title: The Use of Bayesian Networks for Web Effort Estimation: Further Investigation

abstract: The objective of this paper is to further investigate the use of Bayesian Networks (BN) for Web effort estimation when using a cross-company dataset. Four BNs were built; two automatically using the Hugin tool with two training sets; two using a structure elicited by a domain expert, with parameters obtained from automatically fitting the network to the same training sets used in the automated elicitation (hybrid models). The accuracy of all four models was measured using two validation sets, and point estimates. As a benchmark, the BN-based predictions were also compared to predictions obtained using Manual StepWise Regression (MSWR), and Case-Based Reasoning (CBR). The BN model generated using Hugin presented similar accuracy to CBR and Mean effort-based predictions. Our results suggest that Hybrid BN models can provide significantly superior prediction accuracy. However, good results also seem to depend on characteristics of the training and validation sets used.


title: A WordNet-based semantic similarity measurement combining edge-counting and information content theory

abstract: Abstract Semantic similarity measuring between words can be applied to many applications, such as Artificial Intelligence, Information Processing, Medical Care and Linguistics. In this paper, we present a new approach for semantic similarity measuring which is based on edge-counting and information content theory. Specifically, the proposed measure nonlinearly transforms the weighted shortest path length between the compared concepts to achieve the semantic similarity results, and the relation between parameters and the correlation value is discussed in detail. Experimental results show that the proposed approach not only achieves high correlation value against human ratings but also has better distribution characteristics of the correlation coefficient compared with several related works in the literature. In addition, the proposed method is computationally efficient due to the simplified ways of weighting the shortest path length between the concept pairs.


title: Controlling Popularity Bias in Learning-to-Rank Recommendation

abstract: Many recommendation algorithms suffer from popularity bias in their output: popular items are recommended frequently and less popular ones rarely, if at all. However, less popular, long-tail items are precisely those that are often desirable recommendations. In this paper, we introduce a flexible regularization-based framework to enhance the long-tail coverage of recommendation lists in a learning-to-rank algorithm. We show that regularization provides a tunable mechanism for controlling the trade-off between accuracy and coverage. Moreover, the experimental results using two data sets show that it is possible to improve coverage of long tail items without substantial loss of ranking performance.


Let Your Photos Talk: Generating Narrative Paragraph for Photo Stream via Bidirectional Attention Recurrent Neural Networks.


title: Normative Values of Two-Point Discrimination Test among Students of Princess Noura Bint Abdulrahman University in Riyadh

abstract: Present research was performed to generate normative values using Two-Point Discrimination test (TPD) for skin areas of dominant hand. Various studies revealed that TPD test demonstrate the integrity of tactile stimulation. In this study the test was executed on 270 students of art & design, medical and literary backgrounds of age between 20-23 years which were randomly selected from different colleges of Princess Noura Bint Abdulrahman University in Riyadh. TPD values were determined for distal palmar of the hand and tip of middle finger of their dominant hand parallel to the median nerve, which innervate the area of the hand and perpendicular on the fingertips. The examiner addressed the hand skills or any talent and visual acuity scale of the participants and used Michigan hand outcomes questionnaire (MHQ) to measure their perception of hand function, pain, satisfaction and work performance which was focused on all daily living activities of hand (ADL). To meet objectives of the study, test parameters were considered viz. ADL of one and both hands, overall function of hand, normal work; TPD values of distal palm of hand and tip of middle finger. Normative values of TPD test were obtained from range 2-7 mm among participants in distal palm of dominant hand with 4 mm average and 2-3 mm in the tip of middle finger with 2.6 mm average. Obtained normative values were analyzed statistically and compared with referred values. A plot between average TPD values shown that discriminatory sensations were found better in art and design college students. To measure the visual acuity values visual acuity test 6/6 vision was performed on participated students which represented the normal vision. Plot between values of TPD and visual acuity suggested that decrease level of visual acuity of students has better normative values which were obtained 3.4 mm of distal palm of dominant hand and 2.4 mm on the tip of the long finger. A significant relation between age of students and TPD values were estimated p 0.01 for visual acuity. This study estimated that fingertips were the most sensitive part than palm of hand (p<0.01).


title: A course on probability theory for computer scientists

abstract: During the past 20 years, probability theory has become a critical element in the development of many areas in computer science. Commensurately, in this paper, we argue for expanding the coverage of probability in the computing curriculum. Specifically, we present details of a new course we have developed on Probability Theory for Computer Scientists. An analysis of course evaluation data shows that students find the contextualized content of this class more relevant and valuable than general presentations of probability theory. We also discuss different models for expanding the role of probability in different curricular programs that may not have the capacity to teach a full course on the subject.


title: Regular polygon detection

abstract: This paper describes a new robust regular polygon detector. The regular polygon transform is posed as a mixture of regular polygons in a five dimensional space. Given the edge structure of an image, we derive the a posteriori probability for a mixture of regular polygons, and thus the probability density function for the appearance of a mixture of regular polygons. Likely regular polygons can be isolated quickly by discretising and collapsing the search space into three dimensions. The remaining dimensions may be efficiently recovered subsequently using maximum likelihood at the locations of the most likely polygons in the subspace. This leads to an efficient algorithm. Also the a posteriori formulation facilitates inclusion of additional a priori information leading to real-time application to road sign detection. The use of gradient information also reduces noise compared to existing approaches such as the generalised Hough transform. Results are presented for images with noise to show stability. The detector is also applied to two separate applications: real-time road sign detection for on-line driver assistance; and feature detection, recovering stable features in rectilinear environments.


title: A machine learning approach for predicting bank credit worthiness

abstract: Machine learning is an emerging technique for building analytic models for machines to "learn" from data and be able to do predictive analysis. The ability of machines to "learn" and do predictive analysis is very important in this era of big data and it has a wide range of application areas. For instance, banks and financial institutions are sometimes faced with the challenge of what risk factors to consider when advancing credit/loans to customers. For several features/attributes of the customers are normally taken into consideration, but most of these features have little predictive e ect on the credit worthiness or otherwise of the customer. Furthermore, a robust and e ective automated bank credit risk score that can aid in the prediction of customer credit worthiness very accurately is still a major challenge facing many banks. In this paper, we examine a real bank credit data and conduct several machine learning algorithms on the data for comparative analysis and to choose which algorithms are the best fit for learning bank credit data. The algorithms gave over 80% accuracy in prediction. Furthermore, the most important features that determine whether a customer will default or otherwise in paying his/her credit the next month are extracted from a total of 23 features. We then applied these most important features on some selected machine learning algorithms and compare their predictive accuracy with the other algorithms that used all the 23 features. The results show no significant di erence, signifying that these features can accurately determine the credit worthiness of the customers. Finally, we formulate a predictive model using the most important features to predict the credit worthiness of a given customer.


title: Reinforcement learning using associative memory networks

abstract: It is shown that associative memory networks are capable of solving immediate and general reinforcement learning (RL) problems by combining techniques from associative neural networks and reinforcement learning and in particular Q-learning. The modified model is shown to significantly outperform native RL techniques on a stochastic grid world task by developing correct optimal policies. The network contrary to pure RL methods is based on associative memory principles such as distribution of information, pattern completion, Hebbian learning, attractors, and noise tolerance. Because of this, it can be argued that the model possesses more cognitive explanative power than pure reinforcement learning methods or other hybrid models and can be an effective tool for bridging the gap between biological memory models and computational memory models.


title: Advances in Network Simulation

abstract: Network researchers must test Internet protocols under varied conditions to determine whether they are robust and reliable. The paper discusses the Virtual Inter Network Testbed (VINT) project which has enhanced its network simulator and related software to provide several practical innovations that broaden the conditions under which researchers can evaluate network protocols.


title: Utilizing Local Phase Information to Remove Rain from Video

abstract: In the context of extracting information from video, bad weather conditions like rain can have a detrimental effect. In this paper, a novel framework to detect and remove rain streaks from video is proposed. The first part of the proposed framework for rain removal is a technique to detect rain streaks based on phase congruency features. The variation of features from frame to frame is used to estimate the candidate rain pixels in a frame. In order to reduce the number of false candidates due to global motion, frames are registered using phase correlation. The second part of the proposed framework is a novel reconstruction technique that utilizes information from three different sources, which are intensities of the rain affected pixel, spatial neighbors, and temporal neighbors. An optimal estimate for the actual intensity of the rain affected pixel is made based on the minimization of registration error between frames. An optical flow technique using local phase information is adopted for registration. This part of the proposed framework for removing rain is modeled such that the presence of local motion will not distort the features in the reconstructed video. The proposed framework is evaluated quantitatively and qualitatively on a variety of videos with varying complexities. The effectiveness of the algorithm is quantitatively verified by computing a no-reference image quality measure on individual frames of the reconstructed video. From a variety of experiments that are performed on output videos, it is shown that the proposed technique performs better than state-of-the-art techniques.


HACHIRŌ FUKUHARA: THE SCHOLAR-STATESMAN AND INDUSTRIAL SPY?


title: Traffic light detection and recognition based on Haar-like features

abstract: The problem of traffic light detection and recognition is investigated in this paper. Most algorithms used in traffic light detection and recognition are based on color detection. The color-based approach has some difficulties in that if the color of the traffic lights is changed by external factors, they will not be recognized and errors will occur. We propose an algorithm for traffic light detection and recognition based on Haar-like features in this paper. We use Haar-like features to learn about the traffic light image and detect the candidate area based on the learning data. The detected candidate image is verified by the pre-learned SVM(Support Vector Machine) classifier, and binarization and morphology operations are performed on the verified candidate image for detection of the traffic light object. The detected traffic light is divided into respective signal areas to determine the current on/off status of traffic lights. The signal signs in the respective areas are defined by regulation and the sign of traffic lights can be recognized by recognizing on/off of the signals in the respective areas. The experimental study is performed to show that it is possible to detect and recognize traffic lights irrespective of color changes.


title: A 10.5-b ENOB 645 nW 100kS/s SAR ADC with statistical estimation based noise reduction

abstract: This paper presents a power-efficient SNR enhancement technique for SAR ADCs. By accurately estimating the conversion residue, it can suppress both comparator noise and quantization error. Thus, it allows the use of a noisy low-power comparator and a relatively low resolution DAC to achieve high resolution. The proposed technique has low hardware complexity, requiring no change to the standard ADC operation except for repeating the LSB comparisons. A prototype ADC is designed in 65nm CMOS. Its SNR is improved by 7dB with the proposed technique. Overall, it achieves 10.5-b ENOB while operating at 100kS/s and consuming 645nW from a 0.7V power supply.


title: Big data in healthcare: Challenges and opportunities

abstract: Mobile phones, sensors, patients, hospitals, researchers, providers and organizations are nowadays, generating huge amounts of healthcare data. The real challenge in healthcare systems is how to find, collect, analyze and manage information to make people's lives healthier and easier, by contributing not only to understand new diseases and therapies but also to predict outcomes at earlier stages and make real-time decisions. In this paper, we explain the potential benefits of big data to healthcare and explore how it improves treatment and empowers patients, providers and researchers. We also describe the ability of reality mining in collecting large amounts of data to understand people's habits, detect and predict outcomes, and illustrate the benefits of big data analytics through five effective new pathways that could be adopted to promote patients' health, enhance medicine, reduce cost and improve healthcare value and quality. We cover some big data solutions in healthcare and we shed light on implementations, such as Electronic Healthcare Record (HER) and Electronic Healthcare Predictive Analytics (e-HPA) in US hospitals. Furthermore, we complete the picture by highlighting some challenges that big data analytics faces in healthcare.


title: Integrated Networking, Caching, and Computing for Connected Vehicles: A Deep Reinforcement Learning Approach

abstract: The developments of connected vehicles are heavily influenced by information and communications technologies, which have fueled a plethora of innovations in various areas, including networking, caching, and computing. Nevertheless, these important enabling technologies have traditionally been studied separately in the existing works on vehicular networks. In this paper, we propose an integrated framework that can enable dynamic orchestration of networking, caching, and computing resources to improve the performance of next generation vehicular networks. We formulate the resource allocation strategy in this framework as a joint optimization problem, where the gains of not only networking but also caching and computing are taken into consideration in the proposed framework. The complexity of the system is very high when we jointly consider these three technologies. Therefore, we propose a novel deep reinforcement learning approach in this paper. Simulation results with different system parameters are presented to show the effectiveness of the proposed scheme.


title: Resource provisioning of Web applications in heterogeneous clouds

abstract: Cloud computing platforms provide very little guarantees regarding the performance of seemingly identical virtual machine instances. Such instances have been shown to exhibit significantly different performance from each other. This heterogeneity creates two challenges when hosting multi-tier Web applications in the Cloud. First, different machine instances have different processing capacity so balancing equal amounts of load to different instances leads to poor performance. Second, when an application must be reprovisioned, depending on the performance characteristics of the new machine instance it may be more beneficial to add the instance to one tier or another. This paper shows how we can efficiently benchmark the individual performance profile of each individual virtual machine instance when we obtain it from the Cloud. These performance profiles allow us to balance the request load more efficiently than standard load balancers, leading to better performance at lower costs. The performance profiles also allow us to predict the performance that the overall application would have if the new machine instance would be added to any of the application tiers, and therefore to decide how to make best use of newly acquired machine instances. We demonstrate the effectiveness of our techniques by provisioning the TPC-W e-commerce benchmark in the Amazon EC2 platform.


title: An approach to identify duplicated web pages

abstract: A relevant consequence of the expansion of the web and e-commerce is the growth of the demand of new web sites and web applications. As a result, web sites and applications are usually developed without a formalized process, and web pages are directly coded in an incremental way, where new pages are obtained by duplicating existing ones. Duplicated web pages, having the same structure and just differing for the data they include, can be considered as clones. The identification of clones may reduce the effort devoted to test, maintain and evolve web sites and applications. Moreover, clone detection among different web sites aims to detect cases of possible plagiarism. In this paper we propose an approach. based on similarity metrics, to detect duplicated pages in web sites and applications, implemented with HTML language and ASP technology. The proposed approach has been assessed by analyzing several web sites and Web applications. The obtained results are reported in the paper with respect to some case studies.


title: Designing educational games through a conceptual model based on rules and scenarios

abstract: The design of a successful educational game (EG) is a challenging task that requires a lot of knowledge and a variety of skills. EG designers not only have to deal with the inherent technical complexity of game design, but also have to be able to interweave learning activities in a way that is enjoyable and educationally effective at the same time. In order to make available the benefits of game based learning to a wider audience, it is necessary to provide means to alleviate the cost of envisioning new EG by providing tools that might contribute to make the design process easier and quicker. As a first step towards this goal, in this paper we introduce a conceptual model that organizes in a modular way and in different design perspectives the game features. In order to help EG designers, the features that are most often regarded in the literature as significant in producing engaging, fun and educational game experiences, have been included in the model through a set of design entities. Furthermore, the organization of the elements of the model facilitates reusing pieces of the EG designs to quickly produce variants of the same game which can be used to match different learning purposes. The opinions gathered from the educators and game designers that participated of an EG design workshop confirmed that the model can help multidisciplinary EG design teams. Moreover, the model successfully contributed to the process of designing a collection of EGs aimed at raising children ?s awareness of emergencies and domestic risks, whose educational and ludic value was assessed in an experience conducted with students and educators at a primary school in Madrid.


Unsupervised deformable image registration with fully connected generative neural network


title: www.elsevier.com/locate/tcs Topics in the theory of DNA computing

abstract: DNA computing, or, more generally, molecular computing, is an exciting fast developing interdisciplinary area. Research in this area concerns theory, experiments, and applications of DNA computing. In this paper, we demonstrate the theoretical developments by discussing a number of selected topics. We also give an introduction to the basic structure of DNA and the basic DNA processing tools.


title: A functional group characterization of organic PM 2 . 5 exposure : Results from the RIOPA study

abstract: Abstract The functional group (FG) composition of urban residential outdoor, indoor, and personal fine particle (PM 2.5 ) samples is presented and used to provide insights relevant to organic PM 2.5 exposure. PM 2.5 samples (48 h) were collected during the Relationship of Indoor, Outdoor, and Personal Air (RIOPA) study at 219 non-smoking homes (once or twice) in Los Angeles County, CA, Elizabeth, NJ, and Houston, TX. Fourier transform infrared (FTIR) spectra of PM 2.5 samples were collected, and FG absorbances were quantified by partial least squares (PLS) regression, a multivariate calibration method. There is growing evidence in the literature that a large majority of indoor-generated PM 2.5 is organic. The current research suggests that indoor-generated PM 2.5 is enriched in aliphatic carbon–hydrogen (CH) FGs relative to ambient outdoor PM 2.5 . Indoor-generated CH exceeded outdoor-generated CH in 144 of the 167 homes for which indoor or outdoor CH was measurable; estimated indoor emission rates are provided. The strong presence of aliphatic CH FGs in indoor PM 2.5 makes particulate organic matter substantially less polar indoors and in personal exposures than outdoors. This is a substantial new finding. Based on the quantified FGs, the average organic molecular weight (OM) per carbon weight (OC), a measure of the degree of oxygenation of organic PM, is in the range of 1.7–2.6 for outdoor samples and 1.3–1.7 for indoor and personal samples. Polarity or degree of oxygenation effects particle deposition in exposure environments and in the respiratory system.


title: Metamaterial-Based Design of Planar Compact MIMO Monopoles

abstract: A systematic design of planar MIMO monopole antennas with significantly reduced mutual coupling is presented, based on the concept of metamaterials. The design is performed by means of individual rectangular loop resonators, placed in the space between the antenna elements. The underlying principle is that resonators act like small metamaterial samples, thus providing an effective means of controlling electromagnetic wave propagation. The proposed design achieves considerably high levels of isolation between antenna elements, without essentially affecting the simplicity and planarity of the MIMO antenna.


title: Human-Animal Bonds II: The Role of Pets in Family Systems and Family Therapy

abstract: The vast majority of pet owners regard their companion animals as family members, yet the role of pets in family systems and family therapy has received little attention in research, training, and practice. This article first notes the benefits of family pets and their importance for resilience. It then examines their role in couple and family processes and their involvement in relational dynamics and tensions. Next, it addresses bereavement in the loss of a cherished pet, influences complicating grief, and facilitation of mourning and adaptation. Finally, it explores the ways that clients' pets and the use of therapists' companion animals in animal-assisted therapy can inform and enrich couple and family therapy as valuable resources in healing.


title: Annotating evidence-based argumentation in biomedical text

abstract: A new challenge in natural language processing, argumentation mining is the automatic identification of an argument's premises, conclusion, and argumentation scheme, and relationships between arguments. Argumentation mining could provide critical context for information extraction and question answering and support new forms of summarization and citation indexing. However, argumentation mining, in this sense, has not yet been addressed in BioNLP. This theoretical paper contributes towards annotation of argumentation in biomedical/biological corpora. We show that argumentative zone models and models of discourse coherence do not represent the same aspects of discourse as a model of evidence-based argumentation. We explore the challenges of annotation of argumentation in full-text biomedical articles and describe the steps we have taken towards annotating a biomedical corpus for argumentation mining research.


title: Exploring shared control in automated driving

abstract: Automated driving systems that share control with human drivers by using haptic feedback through the steering wheel have been shown to have advantages over fully automated systems and manual driving. Here, we describe an experiment to elicit tacit expectations of behavior from such a system. A gaming steering wheel electronically coupled to the steering wheel in a full-car driving simulator allows two participants to share control of the vehicle. One participant was asked to use the gaming wheel to act as the automated driving agent while another participant acted as the car driver. The course provided different information and visuals to the driving agent and the driver to simulate possible automation failures and conflict situations between automation and the driver. The driving agent was also given prompts that specified a communicative goal at various points along the course. Both participants were interviewed before and after the drive, and vehicle data and drive video were collected. Our results suggest that drivers were able to interpret simple trajectory intentions, such as a lane change, conveyed by the driving agent. However, the driving agent was not able to effectively communicate more nuanced, higher level ideas such as availability, primarily due to the steering wheel being the control mechanism. Torque on the steering wheel without warning was seen most often as a failure of automation. Gentle and steady steering movements were viewed more favorably.


title: Pattern Recognition: Possible Research Areas and Issues

abstract: Pattern recognition is a tough problem for computers, although humans are wired for it. Pattern recognition is becoming increasingly important in the age of automation and information handling and retrieval. This paper reviews possible application areas of Pattern recognition. Author covers various sub-disciplines of pattern recognition based on learning methods, such as supervised, unsupervised, semi-supervised learning and key research areas such as grammar induction. Novel solutions to these possible problems could be well deployed for character recognition, speech analysis, man and machine diagnostics, person identification, industrial inspection and so on. The paper concludes with brief discussion on open issues that need to be addressed by future researchers.


title: A framework of knowledge management systems: issues and challenges for theory and practice

abstract: As the basis of value creation increasingly depends on the leverage of the intangible assets of firms, knowledge management systems (KMS) are emerging as powerful sources of competitive advantage. However, the general recognition of the importance of such systems seems to be accompanied by a technology-induced drive to implement systems with inadequate consideration of the fundamental knowledge problems that the KMS are likely to solve. This paper contributes to the stream of research on knowledge management systems by proposing an inductively developed framework for this important class of information systems, classifying KMS based on the locus of the knowledge and the a priori structuring of contents. This framework provides a means to explore issues related to KMS and unifying dimensions underlying different types of KMS. The contingencies that we discuss—the size and diversity of networks, the maintenance of knowledge flows and the long term effects of the use of KMS—provide a window into work in a number of reference disciplines that would enrich the utility of KMS and also open up fruitful areas for future research.


The Tamiflu fiasco and lessons learnt


title: BRAINPORT: AN ALTERNATIVE INPUT TO THE BRAIN

abstract: Brain Computer Interface (BCI) technology is one of the most rapidly developing areas of modern science; it has created numerous significant crossroads between Neuroscience and Computer Science. The goal of BCI technology is to provide a direct link between the human brain and a computerized environment. The objective of recent BCI approaches and applications have been designed to provide the information flow from the brain to the computerized periphery. The opposite or alternative direction of the flow of information (computer to brain interface, or CBI) remains almost undeveloped. The BrainPort is a CBI that offers a complementary technology designed to support a direct link from a computerized environment to the human brain — and to do so non-invasively. Currently, BrainPort research is pursuing two primary goals. One is the delivery of missing sensory information critical for normal human behavior through an additional artificial sensory channel around the damaged or malfunctioning natural sensory system. The other is to decrease the risk of sensory overload in human-machine interactions by providing a parallel and supplemental channel for information flow to the brain. In contrast, conventional CBI strategies (e.g., Virtual Reality), are usually designed to provide additional or substitution information through pre-existing sensory channels, and unintentionally aggravate the brain overload problem.


title: Mnemosyne: lightweight persistent memory

abstract: New storage-class memory (SCM) technologies, such as phase-change memory, STT-RAM, and memristors, promise user-level access to non-volatile storage through regular memory instructions. These memory devices enable fast user-mode access to persistence, allowing regular in-memory data structures to survive system crashes. In this paper, we present Mnemosyne, a simple interface for programming with persistent memory. Mnemosyne addresses two challenges: how to create and manage such memory, and how to ensure consistency in the presence of failures. Without additional mechanisms, a system failure may leave data structures in SCM in an invalid state, crashing the program the next time it starts. In Mnemosyne, programmers declare global persistent data with the keyword "pstatic" or allocate it dynamically. Mnemosyne provides primitives for directly modifying persistent variables and supports consistent updates through a lightweight transaction mechanism. Compared to past work on disk-based persistent memory, Mnemosyne reduces latency to storage by writing data directly to memory at the granularity of an update rather than writing memory pages back to disk through the file system. In tests emulating the performance characteristics of forthcoming SCMs, we show that Mnemosyne can persist data as fast as 3 microseconds. Furthermore, it provides a 35 percent performance increase when applied in the OpenLDAP directory server. In microbenchmark studies we find that Mnemosyne can be up to 1400% faster than alternative persistence strategies, such as Berkeley DB or Boost serialization, that are designed for disks.


title: A comparative study of the classification techniques in isolated Mandarin syllable tone recognition

abstract: Tonal languages, such as Chinese, use systematic variations of pitch to distinguish lexical or grammatical meaning. Thus, tone recognition is essential for tonal languages. Typically, tone recognition for isolated syllables involves three major steps: fundamental frequency (F0) detection, feature extraction, and classification. The work compares different techniques for these three steps and to answer the questions: for Mandarin Chinese syllables, what combination of fundamental frequency detection and feature extraction methods best prepare data for classification, and what is the most effective classification method for tone recognition. Three types of F0 detection methods (autocorrelation, cross-correlation and cepstrum), two feature extraction schemes (sampled F0 and average F0, slope and energy from three subsegments), four normalization methods (slope only, 0--100 scaled, z-score and T1 shift), and two classification methods (Support Vector Machine (SVM) and Multilayer Perceptron (MLP)) were experimentally studied using 700 collected data samples.


title: CMOS temperature sensor with built-in ADC

abstract: An area efficient temperature sensor using Ring Oscillator (RO) is proposed. It is basically implemented with two ROs, one being temperature-sensitive and the other temperature-insensitive. Temperature measurement is done in two steps. Initially, temperature-to-time conversion is performed when temperature-sensitive RO is enabled and then time-to-digital conversion is performed when temperature-insensitive RO is enabled. This two step process optimizes the area because only one counter is sufficient. The feedback provision enables only one RO at once, providing low dynamic power dissipation. A True Single-Phase Clock (TSPC) based high speed counter is employed to meet the requirement. It is designed using UMC 0.18 μm CMOS technology with the supply votage of 1.8 V and the simulation results are presented. The proposed sensor is capable of measuring the temperatures from −55° C to 125° C with the resolution of 5.68 LSB/°C. It dissipates 650 μW with the conversion rate of 300K Samples/S and occupying 10.05 μm2.


title: Applying Integer Programming to AI Planning

abstract: Despite the historical difference in focus between AI planning techniques and Integer Programming (IP) techniques, recent research has shown that IP techniques show significant promise in their ability to solve AI planning problems. This paper provides approaches to encode AI planning problems as IP problems, describes some of the more significant issues that arise in using IP for AI planning, and discusses promising directions for future research.


title: Privacy-Preserving Queries on Encrypted Data ⋆

abstract: Data confidentiality is a major concern in database systems. Encryption is a useful tool for protecting the confidentiality of sensitive data. However, when data is encrypted, performing queries becomes more challenging. In this paper, we study efficient and provably secure methods for queries on encrypted data stored in an outsourced database that may be susceptible to compromise. Specifically, we show that, in our system, even if an intruder breaks into the database and observes some interactions between the database and its users, he only learns very little about the data stored in the database and the queries performed on the data. Our work consists of several components. First, we consider databases in which each attribute has a finite domain and give a basic solution for certain kinds of queries on such databases. Then, we present two enhanced solutions, one with a stronger security guarantee and the other with accelerated queries. In addition to providing proofs of our security guarantees, we provide empirical performance evaluations. Our experiments demonstrate that our solutions are fast on large-sized real data.


title: Design Challenges/Solutions for Environments Supporting the Analysis of Social Media Data in Crisis Informatics Research

abstract: Crisis informatics investigates how society's pervasive access to technology is transforming how it responds to mass emergency events. To study this transformation, researchers require access to large sets of data that because of their volume and heterogeneous nature are difficult to collect and analyze. To address this concern, we have designed and implemented an environment -- EPIC Analyze -- that supports researchers with the collection and analysis of social media data. Our research has identified the types of components -- such as NoSQL, MapReduce, caching, and search -- needed to ensure that these services are reliable, scalable, extensible, and efficient. We describe the design challenges encountered -- such as data modeling, time vs. Space tradeoffs, and the need for a useful and usable system -- when building EPIC Analyze and discuss its scalability, performance, and functionality.


title: Design of a hydraulic ankle-foot orthosis

abstract: This paper presents the design and simulation of an ankle-foot orthosis (AFO) to assist human walking. Design requirements were established based on a quantitative study of published data, simulations of human walking, and a model of intrinsic and reflex ankle joint stiffness. The design of an AFO that meets these requirements is then presented; it comprises a small linear, hydraulic actuator, a servo-valve, a hydraulic power supply, and an accumulator. Two methods of selecting the kinematic parameters of the AFO are introduced. One is based on force minimization and the other on compactness maximization. The performance expected of the AFO is demonstrated in a simulation study.


Interactions with big data analytics


title: Community Discovery in Dynamic Networks: A Survey

abstract: Several research studies have shown that complex networks modeling real-world phenomena are characterized by striking properties: (i) they are organized according to community structure, and (ii) their structure evolves with time. Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery. A novel and fascinating problem started capturing researcher interest recently: the identification of evolving communities. Dynamic networks can be used to model the evolution of a system: nodes and edges are mutable, and their presence, or absence, deeply impacts the community structure that composes them. ::: ::: This survey aims to present the distinctive features and challenges of dynamic community discovery and propose a classification of published approaches. As a “user manual,” this work organizes state-of-the-art methodologies into a taxonomy, based on their rationale, and their specific instantiation. Given a definition of network dynamics, desired community characteristics, and analytical needs, this survey will support researchers to identify the set of approaches that best fit their needs. The proposed classification could also help researchers choose in which direction to orient their future research.


title: A comparison of 10 sampling algorithms for configurable systems

abstract: Almost every software system provides configuration options to tailor the system to the target platform and application scenario. Often, this configurability renders the analysis of every individual system configuration infeasible. To address this problem, researchers have proposed a diverse set of sampling algorithms. We present a comparative study of 10 state-of-the-art sampling algorithms regarding their fault-detection capability and size of sample sets. The former is important to improve software quality and the latter to reduce the time of analysis. In a nutshell, we found that sampling algorithms with larger sample sets are able to detect higher numbers of faults, but simple algorithms with small sample sets, such as most-enabled-disabled, are the most efficient in most contexts. Furthermore, we observed that the limiting assumptions made in previous work influence the number of detected faults, the size of sample sets, and the ranking of algorithms. Finally, we have identified a number of technical challenges when trying to avoid the limiting assumptions, which questions the practicality of certain sampling algorithms.


title: Quantitative Assessment of Operational Security: Models and Tools

abstract: Abstract This paper proposes a novel approach to help computing system administrators inmonitoring the security of their systems. This approach is based on modeling thesystem as a privilege graph exhibiting operational security vulnerabilities and ontransforming this privilege graph into a Markov chain corresponding to all possiblesuccessful attack scenarios. A set of tools has been developed to generateautomatically the privilege graph of a Unix system, to transform it into thecorresponding Markov chain and to compute characteristic measures of theoperational system security.Keywords: quantitative security evaluation, privilege graph, Markov modeling 1. Introduction Computing system security relies mostly on users, operators and administrators, and eventhe best designed system, if badly operated, would be unsecure. Most authentication andprotection mechanisms can be diverted by malicious or careless users, then allowing possibleintruders to perform security breaches. This is not surprising since most users are lessinterested in security than in efficiency, flexibility and cooperation with other users.A scrupulous system administrator should try to maintain the best security for his systemwith the least incidence on user operation. It is thus tremendously important to assess thesecurity level of the operational system and to monitor the evolution of this security level withrespect to system configuration modifications, application or operation changes andenvironment evolution.This paper develops an approach to evaluate the security of operational computing systems.Examples are taken from Unix™ systems, but the approach could be extended to otheroperating systems and to distributed systems as well. Section 2 shows how to model acomputing system in a way exhibiting vulnerabilities that could be exploited by a possible


title: CORE: A Cloud-based Object Recognition Engine for robotics

abstract: An object recognition engine needs to extract discriminative features from data representing an object and accurately classify the object to be of practical use in robotics. Furthermore, the classification of the object must be rapidly performed in the presence of a voluminous stream of data. These conditions call for a distributed and scalable architecture that can utilize a cloud computing infrastructure for performing object recognition. This paper introduces a Cloud-based Object Recognition Engine (CORE) to address these needs. CORE is able to train on large-scale datasets, perform classification of 3D point cloud data, and efficiently transfer data in a robotic network.


title: DESIGNING THE OCEAN PAVILION: Biomaterial Templating of Structural, Manufacturing, and Environmental Performance

abstract: Driven by novel biomaterials design and natural aqueous formation, this research offers a new structural design perspective combining a crustacean-derived biopolymer with robotic fabrication to shape constructs that interact with the environment utilizing graded material properties for hydrationguided formation. We establish structural, manufacturing, and environmental design templating strategies informing the design of the constructs and their material makeup. We present a biomaterialdriven design process resulting in a novel structural system and a custom robotic manufacturing platform designed to deposit water-based composites unveiling novel functional, mechanical, and optical gradients across length scales. Components are form-found through evaporation patterns informed by the geometrical arrangement of structural members and the hierarchical distribution of material properties. Each component is designed to take shape upon contact with air and dissolve upon contact with water. We present the principles and method applied as a unique case demonstrating the material ecology design approach through additive manufacturing of lightweight, biocompatible and materially heterogeneous structures. Initial results demonstrate a wide range of structural behaviors that represent a novel approach to material-informed biodegradable structure formation by design and hold great promise for the future of sustainable manufacturing.


title: MP-DASH: Adaptive Video Streaming Over Preference-Aware Multipath

abstract: Compared with using only a single wireless path such as WiFi, leveraging multipath (e.g., WiFi and cellular) can dramatically improve users' quality of experience (QoE) for mobile video streaming. However, Multipath TCP (MPTCP), the de-facto multipath solution, lacks the support to prioritize one path over another. When applied to video streaming, it may cause undesired network usage such as substantial over-utilization of the metered cellular link. In this paper, we propose MP-DASH, a multipath framework for video streaming with the awareness of network interface preferences from users. The basic idea behind MP-DASH is to strategically schedule video chunks' delivery and thus satisfy user preferences. MP-DASH can work with a wide range of off-the-shelf video rate adaptation algorithms with very small changes. Our extensive field studies at 33 locations in three U.S. states suggest that MP-DASH is very effective: it can reduce cellular usage by up to 99% and radio energy consumption by up to 85% with negligible degradation of QoE, compared with off-the-shelf MPTCP.


title: Towards international E-stat for monitoring the socio-economic activities across the globe

abstract: We investigate relationship between annual electric power consumption per capita and gross domestic production (GDP) per capita for 131 countries. We found that the relationship can be fitted with a power-law function. We examine the relationship for 47 prefectures in Japan. Furthermore, we investigate values of annual electric power production reported by four international organizations. We collected the data from U.S. Energy Information Administration (EIA), Statistics by International Energy Agency (IEA), OECD Factbook (Economic, Environmental and Social Statistics), and United Nations (UN) Energy Statistics Yearbook. We found that the data structure, values, and unit depend on the organizations. This implies that it is further necessary to establish data standards and an organization to collect, store, and distribute the data on socio-economic systems.


title: Review of control algorithms for robotic ankle systems in lower-limb orthoses, prostheses, and exoskeletons.

abstract: This review focuses on control strategies for robotic ankle systems in active and semiactive lower-limb orthoses, prostheses, and exoskeletons. Special attention is paid to algorithms for gait phase identification, adaptation to different walking conditions, and motion intention recognition. The relevant aspects of hardware configuration and hardware-level controllers are discussed as well. Control algorithms proposed for other actuated lower-limb joints (knee and/or hip), with potential applicability to the development of ankle devices, are also included.


title: Advanced Analytics with the SAP HANA Database

abstract: MapReduce as a programming paradigm provides a simple-to-use yet very powerful abstraction encapsulated in two second-order functions: Map and Reduce. As such, they allow defining single sequentially processed tasks while at the same time hiding many of the framework details about how those tasks are parallelized and scaled out. In this paper we discuss four processing patterns in the context of the distributed SAP HANA database that go beyond the classic MapReduce paradigm. We illustrate them using some typical Machine Learning algorithms and present experimental results that demonstrate how the data flows scale out with the number of parallel tasks.


title: Effect of PWM inverters on AC motor bearing currents and shaft voltages

abstract: This paper investigates AC induction motor shaft voltage problems, current flow through motor bearings and electric discharge current problems within bearings when operated under both pure sinewave and pulse width modulated (PWM) inverter sources. Experience suggests that PWM voltage sources with steep wavefronts especially increase the magnitude of the above electrical problems, leading to motor bearing material erosion and early mechanical failure. Previous literature suggests that shaft voltage-bearing current problems under 60 Hz sinewave operation are predominantly electromagnetically induced. It is proposed that under PWM operation these same problems are now predominantly an electrostatic phenomenon. A system model to describe this phenomenon is characterized and developed. Construction and test of a new electrostatic shielded induction motor (ESIM) verifies this model and is also a possible solution to the bearing current problem under PWM operation. >


title: Methods of drying flooded domestic properties: the perceptions of UK building surveyors

abstract: The prediction of more frequent flooding events in the UK necessitates the development of reinstatement standards for flood-damaged domestic properties. This is because current reinstatement methods exhibit significant variance which ultimately influence cost and time needed for repair works as well as their overall performance. Prior to the commencement of repair works, it is important that appropriate drying methods be used so that subsequent repair works are not damaged by latent defects such as fungal attack. This paper presents the perceptions of 289 building surveyors regarding methods used to dry flooded domestic properties. Findings suggest that surveyors consider various methods to assist drying rather than focusing on a single dominant method. Furthermore, the drying method is not a scientific process but rather one based on experience and subjectivity. These facts highlight the lack of definitive guidance on drying out flooded buildings. Further research is recommended towards developing standardised drying out procedures and techniques.


title: Perceivable Light Fields: Matching the Requirements Between the Human Visual System and Autostereoscopic 3-D Displays

abstract: Recently, there has been a substantial increase in efforts to develop 3-D visualization technologies that can provide the viewers with a realistic 3-D visual experience. Various terms such as “reality communication” have been used to categorize these efforts. In order to provide the viewers with a complete and realistic visual sensation, the display or visualization system and the displayed content need to match the physiological 3-D information sensing capabilities of the human visual system which can be quite complex. These may include spatial and temporal resolutions, depth perception, dynamic range, spectral contents, nonlinear effects, and vergence accommodation effects. In this paper, first we present an overview of some of the 3-D display research efforts which have been extensively pursued in Asia, Europe, and North America among other areas. Based on the limitations and comfort-based requirements of the human visual system when viewing a nonnatural visual input from 3-D displays, we present an analytical framework that combines main perception and human visual requirements with analytical tools and principles used in related disciplines such as optics, computer graphics, computational imaging, and signal processing. Building on the widely used notion of light fields, we define a notion of perceivable light fields to account for the human visual system physiological requirements, and propagate it back to the display device to determine the display device specifications. This helps us clarify the fundamental and practical requirements of the 3-D display devices for reality viewing communication. In view of the proposed analytical framework, we overview various methods that can be applied to overcome the extensive information needed to be displayed in order to meet the requirements imposed by the human visual system.


title: State of the art on vision-based structured light systems for 3D measurements

abstract: This paper aims at giving an overview on the state of the art of vision-based, non-contact 3D measurement systems based on structured light. Such systems are equipped with a camera and a light projector, and are referred to in the literature as active stereo systems. This report is structured into structured light, calibration methods and 3D measurement techniques. Moreover, several selected research works are described in order to illustrate these different topics


title: Parallel simulation of UAV swarm scenarios

abstract: The concept of operations for a micro-UAV system is adopted from nature from the appearance of flocking birds, movement of a school of fish, and swarming bees among others. This "emergent behavior" is the aggregate result of many simple interactions occurring within the flock, school, or swarm. Exploration of this emergent behavior in a swarm is accomplished through a high performance computing parallel discrete event simulation. After design of the system, several experiments are designed, tested, and analyzed for efficiency and effectiveness.


title: Spectroscopic and Microscopic Studies on the Mechanisms of Mitochondrial Toxicity Induced by Different Concentrations of Cadmium

abstract: The deleterious action of Cd2+ on rat liver mitochondria was investigated in this work using spectroscopic and microscopic methods. The concentration dependence of Cd2+ on mitochondrial swelling, membrane potential and membrane fluidity was studied. Our aim was to detect the active sites of Cd2+ in the mitochondrial membrane treatments with cyclosporin A (CsA) and EGTA on the mitochondrial permeability transition (MPT) induced by low and high concentrations of Cd2+. The protective effects of dithiothreitol, human serum albumin and monobromobimane+ on Cd2+-induced MPT were also monitored. All of these investigations indicated that Cd2+ can directly affect MPT at two separate localization sites at different concentrations: the classic Ca2+ triggering site and the thiol (-SH) groups of membrane proteins matched by MPT pore opening (defined as "S" site). At the high concentration of Cd2+, other free -SH groups in the mitochondrial matrix may be involved in this process. These findings were supported by transmission electron microscopy and shed light on the toxic mechanism of Cd2+ on mitochondria.


title: 250 kV sub-nanosecond pulse generator with adjustable pulse-width

abstract: This paper details the construction of a sub-nanosecond pulse generator capable of delivering 250 kV into a high impedance load. The pulse width is approximately 600ps with a voltage rise of up to 1 MV/ns. The pulse rise-time can be adjusted by manipulation of a peaking gap, whereas the pulse-width can be changed by adjusting a novel tail-cut switch located close to the load.


Spontaneous Concept Learning with Deep Autoencoder


title: Approximating the Randomized Hitting Time Distribution of a Non-Stationary Gamma Process

abstract: The non-stationary gamma process is a non-decreasing stochastic process with independent increments. By this monotonic behavior this stochastic process serves as a natural candidate for modelling time-dependent phenomena such as degradation. In condition-based maintenance the first time such a process exceeds a random threshold is used as a model for the lifetime of a device or for the random time between two successive imperfect maintenance actions. Therefore there is a need to investigate in detail the cumulative distribution function (cdf) of this so-called randomized hitting time. We first relate the cdf of the (randomized) hitting time of a non-stationary gamma process to the cdf of a related hitting time of a stationary gamma process. Even for a stationary gamma process this cdf has in general no elementary formula and its evaluation is time-consuming. Hence two approximations are proposed in this paper and both have a clear probabilistic interpretation. Numerical experiments show that these approximations are easy to evaluate and their accuracy depends on the scale parameter of the non-stationary gamma process. Finally, we also consider some special cases of randomized hitting times for which it is possible to give an elementary formula for its cdf.


title: A Classification-Based Study of Covariate Shift in GAN Distributions

abstract: A basic, and still largely unanswered, question in the context of Generative Adversarial Networks (GANs) is whether they are truly able to capture all the fundamental characteristics of the distributions they are trained on. In particular, evaluating the diversity of GAN distributions is challenging and existing methods provide only a partial understanding of this issue. In this paper, we develop quantitative and scalable tools for assessing the diversity of GAN distributions. Specifically, we take a classification-based perspective and view loss of diversity as a form of covariate shift introduced by GANs. We examine two specific forms of such shift: mode collapse and boundary distortion. In contrast to prior work, our methods need only minimal human supervision and can be readily applied to state-of-the-art GANs on large, canonical datasets. Examining popular GANs using our tools indicates that these GANs have significant problems in reproducing the more distributional properties of their training dataset.


title: A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution

abstract: Sparse-view computed tomography (CT) holds great promise for speeding up data acquisition and reducing radiation dose in CT scans. Recent advances in reconstruction algorithms for sparse-view CT, such as iterative reconstruction algorithms, obtained high-quality image while requiring advanced computing power. Lately, deep learning (DL) has been widely used in various applications and has obtained many remarkable outcomes. In this paper, we propose a new method for sparse-view CT reconstruction based on the DL approach. The method can be divided into two steps. First, filter backprojection (FBP) was used to reconstruct the CT image from sparsely sampled sinogram. Then, the FBP results were fed to a DL neural network, which is a DenseNet and deconvolution-based network (DD-Net). The DD-Net combines the advantages of DenseNet and deconvolution and applies shortcut connections to concatenate DenseNet and deconvolution to accelerate the training speed of the network; all of those operations can greatly increase the depth of network while enhancing the expression ability of the network. After the training, the proposed DD-Net achieved a competitive performance relative to the state-of-the-art methods in terms of streaking artifacts removal and structure preservation. Compared with the other state-of-the-art reconstruction methods, the DD-Net method can increase the structure similarity by up to 18% and reduce the root mean square error by up to 42%. These results indicate that DD-Net has great potential for sparse-view CT image reconstruction.


title: Development of a wall-climbing robot with biped-wheel hybrid locomotion mechanism

abstract: This paper presents a wall-climbing robot for reconnaissance in anti-hijacking application. A novel biped-wheel hybrid locomotion mechanism is proposed, which is composed of a planetary gear train, a vacuum adhesion module and a negative pressure adhesion module. The bipedal, wheeled and hybrid locomotion modes are analyzed respectively. A prototype of the wall-climbing robot with compact size and low power consumption has been developed and a lot of performance tests have been conducted. The experimental results demonstrate that the wall-climbing robot has such characteristics as fast moving speed, excellent surface adaptability and obstacle negotiation capability.


title: Understanding Processing Overheads of Network Coding-Based Content Distribution in VANETs

abstract: Content distribution in vehicular networks, such as multimedia file sharing and software updates, poses a great challenge due to network dynamics and high-speed mobility. In recent years, network coding has been shown to efficiently support distribution of content in such dynamic environments, thereby considerably enhancing the performance. However, the related work in the literature has mostly focused on theoretic or algorithmic aspects of network coding so far. In this paper, we provide an in-depth analysis on the implementation issues of network coding in wireless networks. In particular, we study the impact of resource constraints (namely CPU, disk, memory, and bandwidth) on the performance of network coding in the content distribution application. The contribution of this paper is twofold. First, we develop an abstract model of a general network coding process and evaluate the validity of the model via several experiments on real systems. This model enables us to find the key resource constraints that influence the network coding strategy and thus to efficiently configure network coding parameters in wireless networks. Second, we propose schemes that considerably improve the performance of network coding under resource constrained environments. We implement our overhead model in the QualNet network simulator and evaluate these schemes in a large-scale vehicular network. Our results show that the proposed schemes can significantly improve the network coding performance by reducing the coding overhead.


Lessons Learned in Building Information Modeling Applications


title: Censoring Representations with an Adversary

abstract: Abstract: In practice, there are often constraints on the decisions that may be made for a decision problem, or in communicating data. One example of such a constraint is that a decision must not favour a particular group. Another is that data must not have identifying information. We address these two related issues by learning flexible representations that minimize the capability of an adversarial critic. This adversary is trying to predict the relevant sensitive variable from the representation, and so minimizing the performance of the adversary ensures there is little or no information in the representation about the sensitive variable. We demonstrate this in the specific contexts of making decisions free from discrimination and removing private information from images.


title: Hybrid electric vehicle propulsion system architectures of the e-CVT type

abstract: There is now significant interest in hybrid electric vehicle (HEV) propulsion systems globally. Economics play a major role as evidenced by oil prices in North America pressing upwards of $100/Bbl coupled with a customer preference for full size crossover and sport utility vehicles. The situation in Oceania is milder, but emerging markets such as China are experiencing automotive sector growth rates of 37%/year. Europe remains least affected by hybrids since nearly 47% of all new vehicles sold are diesel fueled and have economy ratings on par with that of gasoline-electric hybrids. In the global economy there are presently some 57 Mil new vehicles manufactured each year. Toyota and Honda have projected that HEVs will be 10 % to 15 % of the U.S. market by 2009, with Toyota raising the bar further by stating they will produce 1 Mil hybrids a year in the 2012 time frame. Hybrid propulsion system types are only vaguely comprehended by the buying public, and to a large measure, even by technical professionals. This paper addresses this latter issue by presenting a summary of the globally accepted standard in hybrid power trains-the power split architecture, or more generically and in common usage, the electronic-continuously variable transmission


title: Efficient simulation of inextensible cloth

abstract: Many textiles do not noticeably stretch under their own weight. Unfortunately, for better performance many cloth solvers disregard this fact. We propose a method to obtain very low strain along the warp and weft direction using Constrained Lagrangian Mechanics and a novel fast projection method. The resulting algorithm acts as a velocity filter that easily integrates into existing simulation code.


title: On the Robustness of a Neural Network

abstract: With the development of neural networks based machine learning and their usage in mission critical applications, voices are rising against the black box aspect of neural networks as it becomes crucial to understand their limits and capabilities. With the rise of neuromorphic hardware, it is even more critical to understand how a neural network, as a distributed system, tolerates the failures of its computing nodes, neurons, and its communication channels, synapses. Experimentally assessing the robustness of neural networks involves the quixotic venture of testing all the possible failures, on all the possible inputs, which ultimately hits a combinatorial explosion for the first, and the impossibility to gather all the possible inputs for the second.In this paper, we prove an upper bound on the expected error of the output when a subset of neurons crashes. This bound involves dependencies on the network parameters that can be seen as being too pessimistic in the average case. It involves a polynomial dependency on the Lipschitz coefficient of the neurons' activation function, and an exponential dependency on the depth of the layer where a failure occurs. We back up our theoretical results with experiments illustrating the extent to which our prediction matches the dependencies between the network parameters and robustness. Our results show that the robustness of neural networks to the average crash can be estimated without the need to neither test the network on all failure configurations, nor access the training set used to train the network, both of which are practically impossible requirements.


title: Meta-learning Method for Authomatic Selection of Algorithms for Text Classification

abstract: The paper presents a meta-learning approach for the task of textual document classification as an automatic selection of the optimal algorithm for creation of classifiers. Proposed method is based on the modified MUDOF algorithm, where regression model for prediction of optimizing parameters was replaced by a classification approach, using the kNN algorithm. This approach enables to increase quality and efficiency of semantic annotation procedures supported by text mining classification within collaborative eLearning system KP-Lab, which was taken as a prototype application. Design and implementation of the proposed meta-learning method using the JBowl Java library is described in detail. Finally, experimental results achieved by the meta-learning algorithms as well as their comparison with traditional ways used for text classification are presented and discussed.


title: Morpheme-based feature-rich language models using Deep Neural Networks for LVCSR of Egyptian Arabic

abstract: Egyptian Arabic (EA) is a colloquial version of Arabic. It is a low-resource morphologically rich language that causes problems in Large Vocabulary Continuous Speech Recognition (LVCSR). Building LMs on morpheme level is considered a better choice to achieve higher lexical coverage and better LM probabilities. Another approach is to utilize information from additional features such as morphological tags. On the other hand, LMs based on Neural Networks (NNs) with a single hidden layer have shown superiority over the conventional n-gram LMs. Recently, Deep Neural Networks (DNNs) with multiple hidden layers have achieved better performance in various tasks. In this paper, we explore the use of feature-rich DNN-LMs, where the inputs to the network are a mixture of words and morphemes along with their features. Significant Word Error Rate (WER) reductions are achieved compared to the traditional word-based LMs.


title: Feasibility of Using Fluorescence Spectrophotometry to Develop a Sensitive Dye Immersion Method for Container Closure Integrity Testing of Prefilled Syringes.

abstract: UNLABELLED ::: A feasibility study was conducted for a sensitive and robust dye immersion method for the measurement of container closure integrity of unopened prefilled syringes using fluorescence spectrophotometry as the detection method. A Varian Cary Eclipse spectrofluorometer was used with a custom-made sample holder to position the intact syringe in the sample compartment for fluorescence measurements. Methylene blue solution was initially evaluated as the fluorophore in a syringe with excitation at 607 nm and emission at 682 nm, which generated a limit of detection of 0.05 μg/mL. Further studies were conducted using rhodamine 123, a dye with stronger fluorescence. Using 480 nm excitation and 525 nm emission, the dye in the syringe could be easily detected at levels as low as 0.001 μg/mL. The relative standard deviation for 10 measurements of a sample of 0.005 μg/mL (with repositioning of the syringe after each measurement) was less than 1.1%. A number of operational parameters were optimized, including the photomultiplier tube voltage, excitation, and emission slit widths. The specificity of the testing was challenged by using marketed drug products and a protein sample, which showed no interference to the rhodamine detection. Results obtained from this study demonstrated that using rhodamine 123 for container closure integrity testing with in-situ (in-syringe) fluorescence measurements significantly enhanced the sensitivity and robustness of the testing and effectively overcame limitations of the traditional methylene blue method with visual or UV-visible absorption detection. ::: ::: ::: LAY ABSTRACT ::: Ensuring container closure integrity of injectable pharmaceutical products is necessary to maintain quality throughout the shelf life of a sterile drug product. Container closure integrity testing has routinely been used to evaluate closure integrity during product development and production line qualification of prefilled syringes, vials, and devices. However, container closure integrity testing has recently gained industry attention due to increased regulatory agency scrutiny regarding the analytical rigor of container closure integrity testing methods and expectations to use container closure integrity testing in lieu of sterility tests in stability programs. Methylene blue dye is often used for dye ingress testing of container closure integrity, but we found it unsuitable for reliable detection of small breaches in prefilled syringes of drug product. This work describes the suitability and advantages of using a fluorescent dye and spectroscopic detection for a robust, sensitive, and quality control-friendly container closure integrity testing method for prefilled syringes.


title: Chaotic Time Series Prediction Based on a Novel Robust Echo State Network

abstract: In this paper, a robust recurrent neural network is presented in a Bayesian framework based on echo state mechanisms. Since the new model is capable of handling outliers in the training data set, it is termed as a robust echo state network (RESN). The RESN inherits the basic idea of ESN learning in a Bayesian framework, but replaces the commonly used Gaussian distribution with a Laplace one, which is more robust to outliers, as the likelihood function of the model output. Moreover, the training of the RESN is facilitated by employing a bound optimization algorithm, based on which, a proper surrogate function is derived and the Laplace likelihood function is approximated by a Gaussian one, while remaining robust to outliers. It leads to an efficient method for estimating model parameters, which can be solved by using a Bayesian evidence procedure in a fully autonomous way. Experimental results show that the proposed method is robust in the presence of outliers and is superior to existing methods.


title: Learning from Limited Demonstrations

abstract: We propose a Learning from Demonstration (LfD) algorithm which leverages expert data, even if they are very few or inaccurate. We achieve this by using both expert data, as well as reinforcement signals gathered through trial-and-error interactions with the environment. The key idea of our approach, Approximate Policy Iteration with Demonstration (APID), is that expert's suggestions are used to define linear constraints which guide the optimization performed by Approximate Policy Iteration. We prove an upper bound on the Bellman error of the estimate computed by APID at each iteration. Moreover, we show empirically that APID outperforms pure Approximate Policy Iteration, a state-of-the-art LfD algorithm, and supervised learning in a variety of scenarios, including when very few and/or suboptimal demonstrations are available. Our experiments include simulations as well as a real robot path-finding task.


title: Modeling HTTP/2 Speed from HTTP/1 Traces

abstract: With the standardization of HTTP/2, content providerswant to understand the benefits and pitfalls of transitioning to the new standard. Using a large dataset of HTTP/1.1 resource timing data from production traffic on Akamai’s CDN, and a model of HTTP/2 behavior, we obtain the distribution of performance differences between the protocol versions for nearly 280,000 downloads. We find that HTTP/2 provides significant performanceimprovements in the tail, and, for websites for which HTTP/2 does not improve median performance, we explore how optimizations like prioritization and push can improve performance, and how these improvements relate to page structure.


title: SpeDo: 6 DOF Ego-Motion Sensor Using Speckle Defocus Imaging

abstract: Sensors that measure their motion with respect to the surrounding environment (ego-motion sensors) can be broadly classified into two categories. First is inertial sensors such as accelerometers. In order to estimate position and velocity, these sensors integrate the measured acceleration, which often results in accumulation of large errors over time. Second, camera-based approaches such as SLAM that can measure position directly, but their performance depends on the surrounding scene's properties. These approaches cannot function reliably if the scene has low frequency textures or small depth variations. We present a novel ego-motion sensor called SpeDo that addresses these fundamental limitations. SpeDo is based on using coherent light sources and cameras with large defocus. Coherent light, on interacting with a scene, creates a high frequency interferometric pattern in the captured images, called speckle. We develop a theoretical model for speckle flow (motion of speckle as a function of sensor motion), and show that it is quasi-invariant to surrounding scene's properties. As a result, SpeDo can measure ego-motion (not derivative of motion) simply by estimating optical flow at a few image locations. We have built a low-cost and compact hardware prototype of SpeDo and demonstrated high precision 6 DOF ego-motion estimation for complex trajectories in scenarios where the scene properties are challenging (e.g., repeating or no texture) as well as unknown.


title: TDDC03 Projects, Spring 2004 An Introduction to RFID – Information Security and Privacy Concerns

abstract: This report is written as part of the course TDDC03 Information Security at Linkoping Institute of Technology. It aims to give an introduction to RFID, point out the current and future uses of the technology and to evaluate and discuss the technology from a perspective of information security and privacy with focus on new arising concerns.


title: Methods of combining multiple classifiers and their applications to handwriting recognition

abstract: Possible solutions to the problem of combining classifiers can be divided into three categories according to the levels of information available from the various classifiers. Four approaches based on different methodologies are proposed for solving this problem. One is suitable for combining individual classifiers such as Bayesian, k-nearest-neighbor, and various distance classifiers. The other three could be used for combining any kind of individual classifiers. On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers can be improved significantly. For example, on the US zipcode database, 98.9% recognition with 0.90% substitution and 0.2% rejection can be obtained, as well as high reliability with 95% recognition, 0% substitution, and 5% rejection. >


title: Machine Learned Resume-Job Matching Solution

abstract: Job search through online matching engines nowadays are very prominent and beneficial to both job seekers and employers. But the solutions of traditional engines without understanding the semantic meanings of different resumes have not kept pace with the incredible changes in machine learning techniques and computing capability. These solutions are usually driven by manual rules and predefined weights of keywords which lead to an inefficient and frustrating search experience. To this end, we present a machine learned solution with rich features and deep learning methods. Our solution includes three configurable modules that can be plugged with little restrictions. Namely, unsupervised feature extraction, base classifiers training and ensemble method learning. In our solution, rather than using manual rules, machine learned methods to automatically detect the semantic similarity of positions are proposed. Then four competitive "shallow" estimators and "deep" estimators are selected. Finally, ensemble methods to bag these estimators and aggregate their individual predictions to form a final prediction are verified. Experimental results of over 47 thousand resumes show that our solution can significantly improve the predication precision current position, salary, educational background and company scale.


title: Designing a unified programming model for heterogeneous machines

abstract: While high-efficiency machines are increasingly embracing heterogeneous architectures and massive multithreading, contemporary mainstream programming languages reflect a mental model in which processing elements are homogeneous, concurrency is limited, and memory is a flat undifferentiated pool of storage. Moreover, the current state of the art in programming heterogeneous machines tends towards using separate programming models, such as OpenMP and CUDA, for different portions of the machine. Both of these factors make programming emerging heterogeneous machines unnecessarily difficult. We describe the design of the Phalanx programming model, which seeks to provide a unified programming model for heterogeneous machines. It provides constructs for bulk parallelism, synchronization, and data placement which operate across the entire machine. Our prototype implementation is able to launch and coordinate work on both CPU and GPU processors within a single node, and by leveraging the GASNet runtime, is able to run across all the nodes of a distributed-memory machine.


title: Neural Machine Translation with Latent Semantic of Image and Text

abstract: Although attention-based Neural Machine Translation have achieved great success, attention-mechanism cannot capture the entire meaning of the source sentence because the attention mechanism generates a target word depending heavily on the relevant parts of the source sentence. The report of earlier studies has introduced a latent variable to capture the entire meaning of sentence and achieved improvement on attention-based Neural Machine Translation. We follow this approach and we believe that the capturing meaning of sentence benefits from image information because human beings understand the meaning of language not only from textual information but also from perceptual information such as that gained from vision. As described herein, we propose a neural machine translation model that introduces a continuous latent variable containing an underlying semantic extracted from texts and images. Our model, which can be trained end-to-end, requires image information only when training. Experiments conducted with an English–German translation task show that our model outperforms over the baseline.


title: Evaluations for abuse in young children with subdural hemorrhages: findings based on symptom severity and benign enlargement of the subarachnoid spaces

abstract: OBJECTIVEChildren who have subdural hematomas (SDHs) with no or minimal neurological symptoms (SDH-mild symptoms) often present a forensic challenge. Nonabusive causes of SDH, including birth-related SDH, benign enlargement of the subarachnoid spaces (BESS), and other proposed causes have been offered as etiologies. These alternative causes do not provide explanations for concomitant suspicious injuries (CSIs). If SDH with mild symptoms in young children are frequently caused by these alternative causes, children with SDH-mild symptoms should be more likely to have no other CSIs than those who have SDH with severe symptoms (SDH-severe symptoms). Additionally, if SDH with mild symptoms is caused by something other than abuse, the location and distribution of the SDH may be different than an SDH caused by abuse. The objectives of this study were to determine the prevalence of other CSIs in patients who present with SDH-mild symptoms and to compare that prevalence to patients with SDH-severe symptoms. Additi...


title: Optimized Deployment of Sensor Networks Based on Event-triggered Mechanism

abstract: Aim at solving the communication limitation and energy conservation problems in the deployment of mobile sensor networks, we develop a new optimized coverage scheme based on event-triggered. Firstly, use the Voronoi tessellation to partition the task area, and for the first-order integrator dynamics, an event-triggered control law is designed. This control strategy doesn't need the real-time communication between neighbors, but according to design an adaptive estimated parameter to approximate the centroid at last trigger time and combine the sliding-mode variable structure control to design the control law. Finally, we prove the convergence and no Zeno behavior of this scheme theoretically.


title: Mobile Enterprise Application Development - a Cross-Platform Framework

abstract: In today’s business mobility of information systems is an important topic. Almost no company can get by these days without mobile software solutions. At the same time we are going through a convergence of desktop and mobile, web and native application development. Because of this clear necessarity of mobility and mobile development it is not surprising that there is a large number of different recommendations for software developer using different technologies. This paper is going to point out the basic approach of a cross-platform application framework using the example of PhoneGap and - more importantly - highlights and answers the question if the often promised universal applicability of these frameworks really apply and a one-off source code is enough to deploy an enterprise application.


AROMA: abstract representation of presence supporting mutual awareness


title: The relationship between addiction to smartphone usage and depression among adults: a cross sectional study

abstract: Addiction to smartphone usage is a common worldwide problem among adults, which might negatively affect their wellbeing. This study investigated the prevalence and factors associated with smartphone addiction and depression among a Middle Eastern population. This cross-sectional study was conducted in 2017 using a web-based questionnaire distributed via social media. Responses to the Smartphone Addiction Scale - Short version (10-items) were rated on a 6-point Likert scale, and their percentage mean score (PMS) was commuted. Responses to Beck’s Depression Inventory (20-items) were summated (range 0–60); their mean score (MS) was commuted and categorized. Higher scores indicated higher levels of addiction and depression. Factors associated with these outcomes were identified using descriptive and regression analyses. Statistical significance was set at P < 0.05. Complete questionnaires were 935/1120 (83.5%), of which 619 (66.2%) were females and 316 (33.8%) were males. The mean ± standard deviation of their age was 31.7 ± 11 years. Majority of participants obtained university education 766 (81.9%), while 169 (18.1%) had school education. The PMS of addiction was 50.2 ± 20.3, and MS of depression was 13.6 ± 10.0. A significant positive linear relationship was present between smart phone addiction and depression (y = 39.2 + 0.8×; P < 0.001). Significantly higher smartphone addiction scores were associated with younger age users, (β = − 0.203, adj. P = 0.004). Factors associated with higher depression scores were school educated users (β = − 2.03, adj. P = 0.01) compared to the university educated group and users with higher smart phone addiction scores (β =0.194, adj. P < 0.001). The positive correlation between smartphone addiction and depression is alarming. Reasonable usage of smart phones is advised, especially among younger adults and less educated users who could be at higher risk of depression.


title: Behavioral and Socio-emotional Functioning in Children with Selective Mutism: A Comparison with Anxious and Typically Developing Children Across Multiple Informants

abstract: We examined differences among 158 children, 44 with selective mutism (SM; M = 8.2 years, SD = 3.4 years), 65 with mixed anxiety (MA; M = 8.9 years, SD = 3.2 years), and 49 community controls (M = 7.7 years, SD = 2.6 years) on primary caregiver, teacher, and child reports of behavioral and socio-emotional functioning. Children with SM were rated lower than controls on a range of social skills, but the SM and MA groups did not significantly differ on many of the social skills and anxiety measures. However, children with SM were rated higher than children with MA and controls on social anxiety. Findings suggest that SM may be conceptualized as an anxiety disorder, with primary deficits in social functioning and social anxiety. This interpretation supports a more specific classification of SM as an anxiety disorder for future diagnostic manuals than is currently described in the literature. The present findings also have implications for clinical practice, whereby social skills training merits inclusion in intervention for children with anxiety disorders as well as children with SM.


title: Reduction of unbalanced axial magnetic force in post-fault operation of a novel six-phase double-stator axial flux PM machine using model predictrive control

abstract: This paper investigated the post-fault operation of a novel six-phase double-stator axial flux permanent magnet machine with detached winding configuration, which was found to be superior to existing winding configuration in previous study. However, the unbalanced magnetic force problem still remains unsolved. In this paper, the axial force balancing control principle is proposed and a group of specific current waveforms are deduced. When applying these currents under post-fault condition, magnetic torque, axial magnetic force and rotor losses of the machine are calculated in finite element analysis. The results are compared with normal condition and commonly-used post-fault current waveforms. It is verified that this method reduced the unbalanced axial magnetic force immensely and the torque ripple was also kept at a low level. In order to achieve the proposed current waveform, finite control set model predictive control (FCS-MPC) is adopted. This paper proposed the post-fault model of dual three-phase permanent magnet machines and designed a cost function to track the desired current waveforms. The model of the machine is used to predict the future behavior of the controlled variables and the cost function decides the next step of the inverter by evaluating all the predictions. At last, it is verified by simulation results that the control strategy performs well in both dynamic and steady-state situations.


title: Survey on UAANET Routing Protocols and Network Security Challenges

abstract: UAV Ad hoc Networks (UAANETs) is a subset of the well-known mobile ad hoc network (MANET) paradigm. It refers to the deployment of a swarm of small Unmanned Aerial Vehicles (UAVs) and Ground Control Stations (GCS). These UAVs collaborate in order to relay data (command and control traffic and remotely sensed data) between each other and to the Ground Control Station (GCS). Compared to other types of ad hoc networks, UAANETs have some unique features and bring several major challenges to the research community. One of these is the design of UAANET routing protocol. It must establish an efficient route between UAVs and adjust in real time to the rapidly changing topology. It must also be secured to protect the integrity of the network against malicious attackers. Security of routing protocols has been widely investigated in wired networks and MANETs, but as far as we are aware, there is no previous research paper dealing with the security features of UAANET routing protocols. This paper focuses on characteristics of UAANETs and provides a review of the literature on associated routing protocols. We also highlight the analysis of the security features of these protocols. Security requirements, potential threats and countermeasures are all described.


title: A High Slew-Rate Push–Pull Output Amplifier for Low-Quiescent Current Low-Dropout Regulators With Transient-Response Improvement

abstract: A high slew-rate amplifier with push-pull output driving capability is proposed to enable an ultra-low quiescent current (Iq ~ 1muA) low-dropout (LDO) regulator with improved transient responses. The proposed amplifier eliminates the tradeoff between small Iq and large slew-rate that is imposed by the tail-current in conventional amplifier design. Push-pull output stage is introduced to enhance the output driving ability. Small dropout voltage (Vbo) with large-size pass transistor and ultra-low Iq can thus be used to minimize power loss of LDO regulator without transient-response degradation. The proposed amplifier helps to improve stability of LDO regulators without using any on-chip and off-chip compensation capacitors. This is beneficial to chip-level power management requiring high-area efficiency. An LDO regulator with the proposed amplifier has been implemented in a 0.18- mum standard CMOS process and occupies 0.09 mm2. The LDO regulator can deliver 50-mA load current at 1-V input and ~ 100-mV VDO . It only consumes 1.2 muA Iq and is able to recover within ~ 4 mus even under the worst case scenario.


title: Inside packet sampling techniques: exploring modularity to enhance network measurements

abstract: Summary ::: ::: Traffic sampling is viewed as a prominent strategy contributing to lightweight and scalable network measurements. Although multiple sampling techniques have been proposed and used to assist network engineering tasks, these techniques tend to address a single measurement purpose, without detailing the network overhead and computational costs involved. The lack of a modular approach when defining the components of traffic sampling techniques also makes difficult their analysis. Providing a modular view of sampling techniques and classifying their characteristics is, therefore, an important step to enlarge the sampling scope, improve the efficiency of measurement systems, and sustain forthcoming research in the area. Thus, this paper defines a taxonomy of traffic sampling techniques resorting to a comprehensive analysis of the inner components of existing proposals. After identifying granularity, selection scheme, and selection trigger as the main components differentiating sampling proposals, the study goes deeper on characterizing these components, including insights into their computational weight. Following this taxonomy, a general-purpose architecture is established to sustain the development of flexible sampling-based measurement systems. Traveling inside packet sampling techniques, this paper contributes to a clearer positioning and comparison of existing proposals, providing a road map to assist further research and deployments in the area. Copyright © 2016 John Wiley & Sons, Ltd.


title: Zombie Politics and other Late Modern Monstrosities in the Age of Disposability

abstract: At present Americans are fascinated by a particular kind of monstrosity, by vampires and zombies condemned to live an eternity by feeding off the souls of the living. The preoccupation with such parasitic relations speaks uncannily to the threat most Americans perceive from the shameless blood lust of contemporary captains of industry, which Matt Taibbi, a writer for Rolling Stone, has aptly described as ‘a great vampire squid wrapped around the face of humanity, relentlessly jamming its blood funnel into anything that smells like money’.[3] Media culture, as the enormous popularity of the Twilight television franchise and HBO’s True Blood reveals, is nonetheless enchanted by this seductive force of such omnipotent beings. More frightening, however, than the danger posed by these creatures is the coming revolution enacted by the hordes of the unthinking, caught in the spell of voodoo economics and compelled to acts of obscene violence and mayhem. They are the living dead, whose contagion threatens the very life force of the nation. Only a decade or so ago, citizens feared the wrath of robots – terminators and cyborgs – who wanted to destroy us – the legacy of a highly rationalized, technocratic culture that eludes human regulation, even comprehension. That moment has passed as we are now in the near terminal phase of that same society where instrumental rationality and technocracy still threaten the planet as never before. But now, those who are not part of a technocratic elite are helpless and adrift, caught in the grips of a society that denies them any alternative, condemned to roam the earth with a blind, unthinking rage. Zombies are invading almost every aspect of our daily lives. Not only are the flesh-chomping, blood-lusting, pale-faced creatures with mouths full of black goo appearing in movie theatres, television series, and everywhere in screen culture as shock advertisements, but these flesh-eating zombies have become an apt metaphor for the current state of American politics. Not only do zombies portend a new aesthetic in which hyper-violence is embodied in the form of a carnival of snarling creatures engorging elements of human anatomy, but they also portend the arrival of a revolting politics that has a ravenous appetite for spreading destruction and promoting human suffering and hardship.[4] This is a politics in which cadres of the unthinking and living dead promote civic catastrophes and harbour apocalyptic visions, focusing more on death than life. Death-dealing zombie politicians and their acolytes support modes of corporate and militarized governance through which entire populations now become either redundant, disposable, or criminalized. This is especially true for poor minority youth, who as flawed consumers and unwanted workers are offered the narrow choice of joining the military or going to prison, or are simply exiled into various dead zones in which they become socially embedded and invisible.[5] Zombie values find expression in an aesthetic that is aired daily in the mainstream media, a visual landscape filled with the spectacle of destruction and decay, wrought by human parasites in the


title: A Joint Model for 2D and 3D Pose Estimation from a Single Image

abstract: We introduce a novel approach to automatically recover 3D human pose from a single image. Most previous work follows a pipelined approach: initially, a set of 2D features such as edges, joints or silhouettes are detected in the image, and then these observations are used to infer the 3D pose. Solving these two problems separately may lead to erroneous 3D poses when the feature detector has performed poorly. In this paper, we address this issue by jointly solving both the 2D detection and the 3D inference problems. For this purpose, we propose a Bayesian framework that integrates a generative model based on latent variables and discriminative 2D part detectors based on HOGs, and perform inference using evolutionary algorithms. Real experimentation demonstrates competitive results, and the ability of our methodology to provide accurate 2D and 3D pose estimations even when the 2D detectors are inaccurate.


title: Efficient Distributed Linear Classification Algorithms via the Alternating Direction Method of Multipliers

abstract: Linear classification has demonstrated success in many areas of applications. Modern algorithms for linear classification can train reasonably good models while going through the data in only tens of rounds. However, large data often does not fit in the memory of a single machine, which makes the bottleneck in large-scale learning the disk I/O, not the CPU. Following this observation, Yu et al. (2010) made significant progress in reducing disk usage, and their algorithms now outperform LIBLINEAR. In this paper, rather than optimizing algorithms on a single machine, we propose and implement distributed algorithms that achieve parallel disk loading and access the disk only once. Our large-scale learning algorithms are based on the framework of alternating direction methods of multipliers. The framework derives a subproblem that remains to be solved eciently for which we propose using dual coordinate descent and trust region Newton method. Our experimental evaluations on large datasets demonstrate that the proposed algorithms achieve significant speedup over the classifier proposed by Yu et al. running on a single machine. Our algorithms are faster than existing distributed solvers, such as Zinkevich et al. (2010)’s parallel stochastic gradient descent and Vowpal Wabbit.