


title: Two-Dimensional AoD and AoA Acquisition for Wideband Millimeter-Wave Systems With Dual-Polarized MIMO

abstract: In this paper, a novel two-dimensional super-resolution angle-of-departure (AoD) and angle-of-arrival (AoA) estimation technique is proposed for wideband millimeter-wave multiple-input multiple-output systems with cross-polarized antenna elements. The key ingredient of the proposed method is to form custom designed beam pairs, and devise an invertible function of the AoD/AoA to be estimated from the corresponding beam pairs. Further, a new multi-layer reference signal structure is developed for the proposed method to facilitate angle estimation for wideband channels with cross-polarized antenna elements. To facilitate feedback in closed-loop frequency division duplexing systems, a novel differential feedback strategy is proposed aiming at feedback reduction for the two-dimensional angle estimation. Numerical results demonstrate that by using the proposed method, good azimuth/elevation AoD and AoA estimation performance can be achieved under different levels of signal-to-noise ratio, channel conditions, and antenna array configurations.


title: Improved text-detection methods for a camera-based text reading system for blind persons

abstract: Automatic text recognition from natural images receives a growing attention because of potential applications in image retrieval, robotics and intelligent transport system. Camera-based document analysis becomes a real possibility with the increasing resolution and availability of digital cameras. Our research objective is a system that reads the text encountered in natural scenes with the aim to provide assistance to visually impaired persons. In the case of a blind person, finding the text region is the first important problem that must be addressed, because it cannot be assumed that the acquired image contains only characters. In a previous paper (N. Ezaki et al., 2004), we propose four text-detection methods based on connected components. Finding small characters needed significant improvement. This paper describes a new text-detection method geared for small text characters. This method uses Fisher's discriminant rate (FDR) to decide whether an image area should be binarized using local or global thresholds. Fusing the new method with a previous morphology-based one yields improved results. Using a controllable Webcam and a laptop PC, we developed a prototype that works in real time. At first, our system tries to find in the image areas with small characters. Then it zooms into the found areas to retake higher resolution images necessary for character recognition. Going from this proof-of-concept to a complete system requires further research effort.


title: A BPMN Extension for the Modeling of Security Requirements in Business Processes

abstract: Business Processes are considered a crucial issue by many enterprises because they are the key to maintain competitiveness. Moreover, business processes are important for software developers, since they can capture from them the necessary requirements for software design and creation. Besides, business process modeling is the center for conducting and improving how the business is operated. Security is important for business performance, but traditionally, it is considered after the business processes definition. Empirical studies show that, at the business process level, customers, end users, and business analysts are able to express their security needs. In this work, we will present a proposal aimed at integrating security requirements through business process modeling. We will summarize our Business Process Modeling Notation extension for modeling secure business process through Business Process Diagrams, and we will apply this approach to a typical health-care business process.


title: A Focused Crawler Based on Naive Bayes Classifier

abstract: The exponential growth of information on the World Wide Web makes it increasingly difficult to discover relevant data about a specific topic. In this case, growing interest is emerging in focused crawler, a program that traverses the Internet by choosing relevant pages to a predefined topic and neglecting those out of concern. A new focused crawler based on Naive Bayes classifier was proposed here, which used an improved TF-IDF algorithm to extract the characteristics of page content and adopted Bayes classifier to compute the page rank. Then the crawler developed was compared with a BFS crawler and a PageRank crawler, and the results show that our crawler has better performance than the PageRank crawler and BFS crawler in harvest ratio.


title: Locate the hate: detecting tweets against blacks

abstract: Although the social medium Twitter grants users freedom of speech, its instantaneous nature and retweeting features also amplify hate speech. Because Twitter has a sizeable black constituency, racist tweets against blacks are especially detrimental in the Twitter community, though this effect may not be obvious against a backdrop of half a billion tweets a day. We apply a supervised machine learning approach, employing inexpensively acquired labeled data from diverse Twitter accounts to learn a binary classifier for the labels "racist" and "nonracist" The classifier has a 76% average accuracy on individual tweets, suggesting that with further improvements, our work can contribute data on the sources of anti-black hate speech.


A well grounded education: The role of perception in science and mathematics


title: The Effect of Electronic Word of Mouth on Brand Image and Purchase Intention: An Application Concerning Cell Phone Brands for Youth Consumers in Turkey

abstract: This study aims to explain the effect of Word of mouth on purchase intention through brand image. The focus on cell phone brands specifically. The sample of this study consisted of university students residing in Turkey. Data were obtained with a questionnaire and the face to face method after briefing the participants. The results of the study showed that there is a significant positive relationship between the electronic word of mouth on brand image and purchase intention.


title: Customer gender prediction based on E-commerce data

abstract: Demographic attributes of customers such as gender, age, etc. provide important information for e-commerce service providers in marketing and personalization of web applications. However, online customers often do not provide this kind of information due to privacy issues and other security-related reasons. In this paper, we proposed a method for predicting the gender of customers based on their catalog viewing data on e-commerce systems, such as the date and time of access, list of categories and products viewed, etc. We employ a machine learning approach and investigate a number of features derived from catalog viewing information to predict the gender of viewers. Experiments were conducted on datasets provided by the PAKDD'15 Data Mining Competition and achieved the good result. The results 81.2% on balanced accuracy and 81.4% on macro F1 score showed that basic features such as viewing time, products/categories features used together with more advanced features such as products/categories sequence and transfer features effectively facilitate gender prediction of customers.


title: Successful management of a parasitic ischiopagus conjoined twins in a low‐income setting

abstract: Ischiopagus parasites are fetal defects attached to a relatively normal twin by pelvis. This is the first reported case of parasitic ischiopagus twins without prenatal diagnosis successfully managed in Mozambique. A multidisciplinary team was involved in the supernumerary limbs excision. After 7 months, the infant has a normal development.


title: Modeling of a Point Absorber for Energy Conversion in Italian Seas

abstract: In the present paper, we investigate the feasibility of wave electricity production in Italian seas by the deployment of the Seabased wave energy converter (WEC). A numerical model of the coupled buoy-generator system is presented, which simulates the behavior of the wave energy converter under regular waves of different wave heights and periods. The hydrodynamic forces, including excitation force, radiation impedance and hydrostatic force, are calculated by linear potential wave theory, and an analytical model is used for the linear generator. Two buoys of different radii are considered to explore the effect of buoy dimension on energy conversion and device efficiency. The power output is maximized by adding a submerged object to the floating buoy, in order to bring the system into resonance with the typical wave frequencies of the sites. The simulation results show a very good agreement with the published data on the Seabased WEC. The model is used to estimate energy production at eight Italian offshore locations. The results indicate that the degree of utilization of the device is higher than 20% at the two most energetic Italian sites (Alghero and Mazara del Vallo) and that it can be considerably increased if the floating body is connected to a submerged object, thanks to the resonant behavior of the WEC. In this case, the degree of utilization of the device would be higher than 40% at most of the study sites, with the highest value at Mazara del Vallo. The work enlarges the perspective, to be confirmed by experimental tests and more accurate numerical modeling, on clean electric power production from ocean waves in the Italian seas.


Survey of biometric pattern recognition via machine learning techniques


title: Topological Map Extraction From Overhead Images

abstract: We propose a new approach, named PolyMapper, to circumvent the conventional pixel-wise segmentation of (aerial) images and predict objects in a vector representation directly. PolyMapper directly extracts the topological map of a city from overhead images as collections of building footprints and road networks. In order to unify the shape representation for different types of objects, we also propose a novel sequentialization method that reformulates a graph structure as closed polygons. Experiments are conducted on both existing and self-collected large-scale datasets of several cities. Our empirical results demonstrate that our end-to-end learnable model is capable of drawing polygons of building footprints and road networks that very closely approximate the structure of existing online map services, in a fully automated manner. Quantitative and qualitative comparison to the state-of-the-arts also show that our approach achieves good levels of performance. To the best of our knowledge, the automatic extraction of large-scale topological maps is a novel contribution in the remote sensing community that we believe will help develop models with more informed geometrical constraints.


title: Individual Differences in Executive Functions Are Almost Entirely Genetic in Origin

abstract: Recent psychological and neuropsychological research suggests that executive functions — the cognitive control processes that regulate thought and action — are multifaceted and that different types of executive functions are correlated but separable. The present multivariate twin study of three executive functions (inhibiting dominant responses, updating working memory representations, and shifting between task sets), measured as latent variables, examined why people vary in these executive control abilities and why these abilities are correlated but separable from a behavioral genetic perspective. Results indicated that executive functions are correlated because they are influenced by a highly heritable (99%) common factor that goes beyond general intelligence or perceptual speed, and they are separable because of additional genetic influences unique to particular executive functions. This combination of general and specific genetic influences places executive functions among the most heritable psychological traits. These results highlight the potential of genetic approaches for uncovering the biological underpinnings of executive functions and suggest a need for examining multiple types of executive functions to distinguish different levels of genetic influences.


Fast bilateral filtering for the display of high-dynamic-range images


title: Cuckoo hashing

abstract: We present a simple dictionary with worst case constant lookup time, equaling the theoretical performance of the classic dynamic perfect hashing scheme of Dietzfelbinger et al. [SIAM J. Comput. 23 (4) (1994) 738-761]. The space usage is similar to that of binary search trees. Besides being conceptually much simpler than previous dynamic dictionaries with worst case constant lookup time, our data structure is interesting in that it does not use perfect hashing, but rather a variant of open addressing where keys can be moved back in their probe sequences. An implementation inspired by our algorithm, but using weaker hash functions, is found to be quite practical. It is competitive with the best known dictionaries having an average case (but no nontrivial worst case) guarantee on lookup time.


title: Occupational Therapy Interventions for Employment and Education for Adults With Serious Mental Illness: A Systematic Review

abstract: In this systematic review, we investigated research literature evaluating the effectiveness of occupational therapy interventions focusing on participation and performance in occupations related to paid and unpaid employment and education for people with serious mental illness. The review included occupation- and activity-based interventions and interventions addressing performance skills, aspects of the environment, activity demands, and client factors. The results indicate that strong evidence exists for the effectiveness of supported employment using individual placement and support to result in competitive employment. These outcomes are stronger when combined with cognitive or social skills training. Supported education programs emphasizing goal setting, skill development, and cognitive training result in increased participation in educational pursuits. The evidence for instrumental activities of daily living interventions that targeted specific homemaking occupations and supported parenting was limited but positive. Environmental cognitive supports, such as signs, and other compensatory strategies are useful in managing maladaptive behavior.


title: How dynamic can organizational capabilities be? Towards a dual-process model of capability dynamization

abstract: The recent discussion in the field of strategic management broadly favors the idea of dynamic capabilities in order to overcome potential rigidities of organizational capability building. The major question addressed in this paper is whether capabilities can actually be conceived as being in flux—and if so, to what extent and in which way? After briefly recapitulating the distinguishing features of organizational capabilities, path dependency, structural inertia, and commitment are identified as the main capability-rigidity drivers causing a managerial dilemma. In the search for a resolution of this dilemma different approaches of dynamic capabilities are identified and discussed. The analysis shows that the approaches suffer from inherent conceptual contradictions: the dynamization runs the risk of dissolving the original idea and strength of organizational capability building. Ultimately, capabilities would lose the strategic power attributed to them in the resource-based view. The last section of this paper therefore aims to develop an alternative approach, which aims at preserving the original merits of organizational capability and solving the rigidity issue not by integrating a dynamic dimension into the capability construct but rather by establishing a separate function (‘capability monitoring’). The suggestions mount up to a tier solution. Its logic builds on the dynamics of countervailing processes and second-level observation. Copyright © 2007 John Wiley & Sons, Ltd.


title: Must – A method for Participatory Design

abstract: The paper presents a conceptual framework and a coherent method for design in an organizational context within the PD tradition. The MUST method has been developed throughout 10 projects in Danish and American organizations, and it has recently been evaluated, and adopted by IT professionals within a large Danish organization. The method is based on thorough participation with users and managers, and it combines the use of ethnographic techniques and intervention. The paper describes the application area and perspective of the MUST method, presents six general principles on which the method is based. and describes five main activities providing a stepwise decision making process in relation to the overall design process. The paper concludes with a brief comparison of the MUST method with other approaches and by summing up the main points.


title: DeepBE: Learning Deep Binary Encoding for Multi-label Classification

abstract: The track 2 and track 3 of ChaLearn 2016 can be considered as Multi-Label Classification problems. We present a framework of learning deep binary encoding (DeepBE) to deal with multi-label problems by transforming multi-labels to single labels. The transformation of DeepBE is in a hidden pattern, which can be well addressed by deep convolutions neural networks (CNNs). Furthermore, we adopt an ensemble strategy to enhance the learning robustness. This strategy is inspired by its effectiveness in fine-grained image recognition (FGIR) problem, while most of face related tasks such as track 2 and track 3 are also FGIR problems. By DeepBE, we got 5.45% and 10.84% mean square error for track 2 and track 3 respectively. Additionally, we proposed an algorithm adaption method to treat the multiple labels of track 2 directly and got 6.84% mean square error.


title: Editorial: E-learning and Human-Computer Interaction: Exploring Design Synergies for more Effective Learning Experiences.

abstract: A carbon fiber reinforced carbon composite, obtained by a method comprising impregnating a pitch or a resin into a molded member formed of carbon fibers for densification thereof; forming an impregnated pyrolytic carbon layer by CVI after densification; and forming a coated pyrolytic carbon layer on the impregnated pyrolytic carbon layer by CVD; a method of making the carbon fiber reinforced carbon composite; and a pulling single crystal member made from the carbon fiber reinforced carbon composite.


title: A sparse texture representation using local affine regions

abstract: This paper introduces a texture representation suitable for recognizing images of textured surfaces under a wide range of transformations, including viewpoint changes and nonrigid deformations. At the feature extraction stage, a sparse set of affine Harris and Laplacian regions is found in the image. Each of these regions can be thought of as a texture element having a characteristic elliptic shape and a distinctive appearance pattern. This pattern is captured in an affine-invariant fashion via a process of shape normalization followed by the computation of two novel descriptors, the spin image and the RIFT descriptor. When affine invariance is not required, the original elliptical shape servee as an additional discriminative feature for texture recognition. The proposed approach is evaluated in retrieval and classification tasks using the entire Brodatz database and a publicly available collection of 1,000 photographs of textured surfaces taken from different viewpoints.


title: Deriving Formal Specification Using Z Notation

abstract: Formal Methods are very tough subject to Software Engineering student. It happens because of the mathematics involvement during software development. Students normally feel very difficult to derive formal specification from informal requirement. The problem always happen is to derive the Z notation in the formal specification. The formal specification is all about the operation inside the requirement needed from the customer using mathematical statement. If there do not know the logic of the operation the notation might goes wrong. In this paper, we propose an approach to deriving formal specifications from informal requirement using venn diagram for creating formal specification in term of training environment to make our student understand. The venn diagram that we use is purposely for the basic level. It is used to visualize in the branch of mathematics known as set theory. It shows all of the possible mathematical or logical relationships between sets groups of things. With this venn diagram they can visualize the operation of their notation. To show how to implement the venn diagram we chose a case study. We show how to convert the Venn diagram to formal specification which is the important part during development of Z schema. Then, we do an analysis of an assignment given to a group of student to know whether the venn diagram is really helpful for them or not.


title: Potential environmental and human health impacts of rechargeable lithium batteries in electronic waste.

abstract: Rechargeable lithium-ion (Li-ion) and lithium-polymer (Li-poly) batteries have recently become dominant in consumer electronic products because of advantages associated with energy density and product longevity. However, the small size of these batteries, the high rate of disposal of consumer products in which they are used, and the lack of uniform regulatory policy on their disposal means that lithium batteries may contribute substantially to environmental pollution and adverse human health impacts due to potentially toxic materials. In this research, we used standardized leaching tests, life-cycle impact assessment (LCIA), and hazard assessment models to evaluate hazardous waste classification, resource depletion potential, and toxicity potentials of lithium batteries used in cellphones. Our results demonstrate that according to U.S. federal regulations, defunct Li-ion batteries are classified hazardous due to their lead (Pb) content (average 6.29 mg/L; σ = 11.1; limit 5). However, according to California regulations, all lithium batteries tested are classified hazardous due to excessive levels of cobalt (average 163,544 mg/kg; σ = 62,897; limit 8000), copper (average 98,694 mg/kg; σ = 28,734; limit 2500), and nickel (average 9525 mg/kg; σ = 11,438; limit 2000). In some of the Li-ion batteries, the leached concentrations of chromium, lead, and thallium exceeded the California regulation limits. The environmental impact associated with resource depletion and human toxicity is mainly associated with cobalt, copper, nickel, thallium, and silver, whereas the ecotoxicity potential is primarily associated with cobalt, copper, nickel, thallium, and silver. However, the relative contribution of aluminum and lithium to human toxicity and ecotoxicity could not be estimated due to insufficient toxicity data in the models. These findings support the need for stronger government policy at the local, national, and international levels to encourage recovery, recycling, and reuse of lithium battery materials.


title: Damming the rivers of the Amazon basin

abstract: NSF [FESD-1338694, EAR-1147954, DDRI-1558446]; NASA [NAG5-6120]; National Geographic Society-Research and Exploration Grant [8855-10]; LLILAS-Mellon; Brazilian Council for Scientific and Technological Development-CNPq; CAPES Foundation


title: Multi-hop communication routing (MCR) protocol for heterogeneous wireless sensor networks

abstract: Effective energy management in heterogeneous wireless sensor networks (WSNs) is a more challenging issue compared to homogeneous WSNs. Much of the existing research has often assumed homogeneous sensor nodes in the networks. The energy preservation schemes for the homogeneous WSNs do not perform efficiently when applied to heterogeneous WSNs. In this paper, we have presented an energy-efficient multi-hop communication routing (MCR) protocol in order to address the traditional problem of load balancing, lifetime, stability and energy efficiency in the WSNs. MCR protocol is based on dividing the network into dynamic clusters. The cluster-heads election is based on weighted probability. The cluster's nodes communicate with an elected cluster head node by using single hop communication approach, and then the cluster heads communicate the information to the base station via multi-hop communication approach. Performance studies indicate that MCR effectively solves the problem of load balancing across the network, extends the network lifetime, stability and is more energy efficient in comparison to multi-hop low-energy adaptive clustering hierarchy (M-LEACH) and multilayer energy efficient cluster head communication protocol (MEECHCP).


title: Embedded Fatigue Detection Using Convolutional Neural Networks with Mobile Integration

abstract: Fatigued or drowsy drivers pose a significant risk of causing life-threatening accidents. Yet, many sleep-deprived drivers are behind the wheels exposing lives to danger. In this paper, we propose a low-cost and real-time embedded system for fatigue detection using convolutional neural networks (CNN). Our system starts by spatially processing the video signal using a real-time face detection algorithm to establish a region of interest and reduce computations. The video signal comes from a camera module mounted on the car dashboard connected to an embedded Linux board set to monitor the driver's eyes. Detected faces are then passed to an optimized fatigue recognition CNN binary classifier to detect the event of fatigued or normal driving. When temporally persistent fatigue is detected, alerts are sent to the driver's smart phone, and to possibly others, for prevention measures to be taken before accidents happen. Our testing shows that the system can robustly detect fatigue and can effectively be deployed to address the problem.


title: Unsupervised Representation Learning with Prior-Free and Adversarial Mechanism Embedded Autoencoders

abstract: Most state-of-the-art methods for representation learning are supervised, which require a large number of labeled data. This paper explores a novel unsupervised approach for learning visual representation. We introduce an image-wise discrimination criterion in addition to a pixel-wise reconstruction criterion to model both individual images and the difference between original images and reconstructed ones during neural network training. These criteria induce networks to focus on not only local features but also global high-level representations, so as to provide a competitive alternative to supervised representation learning methods, especially in the case of limited labeled data. We further introduce a competition mechanism to drive each component to increase its capability to win its adversary. In this way, the identity of representations and the likeness of reconstructed images to original ones are alternately improved. Experimental results on several tasks demonstrate the effectiveness of our approach.


title: Background-Suppressed Correlation Filters for Visual Tracking

abstract: Correlation filters (CF) visual object tracking is a powerful framework, with excellent tracking accuracy and beyond realtime frame rate. Its performance, however, can be severely degraded in cluttered background images. In this paper, we propose background-suppressed correlation filters (BSCF), a better CF tracking scheme, which can significantly improve the reliability and accuracy of CF trackers, without harming their beyond real-time speed. Specifically, we present a unified BSCF object function. We show that both the correlation filters and BS weight map can be efficiently and jointly solved in frequency domain. Extensive experiments on OTB-100 benchmark validate the effectiveness and generality of BS in improving multiple CF trackers with higher accuracy and robustness while maintaining their fast tracking speed. We also show BS boosted CF tracker can achieve comparable accuracy of the state-of-the-art spatially-regularized CF tracker but is 14 times faster.


title: Modeling User Satisfaction With an Employee Portal

abstract: User satisfaction with general IS and certain types of IT applications has been thoroughly studied in IS research. With the widespread use of portal technology, there is a need to conduct a user satisfaction study with portal usage, in particular, the business-to-employee (b2e) portal. In this paper, we propose a conceptual model for determining b2e portal user satisfaction, which has been derived from an extensive literature review of user satisfaction scales and the b2e portal. Nine dimensions of the b2e portal user satisfaction were identified: Information Content, Ease of Use, Convenience of Access, Timeliness, Efficiency, Security, Confidentiality, Communication, and Layout.


title: Selective attention gates visual processing in the extrastriate cortex

abstract: Single cells were recorded in the visual cortex of monkeys trained to attend to stimuli at one location in the visual field and ignore stimuli at another. When both locations were within the receptive field of a cell in prestriate area V4 or the inferior temporal cortex, the response to the unattended stimulus was dramatically reduced. Cells in the striate cortex were unaffected by attention. The filtering of irrelevant information from the receptive fields of extrastriate neurons may underlie the ability to identify and remember the properties of a particular object out of the many that may be represented on the retina.


title: Human Performance on Insight Problem Solving: A Review

abstract: The article provides a review of recent research on insight problem-solving performance. We discuss what insight problems are, the different types of classic and newer insight problems, and how we can classify them. We also explain some of the other aspects that affect insight performance, such as hints, analogs, training, thinking aloud, and individual differences. In addition, we describe some of the main theoretical explanations that have been offered. Finally, we present some measures of insight and relevant neuroscience contributions to the area over the last decade.


title: Terrain Classification for a Quadruped Robot

abstract: Using data retrieved from the Puppy II robot at the University of Zurich (UZH), we show that machine learning techniques with non-linearities and fading memory are effective for terrain classification, both supervised and unsupervised, even with a limited selection of input sensors. The results indicate that most information for terrain classification is found in the combination of tactile sensors and proprioceptive joint angle sensors. The classification error is small enough to have a robot adapt the gait to the terrain and hence move more robustly.


title: A novel privacy preserving authentication and access control scheme for pervasive computing environments

abstract: Privacy and security are two important but seemingly contradictory objectives in a pervasive computing environment (PCE). On one hand, service providers want to authenticate legitimate users and make sure they are accessing their authorized services in a legal way. On the other hand, users want to maintain the necessary privacy without being tracked down for wherever they are and whatever they are doing. In this paper, a novel privacy preserving authentication and access control scheme to secure the interactions between mobile users and services in PCEs is proposed. The proposed scheme seamlessly integrates two underlying cryptographic primitives, namely blind signature and hash chain, into a highly flexible and lightweight authentication and key establishment protocol. The scheme provides explicit mutual authentication between a user and a service while allowing the user to anonymously interact with the service. Differentiated service access control is also enabled in the proposed scheme by classifying mobile users into different service groups. The correctness of the proposed authentication and key establishment protocol is formally verified based on Burrows-Abadi-Needham logic


title: SecureCyber: Risk-Based Optimization through Common Vulnerability Systems Scoring Over Energy Smart Grid

abstract: This paper introduces the idea of CVSS-host scores which utilize CVSS parameters to provide impact scoring for Smart Grid Environment. This scoring mechanism presents a novel view of system risk by framing an-upper bounds on the criticality of potential vulnerabilities in that system. Once this scoring system has been established, the CVSS vectors can then be utilized to perform more sophisticated calculations to investigate optimal costs and benefits for future security enhancements.


title: AppScale: Open-Source Platform-As-A-Service

abstract: AppScale is a scalable, distributed, and fault tolerant cloud runtime system that we have developed at the University of California, Santa Barbara as part of our research into the next generation of programming systems [5, 3]. In particular, AppScale is a cloud platform, i.e. a platform-as-a-service (PaaS) cloud fabric, that executes over cluster resources. The cluster resources underlying AppScale can be managed with or without virtualization (e.g. Xen, KVM) or via popular cloud infrastructures including Amazon EC2 [6] and Eucalyptus [8]. The AppScale platform virtualizes, abstracts, and multiplexes cloud and system services across multiple applications, enabling write-one, run-anywhere (WORA) program development for the cloud. In addition to simplifying application development and deployment using cloud systems and modern distributed computing technologies, AppScale brings popular public cloud fabrics to “on-premise”, or private, clusters. To enable this, we emulate key cloud layers from the commercial sector – (i) to engender a user community, (ii) to gain access to and to study real applications, and (iii) to investigate the potential implementations of, and extensions to, public cloud systems using open-source technologies. The first API we have chosen to emulate is that of Google App Engine [11]. Google App Engine is a cloud platform that provides scalable implementations of technologies important for web services (messaging, key-value data storage, multi-tasking, web server support, elasticity, and resource management, among others). Using Google App Engine developers debug and test their programs using an open-source software development kit (SDK) provided by Google that implements non-scalable versions of the APIs. Developers then upload their code and data to Google clusters, and can then use Google resources and services “pay-per-use”, on a free or low-cost rental basis. AppScale implements the (open) Google App Engine APIs such that applications that execute on Google App Engine, also execute on AppScale without modification using private cluster resources or public cloud infrastructures. Our API and service implementations target scale, distribution, fault tolerance, high-performance, and high availability. We leverage mature open-source technologies to the greatest degree possible to enable this. AppScale implements multiple language runtimes (Java, Python, Ruby) as application frontends and a wide range of open source database technologies (key-value or relational) as options for its internal system-wide datastore. AppScale is not a replacement for Google App Engine or any other public cloud technology. Instead, AppScale is a robust and extensive distributed system that provisions the resources it is allocated scalably across multiple applications. The AppScale platform exports services and APIs in addition to those provided by Google App Engine. These technologies are important for application domains beyond those of web services, e.g. data analytics and data and computationally intensive applications. AppScale exports these technologies as services, i.e. AppScale “service-izes” libraries, tools, and software packages, including MapReduce, X10, R, and MPI, and, as it does for the Google App Engine APIs, AppScale provides automatic configuration, deployment, and distribution for them, as well as support for their elasticity, load balancing, and fault tolerance. Since AppScale provides a software layer between applications and the implementations of cloud APIs and services, we are also able to employ existing cloud services for the implementations. As such, AppScale provides a hybrid cloud platform – a programming system through which an application can access services from different clouds (a mix of public and private) at different times or concurrently. Such support can be used by developers to move data between clouds, e.g., for disaster recovery, fault tolerance, data backups, to reduce public cloud costs (to use lower-cost alternatives), and to “fall out” from a private cloud with limited resources to a public cloud on-demand.


title: Multi-stream switching for interactive virtual reality video streaming

abstract: Virtual reality (VR) video provides an immersive 360 viewing experience to a user wearing a head-mounted display: as the user rotates his head, correspondingly different fields-of-view (FoV) of the 360 video are rendered for observation. Transmitting the entire 360 video in high quality over bandwidth-constrained networks from server to client for real-time playback is challenging. In this paper we propose a multi-stream switching framework for VR video streaming: the server pre-encodes a set of VR video streams covering different view ranges that account for server-client round trip time (RTT) delay, and during streaming the server transmits and switches streams according to a user's detected head rotation angle. For a given RTT, we formulate an optimization to seek multiple VR streams of different view ranges and the head-angle-to-stream mapping function simultaneously, in order to minimize the expected distortion subject to bandwidth and storage constraints. We propose an alternating algorithm that, at each iteration, computes the optimal streams while keeping the mapping function fixed and vice versa. Experiments show that for the same bandwidth, our multi-stream switching scheme outperforms a non-switching single-stream approach by up to 2.9dB in PSNR.


title: Community-based features for identifying spammers in online social networks

abstract: The popularity of Online Social Networks (OSNs) is often faced with challenges of dealing with undesirable users and their malicious activities in the social networks. The most common form of malicious activity over OSNs is spamming wherein a bot (fake user) disseminates content, malware/viruses, etc. to the legitimate users of the social networks. The common motives behind such activity include phishing, scams, viral marketing and so on which the recipients do not indent to receive. It is thus a highly desirable task to devise techniques and methods for identifying spammers (spamming accounts) in OSNs. With an aim of exploiting social network characteristics of community formation by legitimate users, this paper presents a community-based framework to identify spammers in OSNs. The framework uses community-based features of OSN users to learn classification models for identification of spamming accounts. The preliminary experiments on a real-world dataset with simulated spammers reveal that proposed approach is promising and that using community-based node features of OSN users can improve the performance of classifying spammers and legitimate users.


title: Learning Adversarially Fair and Transferable Representations

abstract: In this paper, we advocate for representation learning as the key to mitigating unfair prediction outcomes downstream. Motivated by a scenario where learned representations are used by third parties with unknown objectives, we propose and explore adversarial representation learning as a natural method of ensuring those parties act fairly. We connect group fairness (demographic parity, equalized odds, and equal opportunity) to different adversarial objectives. Through worst-case theoretical guarantees and experimental validation, we show that the choice of this objective is crucial to fair prediction. Furthermore, we present the first in-depth experimental demonstration of fair transfer learning and demonstrate empirically that our learned representations admit fair predictions on new tasks while maintaining utility, an essential goal of fair representation learning.


title: Leucine co-ingestion improves post-prandial muscle protein accretion in elderly men

abstract: Summary Background & aims It has been speculated that the amount of leucine in a meal largely determines the post-prandial muscle protein synthetic response to food intake. The present study investigates the impact of leucine co-ingestion on subsequent post-prandial muscle protein accretion following the ingestion of a single bolus of dietary protein in elderly males. Methods Twenty-four elderly men (74.3 ± 1.0 y) were randomly assigned to ingest 20 g intrinsically l -[1-13C]phenylalanine-labeled casein protein with (PRO + LEU) or without (PRO) 2.5 g crystalline leucine. Ingestion of specifically produced intrinsically labeled protein allowed us to create a plasma phenylalanine enrichment pattern similar to the absorption pattern of phenylalanine from the ingested protein and assess the subsequent post-prandial incorporation of l -[1-13C] phenylalanine into muscle protein. Results Plasma amino acid concentrations increased rapidly following protein ingestion in both groups, with higher leucine concentrations observed in the PRO + LEU compared with the PRO group (P l -[1-13C]phenylalanine enrichments increased rapidly and to a similar extent in both groups following protein ingestion. Muscle protein-bound l -[1-13C]phenylalanine enrichments were significantly greater after PRO + LEU when compared with PRO at 2 h (72%; 0.0078 ± 0.0010 vs. 0.0046 ± 0.00100 MPE, respectively; P Conclusion Leucine co-ingestion with a bolus of pure dietary protein further stimulates post-prandial muscle protein synthesis rates in elderly men.


title: A random finite set conjugate prior and application to multi-target tracking

abstract: The objective of multi-object estimation is to simultaneously estimate the number of objects and their states from a set of observations in the presence of data association uncertainty, detection uncertainty, false observations and noise. This estimation problem can be formulated in a Bayesian framework by modeling the (hidden) set of states and set of observations as random finite sets (RFSs) where the model for the observation covers thinning, Markov shifts and superposition of false observations. A prior for the hidden RFS together with the likelihood of the realisation of the observed RFS gives the posterior distribution via the application of Bayes rule. We propose a new class of prior distribution and show that it is a conjugate prior with respect to the multi-target observation likelihood. This result is then applied to develop an analytic implementation of the Bayes multi-target filter for the class of linear Gaussian multi-target models.


title: dbscan: Fast Density-Based Clustering with R

abstract: This article describes the implementation and use of the R package dbscan, which provides complete and fast implementations of the popular density-based clustering algorithm DBSCAN and the augmented ordering algorithm OPTICS. Package dbscan uses advanced open-source spatial indexing data structures implemented in C++ to speed up computation. An important advantage of this implementation is that it is up-to-date with several improvements that have been added since the original algorithms were publications (e.g., artifact corrections and dendrogram extraction methods for OPTICS). We provide a consistent presentation of the DBSCAN and OPTICS algorithms, and compare dbscan's implementation with other popular libraries such as the R package fpc, ELKI, WEKA, PyClustering, SciKit-Learn, and SPMF in terms of available features and using an experimental comparison.


title: Compiling for EDGE Architectures

abstract: Explicit data graph execution (EDGE) architectures offer the possibility of high instruction-level parallelism with energy efficiency. In EDGE architectures, the compiler breaks a program into a sequence of structured blocks that the hardware executes atomically. The instructions within each block communicate directly, instead of communicating through shared registers. The TRIPS EDGE architecture imposes restrictions on its blocks to simplify the microarchitecture: each TRIPS block has at most 128 instructions, issues at most 32 loads and/or stores, and executes at most 32 register bank reads and 32 writes. To detect block completion, each TRIPS block must produce a constant number of outputs (stores and register writes) and a branch decision. The goal of the TRIPS compiler is to produce TRIPS blocks full of useful instructions while enforcing these constraints. This paper describes a set of compiler algorithms that meet these sometimes conflicting goals, including an algorithm that assigns load and store identifiers to maximize the number of loads and stores within a block. We demonstrate the correctness of these algorithms in simulation on SPEC2000, EEMBC, and microbenchmarks extracted from SPEC2000 and others. We measure speedup in cycles over an Alpha 21264 on microbenchmarks.


title: Reprogramming Wireless Sensor Networks by using SDN-WISE: A hands-on demo

abstract: SDN-WISE is a Software Defined Networking solution for Wireless Sensor Networks (WSNs). The aim of SDN-WISE is to simplify the management of the network, the development of novel applications, and the experimentation of new networking solutions in WSNs. Objectives of this demo are to demonstrate how different logical WSNs can coexist exploiting the same sensor nodes and show how easy is it to program the behavior of sensor nodes by few lines of code.


title: Cutty: Aggregate Sharing for User-Defined Windows

abstract: Aggregation queries on data streams are evaluated over evolving and often overlapping logical views called windows. While the aggregation of periodic windows were extensively studied in the past through the use of aggregate sharing techniques such as Panes and Pairs, little to no work has been put in optimizing the aggregation of very common, non-periodic windows. Typical examples of non-periodic windows are punctuations and sessions which can implement complex business logic and are often expressed as user-defined operators on platforms such as Google Dataflow or Apache Storm. The aggregation of such non-periodic or user-defined windows either falls back to expensive, best-effort aggregate sharing methods, or is not optimized at all. In this paper we present a technique to perform efficient aggregate sharing for data stream windows, which are declared as user-defined functions (UDFs) and can contain arbitrary business logic. To this end, we first introduce the concept of User-Defined Windows (UDWs), a simple, UDF-based programming abstraction that allows users to programmatically define custom windows. We then define semantics for UDWs, based on which we design Cutty, a low-cost aggregate sharing technique. Cutty improves and outperforms the state of the art for aggregate sharing on single and multiple queries. Moreover, it enables aggregate sharing for a broad class of non-periodic UDWs. We implemented our techniques on Apache Flink, an open source stream processing system, and performed experiments demonstrating orders of magnitude of reduction in aggregation costs compared to the state of the art.


title: Chinese Short-Text Classification Based on Topic Model with High-Frequency Feature Expansion

abstract: Short text differs from traditional documents in its shortness and sparseness. Feature extension can ease the problem of high sparseness in the vector space model, but it inevitably introduces noise. To resolve this problem, this paper proposes a high-frequency feature expansion method based on a latent Dirichlet allocation (LDA) topic model. High-frequency features are extracted from each category as the feature space, using LDA to derive latent topics from the corpus, and topic words are extended to the short text. Extensive experiments are conducted on Chinese short messages and news titles. The proposed method for classifying Chinese short texts outperforms conventional classification methods.


title: Robust Higher Order Potentials for Enforcing Label Consistency

abstract: This paper proposes a novel framework for labelling problems which is able to combine multiple segmentations in a principled manner. Our method is based on higher order conditional random fields and uses potentials defined on sets of pixels (image segments) generated using unsupervised segmentation algorithms. These potentials enforce label consistency in image regions and can be seen as a strict generalization of the commonly used pairwise contrast sensitive smoothness potentials. The higher order potential functions used in our framework take the form of the robust Pn model. This enables the use of powerful graph cut based move making algorithms for performing inference in the framework [14 ]. We test our method on the problem of multi-class object segmentation by augmenting the conventional CRF used for object segmentation with higher order potentials defined on image regions. Experiments on challenging data sets show that integration of higher order potentials quantitatively and qualitatively improves results leading to much better definition of object boundaries. We believe that this method can be used to yield similar improvements for many other labelling problems.


title: Phinding phish: Evaluating anti-phishing tools

abstract: There are currently dozens of freely available tools to combat phishing and other web-based scams, many of which are web browser extensions that warn users when they are browsing a suspected phishing site. We developed an automated test bed for testing antiphishing tools. We used 200 verified phishing URLs from two sources and 516 legitimate URLs to test the effectiveness of 10 popular anti-phishing tools. Only one tool was able to consistently identify more than 90% of phishing URLs correctly; however, it also incorrectly identified 42% of legitimate URLs as phish. The performance of the other tools varied considerably depending on the source of the phishing URLs. Of these remaining tools, only one correctly identified over 60% of phishing URLs from both sources. Performance also changed significantly depending on the freshness of the phishing URLs tested. Thus we demonstrate that the source of phishing URLs and the freshness of the URLs tested can significantly impact the results of anti-phishing tool testing. We also demonstrate that many of the tools we tested were vulnerable to simple exploits. In this paper we describe our anti-phishing tool test bed, summarize our findings, and offer observations about the effectiveness of these tools as well as ways they might be improved.


title: Image Splicing Detection Using Inherent Lens Radial Distortion

abstract: Image splicing is a common form of image forgery. Such alterations may leave no visual clues of tampering. In recent works camera characteristics consistency across the image has been used to establish the authenticity and integrity of digital images. Such constant camera characteristic properties are inherent from camera manufacturing processes and are unique. The majority of digital cameras are equipped with spherical lens and this introduces radial distortions on images. This aberration is often disturbed and fails to be consistent across the image, when an image is spliced. This paper describes the detection of splicing operation on images by estimating radial distortion from different portions of the image using line-based calibration. For the first time, the detection of image splicing through the verification of consistency of lens radial distortion has been explored in this paper. The conducted experiments demonstrate the efficacy of our proposed approach for the detection of image splicing on both synthetic and real images.


title: Multivehicle Cooperative Driving Using Cooperative Perception: Design and Experimental Validation

abstract: In this paper, we present a multivehicle cooperative driving system architecture using cooperative perception along with experimental validation. For this goal, we first propose a multimodal cooperative perception system that provides see-through, lifted-seat, satellite and all-around views to drivers. Using the extended range information from the system, we then realize cooperative driving by a see-through forward collision warning, overtaking/lane-changing assistance, and automated hidden obstacle avoidance. We demonstrate the capabilities and features of our system through real-world experiments using four vehicles on the road.


title: A Machine Learning Approach to Predicting Blood Glucose Levels for Diabetes Management

abstract: Patients with diabetes must continually monitor their blood glucose levels and adjust insulin doses, striving to keep blood glucose levels as close to normal as possible. Blood glucose levels that deviate from the normal range can lead to serious short-term and long-term complications. An automatic prediction model that warned people of imminent changes in their blood glucose levels would enable them to take preventive action. In this paper, we describe a solution that uses a generic physiological model of blood glucose dynamics to generate informative features for a Support Vector Regression model that is trained on patient specific data. The new model outperforms diabetes experts at predicting blood glucose levels and could be used to anticipate almost a quarter of hypoglycemic events 30 minutes in advance. Although the corresponding precision is currently just 42%, most false alarms are in near-hypoglycemic regions and therefore patients responding to these hypoglycemia alerts would not be harmed by intervention.


title: Automatic Keyword Extraction for Text Summarization in e-Newspapers

abstract: Summarization is the process of reducing a text document to create a summary that retains the most important points of the original document. Extractive summarizers work on the given text to extract sentences that best convey the message hidden in the text. Most extractive summarization techniques revolve around the concept of finding keywords and extracting sentences that have more keywords than the rest. Keyword extraction usually is done by extracting relevant words having a higher frequency than others, with stress on important ones'. Manual extraction or annotation of keywords is a tedious process brimming with errors involving lots of manual effort and time. In this paper, we proposed an algorithm to extract keyword automatically for text summarization in e-newspaper datasets. The proposed algorithm is compared with the experimental result of articles having the similar title in four different e-Newspapers to check the similarity and consistency in summarized results.


title: An inkjet-printed UWB antenna on paper substrate utilizing a novel fractal matching network

abstract: In this work, the smallest reported inkjet-printed UWB antenna is proposed that utilizes a fractal matching network to increase the performance of a UWB microstrip monopole. The antenna is inkjet-printed on a paper substrate to demonstrate the ability to produce small and low-cost UWB antennas with inkjet-printing technology which can enable compact, low-cost, and environmentally friendly wireless sensor network.


title: Gender-Driven Emotion Recognition Through Speech Signals For Ambient Intelligence Applications

abstract: This paper proposes a system that allows recognizing a person's emotional state starting from audio signal registrations. The provided solution is aimed at improving the interaction among humans and computers, thus allowing effective human-computer intelligent interaction. The system is able to recognize six emotions(anger, boredom, disgust, fear, happiness, and sadness) and the neutral state. This set of emotional states is widely used for emotion recognition purposes. It also distinguishes a single emotion versus all the other possible ones, as proven in the proposed numerical results. The system is composed of two subsystems: 1) gender recognition(GR) and 2) emotion recognition(ER). The experimental analysis shows the performance in terms of accuracy of the proposed ER system. The results highlight that the a priori knowledge of the speaker's gender allows a performance increase. The obtained results show also that the features selection adoption assures a satisfying recognition rate and allows reducing the employed features. Future developments of the proposed solution may include the implementation of this system over mobile devices such as smartphones.


title: Road detection algorithm for Autonomous Navigation Systems based on dark channel prior and vanishing point in complex road scenes

abstract: Abstract Vision-based road extraction is essentially important in many fields, such as for intelligent traffic and robot navigation. However, the road detection in urban or ill-structured roads is still very challenging at current stage, and the existing methods often suffer from high computational complexity. This paper reports a novel and efficient method for road detection in challenging scenes. First, the dark channel based image segmentation is proposed to distinguish a rough road region from complex background noise, which is envisioned to reduce the workload of road detection. Furthermore, instead of using the conventional pixel-wise soft voting, a new voting strategy based on the vanishing point and the properties of the segmented regions is proposed to further reduce the computation time of road extraction stage. Finally, the segmented region which has the maximum voting value is selected as the road region. Experimental results demonstrated that the proposed algorithm shows superior performance in different kinds of road scenes. It can remove the interference from pedestrians, vehicles and other obstacles. Our method is about 40 times faster in detection speed, when compared to a recently well-known approach.


title: Progestogens in postmenopausal hormone therapy and the risk of breast cancer

abstract: Hormone therapy is the treatment of choice for the alleviation of menopausal symptoms and the treatment of urogenital atrophy. In women with an intact uterus a progestogen must be added to estrogen therapy to prevent endometrial hyperplasia and cancer. There is a wide variety of marketed progestogens which differ in their pharmacological properties according to their structure. Convincing evidence from both clinical trials and epidemiological studies indicates that combined estrogen–progestogen therapy confers a higher risk of breast cancer compared to estrogen monotherapy. Concerning the different types of progestogens, data from large observational studies suggest that natural progesterone and dydrogesterone are associated with a lower risk of breast cancer compared with the other progestins. Observational studies, furthermore, indicate that sequential estrogen–progestogen regimens may lead to a lower risk elevation compared to continuous regimens. The effect of tibolone on breast cancer is unclear. Concluding, both the type of the progestogen and the mode of HT administration may have an impact on breast cancer risk.


title: A denotational semantics of inheritance and its correctness

abstract: This paper presents a denotational model of inheritance. The model is based on an intuitive motivation of the purpose of inheritance. The correctness of the model is demonstrated by proving it equivalent to an operational semantics of inheritance based upon the method-lookup algorithm of object-oriented languages. Although it was originally developed to explain inheritance in object-oriented languages, the model shows that inheritance is a general mechanism that may be applied to any form of recursive definition.


title: Binary code is not easy

abstract: Binary code analysis is an enabling technique for many applications. Modern compilers and run-time libraries have introduced significant complexities to binary code, which negatively affect the capabilities of binary analysis tool kits to analyze binary code, and may cause tools to report inaccurate information about binary code. Analysts may hence be confused and applications based on these tool kits may have degrading quality. We examine the problem of constructing control flow graphs from binary code and labeling the graphs with accurate function boundary annotations. We identified several challenging code constructs that represent hard-to-analyze aspects of binary code, and show code examples for each code construct. As part of this discussion, we present new code parsing algorithms in our open source Dyninst tool kit that support these constructs, including a new model for describing jump tables that improves our ability to precisely determine the control flow targets, a new interprocedural analysis to determine when a function is non-returning, and techniques for handling tail calls. We evaluated how various tool kits fare when handling these code constructs with real software as well as test binaries patterned after each challenging code construct we found in real software.


title: Food store access and household fruit and vegetable use among participants in the US Food Stamp Program

abstract: Objective: Recent research on access to food among low-income populations in industrialised countries has begun to focus on neighbourhood food availability as a key determinant of dietary behaviour. This study examined the relationship between various measures of food store access and household fruit and vegetable use among participants in the Food Stamp Program, America's largest domestic food assistance programme. Design: A secondary data analysis was conducted using the 1996–97 National Food Stamp Program Survey. The survey employed a 1-week food inventory method, including two at-home interviews, to determine household food use. Separate linear regression models were developed to analyse fruit and vegetable use. Independent variables included distance to store, travel time to store, ownership of a car and difficulty of supermarket access. All models controlled for a full set of socio-economic variables. Subjects: A nationally representative sample of participants (n = 963) in the Food Stamp Program. Results: After controlling for confounding variables, easy access to supermarket shopping was associated with increased household use of fruits (84 grams per adult equivalent per day; 95% confidence interval 5, 162). Distance from home to food store was inversely associated with fruit use by households. Similar patterns were seen with vegetable use, though associations were not significant. Conclusions: Environmental factors are importantly related to dietary choice in a nationally representative sample of low-income households, reinforcing the importance of including such factors in interventions that seek to effect dietary improvements.


title: Risk and safety of repetitive transcranial magnetic stimulation: report and suggested guidelines from the International Workshop on the Safety of Repetitive Transcranial Magnetic Stimulation, June 5–7, 1996

abstract: Single-pulse transcranial magnetic stimulation (TMS) is a safe and useful tool for investigating various aspects of human neurophysiology, particularly corticospinal function, in health and disease. Repetitive TMS (rTMS), however, is a more powerful and potentially dangerous modality, capable of regionally blocking or facilitating cortical processes. Although there is evidence that rTMS is useful for treating clinical depression, and possibly other brain disorders, it had caused 7 known seizures by 1996 and could have other undesirable effects. In June 1996 a workshop was organized to review the available data on the safety of rTMS and to develop guidelines for its safe use. This article summarizes the workshop’s deliberations. In addition to issues of risk and safety, it also addresses the principles and applications of rTMS, nomenclature, and potential therapeutic effects of rTMS. The guidelines for the use of rTMS, which are summarized in an appendix, cover the ethical issues, recommended limits on stimulation parameters, monitoring of subjects (both physiologically and neuropsychologically), expertise and function of the rTMS team, medical and psychosocial management of induced seizures, and contraindications to rTMS. © 1998 Elsevier Science Ireland Ltd.


title: EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals

abstract: Generative adversarial networks (GANs) are recently highly successful in generative applications involving images and start being applied to time series data. Here we describe EEG-GAN as a framework to generate electroencephalographic (EEG) brain signals. We introduce a modification to the improved training of Wasserstein GANs to stabilize training and investigate a range of architectural choices critical for time series generation (most notably up- and down-sampling). For evaluation we consider and compare different metrics such as Inception score, Frechet inception distance and sliced Wasserstein distance, together showing that our EEG-GAN framework generated naturalistic EEG examples. It thus opens up a range of new generative application scenarios in the neuroscientific and neurological context, such as data augmentation in brain-computer interfacing tasks, EEG super-sampling, or restoration of corrupted data segments. The possibility to generate signals of a certain class and/or with specific properties may also open a new avenue for research into the underlying structure of brain signals.


title: A Model of Perceptual Task Effort for Bar Charts and its Role in Recognizing Intention

abstract: This paper presents a model of perceptual task effort for use in hypothesizing the message that a bar chart is intended to convey. It presents our rules, based on research by cognitive psychologists, for estimating perceptual task effort, and discusses the results of an eye tracking experiment that demonstrates the validity of our model. These rules comprise a model that captures the relative difficulty that a viewer would have performing one perceptual task versus another on a specific bar chart. The paper outlines the role of our model of relative perceptual task effort in recognizing the intended message of an information graphic. Potential applications of this work include using this message to provide (1) a more complete representation of the content of the document to be used for searching and indexing in digital libraries, and (2) alternative access to the information graphic for visually impaired users or users of low-bandwidth environments.


title: Data Sparsity Issues in the Collaborative Filtering Framework

abstract: With the amount of available information on the Web growing rapidly with each day, the need to automatically filter the information in order to ensure greater user efficiency has emerged. Within the fields of user profiling and Web personalization several popular content filtering techniques have been developed. In this chapter we present one of such techniques -- collaborative filtering. Apart from giving an overview of collaborative filtering approaches, we present the experimental results of confronting the κ-Nearest Neighbor (kNN) algorithm with Support Vector Machine (SVM) in the collaborative filtering framework using datasets with different properties. While the κ-Nearest Neighbor algorithm is usually used for collaborative filtering tasks, Support Vector Machine is considered a state-of-the-art classification algorithm. Since collaborative filtering can also be interpreted as a classification/regression task, virtually any supervised learning algorithm (such as SVM) can also be applied. Experiments were performed on two standard, publicly available datasets and, on the other hand, on a real-life corporate dataset that does not fit the profile of ideal data for collaborative filtering. We conclude that the quality of collaborative filtering recommendations is highly dependent on the sparsity of available data. Furthermore, we show that kNN is dominant on datasets with relatively low sparsity while SVM-based approaches may perform better on highly sparse data.


title: Cyber-physical security via geometric control: Distributed monitoring and malicious attacks

abstract: Cyber-physical systems are ubiquitous in power systems, transportation networks, industrial processes, and critical infrastructures. These systems need to operate reliably in the face of unforeseen failures and external malicious attacks. This paper summarizes and extends our results on the security of cyber-physical systems based on geometric control theory: (i) we propose a mathematical framework for cyber-physical systems, attacks, and monitors; (ii) we characterize fundamental monitoring limitations from system-theoretic and graph-theoretic perspectives; and (iii) we design centralized and distributed attack detection and identification monitors. Finally, we design an attack strategy for a group of power generators to physically compromise the functionality of other generators. Novel contributions include a more general framework, the design of novel centralized and distributed identification monitors, and the attack design case study.


title: Peak-to-Average Power Ratio Reduction in FBMC-OQAM System

abstract: Multicarrier Modulation has attracted a lot of attention in the modern communication world to satisfy the need for higher bit rate. The most popular MCM technique, OFDM has low efficiency due to the insertion of cyclic prefix and the use of IFFT in OFDM leads to poor stopband behaviour for OFDM prototype filter which leads to spectral leakage among the subchannels. Due to the disadvantages of OFDM, Filter Bank based MultiCarrier system with Offset Quadrature Amplitude Modulation has been proposed. The prototype filter for FBMC-OQAM system is designed using frequency sampling method of filter design from which the whole filterbank is formed by exponential modulation. For every multicarrier system, high PAPR is the main issue. Simple PAPR reduction methods like clipping, A-law comapnding, mu-law companding etc are carried out in the FBMC-OQAM system and the system performance is evaluated using CCDF plot and BER plot. Simulation results show that the proposed scheme has better PAPR reduction performance than the existing methods along with a good BER performance.


title: Sequence segmentation using joint RNN and structured prediction models

abstract: We describe and analyze a simple and effective algorithm for sequence segmentation applied to speech processing tasks. We propose a neural architecture that is composed of two modules trained jointly: a recurrent neural network (RNN) module and a structured prediction model. The RNN outputs are considered as feature functions to the structured model. The overall model is trained with a structured loss function which can be designed to the given segmentation task. We demonstrate the effectiveness of our method by applying it to two simple tasks commonly used in phonetic studies: word segmentation and voice onset time segmentation. Results suggest the proposed model is superior to previous methods, obtaining state-of-the-art results on the tested datasets.


title: Mining duplicate questions in stack overflow

abstract: Stack Overflow is a popular question answering site that is focused on programming problems. Despite efforts to prevent asking questions that have already been answered, the site contains duplicate questions. This may cause developers to unnecessarily wait for a question to be answered when it has already been asked and answered. The site currently depends on its moderators and users with high reputation to manually mark those questions as duplicates, which not only results in delayed responses but also requires additional efforts. In this paper, we first perform a manual investigation to understand why users submit duplicate questions in Stack Overflow. Based on our manual investigation we propose a classification technique that uses a number of carefully chosen features to identify duplicate questions. Evaluation using a large number of questions shows that our technique can detect duplicate questions with reasonable accuracy. We also compare our technique with DupPredictor, a state-of-the-art technique for detecting duplicate questions, and we found that our proposed technique has a better recall rate than that technique.


title: Blockchain Enabled Privacy Audit Logs

abstract: Privacy audit logs are used to capture the actions of participants in a data sharing environment in order for auditors to check compliance with privacy policies. However, collusion may occur between the auditors and participants to obfuscate actions that should be recorded in the audit logs. In this paper, we propose a Linked Data based method of utilizing blockchain technology to create tamper-proof audit logs that provide proof of log manipulation and non-repudiation. We also provide experimental validation of the scalability of our solution using an existing Linked Data privacy audit log model.


title: Compact Structure With Three Attenuation Poles for Improving Stopband Characteristics

abstract: A novel compact structure with three controllable finite attenuation poles at stopband is presented. The new structure is composed of a pair of symmetrical parallel coupled-lines and a capacitive load. With this configuration, three finite attenuation poles are available, which can improve the stopband characteristics of low-pass filters (LPFs) or the upper stopband performances of band-pass filters. The research method is based on a transmission-line model for tuning the finite attenuation poles. In order to examine the feasibility of the proposed structure, a new type of LPF with broad stopband and sharp cutoff frequency response is designed, fabricated, and measured. The experimental results of the fabricated circuit agree well with the simulation and analytical ones


title: Causal Relationships among Dimensions of Consumer-Based Brand Equity and Purchase Intention: Fashion Industry

abstract: Due to the intense competition in market place, customers nowadays are exposed to several brands from across the globe. Building the brand equity becomes an important source of differentiation. It is widely recognized the important role of brand image in establishing brand awareness and brand attitude. Numbers of research are still debating on relationship among the dimensions of brand equity and yet no conclusive decision has been made. The purpose of the research is to investigate the relationship among the dimensions of brand equity and propose a model that predicts the purchase intention in fashion industry. The result confirms the casual relationship among the dimensions of brand equity. Interestingly perceived quality rather than brand image has stronger impact on brand loyalty and brand loyalty rather than perceived quality has stronger impact on purchase intention. This study contributes the original model of customer based brand equity of Aaker by introducing brand image as one of the assets to measure the brand equity concept. Strategic guidelines are provided to managers in building the brand loyalty.


title: Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks

abstract: In recent years, graph neural networks (GNNs) have emerged as a powerful neural architecture to learn vector representations of nodes and graphs in a supervised, end-to-end fashion. Up to now, GNNs have only been evaluated empirically—showing promising results. The following work investigates GNNs from a theoretical point of view and relates them to the 1-dimensional Weisfeiler-Leman graph isomorphism heuristic (1-WL). We show that GNNs have the same expressiveness as the 1-WL in terms of distinguishing non-isomorphic (sub-)graphs. Hence, both algorithms also have the same shortcomings. Based on this, we propose a generalization of GNNs, so-called k-dimensional GNNs (k-GNNs), which can take higher-order graph structures at multiple scales into account. These higher-order structures play an essential role in the characterization of social networks and molecule graphs. Our experimental evaluation confirms our theoretical findings as well as confirms that higher-order information is useful in the task of graph classification and regression.


title: A multilayer IP security protocol for TCP performance enhancement in wireless networks

abstract: Transmission control protocol (TCP) performance enhancement proxy (PEP) mechanisms have been proposed, and in some cases widely deployed, to improve TCP performance in all-Internet protocol (IP) wireless networks. However, this technique is conflicted with IP-security (IPsec)-a standard IP security protocol that will make inroad into wireless networks. This paper analyzes the fundamental problem behind this conflict and develops a solution called multilayer IP-security (ML-IPsec). The basic principle is to use a multilayer protection model and a fine grain access control to make IP security protocols compatible with TCP PEP. It allows wireless network operators or service providers to grant base stations or wireless routers limited and controllable access to the TCP headers for performance enhancement purposes. Through careful design, implementation, and evaluation, we show that we can easily add ML-IPsec to existing IPsec software and the overhead is low. We conclude that ML-IPsec can help wireless networks provide both security and performance.


title: Performance analysis of real-time multimedia transmission in 802.11p based multihop hybrid vehicular networks

abstract: Real-time multimedia communications over vehicular ad-hoc networks (VANET) will play an extremely significant role in the next generation intelligent transport systems. In recent years, there has been an upsurge of interest in the quality-oriented adaptive multimedia delivery, including over VANET. In this paper, a hybrid IEEE 802.11p-based multihop network communication solution is presented, which makes use of both in frastructure and ad-hoc modes in order to deliver quality-oriented real-time multimedia content to high-speed vehicles. Simulation-based testing shows how multimedia delivery to vehicles when using the multihop hybrid mechanism achieves significantly higher throughput in comparison to the case when the in frastructure mode is employed and the vehicles communicate directly with the base station. This result is obtained while the average delay and packet loss in the two cases are similar.


title: Theoretical modeling of amorphous silicon‐based alloy p‐i‐n solar cells

abstract: We have developed a computer model of amorphous silicon based alloy p‐i‐n solar cells based on the solution of the electron and hole continuity equations, together with Poisson’s equation. This model takes into account the spatial and energy variations of the localized state spectrum, nonuniform doping profiles, and nonuniform optical excitation. Heavily doped p+ and n+ regions are used as contacts, and these enable us to realistically set up the boundary conditions. Appropriate boron profiles have been added to the intrinsic layer, with the density of states being dependent on the doping concentration. The computed dark and light current–voltage characteristics are in good agreement with experimental data. In particular, our results indicate the importance of residual boron doping in the intrinsic layer for cells illuminated through the n+ layer, and show that for optimized p‐i‐n devices carrier back diffusion is not a significant effect and that the open circuit voltage is determined by the recombinatio...


title: A Hybrid Clustering Algorithm based on Honey Bees Mating Optimization and Greedy Randomized Adaptive Search Procedure

abstract: This paper introduces a new hybrid algorithmic nature inspired approach based on the concepts of the Honey Bees Mating Optimization Algorithm (HBMO) and of the Greedy Randomized Adaptive Search Procedure (GRASP), for optimally clustering N objects into K clusters. The proposed algorithm for the Clustering Analysis, the Hybrid HBMO-GRASP, is a two phase algorithm which combines a HBMO algorithm for the solution of the feature selection problem and a GRASP for the solution of the clustering problem. This paper shows that the Honey Bees Mating Optimization can be used in hybrid synthesis with other metaheuristics for the solution of the clustering problem with remarkable results both to quality and computational efficiency. Its performance is compared with other popular stochastic/metaheuristic methods like particle swarm optimization, ant colony optimization, genetic algorithms and tabu search based on the results taken from the application of the methodology to data taken from the UCI Machine Learning Repository.


title: Deep learning approach in multi-scale prediction of turbulent mixing-layer

abstract: Achievement of solutions in Navier-Stokes equation is one of challenging quests, especially for its closure problem. For achievement of particular solutions, there are variety of numerical simulations including Direct Numerical Simulation (DNS) or Large Eddy Simulation (LES). These methods analyze flow physics through efficient reduced-order modeling such as proper orthogonal decomposition or Koopman method, showing prominent fidelity in fluid dynamics. Generative adversarial network (GAN) is a reprint of neurons in brain as combinations of linear operations, using competition between generator and discriminator. Current paper propose deep learning network for prediction of small-scale movements with large-scale inspections only, using GAN. Therefore DNS result of three-dimensional mixing-layer was filtered blurring out the small-scaled structures, then is predicted of its detailed structures, utilizing Generative Adversarial Network (GAN). This enables multi-resolution analysis being asked to predict fine-resolution solution with only inspection of blurry one. Within the grid scale, current paper present deep learning approach of modeling small scale features in turbulent flow. The presented method is expected to have its novelty in utilization of unprocessed simulation data, achievement of 3D structures in prediction by processing 3D convolutions, and predicting precise solution with less computational costs.


title: Online active inference and learning

abstract: We present a generalized framework for active inference, the selective acquisition of labels for cases at prediction time in lieu of using the estimated labels of a predictive model. We develop techniques within this framework for classifying in an online setting, for example, for classifying the stream of web pages where online advertisements are being served. Stream applications present novel complications because (i) at the time of label acquisition, we don't know the set of instances that we will eventually see, (ii) instances repeat based on some unknown (and possibly skewed) distribution. We combine ideas from decision theory, cost-sensitive learning, and online density estimation. We also introduce a method for on-line estimation of the utility distribution, which allows us to manage the budget over the stream. The resulting model tells which instances to label so that by the end of each budget period, the budget is best spent (in expectation). The main results show that: (1) our proposed approach to active inference on streams can indeed reduce error costs substantially over alternative approaches, (2) more sophisticated online estimations achieve larger reductions in error. We next discuss simultaneously conducting active inference and active learning. We show that our expected-utility active inference strategy also selects good examples for learning. We close by pointing out that our utility-distribution estimation strategy can also be applied to convert pool-based active learning techniques into budget-sensitive online active learning techniques.


title: Optimal Trajectory Control of LLC Resonant Converters for Soft Start-Up

abstract: In this paper, the soft start-up process for the LLC resonant converter is investigated and optimized based on the graphical state-plane analysis. By setting a current limiting band, several optimal switch patterns are proposed to settle the initial condition. Then, by sensing the output voltage, the optimal frequency is determined within the current limiting band. Therefore, no current and voltage stress in the resonant tank is guaranteed during the soft start-up process. Meanwhile, the output voltage is built up quickly and smoothly.


title: sec-cs: Getting the Most out of Untrusted Cloud Storage

abstract: We present sec-cs, a hash-table-like data structure for file contents on untrusted storage that is both secure and storage-efficient. We achieve authenticity and confidentiality with zero storage overhead using deterministic authenticated encryption. State-of-the-art data deduplication approaches prevent redundant storage of shared parts of different contents irrespective of whether relationships between contents are known a priori or not. ::: Instead of just adapting existing approaches, we introduce novel (multi-level) chunking strategies, ML-SC and ML-CDC, which are significantly more storage-efficient than existing approaches in presence of high redundancy. ::: We prove sec-cs's security, publish a ready-to-use implementation, and present results of an extensive analytical and empirical evaluation that show its suitability for, e.g., future backup systems that should preserve many versions of files on little available cloud storage.


title: Estimation of Crowd Density Based on Wavelet and Support Vector Machine

abstract: Automatic estimation of crowd density is very important for the safety management of the crowds. In particular, when the density of the crowds exceeds a critical level, the safety of people in the crowd may be compromised. This paper describes a novel method to estimate the crowd density based on the combination of multi-scale analysis and a support vector machine. The algorithm will first transform the crowd image into multi-scale formats using wavelet transform. The first-order and second-order statistical features at each scale of the transformed images are then extracted as density character vectors. Furthermore, a classifier based on a support vector machine is designed to classify the extracted density character vectors into different density levels. Compared with the conventional statistical techniques and wavelet energy techniques used in single-scale images, the test results of a set of 300 images show that the proposed algorithm can achieve much improved performance and more detailed information of the crowd density can be captured by the new feature extraction method. Language: en


From the editors: What Grounded Theory is not


title: Safe exploration for reinforcement learning

abstract: In this paper we define and address the problem of safe exploration in the context of reinforcement learning. Our notion of safety is concerned with states or transitions that can lead to damage and thus must be avoided. We introduce the concepts of a safety function for determining a state’s safety degree and that of a backup policy that is able to lead the system under control from a critical state back to a safe one. Moreover, we present a level-based exploration scheme that is able to generate a comprehensive base of observations while adhering safety constraints.We evaluate our approach on a simplified simulation of a gas turbine.


Towards a Resource Aware Scheduler in Hadoop