title: RoboCupRescue 2010- Robot League Team

abstract: The team Hector Darmstadt has been established from a PhD program funded by the German Research Foundation at TU Darm- stadt. It combines expertise from Computer Science and Mechanical En- gineering. In 2009 the team successfully participated for the rst time in the RoboCup rescue league. Several team members have already con- tributed in the past to highly successful teams in the RoboCup four- legged and humanoid leagues and in UAV competitions.


title: Cloudnet: A platform for optimized wan migration of virtual machines

abstract: Cloud computing platforms are growing from clusters of machines within a data center to networks of data centers with resources spread across the globe. Virtual machine migration within the LAN has changed the scale of resource management from allocating resources on a single server to manipulating pools of resources within a data center. We expect WAN migration to likewise transform the scope of provisioning from a single data center to multiple data centers spread across the country or around the world. In this paper we propose a cloud computing platform linked with a VPN based network infrastructure that provides seamless connectivity between enterprise and data center sites, as well as support for live WAN migration of virtual machines. We describe a set of optimizations that minimize the cost of transferring persistent storage and moving virtual machine memory during migrations over low bandwidth, high latency Internet links. Our evaluation on both a local testbed and across two real data centers demonstrates that these improvements can reduce total migration and pause time by over 30%. During simultaneous migrations of four VMs between Texas and Illinois, CloudNet’s optimizations reduce memory migration time by 65% and lower bandwidth consumption for the storage and memory transfer by 20GB, a 57% reduction.


title: Approximating dynamic global illumination in image space

abstract: Physically plausible illumination at real-time framerates is often achieved using approximations. One popular example is ambient occlusion (AO), for which very simple and efficient implementations are used extensively in production. Recent methods approximate AO between nearby geometry in screen space (SSAO). The key observation described in this paper is, that screen-space occlusion methods can be used to compute many more types of effects than just occlusion, such as directional shadows and indirect color bleeding. The proposed generalization has only a small overhead compared to classic SSAO, approximates direct and one-bounce light transport in screen space, can be combined with other methods that simulate transport for macro structures and is visually equivalent to SSAO in the worst case without introducing new artifacts. Since our method works in screen space, it does not depend on the geometric complexity. Plausible directional occlusion and indirect lighting effects can be displayed for large and fully dynamic scenes at real-time frame rates.


title: Activity Theory : Basic Concepts and Applications A summary of a tutorial given at the East West HCI 95 Conference

abstract: The objective of the tutorial is to introduce attendees to Activity Theory, a general theoretical framework for the analysis of human and communal action in the world. After an overview of the theory, focus shifts to how this framework can be utilized in practice. Some examples are shown of how this framework can provide a fresh perspective on certain extant problems in the fields of HCI and CSCW. Hopefully, participants become more aware of the nature and complexity of current controversies concerning the role of theory in the design of computer artifacts. By the end of the tutorial, participants should be able to understand the basic principles of the approach, and to describe their work activities in ways illuminated by this framework


title: Capacitor based SneakPath compensation circuit for transistor-less ReRAM architectures

abstract: With the arrival of crosspoint based memories on the consumer market, high-density resistive memories could be introduced as flash memories replacement or as storage class memory. However, transistor-Less Resistive memory architectures using 1Selector-1resistance bitcells suffer from performances loss due to sneaking current through unselected bitcells. Beyond the back end of line selector design, circuit design solutions have to be pushed in order to improve precision during programming steps. In this paper we propose a novel capacitor based 2-steps SneakPath compensation circuit for transistor-less architectures of resistive memories. Compared to standard SneakPath compensation circuits, it ensures up to 20× of area improvement and more than 3× reduction of the variability effects for a 28nm CMOS node.


title: Empowering Students in the Process of Social Inquiry Learning through Flipping the Classroom

abstract: Introduction The Marzano Learning Sciences Research Lab (Marzano & Toth, 2014) has recently published a research report (namely "Teaching for Rigor: A Call for a Critical Instructional Shift"), revealing that nearly 60% of the classroom time in schools in the United States is still dedicated to direct instruction. The concluding part of the report reminds us again of the importance of learner-centredness in the twenty-first century education. With the advancement in computing and information technologies, people nowadays tend to automate the things that can be computerised and automatized, and spend more time and effort on those that cannot (Xu, Feng, Zou & Huang, 2012). Regarding conveying information and factual knowledge, there has been evidence that video lectures can be as effective as in-person live lectures (e.g., Cohen, Ebeling & Kulik, 1991; Howland, Jonassen & Marra, 2012; Mayer, 2009; Zhang, Zhou, Briggs & Nunamaker, 2006). Therefore, it has become questionable about the traditional use of face-to-face time inside classrooms for merely "transmitting" and "receiving" subject contents (Bishop & Verleger, 2013). Employing today's handy video-recording tools and low-cost Internet access, teachers can create online video lectures by themselves and then assign the videos to students as out-of- class "homework." Hence, the in-class time can be used for conducting higher-order learning and teaching activities that cannot be "automated." This is the central idea of the flipped classroom--a pedagogic strategy that has recently attracted a lot of educational researchers' attention. According to the recent K-12 edition of the New Media Consortium Horizon Report 2015 (Johnson, Adams Becker, Estrada & Freeman, 2015), the flipped classroom is foreseen as one of the most prominent educational strategies in this triennium to transform students from "spoon-fed," passive learners into self-directed, active learners. In Hong Kong, "exploring the feasibility of introducing the flipped classroom into learning and teaching" is one of the key objectives of the government's current three-year master plan on information technology in school education (September 2015-August 2018) (Education Bureau, 2015). Liberal Studies is a "young" compulsory subject of social and humanities education in the new senior secondary education system under the recent education reform in Hong Kong (Curriculum Development Council, 2014; Education Bureau, 2014). A core objective of Liberal Studies is to develop students' knowledge of perennial and contemporary issues upon various social and humanities contexts. Guided social inquiry learning (GSIL), which is a scaffolded constructivist learning approach, is conventionally adopted in learning and teaching of the formal curriculum of this subject. The present research aimed to integrate the pedagogic idea of the flipped classroom into the process of GSIL to promote students' learning achievement and self-efficacy in studying Liberal Studies. A quasi-experimental study was conducted to evaluate the pedagogic effectiveness of this "flipped" social inquiry learning (hereinafter referred as FSIL) approach in comparison with the conventional GSIL approach. It involved a total of 215 Grade-11 students from three schools respectively at the top, middle, and bottom academic banding in Hong Kong. We organise the rest of this paper as follows. The next section is a literature review of the related work. Then, the pedagogic design of FSIL will be elaborated. After that, we will delineate the method, findings, implications, and limitations of the experimental study. At the end of the paper, we will give the concluding observations on this research. Related work Guided social inquiry learning (GSIL) The twenty-first century education encourages students to acquire knowledge constructively on their own rather than receiving it didactically from teachers (Gee, 2013; Prensky, 2012). …


title: Unsupervised Predictive Memory in a Goal-Directed Agent

abstract: Animals execute goal-directed behaviours despite the limited range and scope of their sensors. To cope, they explore environments and store memories maintaining estimates of important information that is not presently available. Recently, progress has been made with artificial intelligence (AI) agents that learn to perform tasks from sensory input, even at a human level, by merging reinforcement learning (RL) algorithms with deep neural networks, and the excitement surrounding these results has led to the pursuit of related ideas as explanations of non-human animal learning. However, we demonstrate that contemporary RL algorithms struggle to solve simple tasks when enough information is concealed from the sensors of the agent, a property called"partial observability". An obvious requirement for handling partially observed tasks is access to extensive memory, but we show memory is not enough; it is critical that the right information be stored in the right format. We develop a model, the Memory, RL, and Inference Network (MERLIN), in which memory formation is guided by a process of predictive modeling. MERLIN facilitates the solution of tasks in 3D virtual reality environments for which partial observability is severe and memories must be maintained over long durations. Our model demonstrates a single learning agent architecture that can solve canonical behavioural tasks in psychology and neurobiology without strong simplifying assumptions about the dimensionality of sensory input or the duration of experiences.


title: Finding stationary subspaces in multivariate time series.

abstract: Identifying temporally invariant components in complex multivariate time series is key to understanding the underlying dynamical system and predict its future behavior. In this Letter, we propose a novel technique, stationary subspace analysis (SSA), that decomposes a multivariate time series into its stationary and nonstationary part. The method is based on two assumptions: (a) the observed signals are linear superpositions of stationary and nonstationary sources; and (b) the nonstationarity is measurable in the first two moments. We characterize theoretical and practical properties of SSA and study it in simulations and cortical signals measured by electroencephalography. Here, SSA succeeds in finding stationary components that lead to a significantly improved prediction accuracy and meaningful topographic maps which contribute to a better understanding of the underlying nonstationary brain processes.


title: Trinity: On Using Trinary Trees for Unsupervised Web Data Extraction

abstract: Web data extractors are used to extract data from web documents in order to feed automated processes. In this article, we propose a technique that works on two or more web documents generated by the same server-side template and learns a regular expression that models it and can later be used to extract data from similar documents. The technique builds on the hypothesis that the template introduces some shared patterns that do not provide any relevant data and can thus be ignored. We have evaluated and compared our technique to others in the literature on a large collection of web documents; our results demonstrate that our proposal performs better than the others and that input errors do not have a negative impact on its effectiveness; furthermore, its efficiency can be easily boosted by means of a couple of parameters, without sacrificing its effectiveness.


3D RECONSTRUCTION OF ARCHITECTURAL SCENES FROM UNCALIBRATED VIDEO SEQUENCES


title: Municipal solid waste management strategies in Turkey.

abstract: Municipal solid waste (MSW) is a major environmental problem in Turkey, as in many developing countries. Problems associated with municipal solid waste are difficult to address, but efforts towards more efficient collection and transportation and environmentally acceptable waste disposal continue in Turkey. Although strict regulations on the management of solid waste are in place, primitive disposal methods such as open dumping and discharge into surface water have been used in various parts of Turkey. This study presents a brief history of the legislative trends in Turkey for MSW management. The study also presents the MSW responsibility and management structure together with the present situation of generation, composition, recycling, and treatment. The results show that approximately 25 million ton of MSW are generated annually in Turkey. About 77% of the population receives MSW services. In spite of efforts to change open dumping areas into sanitary landfills and to build modern recycling and composting facilities, Turkey still has over 2000 open dumps.


title: Living Will, Resuscitation Preferences, and Attitudes towards Life in an Aged Population

abstract: BACKGROUND ::: The growth of life-sustaining medical technology and greater attention to medical care at the end of life have provoked interest in issues related to advance care planning. ::: ::: ::: OBJECTIVE ::: To investigate how having a living will (LW), resuscitation preferences, health condition, and life attitudes are related in home-dwelling elderly people. ::: ::: ::: METHODS ::: In a cross-sectional descriptive study, detailed assessments were made of 378 home-dwelling elderly individuals participating in a cardiovascular prevention study (DEBATE Study). The participants were inquired about a preexistence of a written document (LW) concerning life-sustaining care, preferences of cardiopulmonary resuscitation (CPR) in their current situation, and attitudes towards life. General health, physical and cognitive functioning, the presence of depression, and quality of life were also assessed. ::: ::: ::: RESULTS ::: Forty-four of the 378 participants (12%) had a LW. As compared with those without one (n = 334), there were more women [82% (36/44) vs. 63% (210/334)] and widows [57% (25/44) vs. 41% (135/334)] among those with a LW. They were also more educated and considered their health to be better. Despite having a LW, 46% (20/44) of them preferred CPR in their current condition, a proportion not statistically different from the 58% (194/334) of the individuals without a LW. In the whole sample, 39% (149/378) of the individuals preferred to forgo CPR. As compared with those preferring CPR, they were older, more often women, and widowed. Participants preferring to forgo CPR had a poorer quality of life, were more lonely, and showed signs of depression more often than those preferring CPR. The preference to forgo CPR was related to attitudes towards life regardless of physical or cognitive functioning. ::: ::: ::: CONCLUSIONS ::: Having a LW does not reduce the reported preference of CPR which is related more to current mental status and life attitudes. In-depth assessment of the patient's preferences should be performed in any comprehensive care plan.


title: Behavior of a matrix converter with a feed back control in an input side

abstract: This paper discusses about characteristics of a matrix converter where a current feedback is applied to the input side. When the impedance of the power grid is high, such as a generator, the control stability of the matrix converter will be deteriorated. One of the solutions to this problem is to apply an input current stability control that is proposed. However, the efficiency of the generator will decrease at light load and heavy load. In this paper, the behavior of the input current control is analyzed, then the vector control is applied to the input side in order to obtain a stable operation and the maximum efficiency according to the pole position of the generator. This study confirms the validity of the vector control for the matrix converter input side with experimental results. The input current THD 2.8% is obtained under a RL load.


title: Recurrent and Contextual Models for Visual Question Answering

abstract: We propose a series of recurrent and contextual neural network models for multiple choice visual question answering on the Visual7W dataset. Motivated by divergent trends in model complexities in the literature, we explore the balance between model expressiveness and simplicity by studying incrementally more complex architectures. We start with LSTM-encoding of input questions and answers; build on this with context generation by LSTM-encodings of neural image and question representations and attention over images; and evaluate the diversity and predictive power of our models and the ensemble thereof. All models are evaluated against a simple baseline inspired by the current state-of-the-art, consisting of involving simple concatenation of bag-of-words and CNN representations for the text and images, respectively. Generally, we observe marked variation in image-reasoning performance between our models not obvious from their overall performance, as well as evidence of dataset bias. Our standalone models achieve accuracies up to $64.6\%$, while the ensemble of all models achieves the best accuracy of $66.67\%$, within $0.5\%$ of the current state-of-the-art for Visual7W.


title: Current use of high-resolution mass spectrometry in drug screening relevant to clinical and forensic toxicology and doping control

abstract: Clinical and forensic toxicology and doping control deal with hundreds or thousands of drugs that may cause poisoning or are abused, are illicit, or are prohibited in sports. Rapid and reliable screening for all these compounds of different chemical and pharmaceutical nature, preferably in a single analytical method, is a substantial effort for analytical toxicologists. Combined chromatography-mass spectrometry techniques with standardised reference libraries have been most commonly used for the purpose. In the last ten years, the focus has shifted from gas chromatography-mass spectrometry to liquid chromatography-mass spectrometry, because of progress in instrument technology and partly because of the polarity and low volatility of many new relevant substances. High-resolution mass spectrometry (HRMS), which enables accurate mass measurement at high resolving power, has recently evolved to the stage that is rapidly causing a shift from unit-resolution, quadrupole-dominated instrumentation. The main HRMS techniques today are time-of-flight mass spectrometry and Orbitrap Fourier-transform mass spectrometry. Both techniques enable a range of different drug-screening strategies that essentially rely on measuring a compound's or a fragment's mass with sufficiently high accuracy that its elemental composition can be determined directly. Accurate mass and isotopic pattern acts as a filter for confirming the identity of a compound or even identification of an unknown. High mass resolution is essential for improving confidence in accurate mass results in the analysis of complex biological samples. This review discusses recent applications of HRMS in analytical toxicology.


title: GraRep: Learning Graph Representations with Global Structural Information

abstract: In this paper, we present {GraRep}, a novel model for learning vertex representations of weighted graphs. This model learns low dimensional vectors to represent vertices appearing in a graph and, unlike existing work, integrates global structural information of the graph into the learning process. We also formally analyze the connections between our work and several previous research efforts, including the DeepWalk model of Perozzi et al. as well as the skip-gram model with negative sampling of Mikolov et al. We conduct experiments on a language network, a social network as well as a citation network and show that our learned global representations can be effectively used as features in tasks such as clustering, classification and visualization. Empirical results demonstrate that our representation significantly outperforms other state-of-the-art methods in such tasks.


title: Fraud Detection in Telecommunications: History and Lessons Learned

abstract: Fraud detection is an increasingly important and difficult task in today’s technological environment. As consumers are putting more of their personal information online and transacting much more business over computers, the potential for losses from fraud is in the billions of dollars, not to mention the damage done by identity theft. This paper reviews the history of fraud detection at AT&T, one of the first companies to address fraud in a systematic way to protect its revenue stream. We discuss some of the major fraud schemes and the techniques used to address them, leading to generic conclusions about fraud detection. Specifically, we advocate the use of simple, understandable models, heavy use of visualization, and a flexible environment and emphasize the importance of data management and the need to keep humans in the loop.


title: A Capacitor-Less LDO With High-Frequency PSR Suitable for a Wide Range of On-Chip Capacitive Loads

abstract: This paper presents an on-chip, low drop-out (LDO) voltage regulator with improved power-supply rejection (PSR) able to drive large capacitive loads. The LDO compensation is achieved via a custom, wide bandwidth capacitance multiplier (c-multiplier) that emulates a nanofarad-range capacitance at the LDO output node. The LDO frequency response resembles that of externally compensated LDOs, leading to a wide PSR frequency range without using an off-chip capacitor. To drive large capacitive loads without stability concerns, the supply-line capacitance of the load circuit is incorporated to the design of the LDO compensation scheme. The power-stability-performance tradeoffs involved in the design are discussed in detail. The LDO and the c-multiplier are implemented in 0.18- $\mu \text{m}$ CMOS technology and target applications with load currents in the 10-mA range. Experimental results show that the LDO achieves a PSR better than −39 dB up to 20 MHz at 1.2 V output voltage, while maintaining a 97.4% current efficiency.


title: Share your Model instead of your Data: Privacy Preserving Mimic Learning for Ranking

abstract: Deep neural networks have become a primary tool for solving problems in many fields. They are also used for addressing information retrieval problems and show strong performance in several tasks. Training these models requires large, representative datasets and for most IR tasks, such data contains sensitive information from users. Privacy and confidentiality concerns prevent many data owners from sharing the data, thus today the research community can only benefit from research on large-scale datasets in a limited manner. In this paper, we discuss privacy preserving mimic learning, i.e., using predictions from a privacy preserving trained model instead of labels from the original sensitive training data as a supervision signal. We present the results of preliminary experiments in which we apply the idea of mimic learning and privacy preserving mimic learning for the task of document re-ranking as one of the core IR tasks. This research is a step toward laying the ground for enabling researchers from data-rich environments to share knowledge learned from actual users' data, which should facilitate research collaborations.


title: Comparing Machine Learning Approaches for Table Recognition in Historical Register Books

abstract: We present in this paper experiments on Table Recognition in hand-written registry books. We first explain how the problem of row and column detection is modeled, and then compare two Machine Learning approaches (Conditional Random Field and Graph Convolutional Network) for detecting these table elements. Evaluation was conducted on death records provided by the Archive of the Diocese of Passau. Both methods show similar results, a 89 F1 score, a quality which allows for Information Extraction. Software and dataset are open source/data.


ReBots : A Drag-and-drop High-Performance Simulator for Modular and Self-Reconfigurable Robots


title: Fine-grained Material Classification using Micro-geometry and Reflectance

abstract: In this paper we focus on an understudied computer vision problem, particularly how the micro-geometry and the reflectance of a surface can be used to infer its material. To this end, we introduce a new, publicly available database for fine-grained material classification, consisting of over 2000 surfaces of fabrics (http://ibug.doc.ic.ac.uk/resources/fabrics.). The database has been collected using a custom-made portable but cheap and easy to assemble photometric stereo sensor. We use the normal map and the albedo of each surface to recognize its material via the use of handcrafted and learned features and various feature encodings. We also perform garment classification using the same approach. We show that the fusion of normals and albedo information outperforms standard methods which rely only on the use of texture information. Our methodologies, both for data collection, as well as for material classification can be applied easily to many real-word scenarios including design of new robots able to sense materials and industrial inspection.


title: A Reduced-Part, Triple-Voltage DC-DC Converter for Electric Vehicle Power Management

abstract: Electrical power systems in future hybrid and fuel cell vehicles may consist of three voltage nets; 14 V, 42 V and high voltage (>200 V) buses. A soft-switched, bi-directional dc-dc converter using only four switches was proposed for interconnecting the three nets. This paper presents a reduced- part dc-dc converter, which decreases the converter cost while retaining all the favorable features of the original topology. Simulation and experimental data are included to verify a simple power flow control scheme.


title: Research on application of distributed data mining in anti-money laundering monitoring system

abstract: From the reality, this article presents anti-money laundering real-time system architecture on the base of analyzing custom transaction. At the same time, data process, custom transaction and bank node are discussed.


title: A CMOS Inverter-Based Self-biased Fully Differential Amplifier

abstract: A CMOS self-biased fully differential amplifier is presented. Due to the self-biasing structure of the amplifier and its associated negative feedback, the amplifier is compensated to achieve low sensitivity to process, supply voltage and temperature (PVT) variations. The output common-mode voltage of the amplifier is adjusted through the same biasing voltages provided by the common-mode feedback (CMFB) circuit. The amplifier core is based on a simple structure that uses two CMOS inverters to amplify the input differential signal. Despite its simple structure, the proposed amplifier is attractive to a wide range of applications, specially those requiring low power and small silicon area. As two examples, a sample-and-hold circuit and a second order multi-bit sigma-delta modulator either employing the proposed amplifier are presented. Besides these application examples, a set of amplifier performance parameters is given.


title: Shift-minimal groups, fixed price 1, and the unique trace property

abstract: A countable group \Gamma is called shift-minimal if every non-trivial measure preserving action of \Gamma weakly contained in the Bernoulli shift of \Gamma on ([0,1]^\Gamma ,\lambda ^\Gamma) is free. We show that any group \Gamma whose reduced C^*-algebra admits a unique tracial state is shift-minimal, and that any group \Gamma admitting a free measure preserving action of cost>1 contains a finite normal subgroup N such that \Gamma /N is shift-minimal. Any shift-minimal group in turn is shown to have trivial amenable radical. Recurrence arguments are used in studying invariant random subgroups of a wide variety of shift-minimal groups. We also examine continuity properties of cost in the context of infinitely generated groups and equivalence relations. A number of open questions are discussed which concern cost, shift-minimality, C^*-simplicity, and uniqueness of tracial state on C^*_r(\Gamma).


title: Sparse Factorization Machines for Click-through Rate Prediction

abstract: With the rapid development of E-commerce, recent years have witnessed the booming of online advertising industry, which raises extensive concerns of both academic and business circles. Among all the issues, the task of Click-through rates (CTR) prediction plays a central role, as it may influence the ranking and pricing of online ads. To deal with this task, the Factorization Machines (FM) model is designed for better revealing proper combinations of basic features. However, the sparsity of ads transaction data, i.e., a large proportion of zero elements, may severely disturb the performance of FM models. To address this problem, in this paper, we propose a novel Sparse Factorization Machines (SFM) model, in which the Laplace distribution is introduced instead of traditional Gaussian distribution to model the parameters, as Laplace distribution could better fit the sparse data with higher ratio of zero elements. Along this line, it will be beneficial to select the most important features or conjunctions with the proposed SFM model. Furthermore, we develop a distributed implementation of our SFM model on Spark platform to support the prediction task on mass dataset in practice. Comprehensive experiments on two large-scale real-world datasets clearly validate both the effectiveness and efficiency of our SFM model compared with several state-of-the-art baselines, which also proves our assumption that Laplace distribution could be more suitable to describe the online ads transaction data.


title: Sparse Malicious False Data Injection Attacks and Defense Mechanisms in Smart Grids

abstract: This paper discusses malicious false data injection attacks on the wide area measurement and monitoring system in smart grids. First, methods of constructing sparse stealth attacks are developed for two typical scenarios: 1) random attacks in which arbitrary measurements can be compromised; and 2) targeted attacks in which specified state variables are modified. It is already demonstrated that stealth attacks can always exist if the number of compromised measurements exceeds a certain value. In this paper, it is found that random undetectable attacks can be accomplished by modifying only a much smaller number of measurements than this value. It is well known that protecting the system from malicious attacks can be achieved by making a certain subset of measurements immune to attacks. An efficient greedy search algorithm is then proposed to quickly find this subset of measurements to be protected to defend against stealth attacks. It is shown that this greedy algorithm has almost the same performance as the brute-force method, but without the combinatorial complexity. Third, a robust attack detection method is discussed. The detection method is designed based on the robust principal component analysis problem by introducing element-wise constraints. This method is shown to be able to identify the real measurements, as well as attacks even when only partial observations are collected. The simulations are conducted based on IEEE test systems.


title: Development of algorithms for MAV security

abstract: This paper describes the various security schemes required for incorporating security to the data being transmitted over the wireless link between a Micro Air Vehicle (MAV) and Ground Control Station (GCS). The suggested security schemes are developed in software and is integrated with the open source communication protocol i.e., MAVLink. The ciphers used in the adapted method are, Advanced Encryption Standard in Counter mode (AES-CTR) algorithm is used for data encryption, SHA-256 for key Hashing and Diffie-Hellman(D-H) for key exchange. This provides both encryption and authentication to the MAV communication, So that it results in improved security and integrity of the system in the real time applications.


title: Morphological classification of blood leucocytes by microscope images

abstract: The classification and the count of white blood cells in microscopy images allows the in vivo assessment of a wide range of important hematic pathologies (i.e., from presence of infections to leukemia). Nowadays, the morphological cell classification is typically made by experienced operators. Such a procedure presents undesirable drawbacks: slowness and it presents a not standardized accuracy since it depends on the operator's capabilities and tiredness. Only few attempts of partial/full automated systems based on image-processing systems are present in literature and they are still at prototype stage. This paper presents a methodology to achieve an automated detection and classification of leucocytes by microscope color images. The proposed system firstly individuates in the blood image the leucocytes from the others blood cells, then it extracts morphological indexes and finally it classifies the leucocytes by a neural classifier in Basophil, Eosinophil, Lymphocyte, Monocyte and Neutrophil.


title: A log-structured buffer for database systems using non-volatile memory

abstract: Emerging next-generation non-volatile memory (NVM) technologies, including PCM and STT-MRAM, provide low latency, high bandwidth, non-volatility, and high capacity. Recently, NVM has drawn much attention from the database community to improve transaction processing (e.g., write-ahead logging). As a complement to existing work, we investigate NVM for atomic page updates in database systems. Atomic page update is required for consistent recovery, and database systems generally use redundant writes to support atomic page update (e.g., doublewrite buffer in MySQL/InnoDB). However, redundant writes negatively affect the database and lifetime of storage devices (e.g., flash-based SSD). In this paper, we present LSBM, a log-structured buffer manager which updates pages atomically and efficiently using NVM. LSBM updates pages in an out-of-place manner to achieve atomicity and performs page buffering to improve performance. LSBM also reclaims the buffered pages on the fly to reduce the write traffic to storage. We implement LSBM using an NVDIMM as NVM and ported it to MySQL/InnoDB. The experimental results demonstrate that LSBM improves database performance and reduces write traffic on OLTP workloads.


title: DecoBrush: drawing structured decorative patterns by example

abstract: Structured decorative patterns are common ornamentations in a variety of media like books, web pages, greeting cards and interior design. Creating such art from scratch using conventional software is time consuming for experts and daunting for novices. We introduce DecoBrush, a data-driven drawing system that generalizes the conventional digital "painting" concept beyond the scope of natural media to allow synthesis of structured decorative patterns following user-sketched paths. The user simply selects an example library and draws the overall shape of a pattern. DecoBrush then synthesizes a shape in the style of the exemplars but roughly matching the overall shape. If the designer wishes to alter the result, DecoBrush also supports user-guided refinement via simple drawing and erasing tools. For a variety of example styles, we demonstrate high-quality user-constrained synthesized patterns that visually resemble the exemplars while exhibiting plausible structural variations.


title: Web page classification based on Schema.org collection

abstract: The internet is a library of a huge amount of information and there is a need for categorize its content based on web page classification. Classification of web page content can improve the quality of web search and its accuracy. Unfortunately the high dimensionality of the web pages dataset has made the process of classification difficult. The use of an automatic method for web page classification can simplify the whole process and assist the search engine in getting more relevant results. Nowadays information on the web is generally structured and formatted in a not formal way. This absence of semantics leads to create formal methods to provide more semantics information into web page. Search engines including Bing, Google, Yahoo! and Yandex formed collection of schemas Schema.org to support web page semantics and improve their search results. This paper explores the use of formal source code structure for classifying a large collection of the web content. Is focused on use of schemas collection Schema.org to classify web pages and categorize them unambiguously.


title: GhostVLAD for set-based face recognition

abstract: The objective of this paper is to learn a compact representation of image sets for template-based face recognition. We make the following contributions: first, we propose a network architecture which aggregates and embeds the face descriptors produced by deep convolutional neural networks into a compact fixed-length representation. This compact representation requires minimal memory storage and enables efficient similarity computation. Second, we propose a novel GhostVLAD layer that includes ghost clusters, that do not contribute to the aggregation. We show that a quality weighting on the input faces emerges automatically such that informative images contribute more than those with low quality, and that the ghost clusters enhance the network’s ability to deal with poor quality images. Third, we explore how input feature dimension, number of clusters and different training techniques affect the recognition performance. Given this analysis, we train a network that far exceeds the state-of-the-art on the IJB-B face recognition dataset. This is currently one of the most challenging public benchmarks, and we surpass the state-of-the-art on both the identification and verification protocols.


title: Adversarial Learning of Answer-Related Representation for Visual Question Answering

abstract: Visual Question Answering (VQA) aims to learn a joint embedding of the question sentence and the corresponding image to infer the answer. Existing approaches learn the joint embedding don't consider the answer-related information, which results in that the learned representation is not effective to reflect the answer of the question. To address this problem, this paper proposes a novel method, i.e., Adversarial Learning of Answer-Related Representation (ALARR) for visual question answering, which seeks an effective answer-related representation for the question-image pair based on adversarial learning between two processes. The embedding learning process aims to generate modality-invariant joint representations for the question-image and question-answer pairs, respectively. Meanwhile, it tries to confuse the other process, embedding discriminator, which tries to discriminate the two representations from different modalities of pairs. Specifically, the joint embedding of the question-image pair is learned by a three-level attention model, and the joint representation of the question-answer pair is learned by a semantic integration model. Through the adversarial leaning, the answer-related representation are better preserved. Then an answer predictor is proposed to infer the answer from the answer-related representation. Experiments conducted on two widely used VQA benchmark datasets demonstrate that the proposed model outperforms the state-of-the-art approaches.


title: Compositional coding capsule network with k-means routing for text classification

abstract: Text classification is a challenging problem which aims to identify the category of texts. Recently, Capsule Networks (CapsNets) are proposed for image classification. It has been shown that CapsNets have several advantages over Convolutional Neural Networks (CNNs), while, their validity in the domain of text has less been explored. An effective method named deep compositional code learning has been proposed lately. This method can save many parameters about word embeddings without any significant sacrifices in performance. In this paper, we introduce the Compositional Coding (CC) mechanism between capsules, and we propose a new routing algorithm, which is based on k-means clustering theory. Experiments conducted on eight challenging text classification datasets show the proposed method achieves competitive accuracy compared to the state-of-the-art approach with significantly fewer parameters.


title: ONLINE, REVERSE AUCTIONS: ISSUES, THEMES, AND PROSPECTS FOR THE FUTURE* Invited manuscript forthcoming in the MSI/JAMS Special Issue on Marketing to and Serving Customers Through the Internet: Conceptual Frameworks, Practical Insights and Research Directions

abstract: Online reverse auctions are increasingly being used in industrial sourcing activities. This phenomenon represents a novel emerging area of inquiry with significant implications for sourcing strategies. However, there is little systematic thinking or empirical evidence on the topic. This article reviews the use of these auctions in sourcing activities and highlights four key aspects: (1) the differences from physical auctions and those of the theoretical literature, (2) the conditions for using online reverse auctions, (3) the methods for structuring the auctions, and (4) the evaluations of auction performance. Some empirical evidence on these issues is also provided.


title: Forensic Analysis of Instant Messenger Applications on Android Devices

abstract: modern day Smartphone's have built in apps like "WhatsApp & Viber" which allow users to exchange instant messages, share videos, audio's and images via Smartphone's instead of relying on their desktop Computers or laptop thereby increasing the portability and convenience for a layman smart phone user. An Instant Messenger (IM) can serve as a very useful yet very dangerous platform for the victim and the suspect to communicate. The increased use of Instant messengers on Android phones has turned to be the goldmine for mobile and computer forensic experts. Traces and Evidence left by applications can be held on Android phones and retrieving those potential evidences with right forensic technique is strongly required. This paper focuses on conducting forensic data analysis of 2 widely used IMs applications on Android phones: WhatsApp and Viber. 5 Android phones were analyzed covering 3 different versions of Android OS: Froyo (2.2), GingerBread (2.3.x) and Ice- Cream Sandwich (4.0.x). The tests and analysis were performed with the aim of determining what data and information can be found on the device's internal memory for instant messengers e.g. chat messaging logs and history, send & received image or video files, etc. Determining the location of data found from FileSystem Extraction of the device was also determined. The experiments and results show that heavy amount of potential evidences and valuable data can be found on Android phones by forensic investigators.


title: Light Emission Intensities of Luminescent Y2O3:Eu and Gd2O3:Eu Particles of Various Sizes

abstract: There is great technological interest in elucidating the effect of particle size on the luminescence efficiency of doped rare earth oxides. This study demonstrates unambiguously that there is a size effect and that it is not dependent on the calcination temperature. The Y2O3:Eu and Gd2O3:Eu particles used in this study were synthesized using wet chemistry to produce particles ranging in size between 7 nm and 326 nm and a commercially available phosphor. These particles were characterized using three excitation methods: UV light at 250 nm wavelength, electron beam at 10 kV, and X-rays generated at 100 kV. Regardless of the excitation source, it was found that with increasing particle diameter there is an increase in emitted light. Furthermore, dense particles emit more light than porous particles. These results can be explained by considering the larger surface area to volume ratio of the smallest particles and increased internal surface area of the pores found in the large particles. For the small particles, the additional surface area hosts adsorbates that lead to non-radiative recombination, and in the porous particles, the pore walls can quench fluorescence. This trend is valid across calcination temperatures and is evident when comparing particles from the same calcination temperature.


title: APPLICATION OF CFD IN BUILDING PERFORMANCE SIMULATION FOR THE OUTDOOR ENVIRONMENT

abstract: This article provides an overview of the application of computational fluid dynamics (CFD) in building performance simulation for the outdoor environment, focused on four topics: (1) pedestrian wind environment around buildings, (2) wind-driven rain on building facades, (3) convective heat transfer coefficients at exterior building surfaces and (4) air pollutant dispersion around buildings. For each topic, its background, the need for CFD, an overview of some past CFD studies, a discussion about accuracy and some perspectives for practical application are provided. This article indicates that for all four topics, CFD offers considerable advantages compared with wind tunnel modelling or (semi-)empirical formulae because it can provide detailed whole-flow field data under fully controlled conditions and without similarity constraints. The main limitations are the deficiencies of steady Reynolds-averaged Navier–Stokes modelling, the increased complexity and computational expense of large eddy simulation and the requirement of systematic and time-consuming CFD solution verification and validation studies.


title: Watch Me, but Don't Touch Me! Contactless Control Flow Monitoring via Electromagnetic Emanations

abstract: Trustworthy operation of industrial control systems depends on secure and real-time code execution on the embedded programmable logic controllers (PLCs). The controllers monitor and control the critical infrastructures, such as electric power grids and healthcare platforms, and continuously report back the system status to human operators. We present Zeus, a contactless embedded controller security monitor to ensure its execution control flow integrity. Zeus leverages the electromagnetic emission by the PLC circuitry during the execution of the controller programs. Zeus's contactless execution tracking enables non-intrusive monitoring of security-critical controllers with tight real-time constraints. Those devices often cannot tolerate the cost and performance overhead that comes with additional traditional hardware or software monitoring modules. Furthermore, Zeus provides an air-gap between the monitor (trusted computing base) and the target (potentially compromised) PLC. This eliminates the possibility of the monitor infection by the same attack vectors. Zeus monitors for control flow integrity of the PLC program execution. Zeus monitors the communications between the human machine interface and the PLC, and captures the control logic binary uploads to the PLC. Zeus exercises its feasible execution paths, and fingerprints their emissions using an external electromagnetic sensor. Zeus trains a neural network for legitimate PLC executions, and uses it at runtime to identify the control flow based on PLC's electromagnetic emissions. We implemented Zeus on a commercial Allen Bradley PLC, which is widely used in industry, and evaluated it on real-world control program executions. Zeus was able to distinguish between different legitimate and malicious executions with 98.9% accuracy and with zero overhead on PLC execution by design.


title: Relational.OWL - A Data and Schema Representation Format Based on OWL

abstract: One of the research fields which has recently gained much scientific interest within the database community are Peer-to-Peer databases, where peers have the autonomy to decide whether to join or to leave an information sharing environment at any time. Such volatile data nodes may appear shortly, collect or deliver some data, and disappear again. It even can not be assured that a peer joins the network ever again. In this paper we introduce a representation format fort both, schema and data information based on the Web Ontology Language OWL. According to the advantages of the Semantic Web we are thus able to represent and to transfer every schema and data component of a database to any partner, without having to define a data and schema exchange format explicitly.


title: EEG-Based "Serious" Games Design for Medical Applications

abstract: Recently, EEG-based technology has become more popular in “serious” games designs and developments since new wireless headsets that meet consumer demand for wear ability, price, portability and ease-of-use are coming to the market. Originally, EEG-based technologies were used in neurofeedback games and brain-computer interfaces. Now, such technologies could be used in entertainment, e-learning and new medical applications. In this paper, we review on neurofeedback game designs and algorithms, and propose design, algorithm, and implementation of new EEG-based 2D and 3D concentration games. Possible future medical applications of the games are discussed.


title: Are there too many uncited articles? Zero inflated variants of the discretised lognormal and hooked power law distributions

abstract: Although statistical models fit many citation data sets reasonably well with the best fitting models being the hooked power law and discretised lognormal distribution, the fits are rarely close. One possible reason is that there might be more uncited articles than would be predicted by any model if some articles are inherently uncitable. Using data from 23 different Scopus categories, this article tests the assumption that removing a proportion of uncited articles from a citation dataset allows statistical distributions to have much closer fits. It also introduces two new models, zero inflated discretised lognormal distribution and the zero inflated hooked power law distribution and algorithms to fit them. In all 23 cases, the zero inflated version of the discretised lognormal distribution was an improvement on the standard version and in 16 out of 23 cases the zero inflated version of the hooked power law was an improvement on the standard version. Without zero inflation the discretised lognormal models fit the data better than the hooked power law distribution 6 out of 23 times and with it, the discretised lognormal models fit the data better than the hooked power law distribution 9 out of 23 times. Apparently uncitable articles seem to occur due to the presence of academic-related magazines in Scopus categories. In conclusion, future citation analysis and research indicators should take into account uncitable articles, and the best fitting distribution for sets of citation counts from a single subject and year is either the zero inflated discretised lognormal or zero inflated hooked power law.


title: Example-based synthesis of stylized facial animations

abstract: We introduce a novel approach to example-based stylization of portrait videos that preserves both the subject's identity and the visual richness of the input style exemplar. Unlike the current state-of-the-art based on neural style transfer [Selim et al. 2016], our method performs non-parametric texture synthesis that retains more of the local textural details of the artistic exemplar and does not suffer from image warping artifacts caused by aligning the style exemplar with the target face. Our method allows the creation of videos with less than full temporal coherence [Ruder et al. 2016]. By introducing a controllable amount of temporal dynamics, it more closely approximates the appearance of real hand-painted animation in which every frame was created independently. We demonstrate the practical utility of the proposed solution on a variety of style exemplars and target videos.


title: Dynamics of Platform Competition: Exploring the Role of Installed Base, Platform Quality and Consumer Expectations

abstract: Researchers debate the role of installed base, platform quality and consumer expectations in driving the success of platforms. We analyze a dynamic model in which a new entrant with superior quality competes with an incumbent platform, and examine the long-run market outcomes. We find that the driver of market dynamics depends critically on the strength of indirect network effects and the consumer discount factor of future applications. We empirically examine the competition between the Xbox and PlayStation 2 consoles. We find that Xbox has a small quality advantage over PlayStation 2, and that the strength of indirect network effects and the discount factor in this market are within the range in which the market dynamics are quality driven. Counterfactual experiments suggest that PlayStation 2 could drive Xbox out of the market if the strength of indirect network effects more than doubles or the consumer discount factor increases by fifty percent.


title: Information Retrieval as Card Playing: A Formal Model for Optimizing Interactive Retrieval Interface

abstract: We propose a novel formal model for optimizing interactive information retrieval interfaces. To model interactive retrieval in a general way, we frame the task of an interactive retrieval system as to choose a sequence of interface cards to present to the user. At each interaction lap, the system's goal is to choose an interface card that can maximize the expected gain of relevant information for the user while minimizing the effort of the user with consideration of the user's action model and any desired constraints on the interface card. We show that such a formal interface card model can not only cover the Probability Ranking Principle for Interactive Information Retrieval as a special case by making multiple simplification assumptions, but also be used to derive a novel formal interface model for adaptively optimizing navigational interfaces in a retrieval system. Experimental results show that the proposed model is effective in automatically generating adaptive navigational interfaces, which outperform the baseline pre-designed static interfaces.


A Comparison Between the Silhouette Index and the Davies-Bouldin Index in Labelling IDS Clusters


title: Passive-Set-Position-Modulation Framework for Interactive Robotic Systems

abstract: In this paper, we propose a novel framework, passive set-position modulation (PSPM), which enables us to connect a (continuous-time) robot's position to a sequence of slowly updating/sparse (discrete-time) set-position signal via the simple (yet frequently used in practice) spring coupling with damping injection, while enforcing passivity of the closed-loop robotic system. The PSPM modulates the original set-position signal in such a way that the modulated signal is as close to the original signal as possible (i.e., maximum information recovery for better performance), yet only to the extent permissible by the available energy in the system (i.e., passivity constraints). We present its algorithm and theoretically show its passivity and performance. We also show how this PSPM can be applied for two applications, with some experimental results: Internet teleoperation with varying delay and packet loss; and haptics with slow and variable-rate data update.


title: Stem Cells and Niches: Mechanisms That Promote Stem Cell Maintenance throughout Life

abstract: Niches are local tissue microenvironments that maintain and regulate stem cells. Long-predicted from mammalian studies, these structures have recently been characterized within several invertebrate tissues using methods that reliably identify individual stem cells and their functional requirements. Although similar single-cell resolution has usually not been achieved in mammalian tissues, principles likely to govern the behavior of niches in diverse organisms are emerging. Considerable progress has been made in elucidating how the microenvironment promotes stem cell maintenance. Mechanisms of stem cell maintenance are key to the regulation of homeostasis and likely contribute to aging and tumorigenesis when altered during adulthood.


title: A reflection on information systems strategizing: the role of power and everyday practices

abstract: We review the IS strategizing literature and highlight its main strengths and weaknesses. Strengths include an account given to the relevance of tensions between planned and executed strategy, and associated tradeoffs such as rigidity and flexibility, formal and informal strategizing and the exploitation of static resources vis a vis the exploration of novel capabilities. Weaknesses relate to a predominant focus on an organizational level of analysis and a lack of power considerations. In this paper we aim to build on these strengths and to ameliorate these weaknesses by proposing a comprehensive IS strategizing framework that uses extant IS strategizing research as a foundation, rejuvenated by insights from the emerging strategy-as-practice literature. The paper extends our understanding of IS strategizing in light of the practice perspective by providing a multilevel account and incorporating power considerations. © 2016 Blackwell Publishing Ltd


title: Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda

abstract: Advances in artificial intelligence, sensors and big data management have far-reaching societal impacts. As these systems augment our everyday lives, it becomes increasing-ly important for people to understand them and remain in control. We investigate how HCI researchers can help to develop accountable systems by performing a literature analysis of 289 core papers on explanations and explaina-ble systems, as well as 12,412 citing papers. Using topic modeling, co-occurrence and network analysis, we mapped the research space from diverse domains, such as algorith-mic accountability, interpretable machine learning, context-awareness, cognitive psychology, and software learnability. We reveal fading and burgeoning trends in explainable systems, and identify domains that are closely connected or mostly isolated. The time is ripe for the HCI community to ensure that the powerful new autonomous systems have intelligible interfaces built-in. From our results, we propose several implications and directions for future research to-wards this goal.


title: Optimal medication dosing from suboptimal clinical examples: A deep reinforcement learning approach

abstract: Misdosing medications with sensitive therapeutic windows, such as heparin, can place patients at unnecessary risk, increase length of hospital stay, and lead to wasted hospital resources. In this work, we present a clinician-in-the-loop sequential decision making framework, which provides an individualized dosing policy adapted to each patient's evolving clinical phenotype. We employed retrospective data from the publicly available MIMIC II intensive care unit database, and developed a deep reinforcement learning algorithm that learns an optimal heparin dosing policy from sample dosing trails and their associated outcomes in large electronic medical records. Using separate training and testing datasets, our model was observed to be effective in proposing heparin doses that resulted in better expected outcomes than the clinical guidelines. Our results demonstrate that a sequential modeling approach, learned from retrospective data, could potentially be used at the bedside to derive individualized patient dosing policies.


title: Enumeration of ad-nilpotent ideals of parabolic subalgebras for exceptional types

abstract: Using GAP4, we determine the number of ad-nilpotent and abelian ideals of a parabolic subalgebra of a simple Lie algebra of exceptional types E, F or G.


title: Effect of video game practice on spatial skills in girls and boys

abstract: A study of the effect of video game practice on spatial abilities in girls and boys was carried out. Spatial performance, measured using two subtests of a computerized spatial skills battery, was significantly better in boys than in girls during pretest assessment. Subjects then practiced on an action video game, Marble Madness, or a computerized word game, Conjecture. Video game practice was significantly more effective than the word game in improving spatial performance on the posttest assessment; there was no significant interaction of gender with experimental treatment. However, video game practice was more effective for children who started out with relatively poor spatial skills. The pattern of results suggests that video games may be useful in equalizing individual differences in spatial skill performance, including those associated with gender.


title: Visualization of Origins , Destinations and Flows with OD Maps

abstract: Abstract We present a new technique for the visual exploration of origins (O) and destinations (D) arranged in geographic space. Previous attempts to map the flows between origins and destinations have suffered from problems of occlusion usually requiring some form of generalisation, such as aggregation or flow density estimation before they can be visualized. This can lead to loss of detail or the introduction of arbitrary artefacts in the visual representation. Here, we propose mapping OD vectors as cells rather than lines, comparable with the process of constructing OD matrices, but unlike the OD matrix, we preserve the spatial layout of all origin and destination locations by constructing a gridded two-level spatial treemap. The result is a set of spatially ordered small multiples upon which any arbitrary geographic data may be projected. Using a hash grid spatial data structure, we explore the characteristics of the technique through a software prototype that allows interactive query and visua...


title: A Combined Voxel and Particle Filter-Based Approach for Fast Obstacle Detection and Tracking in Automotive Applications

abstract: In this paper, a new method for real-time detection, motion estimation, and tracking of generic obstacles using just a 3-D point cloud and odometry information as input is presented. In this approach, a simplification of the world is done using voxels, supported by a particle filter-based 3-D object segmentation and a motion estimation scheme. That combination of techniques leverages a fast and reliable object detection, providing also motion speed and direction information. Four detailed studies have been performed in order to assess the suitability of the method, two of them related to the parameterization of the method and its input point cloud. Another one compares the tracking and detection results with other state-of-the-art methods. Last tests are intended for the characterization of the execution times required. Results are encouraging, with a high detection rate, low error rate, and real-time capable computing performance. In the attached video, it is possible to observe the behavior of the method, both using a stereovision and a light-detection and ranging generated point clouds as an input.


title: PLEX Cards: a source of inspiration when designing for playfulness

abstract: Playfulness can be observed in all areas of human activity. It is an attitude of making activities more enjoyable. Designing for playfulness involves creating objects that elicit a playful approach and provide enjoyable experiences. In this paper we introduce the design and evaluation of the PLEX Cards and its two related idea generation techniques. The cards were created to communicate the 22 categories of a Playful Experiences framework to designers and other stakeholders who wish to design for playfulness. We have evaluated the practical use of the cards by applying them in three design cases. The results show that the PLEX Cards are a valuable source of inspiration when designing for playfulness and the techniques help create a large amount of ideas in a short time.


title: SOQDE: A Supervised Learning Based Question Difficulty Estimation Model for Stack Overflow

abstract: StackOverflow (SO), the most popular community Q&A site rewards answerers with reputation scores to encourage answers from volunteer participants. However, irrespective of the difficulty of a question, the contributor of an accepted answer is awarded with the same 'reputation' score, which may demotivate an user's additional efforts to answer a difficult question. To facilitate a question difficulty aware rewarding system, this study proposes SOQDE (Stack Overflow Question Difficulty Estimation), a supervised learning based Question difficulty estimation model for the StackOverflow. To design SOQDE, we randomly selected 936 questions from a SO datadump exported during September 2017. Two of the authors independently labeled those questions into three categories (basic, intermediate, or advanced), where conflicting labels were resolved through tie-breaking votes from a third author. We performed an empirical study to determine how the difficulty of a question impacts its outcomes, such as number of votes, resolution time, and number of votes. Our results suggest that the answers of a basic question receive more votes and therefore would generate more reputation points for an answerer. Due to less incentives relative to efforts spent by an answerer, intermediate and advanced questions encounter significantly more delays than the basic questions, which further validates the need of a model like SOQDE. To build our model, we have identified textual and contextual features of a question and divided them into two categories-pre-hoc and post-hoc features. We observed a model based on Random Forest achieving the highest mean accuracy (67.6%), using only answer-independent pre-hoc features. Accommodating answer-dependent post-hoc features, we were able to improve the mean accuracy of our model to 75.2%.


Automatic Detection and Classification of Skin Cancer


title: Development of a lightweight, underactuated exoskeleton for load-carrying augmentation

abstract: Metabolic studies have shown that there is a metabolic cost associated with carrying load. Several leg exoskeletons have been developed by various groups in an attempt to augment the load carrying capability of the human. Previous research efforts have not fully exploited the passive dynamics of walking and have largely focused on fully actuated exoskeletons that are heavy with large energy requirements. In this paper, a lightweight, underactuated exoskeleton design is presented that runs in parallel to the human and supports the weight of a payload. Two exoskeleton architectures are pursued based on examining human walking data. A first architecture consists of springs at the hip, a variable impedance device at the knee, and springs at the ankle. A second architecture replaces the springs at the hip with a non-conservative actuator to examine the effect of adding power at desired instances throughout the gait cycle. Preliminary studies show that an efficient, underactuated leg exoskeleton can effectively transmit payload forces to the ground during the walking cycle


title: Combating cyber terrorism: countering cyber terrorist advantages of surprise and anonymity

abstract: The paper proposes ways to counter the cyber terrorist (CT) advantages of surprise and anonymity. It recommends the creation of a Cyberint Analysis Center to develop and evaluate methods to improve the ability to detect, identify and deter cyber terrorist attacks. It also proposes ways to implement responsible, accountable and identifiable use of the Internet, and deny anonymity to the attackers.


title: Unmodified Device Driver Reuse and Improved System Dependability via Virtual Machines

abstract: We propose a method to reuse unmodified device drivers and to improve system dependability using virtual machines. We run the unmodified device driver, with its original operating system, in a virtual machine. This approach enables extensive reuse of existing and unmodified drivers, independent of the OS or device vendor, significantly reducing the barrier to building new OS endeavors. By allowing distinct device drivers to reside in separate virtual machines, this technique isolates faults caused by defective or malicious drivers, thus improving a system's dependability. ::: ::: We show that our technique requires minimal support infrastructure and provides strong fault isolation. Our prototype's network performance is within 3-8% of a native Linux system. Each additional virtual machine increases the CPU utilization by about 0.12%. We have successfully reused a wide variety of unmodified Linux network, disk, and PCI device drivers.


MANTIS-A ROBOT WITH ADVANCED LOCOMOTION AND MANIPULATION ABILITIES


title: Design of rare-earth free five-phase outer-rotor IPM motor drive for electric bicycle

abstract: This paper presents the design considerations of a five-phase outer rotor interior permanent magnet motor (IPM) for an electric bicycle (e-bicycle). Conventional e-bicycles employ three phase outer rotor surface permanent magnet motors which are less reliable compared to the five-phase IPM motors. Moreover, the traditional electric motors use rare-earth permanent magnet materials such as neodymium which are expensive. Ferrite based magnets can be a better substitute to rare earth materials to make the motor more affordable. In this study, the design of a highly reliable and economical five-phase ferrite based IPM motor to be accommodated in a hub motor drive (HMD) for an e-bicycle is presented. The proposed motor design is optimized using a lumped parameter model (LPM) based optimizer. Detailed analysis on the design parameters and constraints considered for optimization is discussed. Thorough finite element simulations in terms of torque developed, cogging torque, back-EMF, and mechanical stress have been conducted to verify the effectiveness of the proposed motor design.


title: Flooding-resilient broadcast authentication for VANETs

abstract: Digital signatures are one of the fundamental security primitives in Vehicular Ad-Hoc Networks (VANETs) because they provide authenticity and non-repudiation in broadcast communication. However, the current broadcast authentication standard in VANETs is vulnerable to signature flooding: excessive signature verification requests that exhaust the computational resources of victims. In this paper, we propose two efficient broadcast authentication schemes, Fast Authentication (FastAuth) and Selective Authentication (SelAuth), as two countermeasures to signature flooding. FastAuth secures periodic single-hop beacon messages. By exploiting the sender's ability to predict its own future beacons, FastAuth enables 50 times faster verification than previous mechanisms using the Elliptic Curve Digital Signature Algorithm. SelAuth secures multi-hop applications in which a bogus signature may spread out quickly and impact a significant number of vehicles. SelAuth pro- vides fast isolation of malicious senders, even under a dynamic topology, while consuming only 15%--30% of the computational resources compared to other schemes. We provide both analytical and experimental evaluations based on real traffic traces and NS-2 simulations. With the near-term deployment plans of VANET on all vehicles, our approaches can make VANETs practical.


PROPAGANDA ANALYSIS: A CASE STUDY OF KAZAKHSTAN'S 2006 ADVERTISING CAMPAIGN


title: SDN Enabled 5G-VANET: Adaptive Vehicle Clustering and Beamformed Transmission for Aggregated Traffic

abstract: With the anticipated arrival of autonomous vehicles, supporting vehicle generated data traffic due to the dramatically increased use of in-vehicle mobile Internet access will become extremely challenging in 5G-based vehicular networks. This is mainly due to the high mobility of vehicles on the road and the high complexity of 5G HetNets. In order to support the increasing traffic and improve Het- Net management, an SDN enabled 5G VANET is proposed in this article, where neighboring vehicles are clustered adaptively according to real-time road conditions using SDN's global information gathering and network control capabilities. With proposed dual cluster head design and dynamic beamforming coverage, both trunk link communication quality and network robustness of vehicle clusters are significantly enhanced. Furthermore, an adaptive transmission scheme with selective modulation and power control is proposed to improve the capacity of the trunk link between the cluster head and base station. With cooperative communication between the mobile gateway candidates, the latency of traffic aggregation and distribution is also reduced. Computer simulation results show that the proposed design substantially improved 5G users' bit error rate and trunk link throughput rate.


title: Automated Detection of Adverse Drug Reactions in the Biomedical Literature Using Convolutional Neural Networks and Biomedical Word Embeddings

abstract: Monitoring the biomedical literature for cases of Adverse Drug Reactions (ADRs) is a critically important and time consuming task in pharmacovigilance. The development of computer assisted approaches to aid this process in different forms has been the subject of many recent works. One particular area that has shown promise is the use of Deep Neural Networks, in particular, Convolutional Neural Networks (CNNs), for the detection of ADR relevant sentences. Using token-level convolutions and general purpose word embeddings, this architecture has shown good performance relative to more traditional models as well as Long Short Term Memory (LSTM) models. In this work, we evaluate and compare two different CNN architectures using the ADE corpus. In addition, we show that by de-duplicating the ADR relevant sentences, we can greatly reduce overoptimism in the classification results. Finally, we evaluate the use of word embeddings specifically developed for biomedical text and show that they lead to a better performance in this task.


title: Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only

abstract: We propose a fully unsupervised framework for ad-hoc cross-lingual information retrieval (CLIR) which requires no bilingual data at all. The framework leverages shared cross-lingual word embedding spaces in which terms, queries, and documents can be represented, irrespective of their actual language. The shared embedding spaces are induced solely on the basis of monolingual corpora in two languages through an iterative process based on adversarial neural networks. Our experiments on the standard CLEF CLIR collections for three language pairs of varying degrees of language similarity (English-Dutch/Italian/Finnish) demonstrate the usefulness of the proposed fully unsupervised approach. Our CLIR models with unsupervised cross-lingual embeddings outperform baselines that utilize cross-lingual embeddings induced relying on word-level and document-level alignments. We then demonstrate that further improvements can be achieved by unsupervised ensemble CLIR models. We believe that the proposed framework is the first step towards development of effective CLIR models for language pairs and domains where parallel data are scarce or non-existent.


title: Autonomous Vehicles for Smart and Sustainable Cities: An In-Depth Exploration of Privacy and Cybersecurity Implications

abstract: Amidst rapid urban development, sustainable transportation solutions are required to meet the increasing demands for mobility whilst mitigating the potentially negative social, economic, and environmental impacts. This study analyses autonomous vehicles (AVs) as a potential transportation solution for smart and sustainable development. We identified privacy and cybersecurity risks of AVs as crucial to the development of smart and sustainable cities and examined the steps taken by governments around the world to address these risks. We highlight the literature that supports why AVs are essential for smart and sustainable development. We then identify the aspects of privacy and cybersecurity in AVs that are important for smart and sustainable development. Lastly, we review the efforts taken by federal governments in the US, the UK, China, Australia, Japan, Singapore, South Korea, Germany, France, and the EU, and by US state governments to address AV-related privacy and cybersecurity risks in-depth. Overall, the actions taken by governments to address privacy risks are mainly in the form of regulations or voluntary guidelines. To address cybersecurity risks, governments have mostly resorted to regulations that are not specific to AVs and are conducting research and fostering research collaborations with the private sector.


title: Using mobile device screens for authentication

abstract: Authentication in public spaces, such as ATM PIN entry, is inherently susceptible to security attacks based on observation in person or via cameras. This paper addresses this problem with a system which allows users to enter a PIN on a standard mobile phone and then transmit it securely for authentication using modulated patterns of light shown on the screen and sensed by a cheap bespoke receiver unit. No pre-pairing is required as physical proximity guarantees security. The paper presents several hardware and software variations, evaluates the technical soundness of the system, and presents two user studies addressing usability and security against observation attacks.


title: A framework for designing closed domain virtual assistants

abstract: Highlights? Virtual assistant framework that helps users find useful information in a website. ? It engages users in conversation using natural language, as real assistants. ? Users can omit some words if they are implicit in the conversation (context). ? It answers people's questions and offer related information of the highest interest. ? It is multilingual and can be integrated into every existing website. Since its beginning in 1969, the Internet has grown rapidly, especially over the past few years. Companies and organizations store more and more information about themselves on the Internet. Sometimes, that information is not well organized. Other times, the huge volume of available data makes useful information difficult to be found. The result is that users have to waste their time looking for what they want to know using the traditional menu-driven navigation and keyword search that websites provide. This is a critical issue because it decreases users interest about companies. In order to avoid this problem, in this paper we propose a framework for designing virtual assistants, which are, considering first results, an ideal alternative to help users find, not only the information that they are looking for, but also some related information which could be of the highest interest.


title: The Best Two-Phase Algorithm for Bin Stretching

abstract: Online Bin Stretching is a semi-online variant of bin packing in which the algorithm has to use the same number of bins as an optimal packing, but is allowed to slightly overpack the bins. The goal is to minimize the amount of overpacking, i.e., the maximum size packed into any bin. We give an algorithm for Online Bin Stretching with a stretching factor of 1:5 for any number of bins. We build on previous algorithms and use a two-phase approach. We also show that this approach cannot give better results by proving a matching lower bound.


title: Categorization of display ads using image and landing page features

abstract: We consider the problem of automatically categorizing display ad images into a taxonomy of relevant interest categories. In particular, we focus on the efficacy of using image features extracted by OCR techniques from the ad images, in addition to the features from the text in the title, keywords and body of the landing page of the ad, and the features of the advertiser, in predicting the category of the display ad. An automated ad categorization tool has multiple uses in display advertising including increasing the ad categorization coverage, scaling up the ad categorization capacity to handle large volumes of ads by reducing the amount of human editorial effort and better utilizing the human editorial experts to focus on categorizing difficult ads. The ad image and landing page features extracted in this ad categorization system can also be used to improve the matching and ranking steps of ad selection algorithms in display ad serving systems. We learn multiple one-versus-rest SVM models to categorize the display ads, from a historical dataset of ads labeled into these categories by human editors. The OCR features extracted by common open source tools are by themselves noisy, and models trained using only the OCR features are not competitive with the performance of models trained using the landing page features. However, for categories with a small number of training examples, the OCR features improve the categorization performance metrics when used in addition to the features from the landing page. The OCR features also provide a useful signal to predict the category of an ad when features from the landing pages are not available. Our models have an average precision of 0.6 and recall of 0.37 over more than 1200 categories when evaluated on a hold out dataset. The precision and recall values are considerably higher for categories with larger amounts of training data, with precision larger than 0.84 and recall larger than 0.7 in all the categories that have more than 100,000 samples in the training dataset. Features from the text in the body of the landing page of the ads increase the recall of the categorization models and to a lesser extent increase the precision of these models, especially in categories with a smaller number of training samples.


title: doi: 0.5087/dad.2012.205 G-Asks: An Intelligent Automatic Question Generation System for Academic Writing Support

abstract: Many electronic feedback systems have been proposed for writing support. However, most of these systems only aim at supporting writing to communicate instead of writing to learn, as in the case of literature review writing. Trigger questions are potentially forms of support for writing to learn, but current automatic question generation approaches focus on factual question generation for reading comprehension or vocabulary assessment. This article presents a novel Automatic Question Generation (AQG) system, called G-Asks, which generates specific trigger questions as a form of support for students' learning through writing. We conducted a large-scale case study, including 24 human supervisors and 33 research students, in an Engineering Research Method course at The University of Sydney and compared questions generated by G-Asks with human generated question. The results indicate that G-Asks can generate questions as useful as human supervisors (`useful' is one of five question quality measures) while significantly outperforming Human Peer and Generic Questions in most quality measures after filtering out questions with grammatical and semantic errors. Furthermore, we identified the most frequent question types, derived from the human supervisors' questions and discussed how the human supervisors generate such questions from the source text.


title: How app update affects app download in iOS appstore

abstract: Mobile stores (appstores) are emerging digital distribution platforms with explosive growth, in which the mobile applications (apps) are directly published by the app developers and downloaded by the end users. Appstores attract a lot attention of academia, there are increasingly studies on many aspects of appstores, especially update or release strategy of apps. In this paper, we measured a third-party appstore and introduced a metric, named “Pulling Rate” to quantify the effect of update events on downloads through case study. The results showed that in the first ten days after update, downloads will increase about three times about average downloads. However, after about 45 days after update event, the effect of update event on downloads will exists no more. These results are useful for app developers to help with determining their release strategies.


title: A Natural Language Question Answering System as a Participant in Human Q&A Portals

abstract: LogAnswer is a question answering (QA) system for the German language, aimed at providing concise and correct answers to arbitrary questions. For this purpose LogAnswer is designed as an embedded artificial intelligence system which integrates methods from several fields of AI, namely natural language processing, machine learning, knowledge representation and automated theorem proving. We intend to employ LogAnswer as a virtual user within Internet-based QA forums, where it must be able to identify the questions that it cannot answer correctly, a task that normally receives little attention in QA research compared to the actual answer derivation. The paper presents a machine learning solution to the wrong answer avoidance (WAA) problem, applying a meta classifier to the output of simple term-based classifiers and a rich set of other WAA features. Experiments with a large set of real-world questions from a QA forum show that the proposed method significantly improves the WAA characteristics of our system.


title: Maximizing Influence Propagation in Networks with Community Structure

abstract: We consider the algorithmic problem of selecting a set of target nodes that cause the biggest activation cascade in a network. In case when the activation process obeys the diminishing returns property, a simple hill-climbing selection mechanism has been shown to achieve a provably good performance. Here we study models of influence propagation that exhibit critical behavior, and where the property of diminishing returns does not hold. We demonstrate that in such systems, the structural properties of networks can play a significant role. We focus on networks with two loosely coupled communities, and show that the double-critical behavior of activation spreading in such systems has significant implications for the targeting strategies. In particular, we show that simple strategies that work well for homogeneous networks can be overly sub-optimal, and suggest simple modification for improving the performance, by taking into account the community structure.


title: Uplink Performance Analysis in D2D-Enabled mmWave Cellular Networks

abstract: In this paper, we provide an analytical framework to analyze the uplink performance of device-to-device (D2D)-enabled millimeter wave (mmWave) cellular networks. Signal-to- interference-plus-noise ratio (SINR) outage probabilities are derived for both cellular and D2D links using tools from stochastic geometry. The distinguishing features of mmWave communications such as directional beamforming and having different path loss laws for line-of-sight (LOS) and non-line-of-sight (NLOS) links are incorporated into the outage analysis by employing a flexible mode selection scheme and Nakagami fading. Also, the effect of beamforming alignment errors on the outage probability is investigated to get insight on the performance in practical scenarios.


title: Unique signatures of histograms for local surface description

abstract: This paper deals with local 3D descriptors for surface matching. First, we categorize existing methods into two classes: Signatures and Histograms. Then, by discussion and experiments alike, we point out the key issues of uniqueness and repeatability of the local reference frame. Based on these observations, we formulate a novel comprehensive proposal for surface representation, which encompasses a new unique and repeatable local reference frame as well as a new 3D descriptor. The latter lays at the intersection between Signatures and Histograms, so as to possibly achieve a better balance between descriptiveness and robustness. Experiments on publicly available datasets as well as on range scans obtained with Spacetime Stereo provide a thorough validation of our proposal.


title: An FPGA Implementation for Solving Least Square Problem

abstract: This paper proposes a high performance least square solver on FPGAs using the Cholesky decomposition method. Our design can be realized by iteratively adopting a single triangular linear equation solver for modified Cholesky decomposition and forward/backward substitutions. Good performance is achieved by optimizing the Cholesky factorization algorithms, reordering the computation and thus alleviating the data dependency. Dedicated hardware architecture for solving triangular linear equations is designed and implemented for different precision requirements. Compared to software on a Pentium 4, our design achieves a significant speedup.


Understanding Stories with Large-Scale Common Sense


title: Hybrid differential evolution based on fuzzy C-means clustering

abstract: In this paper, we propose a hybrid Differential Evolution (DE) algorithm based on the fuzzy C-means clustering algorithm, referred to as FCDE. The fuzzy C-means clustering algorithm is incorporated with DE to utilize the information of the population efficiently, and hence it can generate good solutions and enhance the performance of the original DE. In addition, the population-based algorithmgenerator is adopted to efficiently update the population with the clustering offspring. In order to test the performance of our approach, 13 high-dimensional benchmark functions of diverse complexities are employed. The results show that our approach is effective and efficient. Compared with other state-of-the-art DE approaches, our approach performs better, or at least comparably, in terms of the quality of the final solutions and the reduction of the number of fitness function evaluations (NFFEs).


title: Emerging roles of caspase-3 in apoptosis

abstract: Caspases are crucial mediators of programmed cell death (apoptosis). Among them, caspase-3 is a frequently activated death protease, catalyzing the specific cleavage of many key cellular proteins. However, the specific requirements of this (or any other) caspase in apoptosis have remained largely unknown until now. Pathways to caspase-3 activation have been identified that are either dependent on or independent of mitochondrial cytochrome c release and caspase-9 function. Caspase-3 is essential for normal brain development and is important or essential in other apoptotic scenarios in a remarkable tissue-, cell type- or death stimulus-specific manner. Caspase-3 is also required for some typical hallmarks of apoptosis, and is indispensable for apoptotic chromatin condensation and DNA fragmentation in all cell types examined. Thus, caspase-3 is essential for certain processes associated with the dismantling of the cell and the formation of apoptotic bodies, but it may also function before or at the stage when commitment to loss of cell viability is made.


title: General-purpose molecular dynamics simulations on GPU-based clusters

abstract: We present a GPU implementation of LAMMPS, a widely-used parallel molecular dynamics (MD) software package, and show 5x to 13x single node speedups versus the CPU-only version of LAMMPS. This new CUDA package for LAMMPS also enables multi-GPU simulation on hybrid heterogeneous clusters, using MPI for inter-node communication, CUDA kernels on the GPU for all methods working with particle data, and standard LAMMPS C++ code for CPU execution. Cell and neighbor list approaches are compared for best performance on GPUs, with thread-per-atom and block-per-atom neighbor list variants showing best performance at low and high neighbor counts, respectively. Computational performance results of GPU-enabled LAMMPS are presented for a variety of materials classes (e.g. biomolecules, polymers, metals, semiconductors), along with a speed comparison versus other available GPU-enabled MD software. Finally, we show strong and weak scaling performance on a CPU/GPU cluster using up to 128 dual GPU nodes.


title: Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data

abstract: This paper analyzes consumer choices over lunchtime restaurants using data from a sample of several thousand anonymous mobile phone users in the San Francisco Bay Area. The data is used to identify users' approximate typical morning location, as well as their choices of lunchtime restaurants. We build a model where restaurants have latent characteristics (whose distribution may depend on restaurant observables, such as star ratings, food category, and price range), each user has preferences for these latent characteristics, and these preferences are heterogeneous across users. Similarly, each item has latent characteristics that describe users' willingness to travel to the restaurant, and each user has individual-specific preferences for those latent characteristics. Thus, both users' willingness to travel and their base utility for each restaurant vary across user-restaurant pairs. We use a Bayesian approach to estimation. To make the estimation computationally feasible, we rely on variational inference to approximate the posterior distribution, as well as stochastic gradient descent as a computational approach. Our model performs better than more standard competing models such as multinomial logit and nested logit models, in part due to the personalization of the estimates. We analyze how consumers re-allocate their demand after a restaurant closes to nearby restaurants versus more distant restaurants with similar characteristics, and we compare our predictions to actual outcomes. Finally, we show how the model can be used to analyze counterfactual questions such as what type of restaurant would attract the most consumers in a given location.


title: Cascaded Pose Regression Revisited: Face Alignment in Videos

abstract: Automated pose estimation is a fundamental task in computer vision. In this paper, we investigate the generic framework of Cascaded Pose Regression (CPR), which demonstrates practical effectiveness in pose estimation on deformable and articulated objects. In particular, we focus on the use of CPR for face alignment by exploring existing techniques and verifying their performances on different public facial datasets. We show that the correct selection of pose-invariant features is critical to encode the geometric arrangement of landmarks and crucial for the overall regressor learnability. Furthermore, by incorporating strategies that are commonly used among the state-of-the-art, we interpret the CPR training procedure as a repeated clustering problem with explicit regressor representation, which is complementary to the original CPR algorithm. In our experiment, the qualitative evaluation of existing alignment techniques demonstrates the success of CPR for facial pose inference that can be conveniently adopted to video detection and tracking applications.


title: Container-based virtualization for byte-addressable NVM data storage

abstract: Container based virtualization is rapidly growing in popularity for cloud deployments and applications as a virtualization alternative due to the ease of deployment coupled with high-performance. Emerging byte-addressable, nonvolatile memories, commonly called Storage Class Memory or SCM, technologies are promising both byte-addressability and persistence near DRAM speeds operating on the main memory bus. These new memory alternatives open up a new realm of applications that no longer have to rely on slow, block-based persistence, but can rather operate directly on persistent data using ordinary loads and stores through the cache hierarchy coupled with transaction techniques. However, SCM presents a new challenge for container-based applications, which typically access persistent data through layers of block based file isolation. Traditional persistent data accesses in containers are performed through layered file access, which slows byte-addressable persistence and transactional guarantees, or through direct access to drivers, which do not provide for isolation guarantees or security. This paper presents a high-performance containerized version of byte-addressable, non-volatile memory (SCM) for applications running inside a container that solves performance challenges while providing isolation guarantees. We created an open-source container-aware Linux loadable Kernel Module (LKM) called Containerized Storage Class Memory, or CSCM, that presents SCM for application isolation and ease of portability. We performed evaluation using microbenchmarks, STREAMS, and Redis, a popular in-memory data structure store, and found our CSCM driver has near the same memory throughput for SCM applications as a non-containerized application running on a host and much higher throughput than persistent in-memory applications accessing SCM through Docker Storage or Volumes.