title: Forensic Triage for Mobile Phones with DEC0DE

abstract: We present DEC0DE, a system for recovering information from phones with unknown storage formats, a critical problem for forensic triage. Because phones have myriad custom hardware and software, we examine only the stored data. Via flexible descriptions of typical data structures, and using a classic dynamic programming algorithm, we are able to identify call logs and address book entries in phones across varied models and manufacturers. We designed DEC0DE by examining the formats of one set of phone models, and we evaluate its performance on other models. Overall, we are able to obtain high performance for these unexamined models: an average recall of 97% and precision of 80% for call logs; and average recall of 93% and precision of 52% for address books. Moreover, at the expense of recall dropping to 14%, we can increase precision of address book recovery to 94% by culling results that don't match between call logs and address book entries on the same phone.


title: FM wide band panel dipole antenna

abstract: It is very common that when a broadcaster needs to install an FM transmitting antenna within a large metropolitan area he places it on the tallest structure or building available. When the rooftop is already occupied by a large number of other types of transmitting and receiving antennas, the panel dipole antenna should be chosen. This antenna is secured to the side walls of the upper floors with the panel oriented to obtain full coverage of the most desirable areas of the city. For the Buenos Aires area this orientation avoids radiating toward Uruguay and specifically toward Montevideo, some 140 miles away. A wide band antenna operation permits placing the station on the air and at the same time allows future stations to share it without the installation of new antennas. Details of model and full model impedance and radiation pattern measurements during the antenna development are presented in order to show its technical characteristics. The radiation patterns were measured on a scale model in an anechoic chamber. The full scaled model was measured in an outdoor antenna range. Both E and H plane radiation patterns were measured along the FM band in order to observe pattern variations on both planes. Practically no difference in a panel radiation beamwidth from 88 to 108 MHz was observed and at the same time good input impedance was maintained. A really wide band antenna in pattern and VSWR is obtained. Power division for the antenna system is obtained designing an eight port power divider using the microstrip line technique. In this case, however due to high power operation the ground plane and strip are contained in a sealed metallic box and are separated by high pressure dry air like into the 3" feeding coaxial line.


title: A review of single-phase grid-connected inverters for photovoltaic modules

abstract: This review focuses on inverter technologies for connecting photovoltaic (PV) modules to a single-phase grid. The inverters are categorized into four classifications: 1) the number of power processing stages in cascade; 2) the type of power decoupling between the PV module(s) and the single-phase grid; 3) whether they utilizes a transformer (either line or high frequency) or not; and 4) the type of grid-connected power stage. Various inverter topologies are presented, compared, and evaluated against demands, lifetime, component ratings, and cost. Finally, some of the topologies are pointed out as the best candidates for either single PV module or multiple PV module applications.


title: Data Fusion by Matrix Factorization

abstract: For most problems in science and engineering we can obtain data sets that describe the observed system from variousperspectives and record the behavior of its individual components. Heterogeneous data sets can be collectively mined by data fusion. Fusion can focus on a specific target relation and exploit directly associated data together with contextual data and data about system’s constraints. In the paper we describe a data fusion approach with penalized matrix tri-factorization (DFMF) that simultaneouslyfactorizes data matrices to reveal hidden associations. The approach can directly consider any data that can be expressed in a matrix, including those from feature-based representations, ontologies, associations and networks. We demonstrate the utility of DFMF for gene function prediction task with eleven different data sources and for prediction of pharmacologic actions by fusing six data sources. Our data fusion algorithm compares favorably to alternative data integration approaches and achieves higher accuracy than can be obtained from any single data source alone.


title: Beat Frequency Detector--Based High-Speed True Random Number Generators: Statistical Modeling and Analysis

abstract: True random number generators (TRNGs) are crucial components for the security of cryptographic systems. In contrast to pseudo--random number generators (PRNGs), TRNGs provide higher security by extracting randomness from physical phenomena. To evaluate a TRNG, statistical properties of the circuit model and raw bitstream should be studied. In this article, a model for the beat frequency detector--based high-speed TRNG (BFD-TRNG) is proposed. The parameters of the model are extracted from the experimental data of a test chip. A statistical analysis of the proposed model is carried out to derive mean and variance of the counter values of the TRNG. Our statistical analysis results show that mean of the counter values is inversely proportional to the frequency difference of the two ring oscillators (ROSCs), whereas the dynamic range of the counter values increases linearly with standard deviation of environmental noise and decreases with increase of the frequency difference. Without the measurements from the test data, a model cannot be created; similarly, without a model, performance of a TRNG cannot be predicted. The key contribution of the proposed approach lies in fitting the model to measured data and the ability to use the model to predict performance of BFD-TRNGs that have not been fabricated. Several novel alternate BFD-TRNG architectures are also proposed; these include parallel BFD, cascade BFD, and parallel-cascade BFD. These TRNGs are analyzed using the proposed model, and it is shown that the parallel BFD structure requires less area per bit, whereas the cascade BFD structure has a larger dynamic range while maintaining the same mean of the counter values as the original BFD-TRNG. It is shown that 3.25M and 4M random bits can be obtained per counter value from parallel BFD and parallel-cascade BFD, respectively, where M counter values are computed in parallel. Furthermore, the statistical analysis results illustrate that BFD-TRNGs have better randomness and less cost per bit than other existing ROSC-TRNG designs. For example, it is shown that BFD-TRNGs accumulate 150p more jitter than the original two-oscillator TRNG and that parallel BFD-TRNGs require one-third power and one-half area for same number of random bits for a specified period.


title: Cryptographic and Physical Zero-Knowledge Proof Systems for Solutions of Sudoku Puzzles

abstract: We consider cryptographic and physical zero-knowledge proof schemes for Sudoku, a popular combinatorial puzzle. We discuss methods that allow one party, the prover, to convince another party, the verifier, that the prover has solved a Sudoku puzzle, without revealing the solution to the verifier. The question of interest is how a prover can show: (i) that there is a solution to the given puzzle, and (ii) that he knows the solution, while not giving away any information about the solution to the verifier. ::: ::: In this paper we consider several protocols that achieve these goals. Broadly speaking, the protocols are either cryptographic or physical. By a cryptographic protocol we mean one in the usual model found in the foundations of cryptography literature. In this model, two machines exchange messages, and the security of the protocol relies on computational hardness. By a physical protocol we mean one that is implementable by humans using common objects, and preferably without the aid of computers. In particular, our physical protocols utilize items such as scratch-off cards, similar to those used in lotteries, or even just simple playing cards. ::: ::: The cryptographic protocols are direct and efficient, and do not involve a reduction to other problems. The physical protocols are meant to be understood by “lay-people” and implementable without the use of computers.


title: Challenges for Frequency-Reconfigurable Antennas in Small Terminals

abstract: This paper gives an overview of the techniques published over the past years to address continuous frequency tuning. It presents the challenges that have been encountered and relates to each other the parameters that influence the losses of the resulting antenna structure. A mock-up is made with a PIFA and a packaged RF-MEMS tunable capacitor to measure its efficiency as the resonance is tuned towards the LTE-700 band.


title: Unexpected failure in power-rail ESD clamp circuits of CMOS integrated circuits in microelectronics systems during electrical fast transient (EFT) test and the re-design solution

abstract: Four different on-chip power-rail electrostatic discharge (ESD) clamp circuits have been designed to investigate their susceptibility to electrical fast transient (EFT) test. From the experimental results, the feedback loop in two kinds of on-chip power-rail ESD clamp circuits provides the lock function to perform a latchup-like failure after the EFT test. The re-design solution will be developed to overcome this issue to meet the regulation of EFT/EMC test.


Doctor’s Orders: Menopause, Weight Change, and Feminism


title: ParsCit: an Open-source CRF Reference String Parsing Package

abstract: We describe ParsCit, a freely available, open-source implementation of a reference string parsing package. At the core of ParsCit is a trained conditional random field (CRF) model used to label the token sequences in the reference string. A heuristic model wraps this core with added functionality to identify reference strings from a plain text file, and to retrieve the citation contexts. The package comes with utilities to run it as a web service or as a standalone utility. We compare ParsCit on three distinct reference string datasets and show that it compares well with other previously published work.


title: A Hilbert space embedding for distributions

abstract: We describe a technique for comparing distributions without the need for density estimation as an intermediate step. Our approach relies on mapping the distributions into a reproducing kernel Hilbert space. Applications of this technique can be found in two-sample tests, which are used for determining whether two sets of observations arise from the same distribution, covariate shift correction, local learning, measures of independence, and density estimation.


title: Hair meshes

abstract: Despite the visual importance of hair and the attention paid to hair modeling in the graphics research, modeling realistic hair still remains a very challenging task that can be performed by very few artists. In this paper we present hair meshes, a new method for modeling hair that aims to bring hair modeling as close as possible to modeling polygonal surfaces. This new approach provides artists with direct control of the overall shape of the hair, giving them the ability to model the exact hair shape they desire. We use the hair mesh structure for modeling the hair volume with topological constraints that allow us to automatically and uniquely trace the path of individual hair strands through this volume. We also define a set of topological operations for creating hair meshes that maintain these constraints. Furthermore, we provide a method for hiding the volumetric structure of the hair mesh from the end user, thus allowing artists to concentrate on manipulating the outer surface of the hair as a polygonal surface. We explain and show examples of how hair meshes can be used to generate individual hair strands for a wide variety of realistic hair styles.


title: Firewall Policy Diagram: Structures for Firewall Behavior Comprehension

abstract: Communication security and regulatory compliance have made the firewall a vital element for networked computers. They provide the protections between parties that only wish to communicate over an explicit set of channels, expressed through protocols, traveling over a network. These explicit set of channels are described and implemented in a firewall using a set of rules. The firewall implements the will of the organization through an ordered list of these rules, collectively referred to as a policy. In small test environments and networks, firewall policies may be easy to comprehend and understand; however, in real world organizations these devices and policies must be capable of handling large amounts of traffic traversing hundreds or thousands of rules in a particular policy. Added to that complexity is the tendency of a policy to grow substantially more complex over time and the result is often unintended mistakes in comprehending what is allowed, possibly leading to security breaches. Therefore, it is imperative that an organization is able to unerringly and deterministically reason about network traffic, while being presented with hundreds or thousands of rules. This work seeks to address this problem using a data structure, the Firewall Policy Diagram, in an effort to advance the state of large network behavior comprehension.


title: Impact of meditation training on the default mode network during a restful state

abstract: Mindfulness meditation has been shown to promote emotional stability. Moreover, during the processing of aversive and self-referential stimuli, mindful awareness is associated with reduced medial prefrontal cortex (MPFC) activity, a central default mode network (DMN) component. However, it remains unclear whether mindfulness practice influences functional connectivity between DMN regions and, if so, whether such impact persists beyond a state of meditation. Consequently, this study examined the effect of extensive mindfulness training on functional connectivity within the DMN during a restful state. Resting-state data were collected from 13 experienced meditators (with over 1000 h of training) and 11 beginner meditators (with no prior experience, trained for 1 week before the study) using functional magnetic resonance imaging (fMRI). Pairwise correlations and partial


title: Indoor smartphone localization via fingerprint crowdsourcing: challenges and approaches

abstract: Nowadays, smartphones have become indispensable to everyone, with more and more built-in location-based applications to enrich our daily life. In the last decade, fingerprinting based on RSS has become a research focus in indoor localization, due to its minimum hardware requirement and satisfiable positioning accuracy. However, its time-consuming and labor-intensive site survey is a big hurdle for practical deployments. Fingerprint crowdsourcing has recently been promoted to relieve the burden of site survey by allowing common users to contribute to fingerprint collection in a participatory sensing manner. For its promising commitment, new challenges arise to practice fingerprint crowdsourcing. This article first identifies two main challenging issues, fingerprint annotation and device diversity, and then reviews the state of the art of fingerprint crowdsourcing-based indoor localization systems, comparing their approaches to cope with the two challenges. We then propose a new indoor subarea localization scheme via fingerprint crowdsourcing, clustering, and matching, which first constructs subarea fingerprints from crowdsourced RSS measurements and relates them to indoor layouts. We also propose a new online localization algorithm to deal with the device diversity issue. Our experiment results show that in a typical indoor scenario, the proposed scheme can achieve a 95 percent hit rate to correctly locate a smartphone in its subarea.


title: Optimal tuning of PI controller for speed control of DC motor drive using particle swarm optimization

abstract: This paper present a method to determine the optimal tuning of the PI controller parameter on Direct current (DC) motor drive system using particle swarm optimization (PSO) algorithm, Ziegler-Nichols (ZN) tuning and Modified Ziegler-Nichols (MZN) tuning method. The main objective of this paper is to minimize transient response specifications chosen as rise time, settling time and overshoot, for better speed response of DC motor drive. The speed control of DC motor is done using PI and PID controllers. Implementation of PID controller for DC motor speed control is done using ZN and MZN tuning method. For PSO algorithm technique, PI controller is used to improve the performance of DC motor speed control system. A comparison is made on the basis of objective function (rise time, settling time and overshoot) from output Step responses. The proposed approach had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency. Fast tuning of optimum PI controller parameters yields high-quality solution. Compared with traditional ZN method and MZN method, the proposed method is found indeed more efficient and robust in improving the step response of DC motor drive system.


title: Career path recommendation system for UiTM Perlis students using fuzzy logic

abstract: Recommendation systems are commonly used over the Internet to guide customers to find the products or services that best fit with their personal preferences. In Malaysia, choosing a career between students is significant due to the existence of multiple human abilities. Many students have chosen their career path without receiving proper advice from suitable professional or university services. This may potentially cause mismatch between academic achievements, personality, interest and abilities of the students. In order to recommend students in career selection, it is essential to build a recommendation system that provides direction and guidance to students in choosing their career. Hence, this study proposes a career recommendation system driven by fuzzy logic technique. The use of fuzzy logic approach helps students by giving career recommendation based on career test. Based on conventional method that have been applied, they just focus to one job careers without look up the other potentials job careers that also can be match with their skills or abilities. So by implementing this system it give a better result and also students also can involves with this system.


title: Systems development in information systems research

abstract: The authors critically review systems development in information systems (IS) research. Several classification schemes of research are described and systems development is identified as a developmental, engineering, and formulative type of research. A framework of research is proposed to explain the dual nature of systems development as a research methodology and a research domain in IS research. Progress in several disciplinary areas is reviewed to provide a basis to argue that systems development is a valid research methodology. A systems development research process is presented from a methodological perspective. Software engineering, the basic method is applying the systems development research methodology, is then discussed. A framework to classify IS research domain and various research methodologies in studying systems development is presented. It is suggested that systems development and empirical research methodologies are complementary to each other. It is further proposed that an integrated multidimensional and multimethodological approach will generate fruitful research results in IS research. >


title: The impact of virtual mirroring on customer satisfaction

abstract: We investigate the impact of a novel method called “virtual mirroring” to promote employee self-reflection and impact customer satisfaction. The method is based on measuring communication patterns, through social network and semantic analysis, and mirroring them back to the individual. Our goal is to demonstrate that self-reflection can trigger a change in communication behaviors, which lead to increased customer satisfaction. We illustrate and test our approach analyzing e-mails of a large global services company by comparing changes in customer satisfaction associated with team leaders exposed to virtual mirroring (the experimental group). We find an increase in customer satisfaction in the experimental group and a decrease in the control group (team leaders not involved in the virtual mirroring process). With regard to the individual communication indicators, we find that customer satisfaction is higher when employees are more responsive, use a simpler language, are embedded in less centralized communication networks, and show more stable leadership patterns.


title: Changes in the Facial Skeleton With Aging: Implications and Clinical Applications in Facial Rejuvenation

abstract: In principle, to achieve the most natural and harmonious rejuvenation of the face, all changes that result from the aging process should be corrected. Traditionally, soft tissue lifting and redraping have constituted the cornerstone of most facial rejuvenation procedures. Changes in the facial skeleton that occur with aging and their impact on facial appearance have not been well appreciated. Accordingly, failure to address changes in the skeletal foundation of the face may limit the potential benefit of any rejuvenation procedure. Correction of the skeletal framework is increasingly viewed as the new frontier in facial rejuvenation. It currently is clear that certain areas of the facial skeleton undergo resorption with aging. Areas with a strong predisposition to resorption include the midface skeleton, particularly the maxilla including the pyriform region of the nose, the superomedial and inferolateral aspects of the orbital rim, and the prejowl area of the mandible. These areas resorb in a specific and predictable manner with aging. The resultant deficiencies of the skeletal foundation contribute to the stigmata of the aging face. In patients with a congenitally weak skeletal structure, the skeleton may be the primary cause for the manifestations of premature aging. These areas should be specifically examined in patients undergoing facial rejuvenation and addressed to obtain superior aesthetic results.Level of Evidence IVThis journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.


title: Adaptive placement and migration policy for an STT-RAM-based hybrid cache

abstract: Emerging Non-Volatile Memories (NVM) such as Spin-Torque Transfer RAM (STT-RAM) and Resistive RAM (RRAM) have been explored as potential alternatives for traditional SRAM-based Last-Level-Caches (LLCs) due to the benefits of higher density and lower leakage power. However, NVM technologies have long latency and high energy overhead associated with the write operations. Consequently, a hybrid STT-RAM and SRAM based LLC architecture has been proposed in the hope of exploiting high density and low leakage power of STT-RAM and low write overhead of SRAM. Such a hybrid cache design relies on an intelligent block placement policy that makes good use of the characteristics of both STT-RAM and SRAM technology.


title: Accurate interpolation in appearance-based pose estimation

abstract: One problem in appearance-based pose estimation is the need for many training examples, i.e. images of the object in a large number of known poses. Some invariance can be obtained by considering translations, rotations and scale changes in the image plane, but the remaining degrees of freedom are often handled simply by sampling the pose space densely enough. This work presents a method for accurate interpolation between training views using local linear models. As a view representation local soft orientation histograms are used. The derivative of this representation with respect to the image plane transformations is computed, and a Gauss-Newton optimization is used to optimize all pose parameters simultaneously, resulting in an accurate estimate.


title: Fast Fourier Transforms: for fun and profit

abstract: The "Fast Fourier Transform" has now been widely known for about a year. During that time it has had a major effect on several areas of computing, the most striking example being techniques of numerical convolution, which have been completely revolutionized. What exactly is the "Fast Fourier Transform"?


title: Airborne SAR processing of highly squinted data using a chirp scaling approach with integrated motion compensation

abstract: Proposes a new approach for high-resolution airborne SAR data processing, which uses a modified chirp scaling algorithm to accommodate the correction of motion errors, as well as the variations of the Doppler centroid in range and azimuth. By introducing a cubic phase term in the chirp scaling phase, data acquired with a squint angle up to 30/spl deg/ can be processed with no degradation of the impulse response function. The proposed approach is computationally very efficient, since it accommodates the variations of Doppler centroid without using block processing. Furthermore, a motion error extraction algorithm can be incorporated into the proposed approach by means of subaperture processing in azimuth. The new approach, denoted as extended chirp scaling, is considered to be a generalized algorithm suitable for the high-resolution processing of most airborne SAR systems. >


title: Planar fiber packaging method for silicon photonic integrated circuits

abstract: A novel method for fiber packaging silicon waveguides is presented. The process uses angled fibers and capillary action of UV-cure epoxy. The technique is suited to passive alignment and can be scaled for fiber arrays.


title: Structure-preserving image completion with multi-level dynamic patches

abstract: In this paper, we present a novel structure-preserving image completion approach equipped with dynamic patches. We formulate the image completion problem into an energy minimization framework that accounts for coherence within the hole and global coherence simultaneously. The completion of the hole is achieved through iterative optimizations combined with a multi-scale solution. In order to avoid abnormal structure and disordered texture, we utilize a dynamic patch system to achieve efficient structure restoration. Our dynamic patch system functions in both horizontal and vertical directions of the image pyramid. In the horizontal direction, we conduct a parallel search for multi-size patches in each pyramid level and design a competitive mechanism to select the most suitable patch. In the vertical direction, we use large patches in higher pyramid level to maximize the structure restoration and use small patches in lower pyramid level to reduce computational workload. We test our approach on massive images with complex structure and texture. The results are visually pleasing and preserve nice structure. Apart from effective structure preservation, our approach outperforms previous state-of-the-art methods in time consumption.


title: Resource scheduling for infrastructure as a service (IaaS) in cloud computing

abstract: Resource scheduling assigns the precise and accurate task to CPU, network, and storage. The aim behind this is the extreme usage of resources. However, well organized scheduling is needed for both cloud providers and cloud users. This paper is a chronological study of recent issues of resource scheduling in IaaS cloud computing environment. In our study, we investigate resource scheduling schemes and algorithms used by different researchers and categorize these approaches on the basis of problems addressed, schemes and the parameters used in evaluating different approaches. Based on various studies considered in this survey, we perceive that different schemes and algorithms did not consider some essential parameters and enhancement is requisite to improve the performance of the existing schemes. Furthermore, this study will trigger new and innovative methods of handling the problems of resource scheduling in the cloud and will help researchers in understanding the existing methodologies for the future adaptation and enhancements. Presents a review of resource scheduling techniques in IaaS cloud.Classify techniques on the basis of problems addressed and schemes used.Analyzes the parameters used to evaluate techniques used in several studies.Suggests future research areas in cloud computing, regards to resource scheduling.


title: El dolor y su impacto en la calidad de vida y estado anímico de pacientes hospitalizados

abstract: By means of this descriptive traverse study, it was sought to understand the existing relationship between the intensity of the perceived pain, the mood and the quality of life in hospitalized patients. The sample used in this study, consisted of 271 patients (133 men and 138 women; mean age 56.79) of a third level hospital in Granada, Spain. The Nottingham Health Profile (NHP), Goldberg’s Anxiety and Depression Scale (GADS) and the Simple Verbal Scale were administered. As the intensity of the pain increases, the results reflected a worsening in each one of the areas of the quality of life when evaluated with the NHP; as well as in the anxiety and depression levels when measured using GADS. Finally, some recommendations are discussed based on the results obtained.


title: Comparative study of different methods of social network analysis and visualization

abstract: A Social Network is a social structure made of individuals or organizations that are linked by one or more specific types of interdependency, such as friends, kinship, terrorist relation, conflict, financial exchange, disease transmission (epidemiology), airline routes etc. Social Network Analysis is an approach to the study of human or organization social interactions. It can be used to investigate kinship patterns, community structure or the organization of other formal and informal social networks. Recently, email networks have been popular source of data for both analysis and visualization of social network of a person or organization. In this paper, three types of visualization technique to analyze social networks have been considered - Force directed layout, Spherical layout and Clustered layout. Each method was evaluated with various data sets from an organization. Force directed layout is used to view the total network structure (Overview). Spherical layout is a 3D method to reveal communication patterns and relationships between different groups. Clustered graph layout visualizes a large amount of data in an efficient and compact form. It gives a hierarchical view of the network. Among the three methods, Clustered layout is the best to handle large network and group relationship. Finally, the comparative study of these three methods has been given.


title: Twitter games: how successful spammers pick targets

abstract: Online social networks, such as Twitter, have soared in popularity and in turn have become attractive targets of spam. In fact, spammers have evolved their strategies to stay ahead of Twitter's anti-spam measures in this short period of time. In this paper, we investigate the strategies Twitter spammers employ to reach relevant target audiences. Due to their targeted approaches to send spam, we see evidence of a large number of the spam accounts forming relationships with other Twitter users, thereby becoming deeply embedded in the social network. We analyze nearly 20 million tweets from about 7 million Twitter accounts over a period of five days. We identify a set of 14,230 spam accounts that manage to live longer than the other 73% of other spam accounts in our data set. We characterize their behavior, types of tweets they use, and how they target their audience. We find that though spam campaigns changed little from a recent work by Thomas et al., spammer strategies evolved much in the same short time span, causing us to sometimes find contradictory spammer behavior from what was noted in Thomas et al.'s work. Specifically, we identify four major strategies used by 2/3rd of the spammers in our data. The most popular of these was one where spammers targeted their own followers. The availability of various kinds of services that help garner followers only increases the popularity of this strategy. The evolution in spammer strategies we observed in our work suggests that studies like ours should be undertaken frequently to keep up with spammer evolution.


title: The impact of sleep deprivation on decision making: A review.

abstract: Few sleep deprivation (SD) studies involve realism or high-level decision making, factors relevant to managers, military commanders, and so forth, who are undergoing prolonged work during crises. Instead, research has favored simple tasks sensitive to SD mostly because of their dull monotony. In contrast, complex rule-based, convergent, and logical tasks are unaffected by short-term SD, seemingly because of heightened participant interest and compensatory effort. However, recent findings show that despite this effort, SD still impairs decision making involving the unexpected, innovation, revising plans, competing distraction, and effective communication. Decision-making models developed outside SD provide useful perspectives on these latter effects, as does a neuropsychological explanation of sleep function. SD presents particular difficulties for sleep-deprived decision makers who require these latter skills during emergency situations. Most of the studies that examine the effects of sleep deprivation (SD) on behavior and psychological performance have concentrated on measures deemed sensitive to "sleepiness," favoring more basic skills, such as vigilance, reaction time, and aspects of memory (cf. the recent review by Pilcher & Huffcutt, 1996). These tests are usually combined with monotony and lack of environmental stimulation, which, taken together, produce optimum conditions for maximizing the adverse effects of SD. Such monotony is further facilitated by the need to ensure that participants are well trained in the test procedures beforehand to minimize practice effects. The extent to which these more conventional laboratory-based tests relate to real-world tasks is a matter for debate. A good illustration, and an example attracting much attention, concerns junior hospital doctors (interns) who experience SD with long working hours on a routine basis. Much of the SD research, as of this writing, has focused on cognitive processes that have little to do with the true nature of the job or normal working duties (e.g., serial reaction time, vigilance). Sometimes, the overall picture can be confusing, with findings showing no impairments for certain clinical skills and concurrent deterioration in psychological performance tasks of unknown relevance to these and other medical skills. For example, Beatty, Adhern, and Katz (1977) noted that after a night of being on call, anesthesiologists had no difficulty in monitoring vital signs during a surgical simulation, although they


title: Enterprise IoT security and scalability: how unikernels can improve the status Quo

abstract: Cloud computing has been a great enabler for both the Internet of Things and Big Data. However, as with all new computing developments, development of the technology is usually much faster than consideration for, and development of, solutions for security and privacy. In a previous paper, we proposed that a unikernel solution could be used to improve security and privacy in a cloud scenario. In this paper, we outline how we might apply this approach to the Internet of Things, which can demonstrate an improvement over existing approaches.


title: Camallanus cotti (Nematoda: Camallanidae), an introduced parasite of fishes in New Caledonia

abstract: A pathogenic Asian nematode species of Camallanus, C. cotti Fujita, 1927, was found in New Caledonia, South Pacific, for the first time; it was recorded from two native fishes, Awaous guamensis (Valenciennes) (Gobiidae) (prevalence 51%, intensity 1-25) and Kuhlia marginata (Cuvier) (Kuhliidae) (a single specimen found), of the La Foa River, about 100 km north of Nouméa; the latter represents a new host record. Morphological data on C. cotti based on New Caledonian specimens and those previously collected from aquarium-kept Misgurnus anguillicaudatus (Cantor) in Canada have been provided. The SEM examination of C. cotti, applied for the first time in this species, made it possible to study some of its morphological details; first-stage larvae from the female's uterus were found to possess several digit-like processes on the tail tip, not previously reported for any species of the Camallanidae. Camallanus moraveci Petter, Cassone et France, 1974 is considered a junior synonym of C. cotti. A list of hitherto recorded hosts of C. cotti is provided. Camallanus cotti is assumed to be introduced into New Caledonia along with the introduction of the exotic poeciliid fishes, which are known to be among the most common hosts of C. cotti in aquarium cultures worldwide.


title: Mining Educational Data to Improve Students ’ Performance: A Case

abstract: Educational data mining concerns with developing methods for discovering knowledge from data that come from educational domain. In this paper we used educational data mining to improve graduate students’ performance, and overcome the problem of low grades of graduate students. In our case study we try to extract useful knowledge from graduate students data collected from the college of Science and Technology – Khanyounis. The data include fifteen years period [1993-2007]. After preprocessing the data, we applied data mining techniques to discover association, classification, clustering and outlier detection rules. In each of these four tasks, we present the extracted knowledge and describe its importance in educational domain.


title: Cloud-Based Commissioning of Constrained Devices using Permissioned Blockchains

abstract: In this paper we describe a privacy-preserving method for commissioning an IoT device into a cloud ecosystem. The commissioning consists of the device proving its manufacturing provenance in an anonymous fashion without reliance on a trusted third party, and for the device to be anonymously registered through the use of a blockchain system. We introduce the ChainAnchor architecture that provides device commissioning in a privacy-preserving fashion. The goal of ChainAnchor is (i) to support anonymous device commissioning, (ii) to support device-owners being remunerated for selling their device sensor-data to service providers, and (iii) to incentivize device-owners and service providers to share sensor-data in a privacy-preserving manner.


title: Investigation into Normal Background DNA on Adult Necks: Implications for DNA Profiling of Manual Strangulation Victims

abstract: Others have investigated the role that DNA profiling could play as a method for identifying the perpetrator of manual strangulation. These studies have demonstrated that it is possible to collect offender DNA from the skin surface of a victim following physical contact. It is not known whether nonself biological material is normally present on the skin surface due to adventitious transfer occurring during innocent everyday interactions. To test the hypothesis that detectable amounts of nonself DNA are normally present on the skin surface of healthy adult individuals due to the adventitious transfer of DNA occurring during normal day-to-day social interactions, we designed an experiment in three phases. Phase 1 was used to deduce which DNA collection, extraction, and amplification methods were suited to investigating this question. During phase 2, the neck surface of 24 healthy adult volunteers was swabbed. DNA was extracted using the QIAamp DNA mini kit and amplified using the SGM Plus PCR amplification kit, using 28 PCR cycles. The work carried out during phase 3 involved a simulated assault to investigate primary and secondary transfer of DNA during physical contact. It was found that 23% of neck areas swabbed during phase 2 of this investigation showed nondonor alleles in the resulting DNA profile, with 5% of areas showing six or more nondonor alleles. The results of phase 3 showed that primary, secondary, and zero transfer of victim and/or offender DNA could be observed after physical contact and that alleles from an unknown source could still be detected in this more controlled experiment. The data presented in this paper demonstrate that DNA profiles generated after swabbing the skin surface of healthy adults can include components of an unknown source, present due to adventitious transfer. These components, if present in large quantities, have the potential to interfere with DNA profile interpretation of swabs taken for the investigation of physical assault by DNA profiling.


Next-Generation Sweep Tool: A Method For Generating All-Hex Meshes On Two-And-One-Half Dimensional Geomtries


title: Benefits, impediments and critical success factors in B2C e-business adoption

abstract: This paper reports the results of a study carried out to assess the benefits, impediments and major critical success factors in adopting business to consumer e-business solutions. A case study method of investigation was used, and the experiences of six online companies and two bricks and mortar companies were documented. The major impediments identified are: leadership issues, operational issues, technology, and ineffective solution design. The critical success factors in the adoption of e-business are identified as: combining e-business knowledge, value proposition and delivery measurement, customer satisfaction and retention, monitoring internal processes and competitor activity, and finally building trust. Findings suggest that above all, adoption of e-business should be appropriate, relevant, value adding, and operationally as well as strategically viable for an organization instead of being a result of apprehensive compliance.


title: An improvement to the SIFT descriptor for image representation and matching

abstract: Constructing proper descriptors for interest points in images is a critical aspect for local features related tasks in computer vision and pattern recognition. Although the SIFT descriptor has been proven to perform better than the other existing local descriptors, it does not gain sufficient distinctiveness and robustness in image match especially in the case of affine and mirror transformations, in which many mismatches could occur. This paper presents an improvement to the SIFT descriptor for image matching and retrieval. The framework of the proposed descriptor consists of the following steps: normalizing elliptical neighboring region, transforming to affine scale-space, improving the SIFT descriptor with polar histogram orientation bin, as well as integrating the mirror reflection invariant. A comparative evaluation of different descriptors is carried out showing that the present approach provides better results than the existing methods.


title: Fusing Social Media Cues: Personality Prediction from Twitter and Instagram

abstract: Incorporating users' personality traits has shown to be instrumental in many personalized retrieval and recommender systems. Analysis of users' digital traces has become an important resource for inferring personality traits. To date, the analysis of users' explicit and latent characteristics is typically restricted to a single social networking site (SNS). In this work, we propose a novel method that integrates text, image, and users' meta features from two different SNSs: Twitter and Instagram. Our preliminary results indicate that the joint analysis of users' simultaneous activities in two popular SNSs seems to lead to a consistent decrease of the prediction errors for each personality trait.


title: A Piecewise Linear 10 Bit DAC Architecture With Drain Current Modulation for Compact LCD Driver ICs

abstract: A piecewise linear 10 bit DAC for LCD data driver with robust interpolation method of drain current modulation is presented. It has higher effective bit resolution than the linear 10 bit switched-capacitor DAC when applied to nonlinear liquid crystal characteristics. By adopting a simultaneous design flow based on the estimations for the mismatch and nonlinearity effects on channel driver performance, the proposed DAC accomplishes good DNL of 0.37 LSB and excellent channel uniformity such that the mean and the standard deviation of the maximum output voltage deviations are 6.35 mV and 0.54 mV, respectively. The data driver with the new interpolation shows 8.2% shrinkage of chip area in comparison with the conventional 8 bit data driver with R-DAC.


title: An Overview on Evaluating and Predicting Scholarly Article Impact

abstract: Scholarly article impact reflects the significance of academic output recognised by academic peers, and it often plays a crucial role in assessing the scientific achievements of researchers, teams, institutions and countries. It is also used for addressing various needs in the academic and scientific arena, such as recruitment decisions, promotions, and funding allocations. This article provides a comprehensive review of recent progresses related to article impact assessment and prediction. The review starts by sharing some insight into the article impact research and outlines current research status. Some core methods and recent progress are presented to outline how article impact metrics and prediction have evolved to consider integrating multiple networks. Key techniques, including statistical analysis, machine learning, data mining and network science, are discussed. In particular, we highlight important applications of each technique in article impact research. Subsequently, we discuss the open issues and challenges of article impact research. At the same time, this review points out some important research directions, including article impact evaluation by considering Conflict of Interest, time and location information, various distributions of scholarly entities, and rising stars.


title: Learning Noun-Modifier Semantic Relations with Corpus-based and WordNet-based Features

abstract: We study the performance of two representations of word meaning in learning noun-modifier semantic relations. One representation is based on lexical resources, in particular WordNet, the other - on a corpus. We experimented with decision trees, instance-based learning and Support Vector Machines. All these methods work well in this learning task. We report high precision, recall and F-score, and small variation in performance across several 10-fold cross-validation runs. The corpus-based method has the advantage of working with data without word-sense annotations and performs well over the baseline. The WordNet-based method, requiring word-sense annotated data, has higher precision.


title: The impact of goal achievement orientation on student technology usage in the classroom

abstract: The aim of this study was to investigate whether students with differing goal achievement orientations were more likely to engage in on-task or off-task mobile device usage, as well as whether particular devices (specifically, laptops and smartphones) have a positive or negative relationship with specific task usage. The results of this study found the mastery approach goal achievement orientation to be negatively associated with off-task device usage, and the mastery avoidance goal achievement orientation to be positively associated with on-task usage behaviors. In addition, it was found that on-task behaviors were positively associated with laptop usage, while off-task behaviors were positively associated with smartphone usage. This study focused on student behavior in a higher education classroom, although the results may be applicable in any context in which users of personal electronic and internet-enabled devices are focused on specific learning objectives.


title: Bio-inspired computing for launch vehicle design and trajectory optimization

abstract: This paper presents an optimization tool for launch vehicle design and trajectory optimization using bio-inspired computing algorithms and nonlinear programming. The objective is to size a launch vehicle such that the payload to lift-of-weight ratio is maximized (i.e the lift off weight is a minimum). Here, the staging problem is solved using Particle Swarm Optimization (PSO) method. With the above vehicle, an optimal trajectory is arrived at using a Real-Coded Genetic Algorithm (RCGA) and solving a nonlinear programming (NLP) by the direct shooting method. The solutions from PSO and RCGA are used for initialization of NLP variables. A case study is carried out that establishes the advantage of the proposed approach.


title: A Theory of Theories in Information Systems

abstract: The aim of this paper is to explore what is meant by ‘theory’ in Information Systems. Different conceptions of causality are discussed, as they are seen as key to understanding different types of theory. Five different types of theory are distinguished: (i) theory for analysing and describing, (ii) theory for understanding, (iii) theory for predicting, (iv) theory for explaining and predicting, and (v) theory for design and action. Illustrations of relevant work in Information Systems are provided, as are relevant research methods, and the form of contributions to knowledge in each. The limited discussion of the nature of theory in Information Systems indicates that further work is needed, particularly with respect to theory for design and action.


title: ARAG: A Routing Algorithm Based on Incentive Mechanisms for DTN With Nodes’ Selfishness

abstract: A delay tolerant network (DTN) is a mobile ad hoc network with extremely limited network resources. When DTN is applied in the urban environment, nodes are not willing to consume their limited resources to forward messages for other nodes, or they prefer a relay node that encounters the destination node with a higher probability to forward messages for themselves owing to the characteristics of sociality and selfishness of nodes, which results in poor network performance. To address this issue, a routing algorithm based on incentive mechanisms was proposed in this paper, which is called ARAG. In the process of forwarding message, the algorithm determines the probability of messages received by relay nodes based on the resource consumption of the sender node. Simultaneously, when a source node generates a message, the threshold of copies is set based on the consumption of its resources, and the total number of the same message in the network will not exceed the threshold. Simulation results show that the algorithm is superior to the existing Epidemic algorithm, Prophet algorithm, and GTMEA algorithm in terms of the delivery ratio, the average delay, and network overhead.


title: Knowledge based evaluation of software systems: a case study

abstract: Solving software evaluation problems is a particularly difficult software engineering process and many contradictory criteria must be considered to reach a decision. Nowadays, the way that decision support techniques are applied suffers from a number of severe problems, such as naive interpretation of sophisticated methods and generation of counter-intuitive, and therefore most probably erroneous, results. In this paper we identify some common flaws in decision support for software evaluations. Subsequently, we discuss an integrated solution through which significant improvement may be achieved, based on the Multiple Criteria Decision Aid methodology and the exploitation of packaged software evaluation expertise in the form of an intelligent system. Both common mistakes and the way they are overcome are explained through a real world example.


title: A Weakly-supervised Approach to Argumentative Zoning of Scientific Documents

abstract: Argumentative Zoning (AZ) -- analysis of the argumentative structure of a scientific paper -- has proved useful for a number of information access tasks. Current approaches to AZ rely on supervised machine learning (ML). Requiring large amounts of annotated data, these approaches are expensive to develop and port to different domains and tasks. A potential solution to this problem is to use weakly-supervised ML instead. We investigate the performance of four weakly-supervised classifiers on scientific abstract data annotated for multiple AZ classes. Our best classifier based on the combination of active learning and self-training outperforms our best supervised classifier, yielding a high accuracy of 81% when using just 10% of the labeled data. This result suggests that weakly-supervised learning could be employed to improve the practical applicability and portability of AZ across different information access tasks.


title: Single pass text classification by direct feature weighting

abstract: The Feature Weighting Classifier (FWC) is an efficient multi-class classification algorithm for text data that uses Information Gain to directly estimate per-class feature weights in the classifier. This classifier requires only a single pass over the dataset to compute the feature frequencies per class, is easy to implement, and has memory usage that is linear in the number of features. Results of experiments performed on 128 binary and multi-class text and web datasets show that FWC’s performance is at least comparable to, and often better than that of Naive Bayes, TWCNB, Winnow, Balanced Winnow and linear SVM. On a large-scale web dataset with 12,294 classes and 135,973 training instances, FWC trained in 13 s and yielded comparable classification performance to a state of the art multi-class SVM implementation, which took over 15 min to train.


title: THz metrology for active electronic devices: state of the art and challenges

abstract: The area of terahertz science and applications has grown dramatically in the last 30 years. Instrumental platforms for THz measurements have also proliferated, and now include laser-based, electronic, opto-electronic, and microwave photonics devices. As the field matured, metrology and standardization have gained focused attention, resulting in an increasing number of publications and a book devoted to the subject. However, these have been primarily focused on photonic-based free-space techniques and their particular demands and issues. This paper presents a brief review of metrology requirements for active THz electronic devices, the available instrumentation, state of the art, and challenges.


title: Cascaded Multi-scale and Multi-dimension Convolutional Neural Network for Stereo Matching

abstract: Convolutional neural networks (CNN) have been shown to perform better than the conventional stereo algorithms for stereo estimation. Numerous CNN algorithms focus on the pixel-wise matching cost computation, which is the important building block for many state-of-the-art algorithms. However, these architectures are limited to small and single scale receptive fields and use traditional methods for cost aggregation or even ignore cost aggregation. In this paper, we propose a novel architecture called cascaded multi-scale and multi-dimension network (MSMD) to take them both into consideration. Firstly, we propose a new multi-scale matching cost computation sub-network, in which two different sizes of receptive fields are implemented parallelly. In this way, the network can make the best use of both variants to balance the trade-off between the increase of receptive field and the loss of details. Furthermore, we show that our multi-dimension aggregation sub-network which contains 2D convolution and 3D convolution operations can provide rich context and semantic information for estimating an accurate initial disparity. Finally, experiments on challenging stereo benchmark KITTI demonstrate that the proposed method can achieve competitive results even without any additional post-processing.


title: A Comprehensive Comparative Study on Inductive and Ultrasonic Wireless Power Transmission to Biomedical Implants

abstract: This paper presents an accurate comparison between the performance of inductive and ultrasonic links for wireless power transmission (WPT) to biomedical implants with different sizes and implantation depth. These links' performances are evaluated based on the power transmission efficiency (PTE) at their optimized operation frequency (fp) to find out which WPT method is more efficient and robust. The geometry of several sets of inductive and ultrasonic links along with their fp have been optimized for different separation distances (d12) between the transmitter (Tx) and receiver (Rx) and two Rx diameters (D02) to achieve the highest PTE. The simulation results show that the ultrasonic link outperforms the inductive link when the Rx has D02 of 1.2 mm and implanted deep inside the tissue. However, for an Rx with the D02 of 5 mm and above, the inductive link achieves higher PTE, particularly at shorter d12 of 5 mm (46% vs. 40%) and 10 mm (40% vs. 28%). The simulated misalignment results show that the ultrasonic link is more prone to misalignments greater than 1 mm compared to its inductive counterpart.


title: WORK ZONES THAT WORK

abstract: Transportation agencies are increasingly focusing and spending funds on preserving, rehabilitating, and maintaining existing roads. While this work is necessary, it takes a certain percentage of the roadways out of use. At the same time, vehicle travel continues to increase. Work zones make the situation worse because they add to the growing congestion problem. This article offers suggestions for work zone management with an operational perspective. The author first reviews the present situation and then considers workable approaches to improving the dangers and congestions of work zones. Current strategies for real-time communications for work zones include providing general construction information on Web sites, using innovative construction and contracting strategies, and applying intelligent transportation system (ITS) technologies to improve work zone performance. Another section considers road closure strategies, including nighttime construction, and full road closure for all or part of the project; worker protection strategies; quick construction techniques; and innovative contracting techniques. The author also describes the use of a traffic impact analysis tool called QuickZone that enables managers to quantify the queue length and user delay caused by work zones. One sidebar describes three relevant resources (software and workshop series).


title: KG^2: Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings

abstract: The AI2 Reasoning Challenge (ARC), a new benchmark dataset for question answering (QA) has been recently released. ARC only contains natural science questions authored for human exams, which are hard to answer and require advanced logic reasoning. On the ARC Challenge Set, existing state-of-the-art QA systems fail to significantly outperform random baseline, reflecting the difficult nature of this task. In this paper, we propose a novel framework for answering science exam questions, which mimics human solving process in an open-book exam. To address the reasoning challenge, we construct contextual knowledge graphs respectively for the question itself and supporting sentences. Our model learns to reason with neural embeddings of both knowledge graphs. Experiments on the ARC Challenge Set show that our model outperforms the previous state-of-the-art QA systems.


title: On legal contracts, imperative and declarative smart contracts, and blockchain systems

abstract: This paper provides an analysis of how concepts pertinent to legal contracts can influence certain aspects of their digital implementation through smart contracts, as inspired by recent developments in distributed ledger technology. We discuss how properties of imperative and declarative languages including the underlying architectures to support contract management and lifecycle apply to various aspects of legal contracts. We then address these properties in the context of several blockchain architectures. While imperative languages are commonly used to implement smart contracts, we find that declarative languages provide more natural ways to deal with certain aspects of legal contracts and their automated management.


title: Application of Neutrosophic Set to Multicriteria Decision Making by Copras

abstract: The paper presents multicriteria decision making method with single value neutrosophic sets (SVNS), namely COPRAS-SVNS. The complex proportional assessment method (COPRAS) has shown accurate results for the solution of various multicriteria decision making problems in the engineering field.


title: Effects of age and pathology on shear wave speed of the human rotator cuff

abstract: Rotator cuff tears are common and often repaired surgically, but post-operative repair tissue healing and shoulder function can be unpredictable. Tear chronicity is believed to influence clinical outcomes, but conventional clinical approaches for assessing tear chronicity are subjective. Shear wave elastography (SWE) is a promising technique for assessing soft tissue via estimates of shear wave speed (SWS), but this technique has not been used extensively on the rotator cuff. Specifically, the effects of age and pathology on rotator cuff SWS are not well known. The objectives of this study were to assess the association between SWS and age in healthy, asymptomatic subjects, and to compare measures of SWS between patients with a rotator cuff tear and healthy, asymptomatic subjects. SWE images of the supraspinatus muscle and intramuscular tendon were acquired from 19 asymptomatic subjects and 11 patients with a rotator cuff tear. Images were acquired with the supraspinatus under passive and active (i.e., minimal activation) conditions. Mean SWS was positively associated with age in the supraspinatus muscle and tendon under passive and active conditions (p≤0.049). Compared to asymptomatic subjects, patients had a lower mean SWS in their muscle and tendon under active conditions (p≤0.024), but no differences were detected under passive conditions (p≥0.783). These findings identify the influences of age and pathology on SWS in the rotator cuff. These preliminary findings are an important step toward evaluating the clinical utility of SWE for assessing rotator cuff pathology. This article is protected by copyright. All rights reserved


title: Cyberbullying detection using parent-child relationship between comments

abstract: Cyberbullying is a underlying problem in social networking service, threatening users' mental and physical health. Previous research on automated cyberbullying detection is mostly textual or social based methods. Cyberbullying content is identified through a set of textual features within the content in the former method and through social information surrounding the content in the latter method. Those methods can not cater different cyberbullying standard for individual SNS user since each content is evaluated using same features. Therefore, in this article we propose a automated cyberbullying detection method that utilises the parent-child relationship between comments to capture the reaction from a third party to detect cyberbullying comments. We were able to improve the effectiveness of cyberbullying detection using only publicly available data.


title: Conceptualizing Context for Pervasive Advertising

abstract: Profile-driven personalization based on socio-demographics is currently regarded as the most convenient base for successful personalized advertising. However, signs point to the dormant power of context recognition: Advertising systems that can adapt to the situational context of a consumer will rapidly gain importance. While technologies that can sense the environment are increasingly advanced, questions such as what to sense and how to adapt to a consumer’s context are largely unanswered. In this chapter, we analyze the purchase context of a retail outlet and conceptualize it such that adaptive pervasive advertising applications really deliver on their potential: showing the right message at the right time to the right recipient.


title: Learning Hierarchical Semantic Segmentations of LIDAR Data

abstract: This paper investigates a method for semantic segmentation of small objects in terrestrial LIDAR scans in urban environments. The core research contribution is a hierarchical segmentation algorithm where potential merges between segments are prioritized by a learned affinity function and constrained to occur only if they achieve a significantly high object classification probability. This approach provides a way to integrate a learned shape-prior (the object classifier) into a search for the best semantic segmentation in a fast and practical algorithm. Experiments with LIDAR scans collected by Google Street View cars throughout a#x223C; 100 city blocks of New York City show that the algorithm provides better segmentations and classifications than simple alternatives for cars, vans, traffic lights, and street lights.


title: Deep learning for the design of nano-photonic structures

abstract: Our visual perception of our surroundings is ultimately limited by the diffraction-limit, which stipulates that optical information smaller than roughly half the illumination wavelength is not retrievable. Over the past decades, many breakthroughs have led to unprecedented imaging capabilities beyond the diffraction-limit, with applications in biology and nanotechnology. In this context, nano-photonics has had a profound impact on the field of optics by enabling the manipulation of light-matter interaction with subwave-length structures [1, 2, 3]. However, despite the many advances in this field, its impact and penetration in our daily life has been hindered by a convoluted and iterative process, cycling through modeling, nanofabrication and nano-characterization. The fundamental reason is the fact that not only the prediction of the optical response is very time consuming and requires solving Maxwell's equations with dedicated numerical packages [4, 5, 6]. But, more significantly, the inverse problem, i.e. designing a nanostructure with an on-demand optical response, is currently a prohibitive task even with the most advanced numerical tools due to the high non-linearity of the problem [7, 8]. Here, we harness the power of Deep Learning and show its ability to predict the geometry of nanostructures based solely on their far-field response. This approach addresses in a direct way the currently inaccessible inverse problem breaking the ground for on-demand design of optical response with applications such as sensing, imaging and also for Plasmons mediated cancer thermotherapy.


title: Electroporation of cells and tissues

abstract: Electrical pulses that cause the transmembrane voltage of fluid lipid bilayer membranes to reach at least U/sub m//spl ap/0.2 V, usually 0.5-1 V, are hypothesized to create primary membrane "pores" with a minimum radius of -1 nm. Transport of small ions such as Na/sup +/ and Cl/sup -/ through a dynamic pore population discharges the membrane even while an external pulse tends to increase U/sub m/, leading to dramatic electrical behavior. Molecular transport through primary pores and pores enlarged by secondary processes provides the basis for transporting molecules into and out of biological cells. Cell electroporation in vitro is used mainly for transfection by DNA introduction, but many other interventions are possible, including microbial killing. Ex vivo electroporation provides manipulation of cells that are reintroduced into the body to provide therapy. In vivo electroporation of tissues enhances molecular transport through tissues and into their constitutive cells. Tissue electroporation, by longer, large pulses, is involved in electrocution injury. Tissue electroporation by shorter, smaller pulses is under investigation for biomedical engineering applications of medical therapy aimed at cancer treatment, gene therapy, and transdermal drug delivery. The latter involves a complex barrier containing both high electrical resistance, multilamellar lipid bilayer membranes and a tough, electrically invisible protein matrix.


title: Information System for Integrated Watershed Management Using Remote Sensing and GIS

abstract: Watershed management is an endowed approach to mitigate the gap between demand and supply of water and other natural resources, particularly in the fragile arid and semi-arid tropics (SAT). As this is a complex phenomenon, there is need for a reliable Information System/Decision Support System (DSS). Watershed Management Information System (WATMIS) is a viable and generic toolkit for integrated watershed planning and management of its natural resources using multiple technologies like Geographical Information System (GIS), Remote Sensing (RS), Global Positioning System (GPS), hydrological modelling and soft computing tools. In this system, an attempt has been made to integrate dimensions in Agriculture–Water–Soil–Climate continuum for sustainable management of land and water resources judiciously. The application of WATMIS will be useful to various stakeholders such as agriculturists, rural extension community and water resources managers for better decision making.


title: Semantic Labeling: A Domain-Independent Approach

abstract: Semantic labeling is the process of mapping attributes in data sources to classes in an ontology and is a necessary step in heterogeneous data integration. Variations in data formats, attribute names and even ranges of values of data make this a very challenging task. In this paper, we present a novel domain-independent approach to automatic semantic labeling that uses machine learning techniques. Previous approaches use machine learning to learn a model that extracts features related to the data of a domain, which requires the model to be re-trained for every new domain. Our solution uses similarity metrics as features to compare against labeled domain data and learns a matching function to infer the correct semantic labels for data. Since our approach depends on the learned similarity metrics but not the data itself, it is domain-independent and only needs to be trained once to work effectively across multiple domains. In our evaluation, our approach achieves higher accuracy than other approaches, even when the learned models are trained on domains other than the test domain.


title: Metrics for Evaluating Dialogue Strategies in a Spoken Language System

abstract: In this paper, we describe a set of metrics for the evaluation of different dialogue management strategies in an implemented real-time spoken language system. The set of metrics we propose tries to offer useful insights in evaluating how particular choices in the dialogue management can affect the overall quality of the man-machine dialogue. The evaluation makes use of established metrics: the transaction success, the contextual appropriateness of system answers, the calculation of normal and correction turns in a dialogue. We also define a new metric, the implicit recovery, which allows to measure the ability of a dialogue manager to deal with errors by different levels of analysis. We report evaluation data from several experiments, and we compare two different approaches to dialogue repair strategies using the set of metrics we argue for.


title: The Use of Moss Pleurozium schreberi (Brid.) Mitt. as Bioindicator of Radionuclide Contamination in Industrial Areas of Upper Silesia

abstract: Abstract Mosses are good bioaccumulators of radionuclides and from the 60 of the last century, they are used as bioindicators of radioactive contamination in the environment. Concentration of impurities in moss represent the accumulation in mosses during the past 2-3 years. As a result, the moss composition analysis provides information on an average contamination within a few vegetation seasons. During our survey the measurements of radionuclide activity concentrations in P. schreberi transplanted from places relatively clean to heavily contaminated areas of Upper Silesia were carried out. An increase in the radionuclides activity concentrations in P. schreberi transplants may indicate not only deposition of the radionuclides itself, but also an influx of other pollutants. The results showed no relationship between the Pb-210 activity concentration and activity concentrations of Pb-214, Bi-214, also belonging to the uranium-radium decay series. The increased concentration of Pb-210 in P. schreberi may be the result of the radionuclide atmospheric deposition, which appears in the environment as a result of fossil fuels burning. Excess, allogeneic Pb-210 can be used as marker of environmental pollution. In the areas with its higher activity concentration increased pollution can be expected delivered, for example, by local industry. The Project received financial assistance from the funds of the National Science Centre, granted by force of the decision no. UMO-2013/09/B/NZ8/03340 (NCN).


title: Artificial Intelligence and Expert Systems in Accounting Databases: Survey and Extensions

abstract: Abstract The purpose of this paper is to survey and extend the use of Artificial Intelligence and expert systems in accounting database.. The paper elicits a number of concerns often voiced about accounting databases. The use of Artificial Intelligence and expert system is investigated as a basis to mitigate those problems. The literature is surveyed and extended. Demons and objects are found to be useful devises to facilitate the organization, storage and application of intelligence for accounting database systems. Models for their use are presented.


title: A 2 MS/s 10A Hall current sensor SoC with digital compressive sensing encoder in 0.16 µm BCD

abstract: Wide-bandwidth lossless current sensors are critical in numerous applications, from current monitoring in DC-DC converters to non-invasive load monitoring. CMOS Hall sensor is a low-cost solution for current sensing and can be easily integrated as part of mixed-signal system-on-chips (SoCs). State-of-the-art CMOS Hall sensors offer only limited acquisition bandwidths of a few hundred kHz. This paper presents a 1 MHz Hall current sensor SoC integrating a broadband CMOS Hall sensor, two 2 MS/s ADCs and a multi-mode digital compressive sensing encoder for data rate reduction. The complete SoC is implemented in a STM 0.16 µm BCD technology, and occupies 16 mm2 while consuming less than 94 mW at 1.8 V.


title: A New Generic Taxonomy on Hybrid Malware Detection Technique

abstract: M alware is a type of malicious program that replicate from host machine and propagate through network. It has been considered as one type of computer attack and intrusion that can do a variety of malicious activity on a computer. This paper addresses the current trend of malware detection techniques and identifies the significant criteria in each technique to improve malware detection in Intrusion Detection System (IDS). Several existing techniques are analyzing from 48 various researches and the capability criteria of malware detection technique have been reviewed. From the analysis, a new generic taxonomy of malware detection technique have been proposed named Hybrid-Malware Detection Technique (Hybrid-MDT) which consists of Hybrid- Signature and Anomaly detection technique and Hybrid- Specification based and Anomaly detection technique to complement the weaknesses of the existing malware detection technique in detecting known and unknown attack as well as reducing false alert before and during the intrusion occur.


title: Beam-tilting design of microstrip comb-line antenna array in perpendicular plane of feeding line for three-beam switching

abstract: In order to extend design flexibility, we proposed design techniques for beam-tilting in perpendicular plane of feeding line. This technique is applied to develop three antennas with different beams for three-beam switching.


title: Heterogeneous Sensor Data Fusion By Deep Multimodal Encoding

abstract: Heterogeneous sensor data fusion is a challenging field that has gathered significant interest in recent years. Two of these challenges are learning from data with missing values, and finding shared representations for multimodal data to improve inference and prediction. In this paper, we propose a multimodal data fusion framework, the deep multimodal encoder (DME), based on deep learning techniques for sensor data compression, missing data imputation, and new modality prediction under multimodal scenarios. While traditional methods capture only the intramodal correlations, DME is able to mine both the intramodal correlations in the initial layers and the enhanced intermodal correlations in the deeper layers. In this way, the statistical structure of sensor data may be better exploited for data compression. By incorporating our new objective function, DME shows remarkable ability for missing data imputation tasks in sensor data. The shared multimodal representation learned by DME may be used directly for predicting new modalities. In experiments with a real-world dataset collected from a 40-node agriculture sensor network which contains three modalities, DME can achieve a root mean square error (RMSE) of missing data imputation which is only 20% of the traditional methods like K -nearest neighbors and sparse principal component analysis and the performance is robust to different missing rates. It can also reconstruct temperature modality from humidity and illuminance with an RMSE of $7\; {}^{\circ }$ C, directly from a highly compressed (2.1%) shared representation that was learned from incomplete (80% missing) data.


title: Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation

abstract: Using neural networks to generate replies in human-computer dialogue systems is attracting increasing attention over the past few years. However, the performance is not satisfactory: the neural network tends to generate safe, universally relevant replies which carry little meaning. In this paper, we propose a content-introducing approach to neural network-based generative dialogue systems. We first use pointwise mutual information (PMI) to predict a noun as a keyword, reflecting the main gist of the reply. We then propose seq2BF, a "sequence to backward and forward sequences" model, which generates a reply containing the given keyword. Experimental results show that our approach significantly outperforms traditional sequence-to-sequence models in terms of human evaluation and the entropy measure, and that the predicted keyword can appear at an appropriate position in the reply.


A Consistent Approach to Designing Spoken-Dialog Systems


title: Spatial Kernel K-Harmonic Means Clustering for Multi-spectral Image Segmentation

abstract: The problem of image segmentation using intensity clustering approaches has been addressed in the literature. Grouping pixels of similar intensity to form clusters in an image have been tackled using a number of methods, such as the K-means (KM) algorithm. The K-harmonic means (KHM) was proposed to overcome the sensitivity of KM to centre initialisation. The use of a spatial kernel-based KHM (SKKHM) algorithm on the problem of image segmentation has been investigated. Instead of the original Euclidean intensity distance, a robust kernel-based KHM metric is employed to reduce the effect of outliers and noise. Spatial image information is also incorporated in the proposed clustering scheme, derived from Markov random field modelling. An extension of the proposed algorithm to multi-spectral imaging applications is also presented. Experimental results for both single-channel and multi-channel images demonstrate the robust performance of the proposed SKKHM algorithm.


title: Towards a generative syntax of tonal harmony

abstract: This paper aims to propose a hierarchical, generative account of diatonic harmonic progressions and suggest a set of phrase-structure grammar rules. It argues that the structure of harmonic progressions exceeds the simplicity of the Markovian transition tables and proposes a set of rules to account for harmonic progressions with respect to key structure, functional and scale degree features as well as modulations. Harmonic structure is argued to be at least one subsystem in which Western tonal music exhibits recursion and hierarchical organization that may provide a link to overarching linguistic generative grammar on a structural and potentially cognitive level.


title: Examining a Multidimensional Model of Student Motivation and Engagement Using a Construct Validation Approach.

abstract: Background. This study seeks to examine a multidimensional model of student motivation and engagement using within- and between-network construct validation approaches. ::: ::: Aims. The study tests the first- and higher-order factor structure of the motivation and engagement wheel and its corresponding measurement tool, the Motivation and Engagement Scale – High School (MES-HS; formerly the Student Motivation and Engagement Scale). ::: ::: Sample. The study draws upon data from 12,237 high school students from 38 Australian high schools. ::: ::: Methods. The hypothesized 11-factor first-order structure and the four-factor higher-order structure, their relationship with a set of between-network measures (class participation, enjoyment of school, educational aspirations), factor invariance across gender and year-level, and the effects of age and gender are examined using confirmatory factor analysis and structural equation modelling. ::: ::: Results. In terms of within-network validity, (1) the data confirm that the 11-factor and higher-order factor models of motivation and engagement are good fitting and (2) multigroup tests showed invariance across gender and year levels. In terms of between-network validity, (3) correlations with enjoyment of school, class participation and educational aspirations are in the hypothesized directions, and (4) girls reflect a more adaptive pattern of motivation and engagement, and year-level findings broadly confirm hypotheses that middle high school students seem to reflect a less adaptive pattern of motivation and engagement. ::: ::: Conclusion. The first- and higher-order structures hold direct implications for educational practice and directions for future motivation and engagement research.


title: An active metabolite of carbamazepine, carbamazepine-10,11-epoxide, inhibits ion channel-mediated catecholamine secretion in cultured bovine adrenal medullary cells

abstract: We have recently reported inhibitory effects of carbamazepine (CBZ) on ion channel-mediated secretion of catecholamines in bovine adrenal medullary cells. Here, we report the effects of carbamazepine-10,11-epoxide (CBZ-E), an active metabolite of CBZ, and carbamazepine-10,11-diol (CBZ-D), a non-active metabolite, on 22Na+ influx, 45Ca2+ influx and catecholamine secretion in cultured adrenal medullary cells. CBZ-E, but not CBZ-D inhibited 22Na+ influx, 45Ca2+ influx and catecholamine secretion induced by carbachol or veratridine with a half-maximal inhibitory concentration (IC50) of 0.26 or 0.68 μg/ml, respectively. CBZ-E also inhibited high K+-evoked 45Ca2+ influx and catecholamine secretion (IC50 = 0.3 μg/ml), but CBZ-D did not. These findings suggest that CBZ-E, but not CBZ-D, attenuates catecholamine secretion by inhibiting nicotinic acetylcholine receptor-associated ion channels, voltage-dependent Na+ channels and voltage-dependent Ca2+ channels in the cells. This inhibition of CBZ-E as well as CBZ may be related to the clinical effects in neuropsychiatric disorders.


title: The development of wild immature Sumatran orangutans (Pongo abelii) at Ketambe

abstract: Orangutans have the longest immature period and inter-birth interval of all ape species. This may be explained by a slow life history, the need to develop skills or by their relatively solitary lifestyle, which prevents a mother from associating with two offspring. This study of wild immature orangutans at the Ketambe Research Station, Indonesia, describes, with partly cross-sectional, partly longitudinal data, their development to independence. The study subjects ranged from 1 to 11 years of age. Data on their activity budget, diet, mother-offspring proximity and maintenance of proximity, association with conspecifics and play behavior were collected. The results indicate that immature orangutans can provide for their own food and transport, and therefore were independent of direct maternal care, at an age of possibly 3 but more clearly 6 years. This is similar to chimpanzees, and refutes the slow life history hypothesis. Immature orangutans remain within their mother's vicinity until the age of 8 years, indicating a dependence on indirect maternal care, and this coincides with the period during which the mother does not produce another offspring. A female orangutan seems unable to associate with an older immature while caring for a new infant. This is consistent with the solitary-lifestyle hypothesis and corroborates the results obtained with the Sumatran orangutan population at Suaq Balimbing. However, why an immature depends indirectly on its mother for such a long period remains unclear. It is possible that it needs to develop ecological or social skills or needs the protection of its mother. Unfortunately, no data were available to distinguish between these possibilities.


Searching for qualitative research for inclusion in systematic reviews: a structured methodological review


title: Numerical Recipes 3rd Edition: The Art of Scientific Computing

abstract: Co-authored by four leading scientists from academia and industry, Numerical Recipes Third Edition starts with basic mathematics and computer science and proceeds to complete, working routines. Widely recognized as the most comprehensive, accessible and practical basis for scientific computing, this new edition incorporates more than 400 Numerical Recipes routines, many of them new or upgraded. The executable C++ code, now printed in color for easy reading, adopts an object-oriented style particularly suited to scientific applications. The whole book is presented in the informal, easy-to-read style that made earlier editions so popular. Please visit www.nr.com or www.cambridge.org/us/numericalrecipes for more details. New key features: 2 new chapters, 25 new sections, 25% longer than Second Edition Thorough upgrades throughout the text Over 100 completely new routines and upgrades of many more. New Classification and Inference chapter, including Gaussian mixture models, HMMs, hierarchical clustering, Support Vector MachinesNew Computational Geometry chapter covers KD trees, quad- and octrees, Delaunay triangulation, and algorithms for lines, polygons, triangles, and spheres New sections include interior point methods for linear programming, Monte Carlo Markov Chains, spectral and pseudospectral methods for PDEs, and many new statistical distributions An expanded treatment of ODEs with completely new routines Plus comprehensive coverage of linear algebra, interpolation, special functions, random numbers, nonlinear sets of equations, optimization, eigensystems, Fourier methods and wavelets, statistical tests, ODEs and PDEs, integral equations, and inverse theory And much, much more! Visit the authors' web site for information about electronic subscriptions www.nr.com/aboutNR3book.html


title: Aggressive driving with model predictive path integral control

abstract: In this paper we present a model predictive control algorithm designed for optimizing non-linear systems subject to complex cost criteria. The algorithm is based on a stochastic optimal control framework using a fundamental relationship between the information theoretic notions of free energy and relative entropy. The optimal controls in this setting take the form of a path integral, which we approximate using an efficient importance sampling scheme. We experimentally verify the algorithm by implementing it on a Graphics Processing Unit (GPU) and apply it to the problem of controlling a fifth-scale Auto-Rally vehicle in an aggressive driving task.


title: Log management comprehensive architecture in Security Operation Center (SOC)

abstract: With the widespread use of information, variety of security logs have increased greatly, which due need for security log management. Organizations requirements have imposed to collect, store, and analyze tremendous volumes of log data across entire infrastructure for extended durations and at increasingly granular levels. It is the process of generating, transmitting, storing, analyzing, and disposing security log data from network to databases. Due to the wide variety of logs, storing comprises different methods. Recorded events in collection module are processed, normalized and classified. Logs are stored in storage module in order to use in forensic, reviewing, auditing and providing further necessities of correlation module. Routine log correlation analysis is beneficial for identifying security incidents, policy violations, fraudulent activities, troubleshooting and operational network problems. So log management is an important and efficient activity in network monitoring. Finding an effective log management functional architecture for network events analysis is the main goal of this paper. In this paper, we aim to suggest log management architecture with more common functions that are used by vendors. By studying logging architectures the main functions are administration of log collection, normalizing, categorization, queuing prioritizing and storing logged events/alarms by sensors. Log functions are different but the suitable architecture must justify the functions to send a normative, synchronized and prioritized log in an efficient way. The mentioned functions are gathered from SIEM products characteristics. Suggested architecture includes functions and activities in log collection server and storage server.


title: Advertising versus Brokerage Model for Online Trading Platforms

abstract: The two leading online consumer-to-consumer platforms use very different revenue models: eBay.com in the United States uses a brokerage model in which sellers pay eBay on a transaction basis, whereas Taobao.com in China uses an advertising model in which sellers can use basic platform service for free and pay Taobao for advertising service to increase their exposure. This paper studies how the revenue model affects a platform's revenue, buyers' payoffs, sellers' payoffs, and social welfare. We find that matching probability on a platform plays a critical role in determining which revenue model can generate more revenue for the platform, provided a significant proportion of space being dedicated to advertising under the advertising model: If the matching probability is high, the brokerage model generates more revenue for the platform than the advertising model; otherwise, the advertising model generates more revenue. Buyers are always better off under the advertising model because of larger participation by the sellers for the platform's free service. Sellers are better off under the advertising model in most scenarios. The only exception is that when the matching probability is low and platform dedicates a large space to advertising. Under these conditions, those sellers having the payoffs similar to the marginal advertiser (who is indifferent in advertising or not) can be worse off under the advertising model. Lastly, the advertising model generates more social welfare than the brokerage model.


title: Fuzzy spatial relationships for image processing and interpretation: a review

abstract: In spatial reasoning, relationships between spatial entities play a major role. In image interpretation, computer vision and structural recognition, the management of imperfect information and of imprecision constitutes a key point. This calls for the framework of fuzzy sets, which exhibits nice features to represent spatial imprecision at different levels, imprecision in knowledge and knowledge representation, and which provides powerful tools for fusion, decision-making and reasoning. In this paper, we review the main fuzzy approaches for defining spatial relationships including topological (set relationships, adjacency) and metrical relations (distances, directional relative position).


title: Connectionist psycholinguistics : capturing the empirical data

abstract: Connectionist psycholinguistics is an emerging approach to modeling empirical data on human language processing using connectionist computational architectures. For almost 20 years, connectionist models have increasingly been used to model empirical data across many areas of language processing. We critically review four key areas: speech processing, sentence processing, language production, and reading aloud, and evaluate progress against three criteria: data contact, task veridicality, and input representativeness. Recent connectionist modeling efforts have made considerable headway toward meeting these criteria, although it is by no means clear whether connectionist (or symbolic) psycholinguistics will eventually provide an integrated model of full-scale human language processing.


title: Engineering opportunities in cancer immunotherapy

abstract: Immunotherapy has great potential to treat cancer and prevent future relapse by activating the immune system to recognize and kill cancer cells. A variety of strategies are continuing to evolve in the laboratory and in the clinic, including therapeutic noncellular (vector-based or subunit) cancer vaccines, dendritic cell vaccines, engineered T cells, and immune checkpoint blockade. Despite their promise, much more research is needed to understand how and why certain cancers fail to respond to immunotherapy and to predict which therapeutic strategies, or combinations thereof, are most appropriate for each patient. Underlying these challenges are technological needs, including methods to rapidly and thoroughly characterize the immune microenvironment of tumors, predictive tools to screen potential therapies in patient-specific ways, and sensitive, information-rich assays that allow patient monitoring of immune responses, tumor regression, and tumor dissemination during and after therapy. The newly emerging field of immunoengineering is addressing some of these challenges, and there is ample opportunity for engineers to contribute their approaches and tools to further facilitate the clinical translation of immunotherapy. Here we highlight recent technological advances in the diagnosis, therapy, and monitoring of cancer in the context of immunotherapy, as well as ongoing challenges.


title: Curved lane detection using robust feature extraction

abstract: In this paper, we propose a robust curved lane marking detection method by first detecting a straight lane and applying a geometric model of that detected straight lane. In our proposed method, we first detect the straight line and generate 13 candidates of the curved lane by applying a geometric model. We then vote those candidates on the feature image and consider the candidate which acquires the highest number of votes as the final curved lane. The performance and the efficiency of the proposed algorithm are confirmed by the experimental results.


title: Gate Width Dependence of Noise Parameters and Scalable Noise Model for HEMTs

abstract: Explicit expressions describing the gate width dependences of HEMTs noise parameters have been obtained experimentally. The minimum noise figure and optimum source admittance are proportional to gate width, and noise resistance is inversely proportional to gate width. A scalable noise model is then developed, which accurately predicates noise parameters in a broad gate width range. The scalable noise model can be attached to any nonlinear signal model to predicate both noise and nonlinear signal responses.


title: Impact of packet loss on CACC string stability performance

abstract: Recent development in wireless technology enables communication between vehicles. The concept of Co-operative Adaptive Cruise Control (CACC) — which uses wireless communication between vehicles — aims at string stable behaviour in a platoon of vehicles. “String stability” means any non-zero position, speed, and acceleration errors of an individual vehicle in a string do not amplify when they propagate upstream. In this paper, we will discuss the string stability of CACC and evaluate its performance with various packet loss ratios, beacon sending frequencies and time headway in simulations. The simulation framework is built up with a controller prototype, a traffic simulator, and a network simulator.


title: Adversarial Variational Optimization of Non-Differentiable Simulators

abstract: Complex computer simulators are increasingly used across fields of science as generative models tying parameters of an underlying theory to experimental observations. Inference in this setup is often difficult, as simulators rarely admit a tractable density or likelihood function. We introduce Adversarial Variational Optimization (AVO), a likelihood-free inference algorithm for fitting a non-differentiable generative model incorporating ideas from generative adversarial networks, variational optimization and empirical Bayes. We adapt the training procedure of generative adversarial networks by replacing the differentiable generative network with a domain-specific simulator. We solve the resulting non-differentiable minimax problem by minimizing variational upper bounds of the two adversarial objectives. Effectively, the procedure results in learning a proposal distribution over simulator parameters, such that the JS divergence between the marginal distribution of the synthetic data and the empirical distribution of observed data is minimized. We evaluate and compare the method with simulators producing both discrete and continuous data.