title: Confluence: conformity influence in large social networks

text: Conformity is a type of social influence involving a change in opinion or behavior in order to fit in with a group. Employing several social networks as the source for our experimental data, we study how the effect of conformity plays a role in changing users' online behavior. We formally define several major types of conformity in individual, peer, and group levels. We propose Confluence model to formalize the effects of social conformity into a probabilistic model. Confluence can distinguish and quantify the effects of the different types of conformities. To scale up to large social networks, we propose a distributed learning method that can construct the Confluence model efficiently with near-linear speedup. Our experimental results on four different types of large social networks, i.e., Flickr, Gowalla, Weibo and Co-Author, verify the existence of the conformity phenomena. Leveraging the conformity information, Confluence can accurately predict actions of users. Our experiments show that Confluence significantly improves the prediction accuracy by up to 5-10% compared with several alternative methods.


title: On board eco-driving system for varying road-traffic environments using model predictive control

text: This paper presents model predictive control of a vehicle in a varying road-traffic environment for ecological (eco) driving. The vehicle control input is derived by rigorous reasoning approach of model based anticipation of road, traffic and fuel consumption in a crowded road network regulated by traffic signals. Model predictive control with Continuation and generalized minimum residual method for optimization is used to calculate the sequence of control inputs aiming at long run fuel economy maintaining a safe driving. Performance of the proposed eco-driving system is evaluated through simulations in AIMSUN microscopic transport simulator. In spite of nonlinearity and discontinuous movement of other traffic and signals, the proposed system is robust enough to control the vehicle safely. The driving behavior with fuel saving aspects is graphically illustrated, compared and analyzed to signify the prospect of the proposed eco-driving of a vehicle.


title: Joint Computation Partitioning and Resource Allocation for Latency Sensitive Applications in Mobile Edge Clouds

text: The proliferation of mobile devices and ubiquitous access of the wireless network enables many new mobile applications such as augmented reality, mobile gaming and so on. As the applications are latency sensitive, researchers propose to offload the complex computations of these applications to the nearby mobile edge cloud, in order to reduce the latency. Existing works mostly consider the problem of partitioning the computations between the mobile device and the traditional cloud that has abundant resources. The proposed approaches can not be applied in the context of mobile edge cloud, because both the resources in the mobile edge cloud and the wireless access bandwidth to the edge cloud are constrained. In this paper, we study joint computation partitioning and resource allocation problem for latency sensitive applications in mobile edge clouds. The problem is novel in that we combine the computation partitioning and the two-dimensional resource allocations in both the computation resources and the network bandwidth. We develop a new and efficient method, namely Multi-Dimensional Search and Adjust (MDSA), to solve the problem. We compares MDSA with the classic list scheduling method and the SearchAdjust algorithm via comprehensive simulations. The results show that MDSA outperforms the benchmark algorithms in terms of the overall application latency.


title: smartPrediction: a real-time smartphone-based fall risk prediction and prevention system

text: The high risk of falls and the substantial increase in the elderly population have recently stimulated scientific research on Smartphone-based fall detection systems. Even though these systems are helpful for fall detection, the best way to reduce the number of falls and their consequences is to predict and prevent them from happening in the first place. To address the issue of fall prevention, in this paper, we propose a fall prediction system by integrating the sensor data of Smartphones and a Smartshoe. We designed and implemented a Smartshoe that contains four pressure sensors with a Wi-Fi communication module to unobtrusively collect data in any environment. By assimilating the Smartshoe and Smartphone sensors data, we performed an extensive set of experiments to evaluate normal and abnormal walking patterns. The system can generate an alert message in the Smartphone to warn the user about the high-risk gait patterns and potentially save them from an imminent fall. We validated our approach using a decision tree with 10-fold cross validation and found 97.2% accuracy in gait abnormality detection.


title: Evaluation of LoRa LPWAN technology for remote health and wellbeing monitoring

text: Low power consumption, low transceiver chip cost and large coverage area are the main characteristics of the low power wide area networks (LPWAN) technologies. We expect that LPWAN can be part of enabling new human-centric health and wellness monitoring applications. Therefore in this work we study the indoor performance of one LPWAN technology, namely LoRa, by the means of real-life measurements. The measurements were conducted using the commercially available equipment in the main campus of the University of Oulu, Finland, which has an indoor area spanning for over 570 meters North to South and over 320 meters East to West. The measurements were executed for a sensor node operating close to human body that was periodically reporting the sensed data to a base station. The obtained results show that when using 14 dBm transmit power and the largest spreading factor of 12 for the 868 MHz ISM band, the whole campus area can be covered. Measured packet success delivery ratio was 96.7 % without acknowledgements and retransmissions.


title: Executive control in language processing

text: During communication, speakers and listeners need the mechanisms of executive control to organize thoughts and actions along internal goals. Speakers may use executive functions to select the right word over competing alternatives to refer to the concept in mind. Listeners may use executive functions to coordinate the outputs of multiple linguistic processes to reach a coherent interpretation of what others say. Bilinguals may use executive functions to control which language is to use or to switch from one language to another. The control mechanisms recruited in language processing may be similar to those recruited in perception and attention, supported by a network of frontal, parietal and sub-cortical brain structures. Here we review existing evidences regarding the involvement of domain-general executive control in language processing. We will explain how executive functions are employed to control interference in comprehension and production, within and across languages.


title: Soft-Switched CCM Boost Converters With High Voltage Gain for High-Power Applications

text: This paper proposes a new soft-switched continuous-conduction-mode (CCM) boost converter suitable for high-power applications such as power factor correction, hybrid electric vehicles, and fuel cell power conversion systems. The proposed converter achieves zero-voltage-switched (ZVS) turn-on of active switches in CCM and zero-current-switched turn-off of diodes leading to negligible reverse-recovery loss. The components' voltage ratings and energy volumes of passive components of the proposed converter are greatly reduced compared to the conventional zero-voltage-transition converter. Voltage conversion ratio is almost doubled compared to the conventional boost converter. Extension of the proposed concept to realize multiphase dc-dc converters is discussed. Experimental results from a 1.5-kW prototype are provided to validate the proposed concept.


title: An Interleaved Boost Converter With Zero-Voltage Transition

text: This paper proposes a novel soft-switching interleaved boost converter composed of two shunted elementary boost conversion units and an auxiliary inductor. This converter is able to turn on both the active power switches at zero voltage to reduce their switching losses and evidently raise the conversion efficiency. Since the two parallel-operated elementary boost units are identical, operation analysis and design for the converter module becomes quite simple. A laboratory test circuit is built, and the circuit operation shows satisfactory agreement with the theoretical analysis. The experimental results show that this converter module performs very well with the output efficiency as high as 95%.


title: A new active clamping zero-voltage switching PWM current-fed half-bridge converter

text: A new active clamping zero-voltage switching (ZVS) pulse-width modulation (PWM) current-fed half-bridge converter (CFHB) is proposed in this paper. Its active clamping snubber (ACS) can not only absorb the voltage surge across the turned-off switch, but also achieve the ZVS of all power switches. Moreover, it can be applied to all current-fed power conversion topologies and its operation as well as structure is very simple. Since auxiliary switches in the snubber circuit are switched in a complementary way to main switches, an additional PWM IC is not necessary. In addition, it does not need any clamp winding and auxiliary circuit besides additional two power switches and one capacitor while the conventional current-fed half bridge converter has to be equipped with two clamp windings, two ZVS circuits, and two snubbers. Therefore, it can ensure the higher operating frequency, smaller-sized reactive components, lower cost of production, easier implementation, and higher efficiency. The operational principle, theoretical analysis, and design considerations are presented. To confirm the operation, validity, and features of the proposed circuit, experimental results from a 200-W, 24-200Vdc prototype are presented.


title: Landsat continuity : Issues and opportunities for land cover monitoring

text: Initiated in 1972, the Landsat program has provided a continuous record of earth observation for 35 years. The assemblage of Landsat spatial, spectral, and temporal resolutions, over a reasonably sized image extent, results in imagery that can be processed to represent land cover over large areas with an amount of spatial detail that is absolutely unique and indispensable for monitoring, management, and scientific activities. Recent technical problems with the two existing Landsat satellites, and delays in the development and launch of a successor, increase the likelihood that a gap in Landsat continuity may occur. In this communication, we identify the key features of the Landsat program that have resulted in the extensive use of Landsat data for large area land covermapping andmonitoring.We then augment this list of key features by examining the data needs of existing large area land cover monitoring programs. Subsequently, we use this list as a basis for reviewing the current constellation of earth observation satellites to identify potential alternative data sources for large area land cover applications. Notions of a virtual constellation of satellites to meet large area land cover mapping and monitoring needs are also presented. Finally, research priorities that would facilitate the integration of these alternative data sources into existing large area land cover monitoring programs are identified. Continuity of the Landsat program and the measurements provided are critical for scientific, environmental, economic, and social purposes. It is difficult to overstate the importance of Landsat; there are no other systems in orbit, or planned for launch in the short-term, that can duplicate or approach replication, of the measurements and information conferred by Landsat. While technical and political options are being pursued, there is no satellite image data stream poised to enter the National Satellite Land Remote Sensing Data Archive should system failures occur to Landsat-5 and -7. Crown Copyright © 2007 Published by Elsevier Inc. All rights reserved.


title: Subcarrier-index modulation OFDM

text: A new transmission approach, referred to as subcarrier-index modulation (SIM) is proposed to be integrated with the orthogonal frequency division multiplexing (OFDM) systems. More specifically, it relates to adding an additional dimension to the conventional two-dimensional (2-D) amplitude/phase modulation (APM) techniques, i.e. amplitude shift keying (ASK) and quadrature amplitude modulation (QAM). The key idea of SIM is to employ the subcarrier-index to convey information to the receiver. Furthermore, a closed-form analytical bit error ratio (BER) of SIM OFDM in Rayleigh channel is derived. Analytical and simulation results show error probability performance gain of 4 dB over 4-QAM OFDM systems for both coded and uncoded data without power saving policy. Alternatively, power saving policy retains an average gain of 1 dB while using 3 dB less transmit power per OFDM symbol.


Impact of aluminum wire and ribbon bonding technologies on D2PAK package reliability during thermal cycling applications


title: Image Processing using IP Core Generator through FPGA

text: Xilinx CORE Generator System generates and delivers parameterizable cores optimized for Xilinx FPGAs. CORE Generator is mainly used to create high density, high performance designs in Xilinx FPGAs in less time. The CORE Generator is included with the ISE WebPack and ISE Foundation software and comes with an extensive library of Xilinx LogiCORE IP. These include DSP functions, memories, storage elements, math functions and a variety of basic elements. Xilinx provides a flexible Block Memory Generator core to create compact, high-performance memories running at up to 450 MHz. Block Memory Generator provides single port and dual port block memory. These memory types differ in selection of operating modes.Matlab tool is used to convert the image that is being processed to .coe file format.


title: Improvement of the on-body performance of a dual-band textile antenna using an EBG structure

text: A new dual-band wearable textile antenna designed for body-centric communications is introduced. The antenna is fully characterized in free space and on the body model, with and without an electromagnetic band gap (EBG) substrate. The band gap array consists of 3 × 3 elements and is used to reduce the interaction with human tissues. With the EBG back reflector, the radiation into the body is reduced by more than 15 dB. Increases of 5.2 dB and 3 dB gain are noticed at 2.45 GHz and 5.5 GHz, respectively. The results are presented for the return loss, radiation pattern, efficiency, and specific absorption rate (SAR).


title: Procedural City Layout Generation Based on Urban Land Use Models

text: Training and simulation applications in virtual worlds require significant amounts of urban environments. Procedural generation is an efficient way to create such models. Existing approaches for procedural modelling of cities aim at facilitating the work of urban planners and artists, but either require expert knowledge or external input data to generate results that resemble real-life cities, or they have long computation times, and thus are unsuitable for non-experts such as training instructors. We propose a method that procedurally creates layouts of structurally plausible cities from high-level, intuitive user input such as city size, location and historic background. The resulting layouts consist of different kinds of city districts which are arranged using constraints derived from established models of urban land use. Our approach avoids the need for external expert engagement in the creation process, and allows for the generation of large city layouts in seconds, making it significantly faster than comparable agent-based software and thus supporting the needs of non-expert creators of virtual cities for many applications.


title: A modular crawler-driven robot: Mechanical design and preliminary experiments

text: This paper presents a tracked robot composed of the proposed crawler mechanism, in which a planetary gear reducer is employed as the transmission device and provides two outputs in different forms with only one actuator. When the crawler moves in a rough environment, collision between mechanism and environment inevitably occurs. This under-actuated crawler can absorb the impact energy that should be transmitted to the actuator. A modular concept for the crawler is proposed for enlarging its use in robot systems and mechanical design of a modular crawler is conducted. Using this crawler module, a four-crawler-driven robot is realized by easily assembling. Experiments are conducted to verify the proposed concept and mechanical design. A single crawler module can well perform the proposed three locomotion modes. The four-crawler-driven robot has good adaptability to the environment which can get over obstacles both passively and actively.


title: An Algorithm for Training Polynomial Networks

text: We consider deep neural networks, in which the output of each node is a quadratic function of its inputs. Similar to other deep architectures, these networks can compactly represent any function on a finite training set. The main goal of this paper is the derivation of an efficient layer-by-layer algorithm for training such networks, which we denote as the Basis Learner. The algorithm is a universal learner in the sense that the training error is guaranteed to decrease at every iteration, and can eventually reach zero under mild conditions. We present practical implementations of this algorithm, as well as preliminary experimental results. We also compare our deep architecture to other shallow architectures for learning polynomials, in particular kernel learning.


title: Deep Learning Approach for Secondary Structure Protein Prediction based on First Level Features Extraction using a Latent CNN Structure

text: In Bioinformatics, Protein Secondary Structure Prediction (PSSP) has been considered as one of the main challenging tasks in this field. Today, secondary structure protein prediction approaches have been categorized into three groups (Neighbor-based, model-based, and meta predicator-based model). The main purpose of the model-based approaches is to detect the protein sequence-structure by utilizing machine learning techniques to train and learn a predictive model for that. In this model, different supervised learning approaches have been proposed such as neural networks, hidden Markov chain, and support vector machines have been proposed. In this paper, our proposed approach which is a Latent Deep Learning approach relies on detecting the first level features based on using Stacked Sparse Autoencoder. This approach allows us to detect new features out of the set of training data using the sparse autoencoder which will have used later as convolved filters in the Convolutional Neural Network (CNN) structure. The experimental results show that the highest accuracy of the prediction is 86.719% in the testing set of our approach when the backpropagation framework has been used to pre-trained techniques by relying on the unsupervised fashion where the whole network can be fine-tuned in a supervised learning fashion. Keywords—Secondary structure protein prediction; secondary structure; fine-tuning; Stacked Sparse; Deep Learning; CNN


title: A Method for Identification of Basmati Rice grain of India and Its Quality Using Pattern Classification

text: The research work deals with an approach to perform texture and morphological based retrieval on a corpus of Basmati rice grain images. The work has been carried out using Image Warping and Image analysis approach. The method has been employed to normalize food grain images and hence eliminating the effects of orientation using image warping technique with proper scaling. The approach has been tested on sufficient number of basmati rice grain images of rice based on intensity, position and orientation. A digital image analysis algorithm based on color, morphological and textural features was developed to identify the six varieties of basmati rice seeds which are widely planted in India. Nine color and nine morphological and textural features were used for discriminant analysis. A back propagation neural network-based classifier was developed to identify the unknown grain types. The color and textural features were presented to the neural network for training purposes. The trained network was then used to identify the unknown grain types. It is also to find the percentage purity of hulled basmati rice grain sample by image processing technique. Commercially the purity test of basmati rice sample is done according to the size of the grain kernel (full, half or broken). By image processing we can also identify any broken basmati rice grains. Here we discuss the various procedures used to obtain the percentage quality of basmati rice grains. Keywords— warping, Image rectification, Image segmentation, Edge Detection, blurring image, Thresholding, Percentage Purity, Pixel area.


title: OCR binarization and image pre-processing for searching historical documents

text: We consider the problem of document binarization as a pre-processing step for optical character recognition (OCR) for the purpose of keyword search of historical printed documents. A number of promising techniques from the literature for binarization, pre-filtering, and post-binarization denoising were implemented along with newly developed methods for binarization: an error diffusion binarization, a multiresolutional version of Otsu’s binarization, and denoising by despeckling. The OCR in the ABBYY FineReader 7.1 SDK is used as a black box metric to compare methods. Results for 12 pages from six newspapers of differing quality show that performance varies widely by image, but that the classic Otsu method and Otsu-based methods perform best on average. 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.


title: Multi-phase Word Sense Embedding Learning Using a Corpus and a Lexical Ontology

text: Word embeddings play a significant role in many modern NLP systems. However, most prevalent word embedding learning methods learn one representation per word which is problematic for polysemous words and homonymous words. To address this problem, we propose a multi-phase word sense embedding learning method which utilizes both a corpus and a lexical ontology to learn one embedding per word sense. We use word sense definitions and relations between word senses defined in a lexical ontology in a different way from existing systems. Experimental results on word similarity task show that our approach produces word sense embeddings of high quality.


title: A Study of Attribute-based Proxy Re-encryption Scheme in Cloud Environments

text: Attribute-based proxy re-encryption (ABPRE) scheme is one of the proxy cryptography, which can delegate the reencryption capability to the proxy and re-encrypt the encrypted data by using the re-encryption key. ABPRE extending the traditional proxy cryptography and attributes plays an important role. In ABPRE, users are identified by attributes, and the access policy is designed to control the user’s access. Using ABPRE can have these advantages: (i) The proxy can be delegated to execute the re-encryption operation, which reduces the computation overhead of the data owner; (ii) The authorized user just uses his own secret key to decrypt the encrypted data, and he doesn’t need to store an additional decryption key for deciphering; (iii) The sensitive information cannot be revealed to the proxy in re-encryption, and the proxy only complies to the data owner’s command. In this paper, we survey two various access policy attribute-based proxy re-encryption schemes and analyze these schemes. Thereafter, we list the comparisons of them by some criteria.


title: Dual-Band Transmitarrays With Dual-Linear Polarization at Ka-Band

text: Dual-band transmitarray antennas are demonstrated at Ka-band with the capability of forming independent linearly polarized beams with a given polarization in each frequency band, while sharing the same radiating aperture. The proposed three-layer unit-cell is based on identical narrow microstrip patches printed on both receiving and transmitting layers and connected by a metallized via hole. The metal layers are printed on two identical substrates bonded with a thin film, and the designed unit-cell exhibits a 180° phase resolution (i.e., 1-b phase quantization). The dual-band dual-polarized property of the transmitarray is achieved by interleaving unit-cells operating in the down-link and up-link frequency bands. Four different prototypes are characterized to demonstrate the relevance of the proposed concepts. A good agreement is obtained between the radiation patterns, gain curves, and cross-polarization levels measured and computed in both frequency bands and polarizations.


title: The diffusion of political memes in social media: keynote abstract

text: This talk presents ongoing work on the study of information diffusion in social media, focusing in particular on political communication in the Twitter microblogging network. Social media platforms play an important role in shaping political discourse in the US and around the world. The truthy.indiana.edu infrastructure allows us to mine and visualize a large stream of social media data related to political themes. The analyses in this keynote address polarization and cross-ideological communication, and partisan asymmetries in the online political activities of social media users. Machine learning efforts can successfully leverage the structure of meme diffusion networks to detect orchestrated astroturf attacks that simulate grassroots campaigns, and to predict the political affiliation of active users. The retweet network segregates individuals into two distinct, homogenous communities of left- and right-leaning users. The mention network does not exhibit this kind of segregation, instead forming a communication bridge across which information flows between these two partisan communities. We propose a mechanism of action to explain these divergent topologies and provide statistical evidence in support of this hypothesis. Related to political communication are questions about the birth of online social movements. Social media data provides an opportunity to look for signatures that capture these seminal events. Finally, I will introduce a model of the competition for attention in social media. A dynamic of information diffusion emerges from this process, where a few ideas go viral while most do not. I will show that the relative popularity of different topics, the diversity of information to which we are exposed, and the fading of our collective interests for specific memes, can all be explained as deriving from a combination between the competition for limited attention and the structure of social networks. Surprisingly, one can reproduce the massive heterogeneity in the popularity and persistence of ideas without the need to assume different intrinsic values among those ideas.


title: Factorization machines with follow-the-regularized-leader for CTR prediction in display advertising

text: Predicting ad click-through rates is the core problem in display advertising, which has received much attention from the machine learning community in recent years. In this paper, we present an online learning algorithm for click-though rate prediction, namely Follow-The-Regularized-Factorized-Leader (FTRFL), which incorporates the Follow-The-Regularized-Leader (FTRL-Proximal) algorithm with per-coordinate learning rates into Factorization machines. Experiments on a real-world advertising dataset show that the FTRFL method outperforms the baseline with stochastic gradient descent, and has a faster rate of convergence.


title: Culture and diverging views of social events.

text: The authors compared East Asians' and Americans' views of everyday social events. Research suggests that Americans tend to focus more on the self and to have a greater sense of personal agency than East Asians. The authors assessed whether, compared to East Asians, Americans emphasize main characters even when events do not involve the self and whether they see more agency or intentionality in actions, even when the actions are not their own. Whether East Asians would observe more emotions in everyday scenarios than would Americans also was investigated. In Study 1, Chinese and Americans read alleged diary entries of another person. Americans did focus more on main characters and on characters' intentionality. Study 2 replicated these results comparing Taiwanese and Americans on free recall of events concerning the self and of narratives and videos concerning others. Study 2 also found that Taiwanese made more comments about the emotional states of characters.


A Fuzzy Approach for Mining Quantitative Association Rules


title: Secure cloud computing: Benefits, risks and controls

text: Cloud computing presents a new model for IT service delivery and it typically involves over-a-network, on-demand, self-service access, which is dynamically scalable and elastic, utilising pools of often virtualized resources. Through these features, cloud computing has the potential to improve the way businesses and IT operate by offering fast start-up, flexibility, scalability and cost efficiency. Even though cloud computing provides compelling benefits and cost-effective options for IT hosting and expansion, new risks and opportunities for security exploits are introduced. Standards, policies and controls are therefore of the essence to assist management in protecting and safeguarding systems and data. Management should understand and analyse cloud computing risks in order to protect systems and data from security exploits. The focus of this paper is on mitigation for cloud computing security risks as a fundamental step towards ensuring secure cloud computing environments.


title: Improving the Neural Algorithm of Artistic Style

text: In this work we investigate different avenues of improving the Neural Algorithm of Artistic Style [7]. While showing great results when transferring homogeneous and repetitive patterns, the original style representation often fails to capture more complex properties, like having separate styles of foreground and background. This leads to visual artifacts and undesirable textures appearing in unexpected regions when performing style transfer. We tackle this issue with a variety of approaches, mostly by modifying the style representation in order for it to capture more information and impose a tighter constraint on the style transfer result. In our experiments, we subjectively evaluate our best method as producing from barely noticeable to significant improvements in the quality of style transfer.


title: Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream.

text: Converging evidence suggests that the primate ventral visual pathway encodes increasingly complex stimulus features in downstream areas. We quantitatively show that there indeed exists an explicit gradient for feature complexity in the ventral pathway of the human brain. This was achieved by mapping thousands of stimulus features of increasing complexity across the cortical sheet using a deep neural network. Our approach also revealed a fine-grained functional specialization of downstream areas of the ventral stream. Furthermore, it allowed decoding of representations from human brain activity at an unsurpassed degree of accuracy, confirming the quality of the developed approach. Stimulus features that successfully explained neural responses indicate that population receptive fields were explicitly tuned for object categorization. This provides strong support for the hypothesis that object categorization is a guiding principle in the functional organization of the primate ventral stream.


title: High-Resolution Image Inpainting Using Multi-scale Neural Patch Synthesis

text: Recent advances in deep learning have shown exciting promise in filling large holes in natural images with semantically plausible and context aware details, impacting fundamental image manipulation tasks such as object removal. While these learning-based methods are significantly more effective in capturing high-level features than prior techniques, they can only handle very low-resolution inputs due to memory limitations and difficulty in training. Even for slightly larger images, the inpainted regions would appear blurry and unpleasant boundaries become visible. We propose a multi-scale neural patch synthesis approach based on joint optimization of image content and texture constraints, which not only preserves contextual structures but also produces high-frequency details by matching and adapting patches with the most similar mid-layer feature correlations of a deep classification network. We evaluate our method on the ImageNet and Paris Streetview datasets and achieved state-of-the-art inpainting accuracy. We show our approach produces sharper and more coherent results than prior methods, especially for high-resolution images.


title: A path decomposition approach for computing blocking probabilities in wavelength-routing networks

text: We study a class of circuit-switched wavelength-routing networks with fixed or alternate routing and with random wavelength allocation. We present an iterative path decomposition algorithm to evaluate accurately and efficiently the blocking performance of such networks with and without wavelength converters. Our iterative algorithm analyzes the original network by decomposing it into single-path subsystems. These subsystems are analyzed in isolation, and the individual results are appropriately combined to obtain a solution for the overall network. To analyze individual subsystems, we first construct an exact Markov process that captures the behavior of a path in terms of wavelength use. We also obtain an approximate Markov process which has a closed-form solution that can be computed efficiently for short paths. We then develop an iterative algorithm to analyze approximately arbitrarily long paths. The path decomposition approach naturally captures the correlation of both link loads and link blocking events. Our algorithm represents a simple and computationally efficient solution to the difficult problem of computing call-blocking probabilities in wavelength-routing networks. We also demonstrate how our analytical techniques can be applied to gain insight into the problem of converter placement in wavelength-routing networks.


title: A neuro-stimulus chip with telemetry unit for retinal prosthetic device

text: In this retinal prosthesis project, a rehabilitative device is designed to replace the functionality of defective photoreceptors in patients suffering from retinitis pigmentosa (RP) and age-related macular degeneration (AMD). The device consists of an extraocular and an intraocular unit. The implantable component receives power and a data signal via a telemetric inductive link between the two units. The extraocular unit includes a video camera and video processing board, a telemetry protocol encoder chip, and an RF amplifier and primary coil. The intraocular unit consists of a secondary coil, a rectifier and regulator, a retinal chip with a telemetry protocol decoder, a stimulus signal generator, and an electrode array. This paper focuses on the design, fabrication, and testing of a microchip which serves as the telemetry protocol decoder and stimulus signal generator. It is fabricated by MOSIS with 1.2-mm CMOS technology and was demonstrated to provide the desired biphasic current stimulus pulses for an array of 100 retinal electrodes at video frame rates.


title: The Causal Foundations of Structural Equation Modeling

text: The role of causality in SEM research is widely perceived to be, on the one hand, of pivotal methodological importance and, on the other hand, confusing, enigmatic and controversial. The confusion is vividly portrayed, for example, in the influential report of Wilkinson and Task Force’s (1999) on “Statistical Methods in Psychology Journals: Guidelines and Explanations.” In discussing SEM, the report starts with the usual warning: “It is sometimes thought that correlation does not prove causation but ‘causal modeling’ does. [Wrong! There are] dangers in this practice.” But then ends with a startling conclusion: “The use of complicated causal-modeling software [read SEM] rarely yields any results that have any interpretation as causal effects.” The implication being that the entire enterprise of causal modeling, from Sewell Wright (1921) to Blalock (1964) and Duncan (1975), the entire literature in econometric research, including modern advances in graphical and nonparametric structural models have all been misguided, for they have been chasing parameters that have no causal interpretation. The motives for such overstatements notwithstanding, readers may rightly ask: “If SEM methods do not ‘prove’ causation, how can they yield results that have causal interpretation?” Put another way, if the structural coefficients that SEM researchers labor to estimate can legitimately be interpreted as causal effects then, unless these parameters are grossly misestimated, why deny SEM researchers the honor of “establishing causation” or at least of deriving some useful claims about causation. The answer is that a huge logical gap exists between “establishing causation,” which requires careful manipulative experiments, and “interpreting parameters as causal effects,” which may be based on firm scientific knowledge or on previously conducted experiments, perhaps by other researchers. One can legitimately be in a possession of a parameter that stands for a causal effect and still be unable, using statistical means alone, to determine the magnitude of that parameter given non-experimental data. As a matter of fact, we know that


title: Librispeech: An ASR corpus based on public domain audio books

text: This paper introduces a new corpus of read English speech, suitable for training and evaluating speech recognition systems. The LibriSpeech corpus is derived from audiobooks that are part of the LibriVox project, and contains 1000 hours of speech sampled at 16 kHz. We have made the corpus freely available for download, along with separately prepared language-model training data and pre-built language models. We show that acoustic models trained on LibriSpeech give lower error rate on the Wall Street Journal (WSJ) test sets than models trained on WSJ itself. We are also releasing Kaldi scripts that make it easy to build these systems.


title: Study about IOT's application in "Digital Agriculture" construction

text: Along with acceleration of various countries' digitization and informationization, according to the 12th 5-year development plan, the internet of things (IOT) will become one of Chinese emerging strategic industries. Based on the analysis of IOT's basic concept and key technology, this paper studies IOT's application in "Digital Agriculture" construction. The main contents include: IOT's application in agricultural production, IOT's application in logistics and distribution of agricultural products, and IOT's application in agricultural products safety traceability.


title: A circle packing algorithm

text: A circle packing is a configuration P of circles realizing a specified pattern of tangencies. Radii of packin the euclidean and hyperbolic planes may be computed using an iterative process suggested by William T We describe an efficient implementation, discuss its performance, and illustrate recent applications. A cen is played by new and subtle monotonicity results for “flowers” of circles.  2003 Elsevier Science B.V. All rights reserved.


title: Help from the Sky: Leveraging UAVs for Disaster Management

text: This article presents a vision for future unmanned aerial vehicles (UAV)-assisted disaster management, considering the holistic functions of disaster prediction, assessment, and response. Here, UAVs not only survey the affected area but also assist in establishing vital wireless communication links between the survivors and nearest available cellular infrastructure. A perspective of different classes of geophysical, climate-induced, and meteorological disasters based on the extent of interaction between the UAV and terrestrially deployed wireless sensors is presented in this work, with suitable network architectures designed for each of these cases. The authors outline unique research challenges and possible solutions for maintaining connected aerial meshes for handoff between UAVs and for systems-specific, security- and energy-related issues. This article is part of a special issue on drones.


title: Cross-Level Sensor Network Simulation with COOJA

text: Simulators for wireless sensor networks are a valuable tool for system development. However, current simulators can only simulate a single level of a system at once. This makes system development and evolution difficult since developers cannot use the same simulator for both high-level algorithm development and low-level development such as device-driver implementations. We propose cross-level simulation, a novel type of wireless sensor network simulation that enables holistic simultaneous simulation at different levels. We present an implementation of such a simulator, COOJA, a simulator for the Contiki sensor node operating system. COOJA allows for simultaneous simulation at the network level, the operating system level, and the machine code instruction set level. With COOJA, we show the feasibility of the cross-level simulation approach


title: Design considerations and performance evaluation of 1200 V, 100 a SiC MOSFET based converter for high power density application

text: Silicon Carbide (SiC) MOSFET is capable of achieving better efficiency, power density and reliability of power converters due to its low on-state resistance, high temperature operation capability and lower switching losses compared to silicon (Si) IGBT. Operation of power converters at higher switching frequency using SiC devices allows reduction in filter size and hence improves the power to weight ratio of the converter. This paper presents switching characterization of 1200 V, 100 A SiC MOSFET module and compares efficiency of a Two Level Voltage Source Converter (2L-VSC) using SiC MOSFETs and Si IGBTs. Also, various design considerations of the 1200 V, 100 A SiC MOSFET based 2L-VSC including gate drive design, bus bar packaging and thermal management have been elaborated. The designed and developed 2L-VSC is operated to supply 35 kVA load at 20 kHz switching frequency with DC bus voltage at 800 V and the experimental results are presented.


title: A Reconfigurable Fabric for Accelerating Large-Scale Datacenter Services

text:
Datacenter workloads demand high computational capabilities, flexibility, power efficiency, and low cost. It is challenging to improve all of these factors simultaneously. To advance datacenter capabilities beyond what commodity server designs can provide, we designed and built a composable, reconfigurable hardware fabric based on field programmable gate arrays (FPGA). Each server in the fabric contains one FPGA, and all FPGAs within a 48-server rack are interconnected over a low-latency, high-bandwidth network.
 We describe a medium-scale deployment of this fabric on a bed of 1632 servers, and measure its effectiveness in accelerating the ranking component of the Bing web search engine. We describe the requirements and architecture of the system, detail the critical engineering challenges and solutions needed to make the system robust in the presence of failures, and measure the performance, power, and resilience of the system. Under high load, the large-scale reconfigurable fabric improves the ranking throughput of each server by 95% at a desirable latency distribution or reduces tail latency by 29% at a fixed throughput. In other words, the reconfigurable fabric enables the same throughput using only half the number of servers.


title: Work Related Stress, Burnout, Job Satisfaction and General Health of Nurses

text: Gaps in research focusing on work related stress, burnout, job satisfaction and general health of nurses is evident within developing contexts like South Africa. This study identified the relationship between work related stress, burnout, job satisfaction and general health of nurses. A total of 1200 nurses from four hospitals were invited to participate in this cross-sectional study (75% response rate). Participants completed five questionnaires and multiple linear regression analysis was used to determine significant relationships between variables. Staff issues are best associated with burnout as well as job satisfaction. Burnout explained the highest amount of variance in mental health of nurses. These are known to compromise productivity and performance, as well as affect the quality of patient care. Issues, such as security risks in the workplace, affect job satisfaction and health of nurses. Although this is more salient to developing contexts it is important in developing strategies and intervention programs towards improving nurse and patient related outcomes.


title: Performance of PMASynRM With Ferrite Magnets for EV/HEV Applications Considering Productivity

text: Although motors that use rare-earth permanent magnets typically exhibit high performance, their cost is high, and there are concerns about the stability of the raw material supply. This paper proposes a permanent-magnet-assisted synchronous reluctance motor (PMASynRM) with a ferrite magnet that does not use rare-earth materials considering productivity. The performance of the proposed PMASynRM is evaluated based on the finite-element method and an experiment using a prototype machine. The analysis results reveal that the proposed PMASynRM has the same power density and an equivalent efficiency as rare-earth permanent-magnet synchronous motors for hybrid electric vehicles (2003 Toyota Prius). Furthermore, some experimental results are presented in order to validate the analytical results. As a result, the proposed PMASynRM was found to achieve high-power-density and high-efficiency performance.


title: Adaptive Speculative Processing of Out-of-Order Event Streams

text:
Distributed event-based systems are used to detect meaningful events with low latency in high data-rate event streams that occur in surveillance, sports, finances, etc. However, both known approaches to dealing with the predominant out-of-order event arrival at the distributed detectors have their shortcomings: buffering approaches introduce latencies for event ordering, and stream revision approaches may result in system overloads due to unbounded retraction cascades.
 This article presents an adaptive speculative processing technique for out-of-order event streams that enhances typical buffering approaches. In contrast to other stream revision approaches developed so far, our novel technique encapsulates the event detector, uses the buffering technique to delay events but also speculatively processes a portion of it, and adapts the degree of speculation at runtime to fit the available system resources so that detection latency becomes minimal.
 Our technique outperforms known approaches on both synthetical data and real sensor data from a realtime locating system (RTLS) with several thousands of out-of-order sensor events per second. Speculative buffering exploits system resources and reduces latency by 40% on average.


title: Cerebellar ataxia rehabilitation trial in degenerative cerebellar diseases.

text:
OBJECTIVE
To investigate short- and long-term effects of intensive rehabilitation on ataxia, gait, and activities of daily living (ADLs) in patients with degenerative cerebellar disease.

METHODS
A total of 42 patients with pure cerebellar degeneration were randomly assigned to the immediate group or the delayed-entry control group. The immediate group received 2 hours of inpatient physical and occupational therapy, focusing on coordination, balance, and ADLs, on weekdays and 1 hour on weekends for 4 weeks. The control group received the same intervention after a 4-week delay. Short-term outcome was compared between the immediate and control groups. Long-term evaluation was done in both groups at 4, 12, and 24 weeks after the intervention. Outcome measures included the assessment and rating of ataxia, Functional Independence Measure, gait speed, cadence, functional ambulation category, and number of falls.

RESULTS
The immediate group showed significantly greater functional gains in ataxia, gait speed, and ADLs than the control group. Improvement of truncal ataxia was more prominent than limb ataxia. The gains in ataxia and gait were sustained at 12 weeks and 24 weeks, respectively. At least 1 measure was better than at baseline at 24 weeks in 22 patients.

CONCLUSIONS
Short-term benefit of intensive rehabilitation was evident in patients with degenerative cerebellar diseases. Although functional status tended to decline to the baseline level within 24 weeks, gains were maintained in more than half of the participants.


title: ON ATTRIBUTE BASED ACCESS CONTROL SCHEME IN CLOUD ENVIRONMENT

text: *Corresponding author: Email: divyasnkl@gmail.com; Tel.: +91 9486029378


title: Interoperability evaluation models: A systematic review

text: Interoperability is defined as the ability for two (or more) systems or components to exchange information and to use the information that has been exchanged. There is increasing demand for interoperability between individual software systems. Developing an interoperability evaluation model between software and information systems is difficult, and becoming an important challenge. An interoperability evaluation model allows knowing the degree of interoperability, and lead to the improvement of interoperability. This paper describes the existing interoperability evaluation models, and performs a comparative analysis among their findings to determine the similarities and differences in their philosophy and implementation. This analysis yields a set of recommendations for any party that is open to the idea of creating or improving an interoperability evaluation model. 2013 Elsevier B.V. All rights reserved.


title: The partograph for the prevention of obstructed labor.

text: Obstructed labor is an important cause of maternal and perinatal mortality and morbidity. The partograph graphically represents key events in labor and provides an early warning system. The World Health Organization partographs are the best known partographs in low resource settings. Experiences with World Health Organization and other types of partographs in low resource settings suggest that when used with defined management protocols, this inexpensive tool can effectively monitor labor and prevent obstructed labor. However, challenges to implementation exist and these should be addressed urgently.


title: Divertible Protocols and Atomic Proxy Cryptography

text: First, we introduce the notion of divertibility as a protocol property as opposed to the existing notion as a language property (see Okamoto, Ohta [OO90]). We give a definition of protocol divertibility that applies to arbitrary 2-party protocols and is compatible with Okamoto and Ohta’s definition in the case of interactive zero-knowledge proofs. Other important examples falling under the new definition are blind signature protocols. We propose a sufficiency criterion for divertibility that is satisfied by many existing protocols and which, surprisingly, generalizes to cover several protocols not normally associated with divertibility (e.g., Diffie-Hellman key exchange). Next, we introduce atomic proxy cryptography, in which an atomic proxy function, in conjunction with a public proxy key, converts ciphertexts (messages or signatures) for one key into ciphertexts for another. Proxy keys, once generated, may be made public and proxy functions applied in untrusted environments. We present atomic proxy functions for discrete-log-based encryption, identification, and signature schemes. It is not clear whether atomic proxy functions exist in general for all public-key cryptosystems. Finally, we discuss the relationship between divertibility and proxy cryptography.


title: Proxy Cryptography Revisited

text: In this work we revisit and formally study the notion of proxy cryptography. Intuitively, various proxy functions allow two cooperating parties F (the “FBI”) and P (the “proxy”) to duplicate the functionality available to t he third party U (the “user”), without being able to perform this functionality on their own (without cooperation). The concept is closely related to the notion of threshold cryptography, except we deal with only two parties P andF , and place very strict restrictions on the way the operations are performed (which is done for the sake of efficiency, usability and scalability). For example, for decryption (res p. signature)P (F ) sends a single message to F (P ), after which the latter can decrypt (sign) the message. Our formal modeling of proxy cryptography significantly generalizes, simplifies and simultaneously clarifies the model of “atomic proxy” suggested by Blaze and Strauss [4]. In particular, we define bidirectional and unidirectional var iants of our model 1, and show extremely simple generic solutions for proxy signature and encryption in these models. We also give more efficient solutions for several specific schemes. We conclude that proxy cryptography is a relatively simple concept to satisfy when looked from the correct and formal standpoint.


title: A Hard-Core Predicate for all One-Way Functions

text:
A central tool in constructing pseudorandom generators, secure encryption functions, and in other areas are “hard-core” predicates <italic>b</italic> of functions (permutations) ƒ, discovered in [Blum Micali 82]. Such <italic>b</italic>(<italic>x</italic>) cannot be efficiently guessed (substantially better than 50-50) given only ƒ(<italic>x</italic>). Both <italic>b</italic>, ƒ are computable in polynomial time.
[Yao 82] transforms any one-way function ƒ into a more complicated one, ƒ<supscrpt>*</supscrpt>, which has a hard-core predicate. The construction applies the original ƒ to many small pieces of the input to ƒ<supscrpt>*</supscrpt> just to get one “hard-core” bit. The security of this bit may be smaller than any constant positive power of the security of ƒ. In fact, for inputs (to ƒ<supscrpt>*</supscrpt>) of practical size, the pieces effected by ƒ are so small that ƒ can be inverted (and the “hard-core” bit computed) by exhaustive search.
In this paper we show that every one-way function, padded to the form ƒ(<italic>p</italic>, <italic>x</italic>) = (<italic>p</italic>, <italic>g</italic>(<italic>x</italic>)), ‖‖<italic>p</italic>‖‖ = ‖<italic>x</italic>‖, has by itself a hard-core predicate of the same (within a polynomial) security. Namely, we prove a conjecture of [Levin 87, sec. 5.6.2] that the scalar product of Boolean vectors <italic>p</italic>, <italic>x</italic> is a hard-core of every one-way function ƒ(<italic>p</italic>, <italic>x</italic>) = (<italic>p</italic>, <italic>g</italic>(<italic>x</italic>)). The result extends to multiple (up to the logarithm of security) such bits and to any distribution on the <italic>x</italic>'s for which ƒ is hard to invert.


title: Zero-knowledge proofs of identity

text: In this paper we extend the notion of zero knowledge proofs of membership (which reveal one bit of information) to zero knowledge proofs of knowledge (which reveal no information whatsoever). After formally defining this notion, we show its relevance to identification schemes, in which parties prove their identity by demonstrating their knowledge rather than by proving the validity of assertions. We describe a novel scheme which is provably secure if factoring is difficult and whose practical implementations are about two orders of magnitude faster than RSA-based identification schemes. In the last part of the paper we consider the question of sequential versus parallel executions of zero knowledge protocols, define a new notion of “transferable information”, and prove that the parallel version of our identification scheme (which is not known to be zero knowledge) is secure since it reveals no transferable information.


title: Adversarial Localization Network

text: We propose the Adversarial Localization Network, a novel weakly supervised approach to generate object masks in an image. We train a corruption net to mask out regions of the image to reduce prediction confidence of the classifier. To avoid generating adversarial artifacts due to vulnerability of deep networks to adversarial perturbation, we also co-train the classifier to correctly predict the corrupted images. Our approach could efficiently produce reasonable object masks with a simple architecture and few hyper-parameters. We achieve competitive results on the ILSVRC2012 dataset for object localization with only image level labels and no bounding boxes for training.


title: A Multi-Modal Approach to Infer Image Affect

text: e group aect or emotion in an image of people can be inferred by extracting features about both the people in the picture and the overall makeup of the scene. e state-of-the-art on this problem investigates a combination of facial features, scene extraction and even audio tonality. is paper combines three additional modalities, namely, human pose, text-based tagging and CNN extracted features / predictions. To the best of our knowledge, this is the rst time all of the modalities were extracted using deep neural networks. We evaluate the performance of our approach against baselines and identify insights throughout this paper.


title: Privacy by BlockChain Design: A BlockChain-enabled GDPR-compliant Approach for Handling Personal Data

text: This paper takes an initial step forward in bringing to life the certification mechanisms according to Art. 42 of the General Data Protection Regulation (GDPR). These newly established methods of legal specification act not only as a central vehicle for overcoming widely articulated and discussed legal challenges, but also as a sandbox for the much needed close collaboration between computer sciences and legal studies. In order to illustrate, for example, what data protection seals could look like in the future, the authors propose a methodology for "translating" legal requirements into technical guidelines: architectural blueprints designed using legal requirements. The purpose of these blueprints is to show developers how their solutions might comply with the principle of Privacy by Design (Art. 25 GDPR). To demonstrate this methodology, the authors propose an architectural blueprint that embodies the legal concept of the data subject’s consent (Art. 6 sec. 1 lit. a GDPR) and elevates best practice to a high standard of Privacy by Design. Finally, the authors highlight further legal problems concerning blockchain technology under the GDPR that will have to be addressed in order to achieve a comprehensive certification mechanism for Privacy by Blockchain Design in the future. ACM Classification


title: Generating Sentence Planning Variations for Story Telling

text: There has been a recent explosion in applications for dialogue interaction ranging from direction-giving and tourist information to interactive story systems. Yet the natural language generation (NLG) component for many of these systems remains largely handcrafted. This limitation greatly restricts the range of applications; it also means that it is impossible to take advantage of recent work in expressive and statistical language generation that can dynamically and automatically produce a large number of variations of given content. We propose that a solution to this problem lies in new methods for developing language generation resources. We describe the ES-TRANSLATOR, a computational language generator that has previously been applied only to fables, and quantitatively evaluate the domain independence of the EST by applying it to personal narratives from weblogs. We then take advantage of recent work on language generation to create a parameterized sentence planner for story generation that provides aggregation operations, variations in discourse and in point of view. Finally, we present a user evaluation of different personal narrative retellings.


title: Persecutory delusions and catastrophic worry in psychosis: developing the understanding of delusion distress and persistence.

text: In a recent theoretical account of persecutory delusions, it is suggested that anxiety and worry are important factors in paranoid experience [Freeman, D., Garety, P. A., Kuipers, E., Fowler, D., & Bebbington, P. E. (2002). A cognitive model of persecutory delusions. British Journal of Clinical Psychology, 41(4), 331-347]. In emotional disorders worry has been understood in terms of catastrophising. In the current study, the concept of catastrophising is applied for the first time with persecutory delusions. Thirty individuals with current persecutory delusions and 30 non-clinical controls participated in a cross-sectional study. The group with persecutory delusions was also followed up at 3 months to assess predictors of delusion persistence. At its most severe, 21% of individuals with persecutory delusions had clinical worry, 68% had levels of worry comparable with treatment seeking GAD patients. Further, high levels of anxiety, worry and catastrophising were associated with high levels of persecutory delusion distress and with the persistence of delusions over 3 months. If future research replicates these findings, worry reduction interventions for individuals with persecutory delusions may be warranted.


title: Multigranulation decision-theoretic rough sets

text: Article history: Available online 23 March 2013


title: Perfectly Matched Layers for Elastodynamics : A New Absorbing Boundary

text: The use of perfectly matched layers (PML) has recently been introduced by Berenger as a material absorbing boundary condition (ABC) for electromagnetic waves. In this paper, we will rst prove that a ctitious elastodynamic material half-space exists that will absorb an incident wave for all angles and all frequencies. Moreover, the wave is attenuative in the second half-space. As a consequence, layers of such material could be designed at the edge of a computer simulation region to absorb outgoing waves. Since this is a material ABC, only one set of computer codes is needed to simulate an open region. Hence, it is easy to parallelize such codes on multiprocessor computers. For instance, it is easy to program massively parallel computers on the SIMD (single instruction multiple data) mode for such codes. We will show two and three dimensional computer simulations of the PML for the linearized equations of elastodyanmics. Comparison with Liao's ABC will be given.


title: Mitigating IoT security threats with a trusted Network element

text: Securing the growing amount of IoT devices is a challenge for both the end-users bringing IoT devices into their homes, as well as the corporates and industries exposing these devices into the Internet as part of their service or operations. The exposure of these devices, often poorly configured and secured, offers malicious actors an easy access to the private information of their users, or potential to utilize the devices in further activities, e.g., attacks on other devices via Distributed Denial of Service. This paper discusses the current security challenges of IoT devices and proposes a solution to secure these devices via a trusted Network Edge Device. NED offloads the security countermeasures of the individual devices into the trusted network elements. The major benefit of this approach is that the system can protect the IoT devices with user-defined policies, which can be applied to all devices regardless of the constraints of computing resources in the IoT tags. Additional benefit is the possibility to manage the countermeasures of multiple IoT devices/gateways at once, via a shared interface, thus largely avoiding the per-device maintenance operations.


title: The All Relevant Feature Selection using Random Forest

text: In this paper we examine the application of the random forest classifier for the all relevant feature selection problem. To this end we first examine two recently proposed all relevant feature selection algorithms, both being a random forest wrappers, on a series of synthetic data sets with varying size. We show that reasonable accuracy of predictions can be achieved and that heuristic algorithms that were designed to handle the all relevant problem, have performance that is close to that of the reference ideal algorithm. Then, we apply one of the algorithms to four families of semi-synthetic data sets to assess how the properties of particular data set influence results of feature selection. Finally we test the procedure using a well-known gene expression data set. The relevance of nearly all previously established important genes was confirmed, moreover the relevance of several new ones is discovered.


title: Using Indirect Encoding of Multiple Brains to Produce Multimodal Behavior

text: An important challenge in neuroevolution is to evolve complex neural networks with multiple modes of behavior. Indirect encodings can potentially answer this challenge. Yet in practice, indirect encodings do not yield effective multimodal controllers. Thus, this paper introduces novel multimodal extensions to HyperNEAT, a popular indirect encoding. A previous multimodal HyperNEAT approach called situational policy geometry assumes that multiple brains benefit from being embedded within an explicit geometric space. However, experiments here illustrate that this assumption unnecessarily constrains evolution, resulting in lower performance. Specifically, this paper introduces HyperNEAT extensions for evolving many brains without assuming geometric relationships between them. The resulting MultiBrain HyperNEAT can exploit human-specified task divisions to decide when each brain controls the agent, or can automatically discover when brains should be used, by means of preference neurons. A further extension called module mutation allows evolution to discover the number of brains, enabling multimodal behavior with even less expert knowledge. Experiments in several multimodal domains highlight that multi-brain approaches are more effective than HyperNEAT without multimodal extensions, and show that brains without a geometric relation to each other outperform situational policy geometry. The conclusion is that Multi-Brain HyperNEAT provides several promising techniques for evolving complex multimodal behavior.


title: Securing distributed storage: challenges, techniques, and systems

text: The rapid increase of sensitive data and the growing number of government regulations that require longterm data retention and protection have forced enterprises to pay serious attention to storage security. In this paper, we discuss important security issues related to storage and present a comprehensive survey of the security services provided by the existing storage systems. We cover a broad range of the storage security literature, present a critical review of the existing solutions, compare them, and highlight potential research issues.


title: Analysis of Ethernet AVB for automotive networks using Network Calculus

text: Future functions of vehicles to make driving safer, more comfortable and more exciting rely on suitable in-vehicle networks. Legacy Ethernet extended by AVB to meet recent and future requirements is discussed as a proper candidate. Investigation of its forwarding, synchronization and resource reservation mechanisms are an important prerequisite for the object: The progressive vehicle integration unleashing its whole potential as an in-vehicle network. This paper focuses on AVB's forwarding policy by using Network Calculus as a theory for calculating worst case delays.


title: Application of the Laminar Navier-Stokes Equations for Solving 2D and 3D Pathfinding Problems with Static and Dynamic Spatial Constraints: Implementation and Validation in Comsol Multiphysics

text: Pathfinding problems consist in determining the optimal shortest path, or at least one path, between two points in the space. In this paper, we propose a particular approach, based on methods used in Computational Fluid Dynamics, that intends to solve such problems. In particular, we reformulate pathfinding problems as the motion of a viscous fluid via the use of the laminar Navier-Stokes equations completed with suitable boundary conditions corresponding to some characteristics of the considered problem: position of the initial and final points, a-priori information of the terrain, One-way routes and dynamic spatial configuration. The advantages of this technique, regarding existing ones (e.g., A* algorithm) is that it does not require a pre-processing method (e.g., graph conversion) of the environment and can manage complex geometries. Then, we propose a particular numerical implementation of this methodology by using Comsol Multiphysics (i.e., a modeling software based on Finite Element Methods). Finally, we validate our approach by considering several 2D and 3D benchmark cases. Results are compared with the ones returned by a simple A* algorithm. From those numerical tests, we deduce that our algorithms generate suitable routes (but not the shortest ones) for the studied problems in a computational time similar to the considered A*.


title: One-shot learning for fine-grained relation extraction via convolutional siamese neural network

text: Extracting fine-grained relations between entities of interest is of great importance to information extraction and large-scale knowledge graph construction. Conventional approaches on relation extraction require an existing knowledge graph to start with or sufficient observed samples from each relation type in the training process. However, such resources are not always available, and fine-grained manual labeling is extremely time-consuming and requires extensive expertise for specific domains such as healthcare and bioinformatics. Additionally, the distribution of fine-grained relations is often highly imbalanced in practice. We tackle this label scarcity and distribution imbalance issue from a one-shot classification perspective via a convolutional siamese neural network which extracts discriminative semantic-aware features to verify the relations between a pair of input samples. The proposed siamese network effectively extracts uncommon relations with only limited observed samples on the tasks of 1-shot and few-shot classification, demonstrating significant benefits to domain-specific information extraction in practical applications.


title: Fault classification and fault signature production for rolling element bearings in electric machines

text: Most condition monitoring techniques for rolling element bearings are designed to detect the four characteristic fault frequencies. This has lead to the common practice of categorizing bearing faults according to fault location (i.e., inner race, outer race, ball, or cage fault). While the ability to detect the four characteristic fault frequencies is necessary, this approach neglects another important class of faults that arise in many industrial settings. This research introduces the notion of categorizing bearing faults as either single-point defects or generalized roughness. These classes separate bearing faults according to the fault signatures that are produced rather than by the physical location of the fault. Specifically, single-point defects produce the four predictable characteristic fault frequencies while faults categorized as generalized roughness produce unpredictable broadband changes in the machine vibration and stator current. Experimental results are provided from bearings failed in situ via a shaft current. These results illustrate the unpredictable and broadband nature of the effects produced by generalized roughness bearing faults. This issue is significant because a successful bearing condition monitoring scheme must be able to reliably detect both classes of faults.


title: DCA for bot detection

text: Ensuring the security of computers is a non-trivial task, with many techniques used by malicious users to compromise these systems. In recent years a new threat has emerged in the form of networks of hijacked zombie machines used to perform complex distributed attacks such as denial of service and to obtain sensitive data such as password information. These zombie machines are said to be infected with a dasiahotpsila - a malicious piece of software which is installed on a host machine and is controlled by a remote attacker, termed the dasiabotmaster of a botnetpsila. In this work, we use the biologically inspired dendritic cell algorithm (DCA) to detect the existence of a single hot on a compromised host machine. The DCA is an immune-inspired algorithm based on an abstract model of the behaviour of the dendritic cells of the human body. The basis of anomaly detection performed by the DCA is facilitated using the correlation of behavioural attributes such as keylogging and packet flooding behaviour. The results of the application of the DCA to the detection of a single hot show that the algorithm is a successful technique for the detection of such malicious software without responding to normally running programs.


title: Neural systems behind word and concept retrieval

text: Using both the lesion method and functional imaging (positron emission tomography) in large cohorts of subjects investigated with the same experimental tasks, we tested the following hypotheses: (A) that the retrieval of words which denote concrete entities belonging to distinct conceptual categories depends upon partially segregated regions in higher-order cortices of the left temporal lobe; and (B) that the retrieval of conceptual knowledge pertaining to the same concrete entities also depends on partially segregated regions; however, those regions will be different from those postulated in hypothesis A, and located predominantly in the right hemisphere (the second hypothesis tested only with the lesion method). The analyses provide support for hypothesis A in that several regions outside the classical Broca and Wernicke language areas are involved in name retrieval of concrete entities, and that there is a partial segregation in the temporal lobe with respect to the conceptual category to which the entities belong, and partial support for hypothesis B in that retrieval of conceptual knowledge is partially segregated from name retrieval in the lesion study. Those regions identified here are seen as parts of flexible, multi-component systems serving concept and word retrieval for concrete entities belonging to different conceptual categories. By comparing different approaches the article also addresses a number of method issues that have surfaced in recent studies in this field.


title: CortexNet: a Generic Network Family for Robust Visual Temporal Representations

text: In the past five years we have observed the rise of incredibly well performing feed-forward neural networks trained supervisedly for vision related tasks. These models have achieved super-human performance on object recognition, localisation, and detection in still images. However, there is a need to identify the best strategy to employ these networks with temporal visual inputs and obtain a robust and stable representation of video data. Inspired by the human visual system, we propose a deep neural network family, CortexNet, which features not only bottom-up feedforward connections, but also it models the abundant top-down feedback and lateral connections, which are present in our visual cortex. We introduce two training schemes — the unsupervised MatchNet and weakly supervised TempoNet modes — where a network learns how to correctly anticipate a subsequent frame in a video clip or the identity of its predominant subject, by learning egomotion clues and how to automatically track several objects in the current scene. Find the project website at tinyurl.com/CortexNet.


title: Can we trust digital image forensics?

text: Compared to the prominent role digital images play in nowadays multimedia society, research in the field of image authenticity is still in its infancy. Only recently, research on digital image forensics has gained attention by addressing tamper detection and image source identification. However, most publications in this emerging field still lack rigorous discussions of robustness against strategic counterfeiters, who anticipate the existence of forensic techniques. As a result, the question of trustworthiness of digital image forensics arises. This work will take a closer look at two state-of-the-art forensic methods and proposes two counter-techniques; one to perform resampling operations undetectably and another one to forge traces of image origin. Implications for future image forensic systems will be discussed.


title: On Advances in Statistical Modeling of Natural Images

text: Statistical analysis of images reveals two interesting properties: (i) invariance of image statistics to scaling of images, and (ii) non-Gaussian behavior of image statistics, i.e. high kurtosis, heavy tails, and sharp central cusps. In this paper we review some recent results in statistical modeling of natural images that attempt to explain these patterns. Two categories of results are considered: (i) studies of probability models of images or image decompositions (such as Fourier or wavelet decompositions), and (ii) discoveries of underlying image manifolds while restricting to natural images. Applications of these models in areas such as texture analysis, image classification, compression, and denoising are also considered.


title: Tamper Hiding: Defeating Image Forensics

text: This paper introduces novel hiding techniques to counter the detection of image manipulations through forensic analyses. The presented techniques allow to resize and rotate (parts of) bitmap images without leaving a periodic pattern in the local linear predictor coefficients, which has been exploited by prior art to detect traces of manipulation. A quantitative evaluation on a batch of test images proves the proposed method’s efficacy, while controlling for key parameters and for the retained image quality compared to conventional linear interpolation.


title: Statistical Tools for Digital Forensics

text: A digitally altered photograph, often leaving no visual clues of having been tampered with, can be indistinguishable from an authentic photograph. As a result, photographs no longer hold the unique stature as a definitive recording of events. We describe several statistical techniques for detecting traces of digital tampering in the absence of any digital watermark or signature. In particular, we quantify statistical correlations that result from specific forms of digital tampering, and devise detection schemes to reveal these correlations.


title: Validation of Document Clustering based on Purity and Entropy measures

text: Document clustering aims to automatically group related document into clusters. If two documents are close to each other in the original document space, they are grouped into the same cluster. If the two documents are far away from each other in the original document space, they tend to be grouped into different cluster. The classical clustering algorithms assign each data to exactly one cluster, but fuzzy c-means allow data belong to different clusters. Fuzzy clustering is a powerful unsupervised method for the analysis of data. Cluster validity measure is useful in estimating the optimal number of clusters. Purity and Entropy are the validity measures used in this


title: Revised and abbreviated forms of the Genderism and Transphobia Scale: tools for assessing anti-trans prejudice.

text: Many studies of anti-trans prejudice have measured such attitudes using the Genderism and Transphobia Scale (GTS; Hill & Willoughby, 2005). The GTS is unique in assessing negative attitudes and propensity for violence toward trans people. The present research addressed previously observed limitations in the psychometric properties of data produced by the GTS, including inconsistencies in factor structure and subscale scoring across studies. Results across the present 2 studies (Ns = 314 and 250) yielded 2 refined versions of the GTS: the 22-item GTS-Revised (GTS-R) and a more abbreviated 13-item GTS-R-Short Form (GTS-R-SF), each of which produced stable 2-factor structures corresponding with the intended negative attitudes and propensity for violence dimensions of the GTS. The 2 versions differ in that the GTS-R-SF Genderism/Transphobia subscale focuses on more severe expressions of prejudicial attitudes, whereas the longer GTS-R Genderism/Transphobia subscale assesses subtle expressions of prejudice as well. The Gender-Bashing subscale is identical across the 2 versions. Thus, researchers and practitioners may choose the GTS-R or GTS-R-SF depending on the breadth of prejudicial attitudes they wish to assess. Reliability estimates for GTS-R and GTS-R-SF scale and subscale item responses were acceptable and stable across the 2 studies, and validity evidence was garnered in Study 2. These findings can inform use of the GTS-R and GTS-R-SF in research and practice settings, where psychometric precision and efficiency are both critical.


title: Development and Implementation of a Chat Bot in a Social Network

text: This document describes how to implement a Chat Bot on the Twitter social network for entertainment and viral advertising using a database and a simple algorithm. Having as a main theme a successfully implementation of a Chat Bot preventing people classify it as SPAM, as a result of this a Twitter account (@DonPlaticador) that works without the intervention of a person and every day earns more followers was obtained.


title: A comprehensive security architecture for SDN

text: SDN enables the administrators to configure network resources very quickly and to adjust network-wide traffic flow to meet changing needs dynamically. However, there are some challenges for implementing a full-scale carrier SDN. One of the most important challenges is SDN security, which is beginning to receive attention. With new SDN architecture, some security threats are common to traditional networking, but the profile of these threats (including their likelihood and impact and hence their overall risk level) changes. Moreover, there are some new security challenges such as bypassing predefined mandatory policies by overwriting flow entries and data eavesdropping by inserting fraudulent flow entries. This paper is to design open-flow specific security solutions and propose a comprehensive security architecture to provide security services such as enforcing mandatory network policy correctly and receiving network policy securely for SDN in order to solve these common security issues and new security challenges. It can also help the developers to implement security functions to provide security services when developing the SDN controller.


title: Adversarial synthesis learning enables segmentation without target modality ground truth

text: A lack of generalizability is one key limitation of deep learning based segmentation. Typically, one manually labels new training images when segmenting organs in different imaging modalities or segmenting abnormal organs from distinct disease cohorts. The manual efforts can be alleviated if one is able to reuse manual labels from one modality (e.g., MRI) to train a segmentation network for a new modality (e.g., CT). Previously, two stage methods have been proposed to use cycle generative adversarial networks (CycleGAN) to synthesize training images for a target modality. Then, these efforts trained a segmentation network independently using synthetic images. However, these two independent stages did not use the complementary information between synthesis and segmentation. Herein, we proposed a novel end-to-end synthesis and segmentation network (EssNet) to achieve the unpaired MRI to CT image synthesis and CT splenomegaly segmentation simultaneously without using manual labels on CT. The end-to-end EssNet achieved significantly higher median Dice similarity coefficient (0.9188) than the two stages strategy (0.8801), and even higher than canonical multi-atlas segmentation (0.9125) and ResNet method (0.9107), which used the CT manual labels.


title: SVM based forest fire detection using static and dynamic features

text: A novel approach is proposed in this paper for automatic forest fire detection from video. Based on 3D point cloud of the collected sample fire pixels, Gaussian mixture model is built and helps segment some possible flame regions in single image. Then the new specific flame pattern is defined for forest, and three types of fire colors are labeled accordingly. With 11 static features including color distributions, texture parameters and shape roundness, the static SVM classifier is trained and filters the segmented results. Using defined overlapping degree and varying degree, the remained candidate regions are matched among consecutive frames. Subsequently the variations of color, texture, roundness, area, contour are computed, then the average and the mean square deviation of them are obtained. Together with the flickering frequency from temporal wavelet based Fourier descriptors analysis of flame contour, 27 dynamic features are used to train the dynamic SVM classifier, which is applied for final decision. Our approach has been tested with dozens of video clips, and it can detect forest fire while recognize the fire like objects, such as red house, bright light and flying flag. Except for the acceptable accuracy, our detection algorithm performs in real time, which proves its value for computer vision based forest fire surveillance.


title: SEAME: a Mandarin-English code-switching speech corpus in south-east asia

text: In Singapore and Malaysia, people often speak a mixture of Mandarin and English within a single sentence. We call such sentences intra-sentential code-switch sentences. In this paper, we report on the development of a Mandarin-English codeswitching spontaneous speech corpus: SEAME. The corpus is developed as part of a multilingual speech recognition project and will be used to examine how Mandarin-English codeswitch speech occurs in the spoken language in South-East Asia. Additionally, it can provide insights into the development of large vocabulary continuous speech recognition (LVCSR) for code-switching speech. The corpus collected consists of intra-sentential code-switching utterances that are recorded under both interview and conversational settings. This paper describes the corpus design and the analysis of collected corpus.


title: FeatureAnalytics: An approach to derive relevant attributes for analyzing Android Malware

text: Ever increasing number of Android malware, has always been a concern for cybersecurity professionals. Even though plenty of anti-malware solutions exist, a rational and pragmatic approach for the same is rare and has to be inspected further. In this paper, we propose a novel two-set feature selection approach based on Rough Set and Statistical Test named as RSST to extract relevant system calls. To address the problem of higher dimensional attribute set, we derived suboptimal system call space by applying the proposed feature selection method to maximize the separability between malware and benign samples. Comprehensive experiments conducted on a dataset consisting of 3500 samples with 30 RSST derived essential system calls resulted in an accuracy of 99.9%, Area Under Curve (AUC) of 1.0, with 1% False Positive Rate (FPR). However, other feature selectors (Information Gain, CFsSubsetEval, ChiSquare, FreqSel and Symmetric Uncertainty) used in the domain of malware analysis resulted in the accuracy of 95.5% with 8.5% FPR. Besides, empirical analysis of RSST derived system calls outperform other attributes such as permissions, opcodes, API, methods, call graphs, Droidbox attributes and network traces.


title: Comprehensive study of data analytics tools (RapidMiner, Weka, R tool, Knime)

text: In today's era, data has been increasing in volume, velocity and variety. Due to large and complex collection of datasets, it is difficult to process on traditional data processing application. So, this leads to emerging of new technology called data analytics. Data analytics is a science of exploring raw data and elicitation the useful information and hidden pattern. The main aim of data analytics is to use advance analytics techniques for huge and different datasets. Datasets may vary in sizes from terabytes to zettabytes and can be structured or unstructured. The paper gives the comprehensive and theoretical analysis of four open source data analytics tools which are RapidMiner, Weka, R Tool and KNIME. The study describes the technical specification, features, and specialization for each selected tool. By employing the study the choice and selection of tools can be made easy. The tools are evaluated on basis of various parameters like volume of data used, response time, ease of use, price tag, analysis algorithm and handling.


title: Pattern classification with missing data: a review

text: Pattern classification has been successfully applied in many problem domains, such as biometric recognition, document classification or medical diagnosis. Missing or unknown data are a common drawback that pattern recognition techniques need to deal with when solving real-life classification tasks. Machine learning approaches and methods imported from statistical learning theory have been most intensively studied and used in this subject. The aim of this work is to analyze the missing data problem in pattern classification tasks, and to summarize and compare some of the well-known methods used for handling missing values.


title: A scalable purchase intention prediction system using extreme gradient boosting machines with browsing content entropy

text: Nowadays, a prosperity of electronic commerce (E-commerce) not only gives more convenience to consumers but brings more new opportunities in online advertising and marketing. Online advertisers can understand more about consumer preferences based on their daily online shopping and browsing behaviors. The development of big data and cloud computing techniques further empower advertisers and marketers to have a data-driven and consumer-specific preference recommendation based on the online browsing histories. In this research, a decision support system is proposed to predict a consumer purchase intention during browsing sessions. The proposed decision support system categorizes online browsing activities into purchase-oriented and general sessions using extreme boosting machines. With the browsing content entropy features, the proposed method achieves 41.81% recall and 34.35% F score. It further shows its strong predictive capability compared to other benchmark algorithms including logistic regression and traditional ensemble models. The proposed method can be implemented in real-time bidding algorithms for online advertising strategies. Ad deliveries on browsing session with potential purchase intention not only improve the effectiveness of advertisements but significantly increase last-touch attributions for campaign performance.


title: Cluster Ensembles for High Dimensional Clustering : An Empirical Study

text: This paper studies cluster ensembles for high dimensional data clustering. We examine three different approaches to constructing cluster ensembles. To address high dimensionality, we focus on ensemble construction methods that build on two popular dimension reduction techniques, random projection and principal component analysis (PCA). We present evidence showing that ensembles generated by random projection perform better than those by PCA and further that this can be attributed to the capability of random projection to produce diverse base clusterings. We also examine four different consensus functions for combining the clusterings of the ensemble. We compare their performance using two types of ensembles, each with different properties. In both cases, we show that a recent consensus function based on bipartite graph partitioning achieves the best performance.


title: Linear SLAM: A linear solution to the feature-based and pose graph SLAM based on submap joining

text: This paper presents a strategy for large-scale SLAM through solving a sequence of linear least squares problems. The algorithm is based on submap joining where submaps are built using any existing SLAM technique. It is demonstrated that if submaps coordinate frames are judiciously selected, the least squares objective function for joining two submaps becomes a quadratic function of the state vector. Therefore, a linear solution to large-scale SLAM that requires joining a number of local submaps either sequentially or in a more efficient Divide and Conquer manner, can be obtained. The proposed Linear SLAM technique is applicable to both feature-based and pose graph SLAM, in two and three dimensions, and does not require any assumption on the character of the covariance matrices or an initial guess of the state vector. Although this algorithm is an approximation to the optimal full nonlinear least squares SLAM, simulations and experiments using publicly available datasets in 2D and 3D show that Linear SLAM produces results that are very close to the best solutions that can be obtained using full nonlinear optimization started from an accurate initial value. The C/C++ and MATLAB source codes for the proposed algorithm are available on OpenSLAM.


title: Open Database of Epileptic EEG with MRI and Postoperational Assessment of Foci—a Real World Verification for the EEG Inverse Solutions

text: This paper introduces a freely accessible database http://eeg.pl/epi , containing 23 datasets from patients diagnosed with and operated on for drug-resistant epilepsy. This was collected as part of the clinical routine at the Warsaw Memorial Child Hospital. Each record contains (1) pre-surgical electroencephalography (EEG) recording (10–20 system) with inter-ictal discharges marked separately by an expert, (2) a full set of magnetic resonance imaging (MRI) scans for calculations of the realistic forward models, (3) structural placement of the epileptogenic zone, recognized by electrocorticography (ECoG) and post-surgical results, plotted on pre-surgical MRI scans in transverse, sagittal and coronal projections, (4) brief clinical description of each case. The main goal of this project is evaluation of possible improvements of localization of epileptic foci from the surface EEG recordings. These datasets offer a unique possibility for evaluating different EEG inverse solutions. We present preliminary results from a subset of these cases, including comparison of different schemes for the EEG inverse solution and preprocessing. We report also a finding which relates to the selective parametrization of single waveforms by multivariate matching pursuit, which is used in the preprocessing for the inverse solutions. It seems to offer a possibility of tracing the spatial evolution of seizures in time.


title: ResearchGate versus Google Scholar: Which finds more early citations?

text: ResearchGate has launched its own citation index by extracting citations from documents uploaded to the site and reporting citation counts on article profile pages. Since authors may upload preprints to ResearchGate, it may use these to provide early impact evidence for new papers. This article assesses the whether the number of citations found for recent articles is comparable to other citation indexes using 2675 recently-published library and information science articles. The results show that in March 2017, ResearchGate found less citations than did Google Scholar but more than both Web of Science and Scopus. This held true for the dataset overall and for the six largest journals in it. ResearchGate correlated most strongly with Google Scholar citations, suggesting that ResearchGate is not predominantly tapping a fundamentally different source of data than Google Scholar. Nevertheless, preprint sharing in ResearchGate is substantial enough for authors to take seriously.


title: A static hand gesture recognition algorithm using k-mean based radial basis function neural network

text: The accurate classification of static hand gestures is a vital role to develop a hand gesture recognition system which is used for human-computer interaction (HCI) and for human alternative and augmentative communication (HAAC) application. A vision-based static hand gesture recognition algorithm consists of three stages: preprocessing, feature extraction and classification. The preprocessing stage involves following three sub-stages: segmentation which segments hand region from its background images using a histogram based thresholding algorithm and transforms into binary silhouette; rotation that rotates segmented gesture to make the algorithm, rotation invariant; filtering that effectively removes background noise and object noise from binary image by morphological filtering technique. To obtain a rotation invariant gesture image, a novel technique is proposed in this paper by coinciding the 1st principal component of the segmented hand gestures with vertical axes. A localized contour sequence (LCS) based feature is used here to classify the hand gestures. A k-mean based radial basis function neural network (RBFNN) is also proposed here for classification of hand gestures from LCS based feature set. The experiment is conducted on 500 train images and 500 test images of 25 class grayscale static hand gesture image dataset of Danish/international sign language hand alphabet. The proposed method performs with 99.6% classification accuracy which is better than earlier reported technique.