title: A Parallel FastTrack Data Race Detector on Multi-core Systems

abstract: Detecting data races in multithreaded programs is critical to ensure the correctness of the programs. To discover data races precisely without false alarms, dynamic detection approaches are often applied. However, the overhead of the existing dynamic detection approaches, even with recent innovations, is still substantially high. In this paper, we present a simple but efficient approach to parallelize data race detection in multicore SMP (Symmetric Multiprocessing) machines. In our approach, data access information needed for dynamic detection is collected at application threads and passed to de-tection threads. The access information is distributed in a way that the operation performed by each detection thread is inde-pendent of that of other detection threads. As a consequence, the overhead caused by locking operations in data race detection can be alleviated and multiple cores can be fully utilized to speed up and scale up the detection. Furthermore, each detection thread deals with only its own assigned memory access region rather than the whole address space. The executions of detection threads can exploit the spatial locality of accesses leading to an improved cache performance. We have applied our parallel approach on the FastTrack algorithm and demon-strated the validity of our approach on an Intel Xeon machine. Our experimental results show that the parallel FastTrack detector, on average, runs 2.2 times faster than the original FastTrack detector on the 8 core machine.


title: Knowledge-based Correction of Flash-lag Illusion

abstract: A flash is perceived to lag spatially behind a moving object even when the two retinal images are physically aligned (flash-lag effect, FLE). Here we show that this robust illusion can be diminished by a knowledge of letters in the observer's brain. When moving and flashed segments in the FLE made the shape of a Kanji letter (ideographic characters used in Japan), the magnitude of the illusory lag perceived by Japanese subjects was significantly reduced compared to when conventional geometric (nonletter) segments were used. This diminishment was not observed when a pseudo-Kanji letter was presented to Japanese subjects or when non-Japanese English-speakers (who do not have a knowledge of Kanji) saw a real Kanji letter, indicating that the reduction in the FLE was induced by a retrieval of the knowledge (shapes of letters) stored in the observer's brain. Furthermore, measurements of neural activities by magnetoencephalography showed that the initial brain response, in which the effect of the knowledge became evident, occurred as early as 160 msec after the appearance of the flashed segment. These results demonstrated a substantial influence of knowledge on the flash-lag illusion and further suggest a rapid response of the knowledge-based perceptual pathway in the human brain.


title: Hot Row Identification of DRAM Memory in a Multicore System

abstract: Access locality in single-core system is not easily observed in multicore system due to interleaved requests from different cores. Requests that come to DRAM-based main memory is mapped to a specific bank in DRAM and accessed through row buffer in the bank. DRAM row-buffer conflicts occur when a sequence of requests on different rows goes to the same memory bank, causing much higher memory access latency than requests to the same row or to different banks. In this paper, we first show that strong locality exists in memory requests of multicore systems. For many workloads, the accesses to some rows are more frequent than others, which we call them hot rows. Based on the observation, we propose a simple hot row buffer (HRB) scheme that is able to detect and capture these hot rows in DRAM. Results have shown that the proposed scheme is able to provide average 56% hit ratio over all row accesses in a bank for 10 selected workloads from SPEC CPU2006. A simple stream prefetcher prefetching in the hot row is implemented and results show an average of 9.1% IPC improvement over no prefetcher design.


title: Patterns and influences on Dolly Varden migratory timing in the Chignik Lakes, Alaska, and comparison of populations throughout the northeastern Pacific and Arctic oceans

abstract: Dolly Varden (Salvelinus malma) are a facultatively anadromous salmonid common around much of the North Pacific Rim, but little is known about the environmental factors affecting the timing and diversity of their migration. We combined telemetry of anadromous fish with long-term monitoring of Dolly Varden upstream migration timing and environmental data in the Chignik Lakes watershed in Alaska and then compared the timing data with that of other streams where only count data were available. Telemetry revealed two upstream migration modes: midsummer and late fall at the Chignik Lakes. Weir counts indicated that timing fluctuated markedly over the monitoring period (1996–2011) and was negatively correlated with June sea surface temperature. The relationship between sea surface temperature and migration timing in other watersheds with long-term records was as follows: negative (Buskin River), positive (Auke Creek), or nonexistent (Goodnews and Kanektok rivers). Among 18 streams and rivers throughout the east...


title: Actionable information in vision

abstract: I propose a notion of visual information as the complexity not of the raw images, but of the images after the effects of nuisance factors such as viewpoint and illumination are discounted. It is rooted in ideas of J. J. Gibson, and stands in contrast to traditional information as entropy or coding length of the data regardless of its use, and regardless of the nuisance factors affecting it. The non-invertibility of nuisances such as occlusion and quantization induces an “information gap” that can only be bridged by controlling the data acquisition process. Measuring visual information entails early vision operations, tailored to the structure of the nuisances so as to be “lossless” with respect to visual decision and control tasks (as opposed to data transmission and storage tasks implicit in traditional Information Theory). I illustrate these ideas on visual exploration, whereby a “Shannonian Explorer” guided by the entropy of the data navigates unaware of the structure of the physical space surrounding it, while a “Gibsonian Explorer” is guided by the topology of the environment, despite measuring only images of it, without performing 3D reconstruction. The operational definition of visual information suggests desirable properties that a visual representation should possess to best accomplish vision-based decision and control tasks.


title: Affective ranking of movie scenes using physiological signals and content analysis

abstract: In this paper, we propose an approach for affective ranking of movie scenes based on the emotions that are actually felt by spectators. Such a ranking can be used for characterizing the affective, or emotional, content of video clips. The ranking can for instance help determine which video clip from a database elicits, for a given user, the most joy. This in turn will permit video indexing and retrieval based on affective criteria corresponding to a personalized user affective profile. A dataset of 64 different scenes from 8 movies was shown to eight participants. While watching, their physiological responses were recorded; namely, five peripheral physiological signals (GSR - galvanic skin resistance, EMG - electromyograms, blood pressure, respiration pattern, skin temperature) were acquired. After watching each scene, the participants were asked to self-assess their felt arousal and valence for that scene. In addition, movie scenes were analyzed in order to characterize each with various audio- and video-based features capturing the key elements of the events occurring within that scene. Arousal and valence levels were estimated by a linear combination of features from physiological signals, as well as by a linear combination of content-based audio and video features. We show that a correlation exists between arousal- and valence-based rankings provided by the spectator's self-assessments, and rankings obtained automatically from either physiological signals or audio-video features. This demonstrates the ability of using physiological responses of participants to characterize video scenes and to rank them according to their emotional content. This further shows that audio-visual features, either individually or combined, can fairly reliably be used to predict the spectator's felt emotion for a given scene. The results also confirm that participants exhibit different affective responses to movie scenes, which emphasizes the need for the emotional profiles to be user-dependant.


title: Approximation and online algorithms for multidimensional bin packing: A survey

abstract: Abstract The bin packing problem is a well-studied problem in combinatorial optimization. In the classical bin packing problem, we are given a list of real numbers in ( 0 , 1 ] and the goal is to place them in a minimum number of bins so that no bin holds numbers summing to more than 1. The problem is extremely important in practice and finds numerous applications in scheduling, routing and resource allocation problems. Theoretically the problem has rich connections with discrepancy theory, iterative methods, entropy rounding and has led to the development of several algorithmic techniques. In this survey we consider approximation and online algorithms for several classical generalizations of bin packing problem such as geometric bin packing, vector bin packing and various other related problems. There is also a vast literature on mathematical models and exact algorithms for bin packing. However, this survey does not address such exact algorithms. In two-dimensional geometric bin packing , we are given a collection of rectangular items to be packed into a minimum number of unit size square bins. This variant has a lot of applications in cutting stock, vehicle loading, pallet packing, memory allocation and several other logistics and robotics related problems. In d -dimensional vector bin packing , each item is a d -dimensional vector that needs to be packed into unit vector bins. This problem is of great significance in resource constrained scheduling and in recent virtual machine placement in cloud computing. We also consider several other generalizations of bin packing such as geometric knapsack, strip packing and other related problems such as vector scheduling, vector covering etc. We survey algorithms for these problems in offline and online setting, and also mention results for several important special cases. We briefly mention related techniques used in the design and analysis of these algorithms. In the end we conclude with a list of open problems.


title: Design of Smart Modular Variable Stiffness Actuators for Robotic-Assistive Devices

abstract: Sensors and actuators are the core components of all mechatronic systems used in a broad range of diverse applications. A relatively new and rapidly evolving area is the one of rehabilitation and assistive devices that comes to support and improve the quality of human life. Novel exoskeletons have to address many functional and cost-sensitive issues such as safety, adaptability, customization, modularity, scalability, and maintenance. Therefore, a smart variable stiffness actuator was developed. The described approach was to integrate in one modular unit a compliant actuator with all sensors and electronics required for real-time communications and control. This paper also introduces a new method to estimate and control the actuator's torques without using dedicated expensive torque sensors in conditions where the actuator's torsional stiffness can be adjusted by the user. A 6-degrees-of-freedom exoskeleton was assembled and tested using the technology described in this paper, and is introduced as a real-life case study for the mechatronic design, modularity, and integration of the proposed smart actuators, suitable for human–robot interaction. The advantages are discussed together with possible improvements and the possibility of extending the presented technology to other areas of mechatronics.


title: API design for machine learning software: experiences from the scikit-learn project

abstract: Scikit-learn is an increasingly popular machine learning li- brary. Written in Python, it is designed to be simple and efficient, accessible to non-experts, and reusable in various contexts. In this paper, we present and discuss our design choices for the application programming interface (API) of the project. In particular, we describe the simple and elegant interface shared by all learning and processing units in the library and then discuss its advantages in terms of composition and reusability. The paper also comments on implementation details specific to the Python ecosystem and analyzes obstacles faced by users and developers of the library.


title: The Displaced Aggression Questionnaire

abstract: Previous measures of aggressive personality have focused on direct aggression (i.e., retaliation toward the provoking agent). An original self-report measure of trait displaced aggression is presented. Exploratory and confirmatory factor analyses provided support for a 3-factor conceptualization of the construct. These analyses identified an affective dimension (angry rumination), a cognitive dimension (revenge planning), and a behavioral dimension (general tendency to engage in displaced aggression). The trait measure demonstrated good internal consistency and test–retest reliability as well as convergent and discriminant construct validity. Unlike other related personality measures, trait displaced aggression significantly predicted indirect indicators of real-world displaced aggression (i.e., self-reported domestic abuse and road rage) as well as laboratory displaced aggression in 2 experiments.


title: EffiEye: Application-Aware Large Flow Detection in Data Center

abstract: With the rapid development of cloud computing, thousands of servers and various cloud applications are involved in data center. These changes result in more and more complex flows in data center, which motivates the need for faster, lower overhead, more scalable large flow detection technology. This paper firstly shows the shortcomings of the traditional large flow detection technologies. Then it proposes a new method named EffiEye, which efficiently realizes application-aware large flow detection in the controller. EffiEye mainly replies on two different mechanisms: one is the flow classification based on the pre-classification of cloud applications in App Info module, which can ensure the fast detecting speed, the other is the flow-stat triggering supported by OpenFlow 1.5, which can ensure the high detecting accuracy.


Patching: a multicast technique for true video-on-demand services


title: A narrative theory of games

abstract: This paper presents a narrative theory of games, building on standard narratology, as a solution to the conundrum that has haunted computer game studies from the start: How to approach software that combines games and stories?


title: Core-shell nanoparticles as prodrugs: possible cytotoxicological and biomedical impacts of batch-to-batch inconsistencies.

abstract: Numerous samples of magnetite@silica and magnetite@silica@silane core-shell nanoparticles, previously used as prodrugs, were prepared by an experienced chemist, using the same identical equipment and the same lots of reagents. Their surface analyses showed batch-to-batch chemical variations: no two batches were found to have the same surface chemistries, showing unexpected Si-O bond scission and amine oxidation. Because the preparations used reactions recognized to be mild, and bond scission and oxidation were never previously reported for similar reactions on larger surfaces, the Fe(3)O(4) nanoparticles that form the nanoparticle core appear to have acted as catalysts for these side reactions. The intended use of these nanoparticles, as drug carriers, is discussed in terms of cytotoxicological and biomedical consequences.


title: Discovering Geo-Informative Attributes for Location Recognition and Exploration

abstract: This article considers the problem of automatically discovering geo-informative attributes for location recognition and exploration. The attributes are expected to be both discriminative and representative, which correspond to certain distinctive visual patterns and associate with semantic interpretations. For our solution, we analyze the attribute at the region level. Each segmented region in the training set is assigned a binary latent variable indicating its discriminative capability. A latent learning framework is proposed for discriminative region detection and geo-informative attribute discovery. Moreover, we use user-generated content to obtain the semantic interpretation for the discovered visual attributes. Discriminative and search-based attribute annotation methods are developed for geo-informative attribute interpretation. The proposed approach is evaluated on one challenging dataset including GoogleStreetView and Flickr photos. Experimental results show that (1) geo-informative attributes are discriminative and useful for location recognition; (2) the discovered semantic interpretation is meaningful and can be exploited for further location exploration.


title: Large-scale factorization of type-constrained multi-relational data

abstract: The statistical modeling of large multi-relational datasets has increasingly gained attention in recent years. Typical applications involve large knowledge bases like DBpedia, Freebase, YAGO and the recently introduced Google Knowledge Graph that contain millions of entities, hundreds and thousands of relations, and billions of relational tuples. Collective factorization methods have been shown to scale up to these large multi-relational datasets, in particular in form of tensor approaches that can exploit the highly scalable alternating least squares (ALS) algorithms for calculating the factors. In this paper we extend the recently proposed state-of-the-art RESCAL tensor factorization to consider relational type-constraints. Relational type-constraints explicitly define the logic of relations by excluding entities from the subject or object role. In addition we will show that in absence of prior knowledge about type-constraints, local closed-world assumptions can be approximated for each relation by ignoring unobserved subject or object entities in a relation. In our experiments on representative large datasets (Cora, DBpedia), that contain up to millions of entities and hundreds of type-constrained relations, we show that the proposed approach is scalable. It further significantly outperforms RESCAL without type-constraints in both, runtime and prediction quality.


title: MonSamp: A distributed SDN application for QoS monitoring

abstract: Software Defined Networks are intended to be less complex, more flexible, and free of vendor-lock-ins. Therefore the Software Defined Networking (SDN) instantiation OpenFlow has been designed according to these properties. The efforts are expected to result in lower expenditure and operational costs. To reach these objectives, mechanisms of classical networks that provide established functionalities have to be revalued and either transformed or redesigned from scratch to take advantage from SDNs. In this paper we describe our vision on flow sampling suitable for traffic monitoring in those networks. Without the loss of generality our approach was specifically created to monitor the quality of service for flows. We describe monitoring as one of possibly many applications that communicate with the SDN controller via the SDN Northbound API. We implemented a prototype SDN application called MonSamp and performed tests to demonstrate the feasibility of our concept.


title: RDF, Jena, SparQL and the 'Semantic Web'

abstract: The Resource Description Format (RDF) is used to represent information modeled as a "graph": a set of individual objects, along with a set of connections among those objects. In that role, RDF is one of the pillars of the so-called Semantic Web. This paper describes how RDF-XML is used to serialize information represented using graphs, how RDF graphs can be read and written by using the Jena software package, and how distributed graphs can be queried using the SparQL query language. It includes examples showing how SparQL can be used to query data (such as the Gene Ontology) that is structured in hierarchies, and how SparQL queries can be submitted through SparQL endpoints. It does not, however, delve into inference or the Web Ontology Language (OWL), but should provide a foundation for understanding those topics.


title: Design Optimization of Thrust Density for Permanent Magnet Linear Motor Based on Genetic Algorithm

abstract: Electromagnetic catapult takeoff as a new way to take off UAV, can be launched within a short distance UAV accelerated takeoff. Linear motor is the key part of electromagnetic catapult and needs to have the features of small size, light weight and large thrust. In order to improve the maneuverability and large thrust of UAV electromagnetic ejection, this paper presents a genetic algorithm based optimization design method for motor parameters. On the basis of establishing the permanent magnet linear motor model, taking the main structure parameters of the primary core and permanent magnet of the motor as the optimization variables, the thrust density of the motor is the optimization goal, and the genetic algorithm is used to optimize the parameters of the motor. The results show that compared with the initial motor, the motor quality is reduced, the thrust density is increased, and the optimized design is feasible.


title: Parallel Random Number Generators in Java

abstract: A vehicle suspension assembly comprises a transverse box beam for a vehicle chassis, a central mount on an underside of the box beam, a combination composite spring and transverse control arm member having a dihedral for establishing a pre-load of the suspension system for establishing the vehicle ride height mounted at the center thereof to the central mount for supporting wheel assemblies at the outer ends thereof.


title: Detecting DGA malware using NetFlow

abstract: Botnet detection systems struggle with performance and privacy issues when analyzing data from large-scale networks. Deep packet inspection, reverse engineering, clustering and other time consuming approaches are unfeasible for large-scale networks. Therefore, many researchers focus on fast and simple botnet detection methods that use as little information as possible to avoid privacy violations. We present a novel technique for detecting malware using Domain Generation Algorithms (DGA), that is able to evaluate data from large scale networks without reverse engineering a binary or performing Non-Existent Domain (NXDomain) inspection. We propose to use a statistical approach and model the ratio of DNS requests and visited IPs for every host in the local network and label the deviations from this model as DGA-performing malware. We expect the malware to try to resolve more domains during a small time interval without a corresponding amount of newly visited IPs. For this we need only the NetFlow/IPFIX statistics collected from the network of interest. These can be generated by almost any modern router. We show that by using this approach we are able to identify DGA-based malware with zero to very few false positives. Because of the simplicity of our approach we can inspect data from very large networks with minimal computational costs.


Sensing Earthquake Disaster Information: A Named Entity Recognition Approach Using Twitter Collaborative Data


title: Formaldehyde Gas Sensors: A Review

abstract: Many methods based on spectrophotometric, fluorometric, piezoresistive, amperometric or conductive measurements have been proposed for detecting the concentration of formaldehyde in air. However, conventional formaldehyde measurement systems are bulky and expensive and require the services of highly-trained operators. Accordingly, the emergence of sophisticated technologies in recent years has prompted the development of many microscale gaseous formaldehyde detection systems. Besides their compact size, such devices have many other advantages over their macroscale counterparts, including a real-time response, a more straightforward operation, lower power consumption, and the potential for low-cost batch production. This paper commences by providing a high level overview of the formaldehyde gas sensing field and then describes some of the more significant real-time sensors presented in the literature over the past 10 years or so.


title: A Haptic Back Display for Attentional and Directional Cueing

abstract: We have been developing a haptic back display using a 3-by-3 tactor array. This paper reports two studies that investigated the use of such a display for delivering attention- and direction-related information to its user. The first study measured the effectiveness of haptic cues in redirecting an observer’s visual spatial attention. The observer was first tapped on the back, and then asked to detect a change between two similar visual scenes. We found that reaction time decreased by an average of 41% (1630 ms) when the location of the tactor coincided with the quadrant of the visual scene where a change occurred. We also found that reaction time increased by an average of 19% (781 ms) when the locations of the tapping and visual change did not coincide. Such a haptic attentional cueing system can be beneficial to a user who must attend to information in small areas within a large and complex visual display (e.g., an aircraft cockpit). In the second study, sequenced pulses were employed to impart directional information. We found that naive and minimally-trained observers were able to discern the directions of a set of horizontal, vertical and diagonal directional lines with an overall accuracy of 81%. Means of improving the overall accuracy were suggested and tested. These directional lines can be applied to a haptic navigation guidance or situation awareness system.


title: A systematic review of theory use in studies investigating the motivations of software engineers

abstract: Motivated software engineers make a critical contribution to delivering successful software systems. Understanding the motivations of software engineers and the impact of motivation on software engineering outcomes could significantly affect the industry's ability to deliver good quality software systems. Understanding the motivations of people generally in relation to their work is underpinned by eight classic motivation theories from the social sciences. We would expect these classic motivation theories to play an important role in developing a rigorous understanding of the specific motivations of software engineers. In this article we investigate how this theoretical basis has been exploited in previous studies of software engineering. We analyzed 92 studies of motivation in software engineering that were published in the literature between 1980 and 2006. Our main findings are that many studies of software engineers' motivations are not explicitly underpinned by reference to the classic motivation theories. Furthermore, the findings presented in these studies are often not explicitly interpreted in terms of those theories, despite the fact that in many cases there is a relationship between those findings and the theories. Our conclusion is that although there has been a great deal of previous work looking at motivation in software engineering, the lack of reference to classic theories of motivation means that the current body of work in the area is weakened and our understanding of motivation in software engineering is not as rigorous as it may at first appear. This weakness in the current state of knowledge highlights important areas for future researchers to contribute towards developing a rigorous and usable body of knowledge in motivating software engineers.


title: Color image enhancement using retinex with robust envelope

abstract: In this paper, we propose a color image enhancement method that uses retinex with a robust envelope to improve the visual appearance of an image. The word “retinex” is hybird “retina” and “cortex”, suggesting that human visual perception is involved in this color image enhancement. To avoid the gray-world violation, a color-shifting problem, an input RGB color image is transformed into an HVS color image, but only the V component is enhanced. Furthermore, to prevent hallow artifacts, we construct a robust envelope with a gradient-dependent weighting to limit disturbances around intensity gaps such as edges and corners. Our experiment results show that the proposed method yields a better (almost hallow-free) performance than traditional image enhancement methods.


title: Inducing Illusory Ownership of a Virtual Body

abstract: We discuss three experiments that investigate how virtual limbs and bodies can come to feel like real limbs and bodies. The first experiment shows that an illusion of ownership of a virtual arm appearing to project out of a person’s shoulder can be produced by tactile stimulation on a person’s hidden real hand and synchronous stimulation on the seen virtual hand. The second shows that the illusion can be produced by synchronous movement of the person’s hidden real hand and a virtual hand. The third shows that a weaker form of the illusion can be produced when a brain-computer interface is employed to move the virtual hand by means of motor imagery without any tactile stimulation. We discuss related studies that indicate that the ownership illusion may be generated for an entire body. This has important implications for the scientific understanding of body ownership and several practical applications.


title: The effects of breakfast on behavior and academic performance in children and adolescents

abstract: Breakfast consumption is associated with positive outcomes for diet quality, micronutrient intake, weight status and lifestyle factors. Breakfast has been suggested to positively affect learning in children in terms of behaviour, cognitive and school performance. However, these assertions are largely based on evidence which demonstrates acute effects of breakfast on cognitive performance. Less research which examines the effects of breakfast on the ecologically valid outcomes of academic performance or in-class behaviour is available. The literature was searched for articles published between 1950-2013 indexed in Ovid MEDLINE, Pubmed, Web of Science, the Cochrane Library, EMBASE databases and PsychINFO. Thirty-six articles examining the effects of breakfast on in-class behaviour and academic performance in children and adolescents were included. The effects of breakfast in different populations were considered, including undernourished or well-nourished children and adolescents from differing socio-economic status (SES) backgrounds. The habitual and acute effects of breakfast and the effects of school breakfast programs (SBPs) were considered. The evidence indicated a mainly positive effect of breakfast on on-task behaviour in the classroom. There was suggestive evidence that habitual breakfast (frequency and quality) and SBPs have a positive effect on children’s academic performance with clearest effects on mathematic and arithmetic grades in undernourished children. Increased frequency of habitual breakfast was consistently positively associated with academic performance. Some evidence suggested that quality of habitual breakfast, in terms of providing a greater variety of food groups and adequate energy, was positively related to school performance. However, these associations can be attributed, in part, to confounders such as SES and to methodological weaknesses such as the subjective nature of the observations of behaviour in class.


title: Towards Building Robust Natural Language Interfaces to Databases

abstract: We seek to give everyday technical teams the capability to build robust natural language interfaces to their databases, for subsequent use by casual users. We present an approach to the problem which integrates and streamlines earlier work based on light annotation and authoring tools. We model queries in a higher-order version of Codd's tuple calculus and we use synchronous grammars extended with lambda functions to represent semantic grammars. The results of configuration can be applied directly to SQL based databases with general n-ary relations. We have fully implemented our approach and we present initial empirical results for the Geoquery 250 corpus.


title: Advancements in Automotive Antennas

abstract: Today's production vehicles are fitted with a multitude of antennas to facilitate communication and enable a moving vehicle to connect with the outside world. Recent years have seen the introduction of new electronic devices to the automotive environment. These devices are usually designed to aid the driver, increase safety, or enhance the driving experience, and many of them rely on wireless communication to perform their task. Antennas are a necessary part of any wireless communication system, enabling transmission and reception of signals in free-space. At the same time, automobile manufacturers have been seeking to create cost effective, fuel efficient vehicles with attractive styling. This leads to a focus on sleek, lightweight vehicles with reduced aerodynamic drag and improved styling an emphasis that would naturally conflict with fitment of traditional antennas. These market preferences, along with the technological factors, have combined in the past few years to drive significant innovation in the world of vehicular antennas. In this chapter, we review the basics of antennas and radiation and examine the frequencies and services which are commonly used in the automotive environment. We will briefly discuss the antennas traditionally used on vehicles, and then detail the recent developments and trends in automotive antenna research.


title: Chitosan-based delivery systems for protein therapeutics and antigens

abstract: Therapeutic peptides/proteins and protein-based antigens are chemically and structurally labile compounds, which are almost exclusively administered by parenteral injections. Recently, non-invasive mucosal routes have attracted interest for administration of these biotherapeutics. Chitosan-based delivery systems enhance the absorption and/or cellular uptake of peptides/proteins across mucosal sites and have immunoadjuvant properties. Chitosan is a mucoadhesive polysaccharide capable of opening the tight junctions between epithelial cells and it has functional groups for chemical modifications, which has resulted in a large variety of chitosan derivatives with tunable properties for the aimed applications. This review provides an overview of chitosan-based polymers for preparation of both therapeutic peptides/protein and antigen formulations. The physicochemical properties of these carrier systems as well as their applications in protein and antigen delivery through parenteral and mucosal (particularly nasal and pulmonary) administrations are summarized and discussed.


title: EEG oscillations: From correlation to causality.

abstract: Abstract Already in his first report on the discovery of the human EEG in 1929, Berger showed great interest in further elucidating the functional roles of the alpha and beta waves for normal mental activities. Meanwhile, most cognitive processes have been linked to at least one of the traditional frequency bands in the delta, theta, alpha, beta, and gamma range. Although the existing wealth of high-quality correlative EEG data led many researchers to the conviction that brain oscillations subserve various sensory and cognitive processes, a causal role can only be demonstrated by directly modulating such oscillatory signals. In this review, we highlight several methods to selectively modulate neuronal oscillations, including EEG-neurofeedback, rhythmic sensory stimulation, repetitive transcranial magnetic stimulation (rTMS), and transcranial alternating current stimulation (tACS). In particular, we discuss tACS as the most recent technique to directly modulate oscillatory brain activity. Such studies demonstrating the effectiveness of tACS comprise reports on purely behavioral or purely electrophysiological effects, on combination of behavioral effects with offline EEG measurements or on simultaneous (online) tACS-EEG recordings. Whereas most tACS studies are designed to modulate ongoing rhythmic brain activity at a specific frequency, recent evidence suggests that tACS may also modulate cross-frequency interactions. Taken together, the modulation of neuronal oscillations allows to demonstrate causal links between brain oscillations and cognitive processes and to obtain important insights into human brain function.


title: Measurement and Analysis of the Reviews in Airbnb

abstract: Airbnb, a recently emerged online lodging service that allows house and apartment dwellers to lease out their premises to short-term renters like tourists, is reconstructing the value chain of the traditional hotel industry. It works as a platform that connects hosts and travelers and facilities their interaction and exchange. Studying this service could shed light on understanding the emerging sharing economy from a user-centric perspective. In this work, we collect the profiles of 43.8 million Airbnb users, and analyze the reviews they published online. We model the interactions between Airbnb users using a review graph, and study their mobility patterns by investigating their reviews. To the best of our knowledge, our work is the first measurement study of massive Airbnb users on a global scale, and it provides insights of their activities in both cyberspace and the physical world.


title: Unsupervised learning for robust Bitcoin fraud detection

abstract: The rampant absorption of Bitcoin as a cryptographic currency, along with rising cybercrime activities, warrants utilization of anomaly detection to identify potential fraud. Anomaly detection plays a pivotal role in data mining since most outlying points contain crucial information for further investigation. In the financial world which the Bitcoin network is part of by default, anomaly detection amounts to fraud detection. This paper investigates the use of trimmed k-means, that is capable of simultaneous clustering of objects and fraud detection in a multivariate setup, to detect fraudulent activity in Bitcoin transactions. The proposed approach detects more fraudulent transactions than similar studies or reports on the same dataset.


title: A Simple Ontology of Manipulation Actions Based on Hand-Object Relations

abstract: Humans can perform a multitude of different actions with their hands (manipulations). In spite of this, so far there have been only a few attempts to represent manipulation types trying to understand the underlying principles. Here we first discuss how manipulation actions are structured in space and time. For this we use as temporal anchor points those moments where two objects (or hand and object) touch or un-touch each other during a manipulation. We show that by this one can define a relatively small tree-like manipulation ontology. We find less than 30 fundamental manipulations. The temporal anchors also provide us with information about when to pay attention to additional important information, for example when to consider trajectory shapes and relative poses between objects. As a consequence a highly condensed representation emerges by which different manipulations can be recognized and encoded. Examples of manipulations recognition and execution by a robot based on this representation are given at the end of this study.


title: A new mobile-based multi-factor authentication scheme using pre-shared number, GPS location and time stamp

abstract: Lots of Web accounts are being hacked everyday even though the public Web servers like Gmail, Yahoo and Hotmail deploy the best-known security mechanisms. However, attacks usually happen due to personal faults. It is too difficult to hack the Gmail or Yahoo servers, but usually attackers try to gain access to user's system itself to get some information about his password. In this paper a new mechanism of securing Web accounts is proposed to overcome these personal faults. It enhances security by generating a secret hash code based on a pre-shared number and user's current GPS location.


title: Tourists’ Attachment Processes and Behavioral Changes in Social Media: Persuasion and Reference Group Influence Perspective

abstract: As the recent travel trend has changed from package-oriented group travels to free independent travels (FIT), individual travellers are in a situation in which they have to search more information than ever before. By the nature of travel, travel information is searched out of various information sources. In recent years, social media has played an important role. Unlike other types of communication media, social media not only provides users with information, but also allows them to know who the source of the information is. Therefore, travellers can use the respective information with more confidence. However, existing studies have not fully reflected the characteristics of social media as a travel information source. In this respect, the present study intends to empirically verify how social media, which is newly drawing great attention as a travel information source, changes traveller behaviors using the theories of the elaboration likelihood model and reference group influence, and thereafter, provide the implications of the study findings.


title: Building extraction from high resolution imagery based on multi-scale object oriented classification and probabilistic Hough transform

abstract: In this paper, we developed a new building extraction system applied on high resolution remote sensing imagery based on multi-scale object oriented classification and probabilistic Hough transform. This can be divided into two different phases: building roof extraction, and shape reconfiguration. For the first phase, the multispectral and panchromatic high resolution satellite imageries are firstly fused for spatial resolution improvement and color information enhancement. The multiresolution image segmentation is applied on the fused image, resulting in the formation of the different level of polygon primitives at different space scale, providing different view of the scene at different resolution. In addition to the spectral information, the tone, texture, shape, context information is evaluated in an object oriented manner. The classification is based on a fuzzy rule decision tree classifier. By fuzzy evaluating of the shape, texture, context and spectral information, building roofs are extracted by reconstruction and classification from an appropriate space scale of roof polygon primitives. For the shape reconfiguration phase, we adopt the probabilistic Hough transform to delineate the roof dominant line which shows the major orientation of the specific building roof. According to the dominant line, a building squaring algorithm is applied based on rectilinear fitting of the building boundary. It is shown by our experiment that most rectangular building roofs can be correctly detected, extracted and reconfigured, demonstrating the potential application of the method.


title: A Method for Learning From Hints

abstract: We address the problem of learning an unknown function by putting together several pieces of information (hints) that we know about the function. We introduce a method that generalizes learning from examples to learning from hints. A canonical representation of hints is defined and illustrated for new types of hints. All the hints are represented to the learning process by examples, and examples of the function are treated on equal footing with the rest of the hints. During learning, examples from different hints are selected for processing according to a given schedule. We present two types of schedules; fixed schedules that specify the relative emphasis of each hint, and adaptive schedules that are based on how well each hint has been learned so far. Our learning method is compatible with any descent technique that we may choose to use.


title: Cu(II)-intercalated muscovite : An infrared spectroscopic study

abstract: Abstract Diffuse reflectance infrared spectroscopy (DRIFT) has proved to be a useful tool for the investigation of migration paths of intercalated metal cations within the layered structure of phyllosilicates like muscovite. While the fixation of small cations like lithium or copper in clay minerals and their migration paths have been studied extensively, they never have been reported for muscovite. Therefore, the aim of this study was to investigate the effects of a treatment with a supersaturated Cu-nitrate solution on the structure of muscovite. Additional X-ray diffractometric (XRD) data showed the formation of new d (0 0 l) and d (0 0 2) peaks, and thus proved that the new cations already were intercalated into the muscovite interlayers, although the concentration of the original interlayer cation potassium was nearly not affected. This suggested the simultaneous occurrence of both potassium and copper in the interlayers, resulting in an expansion along the c -axis and in a decrease of the a and b parameters. The spectroscopic investigations proved a migration of the cations deep into the tetrahedral sheets. As all bands which can be assigned to vibrations of the octahedral sheet (e.g. at 713 or 903 cm −1 ) were strongly affected by the treatment, a fixation of Cu close to the OH-groups of the octahedral sheets was suggested.


title: Scaling To Very Very Large Corpora For Natural Language Disambiguation

abstract: The amount of readily available on-line text has reached hundreds of billions of words and continues to grow. Yet for most core natural language tasks, algorithms continue to be optimized, tested and compared after training on corpora consisting of only one million words or less. In this paper, we evaluate the performance of different learning methods on a prototypical natural language disambiguation task, confusion set disambiguation, when trained on orders of magnitude more labeled data than has previously been used. We are fortunate that for this particular application, correctly labeled training data is free. Since this will often not be the case, we examine methods for effectively exploiting very large corpora when labeled data comes at a cost.


abstract: CPU scheduling involves a careful examination of pending processes to determine the most efficient way to service the requests. This research works in the field of Round Robin (RR) CPU scheduling by carefully studying various popular RR scheduling algorithms to propose an option, Modified Median Round Robin Algorithm (MMRRA), to compete alongside others. A lot of research papers on dynamic RR have been published and they provide better solution to the Classical RR. Some major challenges to most of these dynamic RR scheduling are: They do not include Average Response Time (ART) as a criteria for comparison, they do not bother much on preempting processes with negligible left over of burst time which will lead to an increase on the number of context switching (NCS). Using MMRRA, variable quantum time is used to eliminate these shortcomings. A simulator program has been designed and tested against five selected algorithms, which in turns proved to perform better in most of the criteria.


title: Network failure detection and graph connectivity

abstract: We consider a model for monitoring the connectivity of a network subject to node or edge failures. In particular, we are concerned with detecting (e, k)-failures: events in which an adversary deletes up to network elements (nodes or edges), after which there are two sets of nodes A and B, each at least an e fraction of the network, that are disconnected from one another. We say that a set D of nodes is an (e k)-detection set if, for any (e k)-failure of the network, some two nodes in D are no longer able to communicate; in this way, D "witnesses" any such failure. Recent results show that for any graph G, there is an is (e k)-detection set of size bounded by a polynomial in k and e, independent of the size of G.In this paper, we expose some relationships between bounds on detection sets and the edge-connectivity λ and node-connectivity κ of the underlying graph. Specifically, we show that detection set bounds can be made considerably stronger when parameterized by these connectivity values. We show that for an adversary that can delete κλ edges, there is always a detection set of size O((κ/e) log (1/e)) which can be found by random sampling. Moreover, an (e, l for node failures, we develop a novel approach for working with the much more complex set of all minimum node-cuts of a graph.


title: A Method for Discovering and Extracting Author Contributions Information from Scientific Biomedical Publications

abstract: Creating scientific publications is a complex process, typically composed of a number of different activities, such as designing the experiments, data preparation, programming software and writing and editing the manuscript. The information about the contributions of individual authors of a paper is important in the context of assessing authors' scientific achievements. Some publications in biomedical disciplines contain a description of authors' roles in the form of a short section written in natural language, typically entitled"Authors' contributions". In this paper, we present an analysis of roles commonly appearing in the content of these sections, and propose an algorithm for automatic extraction of authors' roles from natural language text in scientific publications. During the first part of the study, we used clustering techniques, as well as Open Information Extraction (OpenIE), to semi-automatically discover the most popular roles within a corpus of 2,000 contributions sections obtained from PubMed Central resources. The roles discovered by our approach include: experimenting (1,743 instances, 17% of the entire role set within the corpus), analysis (1,343, 16%), study design (1,132, 13%), interpretation (879, 10%), conceptualization (865, 10%), paper reading (823, 10%), paper writing (724, 8%), paper review (501, 6%), paper drafting (351, 4%), coordination (319, 4%), data collection (76, 1%), paper review (41, 0.5%) and literature review (41, 0.5%). Discovered roles were then used to automatically build a training set for the supervised role extractor, based on Naive Bayes algorithm. According to the evaluation we performed, the proposed role extraction algorithm is able to extract the roles from the text with precision 0.71, recall 0.49 and F1 0.58.


title: Detecting and avoiding likely false‐positive findings – a practical guide

abstract: Recently there has been a growing concern that many published research findings do not hold up in attempts to replicate them. We argue that this problem may originate from a culture of ‘you can publish if you found a significant effect’. This culture creates a systematic bias against the null hypothesis which renders meta-analyses questionable and may even lead to a situation where hypotheses become difficult to falsify. In order to pinpoint the sources of error and possible solutions, we review current scientific practices with regard to their effect on the probability of drawing a false-positive conclusion. We explain why the proportion of published false-positive findings is expected to increase with (i) decreasing sample size, (ii) increasing pursuit of novelty, (iii) various forms of multiple testing and researcher flexibility, and (iv) incorrect P-values, especially due to unaccounted pseudoreplication, i.e. the non-independence of data points (clustered data). We provide examples showing how statistical pitfalls and psychological traps lead to conclusions that are biased and unreliable, and we show how these mistakes can be avoided. Ultimately, we hope to contribute to a culture of ‘you can publish if your study is rigorous’. To this end, we highlight promising strategies towards making science more objective. Specifically, we enthusiastically encourage scientists to preregister their studies (including a priori hypotheses and complete analysis plans), to blind observers to treatment groups during data collection and analysis, and unconditionally to report all results. Also, we advocate reallocating some efforts away from seeking novelty and discovery and towards replicating important research findings of one's own and of others for the benefit of the scientific community as a whole. We believe these efforts will be aided by a shift in evaluation criteria away from the current system which values metrics of ‘impact’ almost exclusively and towards a system which explicitly values indices of scientific rigour.


title: The Self-Compassion Scale is a Valid and Theoretically Coherent Measure of Self-Compassion

abstract: Recently, the Self-Compassion Scale (SCS) has been criticized for problems with psychometric validity. Further, the use of an overall self-compassion score that includes items representing the lack of self-compassion has been called into question. I argue that the SCS is consistent with my definition of self-compassion, which I see as a dynamic balance between the compassionate versus uncompassionate ways that individuals emotionally respond to pain and failure (with kindness or judgment), cognitively understand their predicament (as part of the human experience or as isolating), and pay attention to suffering (in a mindful or over-identified manner). A summary of new empirical evidence is provided using a bi-factor analysis, which indicates that at least 90 % of the reliable variance in SCS scores can be explained by an overall self-compassion factor in five different populations, justifying the use of a total scale score. Support for a six-factor structure to the SCS was also found; however, suggesting the scale can be used in a flexible manner depending on the interests of researchers. I also discuss the issue of whether a two-factor model of the SCS—which collapses self-kindness, common humanity, and mindfulness items into a “self-compassion” factor and self-judgment, isolation, and over-identification items into a “self-criticism” factor—makes theoretical sense. Finally, I present new data showing that self-compassion training increases scores on the positive SCS subscales and decreases scores on the negative subscales, supporting the idea that self-compassion represents more compassionate and fewer uncompassionate responses to suffering.


title: Wavelet versus contourlet features for automatic defect detection on hot rolled steel sheet

abstract: The automatic visual inspection systems (AVIS) are being obvious now-a-days in modern manufacturing industries for quality control, ease of documentation and reduced labor cost. The automatic detection of hot rolled steel surface defects in a real process is challenging due to the localization of it on a large surface and its rare occurrences. In this work an effort has been made to extract a set of features that can effectively address the problem of defect detection on hot rolled steel surface by using machine learning algorithm. It is intended to extract two types of features, namely wavelet and contourlet features with two and three resolution levels separately, and then make a comparison of performance of classification accuracy using these features. Here it is proposed to use state-of-the art support vector machine (SVM) classifier as the machine learning algorithm for detecting the defect surface and normal (defects free) surface. Experimental results on 14 different types of steel surface defects show that ‘haar’ wavelet features with three decomposition levels performs better than two levels ‘haar’ feature set or two and three decomposition levels contourlet feature set.


title: Fault diagnosis of suck rod pumping system via extreme learning machines

abstract: Fault diagnosis of suck rod pumping system is an important research subject of oil extraction engineering. This paper presents a research using Extreme Learning Machine (ELM), which is a simple and useful pattern recognition method, to handle downhole dynamometer card auto recognition problems in a suck rod pumping system. An ELM associated with a set of reasonable dynamometer card features is constructed to recognize faults of the system automatically. The model we proposed is trained and tested by the real data acquired from Yanchang oil fields, China. Finally, we conclude based on the experiment results that ELM model has excellent generalization performance and is applicable to the automatic fault diagnosis of suck rod pumping system.


Explicit equations in the plane for elliptic curves given as space quartics


title: Intrusion detection techniques for mobile cloud computing in heterogeneous 5G

abstract: Mobile cloud computing is applied in multiple industries to obtain cloud-based services by leveraging mobile technologies. With the development of the wireless networks, defending threats from wireless communications have been playing a remarkable role in the Web security domain. Intrusion detection system IDS is an efficient approach for protecting wireless communications in the Fifth Generation 5G context. In this paper, we identify and summarize the main techniques being implemented in IDSs and mobile cloud computing with an analysis of the challenges for each technique. Addressing the security issue, we propose a higher level framework of implementing secure mobile cloud computing by adopting IDS techniques for applying mobile cloud-based solutions in 5G networks. On the basis of the reviews and synthesis, we conclude that the implementation of mobile cloud computing can be secured by the proposed framework because it will provide well-protected Web services and adaptable IDSs in the complicated heterogeneous 5G environment. Copyright © 2015John Wiley & Sons, Ltd.


title: Continuous authentication of UAV flight command data using behaviometrics

abstract: The authentication of flight data in Unmanned Aerial Vehicles (UAVs) is highly critical because processing fake commands by the on-board flight controller can cause fatal consequences. Depending on the criticality level of the UAV mission, multi-layer authentication techniques can be useful to assure higher security levels. This paper proposes a technique for continuous authentication of flight data based on the behavior of the UAV operator, who flies the vehicle in a manual mode. In contrast to one-time authentication, this technique allows for an on-the-fly identification of malicious commands aiming at manipulating, hijacking, or crashing the UAV. The operator behavior is defined by the sequence of flight commands sent to the drone using a standard radio control transmitter. This is based on our assumption that every UAV operator has a distinctive pattern when it comes to controlling a UAV using transmitter's levers or joysticks. To verify this assumption, we captured 22,402 commands from five different operators, who flew a small multicopter UAV using a standard flight transmitter. Machine learning was applied to train a random forest classifier. The results show that the UAV operators can be identified with accuracies between 76% and 88% in a 10-tree configuration. These promising results pave the way for a comprehensive study towards implementing a real-time classifier on the UAV embedded system.


title: Length of Stay Predictions: Improvements Through the Use of Automated Laboratory and Comorbidity Variables

abstract: Background:Length of stay (LOS) is a common measure of hospital resource utilization. Most methods for risk-adjusting LOS are limited by the use of only administrative data. Recent studies suggest that adding automated clinical data to these models improves performance.Objectives:To evaluate the uti


title: Optimizing the Hadoop MapReduce Framework with high-performance storage devices

abstract: Solid-state drives (SSDs) are an attractive alternative to hard disk drives (HDDs) to accelerate the Hadoop MapReduce Framework. However, the SSD characteristics and today's Hadoop framework exhibit mismatches that impede indiscriminate SSD integration. This paper explores how to optimize a Hadoop MapReduce Framework with SSDs in terms of performance, cost, and energy consumption. It identifies extensible best practices that can exploit SSD benefits within Hadoop when combined with high network bandwidth and increased parallel storage access. Our Terasort benchmark results demonstrate that Hadoop currently does not sufficiently exploit SSD throughput. Hence, using faster SSDs in Hadoop does not enhance its performance. We show that SSDs presently deliver significant efficiency when storing intermediate Hadoop data, leaving HDDs for Hadoop Distributed File System (HDFS). The proposed configuration is optimized with the JVM reuse option and frequent heartbeat interval option. Moreover, we examined the performance of a state-of-the-art non-volatile memory express interface SSD within the Hadoop MapReduce Framework. While HDFS read and write throughput increases with high-performance SSDs, achieving complete system performance improvement requires carefully balancing CPU, network, and storage resource capabilities at a system level.


title: Effect of oil viscosity on the flow structure and pressure gradient in horizontal oil-water flow

abstract: Abstract The flow patterns and pressure gradient of immiscible liquids are still subject of immense research interest. This is partly because fluids with different properties exhibit different flow behaviours in different pipe's configurations under different operating conditions. In this study, a combination of oil–water properties ( σ = 20.1 mN/m) not previously reported was used in a 25.4 mm acrylic pipe. Experimental data of flow patterns, pressure gradient and phase inversion in horizontal oil–water flow are presented and analyzed together with comprehensive comments. The effect of oil viscosity on flow structure was assessed by comparing the present work data with those of Angeli and Hewitt (2000) and Raj et al. (2005) . The comparison revealed several important findings. For example, the water velocity required to initiate the transition to non-stratified flow at low oil velocities increased as the oil viscosity increased while it decreased at higher oil velocities. The formation of bubbly and annular flows and the extent of dual continuous region were found to increase as the oil–water viscosity ratio increased. Dispersed oil in water appeared earlier when oil viscosity decreased. The effect of oil viscosity on pressure gradient was also investigated by comparing the results with Angeli and Hewitt (1998) and Chakrabarti et al. (2005) . One of the main findings is the large difference between the pressure gradient results which is attributed to the difference in oil viscosity. The differences between the results become bigger at higher oil velocities. The largest difference in pressure values was observed in flow region where oil is the continuous phase. On the contrary, for dispersed oil in water (Do/w), the pressure gradient values observed at the same conditions are approximately the same. A simple correlation was developed to predict the pressure gradient in this regime. The correlation was validated using new experimental data. Finally, the effect of oil viscosity on pressure gradient prediction was investigated using the two flow model for stratified flow and the homogenous model for oil dispersed in water. Both models showed better prediction for the low oil viscosities.


title: A smartphone-based fall detection system

abstract: Falls are a major cause of injuries and hospital admissions among elderly people. Thus, the caregiving process and the quality of life of older adults can be improved by adopting systems for the automatic detection of falls. This paper presents a smartphone-based fall detection system that monitors the movements of patients, recognizes a fall, and automatically sends a request for help to the caregivers. To reduce the problem of false alarms, the system includes novel techniques for the recognition of those activities of daily living that could be erroneously mis-detected as falls (such as sitting on a sofa or lying on a bed). To limit the intrusiveness of the system, a small external sensing unit can also be used for the acquisition of movement data.


title: Device-Free Presence Detection and Localization With SVM and CSI Fingerprinting

abstract: Presence detection and localization are of importance to a variety of applications. Most previous approaches require the objects to carry electronic devices, while on many occasions device-free presence detection and localization are in need. This paper proposes a device-free presence detection and localization algorithm based on WiFi channel state information (CSI) and support vector machines (SVM). In the area of interest covered with WiFi, human movements may cause observable alteration of WiFi signals. By analyzing the CSI fingerprint patterns, the proposed algorithm is able to detect human presence through SVM classification. By establishing the nonlinear relationship between CSI fingerprints and locations through SVM regression, the proposed algorithm is able to estimate the object locations according to the measured CSI fingerprints. To cope with the noisy WiFi channels, the proposed algorithm applies density-based spatial clustering of applications with noise to reduce the noise in CSI fingerprints, and applies principal component analysis to extract the most contributing features and reduce the dimensionality of CSI fingerprints. Evaluations in two typical scenarios achieved the presence detection precision of over 97%, and the localization accuracy of 1.22 and 1.39 m, respectively.


title: ns3-gym: Extending OpenAI Gym for Networking Research

abstract: OpenAI Gym is a toolkit for reinforcement learning (RL) research. It includes a large number of well-known problems that expose a common interface allowing to directly compare the performance results of different RL algorithms. Since many years, the ns-3 network simulation tool is the de-facto standard for academic and industry research into networking protocols and communications technology. Numerous scientific papers were written reporting results obtained using ns-3, and hundreds of models and modules were written and contributed to the ns-3 code base. Today as a major trend in network research we see the use of machine learning tools like RL. What is missing is the integration of a RL framework like OpenAI Gym into the network simulator ns-3. This paper presents the ns3-gym framework. First, we discuss design decisions that went into the software. Second, two illustrative examples implemented using ns3-gym are presented. Our software package is provided to the community as open source under a GPL license and hence can be easily extended.


GPU-Acceleration of In-Memory Data Analytics


title: Hardware Fingerprinting Using HTML5

abstract: Device fingerprinting over the web has received much attention both by the research community and the commercial market a like. Almost all the fingerprinting features proposed to date depend on software run on the device. All of these features can be changed by the user, thereby thwarting the device's fingerprint. In this position paper we argue that the recent emergence of the HTML5 standard gives rise to a new class of fingerprinting features that are based on the \emph{hardware} of the device. Such features are much harder to mask or change thus provide a higher degree of confidence in the fingerprint. We propose several possible fingerprint methods that allow a HTML5 web application to identify a device's hardware. We also present an initial experiment to fingerprint a device's GPU.


title: Emotion Detection in Email Customer Care

abstract: Prompt and knowledgeable responses to customers' emails are critical in maximizing customer satisfaction. Such emails often contain complaints about unfair treatment due to negligence, incompetence, rigid protocols, unfriendly systems, and unresponsive personnel. In this paper, we refer to these emails as emotional emails. They provide valuable feedback to improve contact center processes and customer care, as well as, to enhance customer retention. This paper describes a method for extracting salient features and identifying emotional emails in customer care. Salient features reflect customer frustration, dissatisfaction with the business, and threats to either leave, take legal action and/or report to authorities. Compared to a baseline system using word ngrams, our proposed approach with salient features resulted in a 20% absolute F-measure improvement.


title: A Generalized Linear Programming Model for Nurse Scheduling

abstract: Abstract This paper presents a 0–1 column generation model with a resource constrained shortest path auxiliary problem for nurse scheduling. The master problem finds a configuration of individual schedules to satisfy the demand coverage constraints while minimizing salary costs and maximizing both employee preferences and team balance. A feasible solution of the auxiliary problem is an acceptable schedule for a given nurse, with respect to collective agreement requirements such as seniority, workload, rotations and days off. We define a new resource structure in the auxiliary problem in order to take into account the complex collective agreement rules specific to the nurse scheduling problem. This model generalizes further the previous formulations discussed in the literature and can be viewed as a general scheme for complex personnel scheduling problems, especially in the context of organizations which operate around the clock. Solution methods and preliminary test results are discussed.


What is happening to caesarean section rates?


title: Behavioural laterality in the shrimp-eating cichlid fish Neolamprologus fasciatus in Lake Tanganyika

abstract: Behavioural laterality has been observed in various vertebrates, including fish, but its significance is little known. This study investigated behavioural laterality, corresponding to morphological asymmetry, in individuals of the cichlid Neolamprologus fasciatus as they hunted shrimp. This species shows lateralized hunting; when aiming at prey, individuals bend with either the left or the right side of the body abutting a rock. In field observations, the numbers of leftward and rightward hunts were recorded during 1-h periods for each of 44 individuals. The frequency distribution of the proportion of rightward hunting was bimodal, and approximately one-third of the observed individuals showed a significant leftward or rightward bias. The degree of behavioural laterality of each fish was associated with that of morphological asymmetry of the mouth but not with any of four bilateral characters (outer teeth in upper jaw, gill rakers, upper lateral line scales and lower lateral line scales); ‘lefties’ (‘righties’) showed more rightward (leftward) hunting. Furthermore, it was suggested that hunts corresponding to each individual's mouth laterality achieved higher hunting success than did reverse hunts. Antisymmetrical hunting behaviour in a population may affect predation efficiency on the basis of frequency-dependent selection.


title: Dynamic time warping for tin whistle partial envelope analysis

abstract: The paper examines techniques for the analysis of the amplitude envelope of harmonic partials in a tin whistle sound. Due to the nature of the envelope, a robust technique is required to estimate the envelope parameters. This is achieved using a dynamic time warping based method. Furthermore, a heuristic scheme for addressing difficulties in envelope modelling is also introduced. The results presented show performance improvements over more established methods.


Fundamentals of Progressive Lens Design By


title: Masking wrong-successor Control Flow Errors employing data redundancy

abstract: Advancements of CMOS technology lead to reduction of the transistor size and operating voltage levels that cause transistors to become more sensitive to cosmic rays. Therefore CMOS devices like memory (i.e., RAMs) are more likely to be hit by transient faults. Up to 77% of the transient faults cause Control Flow Errors (CFEs). One type of CFEs is wrong-successor CFE which is caused by faults in data variables resident in RAM. Previous control flow checking techniques neither detect nor correct this type of errors. A technique with the ability of masking wrong-successor CFEs is proposed in this paper. Since occurrence of these errors is induced by faults in data variables which affect the program execution flow (control variables), in the proposed technique, the control variables are being distinguished from other variables. This step is being followed by a traditional fault masking technique that is applied on the control variables. To evaluate the proposed technique, it was applied on five various benchmarks of the MiBench package. The experimental results demonstrated that the proposed technique is able to mask all 50,000 injected faults in control variables; while it had almost 21% performance overhead with 6% memory overhead. It is reasonable and feasible to apply this technique on the former control flow checking techniques due to its perfect wrong-successor CFE correction coverage and low overheads.


title: Proof: A DHT-Based Peer-to-Peer Search Engine

abstract: In this paper we focus on building a large scale keyword search service over structured Peer-to-Peer (P2P) networks. Current state-of-the-art keyword search approaches for structured P2P systems are based on inverted list intersection. However, the biggest challenge in those approaches is that when the indices are distributed over peers, a simple query may cause a large amount of data to be transmitted over the network. We propose a new P2P keyword search scheme, called "Proof', to reduce network traffic for queries. The key idea is storing a content summary for each web page in the inverted list, so that a query can be processed by only transmitting a small size of candidate results. Our simulation results showed that, compared with previous DHT-based P2P systems, Proof can dramatically reduce network traffic and computation time. It provides 100% precision and 90.09% recall of search results, at an acceptable cost of storage overhead, even when the number of peers and documents increases continually.


title: Structures and luminescent properties of Sm(III) and Dy(III) coordination polymers with 2,4,6-pyridinetricarboxylic acid

abstract: Two rare earth coordination compounds with 2,4,6-pyridinetricarboxylic acid (H3pta) have been synthesized by the hydrothermal method; the formula is {[RE(pta)(H2O)3] · H2O} n [RE = Sm (1) and Dy (2)]. Complexes 1 and 2 are crystallized in the monoclinic crystal system with P21/c space group. X-ray structure analyses show the two complexes have the same structure. Each pta3− connects three rare earth ions. Both the Sm(III) and Dy(III) complexes exhibit characteristic luminescence in the visible region upon excitation with UV-rays.


title: The MASSIF platform: a modular and semantic platform for the development of flexible IoT services

abstract: In the Internet of Things (IoT), data-producing entities sense their environment and transmit these observations to a data processing platform for further analysis. Applications can have a notion of context awareness by combining this sensed data, or by processing the combined data. The processes of combining data can consist both of merging the dynamic sensed data, as well as fusing the sensed data with background and historical data. Semantics can aid in this task, as they have proven their use in data integration, knowledge exchange and reasoning. Semantic services performing reasoning on the integrated sensed data, combined with background knowledge, such as profile data, allow extracting useful information and support intelligent decision making. However, advanced reasoning on the combination of this sensed data and background knowledge is still hard to achieve. Furthermore, the collaboration between semantic services allows to reach complex decisions. The dynamic composition of such collaborative workflows that can adapt to the current context, has not received much attention yet. In this paper, we present MASSIF, a data-driven platform for the semantic annotation of and reasoning on IoT data. It allows the integration of multiple modular reasoning services that can collaborate in a flexible manner to facilitate complex decision-making processes. Data-driven workflows are enabled by letting services specify the data they would like to consume. After thorough processing, these services can decide to share their decisions with other consumers. By defining the data these services would like to consume, they can operate on a subset of data, improving reasoning efficiency. Furthermore, each of these services can integrate the consumed data with background knowledge in its own context model, for rapid intelligent decision making. To show the strengths of the platform, two use cases are detailed and thoroughly evaluated.


title: Comparative Study of ID3/C4.5 Decision tree and Multilayer Perceptron Algorithms for the Prediction of Typhoid Fever

abstract: Data mining is an essential phase in knowledge discovery in database which is actually used to extract hidden patterns from large databases. Data mining concepts and methods can be applied in various fields like marketing, medicine, real estate, customer relationship management, engineering, web mining, etc. The main objective of this paper is to compare the performance accuracy of Multilayer perceptron (MLP) Artificial Neural Network and ID3 (Iterative Dichotomiser 3), C4.5 (also known as J48) Decision Trees algorithms Weka data mining software in predicting Typhoid fever. The data used is the patient’s dataset collected from a well known Nigerian Hospital. ID3, C4.5 Decision tree and MLP Artificial Neural Network WEKA Data mining software was used for the implementation. The data collected were transformed in a form that is acceptable to the data mining software and it was splitted into two sets: The training dataset and the testing dataset so that it can be imported into the system. The training set was used to enable the system to observe relationships between input data and the resulting outcomes in order to perform the prediction. The testing dataset contains data used to test the performance of the model. This model can be used by medical experts both in the private and public hospitals to make more timely and consistent diagnosis of typhoid fever cases which will reduce death rate in our country. The MLP ANN model exhibits good performance in the prediction of typhoid fever disease in general because of the low values generated in the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Relative Absolute Error (RAE) error performance measures.


title: Improved clinical diffusion MRI reliability using a tensor distribution function compared to a single tensor

abstract: Fractional anisotropy derived from the single-tensor model (FADTI) in diffusion MRI (dMRI) is the most widely used metric to characterize white matter (WM) micro-architecture in disease, despite known limitations in regions with extensive fiber crossing. Models such as the tensor distribution function (TDF), which represents the diffusion profile as a probabilistic mixture of tensors, have been proposed to reconstruct multiple underlying fibers. Although complex HARDI acquisition protocols are rare in clinical studies, the TDF and TDF-derived scalar FA metric (FATDF) have been shown to be advantageous even for data with modest angular resolution. However, further evaluation and validation of the metric are necessary. Here we compared the test-retest reliability of FATDF and FADTI in clinical quality data by computing the intra-class correlation (ICC) between dMRI scans collected 3 months apart. When FATDF and FADTI were calculated at various angular resolutions, FATDF ICC in both the corpus callosum and in a full axial slice were consistently more stable across scans, as compared to FADTI.


title: Derivation of the buck-boost PWM DC-DC converter circuit topology

abstract: The derivation of the buck-boost PWM DC-DC converter circuit is presented. The buck and boost converter circuits are cascaded through an inverting stage. Circuit theory and manipulation will be used to simplify the cascaded circuit into the familiar inverting buck-boost PWM DC-DC converter topology.


title: Time-Dependent Models in Collaborative Filtering Based Recommender System

abstract: In recent years, time information is more and more important in collaborative filtering (CF) based recommender system because many systems have collected rating data for a long time, and time effects in user preference is stronger. In this paper, we focus on modeling time effects in CF and analyze how temporal features influence CF. There are four main types of time effects in CF: (1) time bias, the interest of whole society changes with time; (2) user bias shifting, a user may change his/her rating habit over time; (3) item bias shifting, the popularity of items changes with time; (4) user preference shifting, a user may change his/her attitude to some types of items. In this work, these four time effects are used by factorized model, which is called TimeSVD. Moreover, many other time effects are used by simple methods. Our time-dependent models are tested on Netflix data from Nov. 1999 to Dec. 2005. Experimental results show that prediction accuracy in CF can be improved significantly by using time information.


title: A homotopy-based sparse representation for fast and accurate shape prior modeling in liver surgical planning

abstract: Abstract Shape prior plays an important role in accurate and robust liver segmentation. However, liver shapes have complex variations and accurate modeling of liver shapes is challenging. Using large-scale training data can improve the accuracy but it limits the computational efficiency. In order to obtain accurate liver shape priors without sacrificing the efficiency when dealing with large-scale training data, we investigate effective and scalable shape prior modeling method that is more applicable in clinical liver surgical planning system. We employed the Sparse Shape Composition (SSC) to represent liver shapes by an optimized sparse combination of shapes in the repository, without any assumptions on parametric distributions of liver shapes. To leverage large-scale training data and improve the computational efficiency of SSC, we also introduced a homotopy-based method to quickly solve the L 1 -norm optimization problem in SSC. This method takes advantage of the sparsity of shape modeling, and solves the original optimization problem in SSC by continuously transforming it into a series of simplified problems whose solution is fast to compute. When new training shapes arrive gradually, the homotopy strategy updates the optimal solution on the fly and avoids re-computing it from scratch. Experiments showed that SSC had a high accuracy and efficiency in dealing with complex liver shape variations, excluding gross errors and preserving local details on the input liver shape. The homotopy-based SSC had a high computational efficiency, and its runtime increased very slowly when repository’s capacity and vertex number rose to a large degree. When repository’s capacity was 10,000, with 2000 vertices on each shape, homotopy method cost merely about 11.29s to solve the optimization problem in SSC, nearly 2000 times faster than interior point method. The dice similarity coefficient (DSC), average symmetric surface distance (ASD), and maximum symmetric surface distance measurement was 94.31 ± 3.04%, 1.12 ± 0.69 mm and 3.65 ± 1.40 mm respectively.


title: WEIGHTED ASSOCIATION RULE MINING- A REVIEW

abstract: Traditional association rule mining consider support confident measures to find out frequent item sets, it assumes all items are having equal significance. Where as weighted association rule mining assigns weights to items based on different aspects. Because researchers are more concerned with qualitative aspects of attributes (e.g. significance), as compared to considering only quantitative ones (e.g. number of appearances in a database etc). Because qualitative properties are required in order to fully exploit the attributes present in the dataset. In last few years a number of weighted associative rule mining algorithms have been proposed, i.e. WAR, WARM, WFIM, WIP, WUARM, FWARM and others. These algorithms employ different rule discovery, rule ranking, rule pruning, rule prediction methods. This paper focuses on surveying and comparing the weighted associative rule mining techniques with regards to the above criteria.


title: Feature selection techniques for increasing reliability of fault diagnosis of bearings

abstract: Features selection (FS) techniques have an apparent need in many complex engineering applications especially the bearing fault diagnosis of low-speed industrial motor. The main goal of an FS algorithm is to select the most discriminant features subset from a high-dimension features vector that increases the model performance by reducing the redundant and irrelevant fault features. This paper proposes an efficient fault diagnosis model of bearing by incorporating the optimal feature selection approach for increasing the reliability of fault diagnosis of bearing. Also, this paper investigates the feature selection approaches including sequential forward selection (SFS), sequential floating forward selection (SFFS), and genetic algorithm (GA) for identifying the most discriminant subset. The effectiveness of this discriminant features subset is verified with a low-speed bearing fault diagnosis application for identifying bearing failures. The experimental shows up-to-mark diagnosis performance using GA based optimal feature selection method.


title: Expression of serotonin transporter mRNA in rat brain: Presence in neuronal and non-neuronal cells and effect of paroxetine

abstract: The expression of serotonin transporter mRNA in rat brain was-examined by in situ hybridisation. Hybridisation was observed in cells of the known serotonergic nuclei and no other neuronal populations. It was also associated with ependymal cells of the aqueduct which may indicate a specialisation of this part of the ventricular system in anatomical and neurophysiological terms. The effect of the selective serotonin reuptake inhibitor paroxetine on neuronal expression of the serotonin transporter mRNA was examined. Quantitation at the cellular level in the dorsal and median raphe nuclei was carried out by analysis of the mean number of silver grains per cell in autoradiographed sections. No significant change (P > 0.1) in serotonin transporter mRNA expression was observed following 21 day administration of paroxetine (5 mg/kg per day).


title: Design of a WSN for the Sampling of Environmental Variability in Complex Terrain

abstract: In-situ environmental parameter measurements using sensor systems connected to a wireless network have become widespread, but the problem of monitoring large and mountainous areas by means of a wireless sensor network (WSN) is not well resolved. The main reasons for this are: (1) the environmental variability distribution is unknown in the field; (2) without this knowledge, a huge number of sensors would be necessary to ensure the complete coverage of the environmental variability and (3) WSN design requirements, for example, effective connectivity (intervisibility), limiting distances and controlled redundancy, are usually solved by trial and error. Using temperature as the target environmental variable, we propose: (1) a method to determine the homogeneous environmental classes to be sampled using the digital elevation model (DEM) and geometric simulations and (2) a procedure to determine an effective WSN design in complex terrain in terms of the number of sensors, redundancy, cost and spatial distribution. The proposed methodology, based on geographic information systems and binary integer programming can be easily adapted to a wide range of applications that need exhaustive and continuous environmental monitoring with high spatial resolution. The results show that the WSN design is perfectly suited to the topography and the technical specifications of the sensors, and provides a complete coverage of the environmental variability in terms of Sun exposure. However these results still need be validated in the field and the proposed procedure must be refined.


title: A Novel Progressive Learning Technique for Multi-class Classification

abstract: In this paper, a progressive learning technique for multi-class classification is proposed. This newly developed learning technique is independent of the number of class constraints and it can learn new classes while still retaining the knowledge of previous classes. Whenever a new class (non-native to the knowledge learnt thus far) is encountered, the neural network structure gets remodeled automatically by facilitating new neurons and interconnections, and the parameters are calculated in such a way that it retains the knowledge learnt thus far. This technique is suitable for real-world applications where the number of classes is often unknown and online learning from real-time data is required. The consistency and the complexity of the progressive learning technique are analyzed. Several standard datasets are used to evaluate the performance of the developed technique. A comparative study shows that the developed technique is superior.


title: Human brain tractography: A DTI vs DKI comparison analysis

abstract: Water diffusion can be measured using Diffusion Tensor Imaging (DTI). This technique estimates molecular diffusion using a Gaussian model which can account for anisotropy arising, for example, from the presence of myelin sheath. The main direction of diffusion can also be estimated from DTI and used to compute diffusion tracts (tractography), a good tool to analyse the structure of white matter brain pathways. Nevertheless, DTI has limitations, such as ignoring the non-Gaussian properties of biological tissues and the inability to resolve intra-voxel fibre crossings that may lead to the reconstruction of anatomically inaccurate tracts. Diffusion Kurtosis Imaging (DKI) was introduced to ameliorate these problems. By abandoning the Gaussian model, DKI acts as an extension of DTI, allowing the computation of the same scalar parameters as DTI as well as providing measures of tissue heterogeneity and resolving multiple tracts within each voxel[1]. The purpose of this study is to analyse the DKI performance in tractography comparing to the DTI approach. DKI data was acquired from 3 subjects (1 male, mean age: 39±14 years) using Philips Achieva® 3.0T including diffusion weighted (DW) Single Shot Echo Planar images using 64 directions with b-values 0, 1000 and 2000 s/mm2, reconstruction matrix 256×256, slice thickness 1.5 mm, TE/TR 64/7703 ms, FOV 240×240 mm2, 60 slices. DTI data was extracted from DKI considering only b-values 0 and 1000. All DTI and DKI based tracts were computed using DKIu, a Matlab toolbox created by Neto-Henriques et. al., using three different tractography approaches: DTI, Laz-DKI[2] and KT-DKI[1]. The structures considered were the Internal Capsule (IC) and the Corpus Callosum (CC). Using TrackVis for visualisation, DKI tractography was shown to enable improved fibre crossing resolutio n (Figure 1), consistent in all three subjects. The number of streamlines computed in the CC was higher with the DKI approach than with DTI, but lower in the IC. DKI-based tractography appears to successfully address some of the limitations of DTI, as it resolves crossing fibres, providing more useful information regarding the brain's microstructure. This is particularly important in pre-surgical planning and in identifying brain lesions and pathologies.


title: Learning with Symmetric Label Noise: The Importance of Being Unhinged

abstract: Convex potential minimisation is the de facto approach to binary classification. However, Long and Servedio [2010] proved that under symmetric label noise (SLN), minimisation of any convex potential over a linear function class can result in classification performance equivalent to random guessing. This ostensibly shows that convex losses are not SLN-robust. In this paper, we propose a convex, classification-calibrated loss and prove that it is SLN-robust. The loss avoids the Long and Servedio [2010] result by virtue of being negatively unbounded. The loss is a modification of the hinge loss, where one does not clamp at zero; hence, we call it the unhinged loss. We show that the optimal unhinged solution is equivalent to that of a strongly regularised SVM, and is the limiting solution for any convex potential; this implies that strong l2 regularisation makes most standard learners SLN-robust. Experiments confirm the SLN-robustness of the unhinged loss.


title: An IoT based smart solar photovoltaic remote monitoring and control unit

abstract: Using the Internet Of Things Technology for supervising solar photovoltaic power generation can greatly enhance the performance, monitoring and maintenance of the plant. With advancement of technologies the cost of renewable energy equipments is going down globally encouraging large scale solar photovoltaic installations. This massive scale of solar photovoltaic deployment requires sophisticated systems for automation of the plant monitoring remotely using web based interfaces as majority of them are installed in inaccessible locations and thus unable to be monitored from a dedicated location. The discussion in this paper is based on implementation of new cost effective methodology based on IoT to remotely monitor a solar photovoltaic plant for performance evaluation. This will facilitate preventive maintenance, fault detection, historical analysis of the plant in addition to real time monitoring.


title: Tuning curve sharpening for orientation selectivity: coding efficiency and the impact of correlations

abstract: Several studies have shown that the information conveyed by bell-shaped tuning curves increases as their width decreases, leading to the notion that sharpening of tuning curves improves population codes. This notion, however, is based on assumptions that the noise distribution is independent among neurons and independent of the tuning curve width. Here we reexamine these assumptions in networks of spiking neurons by using orientation selectivity as an example. We compare two principal classes of model: one in which the tuning curves are sharpened through cortical lateral interactions, and one in which they are not. We report that sharpening through lateral interactions does not improve population codes but, on the contrary, leads to a severe loss of information. In addition, the sharpening models generate complicated codes that rely extensively on pairwise correlations. Our study generates several experimental predictions that can be used to distinguish between these two classes of model.


title: Online event correlations analysis in system logs of large-scale cluster systems

abstract: It has been long recognized that failure events are correlated, not independent. Previous research efforts have shown the correlation analysis of system logs is helpful to resource allocation, job scheduling and proactive management. However, previous log analysis methods analyze the history logs offline. They fail to capture the dynamic change of system errors and failures. In this paper, we purpose an online log analysis approach to mine event correlations in system logs of large-scale cluster systems. Our contributions are three-fold: first, we analyze the event correlations of system logs of a 260-nodes production Hadoop cluster system, and the result shows that the correlation rules of logs change dramatically in different periods; Second, we present a online log analysis algorithm Apriori-SO; third, based on the online event correlations mining, we present an online event prediction method that can predict diversities of failure events with the great detail. The experiment result of a 260-nodes production Hadoop cluster system shows that our online log analysis algorithm can analyze the log streams to obtain event correlation rules in soft real time, and our online event prediction method can achieve higher precision rate and recall rate than the offline log analysis approach.


title: Graph-theoretic connectivity control of mobile robot networks

abstract: In this paper, we provide a theoretical framework for controlling graph connectivity in mobile robot networks. We discuss proximity-based communication models composed of disk-based or uniformly-fading-signal-strength communica- tion links. A graph-theoretic definition of connectivity is pro- vided, as well as an equivalent definition based on algebraic graph theory, which employs the adjacency and Laplacian matrices of the graph and their spectral properties. Based on these results, we discuss centralized and distributed algorithms to maintain, increase, and control connectivity in mobile robot networks. The various approaches discussed in this paper range from convex optimization and subgradient-descent algo- rithms, for the maximization of the algebraic connectivity of the network, to potential fields and hybrid systems that main- tain communication links or control the network topology in a least restrictive manner. Common to these approaches is the use of mobility to control the topology of the underlying com- munication network. We discuss applications of connectivity control to multirobot rendezvous, flocking and formation con- trol, where so far, network connectivity has been considered an assumption.