title: Automotive Electric Propulsion Systems With Reduced or No Permanent Magnets: An Overview

abstract: Hybrid and electric vehicle technology has seen rapid development in recent years. The motor and the generator are at the heart of the vehicle drive and energy system and often utilize expensive rare-earth permanent magnet (PM) material. This paper reviews and addresses the research work that has been carried out to reduce the amount of rare-earth material that is used while maintaining the high efficiency and performance that rare-earth PM machines offer. These new machines can use either less rare-earth PM material, weaker ferrite magnets, or no magnets; and they need to meet the high performance that the more usual interior PM synchronous motor with sintered neodymium-iron-boron magnets provides. These machines can take the form of PM-assisted synchronous reluctance machines, induction machines, switched reluctance machines, wound rotor synchronous machines (claw pole or biaxially excited), double-saliency machines with ac or dc stator current control, or brushless dc multiple-phase reluctance machines.


title: Towards Defeating the Crossfire Attack using SDN

abstract: In this work, we propose online traffic engineering as a novel approach to detect and mitigate an emerging class of stealthy Denial of Service (DoS) link-flooding attacks. Our approach exploits the Software Defined Networking (SDN) paradigm, which renders the management of network traffic more flexible through centralised flow-level control and monitoring. We implement a full prototype of our solution on an emulated SDN environment using OpenFlow to interface with the network devices. We further discuss useful insights gained from our preliminary experiments as well as a number of open research questions which constitute work in progress.


Determining Feature Weights Using a Genetic Algorithm in a Case-Based Reasoning Approach to Personnel Rostering


title: Mining and Planning Time-aware Routes from Check-in Data

abstract: Location-based services allow users to perform check-in actions, which not only record their geo-spatial activities, but also provide a plentiful source for data scientists to analyze and plan more accurate and useful geographical recommender system. In this paper, we present a novel Time-aware Route Planning (TRP) problem using location check-in data. The central idea is that the pleasure of staying at the locations along a route is significantly affected by their visiting time. Each location has its own proper visiting time due to the category, objective, and population. To consider the visiting time of locations into route planning, we develop a three-stage time-aware route planning framework. First, since there is usually either noise time on existing locations or no visiting information on new locations constructed, we devise an inference method, LocTimeInf, to predict and recover the location visiting time on routes. Second, we aim to find the representative and popular time-aware location-transition behaviors from user check-in data, and a Time-aware Transit Pattern Mining (TTPM) algorithm is proposed correspondingly. Third, based on the mined time-aware transit patterns, we develop a Proper Route Search (PR-Search) algorithm to construct the final time-aware routes for recommendation. Experiments on Gowalla check-in data exhibit the promising effectiveness and efficiency of the proposed methods, comparing to a series of competitors.


title: Efficient Algorithm for Contrast Enhancement of Natural Images

abstract: This paper proposed an efficient algorithm for contrast enhancement of natural images. The contrast of images is very important characteristics by which the quality of images can be judged as good or poor. The proposed algorithm consists of two stages: In the first stage the poor quality of an image is processed by modified sigmoid function. In the second stage the output of the first stage is further processed by contrast limited adaptive histogram equalization to enhance contrast of images. In order to achieve better contrast enhancement of images, a novel mask based on input value together with the modified sigmoid formula that will be used as contrast enhancer in addition to contrast limited adaptive histogram equalization. This new contrast enhancement algorithm passes over the input image which operates on its pixels one by one in spatial domain. Simulation and experimental results on benchmark test images demonstrates that proposed algorithm provides better results as compared to other state*of*art contrast enhancement techniques. Proposed algorithm performs efficiently in different dark and bright images by adjusting their contrast very frequently. Proposed algorithm is very simple and efficient approach for contrast enhancement of image. This algorithm can be used in various applications where images are suffering from different contrast problems.


title: Work Capacity of Regulated Freelance Platforms: Fundamental Limits and Decentralized Schemes

abstract: Crowdsourcing of jobs to online freelance platforms is rapidly gaining popularity. Most crowdsourcing platforms are uncontrolled and offer freedom to customers and freelancers to choose each other. This works well for unskilled jobs (e.g., image classification) with no specific quality requirement since freelancers are functionally identical. For skilled jobs (e.g., software development) with specific quality requirements, however, this does not ensure that the maximum number of job requests is satisfied. In this paper, we determine the capacity of regulated freelance systems, in terms of maximum satisfied job requests, and propose centralized schemes that achieve capacity. To ensure decentralized operation and freedom for customers and freelancers, we propose simple schemes compatible with the operation of current crowdsourcing platforms that approximately achieve capacity. Furthermore, for settings where the number of job requests exceeds capacity, we propose a scheme that is agnostic of that information, but is optimal and fair in declining jobs without wait.


title: What we instagram: A first analysis of instagram photo content and user types

abstract: Instagram is a relatively new form of communication where users can easily share their updates by taking photos and tweaking them using filters. It has seen rapid growth in the number of users as well as uploads since it was launched in October 2010. In spite of the fact that it is the most popular photo capturing and sharing application, it has attracted relatively less attention from the research community. In this paper, we present both qualitative and quantitative analysis on Instagram. We use computer vision techniques to examine the photo content. Based on that, we identify the different types of active users on Instagram using clustering. Our results reveal several insights about Instagram which were never studied before, that include: 1) Eight popular photos categories, 2) Five distinct types of Instagram users in terms of their posted photos, and 3) A user's audience (number of followers) is independent of his/her shared photos on Instagram. To our knowledge, this is the first in-depth study of content and users on Instagram.


title: Starter-Generator design and analysis of start/stop processes for HEVs

abstract: A hybrid vehicle is characterized by the unification of the two functional systems: the generating electricity system in the vehicle's electrical system and the starter system of the internal combustion engine (ICE). The development of power electronics, as well as the growth of power, consumed by the vehicles, allows us to combine the starter system and power supply in a single power unit — the starter-generator. It is the combination of the AC machine (ACM) and the power electronic converter. The AC comes out of the electronic power converter connected to the stator of ACM. The DC comes out of this converter connected to the battery and (or) capacitive energy storage (CES). In the motor mode, ACM starts the ICE. In electric vehicles, the starter-generator allows recuperating electric energy and acts as a part of the braking system. The article is devoted to the design of the induction starter-generator for the Russian automobile “LADA”. The modeling results and analysis of start/stop processes are given.


title: Dual mode resonant capacitive MEMS accelerometer

abstract: This paper reports the design, fabrication and characterization of a dual mode resonant capacitive MEMS accelerometer. The sensor consists of electrically coupled micro resonators and a proof mass. The applied acceleration causes displacement of the proof mass which changes the electrostatic gap between the proof mass and the sensing electrodes. This not only changes the capacitance between the electrodes, but it also affects the resonant frequency of the resonator due to the change in electrostatic spring. The sensor output can be measured differentially in both capacitive and resonant modes.


title: Title

abstract: This article discusses a new idea called cognitive radar. Three ingredients are basic to the constitution of cognitive radar: 1) intelligent signal processing, which builds on learning through interactions of the radar with the surrounding environment; 2) feedback from the receiver to the transmitter, which is a facilitator of intelligence; and 3) preservation of the information content of radar returns, which is realized by the Bayesian approach to target detection through tracking. All three of these ingredients feature in the echo-location system of a bat, which may be viewed as a physical realization (albeit in neurobiological terms) of cognitive radar. Radar is a remote-sensing system that is widely used for surveillance, tracking, and imaging applications, for both civilian and military needs. In this article, we focus on future possibilities of radar with particular emphasis on the issue of cognition. As an illustrative case study along the way, we consider the problem of radar surveillance applied to an ocean environment.


title: The antecedents and consequences of racial/ethnic discrimination during adolescence: Does the source of discrimination matter?

abstract: In the current study, we examined the precursors and consequences of discrimination for 876 Latino, African American, and Asian American adolescents (Mage = 16.9 years, SD = 0.43). The race/ethnic characteristics of schools and neighborhoods influenced adolescents' perceptions of the race/ethnic climates of these contexts. In turn, adolescents who viewed climates more negatively were more likely to perceive discriminatory treatment by school personnel, peers, and societal institutions. Discrimination from these 3 sources exerted differential influence on developmental outcomes: Greater discrimination from school personnel was associated with poorer academic performance, greater discrimination from peers was associated with more psychological maladjustment, and greater societal discrimination was associated with heightened racial awareness. Relations were consistent across race/ethnic groups and gender. Implications for intervening to reduce racial discrimination and other social stigmas are discussed.


title: Identification and Detection of Phishing Emails Using Natural Language Processing Techniques

abstract: Phishing refers to fraudulent social engineering techniques used to elicit sensitive information from unsuspecting victims. In this paper, our scheme is aimed at detecting phishing mails which do not contain any links but bank on the victim's curiosity by luring them into replying with sensitive information. We exploit the common features among all such phishing emails such as non-mentioning of the victim's name in the email, a mention of monetary incentive and a sentence inducing the recipient to reply. This textual analysis can be further combined with header analysis of the email so that a final combined evaluation on the basis of both these scores can be done. We have shown that this method is far better than the existing Phishing Email Detection techniques as this covers emails without links while the pre-existing methods were based on the presumption of link(s).


title: Perceived value in B2B and B2C: A comparative approach and cross-fertilization

abstract: Perceived value is of primary concern for researchers and practitioners in the business-to-business (B2B) and business-to-consumer (B2C) spheres. However, the scientific community's excitement over perceived value, associated with approaches developed in parallel in B2B and B2C, has engendered theoretical confusion about perceived value. To solve these conceptual problems, many researchers developed a unified theoretical framework of value (zoom out approach) at a macro-analytical level. By contrast with this currently mainstream perspective in marketing theory, this article adopts a micro-analytical approach to perceived value (zoom in approach). A comparative approach of literature on value in B2B and B2C leads to cross-fertilization between the two areas, while each retains its own features. This zoom-in approach leads to the proposition of a research agenda and allows us to clarify and to enhance the idea of perceived value in B2B and B2C.


title: Approximate Shortest Distance Computing: A Query-Dependent Local Landmark Scheme

abstract: Shortest distance query between two nodes is a fundamental operation in large-scale networks. Most existing methods in the literature take a landmark embedding approach, which selects a set of graph nodes as landmarks and computes the shortest distances from each landmark to all nodes as an embedding. To handle a shortest distance query between two nodes, the precomputed distances from the landmarks to the query nodes are used to compute an approximate shortest distance based on the triangle inequality. In this paper, we analyze the factors that affect the accuracy of the distance estimation in the landmark embedding approach. In particular we find that a globally selected, query-independent landmark set plus the triangulation based distance estimation introduces a large relative error, especially for nearby query nodes. To address this issue, we propose a query-dependent local landmark scheme, which identifies a local landmark close to the specific query nodes and provides a more accurate distance estimation than the traditional global landmark approach. Specifically, a local landmark is defined as the least common ancestor of the two query nodes in the shortest path tree rooted at a global landmark. We propose efficient local landmark indexing and retrieval techniques, which are crucial to achieve low offline indexing complexity and online query complexity. Two optimization techniques on graph compression and graph online search are also proposed, with the goal to further reduce index size and improve query accuracy. Our experimental results on large-scale social networks and road networks demonstrate that the local landmark scheme reduces the shortest distance estimation error significantly when compared with global landmark embedding.


title: Software-defined Networking : Challenges and Research Opportunities for Future Internet

abstract: Currently many aspects of the classical architecture of the Internet are etched in stone - a so called ossification of the Internet - which has led to major obstacles in IPv6 deployment and difficulty in using IP multicast services. Yet, there exist many reasons to extend the Internet, e.g., for improving intra-domain and inter-domain routing for high availability of the network, providing end-to-end connectivity for users, and allowing dynamic QoS management of network resources for new applications, such as data center, cloud computing, and network virtualization. To address these requirements, the next-generation architecture for the Future Internet has introduced the concept of Software-Defined Networking (SDN). At the core of this emerging paradigm is the separation and centralization of the control plane from the forwarding elements in the network as opposed to the distributed control plane of existing networks. This decoupling allows deployment of control plane software components (e.g., OpenFlow controller) on computer platforms that are much more powerful than traditional network equipment (e.g., switches/routers) while protecting the data and intellectual property of the vendors of such equipment.A critical understanding of this emerging paradigm is necessary to address the multiple challenges in realizing the Future Internet and to resolve the ossification problem of the existing Internet. To address these requirements, this paper surveys existing technologies and the wide range of recent and state-of-the-art projects on SDN followed by an in-depth discussion of the major challenges in this area.


title: 3D modeling with silhouettes

abstract: We present a new sketch-based modeling approach in which models are interactively designed by drawing their 2D silhouettes from different views. The core idea of our paper is to limit the input to 2D silhouettes, removing the need to explicitly create or position 3D elements. Arbitrarily complex models can be constructed by assembling them out of parts defined by their silhouettes, which can be combined using CSG operations. We introduce a new simplified algorithm to compute CSG solids that leverages special properties of silhouette cylinders to convert the 3D CSG problem into one that can be handled entirely with 2D operations, making implementation simpler and more robust. We evaluate our approach by modeling a random sampling of man-made objects taken from the words in WordNet, and show that all of the tested man-made objects can be modeled quickly and easily using our approach.


title: Joint Multilabel Classification With Community-Aware Label Graph Learning

abstract: As an important and challenging problem in machine learning and computer vision, multilabel classification is typically implemented in a max-margin multilabel learning framework, where the inter-label separability is characterized by the sample-specific classification margins between labels. However, the conventional multilabel classification approaches are usually incapable of effectively exploring the intrinsic inter-label correlations as well as jointly modeling the interactions between inter-label correlations and multilabel classification. To address this issue, we propose a multilabel classification framework based on a joint learning approach called label graph learning (LGL) driven weighted Support Vector Machine (SVM). In principle, the joint learning approach explicitly models the inter-label correlations by LGL, which is jointly optimized with multilabel classification in a unified learning scheme. As a result, the learned label correlation graph well fits the multilabel classification task while effectively reflecting the underlying topological structures among labels. Moreover, the inter-label interactions are also influenced by label-specific sample communities (each community for the samples sharing a common label). Namely, if two labels have similar label-specific sample communities, they are likely to be correlated. Based on this observation, LGL is further regularized by the label Hypergraph Laplacian. Experimental results have demonstrated the effectiveness of our approach over several benchmark data sets.


title: Computer communication network design: experience with theory and practice

abstract: The ARPA Network (ARPANET) project brought together many individuals with diverse backgrounds, philosophies, and technical approaches from the fields of computer science, communication theory, operations research and others. The project was aimed at providing an efficient and reliable computer communications system (using message switching techniques) in which computer resources such as programs, data, storage, special purpose hardware etc., could be shared among computers and among many users. The variety of design methods, ranging from theoretical modeling to hardware development, were primarily employed independently, although cooperative efforts among designers occurred on occasion. As of November, 1971, the network has been an operational facility for many months, with about 20 participating sites, a network information center accessible via the net, and well over a hundred researchers, system programmers, computer center directors and other technical and administrative personnel involved in its operation.


title: Detecting ROP with Statistical Learning of Program Characteristics

abstract: Return-Oriented Programming (ROP) has emerged as one of the most widely used techniques to exploit software vulnerabilities. Unfortunately, existing ROP protections suffer from a number of shortcomings: they require access to source code and compiler support, focus on specific types of gadgets, depend on accurate disassembly and construction of Control Flow Graphs, or use hardware-dependent (microarchitectural) characteristics. In this paper, we propose EigenROP, a novel system to detect ROP payloads based on unsupervised statistical learning of program characteristics. We study, for the first time, the feasibility and effectiveness of using microarchitecture-independent program characteristics -- namely, memory locality, register traffic, and memory reuse distance -- for detecting ROP. We propose a novel directional statistics based algorithm to identify deviations from the expected program characteristics during execution. EigenROP works transparently to the protected program, without requiring debug information, source code or disassembly. We implemented a dynamic instrumentation prototype of EigenROP using Intel Pin and measured it against in-the-wild ROP exploits and on payloads generated by the ROP compiler ROPC. Overall, EigenROP achieved significantly higher accuracy than prior anomaly-based solutions. It detected the execution of the ROP gadget chains with 81% accuracy, 80% true positive rate, only 0.8% false positive rate, and incurred comparable overhead to similar Pin-based solutions.


title: Probabilistic Deep Spiking Neural Systems Enabled by Magnetic Tunnel Junction

abstract: Deep spiking neural networks are becoming increasingly powerful tools for cognitive computing platforms. However, most of the existing studies on such computing models are developed with limited insights on the underlying hardware implementation, resulting in area and power expensive designs. Although several neuromimetic devices emulating neural operations have been proposed recently, their functionality has been limited to very simple neural models that may prove to be inefficient at complex recognition tasks. In this paper, we venture into the relatively unexplored area of utilizing the inherent device stochasticity of such neuromimetic devices to model complex neural functionalities in a probabilistic framework in the time domain. We consider the implementation of a deep spiking neural network capable of performing high-accuracy and low-latency classification tasks, where the neural computing unit is enabled by the stochastic switching behavior of a magnetic tunnel junction. The simulation studies indicate an energy improvement of $20 \times $ over a baseline CMOS design in 45-nm technology.


title: Unsupervised Cross-lingual Transfer of Word Embedding Spaces

abstract: Cross-lingual transfer of word embeddings aims to establish the semantic mappings among words in different languages by learning the transformation functions over the corresponding word embedding spaces. Successfully solving this problem would benefit many downstream tasks such as to translate text classification models from resource-rich languages (e.g. English) to low-resource languages. Supervised methods for this problem rely on the availability of cross-lingual supervision, either using parallel corpora or bilingual lexicons as the labeled data for training, which may not be available for many low resource languages. This paper proposes an unsupervised learning approach that does not require any cross-lingual labeled data. Given two monolingual word embedding spaces for any language pair, our algorithm optimizes the transformation functions in both directions simultaneously based on distributional matching as well as minimizing the back-translation losses. We use a neural network implementation to calculate the Sinkhorn distance, a well-defined distributional similarity measure, and optimize our objective through back-propagation. Our evaluation on benchmark datasets for bilingual lexicon induction and cross-lingual word similarity prediction shows stronger or competitive performance of the proposed method compared to other state-of-the-art supervised and unsupervised baseline methods over many language pairs.


title: Traceable Air Baggage Handling System Based on RFID Tags in the Airport

abstract: The RFID is not only a feasible, novel, and cost-effective candidate for daily object identification but it is also considered as a significant tool to provide traceable visibility along different stages of the aviation supply chain. In the air baggage handing application, the RFID tags are used to enhance the ability for baggage tracking, dispatching and conveyance so as to improve the management efficiency and the users' satisfaction. We surveyed current related work and introduce the IATA RP1740c protocol used for the standard to recognize the baggage tags. One distributed aviation baggage traceable application is designed based on the RFID networks. We describe the RFID-based baggage tracking experiment in the BCIA (Beijing Capital International Airport). In this experiment the tags are sealed in the printed baggage label and the RFID readers are fixed in the certain interested positions of the BHS in the Terminal 2. We measure the accurate recognition rate and monitor the baggage's real-time situation on the monitor's screen. Through the analysis of the measured results within two months we emphasize the advantage of the adoption of RFID tags in this high noisy BHS environment. The economical benefits achieved by the extensive deployment of RFID in the baggage handing system are also outlined.


title: A brief introduction to OpenCV

abstract: The purpose of this paper is to introduce and quickly make a reader familiar with OpenCV (Open Source Computer Vision) basics without having to go through the lengthy reference manuals and books. OpenCV is an open source library for image and video analysis, originally introduced more than decade ago by Intel. Since then, a number of programmers have contributed to the most recent library developments. The latest major change took place in 2009 (OpenCV 2) which includes main changes to the C++ interface. Nowadays the library has >2500 optimized algorithms. It is extensively used around the world, having >2.5M downloads and >40K people in the user group. Regardless of whether one is a novice C++ programmer or a professional software developer, unaware of OpenCV, the main library content should be interesting for the graduate students and researchers in image processing and computer vision areas. To master every library element it is necessary to consult many books available on the topic of OpenCV. However, reading such more comprehensive material should be easier after comprehending some basics about OpenCV from this paper.


title: A Detection Statistic for Random-Valued Impulse Noise

abstract: This paper proposes an image statistic for detecting random-valued impulse noise. By this statistic, we can identify most of the noisy pixels in the corrupted images. Combining it with an edge-preserving regularization, we obtain a powerful two-stage method for denoising random-valued impulse noise, even for noise levels as high as 60%. Simulation results show that our method is significantly better than a number of existing techniques in terms of image restoration and noise detection.


title: Recurrent Neural Network based Language Modeling in Meeting Recognition

abstract: We use recurrent neural network (RNN) based language models to improve the BUT English meeting recognizer. On the baseline setup using the original language models we decrease word error rate (WER) more than 1% absolute by n-best list rescoring and language model adaptation. When n-gram language models are trained on the same moderately sized data set as the RNN models, improvements are higher yielding a system which performs comparable to the baseline. A noticeable improvement was observed with unsupervised adaptation of RNN models. Furthermore, we examine the influence of word history on WER and show how to speed-up rescoring by caching common prefix strings. Index Terms: automatic speech recognition, language modeling, recurrent neural networks, rescoring, adaptation


title: Can Twitter be used to predict county excessive alcohol consumption rates?

abstract: OBJECTIVES ::: The current study analyzes a large set of Twitter data from 1,384 US counties to determine whether excessive alcohol consumption rates can be predicted by the words being posted from each county. ::: ::: ::: METHODS ::: Data from over 138 million county-level tweets were analyzed using predictive modeling, differential language analysis, and mediating language analysis. ::: ::: ::: RESULTS ::: Twitter language data captures cross-sectional patterns of excessive alcohol consumption beyond that of sociodemographic factors (e.g. age, gender, race, income, education), and can be used to accurately predict rates of excessive alcohol consumption. Additionally, mediation analysis found that Twitter topics (e.g. 'ready gettin leave') can explain much of the variance associated between socioeconomics and excessive alcohol consumption. ::: ::: ::: CONCLUSIONS ::: Twitter data can be used to predict public health concerns such as excessive drinking. Using mediation analysis in conjunction with predictive modeling allows for a high portion of the variance associated with socioeconomic status to be explained.


title: Penile length-somatometric parameters relationship in healthy Egyptian men.

abstract: This study aimed to assess the penile length-somatometric parameters relationship in healthy Egyptian men. Two thousand physically normal men (22-40 years) were subjected to measurement of stretched penile length, glans penis, testis size, index finger, weight, height, span, body mass index (BMI), waist circumference, hip circumference and waist/hip ratio. The mean stretched penile length of the studied subjects was 13.84 ± 1.35 cm (range 12-19 cm), and the mean glans penis length was 2.6 ± 0.4 cm (range 1.7-3.8 cm). Penile length demonstrated positive significant correlation with glans penis length, index finger length, BMI and significant negative correlation with waist/hip ratio. On the other hand, penile length demonstrated nonsignificant correlation with age, weight, height, waist circumference, span or testicular size. It is concluded that the penile length-somatometric parameters relationship in healthy Egyptian men is mostly related to glans penis and index finger lengths.


title: STEED: An Analytical Database System for TrEE-structured Data

abstract: Tree-structured data formats, such as JSON and Protocol Buffers, are capable of expressing sophisticated data types, including nested, repeated, and missing values. While such expressing power contributes to their popularity in real-world applications, it presents a significant challenge for systems supporting tree-structured data. Existing systems have focused on general-purpose solutions either extending RDBMSs or designing native systems. However, the general-purpose approach often results in sophisticated data structures and algorithms, which may not reflect and optimize for the actual structure patterns in the real world. ::: ::: In this demonstration, we showcase Steed, an analytical database Sy stem for t r ee -structured d ata. We use the insights gained by analyzing representative real-world tree structured data as guidelines in the design of Steed. Steed learns and extracts a schema tree for a data set and uses the schema tree to reduce the storage space and improve the efficiency of data field accesses. We observe that sub-structures in real world data are often simple, while the tree-structured data types can support very sophisticated structures. We optimize the storage structure, the column assembling algorithm, and the in-memory layout for the simple sub-structures (a.k.a. simple paths). Compared to representative state-of-the-art systems (i.e. PostgreSQL/JSON, MongoDB, and Hive+Parquet), Steed achieves orders of magnitude better performance for data analysis queries.


title: Selecting Hidden Markov Model State Number with CrossValidated Likelihood

abstract: The problem of estimating the number of hidden states in a hidden Markov model is considered. Emphasis is placed on cross-validated likelihood criteria. Using cross-validation to assess the number of hidden states allows to circumvent the well-documented technical difficulties of the order identification problem in mixture models. Moreover, in a predictive perspective, it does not require that the sampling distribution belongs to one of the models in competition. However, computing cross-validated likelihood for hidden Markov models for which only one training sample is available, involves difficulties since the data are not independent. Two approaches are proposed to compute cross-validated likelihood for a hidden Markov model. The first one consists of using a deterministic half-sampling procedure, and the second one consists of an adaptation of the EM algorithm for hidden Markov models, to take into account randomly missing values induced by cross-validation. Numerical experiments on both simulated and real data sets compare different versions of cross-validated likelihood criterion and penalised likelihood criteria, including BIC and a penalised marginal likelihood criterion. Those numerical experiments highlight a promising behaviour of the deterministic half-sampling criterion.


Case-Based Reasoning for Explaining Probabilistic Machine Learning


title: Experimental Comparative Study of Different Tribocharging Devices for Triboelectric Separation of Insulating Particles

abstract: Several tribocharging devices are commonly used for charging insulating particles in order to separate granular plastic mixtures using a vertical triboelectric separator. However, there is no comparative analysis of these devices when tribocharging is achieved using identical samples of insulating particles. The objective of this paper is to carry out an experimental study that compares several tribocharging devices built up in the laboratory: a rotating cylinder, a tribo-cyclone, a static tribocharger, a propeller-type unit, and a fluidized bed. The study was performed using two granular samples of polyvinyl chloride (PVC) and high-density polyethylene (HDPE), for two different particle sizes: 1 and 2 mm. Experimental results showed that, when tribocharging pure samples of either PVC or HDPE, tribo-cyclone is the most efficient. However, when mixed samples are processed, the fluidized bed device gives the best results in terms of electric charge and separation efficiency.


title: Generalized frequency division multiplexing: Analysis of an alternative multi-carrier technique for next generation cellular systems

abstract: Generalized frequency division multiplexing (GFDM) is a new concept that can be seen as a generalization of traditional OFDM. The scheme is based on the filtered multi-carrier approach and can offer an increased flexibility, which will play a significant role in future cellular applications. In this paper we present the benefits of the pulse shaped carriers in GFDM. We show that based on the FFT/IFFT algorithm, the scheme can be implemented with reasonable computational effort. Further, to be able to relate the results to the recent LTE standard, we present a suitable set of parameters for GFDM.


title: An Application of Markov Random Fields to Range Sensing

abstract: This paper describes a highly successful application of MRFs to the problem of generating high-resolution range images. A new generation of range sensors combines the capture of low-resolution range images with the acquisition of registered high-resolution camera images. The MRF in this paper exploits the fact that discontinuities in range and coloring tend to co-align. This enables it to generate high-resolution, low-noise range images by integrating regular camera images into the range data. We show that by using such an MRF, we can substantially improve over existing range imaging technology.


title: RF communications in underwater wireless sensor networks

abstract: We investigate the propagation of radio waves underwater and between water and air to facilitate setting up hybrid wireless sensor networks with both surface and subsurface nodes. Our investigation includes signal attenuation, antenna radiation patterns, multipath due to reflections from the surface and substrate, noise, and reflection losses transmitting from one medium to another.


title: CometCloud: An Autonomic Cloud Engine

abstract: Clouds typically have highly dynamic demands for resources with highly heterogeneous and dynamic workloads. For example, the workloads associated with the application can be quite dynamic, both in terms of the number of tasks processed as well as computation requirements of each task. Furthermore, different applications may have very different and dynamic QoS requirements, for example, one application may require high throughput while another may be constrained by a budget, and a third may have to balance both throughput and budget. The performance of a cloud service can also vary based on these varying loads as well as failures, network conditions, etc., resulting in different quality of service to the application. Combining public cloud platforms and integrating them with existing grids and data centers can support on-demand scale-up, scale-down as well as scale-out. Users may want to use resources in their private cloud (or datacenter or grid) first before scaling out onto a public cloud, and may have preference for a particular cloud or may want to combine multiple clouds. However, such integration and interoperability is currently non-trivial. Furthermore, integrating these public cloud platforms with exiting


title: Generalizing to Unseen Entities and Entity Pairs with Row-less Universal Schema

abstract: Universal schema predicts the types of entities and relations in a knowledge base (KB) by jointly embedding the union of all available schema types---not only types from multiple structured databases (such as Freebase or Wikipedia infoboxes), but also types expressed as textual patterns from raw text. This prediction is typically modeled as a matrix completion problem, with one type per column, and either one or two entities per row (in the case of entity types or binary relation types, respectively). Factorizing this sparsely observed matrix yields a learned vector embedding for each row and each column. In this paper we explore the problem of making predictions for entities or entity-pairs unseen at training time (and hence without a pre-learned row embedding). We propose an approach having no per-row parameters at all; rather we produce a row vector on the fly using a learned aggregation function of the vectors of the observed columns for that row. We experiment with various aggregation functions, including neural network attention models. Our approach can be understood as a natural language database, in that questions about KB entities are answered by attending to textual or database evidence. In experiments predicting both relations and entity types, we demonstrate that despite having an order of magnitude fewer parameters than traditional universal schema, we can match the accuracy of the traditional model, and more importantly, we can now make predictions about unseen rows with nearly the same accuracy as rows available at training time.


title: 65nW CMOS temperature sensor for ultra-low power microsystems

abstract: We propose a temperature sensor using a novel process-invariant temperature sensing element and voltage to current converter for battery-operated ultra-low power micro systems. By introducing a new temperature-to-voltage sensing element that outputs only 75mV, the sensor achieves ultra-low power. The sensor was implemented in 180nm CMOS process and uses 0.09mm2 of area. Measurements from test chips show 65nW power consumption, the lowest reported to date, with an inaccuracy of +1.3°C /-1.4°C across 0°C to 100°C after 2-point calibration.


title: Heart sounds measurement using PVDF film sensor and their comparison with RR intervals of ECG signals

abstract: Heart sounds are generated by the fluctuations of blood velocity and the vibrations of cardiac structure. The auscultation of heart sounds is one of the basic tools for patient diagnosis and provides a lot of information associated with cardiovascular systems. For converting mechanical sound waves into electronic signals, PVDF (polyvinylidene fluoride) film sensor was used as heart sounds measurement device. After suited filtering process, the first (S1) and the second (S2) heart sounds components were plainly distinguished and their peak timings were closely corresponded to reference heart sounds signals measured by microphone. Lastly, there was high correlation between S1 intervals (from PVDF signals) and RR intervals (from ECG signals). So it is expected that S1 intervals can replace RR intervals. In future application, these methods have possibilities for non-intrusive heart rate estimation.


Liquid Metal Phagocytosis: Intermetallic Wetting Induced Particle Internalization


title: LIRE: open source image retrieval in Java

abstract: Content based image retrieval has been around for some time. There are lots of different test data sets, lots of published methods and techniques, and manifold retrieval challenges, where content based image retrieval is of interest. LIRE is a Java library, that provides a simple way to index and retrieve millions of images based on the images' contents. LIRE is robust and well tested and is not only recommended by the websites of ImageCLEF and MediaEval, but is also employed in industry. This paper gives an overview on LIRE, its use, capabilities and reports on retrieval and runtime performance.


title: Virtual Fog: A Virtualization Enabled Fog Computing Framework for Internet of Things

abstract: The prosperity of Internet of Things (IoT) and the success of rich Cloud services have expedited the emergence of a new computing paradigm called Fog computing, which promotes the processing of data at the proximity of their sources. Complementary to the Cloud, Fog promises to offer many appealing features, such as low latency, low cost, high multitenancy, high scalability, and to consolidate the IoT ecosystem. Although the Fog concept has been widely adopted in many areas, a comprehensive realization has yet been adequately researched. To address all these issues, in this paper, object virtualization is investigated to overcome obstacles resulting from resource constraints on sensory-level nodes while service virtualization is explored to easily create tailored applications for end users. Moreover, network function virtualization is studied to perform the flexibility of network service provisioning. Grounded on object virtualization, network function virtualization and service virtualization, a layered framework that encompasses smart objects, Fog and Cloud is presented to illustrate the realization of virtual Fog along IoT continuum. This proposed virtual Fog framework is applied to a smart living case for verification, then quantitative analysis is conducted to demonstrate the low latency, low operating expense, high multitenancy and scalability, followed by an experimental evaluation to further confirm that delay and jitter can be decreased through virtualization.


The Obstacles of Sustainable Business Model Innovations.


title: Text Extraction in Complex Color Document Images for Enhanced Readability

abstract: Often we encounter documents with text printed on complex color background. Readability of textual contents in such documents is very poor due to complexity of the background and mix up of color(s) of foreground text with colors of background. Automatic segmentation of foreground text in such document images is very much essential for smooth reading of the document contents either by human or by machine. In this paper we propose a novel approach to extract the foreground text in color document images having complex background. The proposed approach is a hybrid approach which combines connected component and texture feature analysis of potential text regions. The proposed approach utilizes Canny edge detector to detect all possible text edge pixels. Connected component analysis is performed on these edge pixels to identify candidate text regions. Because of background complexity it is also possible that a non-text region may be identified as a text region. This problem is overcome by analyzing the texture features of potential text region corresponding to each connected component. An unsupervised local thresholding is devised to perform foreground segmentation in detected text regions. Finally the text regions which are noisy are identified and reprocessed to further enhance the quality of retrieved foreground. The proposed approach can handle document images with varying background of multiple colors and texture; and foreground text in any color, font, size and orientation. Experimental results show that the proposed algorithm detects on an average 97.12% of text regions in the source document. Readability of the extracted foreground text is illustrated through Optical character recognition (OCR) in case the text is in English. The proposed approach is compared with some existing methods of foreground separation in document images. Experimental results show that our approach performs better.


title: Programming and reasoning with algebraic effects and dependent types

abstract: One often cited benefit of pure functional programming is that pure code is easier to test and reason about, both formally and informally. However, real programs have side-effects including state management, exceptions and interactions with the outside world. Haskell solves this problem using monads to capture details of possibly side-effecting computations --- it provides monads for capturing state, I/O, exceptions, non-determinism, libraries for practical purposes such as CGI and parsing, and many others, as well as monad transformers for combining multiple effects. Unfortunately, useful as monads are, they do not compose very well. Monad transformers can quickly become unwieldy when there are lots of effects to manage, leading to a temptation in larger programs to combine everything into one coarse-grained state and exception monad. In this paper I describe an alternative approach based on handling algebraic effects, implemented in the IDRIS programming language. I show how to describe side effecting computations, how to write programs which compose multiple fine-grained effects, and how, using dependent types, we can use this approach to reason about states in effectful programs.


title: Fault Identification Tool Based on Deep Learning for Fault Big Data

abstract: Many open source software (OSS) are developed under the OSS projects all over the world. Then, the software faults detected in OSS projects are managed by the bug tracking systems. Also, many data sets are recorded on the bug tracking systems by many users and project members. In this paper, we propose the useful method based on the deep learning for the improvement activities of OSS reliability. In particular, we develop an application software for visualization of fault data recorded on OSS. Moreover, several numerical illustrations of the developed application software in the actual OSS project are shown in this paper. Furthermore, we discuss the analysis results based on the developed application software by using the fault data sets of actual OSS projects.


title: A high-speed clockless serial link transceiver

abstract: We present a high-speed, clockless, serial link transceiver for inter-chip communication in asynchronous VLSI systems. Serial link transceivers achieve high off-chip data rates by using multiplexing transmitters and demultiplexing receivers that interface parallel on-chip data paths with high-speed, serial off-chip buses. While synchronous transceivers commonly use multi-phase clocks to control the data multiplexing and demultiplexing, our clockless transceiver uses a token-ring architecture that eliminates complex clock generation and synchronization circuitry. Furthermore, our clockless receiver dynamically self-adjusts its sampling rate to match the bit rate of the transmitter. Our SPICE simulations report that in a 0.18-/spl mu/m CMOS technology this transceiver design operates at up to 3-Gb/s and dissipates 77 mW of power with a 1.8-V supply voltage.


Introducing Thing Descriptions and Interactions: An Ontology for the Web of Things


title: Fast identification of redundant transmission capacity constraints with changeable power and load injections

abstract: Line flow constraints or transmission capacity constraints are among the most important constraints in power systems generation scheduling and system reliability evaluation problems. For real world systems, there are usually a very large number of transmission capacity constraints and this is one of the main difficulties in solving the related problems. However, it has been found that many of the transmission capacity constraints are redundant, i.e., feasible region and optimal solution of the related problem will not be changed if the redundant constraints are eliminated from the problem formulation. The solution efficiency of any method, whereas, will be greatly improved when the redundant constraints are removed. Approaches for identifying the redundant constraints have been reported in literature and a revised method is established in this paper. The new method can be used to quickly identify the redundant constraints in systems with changeable power and load injections. Compared with the reported methods, the new method is much easier to be understood and implemented. Numerical testing is performed and it is found that the new method is efficient.


title: Fucoidan as a Potential Therapeutic for Major Blinding Diseases—A Hypothesis

abstract: Fucoidan is a heterogeneous group of sulfated polysaccharide with a high content of l-fucose, which can be extracted from brown algae and marine invertebrates. It has many beneficial biological activities that make fucoidan an interesting candidate for therapeutic application in a variety of diseases. Age-related macular degeneration and diabetic retinopathy are major causes for vision loss and blindness in the industrialized countries and increasingly in the developing world. Some of the characteristics found in certain fucoidans, such as its anti-oxidant activity, complement inhibition or interaction with the Vascular Endothelial Growth factor, which would be of high interest for a potential application of fucoidan in age-related macular degeneration or diabetic retinopathy. However, the possible usage of fucoidan in ophthalmological diseases has received little attention so far. In this review, biological activities of fucoidan that could be of interest regarding these diseases will be discussed.


Wilson's the Jumping-Off Place


title: PARAMO: A PARAllel predictive MOdeling platform for healthcare analytic research using electronic health records

abstract: Abstract Objective Healthcare analytics research increasingly involves the construction of predictive models for disease targets across varying patient cohorts using electronic health records (EHRs). To facilitate this process, it is critical to support a pipeline of tasks: (1) cohort construction, (2) feature construction, (3) cross-validation, (4) feature selection, and (5) classification. To develop an appropriate model, it is necessary to compare and refine models derived from a diversity of cohorts, patient-specific features, and statistical frameworks. The goal of this work is to develop and evaluate a predictive modeling platform that can be used to simplify and expedite this process for health data. Methods To support this goal, we developed a PARAllel predictive MOdeling (PARAMO) platform which (1) constructs a dependency graph of tasks from specifications of predictive modeling pipelines, (2) schedules the tasks in a topological ordering of the graph, and (3) executes those tasks in parallel. We implemented this platform using Map-Reduce to enable independent tasks to run in parallel in a cluster computing environment. Different task scheduling preferences are also supported. Results We assess the performance of PARAMO on various workloads using three datasets derived from the EHR systems in place at Geisinger Health System and Vanderbilt University Medical Center and an anonymous longitudinal claims database. We demonstrate significant gains in computational efficiency against a standard approach. In particular, PARAMO can build 800 different models on a 300,000 patient data set in 3 h in parallel compared to 9 days if running sequentially. Conclusion This work demonstrates that an efficient parallel predictive modeling platform can be developed for EHR data. This platform can facilitate large-scale modeling endeavors and speed-up the research workflow and reuse of health information. This platform is only a first step and provides the foundation for our ultimate goal of building analytic pipelines that are specialized for health data researchers.


title: Depth from Focusing and Defocusing

abstract: The problem of obtaining depth information from focusing and defocusing is studied. In depth from focusing, instead of the Fibonacci search, which is often trapped in local maxima, the combination of Fibonacci search and curve fitting is proposed. This combination leads to an unprecedentedly accurate result. A model of the blurring effect that takes geometric blurring as well as imaging blurring into consideration in the calibration of the blurring model is proposed. In spectrogram-based depth from defocusing, a maximal resemblance estimation method is proposed to decrease or eliminate the window effect. >


title: Values, Perceived Risks and Benefits, and Acceptability of Nuclear Energy

abstract: We examined how personal values and perceptions of risks and benefits are associated with the acceptability of nuclear energy (NE). A theoretical model is tested in which beliefs about the risks and benefits of NE mediate the relationship between values and acceptability. The results showed that egoistic values are positively related to the perceived benefits and acceptability of NE. In contrast, altruistic and biospheric values were positively related to the perceived risks of NE. Although it has been argued that NE may help to combat climate change through lower CO2 emissions, these environmental benefits were not acknowledged by people with strong biospheric values. Furthermore, results confirmed that the more risks respondents perceived, the less they were inclined to accept NE. In contrast, the more a person believed that NE has beneficial consequences, the more acceptable NE was. Finally, as expected, perceived risks and benefits were found to partly mediate the relationship between personal values and acceptability. We discuss the theoretical and practical implications of these findings.


title: Induction of chromosomal aberrations by DNase I.

abstract: Chinese hamster ovary (CHO) cells were treated with bovine pancreatic DNase I using the method of electroporation. The enzyme induced chromosomal aberrations in a S-phase independent manner. The frequencies of polycentric chromosomes induced in the G1 phase of the cell cycle are positively correlated with the dose of DNase I. The distributions of DNase I-induced polycentric chromosomes were overdispersed.


title: Neural Coreference Resolution with Deep Biaffine Attention by Joint Mention Detection and Mention Clustering

abstract: Coreference resolution aims to identify in a text all mentions that refer to the same real-world entity. The state-of-the-art end-to-end neural coreference model considers all text spans in a document as potential mentions and learns to link an antecedent for each possible mention. In this paper, we propose to improve the end-to-end coreference resolution system by (1) using a biaffine attention model to get antecedent scores for each possible mention, and (2) jointly optimizing the mention detection accuracy and the mention clustering log-likelihood given the mention cluster labels. Our model achieves the state-of-the-art performance on the CoNLL-2012 Shared Task English test set.


title: A novel channel pruning method for deep neural network compression

abstract: In recent years, deep neural networks have achieved great success in the field of computer vision. However, it is still a big challenge to deploy these deep models on resource-constrained embedded devices such as mobile robots, smart phones and so on. Therefore, network compression for such platforms is a reasonable solution to reduce memory consumption and computation complexity. In this paper, a novel channel pruning method based on genetic algorithm is proposed to compress very deep Convolution Neural Networks (CNNs). Firstly, a pre-trained CNN model is pruned layer by layer according to the sensitivity of each layer. After that, the pruned model is fine-tuned based on knowledge distillation framework. These two improvements significantly decrease the model redundancy with less accuracy drop. Channel selection is a combinatorial optimization problem that has exponential solution space. In order to accelerate the selection process, the proposed method formulates it as a search problem, which can be solved efficiently by genetic algorithm. Meanwhile, a two-step approximation fitness function is designed to further improve the efficiency of genetic process. The proposed method has been verified on three benchmark datasets with two popular CNN models: VGGNet and ResNet. On the CIFAR-100 and ImageNet datasets, our approach outperforms several state-of-the-art methods. On the CIFAR-10 and SVHN datasets, the pruned VGGNet achieves better performance than the original model with 8 times parameters compression and 3 times FLOPs reduction.


title: Wake on wireless: an event driven energy saving strategy for battery operated devices

abstract: The demand for an all-in-one phone with integrated personal information management and data access capabilities is beginning to accelerate. While personal digital assistants (PDAs) with built-in cellular, WiFi, and Voice-Over-IP technologies have the ability to serve these needs in a single package, the rate at which energy is consumed by PDA-based phones is very high. Thus, these devices can quickly drain their own batteries and become useless to their owner.In this paper, we introduce a technique to increase the battery lifetime of a PDA-based phone by reducing its idle power, the power a device consumes in a "standby" state. To reduce the idle power, we essentially shut down the device and its wireless network card when the device is not being used---the device is powered only when an incoming call is received. Using this technique, we can increase the battery lifetime by up to 115%.In this paper, we describe the design of our "wake-on-wireless" energy-saving strategy and the prototype device we implemented. To evaluate our technique, we compare it with alternative approaches. Our results show that our technique can provide a significant lifetime improvement over other technologies.


title: Face recognition with radial basis function (RBF) neural networks

abstract: A general and efficient design approach using a radial basis function (RBF) neural classifier to cope with small training sets of high dimension, which is a problem frequently encountered in face recognition, is presented. In order to avoid overfitting and reduce the computational burden, face features are first extracted by the principal component analysis (PCA) method. Then, the resulting features are further processed by the Fisher's linear discriminant (FLD) technique to acquire lower-dimensional discriminant patterns. A novel paradigm is proposed whereby data information is encapsulated in determining the structure and initial parameters of the RBF neural classifier before learning takes place. A hybrid learning algorithm is used to train the RBF neural networks so that the dimension of the search space is drastically reduced in the gradient paradigm. Simulation results conducted on the ORL database show that the system achieves excellent performance both in terms of error rates of classification and learning efficiency.


title: CCured in the real world

abstract: CCured is a program transformation system that adds memory safety guarantees to C programs by verifying statically that memory errors cannot occur and by inserting run-time checks where static verification is insufficient.This paper addresses major usability issues in a previous version of CCured, in which many type casts required the use of pointers whose representation was expensive and incompatible with precompiled libraries. We have extended the CCured type inference algorithm to recognize and verify statically a large number of type casts; this goal is achieved by using physical subtyping and pointers with run-time type information to allow parametric and subtype polymorphism. In addition, we present a new instrumentation scheme that splits CCured's metadata into a separate data structure whose shape mirrors that of the original user data. This scheme allows instrumented programs to invoke external functions directly on the program's data without the use of a wrapper function.With these extensions we were able to use CCured on real-world security-critical network daemons and to produce instrumented versions without memory-safety vulnerabilities.


title: Head Circumference Reference Data: Birth to 18 Years

abstract: Highly reliable data from a sample of 888 white US children, measured serially in a single study, have been used to provide reference data for head circumference from birth to 18 years of age. The present data differ little from those already available for the age range from birth to 36 months of age, but they are considerably higher (about 0.5 cm) at older ages for boys and tend to be slightly higher for girls. These new reference data are smoother across age than those used currently for screening and evaluation. Percentiles for 6-month increments from birth to 6 years have been provided.


The Role of Theory and Theorising in Design Science Research


title: Application of information theory in video abstraction extraction

abstract: With the development of Internet and broadband multimedia business, video becomes one of the main-stream vehicles in modern information dissemination. How to reduce the cost of video storage, classification and indexing by analyzing the video content, as well as how to improve the efficiency, availability and accessibility of the video, has become an urgent problem need to solve. Video abstraction is a good solution. In this paper, information theory is used to calculate the size of mutual information between the adjacent key frames, and then to extract the key frames from the initial key frames to form a video abstraction, and experiment results proved to be better.


title: Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security

abstract: Intrusion detection system (IDS) has become an essential layer in all the latest ICT system due to an urge towards cyber safety in the day-to-day world. Reasons including uncertainty in finding the types of attacks and increased the complexity of advanced cyber attacks, IDS calls for the need of integration of Deep Neural Networks (DNNs). In this paper, DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS). A DNN with 0.1 rate of learning is applied and is run for 1000 number of epochs and KDDCup-‘99’ dataset has been used for training and benchmarking the network. For comparison purposes, the training is done on the same dataset with several other classical machine learning algorithms and DNN of layers ranging from 1 to 5. The results were compared and concluded that a DNN of 3 layers has superior performance over all the other classical machine learning algorithms.


title: Compositionality and String Diagrams for Game Theory

abstract: We introduce string diagrams as a formal mathematical, graphical language to represent, compose, program and reason about games. The language is well established in quantum physics, quantum computing and quantum linguistic with the semantics given by category theory. We apply this language to the game theoretical setting and show examples how to use it for some economic games where we highlight the compositional nature of our higher-order game theory.


title: Image Feature Representation of the Subband Power Distribution for Robust Sound Event Classification

abstract: The ability to automatically recognize a wide range of sound events in real-world conditions is an important part of applications such as acoustic surveillance and machine hearing. Our approach takes inspiration from both audio and image processing fields, and is based on transforming the sound into a two-dimensional representation, then extracting an image feature for classification. This provided the motivation for our previous work on the spectrogram image feature (SIF). In this paper, we propose a novel method to improve the sound event classification performance in severe mismatched noise conditions. This is based on the subband power distribution (SPD) image - a novel two-dimensional representation that characterizes the spectral power distribution over time in each frequency subband. Here, the high-powered reliable elements of the spectrogram are transformed to a localized region of the SPD, hence can be easily separated from the noise. We then extract an image feature from the SPD, using the same approach as for the SIF, and develop a novel missing feature classification approach based on a nearest neighbor classifier (kNN). We carry out comprehensive experiments on a database of 50 environmental sound classes over a range of challenging noise conditions. The results demonstrate that the SPD-IF is both discriminative over the broad range of sound classes, and robust in severe non-stationary noise.


title: New encoding based on the minimum spanning tree for distribution feeder reconfiguration using a genetic algorithm

abstract: Power distribution networks are typically structured in a radial topology with extra tie switches to allow for a manual reconfiguration in case of unexpected failure or scheduled maintenance. With the implementation of the smart grid, it is now realistic to also consider the power demand fluctuation and have real-time reconfiguration of the network to always operate in the optimal topology, minimizing distribution losses. In this paper, we propose the use of a genetic algorithm to find the optimal configuration of the network. The algorithm uses a unique solution encoding based on branch weights and computes the minimum spanning tree to decode the candidate solutions. This novel encoding ensures that the radial topology of the network is maintained without the need for complex operators resulting in an efficient and powerful solver. Finally, the solver is tested on distribution networks ranging from 16 to 4400 buses. The quality of the final solutions is equal or better, the maximum network size considered is much larger and the execution time is significantly shorter than that of state-of-the-art methods.


title: Paradoxical block of parathormone secretion is mediated by increased activity of G alpha subunits.

abstract: The paradox of blunted parathormone (PTH) secretion in patients with severe hypomagnesemia has been known for more than 20 years, but the underlying mechanism is not deciphered. We determined the effect of low magnesium on in vitro PTH release and on the signals triggered by activation of the calcium-sensing receptor (CaSR). Analogous to the in vivo situation, PTH release from dispersed parathyroid cells was suppressed under low magnesium. In parallel, the two major signaling pathways responsible for CaSR-triggered block of PTH secretion, the generation of inositol phosphates, and the inhibition of cAMP were enhanced. Desensitization or pertussis toxin-mediated inhibition of CaSR-stimulated signaling suppressed the effect of low magnesium, further confirming that magnesium acts within the axis CaSR-G-protein. However, the magnesium binding site responsible for inhibition of PTH secretion is not identical with the extracellular ion binding site of the CaSR, because the magnesium deficiency-dependent signal enhancement was not altered on CaSR receptor mutants with increased or decreased affinity for calcium and magnesium. By contrast, when the magnesium affinity of the G alpha subunit was decreased, CaSR activation was no longer affected by magnesium. Thus, the paradoxical block of PTH release under magnesium deficiency seems to be mediated through a novel mechanism involving an increase in the activity of G alpha subunits of heterotrimeric G-proteins.


title: Evolving neural networks to play checkers without relying on expert knowledge

abstract: An experiment was conducted where neural networks compete for survival in an evolving population based on their ability to play checkers. More specifically, multilayer feedforward neural networks were used to evaluate alternative board positions and games were played using a minimax search strategy. At each generation, the extant neural networks were paired in competitions and selection was used to eliminate those that performed poorly relative to other networks. Offspring neural networks were created from the survivors using random variation of all weights and bias terms. After a series of 250 generations, the best-evolved neural network was played against human opponents in a series of 90 games on an Internet website. The neural network was able to defeat two expert-level players and played to a draw against a master. The final rating of the neural network placed it in the "Class A" category using a standard rating system. Of particular importance in the design of the experiment was the fact that no features beyond the piece differential were given to the neural networks as a priori knowledge. The process of evolution was able to extract all of the additional information required to play at this level of competency. It accomplished this based almost solely on the feedback offered in the final aggregated outcome of each game played (i.e., win, lose, or draw). This procedure stands in marked contrast to the typical artifice of explicitly injecting expert knowledge into a game-playing program.


title: Development and Evaluation of Textile Actuators

abstract: Existing actuators in robotics are noisy, rigid and not very lifelike in their movements. There is a need for actuators in especially limb prosthetics and exoskeletons that are silent, softly movin ...


title: Why MAC Address Randomization is not Enough: An Analysis of Wi-Fi Network Discovery Mechanisms

abstract: We present several novel techniques to track (unassociated) mobile devices by abusing features of the Wi-Fi standard. This shows that using random MAC addresses, on its own, does not guarantee privacy. First, we show that information elements in probe requests can be used to fingerprint devices. We then combine these fingerprints with incremental sequence numbers, to create a tracking algorithm that does not rely on unique identifiers such as MAC addresses. Based on real-world datasets, we demonstrate that our algorithm can correctly track as much as 50% of devices for at least 20 minutes. We also show that commodity Wi-Fi devices use predictable scrambler seeds. These can be used to improve the performance of our tracking algorithm. Finally, we present two attacks that reveal the real MAC address of a device, even if MAC address randomization is used. In the first one, we create fake hotspots to induce clients to connect using their real MAC address. The second technique relies on the new 802.11u standard, commonly referred to as Hotspot 2.0, where we show that Linux and Windows send Access Network Query Protocol (ANQP) requests using their real MAC address.


title: A unifying model of physical databases

abstract: A unifying model for the study of database performance is proposed. Applications of the model are shown to relate and extend important work concerning batched searching, transposed files, index selection, dynamic hash-based files, generalized access path structures, differential files, network databases, and multifile query processing.


title: Preventing ARP Attacks Using a Fuzzy-Based Stateful ARP Cache

abstract: ARP cache poisoning is considered to be one of the easiest and dangerous attacks in local area networks. This paper proposes a solution to the ARP poisoning problem by extending the current ARP protocol implementation. Instead of the traditional stateless ARP cache, we use a stateful ARP cache in order to manage and secure the ARP cache. We also use a novel Fuzzy Logic approach to differentiate between normal and malicious ARP replies. The Fuzzy Logic controller uses a dynamically populated data base that adapts to network changes. The limits of the current approaches are discussed and analyzed.


title: Deep neural networks for single-channel multi-talker speech recognition

abstract: We investigate techniques based on deep neural networks (DNNs) for attacking the single-channel multi-talker speech recognition problem. Our proposed approach contains five key ingredients: a multi-style training strategy on artificially mixed speech data, a separate DNN to estimate senone posterior probabilities of the louder and softer speakers at each frame, a weighted finite-state transducer (WFST)-based two-talker decoder to jointly estimate and correlate the speaker and speech, a speaker switching penalty estimated from the energy pattern change in the mixed-speech, and a confidence based system combination strategy. Experiments on the 2006 speech separation and recognition challenge task demonstrate that our proposed DNN-based system has remarkable noise robustness to the interference of a competing speaker. The best setup of our proposed systems achieves an average word error rate (WER) of 18.8% across different SNRs and outperforms the state-of-the-art IBM superhuman system by 2.8% absolute with fewer assumptions.


title: Performance comparison of QoS routing algorithms applicable to large-scale SDN networks

abstract: In Software Defined Networking (SDN) the network control plane is decoupled from the data plane and centralized at programmable controller. One of the advantages of centralized control is possibility to efficiently implement traffic engineering mechanisms, that are still lacking in today's backbone networks due to instability of distributed routing protocols and high management complexity. In this paper, we analysed suitability of different routing algorithms for dynamic setting up of performance guaranteed traffic tunnels in backbone SDN networks. Two routing constraints were considered: bandwidth and path delay. Beside bandwidth rejection ratio, which is commonly used as performance indicator for QoS (Quality of Service) routing algorithms, in the context of SDN low computational time is pointed out as important requirement.


title: PPT: A Push Pull Traffic Algorithm to Improve QoS Provisioning in IoT-NDN Environment

abstract: Internet of Things (IoT) is the convergence of connecting people, things, data, and processes. Named data networking (NDN) is a recent paradigm perceived for the future internet architecture to tackle the exponential increase in the volume of global IoT traffic. Traditionally, NDN supports PULL traffic, but IoT applications embrace both PULL and PUSH traffic. In this letter, we propose a hybrid PUSH–PULL traffic (PPT) model, for efficient data exchange in IoT applications. Simulation results indicate that our PPT algorithm can reduce the network load by 50% compared with the traditional IPv6, which in turn helps in almost 98% packet delivery ratio and no packet drop. The average throughput of our PPT model is 50% better than the IPv6 approach, which ensures a reliable IoT model.


title: Ricci Flow for 3D Shape Analysis

abstract: Ricci flow is a powerful curvature flow method, which is invariant to rigid motion, scaling, isometric, and conformal deformations. We present the first application of surface Ricci flow in computer vision. Previous methods based on conformal geometry, which only handle 3D shapes with simple topology, are subsumed by the Ricci flow-based method, which handles surfaces with arbitrary topology. We present a general framework for the computation of Ricci flow, which can design any Riemannian metric by user-defined curvature. The solution to Ricci flow is unique and robust to noise. We provide implementation details for Ricci flow on discrete surfaces of either euclidean or hyperbolic background geometry. Our Ricci flow-based method can convert all 3D problems into 2D domains and offers a general framework for 3D shape analysis. We demonstrate the applicability of this intrinsic shape representation through standard shape analysis problems, such as 3D shape matching and registration, and shape indexing. Surfaces with large nonrigid anisotropic deformations can be registered using Ricci flow with constraints of feature points and curves. We show how conformal equivalence can be used to index shapes in a 3D surface shape space with the use of Teichmüller space coordinates. Experimental results are shown on 3D face data sets with large expression deformations and on dynamic heart data.


title: Strong data-processing inequalities for channels and Bayesian networks

abstract: The data-processing inequality, that is, I(U; Y ) ≤ I(U; X) for a Markov chain U → X → Y, has been the method of choice for proving impossibility (converse) results in information theory and many other disciplines. Various channel-dependent improvements (called strong data-processing inequalities, or SDPIs) of this inequality have been proposed both classically and more recently. In this note we first survey known results relating various notions of contraction for a single channel. Then we consider the basic extension: given SDPI for each constituent channel in a Bayesian network, how to produce an end-to-end SDPI?


title: A pre-collision control strategy for human-robot interaction based on dissipated energy in potential inelastic impacts

abstract: Enabling human-robot collaboration raises new challenges in safety-oriented robot design and control. Indices that quantitatively describe human injury due to a human-robot collision are needed to propose suitable pre-collision control strategies. This paper presents a novel model-based injury index built on the concept of dissipated kinetic energy in a potential inelastic impact. This quantity represents the fracture energy lost when a human-robot collision occurs, modeling both clamped and unclamped cases. It depends on the robot reflected mass and velocity in the impact direction. The proposed index is expressed in analytical form suitable to be integrated in a constraint-based pre-collision control strategy. The exploited control architecture allows to perform a given robot task while simultaneously bounding our injury assessment and minimizing the reflected mass in the direction of the impact. Experiments have been performed on a lightweight robot ABB FRIDA to validate the proposed injury index as well as the pre-collision control strategy.


title: Breaking for commercials: characterizing mobile advertising

abstract: Mobile phones and tablets can be considered as the first incarnation of the post-PC era. Their explosive adoption rate has been driven by a number of factors, with the most signifcant influence being applications (apps) and app markets. Individuals and organizations are able to develop and publish apps, and the most popular form of monetization is mobile advertising. The mobile advertisement (ad) ecosystem has been the target of prior research, but these works typically focused on a small set of apps or are from a user privacy perspective. In this work we make use of a unique, anonymized data set corresponding to one day of traffic for a major European mobile carrier with more than 3 million subscribers. We further take a principled approach to characterize mobile ad traffic along a number of dimensions, such as overall traffic, frequency, as well as possible implications in terms of energy on a mobile device. Our analysis demonstrates a number of inefficiencies in today's ad delivery. We discuss the benefits of well-known techniques, such as pre-fetching and caching, to limit the energy and network signalling overhead caused by current systems. A prototype implementation on Android devices demonstrates an improvement of 50 % in terms of energy consumption for offline ad-sponsored apps while limiting the amount of ad related traffic.


title: Movie Genre Classification from Plot Summaries Using Bidirectional LSTM

abstract: Movie plot summaries are expected to reflect the genre of movies since many spectators read the plot summaries before deciding to watch a movie. In this study, we perform movie genre classification from plot summaries of movies using bidirectional LSTM (Bi-LSTM). We first divide each plot summary of a movie into sentences and assign the genre of corresponding movie to each sentence. Next, using the word representations of sentences, we train Bi-LSTM networks. We estimate the genres for each sentence separately. Since plot summaries generally contain multiple sentences, we use majority voting for the final decision by considering the posterior probabilities of genres assigned to sentences. Our results reflect that, training Bi-LSTM network after dividing the plot summaries into their sentences and fusing the predictions for individual sentences outperform training the network with the whole plot summaries with the limited amount of data. Moreover, employing Bi-LSTM performs better compared to basic Recurrent Neural Networks (RNNs) and Logistic Regression (LR) as a baseline.


title: Texture-based feature tracking for effective time-varying data visualization

abstract: Analyzing, visualizing, and illustrating changes within time-varying volumetric data is challenging due to the dynamic changes occurring between timesteps. The changes and variations in computational fluid dynamic volumes and atmospheric 3D datasets do not follow any particular transformation. Features within the data move at different speeds and directions making the tracking and visualization of these features a difficult task. We introduce a texture-based feature tracking technique to overcome some of the current limitations found in the illustration and visualization of dynamic changes within time-varying volumetric data. Our texture-based technique tracks various features individually and then uses the tracked objects to better visualize structural changes. We show the effectiveness of our texture-based tracking technique with both synthetic and real world time-varying data. Furthermore, we highlight the specific visualization, annotation, registration, and feature isolation benefits of our technique. For instance, we show how our texture-based tracking can lead to insightful visualizations of time-varying data. Such visualizations, more than traditional visualization techniques, can assist domain scientists to explore and understand dynamic changes.


title: Graph Embedding and Extensions: A General Framework for Dimensionality Reduction

abstract: Over the past few decades, a large family of algorithms - supervised or unsupervised; stemming from statistics or geometry theory - has been designed to provide different solutions to the problem of dimensionality reduction. Despite the different motivations of these algorithms, we present in this paper a general formulation known as graph embedding to unify them within a common framework. In graph embedding, each algorithm can be considered as the direct graph embedding or its linear/kernel/tensor extension of a specific intrinsic graph that describes certain desired statistical or geometric properties of a data set, with constraints from scale normalization or a penalty graph that characterizes a statistical or geometric property that should be avoided. Furthermore, the graph embedding framework can be used as a general platform for developing new dimensionality reduction algorithms. By utilizing this framework as a tool, we propose a new supervised dimensionality reduction algorithm called Marginal Fisher Analysis in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizes the interclass separability. We show that MFA effectively overcomes the limitations of the traditional Linear Discriminant Analysis algorithm due to data distribution assumptions and available projection directions. Real face recognition experiments show the superiority of our proposed MFA in comparison to LDA, also for corresponding kernel and tensor extensions.


title: Phase I/II 90Y-Zevalin (yttrium-90 ibritumomab tiuxetan, IDEC-Y2B8) radioimmunotherapy dosimetry results in relapsed or refractory non-Hodgkin’s lymphoma

abstract: Dosimetry studies in patients with non-Hodgkin’s lymphoma were performed to estimate the radiation absorbed dose to normal organs and bone marrow from 90Y-Zevalin (yttrium-90 ibritumomab tiuxetan, IDEC-Y2B8) treatment in this phase I/II, multicenter trial. The trial was designed to determine the dose of Rituximab (chimeric anti-CD20, Rituxan, IDEC-C2B8, MabThera), the unlabeled antibody given prior to the radioconjugate to clear peripheral blood B cells and optimize distribution, and to determine the maximum tolerated dose of 90Y-Zevalin [7.4, 11, or 15 MBq/kg (0.2, 0.3, or 0.4 mCi/kg)]. Patients received 111In-Zevalin (indium-111 ibritumomab tiuxetan, IDEC-In2B8 ) on day 0 followed by a therapeutic dose of 90Y-Zevalin on day 7. Both doses were preceded by an infusion of the chimeric, unlabeled antibody Rituximab. Following administration of 111In-Zevalin, serial anterior/posterior whole-body scans were acquired. Major-organ radioactivity versus time estimates were calculated using regions of interest. Residence times were computed and entered into the MIRDOSE3 computer software program to calculate estimated radiation absorbed dose to each organ. Initial analyses of estimated radiation absorbed dose were completed at the clinical site. An additional, centralized dosimetry analysis was performed subsequently to provide a consistent analysis of data collected from the seven clinical sites. In all patients with dosimetry data (n=56), normal organ and red marrow radiation absorbed doses were estimated to be well under the protocol-defined upper limit of 20 Gy and 3 Gy, respectively. Median estimated radiation absorbed dose was 3.4 Gy to liver (range 1.2–7.8 Gy), 2.6 Gy to lungs (range 0.72–4.4 Gy), and 0.38 Gy to kidneys (range 0.07–0.61 Gy). Median estimated tumor radiation absorbed dose was 17 Gy (range 5.8–67 Gy). No correlation was noted between hematologic toxicity and the following variables: red marrow radiation absorbed dose, blood T1/2, blood AUC, plasma T1/2, and plasma AUC. It is concluded that 90Y-Zevalin administered at nonmyeloablative maximum tolerated doses results in acceptable radiation absorbed doses to normal organs. The only toxicity of note is hematologic and is not correlated to red marrow radiation absorbed dose estimates or T1/2, reflecting that hematologic toxicity is dependent on bone marrow reserve in this heavily pretreated population.


title: Front‐Door Difference‐in‐Differences Estimators

abstract: We develop front-door difference-in-differences estimators as an extension of front-door estimators. Under one-sided noncompliance, an exclusion restriction, and assumptions analogous to parallel trends assumptions, this extension allows identification when the front-door criterion does not hold. Even if the assumptions are relaxed, we show that the front-door and front-door difference-in-differences estimators may be combined to form bounds. Finally, we show that under one-sided noncompliance, these techniques do not require the use of control units. We illustrate these points with an application to a job training study and with an application to Florida's early in-person voting program. For the job training study, we show that these techniques can recover an experimental benchmark. For the Florida program, we find some evidence that early in-person voting had small positive effects on turnout in 2008. This provides a counterpoint to recent claims that early voting had a negative effect on turnout in 2008.


16 AE-0421 APEX : Autonomous Vehicle Plan Verification and Execution


title: How Much is 'Too Much'?: The Role of a Smartphone Addiction Narrative in Individuals' Experience of Use

abstract: In a mixed methods study of parents and teens (n=200), 87% (n=174) of participants used language consistent with smartphone addiction narratives when asked about their smartphone feelings and use. Mental health researchers and clinicians do not consistently agree about whether smartphone addiction exists nor what it would look like if it does. Our goal in this study was to explore the patterns of responses that people invoked when talking about the role of smartphones in their lives and the lives of those around them. Responses suggested that both parents and teens are aware of and potentially influenced by a narrative that smartphones are addictive and can lead to negative, though largely undefined, consequences. We examine potential origins of this narrative, including media coverage, and examine the critical need for a deeper examination in the CSCW community of how this narrative could be influencing well-being, sense of self, and sensemaking around smartphone use.


A Transformer-Based Multi-Source Automatic Post-Editing System


title: Analysis of the Intel Pentium ’ s Ability to Support a Secure Virtual Machine Monitor

abstract: A virtual machine monitor (VMM) allows multiple operating systems to run concurrently on virtual machines (VMs) on a single hardware platform. Each VM can be treated as an independent operating system platform. A secure VMM would enforce an overarching security policy on its VMs. ::: ::: The potential benefits of a secure VMM for PCs include: a more secure environment, familiar COTS operating systems and applications, and enormous savings resulting from the elimination of the need for separate platforms when both high assurance policy enforcement, and COTS software are required. ::: ::: This paper addresses the problem of implementing secure VMMs on the Intel Pentium architecture. The requirements for various types of VMMs are reviewed. We report an analysis of the virtualizability of all of the approximately 250 instructions of the Intel Pentium platform and address its ability to support a VMM. Current "virtualization" techniques for the Intel Pentium architecture are examined and several security problems are identified. An approach to providing a virtualizable hardware base for a highly secure VMM is discussed.


title: Gaming for Health: A Systematic Review of the Physical and Cognitive Effects of Interactive Computer Games in Older Adults

abstract: This systematic review examined the physical and cognitive effects of physically based interactive computer games (ICGs) in older adults. Literature searching was carried out from January 2000 to June 2011. Eligible studies were trials involving older adults (>65 years) describing the effects of ICGs with a physical component (aerobic, strength, balance, flexibility) on physical or cognitive outcomes. Secondary outcomes included adverse effects, compliance, and enjoyment. Twelve trials met the inclusion criteria. ICG interventions varied in terms of software, game type, and nature of the computer interaction. Although there was preliminary evidence that ICG is a safe and effective exercise intervention for older adults, the dearth of high-quality evidence limits this finding. No major adverse effects were reported and two studies reported minor events. ICG could be improved further by tailoring interventions for older adults; in particular, they should aim to optimize participant safety, motivation, and e...


title: Design of 3-dB Hybrid Coupler Based on RGW Technology

abstract: Hybrid couplers are essential devices in various microwave circuits and systems, such as radar systems and beam forming networks. The development of this device is necessary along with the development of the new communication standards for the most modern guiding structures. One of the most recent and promising guiding technologies is the ridge gap waveguides, which is expected to play an essential role in the millimeter wave and submillimeter wave applications, not only the 5G communications but also other future communication. Therefore, standards are recommended to make use of the high-frequency guiding structures. In this paper, a design procedure for the hybrid couplers is presented. The frequency band of interest is centered at the 15 GHz, which can be deployed for both the 5G mobile communication and the airborne radar applications. The proposed design is fabricated and measured. The measured and simulated results are in excellent agreement.


title: Experimental verification of a 77-GHz synthetic aperture radar system for automotive applications

abstract: We present measurement results collected with an experimental 77-GHz synthetic aperture radar, which is suitable for automotive applications. The presented approach relies on a simplified signal processing algorithm, which is derived by exploiting several approximations valid for typical automotive measurement settings. This leads to an image generation approach which requires only limited computational power. The presented results show that, despite the simplified approach, meaningful radar images can be generated. The experimental data was collected during various test drives without requiring highly precise position information.


Aispace: Interactive tools for learning artificial intelligence


title: Refinements on IEEE 802.11 Distributed Coordination Function Modeling Approaches

abstract: With the popularity of the IEEE 802.11 standards, many analytical saturation throughput studies for the distributed coordination function (DCF) have been reported. In this paper, we outline a number of issues and criticalities raised by previously proposed models. In particular, a careful look at backoff counter decrement rules allows us to conclude that, under saturation conditions, the slot immediately following a successful transmission can be accessed only by the station (STA) that has successfully transmitted in the previous channel access. Moreover, due to the specific acknowledgment (ACK) timeout setting adopted in the standard, the slot immediately following a collision cannot be accessed by any STA. Thus, the hypothesis of uncorrelation between consecutive channel slots and statistical homogeneity is not generally true. We propose a new backoff decrement model that retains the simplicity of traditional DCF models while being able to take into account such a correlation, and we compare the accuracy of our model with that of previously proposed approaches.


Location Based Data Aggregation with Energy Aware Scheduling at RSU for Effective Message Dissemination in VANET


title: Toward discovery science of human brain function

abstract: Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's “functional connectome.” Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain–behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/.