title: Sharing it all : the rewards and struggles of two-career families

abstract: I realize more and more that to some degree psychol- ogists study their own lives. My first studies in the mid-1970s looked at the conflicts that emerge in dual- career couples as the partners combine their various roles. Ideas for the early studies initially came from con- versations with clients, friends, and people I met trav- eling. Soon after the topic of dual careers came up, dis- cussions of guilt and frustration followed. The partici- pants in my first studies turned out to be predominantly women. Men expressed little interest in participating. A common response was, "Talk to my wife. " 7 8 PREFACE Ostensibly, husbands saw their partners, but not themselves, as dealing with conflicting roles. Although I presumed this to be somewhat true, I knew from ob- serving my own husband that my having a career had an immeasurable impact on him. Were men denying something? Were women overly sensitive? Discussions with my husband, which helped me to get a better grasp on what male partners might be feeling, planted the seed for my subsequent book on men in dual-career 1 families, published in 1985. In gathering material for that book, I was struck by the contrast with men's ear- lier disinterest. Husbands appeared eager to talk about their lives. Their greatest concern-what happens to the children?-became the focus of my last set of studies. Why this book? "Sharing it all" symbolizes the es- sence of a two-career marriage.


title: Accounting In Business Decisions Theory Method And Use

abstract: Thank you for reading accounting in business decisions theory method and use. Maybe you have knowledge that, people have look numerous times for their favorite books like this accounting in business decisions theory method and use, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some malicious virus inside their laptop.


title: Real-time multiple objects tracking with occlusion handling in dynamic scenes

abstract: This work presents a real-time system for multiple objects tracking in dynamic scenes. A unique characteristic of the system is its ability to cope with long-duration and complete occlusion without a prior knowledge about the shape or motion of objects. The system produces good segment and tracking results at a frame rate of 15-20 fps for image size of 320 /spl times/ 240, as demonstrated by extensive experiments performed using video sequences under different conditions indoor and outdoor with long-duration and complete occlusions in changing background.


title: Mitigating Pilot Contamination by Pilot Reuse and Power Control Schemes for Massive MIMO Systems

abstract: The performance of massive multiple input multiple output systems may be limited by inter-cell pilot contamination (PC) unless appropriate PC mitigation or avoidance schemes are employed. In this paper we develop techniques based on existing long term evolution (LTE) measurements - open loop power control (OLPC) and pilot sequence reuse schemes, that avoid PC within a group of cells. We compare the performance of simple least-squares channel estimator with the higher-complexity minimum mean square error estimator, and evaluate the performance of the recently proposed coordinated pilot allocation (CPA) technique (which is appropriate in cooperative systems). The performance measures of interest include the normalized mean square error of channel estimation, the downlink signal-to-interference-plus-noise and spectral efficiency when employing maximum ratio transmission or zero forcing precoding at the base station. We find that for terminals moving at vehicular speeds, PC can be effectively mitigated in an operation and maintenance node using both the OLPC and the pilot reuse schemes. Additionally, greedy CPA provides performance gains only for a fraction of terminals, at the cost of degradation for the rest of the terminals and higher complexity. These results indicate that in practice, PC may be effectively mitigated without the need for second-order channel statistics or inter-cell cooperation.


title: TAFE-Net: Task-Aware Feature Embeddings for Efficient Learning and Inference

abstract: Learning good feature embeddings for images often requires substantial training data. As a consequence, in settings where training data is limited (e.g., few-shot and zero-shot learning), we are typically forced to use a general feature embedding across prediction tasks. Ideally, we would like to construct feature embeddings that are tuned for the given task and even input image. In this work, we propose Task-Aware Feature Embedding Networks (TAFE-Nets) to learn how to adapt the image representation to a new task in a meta-learning fashion. Our network is composed of a meta learner and a prediction network, where the meta learner generates parameters for the feature layers in the prediction network based on a task input so that the feature embedding can be accurately adjusted for that task. We show that our TAFE-Net is highly effective in generalizing to new tasks or concepts and offers efficient prediction with low computational cost. We demonstrate the general applicability of TAFE-Net in several tasks including zero-shot/ few-shot learning and dynamic efficient prediction. Our networks exceed or match the state-of-the-art on most tasks. In particular, our approach improves the prediction accuracy of unseen attribute-object pairs by 4 to 15 points on the challenging visual attributes composition task.


title: Domain Alignment with Triplets

abstract: Deep domain adaptation methods can reduce the distribution discrepancy by learning domain-invariant embedddings. However, these methods only focus on aligning the whole data distributions, without considering the class-level relations among source and target images. Thus, a target embeddings of a bird might be aligned to source embeddings of an airplane. This semantic misalignment can directly degrade the classifier performance on the target dataset. To alleviate this problem, we present a similarity constrained alignment (SCA) method for unsupervised domain adaptation. When aligning the distributions in the embedding space, SCA enforces a similarity-preserving constraint to maintain class-level relations among the source and target images, i.e., if a source image and a target image are of the same class label, their corresponding embeddings are supposed to be aligned nearby, and vise versa. In the absence of target labels, we assign pseudo labels for target images. Given labeled source images and pseudo-labeled target images, the similarity-preserving constraint can be implemented by minimizing the triplet loss. With the joint supervision of domain alignment loss and similarity-preserving constraint, we train a network to obtain domain-invariant embeddings with two critical characteristics, intra-class compactness and inter-class separability. Extensive experiments conducted on the two datasets well demonstrate the effectiveness of SCA.


title: Dual-Band Circularly Polarized Cavity-Backed Annular Slot Antenna for GPS Receiver

abstract: A circularly polarized (CP) cavity-backed annular slot antenna for GPS receiver is designed to operate in both the L1 and L2 bands of the Global Positioning System (GPS). The measured impedance bandwidths with VSWR less than 2 are 3.7% (1.19-1.235 GHz) and 1.2% (1.565-1.585 GHz), respectively, the measured 3 dB axial-ratio (AR) bandwidth are 0.9% (1.220-1.231 GHz) and 0.6% (1.572-1.581 GHz), respectively. A cavity is designed to render a unidirectional radiation pattern.


title: Wavelet-Based Neural Network for Power Disturbance Recognition and Classification

abstract: In this paper, a prototype wavelet-based neural-network classifier for recognizing power-quality disturbances is implemented and tested under various transient events. The discrete wavelet transform (DWT) technique is integrated with the probabilistic neural-network (PNN) model to construct the classifier. First, the multiresolution-analysis technique of DWT and the Parseval's theorem are employed to extract the energy distribution features of the distorted signal at different resolution levels. Then, the PNN classifies these extracted features to identify the disturbance type according to the transient duration and the energy features. Since the proposed methodology can reduce a great quantity of the distorted signal features without losing its original property, less memory space and computing time are required. Various transient events tested, such as momentary interruption, capacitor switching, voltage sag/swell, harmonic distortion, and flicker show that the classifier can detect and classify different power disturbance types efficiently.


title: Figaro, hair detection and segmentation in the wild

abstract: Hair is one of the elements that mostly characterize people appearance. Being able to detect hair in images can be useful in many applications, such as face recognition, gender classification, and video surveillance. To this purpose we propose a novel multi-class image database for hair detection in the wild, called Figaro. We tackle the problem of hair detection without relying on a-priori information related to head shape and location. Without using any human-body part classifier, we first classify image patches into hair vs. non-hair by relying on Histogram of Gradients (HOG) and Linear Ternary Pattern (LTP) texture features in a random forest scheme. Then we obtain results at pixel level by refining classified patches by a graph-based multiple segmentation method. Achieved segmentation accuracy (85%) is comparable to state-of-the-art on less challenging databases.


title: Electroactive Elastomeric Actuator for All-Polymer Linear Peristaltic Pumps

abstract: This paper describes the concept of a lightweight and deformable structure with intrinsic distributed electromechanical actuation, potentially suitable to develop soft linear peristaltic pumps for incompressible fluids. The proposed system is represented by a series of radially expanding flexible tubular modules, made of dielectric elastomers (DEs), as one of the most promising classes of electroactive polymers for actuation. Each module consists of a cylindrical hollow DE actuator, working in purely radial mode with specific boundary constraints. The static electromechanical transduction performance of such a module was investigated analytically, numerically, and experimentally. For this purpose, predictions obtained from an analytical model, derived in the hypothesis of linear elasticity, were compared with those provided by a finite-element method. Both models were validated by means of a comparison with experimental data, obtained from a silicone-made prototype module. Results permitted to obtain a simple tool of simulation, suitable to predict the performance of the system in terms of both displaced volume and driving pressure, as a function of the material elastic modulus and the applied voltage.


title: On Hit Inflation Techniques and Detection in Streams of Web Advertising Networks

abstract: Click fraud is jeopardizing the industry of Internet advertising. Internet advertising is crucial for the thriving of the entire Internet, since it allows producers to advertise their products, and hence contributes to the well being of e-commerce. Moreover, advertising supports the intellectual value of the Internet by covering the running expenses of the content publishers' sites. Some publishers are dishonest, and use automation to generate traffic to defraud the advertisers. Similarly, some advertisers automate clicks on the advertisements of their competitors to deplete their competitors ' advertising budgets. In this paper, we describe the advertising network model, and discuss the issue of fraud that is an integral problem in such setting. We propose using online algorithms on aggregate data to accurately and proactively detect automated traffic, preserve surfers' privacy, while not altering the industry model. We provide a complete classification of the hit inflation techniques; and devise stream analysis techniques that detect a variety of fraud attacks. We abstract detecting the fraud attacks of some classes as theoretical stream analysis problems that we bring to the data management research community as open problems. A framework is outlined for deploying the proposed detection algorithms on a generic architecture. We conclude by some successful preliminary findings of our attempt to detect fraud on a real network.


title: Comparative study of different techniques used for medical image segmentation of liver from abdominal CT scan

abstract: Computed tomography (CT) imaging is a fast, safe and accurate non-invasive automatic imaging modality used by pathologists to see the internal organs without operating on it. This provides detail cross sectioned view of internal tissues. in order to do visualize the area of interest and organ measurement. The aim objective of the proposed work is to discuss the possibility of different methods used for segmenting the liver from the abdominal CT imaging. Automatic extraction of liver from CT image is essential and used by radiologists to get second opinion to know and detect tumor present in the liver. This is difficult due to presence of nearby organ will have the same intensity as that of liver. In this work, a various method for segmentation of liver from CT Images is investigated that can be helpful for detection and classification of liver region. The objective is to segment the liver using different algorithms like seeded region growing, label connected, Neutrosophic set with thresholding. The proposed method discusses the comparative study of different methods and how those algorithms are used to detect the liver.


title: Polarity Detection of Foursquare Tips

abstract: In location-based social networks, such as Foursquare, users may post tips with their opinions about visited places. Tips may directly impact the behavior of future visitors, providing valuable feedback to business owners. Sentiment or polarity detection has attracted great attention due to its vast applicability in opinion summarization, ranking or recommendation. However, the automatic detection of polarity of tips faces challenges due to their short sizes and informal content. This paper presents an empirical study of supervised and unsupervised techniques to detect the polarity of Foursquare tips. We evaluate the effectiveness of four methods on two sets of tips, finding that a simpler lexicon-based approach, which does not require costly manual labeling, can be as effective as state-of-the-art supervised methods. We also find that a hybrid approach that combines all considered methods by means of stacking does not significantly outperform the best individual method.


title: A novel ultra-wideband 80 GHz FMCW radar system for contactless monitoring of vital signs

abstract: In this paper an ultra-wideband 80 GHz FMCW-radar system for contactless monitoring of respiration and heart rate is investigated and compared to a standard monitoring system with ECG and CO(2) measurements as reference. The novel FMCW-radar enables the detection of the physiological displacement of the skin surface with submillimeter accuracy. This high accuracy is achieved with a large bandwidth of 10 GHz and the combination of intermediate frequency and phase evaluation. This concept is validated with a radar system simulation and experimental measurements are performed with different radar sensor positions and orientations.


Passing a USA National Bar Exam: a First Corpus for Experimentation


title: Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks

abstract: Recent approaches based on artificial neural networks (ANNs) have shown promising results for short-text classification. However, many short texts occur in sequences (e.g., sentences in a document or utterances in a dialog), and most existing ANN-based systems do not leverage the preceding short texts when classifying a subsequent one. In this work, we present a model based on recurrent neural networks and convolutional neural networks that incorporates the preceding short texts. Our model achieves state-of-the-art results on three different datasets for dialog act prediction.


title: High-$Q$ Narrowband Tunable Combline Bandpass Filters Using MEMS Capacitor Banks and Piezomotors

abstract: This paper presents the design and implementation of a new class of evanescent tunable combline bandpass filters based on electronic tuning with the use of RF microelectromechanical systems (RF-MEMS) capacitor banks and also mechanical tuning using piezomotors. The use of microelectromechanical systems tuning circuit results in compact implementation of the proposed filters with high-Q and near to zero dc power consumption. The proposed filter structures consist of combline resonators with tuning disks that are either mechanically moveable using piezomotors or are loaded with RF-MEMS capacitor banks. Two- and six-pole tunable bandpass filters are designed and measured based on the proposed tuning concept. The two-pole tunable filter operates at 2.5 GHz with a bandwidth of 22 MHz and demonstrates a tuning range of 110 MHz, while the quality factor is better than 374 (1300-374 over the tuning range). The six-pole tunable filter with RF-MEMS capacitor banks operates from 2.634 to 2.59 GHz (44-MHz tuning range). The proposed tunable filter structures can also be implemented using alternative technologies such as barium-strontium-titanate varactors.


title: Semantic integration: a survey of ontology-based approaches

abstract: Semantic integration is an active area of research in several disciplines, such as databases, information-integration, and ontologies. This paper provides a brief survey of the approaches to semantic integration developed by researchers in the ontology community. We focus on the approaches that differentiate the ontology research from other related areas. The goal of the paper is to provide a reader who may not be very familiar with ontology research with introduction to major themes in this research and with pointers to different research projects. We discuss techniques for finding correspondences between ontologies, declarative ways of representing these correspondences, and use of these correspondences in various semantic-integration tasks


title: Estimating software based on use case points

abstract: It is well documented that software product cost estimates are notoriously inaccurate across the software industry. Creating accurate cost estimates for software product development projects early in the product development lifecycle has always been a challenge for the industry. This article describes how a large multi-team software engineering organization (over 450 engineers) estimates project cost accurately and early in the software development lifecycle using Use Case Points, and the process of evaluating metrics to ensure the accuracy of the model.The engineering teams of Agilis Solutions in partnership with FPT Software, provide our customers with accurate estimates for software product projects early in the product lifecycle. The bases for these estimates are initial definitions of Use Cases, given point factors and modified for technical and environmental factors according to the Use Case Point method defined within the Rational Unified Process. After applying the process across hundreds of sizable (60 man-months average) software projects, we have demonstrated metrics that prove an estimating accuracy of less than 9% deviation from actual to estimated cost on 95% of our projects. Our process and this success factor is documented over a period of five years, and across more than 200 projects.


title: Adaptive Spur Cancellation Technique in All-Digital Phase-Locked Loops

abstract: The phenomenon of periodic phase errors (also known as spurs) in phase-locked loops (PLLs) is widely acknowledged and is responsible for posing considerable challenge on development of miniaturized wireless communication devices. The common approach employed today for spur mitigation calls for a digital notch filter within the receive chain, while there is no similar digital scheme for the transmit chain. In addition, this notch filter is not perfect and usually degrades the overall receiver sensitivity. This brief puts forward a novel idea, which is to cancel the spurs inside the PLL such that the local oscillator signal and consequently TX and RX antenna ports become spur-free. The technique is based on a least-mean squares algorithm that features a self-learning capability. The method has been silicon proven in a digital PLL of a transceiver radio test chip realized in a standard nanoscale CMOS technology.


title: Segment Hough Transform -- a Novel Hough-based Algorithm for Curve Detection

abstract: A new Hough-based algorithm termed segment Hough transform (SHT) is proposed to detect the curve in the binary image. The segment-transform, which is the main idea of the novel approach, makes the curve detection very efficient. Thanks to the chain code termed angle chain code (ACC) as well as the modified direction measurement, the segmentation operation is also considerably fast and precise. Compared with the conventional Hough transform (CHT), our algorithm is dramatically faster (by over 20 times) And when the background is complex, the SHT is about four times faster than the random Hough transform (RHT), Furthermore, the proposed algorithm inherits the high robustness from CHT. In some situations, it is even more accurate. We implement the novel algorithm in an embedded system to estimate passengers flow on the bus. The detection rate is over 95% which indicates it is quite suitable for real-time application.


title: A Microcontroller Based Four Fingered Robotic

abstract: In this paper we deal with the design and development of a Four Fingered Robotic Hand (FFRH) using 8-bit microcontroller, sensors and wireless feedback. The design of the system is based on a simple, flexible and minimal control strategy. The robot system has 14 independent commands for all the four fingers open and close, wrist up and down, base clockwise and counter clockwise, Pick and Place and Home position to move the fingers. Implementation of pick and place operation of the object using these commands are discussed. The mechanical hardware design of the Robotic hand based on connected double revolute joint mechanisms is briefed. The tendoning system of the double revolute joint mechanism and wireless feedback network provide the hand with the ability to confirm to object topology and therefore providing the advantage of using a simple control algorithm. Finally, the results of the experimental work for pick and place application are enumerated.


Deep Learning Approaches to the Multi-Instance Multi-Label ( MIML ) Learning Problem


title: Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks

abstract: Pretraining with language modeling and related unsupervised tasks has recently been shown to be a very effective enabling technology for the development of neural network models for language understanding tasks. In this work, we show that although language model-style pretraining is extremely effective at teaching models about language, it does not yield an ideal starting point for efficient transfer learning. By supplementing language model-style pretraining with further training on data-rich supervised tasks, we are able to achieve substantial additional performance improvements across the nine target tasks in the GLUE benchmark. We obtain an overall score of 76.9 on GLUE--a 2.3 point improvement over our baseline system adapted from Radford et al. (2018) and a 4.1 point improvement over Radford et al.'s reported score. We further use training data downsampling to show that the benefits of this supplementary training are even more pronounced in data-constrained regimes.


title: A Quasi-Newton Algorithm on the Orthogonal Manifold for NMF with Transform Learning

abstract: Nonnegative matrix factorization (NMF) is a popular method for audio spectral unmixing. While NMF is traditionally applied to off-the-shelf time-frequency representations based on the short-time Fourier or Cosine transforms, the ability to learn transforms from raw data attracts increasing attention. However, this adds an important computational overhead. When assumed orthogonal (like the Fourier or Cosine transforms), learning the transform yields a non-convex optimization problem on the orthogonal matrix manifold. In this paper, we derive a quasi-Newton method on the manifold using sparse approximations of the Hessian. Experiments on synthetic and real audio data show that the proposed algorithm outperforms state-of-the-art first-order and coordinate-descent methods by orders of magnitude in terms of speed. A Python package for fast TL-NMF is released online at https://github.com/pierreablin/tlnmf.


title: A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization

abstract: In this paper we design and evaluate a Deep-Reinforcement Learning agent that optimizes routing. Our agent adapts automatically to current traffic conditions and proposes tailored configurations that attempt to minimize the network delay. Experiments show very promising performance. Moreover, this approach provides important operational advantages with respect to traditional optimization algorithms.


title: Comparison of single-phase H4, H5, H6 inverters for transformerless photovoltaic applications

abstract: In low-power photovoltaic systems, single-phase inverters are often used to inject the generated power into the grid. To increase the efficiency, the researchers have proposed to eliminate the transformer that connects the photovoltaic system to the grid. However, the absence of this transformer allows a leakage current to circulate through the ground, the parasitic capacitance of the photovoltaic panels and the grid transformer. Several transformerless inverter topologies have been proposed and investigated. This paper tries to experimentally compare the performance of three conversion structures derived from full-bridge inverter, i.e., inverters H4, H5, and H6, each controlled with different modulation strategies. The comparison is based on the same hardware platform, experimental set-up, and operating conditions.


title: Scraping and Clustering Techniques for the Characterization of Linkedin Profiles

abstract: The socialization of the web has undertaken a new dimension after the emergence of the Online Social Networks (OSN) concept. The fact that each Internet user becomes a potential content creator entails managing a big amount of data. This paper explores the most popular professional OSN: LinkedIn. A scraping technique was implemented to get around 5 Million public profiles. The application of natural language processing techniques (NLP) to classify the educational background and to cluster the professional background of the collected profiles led us to provide some insights about this OSN's users and to evaluate the relationships between educational degrees and professional careers.


title: DCRoute: Speeding up Inter-Datacenter Traffic Allocation while Guaranteeing Deadlines

abstract: Datacenters provide the infrastructure for cloud computing services used by millions of users everyday. Many such services are distributed over multiple datacenters at geographically distant locations possibly in different continents. These datacenters are then connected through high speed WAN links over private or public networks. To perform data backups or data synchronization operations, many transfers take place over these networks that have to be completed before a deadline in order to provide necessary service guarantees to end users. Upon arrival of a transfer request, we would like the system to be able to decide whether such a request can be guaranteed successful delivery. If yes, it should provide us with transmission schedule in the shortest time possible. In addition, we would like to avoid packet reordering at the destination as it affects TCP performance. Previous work in this area either cannot guarantee that admitted transfers actually finish before the specified deadlines or use techniques that can result in packet reordering. In this paper, we propose DCRoute, a fast and efficient routing and traffic allocation technique that guarantees transfer completion before deadlines for admitted requests. It assigns each transfer a single path to avoid packet reordering. Through simulations, we show that DCRoute is at least 200 times faster than other traffic allocation techniques based on linear programming (LP) while admitting almost the same amount of traffic to the system.


title: Evidence for increased postprandial distal nephron calcium delivery in hypercalciuric stone-forming patients

abstract: A main mechanism of idiopathic hypercalciuria (IH) in calcium stone-forming patients (IHSF) is postprandial reduction of renal tubule calcium reabsorption that cannot be explained by selective reduction of serum parathyroid hormone levels; the nephron site(s) responsible are not as yet defined. Using fourteen 1-h measurements of the clearances of sodium, calcium, and endogenous lithium during a three-meal day in the University of Chicago General Clinical Research Center, we found reduced postprandial proximal tubule reabsorption of sodium and calcium in IHSF vs. normal subjects. The increased distal sodium delivery is matched by increased distal reabsorption so that urine sodium excretions do not differ, but distal calcium reabsorption does not increase enough to match increased calcium delivery, so hypercalciuria results. In fact, urine calcium excretion and overall renal fractional calcium reabsorption both are high in IHSF vs. normal when adjusted for distal calcium delivery, strongly suggesting a distal as well as proximal reduction of calcium reabsorption. The combination of reduced proximal tubule and distal nephron calcium reabsorption in IHSF is a new finding and indicates that IH involves a complex, presumably genetic, variation of nephron function. The increased calcium delivery into the later nephron may play a role in stone formation via deposition of papillary interstitial apatite plaque.


Synthesis and Characterization of Nanoporous Zirconia


title: PROCRASTINATION , DEADLINES , AND PERFORMANCE : Self-Control by Precommitment

abstract: Procrastination is all too familiar to most people. People delay writing up their research (so we hear!), repeatedly declare they will start their diets tomorrow, or postpone until next week doing odd jobs around the house. Yet people also sometimes attempt to control their procrastination by setting deadlines for themselves. In this article, we pose three questions: (a) Are people willing to self-impose meaningful (i.e., costly) deadlines to overcome procrastination? (b) Are self-imposed deadlines effective in improving task performance? (c) When self-imposing deadlines, do people set them optimally, for maximum performance enhancement? A set of studies examined these issues experimentally, showing that the answer is “yes” to the first two questions, and “nO'’ to the third. People have self-control problems, they recognize them, and they try to control them by self-imposing costly deadlines. These deadlines help people control procrastination, but they are not as effective as some externally imposed dead...


title: Intuitive Use of User Interfaces: Defining a Vague Concept

abstract: In this paper we present a general definition of the concept 'intuitive use of user interfaces' on the basis of our current interdisciplinary work. 'Intuitive use' is regarded as a characteristic of human-machine systems. It refers to a special kind of interaction process between users and technical systems that use the users' intuition. The main part of the paper deals with central aspects of this definition in detail and discusses pre-conditions and restrictions of the use of the concept. The main aspects that we discuss are the design of technical systems, application and non-conscious use of previous knowledge, intuition as a non-conscious process, interaction, and effectiveness. We complement this discussion by addressing the relationship between aesthetics and intuitive use.


title: Artificial emotion expression for a robot by dynamic color change

abstract: We propose a novel method of expressing emotions for a robot by dynamically changing the color luminosity of its body. To identify color and dynamic parameters for representing emotions, we asked subjects to select hue values and frequencies and waveforms of blinking to represent the 24 emotions of Plutchik's Wheel of Emotions. The results indicate that the hue value represents basic types of emotion, i.e., anger-red, anticipation-amber, joy-yellow, trust-green, fear-violet, surprise-red, sadness-blue, and disgust-purple, and the duration and waveform represent emotion intensity, i.e., a rect-angular waveform with high frequency represents intense emotion. Another experiment validated that emotions of a robot expressed using our model impressed other subjects appropriately.


title: Learning convolutional neural networks from few samples

abstract: Learning Convolutional Neural Networks (CNN) is commonly carried out by plain supervised gradient descent. With sufficient training data, this leads to very competitive results for visual recognition tasks when starting from a random initialization. When the amount of labeled data is limited, CNNs reveal their strong dependence on large amounts of training data. However, recent results have shown that a well chosen optimization starting point can be beneficial for convergence to a good generalizing minimum. This starting point was mostly found using unsupervised feature learning techniques such as sparse coding or transfer learning from related recognition tasks. In this work, we compare these two approaches against a simple patch based initialization scheme and a random initialization of the weights. We show that pre-training helps to train CNNs from few samples and that the correct choice of the initialization scheme can push the network's performance by up to 41% compared to random initialization.


title: Controlling a Wheelchair Indoors Using Thought

abstract: Amyotrophic lateral sclerosis, or ALS, is a degenerative disease of the motor neurons that eventually leads to complete paralysis. We are developing a wheelchair system that can help ALS patients, and others who can't use physical interfaces such as joysticks or gaze tracking, regain some autonomy. The system must be usable in hospitals and homes with minimal infrastructure modification. It must be safe and relatively low cost and must provide optimal interaction between the user and the wheelchair within the constraints of the brain-computer interface. To this end, we have built the first working prototype of a brain-controlled wheelchair that can navigate inside a typical office or hospital environment. This article describes the BCW, our control strategy, and the system's performance in a typical building environment. This brain-controlled wheelchair prototype uses a P300 EEG signal and a motion guidance strategy to navigate in a building safely and efficiently without complex sensors or sensor processing


Adaptive Coordination Strategies for Human-Robot Handovers


title: Broadcast Court-Net Sports Video Analysis Using Fast 3-D Camera Modeling

abstract: This paper addresses the automatic analysis of court-net sports video content. We extract information about the players, the playing-field in a bottom-up way until we reach scene-level semantic concepts. Each part of our framework is general, so that the system is applicable to several kinds of sports. A central point in our framework is a camera calibration module that relates the a-priori information of the geometric layout in the form of a court model to the input image. Exploiting this information, several novel algorithms are proposed, including playing-frame detection, players segmentation and tracking. To address the player-occlusion problem, we model the contour map of the player silhouettes using a nonlinear regression algorithm, which enables to locate the players during the occlusions caused by players in the same team. Additionally, a Bayesian-based classifier helps to recognize predefined key events, where the input is a number of real-world visual features. We illustrate the performance and efficiency of the proposed system by evaluating it for a variety of sports videos containing badminton, tennis and volleyball, and we show that our algorithm can operate with more than 91% feature detection accuracy and 90% event detection.


title: A very low power CMOS potentiostat for bioimplantable applications

abstract: In this paper, we describe a new structure for designing low power potentiostats, which are suitable for electrochemical sensors used in biomedical implants. The low power consumption is a result of using just one operational amplifier in the structure. The structure is also inherently very low noise because it amplifies the output current of the sensor in current mode which can then be converted to the desirable variable; i.e., voltage, frequency, pulse width, etc. Finally we present a new topology for the design of a low power operational amplifier dedicated to driving super capacitive chemical sensors.


title: Machine Learning Approaches for Mood Classification of Songs toward Music Search Engine

abstract: Human often wants to listen to music that fits best his current emotion. A grasp of emotions in songs might be a great help for us to effectively discover music. In this paper, we aimed at automatically classifying moods of songs based on lyrics and metadata, and proposed several methods for supervised learning of classifiers. In future, we plan to use automatically identified moods of songs as metadata in our music search engine. Mood categories in a famous contest about Audio Music Mood Classification (MIREX 2007) are applied for our system. The training data is collected from a LiveJournal blog site in which each blog entry is tagged with a mood and a song. Then three kinds of machine learning algorithms are applied for training classifiers: SVM, Naive Bayes and Graph-based methods. The experiments showed that artist, sentiment words, putting more weight for words in chorus and title parts are effective for mood classification. Graph-based method promises a good improvement if we have rich relationship information among songs.


title: Multi-beam and multi-range antenna array for 24 GHz radar applications

abstract: An 8 × 4 microstrip antenna array operating within 24 GHz frequency range has been shown. The presented antenna features two beams: a broadside beam and a dual beam. It allows for detection of targets in two different ranges and at two different angular positions. Additionally, the antenna can be used for the direction finding. The proposed antenna array uses `through-element' series feeding technique for dissipation losses reduction as well as the stacked laminate structure to separate the radiating elements and the feeding network, which allows for achieving satisfactory radiation patterns in a wide frequency range.


title: Siamese Neural Networks for One-shot detection of Railway Track Switches

abstract: Deep Learning methods have been extensively used to analyze video data to extract valuable information by classifying image frames and detecting objects. We describe a unique approach for using video feed from a moving Locomotive to continuously monitor the Railway Track and detect significant assets like Switches on the Track. The technique used here is called Siamese Networks, which uses 2 identical networks to learn the similarity between of 2 images. Here we will use a Siamese network to continuously compare Track images and detect any significant difference in the Track. Switch will be one of those images that will be different and we will find a mapping that clearly distinguishes the Switch from other possible Track anomalies. The same method will then be extended to detect any abnormalities on the Railway Track. Railway Transportation is unique in the sense that is has wheeled vehicles, Trains pulled by Locomotives, running on guided Rails at very high speeds nearing 200 mph. Multiple Tracks on the Rail network are connected to each other using an equipment called Switch or a Turnout. Switch is either operated manually or automatically through command from a Control center and it governs the movement of Trains on different Tracks of the network. Accurate location of these Switches is very important for the railroad and getting a true picture of their state in field is important. Modern trains use high definition video cameras facing the Track that continuously record video from track. Using a Siamese network and comparing to benchmark images, we describe a method to monitor the Track and highlight anomalies.


Inquire and Diagnose : Neural Symptom Checking Ensemble using Deep Reinforcement Learning


title: Flexible IoT security middleware for end-to-end cloud-fog communication

abstract: Abstract IoT (Internet of Things) based smart devices such as sensors have been actively used in edge clouds i.e., ‘fogs’ along with public clouds. They provide critical data during scenarios ranging from e.g., disaster response to in-home healthcare. However, for these devices to work effectively, end-to-end security schemes for the device communication protocols have to be flexible and should depend upon the application requirements as well as the resource constraints at the network-edge. In this paper, we present the design and implementation of a flexible IoT security middleware for end-to-end cloud–fog communications involving smart devices and cloud-hosted applications. The novel features of our middleware are in its ability to cope with intermittent network connectivity as well as device constraints in terms of computational power, memory, energy, and network bandwidth. To provide security during intermittent network conditions, we use a ‘Session Resumption’ algorithm in order for our middleware to reuse encrypted sessions from the recent past, if a recently disconnected device wants to resume a prior connection that was interrupted. In addition, we describe an ‘Optimal Scheme Decider’ algorithm that enables our middleware to select the best possible end-to-end security scheme option that matches with a given set of device constraints. Experiment results show how our middleware implementation also provides fast and resource-aware security by leveraging static properties i.e., static pre-shared keys (PSKs) for a variety of IoT-based application requirements that have trade-offs in higher security or faster data transfer rates.


title: Deep Recurrent Survival Analysis

abstract: Survival analysis is a hotspot in statistical research for modeling time-to-event information with data censorship handling, which has been widely used in many applications such as clinical research, information system and other fields with survivorship bias. Many works have been proposed for survival analysis ranging from traditional statistic methods to machine learning models. However, the existing methodologies either utilize counting-based statistics on the segmented data, or have a pre-assumption on the event probability distribution w.r.t. time. Moreover, few works consider sequential patterns within the feature space. In this paper, we propose a Deep Recurrent Survival Analysis model which combines deep learning for conditional probability prediction at finegrained level of the data, and survival analysis for tackling the censorship. By capturing the time dependency through modeling the conditional probability of the event for each sample, our method predicts the likelihood of the true event occurrence and estimates the survival rate over time, i.e., the probability of the non-occurrence of the event, for the censored data. Meanwhile, without assuming any specific form of the event probability distribution, our model shows great advantages over the previous works on fitting various sophisticated data distributions. In the experiments on the three realworld tasks from different fields, our model significantly outperforms the state-of-the-art solutions under various metrics.


title: Future Directions for Optimizing Compilers

abstract: As software becomes larger, programming languages become higher-level, and processors continue to fail to be clocked faster, we'll increasingly require compilers to reduce code bloat, eliminate abstraction penalties, and exploit interesting instruction sets. At the same time, compiler execution time must not increase too much and also compilers should never produce the wrong output. This paper examines the problem of making optimizing compilers faster, less buggy, and more capable of generating high-quality output.


title: Counterfactual Normalization: Proactively Addressing Dataset Shift and Improving Reliability Using Causal Mechanisms

abstract: Predictive models can fail to generalize from training to deployment environments because of dataset shift, posing a threat to model reliability and the safety of downstream decisions made in practice. Instead of using samples from the target distribution to reactively correct dataset shift, we use graphical knowledge of the causal mechanisms relating variables in a prediction problem to proactively remove relationships that do not generalize across environments, even when these relationships may depend on unobserved variables (violations of the"no unobserved confounders"assumption). To accomplish this, we identify variables with unstable paths of statistical influence and remove them from the model. We also augment the causal graph with latent counterfactual variables that isolate unstable paths of statistical influence, allowing us to retain stable paths that would otherwise be removed. Our experiments demonstrate that models that remove vulnerable variables and use estimates of the latent variables transfer better, often outperforming in the target domain despite some accuracy loss in the training domain.


title: Contextualizing agile software development

abstract: SUMMARY ::: This paper presents a contextual model for software-intensive systems development to guide the adoption and adaptation of agile software development practices. This model was found especially useful when the project context departs significantly from the “agile sweet spot”, that is, the ideal conditions in which agile software development practices originated from, and where they are most likely to succeed, “out of the box”. This is the case for large systems, distributed development environment, safety-critical systems, system requiring a novel architecture, or systems with an unorthodox business model or governance model. Copyright © 2011 John Wiley & Sons, Ltd.


title: Toward high performance epoxy/halloysite nanocomposites: New insights based on rheological, curing, and impact properties

abstract: Abstract The present work is aimed to study the effects of various processing parameters such as mixing methods (ultrasonication and mechanical mixing), curing system and types of halloysite on the rheological, curing and impact behavior of epoxy nanocomposites reinforced with halloysite nanotubes (HNTs). It was shown that proper design of process and material is of crucial importance to get the full benefit of HNTs as reinforcing nanofiller. The viscosity of the uncured epoxy increased after the incorporation of HNTs, while the impact properties of the cured epoxy/HNTs nanocomposites were found to be highly dependent on the characteristics of base epoxy matrix, as determined by changing the type of curing agent. Compared to the commonly used mechanical mixing method, ultrasonication effectively improved the dispersion of HNTs in epoxy matrix, and considerably enhanced the viscosity and impact strength of the epoxy/HNTs nanocomposites, especially at high HNTs loading. Fracture mechanism studies revealed that shear yielding happened in the epoxy and the incorporation of HNTs deteriorated the impact properties of final epoxy nanocomposites. Types of HNTs had a significant effect on the impact properties of epoxy/HNTs nanocomposites in a way that those nanocomposites reinforced with longer and more uniform HNTs showed significantly higher impact strength.


abstract: Compound Analogical Design, Or How to Make a Surfboard Disappear Michael E. Helms, Swaroop Vattam & Ashok Goel Design Intelligence Laboratory, School of Interactive Computing Georgia Institute of Technology, Atlanta, Georgia, USA {mhelms3, svattam, goel}@cc.gatech.edu Abstract Biologically-inspired design uses analogous biological systems to develop novel solutions for human needs. In this paper we describe an in situ cognitive study of biologically inspired engineering design. We found that biologically inspired engineering design often involves compound analogies in which a new design concept is generated by composing the results of multiple cross-domain analogies. This process of compound analogy relies on an opportunistic interaction between two processes: problem decomposition and analogical transfer. Based on this cognitive study, we also present an information- processing account of compound analogies. Keywords: design, cognitive study, biologically inspired, engineering, analogical design. 1. Introduction One of the conundrums in research on creativity is that any solution to any problem has to start from what one already knows: so, how is it possible to create novel solutions? One way of trying to answer this question is to conduct in situ (or in vivo) cognitive studies of creativity in naturalistic settings (e.g., Darden & Cook 1994; Dunbar 1995; Kurz-Milcke, Nersessian & Newstetter 2004; Christensen & Schunn 2008). Kurz- Milcke, Nersessian & Newstetter (2004), for example, describe an in situ cognitive study of scientific research laboratories in bio-medical engineering. We have conducted an in situ cognitive study of biologically inspired design, and in this paper we describe one of the (many) findings from the study. Biologically inspired design is a growing movement in design, driven in part by the need for environmentally sustainable development (e.g., Benyus 1997). Although applications of biologically inspired design can be found in many design domains such as engineering, architecture and computing, our focus is on biologically inspired engineering design (and in the rest of this paper we will use the term biologically inspired design to refer to biologically inspired engineering design only). A recent example from textile engineering is the design of thermally self- regulating clothing based on the design of pinecones (Vincent & Mann 2002). By definition, biologically inspired design is based on cross-domain analogies. We chose this particular domain for study because of our interest in understanding the role, process and content of analogies in creativity. 2. Cognitive Study Our study was conducted in the context of an interdisciplinary course on biologically inspired design offered by Georgia Tech’s Center for Biologically Inspired Design in the fall of 2006. At least 32 of the 45 students in the class had already taken a course in design and/or participated in design projects as part of their undergraduate education. In the rest of this paper, we will refer to the students in the class as designers. The instructors of the interdisciplinary course taught biologically inspired design using a problem-based learning approach. Design projects grouped an interdisciplinary team of 4-5 students together, where each team had one student from biology and the rest were from different engineering disciplines. Each team was responsible for identifying a problem that could be addressed by a biologically inspired solution, exploring solution alternatives, and developing a final solution design based on biological design solutions. As observers, we attended the classroom sessions, collected course materials, documented lecture content, and observed teacher-student and student-student interactions in the classroom. We had no influence on the course design or pedagogical approach. We also did in situ observations of a few of the teams engaged in their design projects. We minimized our intervention, only occasionally asking clarifying questions. Our observations focused on the cognitive practices and products of the designers. We observed and documented the frequently occurring problem-solving and representational activities of designers as part of the design process. Some of these activities were part of the design process explicitly taught by the instructors. Others emerged during practice. In terms of the design products, we observed and documented the natural evolution of the conceptual design over time. We also attended the final oral presentations of the design teams to the class, and read the design briefs the teams submitted with their projects. Vattam, Helms & Goel (2007) provide details of the cognitive study.


title: A vision-based lane detection system combining appearance segmentation and tracking of salient points

abstract: Reliable lane detection is a key component of autonomous vehicles supporting navigation in urban environments. This paper introduces the GOLDIE(Geometric Overture for Lane Detection by Intersections Entirety) system, a vision-based software architecture that uses an on-board single camera to determine the position of road lanes with respect to the vehicle. We propose an efficient vision-based lane-detection system that combines an appearance-based analysis with salient point tracking. The appearance-based analysis consists of segmenting high contrast areas that fit inside a Region-Of-Interest(ROI) on the frame. The salient point tracker selects interesting points based in a reference line, that guides a dynamic ROI. The tracking ROI look for paint lane marks close to the last lane reference found, where road marks are likely to emerge, in order to maintain the usability of the salient point tracker. The tracking is performed with the Lucas-Kanade algorithm and the lane points candidates are selected according to a predefined triangular model. Once such lanes points are detected, the vehicle position is estimated based on the intersection of linearised lanes determined through a vanishing point approach. Experiments and comparisons with other algorithms illustrate the applicability of the method.


title: Deep-Reinforcement-Learning-Based Optimization for Cache-Enabled Opportunistic Interference Alignment Wireless Networks

abstract: Both caching and interference alignment (IA) are promising techniques for next-generation wireless networks. Nevertheless, most of the existing works on cache-enabled IA wireless networks assume that the channel is invariant, which is unrealistic considering the time-varying nature of practical wireless environments. In this paper, we consider realistic time-varying channels. Specifically, the channel is formulated as a finite-state Markov channel (FSMC). The complexity of the system is very high when we consider realistic FSMC models. Therefore, in this paper, we propose a novel deep reinforcement learning approach, which is an advanced reinforcement learning algorithm that uses a deep $Q$ network to approximate the $Q$ value-action function. We use Google TensorFlow to implement deep reinforcement learning in this paper to obtain the optimal IA user selection policy in cache-enabled opportunistic IA wireless networks. Simulation results are presented to show that the performance of cache-enabled opportunistic IA networks in terms of the network's sum rate and energy efficiency can be significantly improved by using the proposed approach.


title: 2D-Densely Connected Convolution Neural Networks for automatic Liver and Tumor Segmentation

abstract: In this paper we propose a fully automatic 2-stage cascaded approach for segmentation of liver and its tumors in CT (Computed Tomography) images using densely connected fully convolutional neural network (DenseNet). We independently train liver and tumor segmentation models and cascade them for a combined segmentation of the liver and its tumor. The first stage involves segmentation of liver and the second stage uses the first stage's segmentation results for localization of liver and henceforth tumor segmentations inside liver region. The liver model was trained on the down-sampled axial slices $(256 \times 256)$, whereas for the tumor model no down-sampling of slices was done, but instead it was trained on the CT axial slices windowed at three different Hounsfield (HU) levels. On the test set our model achieved a global dice score of 0.923 and 0.625 on liver and tumor respectively. The computed tumor burden had an rmse of 0.044.


title: DC-DC converters for electric vehicle applications

abstract: An integral part of any modern day electric vehicle is power electronic circuits (PECs) comprising of DC-AC inverters and DC-DC converters. A DC-AC inverter supplies the high power electric motor and utility loads such as air-conditioning system, whereas a DC-DC converter supplies conventional low-power, low-voltage loads. However, the need for high power bidirectional DC- DC converters in future electric vehicles has led to the development of many new topologies of DC-DC converters. This paper presents an overview of state-of-the-art DC-DC converters used in battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and fuel cell vehicles (FCVs). Several DC-DC converters such as isolated, nonisolated, half-bridge, full-bridge, unidirectional and bidirectional topologies, and their applications in electric vehicles are presented.


title: Feeding Relationships between Hyla japonica and Rana nigromaculata in Rice Fields of Japan

abstract: We examined feeding relationships between Hyla japonica and Rana nigromaculata in rice fields of central Japan, where they forage syntopically. Hyla japonica and recently metamorphosed Rana nigromaculata overlapped greatly in body size and took prey of similar size and type. However, H. japonica foraged in rice fields during its breeding season (May to July), whereas R. nigromaculata began metamor- phosis later in July. Therefore, they seldom coexisted and seemed to partition food resources temporally. By the following spring, R. nigromaculata froglets grew larger than adult H. japonica and used different food resources, suggesting food resources were partitioned by body size. These patterns of food partitioning, which were consistent in two study sites where the dominant species differed, may account for coexistence of H. japonica and R. nigromaculta throughout their wide distributions.


title: Optimizing the docker container usage based on load scheduling

abstract: In this project we introduce an energy-aware processing model used for balancing the load of docker container and job scaling using docker(1). The approach used is to create an energy optimal regime within which the containers operate. An important strategy for energy reduction is concentrating the load on the containers. When the load increases, then by api call a new container can be spawned which can the allocated with jobs to process. The container can be created automatically based upon threshold conditions. This ensures that the rest of the container is not over-loaded with jobs. When the load decreases that container can be killed to save energy. The Energy-Aware scaling algorithm is used which ensures that the largest possible number of containers operate within their respective operational domain.


title: Multiobjective optimization technique for demand side management with load balancing approach in smart grid

abstract: Power companies are unable to withstand the consumer power requirement due to growing population, industries and buildings. The use of automated electrical appliances have increased exponentially in day to day activity. To maintain a possible balance between the supply and demand the power companies are introducing the demand side management approach. As a result, consumers are adopted for load shifting or scheduling their loads into off-peak hours to reduce the electricity bill. When all the consumers are trying to run the scheduled electrical appliances at the same time then the usage of energy in the off peak hour curve is marginally high. However, service providers are in need of a load balancing mechanism to avoid over or under utilization of the power grid. In the existing works, threshold limit is applied for a home to maintain the balanced load and if the consumer exceeds it then the additional charges are applied in the bill. To overcome the above mentioned drawbacks there is a need to increase the power usage with minimum cost and reducing the waiting time. For this purpose, in this paper we implement multi-objective evolutionary algorithm, which results in the cost reduction for energy usage and minimize the waiting time for appliance execution. The result reveals that if the consumer exceeds the threshold limit, the scheduled running electrical appliances temporarily stops to maintain the energy usage under threshold level for cost benefit and resumes the stopped appliances later. Further, the proposed technique minimizes the overall electricity bill and waiting time for the execution of electrical appliances.


Digital Innovation: The Hackathon Phenomenon


title: Design of a bandpass filter using microstrip Hairpin resonators

abstract: The implementation of devices developed in microstrip technology is commonly found in radio frequency and microwave communications, where it has evolved with a variety of applications, among which fast response filters stand out. In this study, we present the design and behavior of a microstrip Hairpin bandpass filter for a frequency of 2.45 GHz, with a bandwidth greater than 100 MHz, and an insertion loss (S 21 ) higher than −3 dB. The filter was designed and modeled with the COMSOL® Multiphysics software using, the specific radio frequency module, where S 11 and S 21 parameters were simulated. This filter was made on an FR4 substrate, and its dispersion parameters were measured with a vector network analyzer (VNA), to compare its results with the one obtained in the multiphysics model. Finally, a bandpass filter was achieved for 2.45 GHz, with a bandwidth of 389 MHz, and an insertion loss (peak) of −2.1 dB.


title: Cooperative Regulation and Trading of Emissions Using Plug-in Hybrid Vehicles

abstract: We present a new approach to regulate traffic-related pollution in urban environments by utilizing hybrid vehicles. To do this, we orchestrate the way that each vehicle in a large fleet combines its two engines based on simple communication signals from a central infrastructure. Our approach can be viewed both as a control algorithm and as an optimization algorithm. The primary goal is to regulate emissions, and we discuss a number of control strategies to achieve this goal. Second, we want to allocate the available pollution budget in a fair way among the participating vehicles; again, we explore several different notions of fairness that can be achieved. The efficacy of our approach is exemplified both by the construction of a proof-of-concept vehicle and by extensive simulations, and is verified by mathematical analysis.


title: Anomaly Detection and Localization in Crowded Scenes

abstract: The detection and localization of anomalous behaviors in crowded scenes is considered, and a joint detector of temporal and spatial anomalies is proposed. The proposed detector is based on a video representation that accounts for both appearance and dynamics, using a set of mixture of dynamic textures models. These models are used to implement 1) a center-surround discriminant saliency detector that produces spatial saliency scores, and 2) a model of normal behavior that is learned from training data and produces temporal saliency scores. Spatial and temporal anomaly maps are then defined at multiple spatial scales, by considering the scores of these operators at progressively larger regions of support. The multiscale scores act as potentials of a conditional random field that guarantees global consistency of the anomaly judgments. A data set of densely crowded pedestrian walkways is introduced and used to evaluate the proposed anomaly detector. Experiments on this and other data sets show that the latter achieves state-of-the-art anomaly detection results.


title: Story-Driven Summarization for Egocentric Video

abstract: We present a video summarization approach that discovers the story of an egocentric video. Given a long input video, our method selects a short chain of video sub shots depicting the essential events. Inspired by work in text analysis that links news articles over time, we define a random-walk based metric of influence between sub shots that reflects how visual objects contribute to the progression of events. Using this influence metric, we define an objective for the optimal k-subs hot summary. Whereas traditional methods optimize a summary's diversity or representative ness, ours explicitly accounts for how one sub-event "leads to" another-which, critically, captures event connectivity beyond simple object co-occurrence. As a result, our summaries provide a better sense of story. We apply our approach to over 12 hours of daily activity video taken from 23 unique camera wearers, and systematically evaluate its quality compared to multiple baselines with 34 human subjects.


title: Analyzing Newspaper Crime Reports for Identification of Safe Transit Paths

abstract: In this paper, we propose a method to find the safest path between two locations, based on the geographical model of crime intensities. We consider the police records and news articles for finding crime density of different areas of the city. It is essential to consider news articles as there is a significant delay in updating police crime records. We address this problem by updating the crime intensities based on current news feeds. Based on the updated crime intensities, we identify the safest path. It is this real time updation of crime intensities which makes our model way better than the models that are presently in use. Our model would also inform the user of crime sprees in a particular area thereby ensuring that user avoids these crime hot spots.


title: Study of the highly underexpanded sonic jet.

abstract: A device for cutting glass which can cut a sheet of glass of indeterminate length has a central bridge member with a channel formed therein. It has one or two outwardly extending arms which may be perpendicular to the bridge member. A glass cutter wheel is interlockable with the central bridge member channel through a cutting support member. The bridge member has distance markings inscribed on it. The cutter support member is adapted to be engaged by a pointer member which will indicate glass cutter position in respect to the bridge member.


title: A model of head-related transfer functions based on principal components analysis and minimum-phase reconstruction.

abstract: Free-field to eardrum transfer functions (HRTFs) were measured from both ears of 10 subjects with sound sources at 265 different positions. A principal components analysis of the resulting 5300 HRTF magnitude functions revealed that the HRTFs can be modeled as a linear combination of five basic spectral shapes (basis functions), and that this representation accounts for approximately 90% of the variance in the original HRTF magnitude functions. HRTF phase was modeled by assuming that HRTFs are minimum-phase functions and that interaural phase differences can be approximated by a simple time delay. Subjects' judgments of the apparent directions of headphone-presented sounds that had been synthesized from the modeled HRTFs were nearly identical to their judgments of sounds synthesized from measured HRTFs. With fewer than five basis functions used in the model, a less faithful reconstruction of the HRTF was produced, and the frequency of large localization errors increased dramatically.


title: The Big and the Small: Challenges of Imaging the Brain's Circuits

abstract: The relation between the structure of the nervous system and its function is more poorly understood than the relation between structure and function in any other organ system. We explore why bridging the structure-function divide is uniquely difficult in the brain. These difficulties also explain the thrust behind the enormous amount of innovation centered on microscopy in neuroscience. We highlight some recent progress and the challenges that remain.


title: Collectively Embedding Multi-Relational Data for Predicting User Preferences

abstract: Matrix factorization has found incredible success and widespread application as a collaborative filtering based approach to recommendations. Unfortunately, incorporating additional sources of evidence, especially ones that are incomplete and noisy, is quite difficult to achieve in such models, however, is often crucial for obtaining further gains in accuracy. For example, additional information about businesses from reviews, categories, and attributes should be leveraged for predicting user preferences, even though this information is often inaccurate and partially-observed. Instead of creating customized methods that are specific to each type of evidences, in this paper we present a generic approach to factorization of relational data that collectively models all the relations in the database. By learning a set of embeddings that are shared across all the relations, the model is able to incorporate observed information from all the relations, while also predicting all the relations of interest. Our evaluation on multiple Amazon and Yelp datasets demonstrates effective utilization of additional information for held-out preference prediction, but further, we present accurate models even for the cold-starting businesses and products for which we do not observe any ratings or reviews. We also illustrate the capability of the model in imputing missing information and jointly visualizing words, categories, and attribute factors.


title: Credit Rating Analysis With Support Vector Machines and Neural Networks: A Market Comparative Study

abstract: Corporate credit rating analysis has attracted lots of research interests in the literature. Recent studies have shown that Artificial Intelligence (AI) methods achieved better performance than traditional statistical methods. This article introduces a relatively new machine learning technique, support vector machines (SVM), to the problem in attempt to provide a model with better explanatory power. We used backpropagation neural network (BNN) as a benchmark and obtained prediction accuracy around 80% for both BNN and SVM methods for the United States and Taiwan markets. However, only slight improvement of SVM was observed. Another direction of the research is to improve the interpretability of the AI-based models. We applied recent research results in neural network model interpretation and obtained relative importance of the input financial variables from the neural network models. Based on these results, we conducted a market comparative analysis on the differences of determining factors in the United States and Taiwan markets.


title: Comparison of critical current filaments in IGBT short circuit and during diode turn-off

abstract: Device simulations of two-dimensional structures with a huge lateral extension indicate that the combination of lateral current flow and a vertical reduction of space-charge region are necessary for the filament formation during the short-circuit operation of IGBTs and the turn-off of diodes. The shape of the electric-field distribution is introduced as new criterion to estimate the rising branch of the short-circuit destruction curve in the Ic-Vce SOA-diagram. The impact of the stray inductance Lstray and the gate resistor RG on the short-circuit destruction limit are experimentally investigated. The presented results allow a deeper understanding of the different electrical short-circuit failure mechanisms (turn-on, quasi-stationary state, turn-off) and their relation to one another.


title: Question Answering System : A Review On Question Analysis, Document Processing, And Answer Extraction Techniques

abstract: Question Answering System could automatically provide an answer to a question posed by human in natural languages. This system consists of question analysis, document processing, and answer extraction module. Question Analysis module has task to translate query into a form that can be processed by document processing module. Document processing is a technique for identifying candidate documents, containing answer relevant to the user query. Furthermore, answer extraction module receives the set of ::: passages from document processing module, then determine the best answers to user. Challenge to optimize Question Answering framework is to increase the performance of all modules in the framework. The performance of all modules that has not been optimized has led to the less accurate answer from question answering systems. Based on this issues, the objective of this study is to review the current state of question analysis, document processing, and answer extraction techniques. Result from this study reveals the potential research issues, namely morphology analysis, question classification, and term weighting ::: algorithm for question classification.


title: Sliced Wasserstein Kernels for Probability Distributions

abstract: Optimal transport distances, otherwise known as Wasserstein distances, have recently drawn ample attention in computer vision and machine learning as a powerful discrepancy measure for probability distributions. The recent developments on alternative formulations of the optimal transport have allowed for faster solutions to the problem and has revamped its practical applications in machine learning. In this paper, we exploit the widely used kernel methods and provide a family of provably positive definite kernels based on the Sliced Wasserstein distance and demonstrate the benefits of these kernels in a variety of learning tasks. Our work provides a new perspective on the application of optimal transport flavored distances through kernel methods in machine learning tasks.


title: Quality Metrics in optical modulation analysis: EVM and its relation to Q-factor, OSNR, and BER

abstract: The quality of optical signals is a very important parameter in optical communications. Several metrics are in common use, like optical signal-to-noise power ratio (OSNR), Q-factor, error vector magnitude (EVM) and bit error ratio (BER). A measured raw BER is not necessarily useful to predict the final BER after soft-decision forward error correction (FEC), if the statistics of the noise leading to errors is unknown. In this respect the EVM is superior, as it allows an estimation of the error statistics. We compare various metrics analytically, by simulation, and through experiments. We employ six quadrature amplitude modulation (QAM) formats at symbol rates of 20 GBd and 25 GBd. The signals were generated by a software-defined transmitter. We conclude that for optical channels with additive Gaussian noise the EVM metric is a reliable quality measure. For nondata-aided QAM reception, BER in the range 10−6…10−2 can be reliably estimated from measured EVM.


Artifact-Centric Process Mining ( preprint 1 )


title: Suitability of a Consensual Fuzzy Inference System to Evaluate Suppliers of Strategic Products

abstract: This paper designs a bidding and supplier evaluation model focused on strategic product procurement, and develops their respective evaluation knowledge bases. The model is built using the most relevant variables cited in the reviewed procurement literature and allows to compare two evaluation methods: a factor weighting method (WM) and a fuzzy inference system (FIS). By consulting an expert panel and using a two-tuples symbolic translation system, strong fuzzy partitions for all model variables are built. The method, based on central symmetry, permits to obtain the fuzzy label borders from their cores, which have been previously agreed among experts. The system also allows to agree the fuzzy rules to embed in the FIS. The results show the FIS method’s superiority as it allows to better manage the non-linear behavior and the uncertainty inherent to the supplier evaluation process.


title: Cyber security in healthcare networks

abstract: The healthcare network can be seen as a cyber-physical system where interconnected physical systems uses cyber technologies to interact and function. Because of the increasing complexity of the healthcare system new vulnerabilities appear in terms of security. In this article, we present the challenges of cyber security in healthcare and cyber-attacks classification based on their targeted security goal.


title: Brain Tumor Detection using Curvelet Transform and Support Vector Machine

abstract: The prevalent cause of death in human being is brain tumor. A brain tumor is a mass or growth of anomalous cells in brain. The detection of brain tumor is difficult task. Image processing provides relevant techniques for efficient detection. In the proposed technique, first the features of MRI (Magnetic Resonance Imaging) images are extracted with curvelet transform, and then these features are applied to the support vector machine for successful identification. This proposed methodology gives efficient results.


title: Annotated Bibliographical Reference Corpora in Digital Humanities

abstract: In this paper, we present new bibliographical reference corpora in digital humanities (DH) that have been developed under a research project, Robust and Language Independent Machine Learning Approaches for Automatic Annotation of Bibliographical References in DH Books supported by Google Digital Humanities Research Awards. The main target is the bibliographical references in the articles of Revues.org site, an oldest French online journal platform in DH field. Since the final object is to provide automatic links between related references and articles, the automatic recognition of reference fields like author and title is essential. These fields are therefore manually annotated using a set of carefully defined tags. After providing a full description of three corpora, which are separately constructed according to the difficulty level of annotation, we briefly introduce our experimental results on the first two corpora. A popular machine learning technique, Conditional Random Field (CRF) is used to build a model, which automatically annotates the fields of new references. In the experiments, we first establish a standard for defining features and labels adapted to our DH reference data. Then we show our new methodology against less structured references gives a meaningful result.


A Survey on Mathematical models of Swarm Robotics Purushotham


title: Artificial bee colony algorithm solution for optimal reactive power flow

abstract: Artificial bee colony (ABC) algorithm is an optimization algorithm based on the intelligent foraging behavior of honeybee swarm. Optimal reactive power flow (ORPF) based on ABC algorithm to minimize active power loss in power systems is studied in this paper. The advantage of ABC algorithm is that it does not require these parameters, because it is very difficult to determine external parameters such as cross over rate and mutation rate as in case of genetic algorithm and differential evolution. The other advantage is that global search ability of the algorithm is implemented by introducing a neighborhood source production mechanism which is similar to mutation process. Because of these features, ABC algorithm attracts much attention in recent years and has been used successfully in many areas. ORPF problem is one of these areas. In this paper, proposed algorithm is tested on both standard IEEE 30-bus test system and IEEE 118-bus test system. To show the effectiveness of proposed algorithms, the obtained results are compared with different approaches as available in the literature.


title: New Methods of Analysis of Narrative and Semantics in Support of Interactivity

abstract: Abstract Our work has focused on support for film or television scriptwriting. Since this involves potentially varied story-lines, we note the implicit or latent support for interactivity. Furthermore the film, television, games, publishing and other sectors are converging, so that cross-over and re-use of one form of product in another of these sectors is ever more common. Technically our work has been largely based on mathematical algorithms for data clustering and display. Operationally, we also discuss how our algorithms can support collective, distributed problem-solving.


- LEVEL ACCURACY WITH 50 X FEWER PARAMETERS AND < 0 . 5 MB MODEL SIZE


title: What I experienced yesterday is who I am today: Relationship of work motivations and behaviors to within-individual variation in the five-factor model of personality.

abstract: Historically, organizational and personality psychologists have ignored within-individual variation in personality across situations or have treated it as measurement error. However, we conducted a 10-day experience sampling study consistent with whole trait theory (Fleeson, 2012), which conceptualizes personality as a system of stable tendencies and patterns of intraindividual variation along the dimensions of the Big Five personality traits (Costa & McCrae, 1992). The study examined whether (a) internal events (i.e., motivation), performance episodes, and interpersonal experiences at work predict deviations from central tendencies in trait-relevant behavior, affect, and cognition (i.e., state personality), and (b) there are individual differences in responsiveness to work experiences. Results revealed that personality at work exhibited both stability and variation within individuals. Trait measures predicted average levels of trait manifestation in daily behavior at work, whereas daily work experiences (i.e., organizational citizenship, interpersonal conflict, and motivation) predicted deviations from baseline tendencies. Additionally, correlations of neuroticism with standard deviations in the daily personality variables suggest that, although work experiences influence state personality, people higher in neuroticism exhibit higher levels of intraindividual variation in personality than do those who are more emotionally stable.


title: Coupled Two-Way Clustering Analysis of Gene Microarray Data

abstract: We present a coupled two-way clustering approach to gene microarray data analysis. The main idea is to identify subsets of the genes and samples, such that when one of these is used to cluster the other, stable and significant partitions emerge. The search for such subsets is a computationally complex task. We present an algorithm, based on iterative clustering, that performs such a search. This analysis is especially suitable for gene microarray data, where the contributions of a variety of biological mechanisms to the gene expression levels are entangled in a large body of experimental data. The method was applied to two gene microarray data sets, on colon cancer and leukemia. By identifying relevant subsets of the data and focusing on them we were able to discover partitions and correlations that were masked and hidden when the full dataset was used in the analysis. Some of these partitions have clear biological interpretation; others can serve to identify possible directions for future research.


title: Sentiment analysis in twitter using machine learning techniques

abstract: Sentiment analysis deals with identifying and classifying opinions or sentiments expressed in source text. Social media is generating a vast amount of sentiment rich data in the form of tweets, status updates, blog posts etc. Sentiment analysis of this user generated data is very useful in knowing the opinion of the crowd. Twitter sentiment analysis is difficult compared to general sentiment analysis due to the presence of slang words and misspellings. The maximum limit of characters that are allowed in Twitter is 140. Knowledge base approach and Machine learning approach are the two strategies used for analyzing sentiments from the text. In this paper, we try to analyze the twitter posts about electronic products like mobiles, laptops etc using Machine Learning approach. By doing sentiment analysis in a specific domain, it is possible to identify the effect of domain information in sentiment classification. We present a new feature vector for classifying the tweets as positive, negative and extract peoples' opinion about products.


title: Hiding Traces of Resampling in Digital Images

abstract: Resampling detection has become a standard tool for forensic analyses of digital images. This paper presents new variants of image transformation operations which are undetectable by resampling detectors based on periodic variations in the residual signal of local linear predictors in the spatial domain. The effectiveness of the proposed method is supported with evidence from experiments on a large image database for various parameter settings. We benchmark detectability as well as the resulting image quality against conventional linear and bicubic interpolation and interpolation with a sinc kernel. These early findings on ldquocounter-forensicrdquo techniques put into question the reliability of known forensic tools against smart counterfeiters in general, and might serve as benchmarks and motivation for the development of much improved forensic techniques.


title: Interference Mitigation in Cognitive-Radio-Based Femtocells

abstract: Femtocell architecture involves the use of two separate layers—the macrocell and femtocell layers. In this architecture, the former is the conventional cellular network, whereas the latter incorporates a range of shorter range cells. Femtocells are designed to coexist alongside macrocells, providing spatial frequency reuse, higher spectrum efficiency, and cover areas where macrocells cannot. Femtocells positioned in the macrocell considerably improve the indoor coverage and provide better user experience. However, interference between the two layers is imminent; therefore, ways to manage it must be employed to efficiently avoid problems such as coverage holes in the macrocells. Essential limits of capacity and attainable data rates also mainly depend on the interference faced by a femtocell network. Recently, cognitive radio (CR), which has the ability to sense its environment and accordingly alter its characteristics, e.g., transmission parameters, has been merged with femtocells to exploit the capabilities of the former in the latter. CR-enabled femtocells in a two-tier network can sense the environment and opportunistically allocate both licensed and unlicensed frequency bands to user equipments to avoid interference. This paper examines interference mitigation in femtocells using CR and provides comprehensive survey of different CR-enabled interference mitigation schemes. Presented schemes such as power control, spectrum access, antenna, and joint schemes are classified before they are compared for pros and cons. Finally, tradeoffs and cost of using CR in femtocells are highlighted with some insight into future research issues and challenges.


title: Local Influence in Bayesian Elliptically Contoured-Ordinal Model for Mixed Data

abstract: This paper develops a new class of joint modeling of mixed correlated ordinal and continuous responses with elliptically contoured errors. This joint model includes the latent variable approach of using an elliptically contoured distribution for mixed ordinal and continuous responses. A Markov Chain Monte Carlo sampling algorithm is described for estimating the posterior distribution of the parameters. For sensitivity analysis to investigate the perturbation from associate responses, it is demonstrated how one can use some elements of covariance structure. Influence of small perturbation of these elements on the posterior normal curvature is also studied. To illustrate the application of such modeling the data (medical) is analyzed.


title: Generative Bridging Network in Neural Sequence Prediction

abstract: Maximum Likelihood Estimation (MLE) suffers from data sparsity problem in sequence prediction tasks where training resource is rare. In order to alleviate this problem, in this paper, we propose a novel generative bridging network (GBN) to train sequence prediction models, which contains a generator and a bridge. Unlike MLE directly maximizing the likelihood of the ground truth, the bridge extends the point-wise ground truth to a bridge distribution (containing inexhaustible examples), and the generator is trained to minimize their KL-divergence. In order to guide the training of generator with additional signals, the bridge distribution can be set or trained to possess specific properties, by using different constraints. More specifically, to increase output diversity, enhance language smoothness and relieve learning burden, three different regularization constraints are introduced to construct bridge distributions. By combining these bridges with a sequence generator, three independent GBNs are proposed, namely uniform GBN, language-model GBN and coaching GBN. Experiment conducted on two recognized sequence prediction tasks (machine translation and abstractive text summarization) shows that our proposed GBNs can yield significant improvements over strong baseline systems. Furthermore, by analyzing samples drawn from bridge distributions, expected influences on the sequence model training are verified.


title: Data transformation and semantic log purging for process mining

abstract: Existing process mining approaches are able to tolerate a certain degree of noise in the process log. However, processes that contain infrequent paths, multiple (nested) parallel branches, or have been changed in an ad-hoc manner, still pose major challenges. For such cases, process mining typically returns "spaghetti-models", that are hardly usable even as a starting point for process (re-)design. In this paper, we address these challenges by introducing data transformation and pre-processing steps that improve and ensure the quality of mined models for existing process mining approaches. We propose the concept of semantic log purging, the cleaning of logs based on domain specific constraints utilizing semantic knowledge which typically complements processes. Furthermore we demonstrate the feasibility and effectiveness of the approach based on a case study in the higher education domain. We think that semantic log purging will enable process mining to yield better results, thus giving process (re-)designers a valuable tool.


title: Reconstruction of Single Image from Multiple Blurry Measured Images

abstract: The problem of blind image recovery using multiple blurry images of the same scene is addressed in this paper. To perform blind deconvolution, which is also called blind image recovery, the blur kernel and image are represented by groups of sparse domains to exploit the local and nonlocal information such that a novel joint deblurring approach is conceived. In the proposed approach, the group sparse regularization on both the blur kernel and image is provided, where the sparse solution is promoted by -norm. In addition, the reweighted data fidelity is developed to further improve the recovery performance, where the weight is determined by the estimation error. Moreover, to reduce the undesirable noise effects in group sparse representation, distance measures are studied in the block matching process to find similar patches. In such a joint deblurring approach, a more sophisticated two-step interactive process is needed in which each step is solved by means of the well-known split Bregman iteration algorithm, which is generally used to efficiently solve the proposed joint deblurring problem. Finally, numerical studies, including synthetic and real images, demonstrate that the performance of this joint estimation algorithm is superior to the previous state-of-the-art algorithms in terms of both objective and subjective evaluation standards. The recovery results of real captured images using unmanned aerial vehicles are also provided to further validate the effectiveness of the proposed method.


title: The Role of Context Types and Dimensionality in Learning Word Embeddings

abstract: We provide the first extensive evaluation of how using different types of context to learn skip-gram word embeddings affects performance on a wide range of intrinsic and extrinsic NLP tasks. Our results suggest that while intrinsic tasks tend to exhibit a clear preference to particular types of contexts and higher dimensionality, more careful tuning is required for finding the optimal settings for most of the extrinsic tasks that we considered. Furthermore, for these extrinsic tasks, we find that once the benefit from increasing the embedding dimensionality is mostly exhausted, simple concatenation of word embeddings, learned with different context types, can yield further performance gains. As an additional contribution, we propose a new variant of the skip-gram model that learns word embeddings from weighted contexts of substitute words.


459 Drugs as inhibitors of drug metabolism


title: Software Requirements: Objects, Functions and States

abstract: 1. Introduction. 2. Problem Analysis. 3. The Software Requirements Specification. 4. Specifying Behavioral Requirements. 5. Specifying Nonbehavioral Requirements. 6. Requirements Prototyping. 7. Some Final Thoughts. Glossary. Annotated Bibliography.


title: Tunable Terahertz Filter and Modulator Based on Electrostatic MEMS Reconfigurable SRR Array

abstract: This paper demonstrates a tunable terahertz (THz) filter and a modulator based on an electrostatic micro electro mechanical systems (MEMS) reconfigurable split ring resonator (SRR) array. The device uses tunable metamaterial concept, in which an RF-MEMS (radio frequency-MEMS) capacitor is embedded in each SRR unit cell to tune the electromagnetic resonant frequency. By utilizing THz time domain spectroscopy (THz-TDS) measurement and through the voltage actuation, we have controlled the THz transmission of the metamaterial device, and thus demonstrated a tunable filter. Since the low loss material of fused quartz is used as the substrate, the device shows a high contrast ratio THz switch at a given frequency by controlling the capacitance of the RF-MEMS capacitor. The dynamic performance of the switch, i.e., the modulation ability, is also characterized by THz-TDS. The measurement results show up to 2 kHz of the modulation speed with the TDS operated. The device is developed by the surface micromachining process with three photolithography steps.


title: Using TCAD Modeling to Compare Heavy-Ion and Laser-Induced Single Event Transients in SiGe HBTs

abstract: Silicon-germanium heterojunction bipolar transistor (SiGe HBT) models are used in technology computer aided design (TCAD) to investigate single event transients induced by heavy-ion broadbeam and pulsed-laser two-photon absorption sources. A comparison between transient waveforms is provided, the proper extraction of heavy-ion broadbeam transients is discussed (along with circuit implications), and basic laser strike profiles are implemented in TCAD to provide insight into future design practices for simulation software to be used to describe laser-induced upsets in terms of an effective linear energy transfer (LET).


title: High Quality Monolithic 8-Shaped Inductors for Silicon RF IC Design

abstract: In this paper, RF characterization results of 8-shaped inductors on high resistivity silicon (HRS) substrate are presented. Electrical performances reported to area occupation of 8-shaped inductors are compared to those of classical octagonal/rectangular inductors. It is observed that 8-shaped inductors lead to inductance values 60% higher than what could be expected from classical octagonal/rectangular devices. A predictive electrical model is proposed based on previous investigations. Correlations between measurements and simulation data are found satisfactory.


Author ' s personal copy Modeling viscoelastic dielectrics


title: Recurrent Generative Adversarial Networks for Proximal Learning and Automated Compressive Image Recovery

abstract: Recovering images from undersampled linear measurements typically leads to an ill-posed linear inverse problem, that asks for proper statistical priors. Building effective priors is however challenged by the low train and test overhead dictated by real-time tasks; and the need for retrieving visually "plausible" and physically "feasible" images with minimal hallucination. To cope with these challenges, we design a cascaded network architecture that unrolls the proximal gradient iterations by permeating benefits from generative residual networks (ResNet) to modeling the proximal operator. A mixture of pixel-wise and perceptual costs is then deployed to train proximals. The overall architecture resembles back-and-forth projection onto the intersection of feasible and plausible images. Extensive computational experiments are examined for a global task of reconstructing MR images of pediatric patients, and a more local task of superresolving CelebA faces, that are insightful to design efficient architectures. Our observations indicate that for MRI reconstruction, a recurrent ResNet with a single residual block effectively learns the proximal. This simple architecture appears to significantly outperform the alternative deep ResNet architecture by 2dB SNR, and the conventional compressed-sensing MRI by 4dB SNR with 100x faster inference. For image superresolution, our preliminary results indicate that modeling the denoising proximal demands deep ResNets.


title: Converter Topologies and Power Semiconductors for Industrial Medium Voltage Converters

abstract: Today there are numerous different converter topologies and power semiconductor devices used in medium- voltage drive systems. This paper provides a general overview of the common converter topologies available on the market and their corresponding major characteristics. The different topologies are compared and evaluated with respect to their semiconductor effort. Due to the available power semiconductor devices with maximum blocking voltages of 6.5 kV, the drive market with power ratings up to 25 MW is dominated by voltage source inverters in IGBT as well as in IGCT technology. For higher power demands and special applications, thyristor converters are still frequently used.