title: Model-Driven Management of Docker Containers

abstract: With the emergence of Docker, it becomes easier to encapsulate applications and their dependencies into lightweight Linux containers and make them available to the world by deploying them in the cloud. Compared to hypervisor-based virtualization approaches, the use of containers provides faster start-ups times and reduces the consumption of computer resources. However, Docker lacks of deployability verification tool for containers at design time. Currently, the only way to be sure that the designed containers will execute well is to test them in a running system. If errors occur, a correction is made but this operation can be repeated several times before the deployment becomes operational. Docker does not provide a solution to increase or decrease the size of container resources in demand. Besides the deployment of containers, Docker lacks of synchronization between the designed containers and those deployed. Moreover, container management with Docker is done at low level, and therefore requires users to focus on low level system issues. In this paper we focus on these issues related to the management of Docker containers. In particular, we propose an approach for modeling Docker containers. We provide tooling to ensure the deployability and the management of Docker containers. We illustrate our proposal using an event processing application and show how our solution provides a significantly better compromise between performance and development costs than the basic Docker container solution.


title: Nastalique segmentation-based approach for Urdu OCR

abstract: Much work on Arabic language optical character recognition (OCR) has been on Naskh writing style. Nastalique style, used for most of languages using Arabic script across Southern Asia, is much more challenging to process due to its compactness, cursiveness, higher context sensitivity and diagonality. This makes the Nastalique writing more complex with multiple letters horizontally overlapping each other. Due to these reasons, existing methods used for Naskh would not work for Nastalique and therefore most work on Nastalique has used non-segmentation methods. The current paper presents new approach for segmentation-based analysis for Nastalique style. The paper explains the complexity of Nastalique, why Naskh based techniques cannot work for Nastalique, and proposes a segmentation-based method for developing Nastalique OCR, deriving principles and techniques for the pre-processing and recognition. The OCR is developed for Urdu language. The system is optimized using 79,093 instances of 5249 main bodies derived from a corpus of 18 million words, giving recognition accuracy of 97.11 %. The system is then tested on document images of books with 87.44 % main body recognition accuracy. The work is extensible to other languages using Nastalique.


title: A set of principles for conducting critical research in information systems

abstract: While criteria or principles for conducting positivist and interpretive research have been widely discussed in the IS research literature, criteria or principles for critical research are lacking. Therefore, the purpose of this paper is to propose a set of principles for the conduct of critical research in information systems. We examine the nature of the critical research perspective, clarify its significance, and review its major discourses, recognizing that its mission and methods cannot be captured by a fixed set of criteria once and for all, particularly as multiple approaches are still in the process of defining their identity. However, we suggest it is possible to formulate a set of principles capturing some of the commonalities of those approaches that have so far become most visible in the IS research literature. The usefulness of the principles is illustrated by analyzing three critical field studies in information systems. We hope that this paper will further reflection and debate on the important subject of grounding critical research methodology.


title: Best Practices for Convolutional Neural Networks Applied to Object Recognition in Images

abstract: This research project studies the impact of convolutional neural networks (CNN) in image classification tasks. We explore different architectures and training configurations with the use of ReLUs, Nesterov's accelerated gradient, dropout and maxout networks. We work with the CIFAR-10 dataset as part of a Kaggle competition to identify objects in images. Initial results show that CNNs outperform our baseline by acting as invariant feature detectors. Comparisons between different preprocessing procedures show better results for global contrast normalization and ZCA whitening. ReLUs are much faster than tanh units and outperform sigmoids. We provide extensive details about our training hyperparameters, providing intuition for their selection that could help enhance learning in similar situations. We design 4 models of convolutional neural networks that explore characteristics such as depth, number of feature maps, size and overlap of kernels, pooling regions, and different subsampling techniques. Results favor models of moderate depth that use an extensive number of parameters in both convolutional and dense layers. Maxout networks are able to outperform rectifiers on some models but introduce too much noise as the complexity of the fully-connected layers increases. The final discussion explains our results and provides additional techniques that could improve performance.


title: Driving cycle-based design optimization of interior permanent magnet synchronous motor drives for electric vehicle application

abstract: The paper discusses the influence of driving cycles on the design optimization of permanent magnet synchronous machines. A bi-objective design optimization is presented for synchronous machines with V-shaped buried magnets. The machine length and the loss energy over a given driving cycle are defined as objectives. A total of 14 parameters defining geometry and winding layout are chosen as design parameters. Additionally some constraints like speed-torque-requirements and minimal stray field bridge widths are set. The optimization problem is solved using 2D finite element analysis and a high-performance differential evolution algorithm. The analyses are performed for the ARTEMIS driving cycle due to the more realistic driving behavior in comparison to the most commonly used New European Driving Cycle. Furthermore, a reference design optimization against the rated point loss energy is presented. The results show a much better performance of the driving cycle optimized machines in comparison to the rated point optimized machines in terms of the cycle-based loss energy. Loss savings depend strongly on the machine length and are approximately in between 15% and 45%.


title: BoneScalpel ™ UltraSonic Bone DiSSector: applicationS in Spine SUrgery anD SUrgical techniqUe gUiDe

abstract: The Misonix BoneScalpel is a novel ultrasonic surgical device that cuts bone and spares soft tissues. This relative selectivity for bone ablation makes BoneScalpel ideally suited for spine applications where bone must be cut adjacent to dura and neural structures. Extensive clinical experience with this device confirms its safety and efficacy in spine surgery. The aim of this report is to describe BoneScalpel’s mechanism of action and the basis for its tissue selectivity, review the expanding clinical experience with BoneScalpel (including the author’s personal experience), and provide a few recommendations and recipes for en bloc bone removal with this revolutionary device.


title: Robust Denoising using Feature and Color Information

abstract: We propose a method that robustly combines color and feature buffers to denoise Monte Carlo renderings. On ::: one hand, feature buffers, such as per pixel normals, textures, or depth, are effective in determining denoising ::: filters because features are highly correlated with rendered images. Filters based solely on features, however, are ::: prone to blurring image details that are not well represented by the features. On the other hand, color buffers ::: represent all details, but they may be less effective to determine filters because they are contaminated by the noise ::: that is supposed to be removed. We propose to obtain filters using a combination of color and feature buffers in an ::: NL-means and cross-bilateral filtering framework. We determine a robust weighting of colors and features using ::: a SURE-based error estimate. We show significant improvements in subjective and quantitative errors compared ::: to the previous state-of-the-art. We also demonstrate adaptive sampling and space-time filtering for animations.


title: Bedtime procrastination: introducing a new area of procrastination

abstract: Background. Procrastination is a prevalent and problematic phenomenon that has mostly been studied in the domain of academic behavior. The current study shows that procrastination may also lead to harmful outcomes in the area of health behavior, introducing bedtime procrastination as an important factor related to getting insufficient sleep and consequently affecting individual well-being. Bedtime procrastination is defined as failing to go to bed at the intended time, while no external circumstances prevent a person from doing so. Methods. To empirically support the conceptual introduction of bedtime procrastination, an online survey study was conducted among a community sample (N = 177). The relationship between bedtime procrastination and individual difference variables related to self-regulation and general procrastination was assessed. Moreover, it was investigated whether bedtime procrastination was a predictor of self-reported sleep outcomes (experienced insufficient sleep, hours of sleep, fatigue during the day). Results. Bedtime procrastination was negatively associated with self-regulation: people who scored lower on self-regulation variables reported more bedtime procrastination. Moreover, self-reported bedtime procrastination was related to general reports of insufficient sleep above and beyond demographics and self-regulation. Conclusions. Introducing a novel domain in which procrastinators experience problems, bedtime procrastination appears to be a prevalent and relevant issue that is associated with getting insufficient sleep.


NON-PARAMETRIC LOGISTIC REGRESSION


title: Automatically building research reading lists

abstract: All new researchers face the daunting task of familiarizing themselves with the existing body of research literature in their respective fields. Recommender algorithms could aid in preparing these lists, but most current algorithms do not understand how to rate the importance of a paper within the literature, which might limit their effectiveness in this domain. We explore several methods for augmenting existing collaborative and content-based filtering algorithms with measures of the influence of a paper within the web of citations. We measure influence using well-known algorithms, such as HITS and PageRank, for measuring a node's importance in a graph. Among these augmentation methods is a novel method for using importance scores to influence collaborative filtering. We present a task-centered evaluation, including both an offline analysis and a user study, of the performance of the algorithms. Results from these studies indicate that collaborative filtering outperforms content-based approaches for generating introductory reading lists.


title: RapiLog: reducing system complexity through verification

abstract: Database management systems provide updates with guaranteed durability in the presence of OS crashes or power failures. Durability is achieved by performing synchronous writes to a transaction log on stable, non-volatile storage. The procedure is expensive and several techniques have been devised to ameliorate the impact on overall performance at the cost of increased system complexity. In this paper we explore the possibility of reducing the system complexity around logging by leveraging verification instead of using specialised/dedicated hardware or complicated optimisations. The prototype system, RapiLog, uses a dependable hypervisor based on seL4 to buffer log data outside the database system and its OS, and performs the physical disk writes asynchronously with respect to the operation of the database. RapiLog guarantees that the log data will eventually be written to the disk even if the database system or the underlying OS crash or electrical power is cut. We evaluate RapiLog with multiple open-source and commercial database engines and find that performance is never degraded (beyond the virtualisation overhead), and at times is significantly improved.


title: A protocol for a secure remote keyless entry system applicable in vehicles using symmetric-key cryptography

abstract: In our modern society comfort became a standard. This comfort, especially in cars can only be achieved by equipping the car with more electronic devices. Some of the electronic devices must cooperate with each other and thus they require a communication channel, which can be wired or wireless. In these days, it would be hard to sell a new car operating with traditional keys. Almost all modern cars can be locked or unlocked with a Remote Keyless System. A Remote Keyless System consists of a key fob that communicates wirelessly with the car transceiver that is responsible for locking and unlocking the car. However there are several threats for wireless communication channels. ::: This paper describes the possible attacks against a Remote Keyless System and introduces a secure protocol as well as a lightweight Symmetric Encryption Algorithm for a Remote Keyless Entry System applicable in vehicles.


title: Learning and Transfer: A General Role for Analogical Encoding

abstract: Teaching by examples and cases is widely used to promote learning, but it varies widely in its effectiveness. The authors test an adaptation to case-based learning that facilitates abstracting problem-solving schemas from examples and using them to solve further problems: analogical encoding, or learning by drawing a comparison across examples. In 3 studies, the authors examined schema abstraction and transfer among novices learning negotiation strategies. Experiment 1 showed a benefit for analogical learning relative to no case study. Experiment 2 showed a marked advantage for comparing two cases over studying the 2 cases separately. Experiment 3 showed that increasing the degree of comparison support increased the rate of transfer in a face-to-face dynamic negotiation exercise. (PsycINFO Database Record (c) 2016 APA, all rights reserved)


Automated Object Recognition in Baggage Screening Using Multiple X-ray Views


title: Recommending friends and locations based on individual location history

abstract: The increasing availability of location-acquisition technologies (GPS, GSM networks, etc.) enables people to log the location histories with spatio-temporal data. Such real-world location histories imply, to some extent, users' interests in places, and bring us opportunities to understand the correlation between users and locations. In this article, we move towards this direction and report on a personalized friend and location recommender for the geographical information systems (GIS) on the Web. First, in this recommender system, a particular individual's visits to a geospatial region in the real world are used as their implicit ratings on that region. Second, we measure the similarity between users in terms of their location histories and recommend to each user a group of potential friends in a GIS community. Third, we estimate an individual's interests in a set of unvisited regions by involving his/her location history and those of other users. Some unvisited locations that might match their tastes can be recommended to the individual. A framework, referred to as a hierarchical-graph-based similarity measurement (HGSM), is proposed to uniformly model each individual's location history, and effectively measure the similarity among users. In this framework, we take into account three factors: 1) the sequence property of people's outdoor movements, 2) the visited popularity of a geospatial region, and 3) the hierarchical property of geographic spaces. Further, we incorporated a content-based method into a user-based collaborative filtering algorithm, which uses HGSM as the user similarity measure, to estimate the rating of a user on an item. We evaluated this recommender system based on the GPS data collected by 75 subjects over a period of 1 year in the real world. As a result, HGSM outperforms related similarity measures, namely similarity-by-count, cosine similarity, and Pearson similarity measures. Moreover, beyond the item-based CF method and random recommendations, our system provides users with more attractive locations and better user experiences of recommendation.


title: Role of transformational and transactional leadership on job satisfaction and career satisfaction

abstract: Effective leadership always plays an important role in the growth and better performance of the organization. This research has been conducted to determine the impact of transformational and transactional leadership style on job success and career satisfaction. A total of 240 responses (n = 240) from various private organizations working in the capital city of Pakistan were collected using various measures of TLI Questionnaire along with items of job success and career satisfaction. Results showed positive trends of all variables. Transactional leadership is found significantly related to job success while transformational leadership and job success are found highly related with career satisfaction. The results of regression analysis show that job success is more dependent on transformational and transactional leadership as compared to career satisfaction. Managerial implications are presented based upon these results.


title: Why should i share? examining social capital and knowledge contribution in electronic networks of practice

abstract: Electronic networks of practice are computer-mediated discussion forums focused on problems of practice that enable individuals to exchange advice and ideas with others based on common interests. However, why individuals help strangers in these electronic networks is not well understood: there is no immediate benefit to the contributor, and free-riders are able to acquire the same knowledge as everyone else. To understand this paradox, we apply theories of collective action to examine how individual motivations and social capital influence knowledge contribution in electronic networks. This study reports on the activities of one electronic network supporting a professional legal association. Using archival, network, survey, and content analysis data, we empirically test a model of knowledge contribution. We find that people contribute their knowledge when they perceive that it enhances their professional reputations, when they have the experience to share, and when they are structurally embedded in the network. Surprisingly, contributions occur without regard to expectations of reciprocity from others or high levels of commitment to the network.


title: Updating dopamine reward signals

abstract: Recent work has advanced our knowledge of phasic dopamine reward prediction error signals. The error signal is bidirectional, reflects well the higher order prediction error described by temporal difference learning models, is compatible with model-free and model-based reinforcement learning, reports the subjective rather than physical reward value during temporal discounting and reflects subjective stimulus perception rather than physical stimulus aspects. Dopamine activations are primarily driven by reward, and to some extent risk, whereas punishment and salience have only limited activating effects when appropriate controls are respected. The signal is homogeneous in terms of time course but heterogeneous in many other aspects. It is essential for synaptic plasticity and a range of behavioural learning situations.


title: Learning entity and relation embeddings for knowledge graph completion

abstract: Knowledge graph completion aims to perform link prediction between entities. In this paper, we consider the approach of knowledge graph embeddings. Recently, models such as TransE and TransH build entity and relation embeddings by regarding a relation as translation from head entity to tail entity. We note that these models simply put both entities and relations within the same semantic space. In fact, an entity may have multiple aspects and various relations may focus on different aspects of entities, which makes a common space insufficient for modeling. In this paper, we propose TransR to build entity and relation embeddings in separate entity space and relation spaces. Afterwards, we learn embeddings by first projecting entities from entity space to corresponding relation space and then building translations between projected entities. In experiments, we evaluate our models on three tasks including link prediction, triple classification and relational fact extraction. Experimental results show significant and consistent improvements compared to state-of-the-art baselines including TransE and TransH. The source code of this paper can be obtained from https://github.com/mrlyk423/relation_extraction.


title: Playing with strangers: understanding temporary teams in league of legends

abstract: Game researchers have extensively studied how players form long-term social organizations such as guilds and clans to accomplish complex tasks such as raiding in online games. Few studies have paid attention to how temporary teams (or pickup groups) composed of strangers fulfill complex tasks. Riot Games' League of Legends, a team-based competitive online game, is played by two temporary teams. Players must collaborate with strangers in a relatively short time (about 30-50 minutes). How do players interact and collaborate with their teammates in temporary teams? To answer this question, we conducted an ethnographic study within the League of Legends community. We conducted 30 semi-structured interviews with experienced players. We found that rich social interaction exists within temporary teams. Players want to collaborate with strangers through communication and coordination. They discipline their own ways of interaction to facilitate collaboration. They try to exert influence over their teammates. We further discuss design implications for facilitating collaboration among strangers.


title: Cytogenetic characterization and genome size of the medicinal plant Catharanthus roseus (L.) G. Don

abstract: Background and aims Catharanthus roseus is a highly valuable medicinal plant producing several terpenoid indole alkaloids (TIAs) with pharmaceutical applications, including the anticancer agents vinblastine and vincristine. Due to the interest in its TIAs, C. roseus is one of the most extensively studied medicinal plants and has become a model species for the study of plant secondary metabolism. However, very little is known about the cytogenetics and genome size of this species, in spite of their importance for breeding programmes, TIA genetics and emerging genomic research. Therefore, the present paper provides a karyotype description and fluorescence in situ hybridization (FISH) data for C. roseus, as well as a rigorous characterization of its genome size. Methodology The organization of C. roseus chromosomes was characterized using several DNA/chromatin staining techniques and FISH of rDNA. Genome size was investigated by flow cytometry using an optimized methodology. Principal results The C. roseus full chromosome complement of 2n ¼ 16 includes two metacentric, four subtelocentric and two telocentric chromosome pairs, with the presence of a single nucleolus organizer region in chromosome 6. An easy and reliable flow cytometry protocol for nuclear genome analysis of C. roseus was optimized, and the C-value of this species was estimated to be 1C ¼ 0.76 pg, corresponding to 738 Mbp. Conclusions The organization and size of the C. roseus genome were characterized, providing an important basis for future studies of this important medicinal species, including further cytogenetic mapping, genomics, TIA genetics and breeding programmes.


title: Understanding soft evidence as probabilistic evidence: Illustration with several use cases

abstract: This paper aims to get a better understanding of the notions of evidence, probabilistic evidence and likelihood evidence in Bayesian Networks. Evidence comes from an observation of one or several variables. Soft evidence is probabilistic evidence, since the observation consists in a local probability distribution on a subset of variables that has to replace any former belief on these variables. It has to be clearly distinguished from likelihood evidence, also called virtual evidence, for which the evidence is specified as a likelihood ratio. Since the notion of soft evidence is not yet widely understood, most of the Bayesian Networks engines do not propose related propagation functions and the terms used to describe such evidence are not stabilised. First, we present the different types of evidence on a simple example with an illustrative context. Then, we discuss the understanding of both notions in terms of knowledge and observation. Next, we propose to use soft evidence to represent certain evidence on a continuous variable, after fuzzy discretization.


title: Ant Colony System-Based Applications to Electrical Distribution System Optimization

abstract: Electrical distribution networks are structurally weakly meshed, but are typically operated in radial configurations to simplify the network protection schemes. This implies the need to carry out suitable selection of the redundant network branches to open in normal conditions, as well as to define the control variables to set up in order to guarantee effective system operation and voltage quality. Furthermore, the structure of the distribution networks has to be upgraded to meet future needs, according to economic objective functions defined for distribution system planning. Finally, distribution systems need to face possible interruptions through the formulation of appropriate service restoration strategies, based on switching on and off a number of branches, reducing the effects of faults occurring in the networks by defining a time-dependent path to restore electricity supply to all consumers. All these aspects can be addressed by formulating and solving suitable optimization problems for distribution network operation and planning. Typically these problems are defined within discrete domains for the decision variables. However, for large real distribution systems the highly-dimensional and/or combinatorial nature of the optimization problems make it impracticable to resort to exhaustive search, since the number of combinations would be excessively high to be processed in reasonable computation times. Global optimizations can then be solved in terms of adopting suitable meta-heuristics. In this context, ant colony optimization (ACO) provides viable solutions to the various distribution system optimization problems. Key features of ACO, such as parallel search, shortest path finding, adaptability to changes in the search space, long-term memory and information sharing, have been fully exploited in their classical formulations, in advanced versions such as the hyper-cube ACO framework, as well as in hybrid formulations combining the ACO properties with those of other meta-heuristics. This chapter summarizes the formulation and use of ACO algorithms and variants to solve different types of electrical distribution system optimization problems, namely: • Distribution system optimal reconfiguration and voltage support. Various objective functions to minimize can be set up in the presence of a set of structural and operational constraints. The objectives include total distribution system losses, voltage deviations with respect to their reference values, maximum load on system components, load balancing, and capacitor placement. Performing network configuration changes and setting up the level of insertion of compensating devices or distributed generation and


title: An experimental study of security and privacy risks with emerging household appliances

abstract: Smart household appliances, ranging from light-bulbs and door-locks to power switches and smoke-alarms, are rapidly emerging in the marketplace, with predictions that over 2 billion devices will be installed within the next four years. However, security implementations vary widely across these devices, while privacy implications are unclear to users. In this paper we dissect the behavior of a few household devices, specifically the Phillips Hue light-bulb, the Belkin WeMo power switch, and the Nest smoke-alarm, and highlight the ease with which security and privacy can be compromised. We then propose a new solution to protect such devices by restricting access at the network-level. Our solution does not require changes from device manufacturers, reduces burden on the end-users, and allows security to be offered as an overlay service by the ISP or from a specialist provider in the cloud.


title: FINCH: A SYSTEM FOR EVOLVING JAVA (BYTECODE)

abstract: The established approach in genetic programming (GP) involves the definition of functions and terminals appropriate to the problem at hand, after which evolution of expressions using these definitions takes place. We have recently developed a system, dubbed FINCH (Fertile Darwinian Bytecode Harvester), to evolutionarily improve actual, extant software, which was not intentionally written for the purpose of serving as a GP representation in particular, nor for evolution in general. This is in contrast to existing work that uses restricted subsets of the Java bytecode instruction set as a representation language for individuals in genetic programming. The ability to evolve Java programs will hopefully lead to a valuable new tool in the software engineer’s toolkit.


title: Real-Time Machine Learning: The Missing Pieces

abstract: Machine learning applications are increasingly deployed not only to serve predictions using static models, but also as tightly-integrated components of feedback loops involving dynamic, real-time decision making. These applications pose a new set of requirements, none of which are difficult to achieve in isolation, but the combination of which creates a challenge for existing distributed execution frameworks: computation with millisecond latency at high throughput, adaptive construction of arbitrary task graphs, and execution of heterogeneous kernels over diverse sets of resources. We assert that a new distributed execution framework is needed for such ML applications and propose a candidate approach with a proof-of-concept architecture that achieves a 63x performance improvement over a state-of-the-art execution framework for a representative application.


title: Coming of age: ten years of next-generation sequencing technologies

abstract: Advances in DNA sequencing technologies have led to vast increases in the diversity of sequencing-based applications and in the amount of data generated. This Review discusses the current state-of-the-art technologies in both short-read and long-read DNA sequencing, their underlying mechanisms, relative strengths and limitations, and emerging applications.


title: Declarative Data Cleaning: Language, Model, and Algorithms

abstract: The problem of data cleaning, which consists of emoving inconsistencies and errors from original data sets, is well known in the area of decision support systems and data warehouses. However, for non-conventional applications, such as the migration of largely unstructured data into structured one, or the integration of heterogeneous scientific data sets in inter-discipl- inary fields (e.g., in environmental science), existing ETL (Extraction Transformation Loading) and data cleaning tools for writing data cleaning programs are insufficient. The main challenge with them is the design of a data flow graph that effectively generates clean data, and can perform efficiently on large sets of input data. The difficulty with them comes from (i) a lack of clear separation between the logical specification of data transformations and their physical implementation and (ii) the lack of explanation of cleaning results and user interaction facilities to tune a data cleaning program. This paper addresses these two problems and presents a language, an execution model and algorithms that enable users to express data cleaning specifications declaratively and perform the cleaning efficiently. We use as an example a set of bibliographic references used to construct the Citeseer Web site. The underlying data integration problem is to derive structured and clean textual records so that meaningful queries can be performed. Experimental results report on the assessement of the proposed framework for data cleaning.


title: Occupational therapy interventions for work-related injuries and conditions of the forearm, wrist, and hand: a systematic review.

abstract: A systematic review of the literature related to effective occupational therapy interventions in rehabilitation of individuals with work-related forearm, wrist, and hand injuries and illnesses was conducted as part of the Evidence-Based Literature Review Project of the American Occupational Therapy Association. This review provides a comprehensive overview and analysis of 36 studies that addressed many of the interventions commonly used in hand rehabilitation. Findings reveal that the use of occupation-based activities has reasonable yet limited evidence to support its effectiveness. This review supports the premise that many client factors can be positively affected through the use of several commonly used occupational therapy-related modalities and methods. The implications for occupational therapy practice, research, and education and limitations of reviewed studies are also discussed.


title: A proposal for mobile game based learning to promote knowledge of the a famosa fortress

abstract: The A Famosa fortress in Melaka is an important historical site which offers a significant insight into the history of Melaka and Malaysia. A research project was carried out over the period between 2007 to 2010 in order to develop a conjectural layout of the original fortress with the aim of developing a 3D model for use in future conservation and heritage projects. Based on the results of an evaluation of the final output, it was found that the 3D model had the potential to improve users' knowledge of the fortress. From this context, the current research is aimed at an investigation of how a Game Based Learning methodology could be utilised to support an enhanced learning environment in respect of delivering accessible knowledge regarding the site and its heritage. The objective of this project is to present the history of A Famosa in a more effective and exciting way. The 3D model of the fortress will be integrated into online mapping and a game storyline will be developed. Multimedia elements such as audio, video and text will be embedded in the game-play. We will propose how this application is structured, its game play, features and future recommendations. In addition, illustrations and a proposal as to how this application could be implemented on site are also presented.


title: Performance Comparison between Naïve Bayes , Decision Tree and k-Nearest Neighbor in Searching Alternative Design in an Energy Simulation Tool

abstract: Energy simulation tool is a tool to simulate energy use by a building prior to the erection of the building. Commonly it has a feature providing alternative designs that are better than the user's design. In this paper, we propose a novel method in searching alternative design that is by using classification method. The classifiers we use are Naive Bayes, Decision Tree, and k-Nearest Neighbor. Our experiments hows that Decision Tree has the fastest classification time followed by Naive Bayes and k-Nearest Neighbor. The differences between classification time of Decision Tree and Naive Bayes also between Naive Bayes and k-NN are about an order of magnitude. Based on Percision, Recall, F- measure, Accuracy, and AUC, the performance of Naive Bayes is the best. It outperforms Decision Tree and k-Nearest Neighbor on all parameters but precision. Energy simulation tool is a tool to simulate energy use by a building prior to the erection of the building. The output of such simulation is a value in kWh/m 2 called energy performance. The calculation of the building energy performance must be carried out by developers as part of requirements to get permit to build the building. The building can only be built if the energy performance is below the allowable standard. In order to get building energy performance below the standard, architects must revise the design several times. And in order to ease the design work of the architects, an energy simulation tool must have a feature that suggests a better alternative design. Since the alternative design search is actually a classification problem, hence in this paper we propose a novel method to search alternative design by using classification method. The classification methods used in here are Decision Tree, Naive Bayes, and k-Nearest Neighbor. We will then compare the performance of these three methods in searching alternative design in an energy simulation tools.


title: A novel high-voltage 5.5 V resilient, floating and full-scale 3.3 V pulse-triggered level-shifter

abstract: This paper presents a novel 5.5 V resilient Pulse-Triggered-Level-Shifter (PTLS) with enhanced toggling speed. This PTLS is a based on a cascoded High-Voltage (HV) level-shifters and only uses 3.3 V, near minimum size, CMOS transistors. This HV level-shifter generates both floating and a full-scale output signals and is able to operate under supplies from 2.6 V (down to 1.8 V with strategic contextual gate control on cascode and protection transistors) up to 5.5 V, without endangering any transistors. This PTLS can drive pMOS logic, nMOS logic or 5.5 V I/Os simultaneously with loads up to 250 fF on every output nodes (1.25 pF total), using only one buffering stage. The proposed circuit was extensively validated by simulations for every technological corners, temperatures from −40 °C up to 125 °C, operating voltages and output capacitances, so that this HV level-shifter is reliable under any operating conditions. The simulated circuit shows a 2 39 ns maximum delay under typical conditions and a worst case of 10.4 ns. The circuit draws no static power and shows a typical dynamic power consumption of 25.92 μW/MHz with maximum operating frequency of 261.8 MHz.


title: An Improved Fuzzy c-Means Clustering Algorithm Based on Shadowed Sets and PSO

abstract: To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c-means algorithm (SP-FCM) based on particle swarm optimization (PSO) and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries. This new method uses Xie-Beni index as cluster validity and automatically finds the optimal cluster number within a specific range with cluster partitions that provide compact and well-separated clusters. Experiments show that the proposed approach significantly improves the clustering effect.


title: Towards Real World Human Parsing: Multiple-Human Parsing in the Wild

abstract: The recent progress of human parsing techniques has been largely driven by the availability of rich data resources. In this work, we demonstrate some critical discrepancies between the current benchmark datasets and the real world human parsing scenarios. For instance, all the human parsing datasets only contain one person per image, while usually multiple persons appear simultaneously in a realistic scene. It is more practically demanded to simultaneously parse multiple persons, which presents a greater challenge to modern human parsing methods. Unfortunately, absence of relevant data resources severely impedes the development of multiple-human parsing methods. ::: To facilitate future human parsing research, we introduce the Multiple-Human Parsing (MHP) dataset, which contains multiple persons in a real world scene per single image. The MHP dataset contains various numbers of persons (from 2 to 16) per image with 18 semantic classes for each parsing annotation. Persons appearing in the MHP images present sufficient variations in pose, occlusion and interaction. To tackle the multiple-human parsing problem, we also propose a novel Multiple-Human Parser (MH-Parser), which considers both the global context and local cues for each person in the parsing process. The model is demonstrated to outperform the naive "detect-and-parse" approach by a large margin, which will serve as a solid baseline and help drive the future research in real world human parsing.


title: Expressing Emotions through Color, Sound, and Vibration with an Appearance-Constrained Social Robot

abstract: Many researchers are now dedicating their efforts to studying interactive modalities such as facial expressions, natural language, and gestures. This phenomenon makes communication between robots and individuals become more natural. However, many robots currently in use are appearance constrained and not able to perform facial expressions and gestures. In addition, although humanoid-oriented techniques are promising, they are time and cost consuming, which leads to many technical difficulties in most research studies. To increase interactive efficiency and decrease costs, we alternatively focus on three interaction modalities and their combinations, namely color, sound, and vibration. We conduct a structured study to evaluate the effects of the three modalities on a human's emotional perception towards our simple-shaped robot "Maru." Our findings offer insights into human-robot affective interactions, which can be particularly useful for appearance-constrained social robots. The contribution of this work is not so much the explicit parameter settings but rather deepening the understanding of how to express emotions through the simple modalities of color, sound, and vibration while providing a set of recommended expressions that HRI researchers and practitioners could readily employ.


title: Rendering of Eyes for Eye-Shape Registration and Gaze Estimation

abstract: Images of the eye are key in several computer vision problems, such as shape registration and gaze estimation. Recent large-scale supervised methods for these problems require time-consuming data collection and manual annotation, which can be unreliable. We propose synthesizing perfectly labelled photo-realistic training data in a fraction of the time. We used computer graphics techniques to build a collection of dynamic eye-region models from head scan geometry. These were randomly posed to synthesize close-up eye images for a wide range of head poses, gaze directions, and illumination conditions. We used our model's controllability to verify the importance of realistic illumination and shape variations in eye-region training data. Finally, we demonstrate the benefits of our synthesized training data (SynthesEyes) by out-performing state-of-the-art methods for eye-shape registration as well as cross-dataset appearance-based gaze estimation in the wild.


title: Swarm robotics reviewed

abstract: SUMMARY We present a review of recent activities in swarm robotic research, and analyse existing literature in the field to determine how to get closer to a practical swarm robotic system for real world applications. We begin with a discussion of the importance of swarm robotics by illustrating the wide applicability of robot swarms in various tasks. Then a brief overview of various robotic devices that can be incorporated into swarm robotic systems is presented. We identify and describe the challenges that should be resolved when designing swarm robotic systems for real world applications. Finally, we provide a summary of a series of issues that should be addressed to overcome these challenges, and propose directions for future swarm robotic research based on our extensive analysis of the reviewed literature.


title: Automatic Segmentation of Pulmonary Segments From Volumetric Chest CT Scans

abstract: Automated extraction of pulmonary anatomy provides a foundation for computerized analysis of computed tomography (CT) scans of the chest. A completely automatic method is presented to segment the lungs, lobes and pulmonary segments from volumetric CT chest scans. The method starts with lung segmentation based on region growing and standard image processing techniques. Next, the pulmonary fissures are extracted by a supervised filter. Subsequently the lung lobes are obtained by voxel classification where the position of voxels in the lung and relative to the fissures are used as features. Finally, each lobe is subdivided in its pulmonary segments by applying another voxel classification that employs features based on the detected fissures and the relative position of voxels in the lobe. The method was evaluated on 100 low-dose CT scans obtained from a lung cancer screening trial and compared to estimates of both interobserver and intraobserver agreement. The method was able to segment the pulmonary segments with high accuracy (77%), comparable to both interobserver and intraobserver accuracy (74% and 80%, respectively).


title: Abstractive Multi-document Summarization with Semantic Information Extraction

abstract: This paper proposes a novel approach to generate abstractive summary for multiple documents by extracting semantic information from texts. The concept of Basic Semantic Unit (BSU) is defined to describe the semantics of an event or action. A semantic link network on BSUs is constructed to capture the semantic information of texts. Summary structure is planned with sentences generated based on the semantic link network. Experiments demonstrate that the approach is effective in generating informative, coherent and compact summary.


Second Language Speech Learning Theory , Findings , and Problems


title: Understanding how image quality affects deep neural networks

abstract: Image quality is an important practical challenge that is often overlooked in the design of machine vision systems. Commonly, machine vision systems are trained and tested on high quality image datasets, yet in practical applications the input images can not be assumed to be of high quality. Recently, deep neural networks have obtained state-of-the-art performance on many machine vision tasks. In this paper we provide an evaluation of 4 state-of-the-art deep neural network models for image classification under quality distortions. We consider five types of quality distortions: blur, noise, contrast, JPEG, and JPEG2000 compression. We show that the existing networks are susceptible to these quality distortions, particularly to blur and noise. These results enable future work in developing deep neural networks that are more invariant to quality distortions.


title: Non-surgical Management of Congenital Auricular Deformities

abstract: BACKGROUND ::: Unlike congenital auricular malformations which are identified by underdevelopment of dermal and cartilaginous tissues, deformed ears are less sever congenital anomalies characterized only by a misshaped pinna structure and can be improved with acceptable cosmetic results and minimal cost through ear molding if treated in early neonatal period. In this study, authors present the first report of using splinting techniques for treatment of deformational auricular anomalies in Iranian children. ::: ::: ::: METHODS ::: Our case load consisted of a series of 29 patients (Male=16, Female=13) who were referred to Plastic Surgery Unit of Shiraz University of Medical Sciences from September 2011 to December 2014. Children aged more than 6 moths were excluded. Twenty-nine children affected by various deformities including prominent ears (n=11), lop ears (n=8) and constricted ears (n=10) were treated by splintage as a nonsurgical technique. The mean time of treatment was 13.33±2 weeks. ::: ::: ::: RESULTS ::: Eight (27.6%) patients did not complete the treatment. Splinting resulted in excellent or satisfactory results in 12 (57.14%) of treated cases. No improvement was observed at the end of the molding treatment in 9 patients. No complication was observed during the treatment in any of the patients. ::: ::: ::: CONCLUSION ::: The nonsurgical molding can be used as an effective approach for achieving natural outcomes and correcting cosmetic abnormalities. Rate of satisfaction is dependent on type of deformity, the neonatal age in which treatment started and also parents' adherence to treatment methods and principals. Concerning the low rate of complications and high satisfactory results the method can be used instead of surgery in appropriate cases.


title: A design framework for all-digital mmWave massive MIMO with per-antenna nonlinearities

abstract: Millimeter wave MIMO combines the benefits of compact antenna arrays with a large number of elements and massive bandwidths, so that fully digital beamforming has the potential of supporting a large number of simultaneous users with {\it per user} data rates of multiple gigabits/sec (Gbps). In this paper, we develop an analytical model for the impact of nonlinearities in such a system, and illustrate its utility in providing hardware design guidelines regarding two key challenges: the low available precision of analog-to-digital conversion at high sampling rates, and nonlinearities in ultra-high speed radio frequency (RF) and baseband circuits. We consider linear minimum mean square error (LMMSE) reception for a multiuser MIMO uplink, and provide performance guarantees based on two key concepts: (a) summarization of the impact of per-antenna nonlinearities via a quantity that we term the "intrinsic SNR", (b) using linear MMSE performance in an ideal system without nonlinearities to bound that in our non-ideal system. For our numerical results, we employ nominal parameters corresponding to outdoor picocells operating at a carrier frequency of 140 GHz, with a data rate of 10 Gbps per user.


title: Simple, fast, and practical non-blocking and blocking concurrent queue algorithms

abstract: Drawing ideas from previous authors, we present a new non-blocking concurrent queue algorithm and a new two-lock queue algorithm in which one enqueue and one dequeue can proceed concurrently. Both algorithms are simple, fast, and practical; we were surprised not to find them in the literature. Experiments on a 12-node SGI Challenge multiprocessor indicate that the new non-blocking queue consistently outperforms the best known alternatives; it is the clear algorithm of choice for machines that provide a universal atomic primitive (e.g., compare_and_swap or load_linked/store_conditional). The two-lock concurrent queue outperforms a single lock when several processes are competing simultaneously for access; it appears to be the algorithm of choice for busy queues on machines with non-universal atomic primitives (e.g., test_and_set). Since much of the motivation for non-blocking algorithms is rooted in their immunity to large, unpredictable delays in process execution, we report experimental results both for systems with dedicated processors and for systems with several processes multiprogrammed on each processor.


The science of food security


title: An On-Chip Temperature Sensor With a Self-Discharging Diode in 32-nm SOI CMOS

abstract: We report a 39 μm × 27 μm on-chip temperature sensor which uses the temperature-dependent reverse-bias leakage current of a lateral silicon on insulator (SOI) CMOS p-n diode to monitor the thermal profile of a 32-nm microprocessor core. In this sensor, the diode junction capacitance is first charged to a fixed voltage. Subsequently, the diode capacitance is allowed to self-discharge through its temperature-dependent reverse-bias current. Next, by using a time-to-digital-converter circuit, the discharge voltage is converted to a temperature-dependent time pulse, and finally, its width is measured by using a digital counter. This compact temperature sensor demonstrates a 3σ measurement inaccuracy of ±1.95°C across the 5°C-100°C temperature range while consuming only 100 μW from a single 1.65-V supply.


title: A 500-W high efficiency LDMOS classical three-way Doherty amplifier for base-station applications

abstract: A 500W classical three-way Doherty power amplifier (DPA) with LDMOS devices at 1.8GHz is presented. Optimized device ratio is selected to achieve maximum efficiency as well as linearity. With a simple passive input driving network implementation, the demonstrator exhibits more than 55% efficiency with 9.9PAR WCDMA signal from 1805MHz–1880MHz. It can be linearized at −60dBc level with 20MHz LTE signal at an average output power of 49dBm.


title: Leveraging Meta-path based Context for Top- N Recommendation with A Neural Co-Attention Model

abstract: Heterogeneous information network (HIN) has been widely adopted in recommender systems due to its excellence in modeling complex context information. Although existing HIN based recommendation methods have achieved performance improvement to some extent, they have two major shortcomings. First, these models seldom learn an explicit representation for path or meta-path in the recommendation task. Second, they do not consider the mutual effect between the meta-path and the involved user-item pair in an interaction. To address these issues, we develop a novel deep neural network with the co-attention mechanism for leveraging rich meta-path based context for top-N recommendation. We elaborately design a three-way neural interaction model by explicitly incorporating meta-path based context. To construct the meta-path based context, we propose to use a priority based sampling technique to select high-quality path instances. Our model is able to learn effective representations for users, items and meta-path based context for implementing a powerful interaction function. The co-attention mechanism improves the representations for meta-path based con- text, users and items in a mutual enhancement way. Extensive experiments on three real-world datasets have demonstrated the effectiveness of the proposed model. In particular, the proposed model performs well in the cold-start scenario and has potentially good interpretability for the recommendation results.


title: How to Create Public Value Through Open Data Driven Co-Creation: A Survey of the Literature

abstract: The drive for openness in government, with open data as a key component, has seen governments around the world devote a large amount of resources to publishing government collected and held data. Scarce resources are being devoted to this goal with the primary goals designated as economic growth and increased innovation. A somewhat overlooked aim is the creation of public value which can be deployed as an openness and trust enhancing apparatus. This preliminary work addresses this gap by aiming to develop the core knowledge of how public value can be co-created with open data. Through an extensive survey of the relevant literature, this research seeks to build an initial framework of public value as applied to co-created open data. Grounded in the structured literature review technique, the authors surveyed the pertinent literature to identify the primary factors that enable value co-creation from the citizens point of view. Furthermore, this research proposes an adapted collection of public values as they relate to open data.


title: Genre classification and the invariance of MFCC features to key and tempo

abstract: Musical genre classification is a promising yet difficult task in the field of musical information retrieval. As a widely used feature in genre classification systems, MFCC is typically believed to encode timbral information, since it represents short-duration musical textures. In this paper, we investigate the invariance of MFCC to musical key and tempo, and show that MFCCs in fact encode both timbral and key information. We also show that musical genres, which should be independent of key, are in fact influenced by the fundamental keys of the instruments involved. As a result, genre classifiers based on the MFCC features will be influenced by the dominant keys of the genre, resulting in poor performance on songs in less common keys.We propose an approach to address this problem, which consists of augmenting classifier training and prediction with various key and tempo transformations of the songs. The resulting genre classifier is invariant to key, and thus more timbreoriented, resulting in improved classification accuracy in our experiments.


title: Scalable Fog Computing with Service Offloading in Bus Networks

abstract: With the rapid increase of mobile devices, the computing load of roadside cloudlets is fast growing. When the computation tasks of the roadside cloudlet reach the limit, the overload may generate heat radiation problem and unacceptable delay to mobile users. In this paper, we leverage the characteristics of buses and propose a scalable fog computing paradigm with servicing offloading in bus networks. The bus fog servers not only provide fog computing services for the mobile users on bus, but also are motivated to accomplish the computation tasks offloaded by roadside cloudlets. By this way, the computing capability of roadside cloudlets is significantly extended. We consider an allocation strategy using genetic algorithm (GA). With this strategy, the roadside cloudlets spend the least cost to offload their computation tasks. Meanwhile, the user experience of mobile users are maintained. The simulations validate the advantage of the propose scheme.


title: A shorted waveguide-stub coupling mechanism for narrow-band multimode coupled resonator filters

abstract: For original article by W. Steyn and P. Meyer see ibid., vol.52, no.6, p.1622-5, June 2004. For comments by F. Arndt see ibid., vol.53, no.1, p.414-15 January 2005.


title: Ebook Readers in Higher Education

abstract: Ebook readers have received a mixed press, with some hailing them as the future of reading and others believing that they will never be popular. The study outlined here aims to understand the attitudes of, and issues of importance to, lecturers in UK academia, with a view to improving the design of ebook readers for education in the future. An evaluation of five portable devices is presented, in which lecturers were given the opportunity to read an ebook and provide feedback via a questionnaire. Results are compared with concerns arising from other experiments in the same field, and recommendations are made for successful ebook design.


MODEL-DRIVEN AND DATA-DRIVEN APPROACHES USING LIDAR DATA : ANALYSIS AND COMPARISON


title: Howard: A Dynamic Excavator for Reverse Engineering Data Structures

abstract: Even the most advanced reverse engineering techniques and products are weak in recovering data structures in stripped binaries—binaries without symbol tables. Unfortunately, forensics and reverse engineering without data structures is exceedingly hard. We present a new solution, known as Howard, to extract data structures from C binaries without any need for symbol tables. Our results are significantly more accurate than those of previous methods — sufficiently so to allow us to generate our own (partial) symbol tables without access to source code. Thus, debugging such binaries becomes feasible and reverse engineering becomes simpler. Also, we show that we can protect existing binaries from popular memory corruption attacks, without access to source code. Unlike most existing tools, our system uses dynamic analysis (on a QEMU-based emulator) and detects data structures by tracking how a program uses memory.


title: An Evolutionary Algorithm that Constructs Recurrent Neural Networks

abstract: Standard methods for simultaneously inducing the structure and weights of recurrent neural networks limit every task to an assumed class of architectures. Such a simplification is necessary since the interactions between network structure and function are not well understood. Evolutionary computations, which include genetic algorithms and evolutionary programming, are population-based search methods that have shown promise in many similarly complex tasks. This paper argues that genetic algorithms are inappropriate for network acquisition and describes an evolutionary program, called GNARL, that simultaneously acquires both the structure and weights for recurrent networks. GNARL's empirical acquisition method allows for the emergence of complex behaviors and topologies that are potentially excluded by the artificial architectural constraints imposed in standard network induction methods.


title: A new virtual reality environment used for e-Learning

abstract: Virtual reality has been successful used in real estate, urban planning and video games etc. In this paper, we proposed to build an interactive virtual e-Learning environment using virtual reality technologies. With the help of Multi-Modal user interface, students can control their avatars to interactively communicate with virtual teachers and environment. This interactive e-Learning environment can increase the autonomy of students and enhance students' interest in learning.


title: ATGV-Net: Accurate Depth Super-Resolution

abstract: In this work we present a novel approach for single depth map super-resolution. Modern consumer depth sensors, especially Time-of-Flight sensors, produce dense depth measurements, but are affected by noise and have a low lateral resolution. We propose a method that combines the benefits of recent advances in machine learning based single image super-resolution, i.e. deep convolutional networks, with a variational method to recover accurate high-resolution depth maps. In particular, we integrate a variational method that models the piecewise affine structures apparent in depth data via an anisotropic total generalized variation regularization term on top of a deep network. We call our method ATGV-Net and train it end-to-end by unrolling the optimization procedure of the variational method. To train deep networks, a large corpus of training data with accurate ground-truth is required. We demonstrate that it is feasible to train our method solely on synthetic data that we generate in large quantities for this task. Our evaluations show that we achieve state-of-the-art results on three different benchmarks, as well as on a challenging Time-of-Flight dataset, all without utilizing an additional intensity image as guidance.


title: A chaos embedded GSA-SVM hybrid system for classification

abstract: Parameter optimization and feature selection influence the classification accuracy of support vector machine (SVM) significantly. In order to improve classification accuracy of SVM, this paper hybridizes chaotic search and gravitational search algorithm (GSA) with SVM and presents a new chaos embedded GSA-SVM (CGSA-SVM) hybrid system. In this system, input feature subsets and the SVM parameters are optimized simultaneously, while GSA is used to optimize the parameters of SVM and chaotic search is embedded in the searching iterations of GSA to optimize the feature subsets. Fourteen UCI datasets are employed to calculate the classification accuracy rate in order to evaluate the developed CGSA-SVM approach. The developed approach is compared with grid search and some other hybrid systems such as GA-SVM, PSO-SVM and GSA-SVM. The results show that the proposed approach achieves high classification accuracy and efficiency compared with well-known similar classifier systems.


UW-Stanford System Description for AESW 2016 Shared Task on Grammatical Error Detection


Multimodal Topic Labelling


title: Critical success factors revisited: success and failure cases of information systems for senior executives

abstract: Abstract The literature suggests the existence of critical success factors (CSFs) for the development of information systems that support senior executives. Our study of six organizations gives evidence for this notion of CSFs. The study further shows an interesting pattern, namely that companies either “get it right”, and essentially succeed on all CSFs, or “get it completely wrong”, that is, fall short on each of the CSFs. Among the six cases for which data were collected through in-depth interviews with company executives, three organizations seemed to manage all the CSFs properly, while two others managed all CSFs poorly. Only one organization showed a mixed scorecard, managing some factors well and some not so well. At the completion of the study, this organization could neither be judged as a success, nor as a failure. This dichotomy between success and failure cases suggests the existence of an even smaller set of “meta-success” factors. Based on our findings, we speculate that these “meta-success” factors are “championship”, “availability of resources”, and “link to organization objectives”.


Demonstration of the Quantum No-Hiding Theorem in a Category Theoretic Framework: An IBM Quantum Experience


title: Ontologies for Describing the Context of Scientific Experiment Processes

abstract: The re-usability and repeatability of e-Science experiments is widely understood as a requirement of validating and reusing previous work in data-intensive domains. Experiments are, however, often complex chains of processing, involving a number of data sources, computing infrastructure, software tools, or external and third-party services, rendering repeatability a challenging task. Another important aspect of many experiments is in the social and organisational dimension -- very often, knowledge on how experiments are performed is tacit and remains with the researcher, and the collaborative and distributed aspects especially of larger collaborative experiments adds to this challenge. Therefore, a number of approaches have tackled this issue from various angles -- initiatives for data sharing, code versioning and publishing as open source, the use of workflow engines to formalise the steps taken in an experiment, to ways to describe the complex environment an experiment is executed in, e.g. via Research Objects. In this paper, we present a model that has a specific focus on the technical infrastructure that is the basis of the research experiment. We demonstrate how this model can be applied to describe e-Science experiments, and align and compare it to Research Objects.


title: Reference configurations for ear-EEG steady-state responses

abstract: Ear-EEG is a non-invasive EEG recording method, where EEG is recorded from electrodes placed in the ear. Ear-EEG could be implemented into hearing aids, and provide neurofeedback for e.g. objective hearing assessment through measurements of the auditory steady-state response. In cases where the objective is to measure a specific feature of an event-related potential, there will be a subject specific optimal reference configuration. This work presents a method for optimizing the reference configuration for steady-state type potentials. For given electrode positions, the method maximizes the signal-to-noise (SNR) ratio of the first harmonic of the steady-state response. This is obtained by estimating a set of weights applied to the electrode signals. The method was validated on a dataset recorded from 12 subjects. The weights were estimated from one part of the dataset, and the validation was performed on another part of the dataset. For all subjects the proposed method demonstrated a robust SNR estimate, yielding on par or better SNR compared to other well-known methods.


title: Trust On the Security of Wireless Vehicular Ad-hoc Networking

abstract: Vehicular Ad-hoc Network (VANET) security is one of the central issues in vehicular communications since each vehicle has to rely on messages sent out by the peers where the received message could be malicious. In order to protect VANETs from malicious actions, each vehicle must be able to evaluate, decide and react locally on the information received from other vehicles. In this paper, we analyze probabilistic and deterministic approaches (individually and combined) to estimate trust for VANET security. The probabilistic approach determines the trust level of the peer vehicles based on received information. The trust level is used to determine legitimacy of the message, which is used to decide whether the message would be considered for further transmission over the VANET or dropped. The deterministic approach measures the trust level of the received message by using distances calculated using received signal strength (RSS) and the vehicle’s geolocation (position coordinate). Combination of probabilistic and deterministic approach gives better results compared to individual approaches. The proposed algorithms are illustrated with numerical results obtained from simulations.


Plants used against cancer : a survey


title: Analyzing the Windkessel Model as a Potential Candidate for Correcting Oscillometric Blood-Pressure Measurements

abstract: Developing a good model for oscillometric blood-pressure measurements is a hard task. This is mainly due to the fact that the systolic and diastolic pressures cannot be directly measured by noninvasive automatic oscillometric blood-pressure meters (NIBP) but need to be computed based on some kind of algorithm. This is in strong contrast with the classical Korotkoff method, where the diastolic and systolic blood pressures can be directly measured by a sphygmomanometer. Although an NIBP returns results similar to the Korotkoff method for patients with normal blood pressures, a big discrepancy exist between both methods for severe hyper- and hypotension. For these severe cases, a statistical model is needed to compensate or calibrate the oscillometric blood-pressure meters. Although different statistical models have been already studied, no immediate calibration method has been proposed. The reason is that the step from a model, describing the measurements, to a calibration, correcting the blood-pressure meters, is a rather large leap. In this paper, we study a “databased” Fourier series approach to model the oscillometric waveform and use the Windkessel model for the blood flow to correct the oscillometric blood-pressure meters. The method is validated on a measurement campaign consisting of healthy patients and patients suffering from either hyper- or hypotension.


3D ShapeNets for 2.5D Object Recognition and Next-Best-View Prediction.


title: Technology as an Assessment Tool in Language Learning

abstract: The present paper examines the employment of different technology-based tools such as computer, podcast, and chat for assessing language proficiency in EFL classrooms. These methods which are referred to as alternative assessments differ from traditional paper-and–pencil assessments. The technology-based assessment incorporates innovative methods and techniques to measure language proficiency improvement. This kind of assessment is effective when it gives the language learners a chance to use what they have learnt. On the contrary, traditional methods of assessment fail to show what learners can do with their acquired language. This paper aims at illustrating several activities that teachers can use with the help of technology to measure and monitor their students’ achievements in language learning.


title: Clean fossil-fuelled power generation

abstract: Using fossil fuels is likely to remain the dominant means of producing electricity in 2030 and even 2050, partly because power stations have long lives. There are two main ways of reducing CO2 emissions from fossil-fuelled power plants. These are carbon capture and storage (CCS), which can produce near-zero CO2 emissions, and increases in plant efficiency, which can give rise to significant reductions in CO2 emissions and to reduced costs. If a typical UK coal-fired plant was replaced by today's best available technology, it would lead to reductions of around 25% in emissions of CO2 per MWÂ h of electricity produced. Future technologies are targeting even larger reductions in emissions, as well as providing a route, with CCS, to zero emissions. These two routes are linked and they are both essential activities on the pathway to zero emissions. This paper focuses on the second route and also covers an additional third route for reducing emissions, the use of biomass. It discusses the current status of the science and technologies for fossil-fuelled power generation and outlines likely future technologies, development targets and timescales. This is followed by a description of the scientific and technological developments that are needed to meet these challenges. Once built, a power plant can last for over 40 years, so the ability to upgrade and retrofit a plant during its lifetime is important.


title: A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features

abstract: This paper presents a new supervised method for blood vessel detection in digital retinal images. This method uses a neural network (NN) scheme for pixel classification and computes a 7-D vector composed of gray-level and moment invariants-based features for pixel representation. The method was evaluated on the publicly available DRIVE and STARE databases, widely used for this purpose, since they contain retinal images where the vascular structure has been precisely marked by experts. Method performance on both sets of test images is better than other existing solutions in literature. The method proves especially accurate for vessel detection in STARE images. Its application to this database (even when the NN was trained on the DRIVE database) outperforms all analyzed segmentation approaches. Its effectiveness and robustness with different image conditions, together with its simplicity and fast implementation, make this blood vessel segmentation proposal suitable for retinal image computer analyses such as automated screening for early diabetic retinopathy detection.


title: Establishing and maintaining long-term human-computer relationships

abstract: This research investigates the meaning of “human-computer relationship” and presents techniques for constructing, maintaining, and evaluating such relationships, based on research in social psychology, sociolinguistics, communication and other social sciences. Contexts in which relationships are particularly important are described, together with specific benefits (like trust) and task outcomes (like improved learning) known to be associated with relationship quality. We especially consider the problem of designing for long-term interaction, and define relational agents as computational artifacts designed to establish and maintain long-term social-emotional relationships with their users. We construct the first such agent, and evaluate it in a controlled experiment with 101 users who were asked to interact daily with an exercise adoption system for a month. Compared to an equivalent task-oriented agent without any deliberate social-emotional or relationship-building skills, the relational agent was respected more, liked more, and trusted more, even after four weeks of interaction. Additionally, users expressed a significantly greater desire to continue working with the relational agent after the termination of the study. We conclude by discussing future directions for this research together with ethical and other ramifications of this work for HCI designers.


RoboCop : Crime Classification and Prediction in San Francisco


title: An Accurate and Robust Strip-Edge-Based Structured Light Means for Shiny Surface Micromeasurement in 3-D

abstract: Three-dimensional measurement of shiny or reflective surface is a challenging issue for optical-based instrumentations. In this paper, we present a novel structured light approach for direct measurement of shiny target so as to skip the coating preprocedure. In comparison with traditional image-intensity-based structured light coding strategies like sinusoidal and line patterns, strip edges not raw image intensities are encoded in the illuminated patterns. With strip edges generally better preserved than individual image intensity in the image data in the presence of surface reflections, such a coding strategy is more robust. To remove the periodic ambiguity within strip patterns, traditional Gray code patterns are adopted. To localize the strip edges more precisely, both positive and negative strip patterns are used. An improved zero-crossing feature detector that has subpixel accuracy is proposed for strip-edge localization. The experimental setup is configured with merely an off-the-shelf pico-projector and a camera. Extensive experiments including accuracy evaluation, comparison with previous structured light algorithms, and the reconstruction of some real shiny objects are shown to demonstrate the system's accuracy and endurance against reflective nature of surfaces.


title: XBOS: An Extensible Building Operating System

abstract: We present XBOS, an eXtensible Building Operating System for integrated management of previously isolated building subsystems. The key contribution of XBOS is the development of the Building Profile, a canonical, executable description of a building and its subsystems that changes with a building and evolves the control processes and applications accordingly. We discuss the design and implementation of XBOS in the context of 6 dimensions of an effective BAS – hardware presentation layer, canonical metadata, control process management, building evolution management, security, scalable UX and API – and evaluate against several recent building and sensor management systems. Lastly, we examine the evolution of a real, 10 month deployment of XBOS in a 7000 sq ft office building.


Software quality and the Capability Maturity Model


title: A Development and Execution Environment for Early Warning Systems for Natural Disasters

abstract: Early Warning Systems (EWS) may become a powerful tool for mitigating the negative impact of natural disasters, especially when combined with advanced IT solutions - such as on-demand scenario simulations, semi-automatic impact assessment, or real-time analysis of measurements from in-situ sensors. However, such complex systems require a proper computing environment supporting their development and operation. We propose the Common Information Space (CIS), a software framework facilitating design, deployment and execution of early warning systems based on real-time monitoring of natural phenomena and computationally intensive, time-critical computations. CIS provides a service-oriented technology stack which helps design 'blueprints' for early warning application scenarios and deploy these blueprints as services - system factories enabling users to rapidly deploy new EWSs in new settings. CIS also provides advanced runtime services and resource orchestration capabilities in order to address the specific requirements of EWSs: continuous operation, highly variable resource demands and mission-critical computations. The CIS concept is validated through the Flood Early Warning System whose goal is to monitor embankments in urban areas and assist in rapid decision making whenever a dike failure results in a flooding threat.


title: An enhanced beamforming algorithm for three dimensional MIMO in LTE-advanced networks

abstract: Multiple input multiple output (MIMO) system with large number of antennas, referred to as massive MIMO, has drawn increasing attention for it enables very high throughput in LTE-Advanced networks. Massive MIMO arrays are expected to be implemented in a three dimensional (3D) array structure and 3D MIMO is introduced. Antenna propagation was considered only in horizontal domain during the past few years. In this paper, vertical domain of antenna is taken into account and the modeling of 3D channel is investigated. Furthermore, an enhanced dynamic beamforming algorithm is proposed where beamforming vector can be obtained according to both users' horizontal and vertical directions. Enhanced performance can be obtained by the proposed algorithm compared with conventional 2D beamforming. In addition, the impact of sector radius on spectral efficiency as well as cell coverage is analyzed and the optimal downtilts for different senarios are explored.


title: The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013

abstract: ChEBI (http://www.ebi.ac.uk/chebi) is a database and ontology of chemical entities of biological interest. Over the past few years, ChEBI has continued to grow steadily in content, and has added several new features. In addition to incorporating all user-requested compounds, our annotation efforts have emphasized immunology, natural products and metabolites in many species. All database entries are now ‘is_a’ classified within the ontology, meaning that all of the chemicals are available to semantic reasoning tools that harness the classification hierarchy. We have completely aligned the ontology with the Open Biomedical Ontologies (OBO) Foundry-recommended upper level Basic Formal Ontology. Furthermore, we have aligned our chemical classification with the classification of chemical-involving processes in the Gene Ontology (GO), and as a result of this effort, the majority of chemical-involving processes in GO are now defined in terms of the ChEBI entities that participate in them. This effort necessitated incorporating many additional biologically relevant compounds. We have incorporated additional data types including reference citations, and the species and component for metabolites. Finally, our website and web services have had several enhancements, most notably the provision of a dynamic new interactive graph-based ontology visualization.


Towards Microblogging Success Factors : An Empirical Survey on Twitter Usage of Austrian Universities


title: Leveraging Support Vector Machine for Opcode Density Based Detection of Crypto-Ransomware

abstract: Ransomware is a significant global threat, with easy deployment due to the prevalent ransomware-as-a-service model. Machine learning algorithms incorporating the use of opcode characteristics and Support Vector Machine have been demonstrated to be a successful method for general malware detection. This research focuses on crypto-ransomware and uses static analysis of malicious and benign Portable Executable files to extract 443 opcodes across all samples, representing them as density histograms within the dataset. Using the SMO classifier and PUK kernel in the WEKA machine learning toolset it demonstrates that this methodology can achieve 100% precision when differentiating between ransomware and goodware, and 96.5% when differentiating between 5 cryptoransomware families and goodware. Moreover, 8 different attribute selection methods are evaluated to achieve significant feature reduction. Using the CorrelationAttributeEval method close to 100% precision can be maintained with a feature reduction of 59.5%. The CFSSubset filter achieves the highest feature reduction of 97.7% however with a slightly lower precision at 94.2%.


title: 3D-CODED : 3D Correspondences by Deep Deformation

abstract: We present a new deep learning approach for matching deformable shapes by introducing {\it Shape Deformation Networks} which jointly encode 3D shapes and correspondences. This is achieved by factoring the surface representation into (i) a template, that parameterizes the surface, and (ii) a learnt global feature vector that parameterizes the transformation of the template into the input surface. By predicting this feature for a new shape, we implicitly predict correspondences between this shape and the template. We show that these correspondences can be improved by an additional step which improves the shape feature by minimizing the Chamfer distance between the input and transformed template. We demonstrate that our simple approach improves on state-of-the-art results on the difficult FAUST-inter challenge, with an average correspondence error of 2.88cm. We show, on the TOSCA dataset, that our method is robust to many types of perturbations, and generalizes to non-human shapes. This robustness allows it to perform well on real unclean, meshes from the the SCAPE dataset.


title: ETCF: An Ensemble Model for CTR Prediction

abstract: Online advertising has attracted lots of attention in both academic and industrial domains. Among many realworld advertising systems, click through rate (CTR) prediction plays a central role. With a large volume of user history log and given its various features, it is quite a challenge to fully extract the meaningful information inside that amount of data. What's more, for many machine learning models, in order to achieve the best performance of the CTR prediction, a lot of hyper-parameters need to be tuned, which often costs plenty of time. In this paper, we propose an ensemble model named ETCF, which cascades GBDT with gcForest to tackle the practical problems of CTR prediction and do not need much hyper-parameter tuning work to realize its best performance. Experimental results validate the effective prediction power of ETCF against classical baseline models, and the superiority of GBDT transformed features.


title: DYAN: A Dynamical Atoms-Based Network for Video Prediction

abstract: The ability to anticipate the future is essential when making real time critical decisions, provides valuable information to understand dynamic natural scenes, and can help unsupervised video representation learning. State-of-art video prediction is based on LSTM recursive networks and/or generative adversarial network learning. These are complex architectures that need to learn large numbers of parameters, are potentially hard to train, slow to run, and may produce blurry predictions. In this paper, we introduce DYAN, a novel network with very few parameters and easy to train, which produces accurate, high quality frame predictions, significantly faster than previous approaches. DYAN owes its good qualities to its encoder and decoder, which are designed following concepts from systems identification theory and exploit the dynamics-based invariants of the data. Extensive experiments using several standard video datasets show that DYAN is superior generating frames and that it generalizes well across domains.


title: Multi-Vehicle Trajectories Generation for Vehicle-to-Vehicle Encounters

abstract: Generating multi-vehicle trajectories analogous to these in real world can provide reliable and versatile testing scenarios for autonomous vehicle. This paper presents an unsupervised learning framework to achieve this. First, we implement variational autoencoder (VAE) to extract interpretable and controllable representatives of vehicle encounter trajectory. Through sampling from the distribution of these representatives, we are able to generate new meaningful driving encounters with a developed Multi-Vehicle Trajectory Generator (MTG). A new metric is also proposed to comprehensively analyze and compare disentangled models. It can reveal the robustness of models and the dependence among latent codes, thus providing guidance for practical application to improve system performance. Experimental results demonstrate that our proposed MTG outperforms infoGAN and vanilla VAE in terms of disentangled ability and traffic awareness. These generations can provide abundant and controllable driving scenarios, thus providing testbeds and algorithm design insights for autonomous vehicle development.


title: Development of small-sized pixel structures for high-resolution CMOS image sensors

abstract: We present our studies on small-sized pixel structures for high-resolution CMOS image sensors. To minimize the number of pixel components, single-transistor pixel and 2T pixel architecture were proposed. To deal with crosstalk between pixels, MOS capacitor deep trench isolation (CDTI) was integrated. CDTI-integrated pixel allows better achievements in dark current and full-well capacity in comparison with the configuration integrating oxide-filled deep trench isolation (DTI). To improve quantum efficiency (QE) and minimize optical crosstalk, back-side illumination (BSI) was developed. Also, vertical photodiode was proposed to maximize its charge-collection region. To take advantages of these structures/technologies, we developed two pixel options (P-type and N-type) combining CDTI or DTI, BSI and vertical photodiode. All the presented pixel structures were designed in 1.4µm-pitch sensor arrays, fabricated and tested.


title: GaN-on-Si Vertical Schottky and p-n Diodes

abstract: This letter demonstrates GaN vertical Schottky and p-n diodes on Si substrates for the first time. With a total GaN drift layer of only 1.5- $\mu{\rm m}$ thick, a breakdown voltage (BV) of 205 V was achieved for GaN-on-Si Schottky diodes, and a soft BV higher than 300 V was achieved for GaN-on-Si p-n diodes with a peak electric field of 2.9 MV/cm in GaN. A trap-assisted space-charge-limited conduction mechanism determined the reverse leakage and breakdown mechanism for GaN-on-Si vertical p-n diodes. The on-resistance was 6 and 10 ${\rm m}\Omega\cdot{\rm cm}^{2}$ for the vertical Schottky and p-n diode, respectively. These results show the promising performance of GaN-on-Si vertical devices for future power applications.


title: Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs

abstract: The multi-channel fully convolutional networks is designed.We segment liver tumors from multiphase contrast-enhanced CT images.We train one network for each phase of CT images and fuse their high-layer features together.This method can make full use of the characteristics of different enhancement phases of CT images.The results showed our model provided greater accuracy and robustness than previous methods. This paper presents a novel, fully automatic approach based on a fully convolutional network (FCN) for segmenting liver tumors from CT images. Specifically, we designed a multi-channel fully convolutional network (MC-FCN) to segment liver tumors from multiphase contrast-enhanced CT images. Because each phase of contrast-enhanced data provides distinct information on pathological features, we trained one network for each phase of the CT images and fused their high-layer features together. The proposed approach was validated on CT images taken from two databases: 3Dircadb and JDRD. In the case of 3Dircadb, using the FCN, the mean ratios of the volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root mean square symmetric surface distance (RMSD) and maximum symmetric surface distance (MSSD) were 15.64.3%, 5.83.5%, 2.00.9%, 2.91.5mm, 7.16.2mm, respectively. For JDRD, using the MC-FCN, the mean ratios of VOE, RVD, ASD, RMSD, and MSSD were 8.14.5%, 1.71.0%, 1.50.7%, 2.01.2mm, 5.26.4mm, respectively. The test results demonstrate that the MC-FCN model provides greater accuracy and robustness than previous methods.


title: The case study approach

abstract: The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.


title: OpenGM: A C++ Library for Discrete Graphical Models

abstract: OpenGM is a C++ template library for defining discrete graphical models and performing inference on these models, using a wide range of state-of-the-art algorithms. No restrictions are imposed on the factor graph to allow for higher-order factors and arbitrary neighborhood structures. Large models with repetitive structure are handled efficiently because (i) functions that occur repeatedly need to be stored only once, and (ii) distinct functions can be implemented differently, using different encodings alongside each other in the same model. Several parametric functions (e.g. metrics), sparse and dense value tables are provided and so is an interface for custom C++ code. Algorithms are separated by design from the representation of graphical models and are easily exchangeable. OpenGM, its algorithms, HDF5 file format and command line tools are modular and extendible.


title: The effects of scapular stabilization based exercise therapy on pain, posture, flexibility and shoulder mobility in patients with shoulder impingement syndrome: a controlled randomized clinical trial

abstract: Background: Dysfunction in the kinetic chain caused by poor scapula stabilization can contribute to shoulder injuries and Shoulder Impingement Syndrome (SIS). The purpose of this study was to compare the effectiveness of two treatment approaches scapular stabilization based exercise therapy and physical therapy in patients with SIS. ::: ::: Methods: The study is a randomized clinical trial in which 68 patients with SIS were randomly assigned in two groups of exercise therapy (ET) and physical therapy (PT) and received 18 sessions of treatment. Pain, shoulders' range of abduction and external rotation, shoulder protraction, scapular rotation and symmetry as well as postural assessment and Pectoralis minor length were evaluated pre and post intervention. The paired-sample t test and the independent sample t test were applied respectively to determine the differences in each group and between two groups. ::: ::: Results: Our findings indicated significant differences in abduction and external rotation range, improvement of forward shoulder translation and increase in the flexibility of the involved shoulder between the two groups (respectively ; p=0.024, p=0.001, p<0/0001, p<0/0001). No significant difference was detected in pain reduction between the groups (p=0.576). Protraction of the shoulder (p<0.0001), forward head posture (p<0/0001) and mid thoracic curvature (p<0.0001) revealed a significant improvement in the ET group. Apparent changes occurred in scapular rotation and symmetry in both groups but no significant differences were observed between the two groups (respectively; p=0.183, p=0.578). ::: ::: Conclusion: The scapular stabilization based exercise intervention was successful in increasing shoulder range, decreasing forward head and shoulder postures and Pectoralis minor flexibility.


title: Rijke. Contextual Bandits for Information Retrieval

abstract: In this paper we give an overview of and outlook on research at the intersection of information retrieval (IR) and contextual bandit problems. A critical problem in information retrieval is online learning to rank, where a search engine strives to improve the quality of the ranked result lists it presents to users on the basis of those users’ interactions with those result lists. Recently, researchers have started to model interactions between users and search engines as contextual bandit problems, and initial methods for learning in this setting have been devised. Our research focuses on two aspects: balancing exploration and exploitation and inferring preferences from implicit user interactions. This paper summarizes our recent work on online learning to rank for information retrieval and points out challenges that are characteristic of this application area.


title: Enhancement of Residual Echo for Robust Acoustic Echo Cancellation

abstract: This paper examines the technique of using a noise-suppressing nonlinearity in the adaptive filter error feedback-loop of an acoustic echo canceler (AEC) based on the least mean square (LMS) algorithm when there is an interference at the near end. The source of distortion may be linear, such as local speech or background noise, or nonlinear due to speech coding used in the telecommunication networks. Detailed derivation of the error recovery nonlinearity (ERN), which “enhances” the filter estimation error prior to the adaptation in order to assist the linear adaptation process, will be provided. Connections to other existing AEC and signal enhancement techniques will be revealed. In particular, the error enhancement technique is well-founded in the information-theoretic sense and has strong ties to independent component analysis (ICA), which is the basis for blind source separation (BSS) that permits unsupervised adaptation in the presence of multiple interfering signals. The single-channel AEC problem can be viewed as a special case of semi-blind source separation (SBSS) where one of the source signals is partially known, i.e., the far-end microphone signal that generates the near-end acoustic echo. The system approach to robust AEC will be motivated, where a proper integration of the LMS algorithm with the ERN into the AEC “system” allows for continuous and stable adaptation even during double talk without precise estimation of the signal statistics. The error enhancement paradigm encompasses many traditional signal enhancement techniques and opens up an entirely new avenue for solving the AEC problem in a real-world setting.