title: Characteristics of internet background radiation

abstract: Monitoring any portion of the Internet address space reveals incessant activity. This holds even when monitoring traffic sent to unused addresses, which we term "background radiation. " Background radiation reflects fundamentally nonproductive traffic, either malicious (flooding backscatter, scans for vulnerabilities, worms) or benign (misconfigurations). While the general presence of background radiation is well known to the network operator community, its nature has yet to be broadly characterized. We develop such a characterization based on data collected from four unused networks in the Internet. Two key elements of our methodology are (i) the use of filtering to reduce load on the measurement system, and (ii) the use of active responders to elicit further activity from scanners in order to differentiate different types of background radiation. We break down the components of background radiation by protocol, application, and often specific exploit; analyze temporal patterns and correlated activity; and assess variations across different networks and over time. While we find a menagerie of activity, probes from worms and autorooters heavily dominate. We conclude with considerations of how to incorporate our characterizations into monitoring and detection activities.


title: A model of adoption determinants of ERP within T-O-E framework

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to attempts to provide further insight into IS adoption by investigating how 12 factors within the technology-organization-environment framework explain small- and medium-sized enterprises’ (SMEs) adoption of enterprise resource planning (ERP) software. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The approach for data collection was questionnaire survey involving executives of SMEs drawn from six fast service enterprises with strong operations in Port Harcourt. The mode of sampling was purposive and snow ball and analysis involves logistic regression test; the likelihood ratios, Hosmer and Lemeshow’s goodness of fit, and Nagelkerke’s R2 provided the necessary lenses. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The 12 hypothesized relationships were supported with each factor differing in its statistical coefficient and some bearing negative values. ICT infrastructures, technical know-how, perceived compatibility, perceived values, security, and firm’s size were found statistically significant adoption determinants. Although, scope of business operations, trading partners’ readiness, demographic composition, subjective norms, external supports, and competitive pressures were equally critical but their negative coefficients suggest they pose less of an obstacle to adopters than to non-adopters. Thus, adoption of ERP by SMEs is more driven by technological factors than by organizational and environmental factors. ::: ::: ::: ::: ::: Research limitations/implications ::: ::: ::: ::: ::: The study is limited by its scope of data collection and phases, therefore extended data are needed to apply the findings to other sectors/industries and to factor in the implementation and post-adoption phases in order to forge a more integrated and holistic adoption framework. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: The model may be used by IS vendors to make investment decisions, to meet customers’ needs, and to craft informed marketing programs that would appeal to actual and potential adopters and cause them to progress in the customer loyalty ladder. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: The paper contributes to the growing research on IS innovations’ adoption by using factors within the T-O-E framework to explains SMEs’ adoption of ERP.


title: Query Segmentation and Resource Disambiguation Leveraging Background Knowledge

abstract: Accessing the wealth of structured data available on the Data Web is still a key challenge for lay users. Keyword search is the most convenient way for users to access information (e.g., from data reposito- ries). In this paper we introduce a novel approach for determining the correct resources for user-supplied keyword queries based on a hidden Markov model. In our approach the user-supplied query is modeled as the observed data and the background knowledge is used for parameter estimation. Instead of learning parameter estimation from training data, we leverage the semantic relationships between data items for comput- ing the parameter estimations. In order to maximize accuracy and us- ability, query segmentation and resource disambiguation are mutually tightly interwoven. First, an initial set of potential segmentations is ob- tained leveraging the underlying knowledge base; then the nal correct set of segments is determined after the most likely resource mapping was computed using a scoring function. While linguistic methods like named entity, multi-word unit recognition and POS-tagging fail in the case of an incomplete sentences (e.g. for keyword-based queries), we will show that our statistical approach is robust with regard to query expression variance. Our experimental results when employing the hidden Markov model for resource identication in keyword queries reveal very promising results.


Towards Crowdsourcing Clickbait Labels for YouTube Videos


title: Urinary calcium/creatinin ratio with different dosages of vitamin D3 prophylaxis in infants.

abstract: Objective: The requirement of vitamin D for breast-fed term infants remains an area of controversy. Different dosage is needed according to environmental factors such as sunlight exposure. Reception of more than 400 I.U. of vitamin D daily produces hypercalcemia. A random urine calcium/creatinin ratio (UCa/Cr) is a screening test for detection of hypercalciuria. Setting adequate values of vitamin D3 prophylaxis by random UCa/Cr in infant population of Yazd city in Iran, is the aim of present study. Methods: A total of 90 healthy, full term newborns of both genders were enrolled in the study. They were divided equally into three receiving vitamin D3 prophylaxis groups as follow: (I) 200 IU/daily, (II) 400 IU/daily and (III) 50000 IU two times in fifteen and sixty days after birth. A random urine specimen from each subject was analyzed for calcium, creatinin and serum 25-hydroxy-vitamin D at the end of three months of life. Findings: From all 90 studied infants, 25 (83.3%) infants with 200 IU/daily vitamin D3, 23 (76.7%) infants with 400 IU/daily vitamin D3 and 28 (93.3%) who received 50000 IU two times (76 infants, 84.4%) had hypercalciuria and 14 (15.6%) infants remained with low values of Ca/Cr ratios. Conclusion: According to high prevalence of hypercalciuria in this survey, it is recommended to evaluate the vitamin D level in infants and use proper amount of supplemented vitamin D. It seems that 200 IU/daily could be used in infants in our area.


title: The curse of dimensionality in data mining and time series prediction

abstract: Modern data analysis tools have to work on high-dimensional data, whose components are not independently distributed. High-dimensional spaces show surprising, counter-intuitive geometrical properties that have a large influence on the performances of data analysis tools. Among these properties, the concentration of the norm phenomenon results in the fact that Euclidean norms and Gaussian kernels, both commonly used in models, become inappropriate in high-dimensional spaces. This papers presents alternative distance measures and kernels, together with geometrical methods to decrease the dimension of the space. The methodology is applied to a typical time series prediction example.


title: Face alignment using cascade Gaussian process regression trees

abstract: In this paper, we propose a face alignment method that uses cascade Gaussian process regression trees (cGPRT) constructed by combining Gaussian process regression trees (GPRT) in a cascade stage-wise manner. Here, GPRT is a Gaussian process with a kernel defined by a set of trees. The kernel measures the similarity between two inputs as the number of trees where the two inputs fall in the same leaves. Without increasing prediction time, the prediction of cGPRT can be performed in the same framework as the cascade regression trees (CRT) but with better generalization. Features for GPRT are designed using shape-indexed difference of Gaussian (DoG) filter responses sampled from local retinal patterns to increase stability and to attain robustness against geometric variances. Compared with the previous CRT-based face alignment methods that have shown state-of-the-art performances, cGPRT using shape-indexed DoG features performed best on the HELEN and 300-W datasets which are the most challenging dataset today.


title: Personality and Recommender Systems

abstract: Personality, as defined in psychology, accounts for the individual differences in users’ preferences and behaviour. It has been found that there are significant correlations between personality and users’ characteristics that are traditionally used by recommender systems (e.g. music preferences, social media behaviour, learning styles etc.). Among the many models of personality, the Five Factor Model (FFM) appears suitable for usage in recommender systems as it can be quantitatively measured (i.e. numerical values for each of the factors, namely, openness, conscientiousness, extraversion, agreeableness and neuroticism). The acquisition of the personality factors for an observed user can be done explicitly through questionnaires or implicitly using machine learning techniques with features extracted from social media streams or mobile phone call logs. There are, although limited, a number of available datasets to use in offline recommender systems experiment. Studies have shown that personality was successful at tackling the cold-start problem, making group recommendations, addressing cross-domain preferences and at generating diverse recommendations. However, a number of challenges still remain.


Harnessing Tidal Energy Using Vertical Axis Tidal Turbine


title: Comparative study of 12 thread shapes of dental implant designs : a three-dimensional finite element analysis

abstract: The purpose of this study was to determine the optimal thread shape for an experimental cylinder implant. Twelve three-dimensional finite element models with an implant of different thread shape were created: 1mm, 2mm, and 3mm square designs (S-1, S-2, and S-3); 90 o , 60 o , and 30 o V-shaped designs (V-1, V-2, and V-3); 45 o , 30 o , and 15 o buttress designs (B-1, B-2, and B-3); 45 o , 30 o , and 15 o reverse buttress designs (R-1, R-2, andR-3). The stress distributions and the maximum Von Mises stresses in cortical and cancellous bone of all models were compared under axial and buccolingual loads. It was found that model S-2 in square design, model V-3 in V-shaped design, and model B-3 in buttress design showed the best stress distribution respectively. And the stress distribution in models R-1, R-2, and R-3 were as good as those in models S-2, V-3, and B-3.The results imply that S-2, V-3, B-3, R-1, R-2, and R-3 thread shapes all appear to be suitable for use in a cylinder implant. And the wide top angle thread should be avoided in a cylinder implant.


title: Dgpf – an adaptable framework for distributed multi-objective search algorithms applied to the genetic programming of sensor networks

abstract: We present DGPF, a framework providing multi-objective, auto-adaptive search algorithms with a focus on Genetic Programming. We first introduce a Common Search API, suitable to explore arbitrary problem spaces with different search algorithms. Using our implementation of Genetic Algorithms as an example, we elaborate on the distribution utilities of the framework which enable local, Master/Slave, Peer-To-Peer, and P2P/MS hybrid distributed search execution. We also discuss how heterogeneous searches consisting of multiple, cooperative search algorithms can be constructed. Sensor networks are distributed systems of nodes with scarce resources. We demonstrate how Genetic Programming based on our framework can be applied to create algorithms for sensor nodes that use these resources very efficiently.


title: MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Face Images

abstract: This paper is aimed at creating extremely small and fast convolutional neural networks (CNN) for the problem of facial expression recognition (FER) from frontal face images. To this end, we employed the popular knowledge distillation (KD) method and identified two major shortcomings with its use: 1) a fine-grained grid search is needed for tuning the temperature hyperparameter and 2) to find the optimal size-accuracy balance, one needs to search for the final network size (or the compression rate). On the other hand, KD is proved to be useful for model compression for the FER problem, and we discovered that its effects gets more and more significant with the decreasing model size. In addition, we hypothesized that translation invariance achieved using max-pooling layers would not be useful for the FER problem as the expressions are sensitive to small, pixel-wise changes around the eye and the mouth. However, we have found an intriguing improvement on generalization when max-pooling is used. We conducted experiments on two widely-used FER datasets, CK+ and Oulu-CASIA. Our smallest model (MicroExpNet), obtained using knowledge distillation, is less than 1MB in size and works at 1851 frames per second on an Intel i7 CPU. Despite being less accurate than the state-of-the-art, MicroExpNet still provides significant insights for designing a microarchitecture for the FER problem.


title: Based on UCC3895 phase-shift full bridge ZVZCS with average current control closed loop simulation and design

abstract: Based on UCC3895 chips for the lagging arm series diode ZVZCS converter's phase shifting control, combined with a average current control part of the compensation network for feedback, it is good for soft switch, reduces the circuit switch loss, improves the overall efficiency of the converter, and can achieve stable performance and a fast dynamic response. Design a switching power supply for electric cars, that is, a 1.2 kW output of on-board DC/DC converter as an example, through the PSIM closed-loop simulation to the whole circuit, verifies the correctness of the result.


title: Multilayer Antenna Package for IEEE 802.11ad Employing Ultralow-Cost FR4

abstract: This paper demonstrates the possibility and feasibility of an ultralow-cost antenna-in-package (AiP) solution for the upcoming generation of wireless local area networks (WLANs) denoted as IEEE802.11ad. The iterative design procedure focuses on maximally alleviating the inherent disadvantages of high-volume FR4 process at 60 GHz such as its relatively high material loss and fabrication restrictions. Within the planar antenna package, the antenna element, vertical transition, antenna feedline, and low- and high-speed interfaces are allocated in a vertical schematic. A circular stacked patch antenna renders the antenna package to exhibit 10-dB return loss bandwidth from 57-66 GHz. An embedded coplanar waveguide (CPW) topology is adopted for the antenna feedline and features less than 0.24 dB/mm in unit loss, which is extracted from measured parametric studies. The fabricated single antenna package is 9 mm × 6 mm × 0.404 mm in dimension. A multiple-element antenna package is fabricated, and its feasibility for future phase array applications is studied. Far-field radiation measurement using an inhouse radio-frequency (RF) probe station validates the single-antenna package to exhibit more than 4.1-dBi gain and 76% radiation efficiency.


title: High-Performance Publish-Subscribe Matching Using Parallel Hardware

abstract: Matching incoming event notifications against received subscriptions are a fundamental part of every publish-subscribe infrastructure. In the case of content-based systems this is a fairly complex and time consuming task, whose performance impacts that of the entire system. In the past, several algorithms have been proposed for efficient content-based event matching. While they differ in most aspects, they have in common the fact of being conceived to run on conventional, sequential hardware. On the other hand, parallel hardware is becoming available off-the-shelf: the number of cores inside CPUs is constantly increasing, and CUDA makes it possible to access the power of GPU hardware for general purpose computing. In this paper, we describe a new publish-subscribe content-based matching algorithm designed to run efficiently both on multicore CPUs and CUDA GPUs. A detailed comparison with two state-of-the-art sequential matching algorithms demonstrates how the use of parallel hardware can bring impressive speedups in content-based matching. At the same time, our analysis identifies the characteristic aspects of multicore and CUDA programming that mostly impact performance.


title: Feature-based comparison and selection of Software Defined Networking (SDN) controllers

abstract: Software Defined Networking (SDN) is seen as one way to solve some problems of the Internet including security, managing complexity, multi-casting, load balancing, and energy efficiency. SDN is an architectural paradigm that separates the control plane of a networking device (e.g., a switch / router) from its data plane, making it feasible to control, monitor, and manage a network from a centralized node (the SDN controller). However, today there exists many SDN controllers including POX, FloodLight, and OpenDaylight. The question is, which of the controllers is to be selected and used? To find out the answer to this question, a decision making template is proposed in this paper to help researchers choose the SDN controller that best fits their needs. The method works as follows; first, several existing open-source controllers are analyzed to collect their properties. For selecting the suitable controller based on the derived requirements (for example, a “Java” interface must be provided by the controller), a matching mechanism is used to compare the properties of the controllers with the requirements. Additionally, for selecting the best controller based on optional requirements (for example, GUI will be extremely preferred over the age of the controller), a Multi-Criteria Decision Making (MCDM) method named Analytic Hierarchy Process (AHP) has been adapted by a monotonic interpolation / extrapolation mechanism which maps the values of the properties to a value in a pre-defined scale. By using the adapted AHP, the topmost five controllers have been compared and “Ryu” is selected to be the best controller based on our requirements.


title: A 7.6 mW, 414 fs RMS-Jitter 10 GHz Phase-Locked Loop for a 40 Gb/s Serial Link Transmitter Based on a Two-Stage Ring Oscillator in 65 nm CMOS

abstract: This paper describes the design of a 10 GHz phase-locked loop (PLL) for a 40 Gb/s serial link transmitter (TX). A two-stage ring oscillator is used to provide a four-phase, 10 GHz clock for a quarter-rate TX. Several analyses and verification techniques, ranging from the clocking architectures for a 40 Gb/s TX to oscillation failures in a two-stage ring oscillator, are addressed in this paper. A tri-state-inverter-based frequency-divider and an AC-coupled clock-buffer are used for high-speed operations with minimal power and area overheads. The proposed 10 GHz PLL fabricated in the 65 nm CMOS technology occupies an active area of 0.009 mm2 with an integrated-RMS-jitter of 414 fs from 10 kHz to 100 MHz while consuming 7.6 mW from a 1.2-V supply. The resulting figure-of-merit is -238.8 dB, which surpasses that of the state-of-the-art ring-PLLs by 4 dB.


title: Comparison of multiple features and modeling methods for text-dependent speaker verification

abstract: Text-dependent speaker verification is becoming popular in the speaker recognition society. However, the conventional i-vector framework which has been successful for speaker identification and other similar tasks works relatively poorly in this task. Researchers have proposed several new methods to improve performance, but it is still unclear that which model is the best choice, especially when the pass-phrases are prompted during enrollment and test. In this paper, we introduce four modeling methods and compare their performance on the newly published RedDots dataset. To further explore the influence of different frame alignments, Viterbi and forward-backward algorithms are both used in the HMM-based models. Several bottleneck features are also investigated. Our experiments show that, by explicitly modeling the lexical content, the HMM-based modeling achieves good results in the fixed-phrase condition. In the prompted-phrase condition, GMM-HMM and i-vector/HMM are not as successful. In both conditions, the forward-backward algorithm brings more benefits to the i-vector/HMM system. Additionally, we also find that even though bottleneck features perform well for text-independent speaker verification, they do not outperform MFCCs on the most challenging Imposter-Correct trials on RedDots.


title: Categorizing User Sessions at Pinterest

abstract: Different users can use a given Internet application in many different ways. The ability to record detailed event logs of user in-application activity allows us to discover ways in which the application is being used. This enables personalization and also leads to important insights with actionable business and product outcomes. ::: Here we study the problem of user session categorization, where the goal is to automatically discover categories/classes of user in-session behavior using event logs, and then consistently categorize each user session into the discovered classes. We develop a three stage approach which uses clustering to discover categories of sessions, then builds classifiers to classify new sessions into the discovered categories, and finally performs daily classification in a distributed pipeline. An important innovation of our approach is selecting a set of events as long-tail features, and replacing them with a new feature that is less sensitive to product experimentation and logging changes. This allows for robust and stable identification of session types even though the underlying application is constantly changing. We deploy the approach to Pinterest and demonstrate its effectiveness. We discover insights that have consequences for product monetization, growth, and design. Our solution classifies millions of user sessions daily and leads to actionable insights.


title: Securing Sensor to Cloud Ecosystem using Internet of Things (IoT) Security Framework

abstract: The Internet of things (IoT) refers to every object, which is connected over a network with the ability to transfer data. Users perceive this interaction and connection as useful in their daily life. However any improperly designed and configured technology will exposed to security threats. Therefore an ecosystem for IoT should be designed with security embedded in each layer of its ecosystem. This paper will discussed the security threats to IoT and then proposed an IoT Security Framework to mitigate it. Then IoT Security Framework will be used to develop a Secure IoT Sensor to Cloud Ecosystem.


title: SUDEP counselling: How and when?

abstract: Sudden unexpected death in epilepsy (SUDEP) is not a recent phenomenon. Although not understood, it has long been recognised as one possible outcome of a diffi cult illness. However, as modern management has offered improved seizure control, epilepsy has acquired the perception of a more benign condition. In this setting, deaths which occur can be both unexpected and shocking. Bereaved families have responded by calling for research and education. However, with no known cause for SUDEP health workers have been uncertain how to discuss the issue. Fortunately, epidemiological research is providing insight into risk factors, thereby opening the door for doctor-patient discussion on personal risk evaluation and SUDEP. Global practice varies but health care policies are starting to promote the routine inclusion of SUDEP information in patient counselling. Early understanding of risk may improve patient choices regarding adherence to treatment and the need to strive for optimum seizure control.


title: Analysis and Derivations for a Family ZVS Converter Based on a New Active Clamp ZVS Cell

abstract: A new zero voltage switching (ZVS) cell is proposed based on the active clamping technique. With a small clamping diode in the cell, the voltage ringing caused by the auxiliary inductor and the parasitic capacitance of the diode is eliminated, and therefore, the circulating loss is minimized. A new ZVS boost converter is presented and analyzed in detail to demonstrate the operating principle of the new ZVS cell. A new family of ZVS dc-dc converters can be derived based on the proposed ZVS cell and typical nonisolated dc-dc converters. A 500 W/190 kHz ZVS boost prototype is made to verify the analysis.


title: Single Cesium Lead Halide Perovskite Nanocrystals at Low Temperature: Fast Single-Photon Emission, Reduced Blinking, and Exciton Fine Structure

abstract: Metal-halide semiconductors with perovskite crystal structure are attractive due to their facile solution processability, and have recently been harnessed very successfully for high-efficiency photovoltaics and bright light sources. Here, we show that at low temperature single colloidal cesium lead halide (CsPbX3, where X = Cl/Br) nanocrystals exhibit stable, narrow-band emission with suppressed blinking and small spectral diffusion. Photon antibunching demonstrates unambiguously nonclassical single-photon emission with radiative decay on the order of 250 ps, representing a significant acceleration compared to other common quantum emitters. High-resolution spectroscopy provides insight into the complex nature of the emission process such as the fine structure and charged exciton dynamics.


title: Using Scalable Video Coding for Dynamic Adaptive Streaming over HTTP in mobile environments

abstract: Dynamic Adaptive Streaming over HTTP (DASH) is a convenient approach to transfer videos in an adaptive and dynamic way to the user. As a consequence, this system provides high bandwidth flexibility and is especially suitable for mobile use cases where the bandwidth variations are tremendous. In this paper we have integrated the Scalable Video Coding (SVC) extensions of the Advanced Video Coding (AVC) standard into the recently ratified MPEG-DASH standard. Furthermore, we have evaluated our solution under restricted conditions using bandwidth traces from mobile environments and compared it with an improved version of our MPEG-DASH implementation using AVC as well as major industry solutions.


title: Neural Random-Access Machines

abstract: In this paper, we propose and investigate a new neural network architecture called Neural Random Access Machine. It can manipulate and dereference pointers to an external variable-size random-access memory. The model is trained from pure input-output examples using backpropagation. We evaluate the new model on a number of simple algorithmic tasks whose solutions require pointer manipulation and dereferencing. Our results show that the proposed model can learn to solve algorithmic tasks of such type and is capable of operating on simple data structures like linked-lists and binary trees. For easier tasks, the learned solutions generalize to sequences of arbitrary length. Moreover, memory access during inference can be done in a constant time under some assumptions.


title: Multi-domain learning and generalization in dialog state tracking

abstract: Statistical approaches to dialog state tracking synthesize information across multiple turns in the dialog, overcoming some speech recognition errors. When training a dialog state tracker, there is typically only a small corpus of well-matched dialog data available. However, often there is a large corpus of mis-matched but related data ‐ perhaps pertaining to different semantic concepts, or from a different dialog system. It would be desirable to use this related dialog data to supplement the small corpus of well-matched dialog data. This paper addresses this task as multi-domain learning, presenting 3 methods which synthesize data from different slots and different dialog systems. Since deploying a new dialog state tracker often changes the resulting dialogs in ways that are difficult to predict, we study how well each method generalizes to unseen distributions of dialog data. Our main result is the finding that a simple method for multi-domain learning substantially improves performance in highly mis-matched conditions.


title: Online Sequence Training of Recurrent Neural Networks with Connectionist Temporal Classification

abstract: Connectionist temporal classification (CTC) based supervised sequence training of recurrent neural networks (RNNs) has shown great success in many machine learning areas including end-to-end speech and handwritten character recognition. For the CTC training, however, it is required to unroll (or unfold) the RNN by the length of an input sequence. This unrolling requires a lot of memory and hinders a small footprint implementation of online learning or adaptation. Furthermore, the length of training sequences is usually not uniform, which makes parallel training with multiple sequences inefficient on shared memory models such as graphics processing units (GPUs). In this work, we introduce an expectation-maximization (EM) based online CTC algorithm that enables unidirectional RNNs to learn sequences that are longer than the amount of unrolling. The RNNs can also be trained to process an infinitely long input sequence without pre-segmentation or external reset. Moreover, the proposed approach allows efficient parallel training on GPUs. For evaluation, phoneme recognition and end-to-end speech recognition examples are presented on the TIMIT and Wall Street Journal (WSJ) corpora, respectively. Our online model achieves 20.7% phoneme error rate (PER) on the very long input sequence that is generated by concatenating all 192 utterances in the TIMIT core test set. On WSJ, a network can be trained with only 64 times of unrolling while sacrificing 4.5% relative word error rate (WER).


title: Modelling errors in automatic speech recognition for dysarthric speakers

abstract: Dysarthria is a motor speech disorder characterized by weakness, paralysis, or poor coordination of the muscles responsible for speech. Although automatic speech recognition (ASR) systems have been developed for disordered speech, factors such as low intelligibility and limited phonemic repertoire decrease speech recognition accuracy, making conventional speaker adaptation algorithms perform poorly on dysarthric speakers. In this work, rather than adapting the acoustic models, we model the errors made by the speaker and attempt to correct them. For this task, two techniques have been developed: (1) a set of "metamodels" that incorporate a model of the speaker's phonetic confusion matrix into the ASR process; (2) a cascade of weighted finite-state transducers at the confusion matrix, word, and language levels. Both techniques attempt to correct the errors made at the phonetic level and make use of a language model to find the best estimate of the correct word sequence. Our experiments show that both techniques outperform standard adaptation techniques.


title: An automated version of the operation span task

abstract: We present an easy-to-administer and automated version of a popular working memory (WM) capacity task (operation span; Ospan) that is mouse driven, scores itself, and requires little intervention on the part of the experimenter. It is shown that this version of Ospan correlates well with other measures of WM capacity and has both good internal consistency (alpha=.78) and test-retest reliability (.83). In addition, the automated version of Ospan (Aospan) was shown to load on the same factor as two other WM measures. This WM capacity factor correlated with a factor composed of fluid abilities measures. The utility of the Aospan was further demonstrated by analyzing response times (RTs) that indicated that RT measures obtained in the task accounted for additional variance in predicting fluid abilities. Our results suggest that Aospan is a reliable and valid indicator of WM capacity that can be applied to a wide array of research domains.


title: The Where and When of Finding New Friends: Analysis of a Location-based Social Discovery Network

abstract: With more people accessing Online Social Networks (OSN) using their mobile devices, location-based features have become an important part of the social networking. In this paper, we present the first measurement study of a new category of location-based online social networking services, a location-based social discovery (LBSD) network, that enables users to discover and communicate with nearby people. Unlike popular check-in-based social networks, LBSD allows users to publicly reveal their locations without being associ- ated to a specific “venue” and their usage is not influenced by the incentive mechanisms of the underlying virtual community. By analyzing over 8 million user profiles and around 150 million location updates collected from a popular new LBSD network, we first present the characteristics of spatial- temporal usage patterns of the observed users, showing that 40% of updates are from the user’s primary location and 80% are from their top 10 locations. We identify events that trigger bursts of growth in subscriber numbers, showing the importance of social media marketing. Finally, we investigate how usage patterns may be utilized to re-identify individuals with e.g. different identifiers or from datasets belonging to different online services. We evaluate re-identification by usage, spatial and spatial-temporal patterns and using a number of metrics and show that the best results can be achieved using location data, with a high accuracy: our experiments demonstrate that we can re-identify up-to 85% of users with a precision of 77% using monitored spatial data. Overall, we find that although users exhibit strong periodic behavior in their usage pattern and movements, the success rate of re-identification is highly dependent on the level of activeness and the lifetime of the users in the network.


title: Coupled Generative Adversarial Networks

abstract: We propose coupled generative adversarial network (CoGAN) for learning a joint distribution of multi-domain images. In contrast to the existing approaches, which require tuples of corresponding images in different domains in the training set, CoGAN can learn a joint distribution without any tuple of corresponding images. It can learn a joint distribution with just samples drawn from the marginal distributions. This is achieved by enforcing a weight-sharing constraint that limits the network capacity and favors a joint distribution solution over a product of marginal distributions one. We apply CoGAN to several joint distribution learning tasks, including learning a joint distribution of color and depth images, and learning a joint distribution of face images with different attributes. For each task it successfully learns the joint distribution without any tuple of corresponding images. We also demonstrate its applications to domain adaptation and image transformation.


title: Abnormal event detection in crowded scenes based on deep learning

abstract: In this paper, we propose to use the deep learning technique for abnormal event detection by extracting spatiotemporal features from video sequences. Human eyes are often attracted to abnormal events in video sequences, thus we firstly extract saliency information (SI) of video frames as the feature representation in the spatial domain. Optical flow (OF) is estimated as an important feature of video sequences in the temporal domain. To extract the accurate motion information, multi-scale histogram optical flow (MHOF) can be obtained through OF. We combine MHOF and SI into the spatiotemporal features of video frames. Finally a deep learning network, PCANet, is adopted to extract high-level features for abnormal event detection. Experimental results show that the proposed abnormal event detection method can obtain much better performance than the existing ones on the public video database.


title: Dyslexia: Cultural Diversity and Biological Unity

abstract: The recognition of dyslexia as a neurodevelopmental disorder has been hampered by the belief that it is not a specific diagnostic entity because it has variable and culture-specific manifestations. In line with this belief, we found that Italian dyslexics, using a shallow orthography which facilitates reading, performed better on reading tasks than did English and French dyslexics. However, all dyslexics were equally impaired relative to their controls on reading and phonological tasks. Positron emission tomography scans during explicit and implicit reading showed the same reduced activity in a region of the left hemisphere in dyslexics from all three countries, with the maximum peak in the middle temporal gyrus and additional peaks in the inferior and superior temporal gyri and middle occipital gyrus. We conclude that there is a universal neurocognitive basis for dyslexia and that differences in reading performance among dyslexics of different countries are due to different orthographies.


title: Integrating web mining and neural network for personalized e-commerce automatic service

abstract: Electronic commerce (EC) has become a trend in the world nowadays. However, most researches neglect a fundamental issue - the user's product-specific knowledge on which the useful intelligent systems are based. This research employs the user's product-specific knowledge and mine his/her interior desire on appropriate target products as a part of personalization process to construct the overall EC strategy for businesses. This paper illustrates a novel web usage mining approach, based on the sequence mining technique applied to user's navigation behaviour, to discover patterns in the navigation of websites. Three critical contributions are made in this paper: (1) using the footstep graph to visualize the user's click-stream data and any interesting pattern can be detected more easily and quickly; (2) illustrating a novel sequence mining approach to identify pre-designated user navigation patterns automatically and integrates back-propagation network (BPN) model smoothly; and (3) applying the empirical research to indicate that the proposed approach can predict and categorize the users' navigation behaviour with high accuracy.


abstract: Factorization Machine (FM) provides a generic framework that combines the prediction quality of factorization models with the flexibility of feature engineering that discriminative models like SVM offer. The Bayesian Factorization Machine [11], with its impressive predictive performance and the convenience of automatic tuning of parameters, has been one of the most successful and efficient approaches within this framework. However, this model has two major drawbacks. Firstly, it assumes that the data is generated from Gaussian distributions that may not be the best assumption for count data such as integer-valued ratings. Secondly, to get the best performance, one needs to cross-validate over the number of latent factors used for modeling the pairwise interaction in FM, a process that is computationally intensive. This paper introduces the Nonparametric Poisson Factorization Machine (NPFM), which models count data using the Poisson distribution, which provides both modeling and computational advantages for sparse data. The ideal number of latent factors is estimated from the data itself, thereby addressing a key limitation of existing approaches to FM. Additionally, NPFM has linear time complexity with respect to the number of non-zero observations.


title: Compensating for Motion during Direct-Global Separation

abstract: Separating the direct and global components of radiance can aid shape recovery algorithms and can provide useful information about materials in a scene. Practical methods for finding the direct and global components use multiple images captured under varying illumination patterns and require the scene, light source and camera to remain stationary during the image acquisition process. In this paper, we develop a motion compensation method that relaxes this condition and allows direct-global separation to be performed on video sequences of dynamic scenes captured by moving projector-camera systems. Key to our method is being able to register frames in a video sequence to each other in the presence of time varying, high frequency active illumination patterns. We compare our motion compensated method to alternatives such as single shot separation and frame interleaving as well as ground truth. We present results on challenging video sequences that include various types of motions and deformations in scenes that contain complex materials like fabric, skin, leaves and wax.


title: Data mining for web personalization

abstract: In this chapter we present an overview of Web personalization process viewed as an application of data mining requiring support for all the phases of a typical data mining cycle. These phases include data collection and pre-processing, pattern discovery and evaluation, and finally applying the discovered knowledge in real-time to mediate between the user and the Web. This view of the personalization process provides added flexibility in leveraging multiple data sources and in effectively using the discovered models in an automatic personalization system. The chapter provides a detailed discussion of a host of activities and techniques used at different stages of this cycle, including the preprocessing and integration of data from multiple sources, as well as pattern discovery techniques that are typically applied to this data. We consider a number of classes of data mining algorithms used particularly forWeb personalization, including techniques based on clustering, association rule discovery, sequential pattern mining, Markov models, and probabilistic mixture and hidden (latent) variable models. Finally, we discuss hybrid data mining frameworks that leverage data from a variety of channels to provide more effective personalization solutions.


title: Explanation in Computational Stylometry

abstract: Computational stylometry, as in authorship attribution or profiling, has a large potential for applications in diverse areas: literary science, forensics, language psychology, sociolinguistics, even medical diagnosis. Yet, many of the basic research questions of this field are not studied systematically or even at all. In this paper we will go into these problems, and suggest that a reinterpretation of current and historical methods in the framework and methodology of machine learning of natural language processing would be helpful. We also argue for more attention in research for explanation in computational stylometry as opposed to purely quantitative evaluation measures and propose a strategy for data collection and analysis for achieving progress in computational stylometry. We also introduce a fairly new application of computational stylometry in internet security.


title: E-Commerce Trust Metrics and Models

abstract: Traditional models of trust between vendors and buyers fall short of requirements for an electronic marketplace, where anonymous transactions cross territorial and legal boundaries as well as traditional value-chain structures. Alternative quantifications of trust may offer better evaluations of transaction risk in this environment. This article introduces a notion of quantifiable trust and then develops models that can use these metrics to verify e-commerce transactions in ways that might be able to satisfy the requirements of mutual trust. The article uses two examples in illustrating these concepts: one for an e-commerce printing enterprise and the other for Internet stock trading.


title: Method for estimating body weight in persons with lower-limb amputation and its implication for their nutritional assessment

abstract: BACKGROUND ::: Body weight is a good indicator of a person's size and is widely used in clinical assessment. However, health-status assessment based on observed body weight (W(O)) is incorrect for persons with limb amputation. ::: ::: ::: OBJECTIVES ::: The objectives were 1) to develop a more accurate and generalized method for estimating body weight in persons with limb amputation, 2) to determine whether corrected body weight can be used to assess nutritional status in persons with limb amputation, and 3) to test the validity of the estimation by using empirical data. ::: ::: ::: DESIGN ::: Anthropometric data were collected from men from Calcutta and adjoining areas with unilateral lower-extremity amputation (n = 102). Mathematic formulas were developed for determining estimated body weight (W(E)) and body mass index (BMI) calculated from both W(O) and W(E) (ie, BMI(O) and BMI(E), respectively). We assessed nutritional status by using BMI(O) and BMI(E) and tested the validity of each by considering the result of nutritional assessment from midupper arm circumference as the gold standard. We also compared the nutritional status results for the subjects with limb amputation with those for a similar sample size of healthy control subjects. ::: ::: ::: RESULTS ::: BMI(E) had a stronger association with midupper arm circumference and a higher efficiency (ie, proportion of correct results given by any test method) than did BMI(O). Moreover, the results obtained with BMI(E) were similar to those obtained with BMI in healthy control subjects. However, the nutritional assessments made with BMI(O) and BMI(E) did not differ significantly from one another. ::: ::: ::: CONCLUSION ::: For persons with limb amputation, W(E) provides a better basis for appropriate nutritional evaluation than does W(O).


title: Metrics for Polyphonic Sound Event Detection

abstract: This paper presents and discusses various metrics proposed for evaluation of polyphonic sound event detection systems used in realistic situations where there are typically multiple sound sources active simultaneously. The system output in this case contains overlapping events, marked as multiple sounds detected as being active at the same time. The polyphonic system output requires a suitable procedure for evaluation against a reference. Metrics from neighboring fields such as speech recognition and speaker diarization can be used, but they need to be partially redefined to deal with the overlapping events. We present a review of the most common metrics in the field and the way they are adapted and interpreted in the polyphonic case. We discuss segment-based and event-based definitions of each metric and explain the consequences of instance-based and class-based averaging using a case study. In parallel, we provide a toolbox containing implementations of presented metrics.


title: Towards a theory of natural language interfaces to databases

abstract: The need for Natural Language Interfaces to databases (NLIs) has become increasingly acute as more and more people access information through their web browsers, PDAs, and cell phones. Yet NLIs are only usable if they map natural language questions to SQL queries correctly. As Schneiderman and Norman have argued, people are unwilling to trade reliable and predictable user interfaces for intelligent but unreliable ones. In this paper, we introduce a theoretical framework for reliable NLIs, which is the foundation for the fully implemented Precise NLI. We prove that, for a broad class of semantically tractable natural language questions, Precise is guaranteed to map each question to the corresponding SQL query. We report on experiments testing Precise on several hundred questions drawn from user studies over three benchmark databases. We find that over 80% of the questions are semantically tractable questions, which Precise answers correctly. Precise automatically recognizes the 20% of questions that it cannot handle, and requests a paraphrase. Finally, we show that Precise compares favorably with Mooney's learning NLI and with Microsoft's English Query product


title: Localization for robot mowers covering unmarked operational area

abstract: The accurate localization is significant for both the accurate terrain acquisition and the successful area covering. Our research aims at the operational area without any manmade marks, the robot utilizes its localization system to establish the digital boundaries of the unmarked operational area. According to the specialties of outdoor environment and the practical mowing requirements, the localization system with combined sensors and the localization algorithm based on neural network are designed. The results of experiment show that the designed neural network can make the accuracy of the localization well improved. With the measurements of the ultrasonic sensors, an effective error-correction method based on the database of environmental features knowledge is proposed for the robot mower to correct its position errors in the real-time coverage operation. The localization information is reliable to ensure the intelligent behaviors of the robot mower. The technical presentations in this paper can facilitate the development of the environmental robotics.


title: Q-FDBA: improving QoE fairness for video streaming

abstract: Multiplayer video streaming scenario can be seen everywhere today as the video traffic is becoming the “killer” traffic over the Internet. The Quality of Experience fairness is critical for not only the users but also the content providers and ISP. Consequently, a QoE fairness adaptive method of multiplayer video streaming is of great importance. Previous studies focus on client-side solutions without network global view or network-assisted solution with extra reaction to client. In this paper, a pure network-based architecture using SDN is designed for monitoring network global performance information. With the flexible programming and network mastery capacity of SDN, we propose an online Q-learning-based dynamic bandwidth allocation algorithm Q-FDBA with the goal of QoE fairness. The results show the Q-FDBA could adaptively react to high frequency of bottleneck bandwidth switches and achieve better QoE fairness within a certain time dimension.


A Privacy-Preserving, Mutual PUF-Based Authentication Protocol


title: A Meta-Learning Method to Select Under-Sampling Algorithms for Imbalanced Data Sets

abstract: Imbalanced data sets originating from real world problems, such as medical diagnosis, can be found pervasive. Learning from imbalanced data sets poses its own challenges, as common classifiers assume a balanced distribution of examples' classes in the data. Sampling techniques overcome the imbalance in the data by modifying the examples' classes distribution. Unfortunately, selecting a sampling technique together with its parameters is still an open problem. Current solutions include the brute-force approach (try as many techniques as possible), and the random search approach (choose the most appropriate from a random subset of techniques). In this work, we propose a new method to select sampling techniques for imbalanced data sets. It uses Meta-Learning and works by recommending a technique for an imbalanced data set based on solutions to previous problems. Our experimentation compared the proposed method against the brute-force approach, all techniques with their default parameters, and the random search approach. The results of our experimentation show that the proposed method is comparable to the brute-force approach, outperforms the techniques with their default parameters most of the time, and always surpasses the random search approach.


title: Teaching the Notion of Speed in Kindergarten Using the Sphero SPRK Robot

abstract: Educational research in Kindergarten is gradually gaining more attention. Children who attend quality care and education programs in their early years are benefited in their future life. Also, Kindergarten lately attracts the attention of STEM (Science, Technology, Engineering and Mathematics) approaches and seems that such activities can be implemented easily in this level of education with important positive results in children's life. In this paper the results of a teaching intervention for approaching the notion of speed in Kindergarten, utilizing the Sphero robot, are presented, indicating the feasibility of designing robotic-enhanced activities for fostering STEM education in Kindergarten.


title: A Computer Vision System for Virtual Rehabilitation

abstract: A Kinect-based pose estimation system is presented for the study of movement problems within a rehab context. The performance of the system is compared to ground-truth data obtained by an expensive MoCap system. The results show that the proposed system performs well and could be used within a virtual rehabilitation context synchronized with other systems (e.g., robots).


title: Synthesis of Artificial Magnetic Conductors Using Structure-Based Evolutionary Design

abstract: An evolutionary programming approach, the so-called structure based evolutionary design, is applied to the synthesis of planar periodic electronic band gap in order to obtain an artificial magnetic conductor surface. We show that this strategy, in conjunction with a flexible aperture-oriented approach, allows for obtaining new and effective structures. This almost unique ability is exploited to obtain an artificial magnetic conductor periodic surface with a bandwidth larger than the most popular surfaces known so far.


title: The node distribution of the random waypoint mobility model for wireless ad hoc networks

abstract: The random waypoint model is a frequently used mobility model for simulation–based studies of wireless ad hoc networks. This paper investigates the spatial node distribution that results from using this model. We show and interpret simulation results on a square and circular system area, derive an analytical expression of the expected node distribution in one dimension, and give an approximation for the two–dimensional case. Finally, the concept of attraction areas and a modified random waypoint model, the random borderpoint model, is analyzed by simulation.


title: Fun Facts: Automatic Trivia Fact Extraction from Wikipedia

abstract: A significant portion of web search queries directly refers to named entities. Search engines explore various ways to improve the user experience for such queries. We suggest augmenting search results with trivia facts about the searched entity. Trivia is widely played throughout the world, and was shown to increase users' engagement and retention. Most random facts are not suitable for the trivia section. There is skill (and art) to curating good trivia. In this paper, we formalize a notion of trivia-worthiness and propose an algorithm that automatically mines trivia facts from Wikipedia. We take advantage of Wikipedia's category structure, and rank an entity's categories by their trivia-quality. Our algorithm is capable of finding interesting facts, such as Obama's Grammy or Elvis' stint as a tank gunner. In user studies, our algorithm captures the intuitive notion of "good trivia" 45% higher than prior work. Search-page tests show a 22% decrease in bounce rates and a 12% increase in dwell time, proving our facts hold users' attention.


title: EGR-VGT Control and Tuning for Pumping Work Minimization and Emission Control

abstract: A control structure is proposed and investigated for coordinated control of exhaust gas recirculation (EGR) valve and variable geometry turbochargers (VGT) position in heavy duty diesel engines. Main control goals are to fulfill the legislated emission levels, to reduce the fuel consumption, and to fulfill safe operation of the turbocharger. These goals are achieved through regulation of normalized oxygen/fuel ratio, λO, and intake manifold EGR-fraction. These are chosen both as main performance variables and feedback variables since they contain information about when it is possible to decrease the fuel consumption by minimizing the pumping work. Based on this a novel and simple pumping work minimization strategy is developed. The proposed performance variables are also strongly coupled to the emissions which makes it easier to adjust set-points, e.g., depending on measured emissions during an emission calibration process, since it is more straightforward than control of manifold pressure and air mass flow. Further, internally the controller is structured to handle the different control objectives. Controller tuning is important for performance but can be time consuming so the controller objectives are captured in a cost function, which makes automatic tuning possible even though objectives are conflicting. Performance tradeoffs are necessary and are illustrated on the European Transient Cycle. The controller is validated in an engine test cell, where it is experimentally demonstrated that the controller achieves all the control objectives and that the current production controller has at least 26% higher pumping losses compared to the proposed controller.


title: Automated Software Size Estimation based on Function Points using

abstract: A systematic approach to software size estimation is important for accurate project planning. In this paper, we will propose the unified mapping of UML models into function points. The mapping is formally described to enable the automation of the counting procedure. Three estimation levels are defined that correspond to the different abstraction levels of the software system. The level of abstraction influences an estimate's accuracy. Our research, based on a small data set, proved that accuracy increases with each subsequent abstraction level. Changes to the FPA complexity tables for transactional functions will also be proposed in order to better quantify the characteristics of object-oriented software.


title: Modeling Human Errors in Security Protocols

abstract: Many security protocols involve humans, not machines, as endpoints. The differences are critical: humans are not only computationally weaker than machines, they are naive, careless, and gullible. In this paper, we provide a model for formalizing and reasoning about these inherent human limitations and their consequences. Specifically, we formalize models of fallible humans in security protocols as multiset rewrite theories. We show how the Tamarin tool can then be used to automatically analyze security protocols involving human errors. We provide case studies of authentication protocols that show how different protocol constructions and features differ in their effectiveness with respect to different kinds of fallible humans. This provides a starting point for a fine-grained classification of security protocols from a usable-security perspective.


title: Atrial Fibrillation Detection via Accelerometer and Gyroscope of a Smartphone

abstract: We present a smartphone-only solution for the detection of atrial fibrillation (AFib), which utilizes the built-in accelerometer and gyroscope sensors [inertial measurement unit, (IMU)] in the detection. Depending on the patient's situation, it is possible to use the developed smartphone application either regularly or occasionally for making a measurement of the subject. The smartphone is placed on the chest of the patient who is adviced to lay down and perform a noninvasive recording, while no external sensors are needed. After that, the application determines whether the patient suffers from AFib or not. The presented method has high potential to detect paroxysmal (“silent”) AFib from large masses. In this paper, we present the preprocessing, feature extraction, feature analysis, and classification results of the envisioned AFib detection system based on clinical data acquired with a standard mobile phone equipped with Google Android OS. Test data was gathered from 16 AFib patients (validated against ECG), as well as a control group of 23 healthy individuals with no diagnosed heart diseases. We obtained an accuracy of 97.4% in AFib versus healthy classification (a sensitivity of 93.8% and a specificity of 100%). Due to the wide availability of smart devices/sensors with embedded IMU, the proposed methods could potentially also scale to other domains such as embedded body-sensor networks.


title: Personalized Physical Activity Coaching: A Machine Learning Approach

abstract: Living a sedentary lifestyle is one of the major causes of numerous health problems. To encourage employees to lead a less sedentary life, the Hanze University started a health promotion program. One of the interventions in the program was the use of an activity tracker to record participants' daily step count. The daily step count served as input for a fortnightly coaching session. In this paper, we investigate the possibility of automating part of the coaching procedure on physical activity by providing personalized feedback throughout the day on a participant's progress in achieving a personal step goal. The gathered step count data was used to train eight different machine learning algorithms to make hourly estimations of the probability of achieving a personalized, daily steps threshold. In 80% of the individual cases, the Random Forest algorithm was the best performing algorithm (mean accuracy = 0.93, range = 0.88–0.99, and mean F1-score = 0.90, range = 0.87–0.94). To demonstrate the practical usefulness of these models, we developed a proof-of-concept Web application that provides personalized feedback about whether a participant is expected to reach his or her daily threshold. We argue that the use of machine learning could become an invaluable asset in the process of automated personalized coaching. The individualized algorithms allow for predicting physical activity during the day and provides the possibility to intervene in time.


title: Synthetic data augmentation using GAN for improved liver lesion classification

abstract: In this paper, we present a data augmentation method that generates synthetic medical images using Generative Adversarial Networks (GANs). We propose a training scheme that first uses classical data augmentation to enlarge the training set and then further enlarges the data size and its diversity by applying GAN techniques for synthetic data augmentation. Our method is demonstrated on a limited dataset of computed tomography (CT) images of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). The classification performance using only classic data augmentation yielded 78.6% sensitivity and 88.4% specificity. By adding the synthetic data augmentation the results significantly increased to 85.7% sensitivity and 92.4% specificity.


title: Green supply chain management practices and performance

abstract: Purpose – The purpose of this study is to examine the deployment of pro‐active and re‐active practices in the implementation of green supply chain management (GSCM) and analyze their impact on environmental, economic, and intangible performance by considering business strategy as organizational focus.Design/methodology/approach – Data were collected from a sample of 190 ISO 14001 certified manufacturing companies in Thailand and used to test the research hypotheses. Factor analysis was used to examine the construct validity while multivariate linear regression was used to test criteria validity.Findings – The threat of legislation and regulation (re‐active practices) was a consideration that resulted in companies enhancing their environmental, economic, and intangible performance. Reverse logistics practices (pro‐active practices) had low levels of adoption and do not have a significant impact on GSCM performance.Research limitations/implications – This study did not consider some aspects of organizationa...


title: POSIX abstractions in modern operating systems: the old, the new, and the missing

abstract: The POSIX standard, developed 25 years ago, comprises a set of operating system (OS) abstractions that aid application portability across UNIX-based OSes. While OSes and applications have evolved tremendously over the last 25 years, POSIX, and the basic set of abstractions it provides, has remained largely unchanged. Little has been done to measure how and to what extent traditional POSIX abstractions are being used in modern OSes, and whether new abstractions are taking form, dethroning traditional ones. We explore these questions through a study of POSIX usage in modern desktop and mobile OSes: Android, OS X, and Ubuntu. Our results show that new abstractions are taking form, replacing several prominent traditional abstractions in POSIX. While the changes are driven by common needs and are conceptually similar across the three OSes, they are not converging on any new standard, increasing fragmentation.


title: Charge pump with perfect current matching characteristics in phase-locked loops

abstract: Conventional CMOS charge pump circuits have some current mismatching characteristics. The current mismatch of the charge pump in the PLLs generates a phase offset, which increases spurs in the PLL output signals. In particular, it reduces the locking range in wide range PLLs with a dual loop scheme. A new charge pump circuit with perfect current matching characteristics is proposed. By using an error amplifier and reference current sources, one can achieve a charge pump with good current matching characteristics. It shows nearly perfect current matching characteristics over the whole VCO input range, and the amount of the reference spur is <-75 dBc in the PLL output signal. The charge pump circuit is implemented in a 0.25 /spl mu/m CMOS process.


title: A VLSI high-performance encoder with priority lookahead

abstract: In this paper we introduce a VLSI priority encoder that uses a novel priority lookahead scheme to reduce the delay for the worst case operation of the circuit, while maintaining a very low transistor count. The encoder's topmost input request has the highest priority; this priority descends linearly. Two design approaches for the priority encoder are presented, one without a priority lookahead scheme and one with a priority lookahead scheme. For an N-bit encoder, the circuit with the priority lookahead scheme requires only 1.094 times the number of transistors of the circuit without the priority lookahead scheme. Having a 32-bit encoder as an example, the circuit with the priority lookahead scheme is 2.59 times faster than the circuit without the priority lookahead. The worst case operation delay is 4.4 ns for this lookahead encoder, using a 1-/spl mu/m scalable CMOS technology. The proposed lookahead scheme can be extended to larger encoders.


title: Intrusion detection techniques in mobile ad hoc and wireless sensor networks

abstract: Mobile ad hoc networks and wireless sensor networks have promised a wide variety of applications. However, they are often deployed in potentially adverse or even hostile environments. Therefore, they cannot be readily deployed without first addressing security challenges. Intrusion detection systems provide a necessary layer of in-depth protection for wired networks. However, relatively little research has been performed about intrusion detection in the areas of mobile ad hoc networks and wireless sensor networks. In this article, first we briefly introduce mobile ad hoc networks and wireless sensor networks and their security concerns. Then, we focus on their intrusion detection capabilities. Specifically, we present the challenge of constructing intrusion detection systems for mobile ad hoc networks and wireless sensor networks, survey the existing intrusion detection techniques, and indicate important future research directions.


title: DANoC: An Efficient Algorithm and Hardware Codesign of Deep Neural Networks on Chip

abstract: Deep neural networks (NNs) are the state-of-the-art models for understanding the content of images and videos. However, implementing deep NNs in embedded systems is a challenging task, e.g., a typical deep belief network could exhaust gigabytes of memory and result in bandwidth and computational bottlenecks. To address this challenge, this paper presents an algorithm and hardware codesign for efficient deep neural computation. A hardware-oriented deep learning algorithm, named the deep adaptive network, is proposed to explore the sparsity of neural connections. By adaptively removing the majority of neural connections and robustly representing the reserved connections using binary integers, the proposed algorithm could save up to 99.9% memory utility and computational resources without undermining classification accuracy. An efficient sparse-mapping-memory-based hardware architecture is proposed to fully take advantage of the algorithmic optimization. Different from traditional Von Neumann architecture, the deep-adaptive network on chip (DANoC) brings communication and computation in close proximity to avoid power-hungry parameter transfers between on-board memory and on-chip computational units. Experiments over different image classification benchmarks show that the DANoC system achieves competitively high accuracy and efficiency comparing with the state-of-the-art approaches.


title: Portfolio management of R & D projects : Implications for innovation management forthcoming in Technovation

abstract: Globalization of markets and new business practices are prompting high-tech firms to reconsider their competitive strategy. The increasing complexity of technologies in addition to shorter product life cycles are also forcing firms to rely on R&D as a source of strategy. More importantly, firms are inclined to evaluate their technologies from a portfolio’s perspective in which a set or a sub-set of R&D projects is evaluated together, in relation to each other. Portfolio techniques can help strategic managers in evaluating whether a portfolio of products is adequate from the perspective of long-term corporate growth and profitability. Obviously, when R&D projects are evaluated relative to one another, technical capability management of such projects must be carried out concurrently. In this paper, R&D Project Portfolio Matrix is used as a tool for analyzing a portfolio of R&D projects by linking competitive advantages of a firm to benefits these projects may provide to customers. Examples of batteries for electric vehicles (EV) and hybrid electric vehicles (HEV) are provided to illustrate how such a matrix is used, and some of the implications for innovation management of such projects.  2001 Elsevier Science Ltd. All rights reserved.


title: Lightweight security scheme for vehicle tracking system using CoAP

abstract: In this paper we present a lightweight security scheme for authentication and key management to establish a secure channel for Intelligent Transportation System (ITS) for an IoT (Internet of Things) application. We choose Constrained Application Protocol (CoAP) as lightweight application layer protocol. Low overhead security is still an open challenge for CoAP. We propose a payload embedded low cost symmetric-key based robust authentication and key management mechanism on CoAP. This minimizes the security overhead by eliminating expensive handshaking and ciphersuite agreement of standard TLS and DTLS. We propose some unique modification in the CoAP header to invoke its secure mode in an optimized manner. Further, we propose a secure channel with adaptive reliability which reduces the overall communication cost. Such a low overhead security scheme for CoAP is hitherto unexplored. The efficacy of our proposed scheme is demonstrated through laboratory experiments in an emulated environment.


title: Cloud Computing in Education in Developing Countries

abstract: The main advantage of cloud computing is to reduce the IT cost. By using cloud computing, organisations do not have to set up an IT infrastructure, and instead are able to rent resources and give payment only for the using services. Even with the appealing of cloud computing benefits, it is still in infancy in developing countries due to many reasons. Technology adoption has been explored to a limited degree in developing countries, particularly in relation to cloud computing in the tertiary education sector. Existing studies have examined technology adoption in developed countries and to a lesser extent in developing countries in non-education contexts such as e-government. This paper contributes to the cloud computing adoption literature in developing countries, and specifically in Saudi Arabian universities.


title: Comparing the Usability of Cryptographic APIs

abstract: Potentially dangerous cryptography errors are well-documented in many applications. Conventional wisdom suggests that many of these errors are caused by cryptographic Application Programming Interfaces (APIs) that are too complicated, have insecure defaults, or are poorly documented. To address this problem, researchers have created several cryptographic libraries that they claim are more usable, however, none of these libraries have been empirically evaluated for their ability to promote more secure development. This paper is the first to examine both how and why the design and resulting usability of different cryptographic libraries affects the security of code written with them, with the goal of understanding how to build effective future libraries. We conducted a controlled experiment in which 256 Python developers recruited from GitHub attempt common tasks involving symmetric and asymmetric cryptography using one of five different APIs. We examine their resulting code for functional correctness and security, and compare their results to their self-reported sentiment about their assigned library. Our results suggest that while APIs designed for simplicity can provide security benefits – reducing the decision space, as expected, prevents choice of insecure parameters – simplicity is not enough. Poor documentation, missing code examples, and a lack of auxiliary features such as secure key storage, caused even participants assigned to simplified libraries to struggle with both basic functional correctness and security. Surprisingly, the availability of comprehensive documentation and easy-to-use code examples seems to compensate for more complicated APIs in terms of functionally correct results and participant reactions, however, this did not extend to security results. We find it particularly concerning that for about 20% of functionally correct tasks, across libraries, participants believed their code was secure when it was not. Our results suggest that while new cryptographic libraries that want to promote effective security should offer a simple, convenient interface, this is not enough: they should also, and perhaps more importantly, ensure support for a broad range of common tasks and provide accessible documentation with secure, easy-to-use code examples.


title: On active learning of record matching packages

abstract: We consider the problem of learning a record matching package (classifier) in an active learning setting. In active learning, the learning algorithm picks the set of examples to be labeled, unlike more traditional passive learning setting where a user selects the labeled examples. Active learning is important for record matching since manually identifying a suitable set of labeled examples is difficult. Previous algorithms that use active learning for record matching have serious limitations: The packages that they learn lack quality guarantees and the algorithms do not scale to large input sizes. We present new algorithms for this problem that overcome these limitations. Our algorithms are fundamentally different from traditional active learning approaches, and are designed ground up to exploit problem characteristics specific to record matching. We include a detailed experimental evaluation on realworld data demonstrating the effectiveness of our algorithms.


title: Particle swarm optimization

abstract: Particle swarm optimization (PSO) has undergone many changes since its intro- duction in 1995. As researchers have learned about the technique, they have derived new versions, developed new applications, and published theoretical studies of the effects of the various parameters and aspects of the algorithm. This paper comprises a snapshot of particle swarming from the authors' perspective, including variations in the algorithm, current and ongoing research, applications and open problems.


title: Code-pointer integrity

abstract: Systems code is often written in low-level languages like C/C++, which offer many benefits but also delegate memory management to programmers. This invites memory safety bugs that attackers can exploit to divert control flow and compromise the system. Deployed defense mechanisms (e.g., ASLR, DEP) are incomplete, and stronger defense mechanisms (e.g., CFI) often have high overhead and limited guarantees [19, 15, 9]. ::: ::: We introduce code-pointer integrity (CPI), a new design point that guarantees the integrity of all code pointers in a program (e.g., function pointers, saved return addresses) and thereby prevents all control-flow hijack attacks, including return-oriented programming. We also introduce code-pointer separation (CPS), a relaxation of CPI with better performance properties. CPI and CPS offer substantially better security-to-overhead ratios than the state of the art, they are practical (we protect a complete FreeBSD system and over 100 packages like apache and postgresql), effective (prevent all attacks in the RIPE benchmark), and efficient: on SPEC CPU2006, CPS averages 1.2% overhead for C and 1.9% for C/C++, while CPI's overhead is 2.9% for C and 8.4% for C/C++. ::: ::: A prototype implementation of CPI and CPS can be obtained from http://levee.epfl.ch.


Natural Language Processing and Language Learning


title: Brain activity correlates differentially with increasing temporal complexity of rhythms during initialisation, synchronisation, and continuation phases of paced finger tapping

abstract: Activity in parts of the human motor system has been shown to correlate with the complexity of performed motor sequences in terms of the number of limbs moved, number of movements, and number of trajectories. Here, we searched for activity correlating with temporal complexity, in terms of the number of different intervals produced in the sequence, using an overlearned tapping task. Our task was divided into three phases: movement selection and initiation (initiate), synchronisation of finger tapping with an external auditory cue (synchronise), and continued tapping in absence of the auditory pacer (continue). Comparisons between synchronisation and continuation showed a pattern in keeping with prior neuroimaging studies of paced finger tapping. Thus, activation of bilateral SMA and basal ganglia was greater in continuation tapping than in synchronisation tapping. Parametric analysis revealed activity correlating with temporal complexity during initiate in bilateral supplementary and pre-supplementary motor cortex (SMA and preSMA), rostral dorsal premotor cortex (PMC), basal ganglia, and dorsolateral prefrontal cortex (DLPFC), among other areas. During synchronise, correlated activity was observed in bilateral SMA, more caudal dorsal and ventral PMC, right DLPFC and right primary motor cortex. No correlated activity was observed during continue at P< 0.01 (corrected, cluster level), though left angular gyrus was active at P< 0.05. We suggest that the preSMA and rostral dorsal PMC activities during initiate may be associated with selection of timing parameters, while activation in centromedial prefrontal cortex during both initiate and synchronise may be associated with temporal error monitoring or correction. The absence of activity significantly correlated with temporal complexity during continue suggests that, once an overlearned timed movement sequence has been selected and initiated, there is no further adjustment of the timing control processes related to its continued production in absence of external cues. 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27


title: Parallel simulation: distributed simulation systems

abstract: An overview of technologies concerned with distributing the execution of simulation programs across multiple processors is presented. Here, particular emphasis is placed on discrete event simulations. The High Level Architecture (HLA) developed by the Department of Defense in the United States is first described to provide a concrete example of a contemporary approach to distributed simulation. The remainder of this paper is focused on time management, a central issue concerning the synchronization of computations on different processors. Time management algorithms broadly fall into two categories, termed conservative and optimistic synchronization. A survey of both conservative and optimistic algorithms is presented focusing on fundamental principles and mechanisms. Finally, time management in the HLA is discussed as a means to illustrate how this standard supports both approaches to synchronization.


title: Episodic volunteering in open source communities

abstract: Episodic volunteers, who prefer short term engagement to habitual contributions, are present in Free/Libre and Open Source Software (FLOSS) communities. Little is known about how they are viewed within their communities, how they view their communities, how community managers are managing them, or even how many episodic volunteers contribute to FLOSS projects and how much they contribute. Knowing more about the prevalence and management of episodic volunteers in FLOSS will help community managers make better decisions for engaging and utilizing these volunteers. My dissertation addresses these questions, providing a picture of what episodic volunteering looks like in the context of FLOSS communities.


title: Consumer Choice and Autonomy in the Age of Artificial Intelligence and Big Data

abstract: Recent developments in the field of artificial intelligence and data analytics are facilitating the automation of some consumer chores (e.g., in smart homes and in self-driving cars) and allow the emergence of big-data-driven, micro-targeting marketing practices (e.g., personalized content recommendation algorithms). We contend that those developments can generate a tension for marketers, consumers, and policy makers: They can, on the one hand, contribute to consumer well-being by making consumer choices easier, more practical, and more efficient. On the other hand, they can also undermine consumers’ sense of autonomy, the absence of which can be detrimental to consumer well-being. Drawing on diverse perspectives from marketing, economics, philosophy, neuroscience, and psychology, we explore how consumers’ sense of autonomy in making choices affects their well-being. We discuss how new technologies may enhance or diminish consumers’ perceptions of being in control of their choices and how either of those can, in turn, enhance of detract from consumer well-being. Building on this, we identify open research questions in the domain of choice, well-being, and consumer welfare, and suggest avenues for future research.


title: A new anti-spam model based on e-mail address concealment technique

abstract: To deal with the junk e-mail problem caused by the e-mail address leakage for a majority of Internet users, this paper presents a new privacy protection model in which the e-mail address of the user is treated as a piece of privacy information concealed. Through an interaction pattern that involves three parties and uses an e-mail address code in the place of an e-mail address, the proposed model can prevent the e-mail address from being leaked, thus effectively resolving the junk e-mail problem. We compare the proposed anti-spam method with the filtering technology based on machine learning. The result shows that 100% spams can be filtered out in our scheme, indicating the effectiveness of the proposed anti-spam method.


title: Churn analysis of online social network users using data mining techniques

abstract: A churn is defined as the loss of a user in an online social network (OSN). Detecting and analyzing user churn at an early stage helps to provide timely delivery of retention solutions (e.g., interventions, customized services, and better user interfaces) that are useful for preventing users from churning. In this paper we develop a prediction model based on a clustering scheme to analyze the potential churn of users. In the experiment, we test our approach on a real-name OSN which contains data from 77, 448 users. A set of 24 attributes is extracted from the data. A decision tree classifier is used to predict churn and non-churn users of the future month. In addition, k-means algorithm is employed to cluster the actual churn users into different groups with different online social networking behaviors. Results show that the churn and non-churn prediction accuracies of ∼65% and ∼77% are achieved respectively. Furthermore, the actual churn users are grouped into Ave clusters with distinguished OSN activities and some suggestions of retaining these users are provided.


e³-value: Design and Evaluation of e-Business Models


INNOVATION IN THE ENERGY SECTOR : EVIDENCE FROM THE COLLECTIVE SWITCHING CASE IN EUROPE November 2018


title: Defects activated photoluminescence in two-dimensional semiconductors: interplay between bound, charged, and free excitons

abstract: Point defects in semiconductors can trap free charge carriers and localize excitons. The interaction between these defects and charge carriers becomes stronger at reduced dimensionalities, and is expected to greatly influence physical properties of the hosting material. We investigated effects of anion vacancies in monolayer transition metal dichalcogenides as two-dimensional (2D) semiconductors where the vacancies density is controlled bya-particle irradiation or thermal-annealing. We found a new, sub-bandgap emission peak as well as increase in overall photoluminescence intensity as a result of the vacancy generation. Interestingly, these effects are absent when measured in vacuum. We conclude that in opposite to conventional wisdom, optical quality at room temperature cannot be used as criteria to assess crystal quality of the 2D semiconductors. Our results not only shed light on defect and exciton physics of 2D semiconductors, but also offer a new route toward tailoring optical properties of 2D semiconductors by defect engineering.


title: Compressed sensing

abstract: Suppose x is an unknown vector in Ropfm (a digital image or signal); we plan to measure n general linear functionals of x and then reconstruct. If x is known to be compressible by transform coding with a known transform, and we reconstruct via the nonlinear procedure defined here, the number of measurements n can be dramatically smaller than the size m. Thus, certain natural classes of images with m pixels need only n=O(m1/4log5/2(m)) nonadaptive nonpixel samples for faithful recovery, as opposed to the usual m pixel samples. More specifically, suppose x has a sparse representation in some orthonormal basis (e.g., wavelet, Fourier) or tight frame (e.g., curvelet, Gabor)-so the coefficients belong to an lscrp ball for 0<ples1. The N most important coefficients in that expansion allow reconstruction with lscr2 error O(N1/2-1p/). It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients. Moreover, a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing. The nonadaptive measurements have the character of "random" linear combinations of basis/frame elements. Our results use the notions of optimal recovery, of n-widths, and information-based complexity. We estimate the Gel'fand n-widths of lscrp balls in high-dimensional Euclidean space in the case 0<ples1, and give a criterion identifying near- optimal subspaces for Gel'fand n-widths. We show that "most" subspaces are near-optimal, and show that convex optimization (Basis Pursuit) is a near-optimal way to extract information derived from these near-optimal subspaces


title: Reinforcement Learning with Perturbed Rewards

abstract: Recent studies have shown that reinforcement learning (RL) models are vulnerable in various noisy scenarios. For instance, the observed reward channel is often subject to noise in practice (e.g., when rewards are collected through sensors), and is therefore not credible. In addition, for applications such as robotics, a deep reinforcement learning (DRL) algorithm can be manipulated to produce arbitrary errors by receiving corrupted rewards. In this paper, we consider noisy RL problems with perturbed rewards, which can be approximated with a confusion matrix. We develop a robust RL framework that enables agents to learn in noisy environments where only perturbed rewards are observed. Our solution framework builds on existing RL/DRL algorithms and firstly addresses the biased noisy reward setting without any assumptions on the true distribution (e.g., zero-mean Gaussian noise as made in previous works). The core ideas of our solution include estimating a reward confusion matrix and defining a set of unbiased surrogate rewards. We prove the convergence and sample complexity of our approach. Extensive experiments on different DRL platforms show that trained policies based on our estimated surrogate reward can achieve higher expected rewards, and converge faster than existing baselines. For instance, the state-of-the-art PPO algorithm is able to obtain 84.6% and 80.8% improvements on average score for five Atari games, with error rates as 10% and 30% respectively.


A Background Model Initialization Algorithm for Video Surveillance


title: Xampling: Analog to Digital at Sub-Nyquist Rates

abstract: We present a sub-Nyquist analog-to-digital converter of wideband inputs. Our circuit realizes the recently proposed modulated wideband converter, which is a flexible platform for sampling signals according to their actual bandwidth occupation. The theoretical work enables, for example, a sub-Nyquist wideband receiver, which has no prior information on the transmitter carrier positions. Our design supports input signals with 2 GHz Nyquist rate and 120 MHz spectrum occupancy, with arbitrary transmission frequencies. The sampling rate is as low as 280 MHz. To the best of our knowledge, this is the first reported wideband hardware for sub-Nyquist conversion. Furthermore, the modular design is proven to compete with state-of-the-art Nyquist ADCs in terms of resolution bits and full-scale range. We describe the various circuit design considerations, with an emphasis on the nonordinary challenges the converter introduces: mixing a signal with a multiple set of sinusoids, rather than a single local oscillator, and generation of highly-transient periodic waveforms, with transient intervals on the order of the Nyquist rate. A series of hardware experiments validates the design and demonstrate sub-Nyquist sampling.


title: Penile Prosthesis Surgery: A Review of Prosthetic Devices and Associated Complications

abstract: INTRODUCTION ::: Although more invasive than some of the other currently available therapies, penile prosthesis surgery has the advantages of high patient satisfaction rates and avoidance of systemic adverse events in the vast majority of cases. ::: ::: ::: AIM ::: This article provides a review of the more widely used implants and some of the more frequently encountered complications of penile prosthesis surgery. ::: ::: ::: METHODS ::: A retrospective review peer reviewed publications relevant to the field of penile prosthesis surgery. ::: ::: ::: MAIN OUTCOME MEASURES ::: Review of historical milestones and newer penile prostheses, as well as a review of prosthesis surgery complications. ::: ::: ::: RESULTS ::: Improved designs and materials have resulted in decreased incidence of mechanical failures or infectious complications while simultaneously simplifying the operation of these devices. ::: ::: ::: CONCLUSIONS ::: Penile prosthesis surgery remains an excellent alternative for restoring erectile function to those in whom medical therapies such as phosphodiesterase inhibitors are contraindicated or who have failed more conservative measures.


A Design Of Omni-Directional Mobile Robot Based On Mecanum Wheels


title: Why is the Snowflake Schema a Good Data Warehouse Design

abstract: Database design for data warehouses is based on the notion of the snowflake schema and its important special case, the star schema. The snowflake schema represents a dimensional model which is composed of a central fact table and a set of constituent dimension tables which can be further broken up into subdimension tables. We formalise the concept of a snowflake schema in terms of an acyclic database schema whose join tree satisfies certain structural properties. We then define a normal form for snowflake schemas which captures its intuitive meaning with respect to a set of functional and inclusion dependencies. We show that snowflake schemas in this normal form are independent as well as separable when the relation schemas are pairwise incomparable. This implies that relations in the data warehouse can be updated independently of each other as long as referential integrity is maintained. In addition, we show that a data warehouse in snowflake normal form can be queried by joining the relation over the fact table with the relations over its dimension and subdimension tables. We also examine an information-theoretic interpretation of the snowflake schema and show that the redundancy of the primary key of the fact table is zero.


title: Sensorless estimation of the pumping process characteristics by a frequency converter

abstract: Energy consumption in pumping systems is affected by the surrounding process. As frequency converters allow sensorless estimation of the pump operating state, they can also be used for the process characteristics identification. In this paper, two frequency-converter-based methods to identify process characteristics are proposed, and the start-up test method is more closely studied by laboratory tests.


title: Discovering Anomalous Aviation Safety Events Using Scalable Data Mining Algorithms

abstract: The worldwide civilian aviation system is one of the most complex dynamical systems created. Most modern commercial aircraft have onboard flight data recorders that record several hundred discrete and continuous parameters at approximately 1 Hz for the entire duration of the flight. These data contain information about the flight control systems, actuators, engines, landing gear, avionics, and pilot commands. In this paper, recent advances in the development of a novel knowledge discovery process consisting of a suite of data mining techniques for identifying precursors to aviation safety incidents are discussed. The data mining techniques include scalable multiple-kernel learning for large-scale distributed anomaly detection. A novel multivariate time-series search algorithm is used to search for signatures of discovered anomalies on massive datasets. The process can identify operationally significant events due to environmental, mechanical, and human factors issues in the high-dimensional flight operati...


title: The RADAN series of compact pulsed power Generators and their applications

abstract: This paper presents results of development of a compact pulsed power high-voltage generators and high-current electron accelerators of the RADAN series. The basic high-voltage units of RADAN instruments are built around coaxial pulsed forming lines and efficient charging device represented by a Tesla transformer. The fields of applications in science and in practice are rather wide and include formation of nanosecond and subnanosecond voltage and ultrawideband RF pulses, high-power microwave generation, X-ray radiography, radiation physics, chemistry, and biology. The designed technique provided achievements of outstanding specific parameters of dense e-beams, microwaves, and ultrawideband pulses.


title: Identity-based encryption with efficient revocation

abstract: Identity-based encryption (IBE) is an exciting alternative to public-key encryption, as IBE eliminates the need for a Public Key Infrastructure (PKI). The senders using an IBE do not need to look up the public keys and the corresponding certificates of the receivers, the identities (e.g. emails or IP addresses) of the latter are sufficient to encrypt. Any setting, PKI- or identity-based, must provide a means to revoke users from the system. Efficient revocation is a well-studied problem in the traditional PKI setting. However in the setting of IBE, there has been little work on studying the revocation mechanisms. The most practical solution requires the senders to also use time periods when encrypting, and all the receivers (regardless of whether their keys have been compromised or not) to update their private keys regularly by contacting the trusted authority. We note that this solution does not scale well -- as the number of users increases, the work on key updates becomes a bottleneck. We propose an IBE scheme that significantly improves key-update efficiency on the side of the trusted party (from linear to logarithmic in the number of users), while staying efficient for the users. Our scheme builds on the ideas of the Fuzzy IBE primitive and binary tree data structure, and is provably secure.


title: Spatial attention modulates activity in a posterior “where” auditory pathway

abstract: Abstract Selectively listening to a single location in space modulates both the behavioral and electrophysiological responses to auditory stimuli presented at that location. Transient attention oriented in cue–target or target–target paradigms results in several modulations of the auditory event-related potential known as the Nd1, Nd2, and Nd3. By employing electrical source analysis we tested the hypothesis that the earliest component (the Nd1) reflects modulation of neurons in parietal rather than auditory cortex. It was found that the most likely sources of the Nd1 modulation were posterior to primary auditory cortex within or near the temporo-parietal junction (TPJ). This location is within the putative auditory “where” pathway.