title: Snap ML: A Hierarchical Framework for Machine Learning

abstract: We describe a new software framework for fast training of generalized linear models. The framework, named Snap Machine Learning (Snap ML), combines recent advances in machine learning systems and algorithms in a nested manner to reflect the hierarchical architecture of modern computing systems. We prove theoretically that such a hierarchical system can accelerate training in distributed environments where intra-node communication is cheaper than inter-node communication. Additionally, we provide a review of the implementation of Snap ML in terms of GPU acceleration, pipelining, communication patterns and software architecture, highlighting aspects that were critical for achieving high performance. We evaluate the performance of Snap ML in both single-node and multi-node environments, quantifying the benefit of the hierarchical scheme and the data streaming functionality, and comparing with other widely-used machine learning software frameworks. Finally, we present a logistic regression benchmark on the Criteo Terabyte Click Logs dataset and show that Snap ML achieves the same test loss an order of magnitude faster than any of the previously reported results, including those obtained using TensorFlow and scikit-learn.


Area V5—a microcosm of the visual brain


title: Learning iterative image reconstruction

abstract: Successful image reconstruction requires the recognition of a scene and the generation of a clean image of that scene. In this chapter, I show how to use Neural Abstraction Pyramid networks for both analysis and synthesis of images. The networks have a hierarchical architecture which represents images in multiple scales with different degrees of abstraction. The mapping between these representations is mediated by a local recurrent connection structure.


title: A Vision Approach for Expiry Date Recognition using Stretched Gabor Features

abstract: Product"expiry date represent important information for products consumption. They must contain clear information in the label. The expiry date information stamped on the cover of product faced some challenges due to their writing in pencil and distorted characters. In this paper, an automated vision approach for recognizing expiry date numerals of industrial product is presented. The system consists of four stages namely, numeral string pre"proc essing, numerals string segmentation, features extraction and numeral recognition. In pre"processing module, we convert the im age to binary image based on threshold. A vertical projection process is adopted to isolate numerals, in the segmentation module. In the features extraction module, Fourier Magnitude (FM), Local Energy (LE) and Complex Moments (CM) derived from Stretched Gabor (S"Gabor) filters outputs are extracted at various filter orientations. Also, the mean and the variance of each feature map are extracted. The recognition process is achieved by classifying the extracted features, which represent the numeral image, with trained Multilayer Neural Network (MNN) using k"fol d cross validation procedure. Through experiments, we demonstrate the richness of the S"Gabor features of information is highlighted. Consequently, the set of features shows its usefulness for practical usage. Keyword : Computer vision, FM, CM, LE, numeral recognition, NN, S"Gabor filters.


title: Rapid casting of patterned vascular networks for perfusable engineered 3D tissues

abstract: In the absence of perfusable vascular networks, three-dimensional (3D) engineered tissues densely populated with cells quickly develop a necrotic core [1]. Yet the lack of a general approach to rapidly construct such networks remains a major challenge for 3D tissue culture [2–4]. Here, we 3D printed rigid filament networks of carbohydrate glass, and used them as a cytocompatible sacrificial template in engineered tissues containing living cells to generate cylindrical networks which could be lined with endothelial cells and perfused with blood under high-pressure pulsatile flow. Because this simple vascular casting approach allows independent control of network geometry, endothelialization, and extravascular tissue, it is compatible with a wide variety of cell types, synthetic and natural extracellular matrices (ECMs), and crosslinking strategies. We also demonstrated that the perfused vascular channels sustained the metabolic function of primary rat hepatocytes in engineered tissue constructs that otherwise exhibited suppressed function in their core.


title: Finding Academic Experts on a MultiSensor Approach using Shannon's Entropy

abstract: Expert finding is an information retrieval task concerned with the search for the most knowledgeable people, in some topic, with basis on documents describing peoples activities. The task involves taking a user query as input and returning a list of people sorted by their level of expertise regarding the user query. This paper introduces a novel approach for combining multiple estimators of expertise based on a multisensor data fusion framework together with the Dempster–Shafer theory of evidence and Shannon’s entropy. More specifically, we defined three sensors which detect heterogeneous information derived from the textual contents, from the graph structure of the citation patterns for the community of experts, and from profile information about the academic experts. Given the evidences collected, each sensor may define different candidates as experts and consequently do not agree in a final ranking decision. To deal with these conflicts, we applied the Dempster–Shafer theory of evidence combined with Shannon’s Entropy formula to fuse this information and come up with a more accurate and reliable final ranking list. Experiments made over two datasets of academic publications from the Computer Science domain attest for the adequacy of the proposed approach over the traditional state of the art approaches. We also made experiments against representative supervised state of the art algorithms. Results revealed that the proposed method achieved a similar performance when compared to these supervised techniques, confirming the capabilities of the proposed framework.


title: Reducing the human overhead in text categorization

abstract: Many applications in text processing require significant human effort for either labeling large document collections (when learning statistical models) or extrapolating rules from them (when using knowledge engineering). In this work, we describe away to reduce this effort, while retaining the methods' accuracy, by constructing a hybrid classifier that utilizes human reasoning over automatically discovered text patterns to complement machine learning. Using a standard sentiment-classification dataset and real customer feedback data, we demonstrate that the resulting technique results in significant reduction of the human effort required to obtain a given classification accuracy. Moreover, the hybrid text classifier also results in a significant boost in accuracy over machine-learning based classifiers when a comparable amount of labeled data is used.


title: Accident Detection System Using Dynamic Fuzzy Logic Control

abstract: Development of Traffic signal control systems has become one of the most important topics in this era. Traffic light controllers need to be developed continuously to solve the traffic problems. This paper discussed the proposed accident detection system and demonstrated how the system works from the beginning of the first flag "decrease of cross ratio" and through the member functions with the status of the accident. The focus of this paper is to discuss the Accident Detection System of the proposed dynamic fuzzy control system, which depended on fuzzy logic and its components.


title: The MoveLab: Developing Congruence Between Students' Self-Concepts and Computing

abstract: The MoveLab was an educational research intervention centering on a community of African American and Hispanic girls as they began to transform their self-concept in relation to computing and dance while creating technology enhanced dance performances. Students within underrepresented populations in computing often do not perceive the identity of a computer scientist as aligning with their interests or value system, leading to rejection of opportunities to participate within the discipline. To engage diverse populations in computing, we need to better understand how to support students in navigating conflicts between identities with computing and their personal interest and values. Using the construct of self-concept, we observed students in the workshop creating both congruence and dissension between their self-concept and computing. We found that creating multiple roles for participation, fostering a socially supportive community, and integrating student values within the curriculum led to students forming congruence between their self-concept and the disciplines of computing and dance.


title: SKOSYN: Simple Knowledge Organization System for synonym search on content management system

abstract: The development of Content Management System (CMS) technology undoubtedly eases website content organization. Creative writing is a common challenge in producing written content. One solution is to use synonyms, which conventionally can be found in a thesaurus. In this research, Simple Knowledge Organization System (SKOS) data model, which is suitable for organizing knowledge such as thesauri, is implemented as a plugin for searching synonyms in Bahasa Indonesia. This plugin will complement the default text editor on CMS to edit the written content in a convenient way. This plugin also provides a feature for getting definitions using the XML web service from Kateglo, so that users can understand exactly the meaning of synonyms they use. Based on experiments done in this research, SKOS data can be loaded into triplestore by ARC2 in shorter time than the regular MySQL database which uses integrity constraints. Moreover, the experiments have also shown that time stability for getting synonyms from triplestore using ARC2 can be more maintained than obtaining synonyms from XML document.


title: The Intrapsychics of Gender: A Model of Self-Socialization.

abstract: This article outlines a model of the structure and the dynamics of gender cognition in childhood. The model incorporates 3 hypotheses featured in different contemporary theories of childhood gender cognition and unites them under a single theoretical framework. Adapted from Greenwald et al. (2002), the model distinguishes three constructs: gender identity, gender stereotypes, and attribute self-perceptions. The model specifies 3 causal processes among the constructs: Gender identity and stereotypes interactively influence attribute self-perceptions (stereotype emulation hypothesis); gender identity and attribute self-perceptions interactively influence gender stereotypes (stereotype construction hypothesis) ; and gender stereotypes and attribute self-perceptions interactively influence identity (identity construction hypothesis). The model resolves nagging ambiguities in terminology, organizes diverse hypotheses and empirical findings under a unifying conceptual umbrella, and stimulates many new research directions.


title: Acoustic echo suppression based on speech presence probability

abstract: This paper proposes a novel acoustic echo suppression (AES) algorithm based on speech presence probability in a frequency domain. Double talk detection algorithm based on two cross-correlation coefficients modeled by Beta distribution controls the update of echo path response to improve the quality of near-end speech. The near-end speech presence probability combined with the Wiener gain function is used to reduce the residual echo. The performance of the proposed algorithm is evaluated by objective tests. High echo-return-loss enhancement and perceptual evaluation of speech quality (PESQ) scores are obtained by comparing with the conventional AES method.


title: Coordinated Adaptive Cruise Control System With Lane-Change Assistance

abstract: To address the problem caused by a conventional adaptive cruise control (ACC) system, which hinders drivers from changing lanes, in this study we propose a novel coordinated ACC system with a lane-change assistance function, which enables dual-target tracking, safe lane change, and longitudinal ride comfort. We first analyze lane-change risk by calculating minimum safety spacing between the host vehicle and surrounding vehicles and then develop a coordinated control algorithm using model predictive control theory. Tracking performance is designed on the basis of tracking errors of the host car and two leading vehicles, safety performance is realized by considering the safe distance between the host car and surrounding vehicles, and ride comfort performance is realized by limiting the vehicle's longitudinal acceleration. Driver-in-the-loop tests performed on a driving simulator confirm that the proposed ACC system can overcome the disadvantages of conventional ACC and achieves multiobjective coordination in the lane-change process.


title: Neural Turing Machines

abstract: We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-to-end, allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples.


title: Using a packet manipulation tool for security analysis of industrial network protocols

abstract: Scapy is a free and open source packet manipulation environment written in Python language. In this paper we present a Modbus extension to Scapy, and show how this environment can be used to build tools for security analysis of industrial network protocols. Our implementation can be extended to other industrial network protocols and can help security analysts to understand how these protocols work under attacks or adverse conditions.


title: Models of Grid Cell Spatial Firing Published 2005–2011

abstract: Since the discovery of grid cells in rat entorhinal cortex, many models of their hexagonally-arrayed spatial firing fields have been suggested. We review the models and organize them according to the mechanisms they use to encode position, update the positional code, read it out in the spatial grid pattern, and learn any patterned synaptic connections needed. We mention biological implementations of the models, but focus on on the models on Marr's algorithmic level, where they are not things to individually prove or disprove, but rather are a valuable collection of metaphors of the grid cell system for guiding research that are all likely true to some degree, with each simply emphasizing different aspects of the system. For the convenience of interested researchers, MATLAB implementations of the discussed grid cell models are provided at ModelDB accession 144006 or http://people.bu.edu/zilli/gridmodels.html.


title: Beyond "local", "categories" and "friends": clustering foursquare users with latent "topics"

abstract: In this work, we use foursquare check-ins to cluster users via topic modeling, a technique commonly used to classify text documents according to latent "themes". Here, however, the latent variables which group users can be thought of not as themes but rather as factors which drive check in behaviors, allowing for a qualitative understanding of influences on user check ins. Our model is agnostic of geo-spatial location, time, users' friends on social networking sites and the venue categories-we treat the existence of and intricate interactions between these factors as being latent, allowing them to emerge entirely from the data. We instantiate our model on data from New York and the San Francisco Bay Area and find evidence that the model is able to identify groups of people which are of different types (e.g. tourists), communities (e.g. users tightly clustered in space) and interests (e.g. people who enjoy athletics).


title: New Approaches to Optical Music Recognition

abstract: We present the beginnings of a new system for optical music recognition (OMR), aimed toward the score images of the International Music Score Library Project (IMSLP). Our system focuses on measures as the basic unit of recognition. We identify candidate composite symbols (chords and beamed groups) using grammatically-formulated top-down model-based methods, while employing template matching to find isolated rigid symbols. We reconcile these overlapping symbols by seeking non-overlapping variants of the composite symbols that best account for the pixel data. We present results on a representative score from the IMSLP.


title: Lies in Conversation: An Examination of Deception Using Automated Linguistic Analysis

abstract: The present study investigated changes in both the sender’s and the receiver’s linguistic style across truthful and deceptive dyadic communication. A computer-based analysis of 242 transcripts revealed that senders used more words overall, increased references to others, and used more sense-based descriptions (e.g., seeing, touching) when lying as compared to telling the truth. Receivers naive to the deception manipulation produced more words and sense terms, and asked more questions with shorter sentences when they were being lied to than when they were being told the truth. These findings are discussed in terms of their implications for linguistic style matching.


title: High Efficiency 5W/10W 32 - 38GHz Power Amplifier MMICs Utilizing Advanced 0.15µm GaN HEMT Technology

abstract: This paper describes the design and measured performance of two high efficiency Ka-band 32 - 38 GHz power amplifier MMICs fabricated with an advanced 0.15μm Gallium Nitride (GaN) HEMT technology process. The process features a 50μm thin Silicon Carbide (SiC) substrate and compact transistor layouts with individual source via (ISV) grounding. The designs utilize an optimum transistor arrangement with both single-ended and balanced approaches. The 2-stage single-ended power amplifier MMIC demonstrates 4.5 - 5.2 W of output power with 25 - 34% PAE over 32 - 38 GHz band. For the second MMIC, two 3-stage power amplifiers are combined to achieve 9.0 - 11.2 W output power with 30 - 35% PAE over the same frequency range. A benchmark of 1.45W/mm2 FOM (output power to die area ratio) with high efficiency is reported at this frequency band.


title: Proof Support for Common Logic

abstract: We present an extension of the Heterogeneous Tool Set HETS that enables proof support for Common Logic. This is achieved via logic translations that relate Common Logic and some of its sublogics to already supported logics and automated theorem proving systems. We thus provide the first full theorem proving support for Common Logic, including the possibility of verifying meta-theoretical relationships between Common Logic theories.


title: Web document clustering: a feasibility demonstration

abstract: Users of Web search engines are often forced to sift through the long ordered list of document "snippets" returned by the engines. The IR community has explored document clustering as an alternative method of organizing retrieval results, but clustering has yet to be deployed on the major search engines. The paper articulates the unique requirements of Web document clustering and reports on the first evaluation of clustering methods in this domain. A key requirement is that the methods create their clusters based on the short snippets returned by Web search engines. Surprisingly, we find that clusters based on snippets are almost as good as clusters created using the full text of Web documents. To satisfy the stringent requirements of the Web domain, we introduce an incremental, linear time (in the document collection size) algorithm called Suffix Tree Clustering (STC). which creates clusters based on phrases shared between documents. We show that STC is faster than standard clustering methods in this domain, and argue that Web document clustering via STC is both feasible and potentially beneficial.


title: Blockchain Design for Trusted Decentralized IoT Networks

abstract: The use of Blockchain in the Internet of Things (IoT) promises to provide an avenue of decentralized, fault-resistant management and data immutability. The wide range of research on blockchain, focused on how to improve upon its drawbacks of latency and resource consumption, demonstrates that the blockchain design has flexibility. Almost every aspect of blockchain can be tailored to fit the requirements of a desired application. However, the wide range of options in configuring blockchains presents challenges in the adoption of blockchains to secure IoT. In this paper, we present the opportunities and challenges of implementing blockchains and present a use case of integrating blockchain into an IoT framework for securing sensor data acquisition.


title: MouseLight: bimanual interactions on digital paper using a pen and a spatially-aware mobile projector

abstract: MouseLight is a spatially-aware standalone mobile projector with the form factor of a mouse that can be used in combination with digital pens on paper. By interacting with the projector and the pen bimanually, users can visualize and modify the virtually augmented contents on top of the paper, and seamlessly transition between virtual and physical information. We present a high fidelity hardware prototype of the system and demonstrate a set of novel interactions specifically tailored to the unique properties of MouseLight. MouseLight differentiates itself from related systems such as PenLight in two aspects. First, MouseLight presents a rich set of bimanual interactions inspired by the ToolGlass interaction metaphor, but applied to physical paper. Secondly, our system explores novel displaced interactions, that take advantage of the independent input and output that is spatially aware of the underneath paper. These properties enable users to issue remote commands such as copy and paste or search. We also report on a preliminary evaluation of the system which produced encouraging observations and feedback.


title: Internal actuation of underwater robots using Control Moment Gyros

abstract: A novel control scheme based on internal actuation using Control Moment Gyros (CMGs) is proposed as the basis of an unrestricted attitude control system suitable for application to Autonomous Underwater Vehicles (AUVs). The coupled dynamic equations of a CMG actuated underwater robot are derived, taking into account interactions with the fluid environment. A nonlinear feedback control law is developed based on energy considerations of the coupled system. Singularities, redundancy and null motion are discussed in the context of CMGs and a mathematical escapability condition is developed based on the differential geometry of null motion. A geometric study of a CMG pyramid's singularities forms the basis of a global steering law that guarantees real-time singularity avoidance whilst maintaining exactness in some constrained workspace. The practical design considerations of a CMG pyramid developed for a laboratory scale underwater robot are discussed. Finally the results of practical experiments using the CMG pyramid are presented to demonstrate the steering and control performance of the system.


title: Streamline Variability Plots for Characterizing the Uncertainty in Vector Field Ensembles

abstract: We present a new method to visualize from an ensemble of flow fields the statistical properties of streamlines passing through a selected location. We use principal component analysis to transform the set of streamlines into a low-dimensional Euclidean space. In this space the streamlines are clustered into major trends, and each cluster is in turn approximated by a multivariate Gaussian distribution. This yields a probabilistic mixture model for the streamline distribution, from which confidence regions can be derived in which the streamlines are most likely to reside. This is achieved by transforming the Gaussian random distributions from the low-dimensional Euclidean space into a streamline distribution that follows the statistical model, and by visualizing confidence regions in this distribution via iso-contours. We further make use of the principal component representation to introduce a new concept of streamline-median, based on existing median concepts in multidimensional Euclidean spaces. We demonstrate the potential of our method in a number of real-world examples, and we compare our results to alternative clustering approaches for particle trajectories as well as curve boxplots.


title: Design and control of underactuated tendon-driven mechanisms

abstract: Many robotic hands or prosthetic hands have been developed in the last several decades, and many use tendon-driven mechanisms for their transmissions. Robotic hands are now built with underactuated mechanisms, which have fewer actuators than degrees of freedom, to reduce mechanical complexity or to realize a biomimetic motion such as flexion of an index finger. The design is heuristic and it is useful to develop design methods for the underactuated mechanisms. This paper classifies mechanisms driven by tendons into three classes, and proposes a design method for them. The two classes are related to underactuated tendon-driven mechanisms, and these have been used without distinction so far. An index finger robot, which has four active tendons and two passive tendons, is developed and controlled with the proposed method.


title: A Comparison of Deep Learning Architectures for Semantic Mapping of Very High Resolution Images

abstract: Semantic mapping of land cover is a key, but challenging, problem in remote sensing. Recent advances in deep learning, especially deep convolutional neural networks (CNNs), have shown outstanding performance in this task. In order to develop refined deep learning pipeline for meeting the rising need for accurate semantic mapping in remote sensing images, this paper study and compare a number of advanced deep learning segmentation architectures, which have obtained state-of-the-art results on computer vision contests like the Pascal VOC. To further analyze and compare the effectiveness of some elaborate layers and underlying structures introduced by these architectures, we evaluate them by re-implementing, train and test them on ISPRS Potsdam dataset. Our results show that a promising performance with overall Fl_score above 87% and mIoU of 79% can be obtained by only using the RGB images, without any post-processing such as conditional random field (CRF) smoothing. At last, we propose several possible approaches to further enhance the deep learning architectures to better deal with high-resolution aerial images. We therefore consider this work to be helpful for the remote sensing research community.


title: Low rank approximation and regression in input sparsity time

abstract: We design a new distribution over poly(r e-1) x n matrices S so that for any fixed n x d matrix A of rank r, with probability at least 9/10, SAx2 = (1 pm e)Ax2 simultaneously for all x ∈ Rd. Such a matrix S is called a subspace embedding. Furthermore, SA can be computed in O(nnz(A)) + ~O(r2e-2) time, where nnz(A) is the number of non-zero entries of A. This improves over all previous subspace embeddings, which required at least Ω(nd log d) time to achieve this property. We call our matrices S sparse embedding matrices. Using our sparse embedding matrices, we obtain the fastest known algorithms for overconstrained least-squares regression, low-rank approximation, approximating all leverage scores, and lp-regression: to output an x' for which Ax'-b2 ≤ (1+e)minx Ax-b2 for an n x d matrix A and an n x 1 column vector b, we obtain an algorithm running in O(nnz(A)) + ~O(d3e-2) time, and another in O(nnz(A)log(1/e)) + ~O(d3log(1/e)) time. (Here ~O(f) = f ⋅ logO(1)(f).) to obtain a decomposition of an n x n matrix A into a product of an n x k matrix L, a k x k diagonal matrix D, and a n x k matrix W, for which F{A - L D W} ≤ (1+e)F{A-Ak}, where Ak is the best rank-k approximation, our algorithm runs in O(nnz(A)) + ~O(nk2 e-4log n + k3e-5log2n) time. to output an approximation to all leverage scores of an n x d input matrix A simultaneously, with constant relative error, our algorithms run in O(nnz(A) log n) + ~O(r3) time. to output an x' for which Ax'-bp ≤ (1+e)minx Ax-bp for an n x d matrix A and an n x 1 column vector b, we obtain an algorithm running in O(nnz(A) log n) + poly(r e-1) time, for any constant 1 ≤ p


title: A survey on IDS alerts processing techniques

abstract: When an attacker tries to penetrate the network, there are many defensive systems, including intrusion detection systems (IDSs). Most IDSs are capable of detecting many attacks, but can not provide a clear idea to the analyst because of the huge number of false alerts generated by these systems. This weakness in the IDS has led to the emergence of many methods in which to deal with these alerts, minimize them and highlight the real attacks. It has come to a stage to take a stock of the research results a comprehensive view so that further research in this area will be motivated objectively to fulfill the gaps exists till now.


title: Dark Image Enhancement Using Perceptual Color Transfer

abstract: In this paper, we introduce an image enhancing approach for transforming dark images into lightened scenes, and we evaluate such method in different perceptual color spaces, in order to find the best-suited for this particular task. Specifically, we use a classical color transfer method where we obtain first-order statistics from a target image and transfer them to a dark input, modifying its hue and brightness. Two aspects are particular to this paper, the application of color transfer on dark imagery and in the search for the best color space for the application. In this regard, the tests performed show an accurate transference of colors when using perceptual color spaces, being RLAB the best color space for the procedure. Our results show that the methodology presented in this paper can be a good alternative to low-light or night vision processing techniques. Besides, the proposed method has a low computational complexity, property that is important for real time applications or for low-resource systems. This method can be used as a preprocessing step in order to improve the recognition and interpretation of dark imagery in a wide range of applications.


title: Deep learning approach for large scale land cover mapping based on remote sensing data fusion

abstract: In the paper we propose the methodology for solving the large scale classification and area estimation problems in the remote sensing domain on the basis of deep learning paradigm. It is based on a hierarchical model that includes self-organizing maps (SOM) for data preprocessing and segmentation (clustering), ensemble of multi-layer perceptrons (MLP) for data classification and heterogeneous data fusion and geospatial analysis for post-processing. The proposed methodology is applied for generation of high resolution land cover and land use maps for the territory of Ukraine from 1990 to 2010 and 2015.


title: Network function placement for NFV chaining in packet/optical data centers

abstract: Intelligent network function (NF) placement can minimize optical/electrical/optical (O/E/O) conversions for NF chaining in packet/optical data centers hosting NF virtualization (NFV). We design an efficient algorithm for NF placement, and show it can achieve near-minimum O/E/O conversions.


title: Binary-Decomposed DCNN for Accelerating Computation and Compressing Model Without Retraining

abstract: Recent trends show recognition accuracy increasing even more profoundly. Inference process of Deep Convolutional Neural Networks (DCNN) has a large number of parameters, requires a large amount of computation, and can be very slow. The large number of parameters also require large amounts of memory. This is resulting in increasingly long computation times and large model sizes. To implement mobile and other low performance devices incorporating DCNN, model sizes must be compressed and computation must be accelerated. To that end, this paper proposes Binary-decomposed DCNN, which resolves these issues without the need for retraining. Our method replaces real-valued inner-product computations with binary inner-product computations in existing network models to accelerate computation of inference and decrease model size without the need for retraining. Binary computations can be done at high speed using logical operators such as XOR and AND, together with bit counting. In tests using AlexNet with the ImageNet classification task, speed increased by a factor of 1.79, models were compressed by approximately 80%, and increase in error rate was limited to 1.20%. With VGG-16, speed increased by a factor of 2.07, model sizes decreased by 81%, and error increased by only 2.16%.


title: SAR image despeckling through convolutional neural networks

abstract: In this paper we investigate the use of discriminative model learning through Convolutional Neural Networks (CNNs) for SAR image despeckling. The network uses a residual learning strategy, hence it does not recover the filtered image, but the speckle component, which is then subtracted from the noisy one. Training is carried out by considering a large multitemporal SAR image and its multilook version, in order to approximate a clean image. Experimental results, both on synthetic and real SAR data, show the method to achieve better performance with respect to state-of-the-art techniques.


title: A ring-resonator-based silicon photonics transceiver with bias-based wavelength stabilization and adaptive-power-sensitivity receiver

abstract: Silicon photonic links based on ring-resonator devices provide a unique opportunity to deliver distance-independent connectivity, whose pin-bandwidth scales with the degree of wavelength-division multiplexing. However, reliability and robustness are major challenges to widespread adoption of ring-based silicon photonics. In this work, a CMOS photonic transceiver architecture is demonstrated that incorporates the following enhancements: transmitters with independent dual-edge pre-emphasis to compensate for modulator bandwidth limitations; a bias-based tuning loop to calibrate for resonance wavelength variations; and an adaptive sensitivity-bandwidth receiver that can self-adapt for insitu variations in input capacitance, modulator/photodetector performance, and link budget.


title: TubeSpam: Comment Spam Filtering on YouTube

abstract: The profitability promoted by Google in its brand new video distribution platform YouTube has attracted an increasing number of users. However, such success has also attracted malicious users, which aim to self-promote their videos or disseminate viruses and malwares. Since YouTube offers limited tools for comment moderation, the spam volume is shockingly increasing which lead owners of famous channels to disable the comments section in their videos. Automatic comment spam filtering on YouTube is a challenge even for established classification methods, since the messages are very short and often rife with slangs, symbols and abbreviations. In this work, we have evaluated several top-performance classification techniques for such purpose. The statistical analysis of results indicate that, with 99.9% of confidence level, decision trees, logistic regression, Bernoulli Naive Bayes, random forests, linear and Gaussian SVMs are statistically equivalent. Based on this, we have also offered the TubeSpam -- an accurate online system to filter comments posted on YouTube.


title: The Horror of Stigma: Psychosis and Mental Health Care Environments in Twenty-First-Century Horror Film (Part I)

abstract: PURPOSE ::: This paper explores the manner in which modern horror films present stigmatizing depictions of psychosis and mental health care environments. ::: ::: ::: CONCLUSIONS ::: Horror films will often include stigmatizing representations of psychosis and mental health care environments. Cinematic techniques can create stigmatizing depictions of psychosis and mental health care environments. Misinformation is often communicated. Due to these stigmatizing representations, people experiencing mental ill health may be rejected by the public. ::: ::: ::: PRACTICE IMPLICATIONS ::: Stigma is a serious problem affecting the mental health services. It is important for practitioners to understand where stigma arises in order to challenge beliefs and attitudes.


title: The neuroscience of human intelligence differences

abstract: Neuroscience is contributing to an understanding of the biological bases of human intelligence differences. This work is principally being conducted along two empirical fronts: genetics--quantitative and molecular--and brain imaging. Quantitative genetic studies have established that there are additive genetic contributions to different aspects of cognitive ability--especially general intelligence--and how they change through the lifespan. Molecular genetic studies have yet to identify reliably reproducible contributions from individual genes. Structural and functional brain-imaging studies have identified differences in brain pathways, especially parieto-frontal pathways, that contribute to intelligence differences. There is also evidence that brain efficiency correlates positively with intelligence.


title: An empirical performance comparison of machine learning methods for spam e-mail categorization

abstract: The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a need for reliable antispam filters. Using a classifier based on machine learning techniques to automatically filter out spam e-mail has drawn many researchers' attention. In this paper, we review some of relevant ideas and do a set of systematic experiments on e-mail categorization, which has been conducted with four machine learning algorithms applied to different parts of e-mail. Experimental results reveal that the header of e-mail provides very useful information for all the machine learning algorithms considered to detect spam e-mail.


title: Dynamic Feature Induction: The Last Gist to the State-of-the-Art

abstract: We introduce a novel technique called dynamic feature induction that keeps inducing high dimensional features automatically until the feature space becomes ‘more’ linearly separable. Dynamic feature induction searches for the feature combinations that give strong clues for distinguishing certain label pairs, and generates joint features from these combinations. These induced features are trained along with the primitive low dimensional features. Our approach was evaluated on two core NLP tasks, part-of-speech tagging and named entity recognition, and showed the state-of-the-art results for both tasks, achieving the accuracy of 97.64 and the F1-score of 91.00 respectively, with about a 25% increase in the feature space.


title: Vertical and horizontal proportions of the face in young adult North American Caucasians: revision of neoclassical canons.

abstract: The validity of nine neoclassical formulas of facial proportions was tested in a group of 153 young adult North American Caucasians. Age-related qualities were investigated in six of the nine canons in 100 six-year-old, 105 twelve-year-old, and 103 eighteen-year-old healthy subjects divided equally between the sexes. The two canons found to be valid most often in young adults were both horizontal proportions (interorbital width equals nose width in 40 percent and nose width equals 1/4 face width in 37 percent). The poorest correspondences are found in the vertical profile proportions, showing equality of no more than two parts of the head and face. Sex does not influence the findings significantly, but age-related differences were observed. Twenty-four variations derived from three vertical profile, four horizontal facial, and two nasoaural neoclassical canons were identified in the group of young adults. For each of the new proportions, the mean absolute and relative differences were calculated. The absolute differences were greater between the facial profile sections (vertical canons) and smaller between the horizontally oriented facial proportions. This study shows a large variability in size of facial features in a normal face. While some of the neoclassical canons may fit a few cases, they do not represent the average facial proportions and their interpretation as a prescription for ideal facial proportions must be tested.


title: ReGAN: A pipelined ReRAM-based accelerator for generative adversarial networks

abstract: Generative Adversarial Networks (GANs) have recently drawn tremendous attention in many artificial intelligence (AI) applications including computer vision, speech recognition, and natural language processing. While GANs deliver state-of-the-art performance on these AI tasks, it comes at the cost of high computational complexity. Although recent progress demonstrated the promise of using ReRMA-based Process-In-Memory for acceleration of convolutional neural networks (CNNs) with low energy cost, the unique training process required by GANs makes them difficult to run on existing neural network acceleration platforms: two competing networks are simultaneously co-trained in GANs, and hence, significantly increasing the need of memory and computation resources. In this work, we propose ReGAN - a novel ReRAM-based Process-In-Memory accelerator that can efficiently reduce off-chip memory accesses. Moreover, ReGAN greatly increases system throughput by pipelining the layer-wise computation. Two techniques, namely, Spatial Parallelism and Computation Sharing are particularly proposed to further enhance training efficiency of GANs. Our experimental results show that ReGAN can achieve 240× performance speedup compared to GPU platform averagely, with an average energy saving of 94×.


title: SeDMiD for Confusion Detection: Uncovering Mind State from Time Series Brain Wave Data

abstract: Understanding how brain functions has been an intriguing topic for years. With the recent progress on collecting massive data and developing advanced technology, people have become interested in addressing the challenge of decoding brain wave data into meaningful mind states, with many machine learning models and algorithms being revisited and developed, especially the ones that handle time series data because of the nature of brain waves. However, many of these time series models, like HMM with hidden state in discrete space or State Space Model with hidden state in continuous space, only work with one source of data and cannot handle different sources of information simultaneously. In this paper, we propose an extension of State Space Model to work with different sources of information together with its learning and inference algorithms. We apply this model to decode the mind state of students during lectures based on their brain waves and reach a significant better results compared to traditional methods.


title: Spectral Clustering Wikipedia Keyword-Based Search Results

abstract: The paper presents an application of spectral clustering algorithms used for grouping Wikipedia search results. The main contribution of the paper is a representation method for Wikipedia articles that has been based on combination of words and links and it has been used to categorize search result in this repository. We evaluate the proposed approach with Primary Component Analysis and show, on the test data, how usage of cosine transformation to create combined representations influence data variability. On sample test datasets we also show how combined representation improves the data separation that increases overall results of data categorization. The paper reviews the three main spectral clustering methods and we test their usability for text categorization comparing them using external validation criteria with standard clustering quality measures. Discussion on descriptiveness of evaluation measures and performed experiments on test datasets allows us to select the one spectral clustering algorithm that has been implemented in our system. We give a brief description of the system architecture that groups on-line Wikipedia articles retrieved with user-specified keywords. Using the system we show how clustering increases information retrieval effectiveness for Wikipedia data repository.


title: K-modestream algorithm for clustering categorical data streams

abstract: Clustering categorical data streams is a challenging problem because new data points are continuously adding to the already existing database at rapid pace and there exists no natural order among the categorical values. Recently, some algorithms have been discussed to tackle the problem of clustering the categorical data streams. However, in all these schemes the user needs to pre-specify the number of clusters, which is not trivial, and it renders to inefficient in the data stream environment. In this paper, we propose a new clustering algorithm, named it as k-modestream, which follows the k-modes algorithm paradigm to dynamically cluster the categorical data streams. It automatically computes the number of clusters and their initial modes simultaneously at regular time intervals. We analyse the time complexity of our scheme and perform various experiments using the synthetic and real world datasets to evaluate its efficacy.


title: Treatment of acne scars using the plasma skin regeneration (PSR) system

abstract: Background and Objectives ::: ::: Acne scarring is a common and difficult to treat condition. The plasma skin regeneration (PSR) system is a novel device that causes delayed ablation of the epidermis and controlled thermal modification to the underlying dermis. PSR has previously been shown to be a safe and effective treatment for facial rhytides and benign skin lesions. In this study, we investigated the safety and efficacy of single-treatment, high-energy, double-pass PSR for the treatment of acne scarring. ::: ::: ::: ::: Study Design/Materials and Methods ::: ::: Ten patients with acne scarring and Fitzpatrick skin types I–III were included in the study. All patients underwent a single PSR treatment with two high-energy passes (3.5–4.0 J). Treatments were performed in an outpatient clinic setting. Nine patients completed 6 months of follow-up. Improvement was determined by patient questionnaires and physician evaluation of digital photographs taken prior to treatment and at 3 and 6 months post-treatment. ::: ::: ::: ::: Results ::: ::: On average, patients reported 34% improvement in their acne scarring at 3 months and 33% improvement at 6 months. Blinded physician ratings of patient photos demonstrated 19% improvement at 3 months and 34% at 6 months. Re-epithelialization was complete by 4–6 days after treatment, and no serious adverse events were encountered. ::: ::: ::: ::: Conclusion ::: ::: PSR appears to provide a safe and effective single treatment, minimal downtime alternative for the treatment of acne scarring. Additional studies are warranted to further demonstrate the safety and efficacy of this device. Lesers Surg. Med. 40:124–127, 2008. © 2008 Wiley-Liss, Inc.


title: A CMOS MEMS Pirani vacuum gauge with complementary bump heat sink and cavity heater

abstract: A novel CMOS-MEMS Pirani vacuum gauge with complementary bump heat-sink and cavity heater design has been proposed and demonstrated. This design using CMOS-MEMS process to offer the following advantages for Pirani gauge: (1) The bump heat-sink vertical integrates with cavity heater increases the dynamic range and sensitivity without changing device footprint size, (2) The cavity in heater reduces the thermal mass for low-power operation, and (3) Easy integration with packaged CMOS-MEMS devices for pressure monitoring [1]. The design is implemented using the standard TSMC 0.18μm 1P6M CMOS process. A 120μm×120μm die size with 0.53μm sensing gap is demonstrated. Measurement indicates the gauge has sensing range 0.3-100torr with sensitivity of 1.53×104(K/W)/torr. The power consumption is 67μW for 1% resistance change. In comparison, the gauge with typical heat-sink/heater design has sensing range 1-100torr with sensitivity of 0.99×104(K/W)/torr and power consumption of 119μW.


title: Smoothness-Increasing Accuracy-Conserving (SIAC) Filtering for Discontinuous Galerkin Solutions: Improved Errors Versus Higher-Order Accuracy

abstract: Smoothness-increasing accuracy-conserving (SIAC) filtering has demonstrated its effectiveness in raising the convergence rate of discontinuous Galerkin solutions from order $k+\frac{1}{2}$ to order 2k+1 for specific types of translation invariant meshes (Cockburn et al. in Math. Comput. 72:577---606, 2003; Curtis et al. in SIAM J. Sci. Comput. 30(1):272---289, 2007; Mirzaee et al. in SIAM J. Numer. Anal. 49:1899---1920, 2011). Additionally, it improves the weak continuity in the discontinuous Galerkin method to k?1 continuity. Typically this improvement has a positive impact on the error quantity in the sense that it also reduces the absolute errors. However, not enough emphasis has been placed on the difference between superconvergent accuracy and improved errors. This distinction is particularly important when it comes to understanding the interplay introduced through meshing, between geometry and filtering. The underlying mesh over which the DG solution is built is important because the tool used in SIAC filtering--convolution--is scaled by the geometric mesh size. This heavily contributes to the effectiveness of the post-processor. In this paper, we present a study of this mesh scaling and how it factors into the theoretical errors. To accomplish the large volume of post-processing necessary for this study, commodity streaming multiprocessors were used; we demonstrate for structured meshes up to a 50× speed up in the computational time over traditional CPU implementations of the SIAC filter.


title: Data, information, knowledge and principle: back to metabolism in KEGG

abstract: In the hierarchy of data, information and knowledge, computational methods play a major role in the initial processing of data to extract information, but they alone become less effective to compile knowledge from information. The Kyoto Encyclopedia of Genes and Genomes (KEGG) resource (http://www.kegg.jp/ or http://www.genome.jp/kegg/) has been developed as a reference knowledge base to assist this latter process. In particular, the KEGG pathway maps are widely used for biological interpretation of genome sequences and other high-throughput data. The link from genomes to pathways is made through the KEGG Orthology system, a collection of manually defined ortholog groups identified by K numbers. To better automate this interpretation process the KEGG modules defined by Boolean expressions of K numbers have been expanded and improved. Once genes in a genome are annotated with K numbers, the KEGG modules can be computationally evaluated revealing metabolic capacities and other phenotypic features. The reaction modules, which represent chemical units of reactions, have been used to analyze design principles of metabolic networks and also to improve the definition of K numbers and associated annotations. For translational bioinformatics, the KEGG MEDICUS resource has been developed by integrating drug labels (package inserts) used in society.


title: Learning Data-Driven Reflectance Priors for Intrinsic Image Decomposition

abstract: We propose a data-driven approach for intrinsic image decomposition, which is the process of inferring the confounding factors of reflectance and shading in an image. We pose this as a two-stage learning problem. First, we train a model to predict relative reflectance ordering between image patches ('brighter', 'darker', 'same') from large-scale human annotations, producing a data-driven reflectance prior. Second, we show how to naturally integrate this learned prior into existing energy minimization frame-works for intrinsic image decomposition. We compare our method to the state-of-the-art approach of Bell et al. [7] on both decomposition and image relighting tasks, demonstrating the benefits of the simple relative reflectance prior, especially for scenes under challenging lighting conditions.


title: Online Affect Detection and Robot Behavior Adaptation for Intervention of Children With Autism

abstract: Investigation into robot-assisted intervention for children with autism spectrum disorder (ASD) has gained momentum in recent years. Therapists involved in interventions must overcome the communication impairments generally exhibited by children with ASD by adeptly inferring the affective cues of the children to adjust the intervention accordingly. Similarly, a robot must also be able to understand the affective needs of these children-an ability that the current robot-assisted ASD intervention systems lack-to achieve effective interaction that addresses the role of affective states in human-robot interaction and intervention practice. In this paper, we present a physiology-based affect-inference mechanism for robot-assisted intervention where the robot can detect the affective states of a child with ASD as discerned by a therapist and adapt its behaviors accordingly. This paper is the first step toward developing ldquounderstandingrdquo robots for use in future ASD intervention. Experimental results with six children with ASD from a proof-of-concept experiment (i.e., a robot-based basketball game) are presented. The robot learned the individual liking level of each child with regard to the game configuration and selected appropriate behaviors to present the task at his/her preferred liking level. Results show that the robot automatically predicted individual liking level in real time with 81.1% accuracy. This is the first time, to our knowledge, that the affective states of children with ASD have been detected via a physiology-based affect recognition technique in real time. This is also the first time that the impact of affect-sensitive closed-loop interaction between a robot and a child with ASD has been demonstrated experimentally.


title: Text frame classification and recognition using segmentation technique

abstract: Text detection techniques assume that the frames given to them are all text frames. When a non-text frame is fed to the text detector there is an increase in the number of false positives which is a major issue in text detection and recognition. Hence, classification of text frames in a video sequence is important. The duplicate frames are eliminated and label each frame as text and non-text before it can be given for detection and recognition of text in a frame. Text in a frame is detected using bounding box technique. Once the text regions are marked, Fuzzy C Means (FCM) algorithm is used to classify each frame as text and non-text. The performance of the proposed work is measured in terms of precision, recall and F-measure and is compared with the existing approaches. The classified text frames are taken as input and are sent to the OCR Engine for character recognition. Several applications such as character recognition, video content analysis, and content based image retrieval, video summarization, and script identification etc., uses text detection.


title: The Reward Circuit: Linking Primate Anatomy and Human Imaging

abstract: Although cells in many brain regions respond to reward, the cortical-basal ganglia circuit is at the heart of the reward system. The key structures in this network are the anterior cingulate cortex, the orbital prefrontal cortex, the ventral striatum, the ventral pallidum, and the midbrain dopamine neurons. In addition, other structures, including the dorsal prefrontal cortex, amygdala, hippocampus, thalamus, and lateral habenular nucleus, and specific brainstem structures such as the pedunculopontine nucleus, and the raphe nucleus, are key components in regulating the reward circuit. Connectivity between these areas forms a complex neural network that mediates different aspects of reward processing. Advances in neuroimaging techniques allow better spatial and temporal resolution. These studies now demonstrate that human functional and structural imaging results map increasingly close to primate anatomy.


title: Expressing thought: improving robot readability with animation principles

abstract: The animation techniques of anticipation and reaction can help create robot behaviors that are human readable such that people can figure out what the robot is doing, reasonably predict what the robot will do next, and ultimately interact with the robot in an effective way. By showing forethought before action and expressing a reaction to the task outcome (success or failure), we prototyped a set of human-robot interaction behaviors. In a 2 (forethought vs. none: between) x 2 (reaction to outcome vs. none: between) x 2 (success vs. failure task outcome: within) experiment, we tested the influences of forethought and reaction upon people's perceptions of the robot and the robot's readability. In this online video prototype experiment (N=273), we have found support for the hypothesis that perceptions of robots are influenced by robots showing forethought, the task outcome (success or failure), and showing goal-oriented reactions to those task outcomes. Implications for theory and design are discussed.


title: Software reuse: metrics and models

abstract: As organizations implement systematic software reuse programs to improve productivity and quality, they must be able to measure their progress and identify the most effective reuse strategies. This is done with reuse metrics and models. In this article we survey metrics and models of software reuse and reusability, and provide a classification structure that will help users select them. Six types of metrics and models are reviewed: cost-benefit models, maturity assessment models, amount of reuse metrics, failure modes models, reusability assessment models, and reuse library metrics.


title: Wide & Deep Learning for Recommender Systems

abstract: Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort. With less feature engineering, deep neural networks can generalize better to unseen feature combinations through low-dimensional dense embeddings learned for the sparse features. However, deep neural networks with embeddings can over-generalize and recommend less relevant items when the user-item interactions are sparse and high-rank. In this paper, we present Wide & Deep learning---jointly trained wide linear models and deep neural networks---to combine the benefits of memorization and generalization for recommender systems. We productionized and evaluated the system on Google Play, a commercial mobile app store with over one billion active users and over one million apps. Online experiment results show that Wide & Deep significantly increased app acquisitions compared with wide-only and deep-only models. We have also open-sourced our implementation in TensorFlow.


title: HinDroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network

abstract: With explosive growth of Android malware and due to the severity of its damages to smart phone users, the detection of Android malware has become increasingly important in cybersecurity. The increasing sophistication of Android malware calls for new defensive techniques that are capable against novel threats and harder to evade. In this paper, to detect Android malware, instead of using Application Programming Interface (API) calls only, we further analyze the different relationships between them and create higher-level semantics which require more effort for attackers to evade the detection. We represent the Android applications (apps), related APIs, and their rich relationships as a structured heterogeneous information network (HIN). Then we use a meta-path based approach to characterize the semantic relatedness of apps and APIs. We use each meta-path to formulate a similarity measure over Android apps, and aggregate different similarities using multi-kernel learning. Then each meta-path is automatically weighted by the learning algorithm to make predictions. To the best of our knowledge, this is the first work to use structured HIN for Android malware detection. Comprehensive experiments on real sample collections from Comodo Cloud Security Center are conducted to compare various malware detection approaches. Promising experimental results demonstrate that our developed system HinDroid outperforms other alternative Android malware detection techniques.


title: Interaction of photoperiod and temperature in flowering-control of Gypsophila paniculata L

abstract: Long day promotes flowering of Gysophila paniculata L cultivar ‘Bristol Fairy’. Repeated treatments with GA3 or GA4 + 7 in short days did not promote flowering. The long photoperiod is effective only at relatively high temperatures. At night temperatures below 12°C, the plants remain vegetative even in long days. Efficient artificial lighting is from incandescent lamps at 60–100 lux. Fluorescent lighting (Cool-White) is not effective. Lighting of 4 hours as a night-break or at the end of the night were equally effective, but 4 hours lighting as a day-extension was less effective. Whole-night lighting promoted flowering more than any of the 4-hour lighting regimes. Cyclic lighting of one third light in each cycle promoted flowering to the same extent as continuous lighting. Light intensity during the day has a decisive effect on flower production.


title: Prediction of air-to-ground communication strength for relay UAV trajectory planner in urban environments

abstract: This paper proposes the use of a learning approach to predict air-to-ground (A2G) communication strength in support of the communication relay mission using UAVs in an urban environment. To plan an efficient relay trajectory, A2G communication link quality needs to be predicted between the UAV and ground nodes. However, due to frequent occlusions by buildings in the urban environment, modelling and predicting communication strength is a difficult task. Thus, a need for learning techniques such as Gaussian Process (GP) arises to learn about inaccuracies in a pre-defined communication model and the effect of line-of-sight obstruction. Two ways of combining GP with a relay trajectory planner are presented: i) scanning the area of interest with the UAV to collect communication strength data first and then using learned data in the trajectory planner and ii) collecting data and running the trajectory planner simultaneously. The performance of both approaches is compared with Monte Carlo simulations. It is shown that the first implementation results in slightly better predictions, however the second one benefits from being able to start the relay mission immediately.


title: Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning

abstract: Driving Scene understanding is a key ingredient for intelligent transportation systems. To achieve systems that can operate in a complex physical and social environment, they need to understand and learn how humans drive and interact with traffic scenes. We present the Honda Research Institute Driving Dataset (HDD), a challenging dataset to enable research on learning driver behavior in real-life environments. The dataset includes 104 hours of real human driving in the San Francisco Bay Area collected using an instrumented vehicle equipped with different sensors. We provide a detailed analysis of HDD with a comparison to other driving datasets. A novel annotation methodology is introduced to enable research on driver behavior understanding from untrimmed data sequences. As the first step, baseline algorithms for driver behavior detection are trained and tested to demonstrate the feasibility of the proposed task.


title: Recognizing Nested Named Entities In GENIA Corpus

abstract: Nested Named Entities (nested NEs), one containing another, are commonly seen in biomedical text, e.g., accounting for 16.7% of all named entities in GENIA corpus. While many works have been done in recognizing non-nested NEs, nested NEs have been largely neglected. In this work, we treat the task as a binary classification problem and solve it using Support Vector Machines. For each token in nested NEs, we use two schemes to set its class label: labeling as the outmost entity or the inner entity. Our preliminary results show that while the outmost labeling tends to work better in recognizing the outmost entities, the inner labeling recognizes the inner NEs better. This result should be useful for recognition of nested NEs.


title: Site placement and site selection algorithms for UMTS radio planning with quality constraints

abstract: In this paper, we address the problem of finding the optimal deployment of UMTS base stations (BSs) in a certain geographic area with a given user distribution to minimize the total BS transmit power while maintaining a satisfactory level of downlink signal-to-interference ratio (SIR) for all users and satisfying the base station maximum power constraint. We define a continuous component of the problem, referred to as the site placement problem, and an integer component, referred to as the site selection problem. The continuous component involves finding the optimal BS locations given a fixed number of BSs in the area of interest. We model the continuous problem as a constrained optimization problem with the BS locations as decision variables and the powers allocated to their assigned users as state variables, and we propose and implement an algorithm using pattern search techniques. On the other hand, the integer component is a combinatorial optimization problem which involves finding the minimum number of BSs given fixed candidate BS locations to satisfy the SIR constraints. We devise an efficient algorithm based on successive elimination of BSs in order to obtain a minimal BS set. Finally, experimental results are presented for different scenarios and different user distributions.


n ensemble learning framework for anomaly detection in building nergy consumption


title: Detection of outliers in time series data

abstract: DETECTION OF OUTLIERS IN TIME SERIES DATA Samson Kiware, B.A. Marquette University, 2010 This thesis presents the detection of time series outliers. The data set used in this work is provided by the GasDay Project at Marquette University, which produces mathematical models to predict the consumption of natural gas for Local Distribution Companies (LDCs). Flow with no outliers is required to develop and train accurate models. GasDay is using statistical approaches motivated by normally distributed samples such as the 3 − σ rule and the 5 − σ rule to aid the experts in detecting outliers in residuals from the models. However, the Jarque-Bera statistical test shows that the residuals from the GasDay models are not normally distributed. We present an explanation of Density Based Spatial Clustering of Applications with Noise (DBSCAN) and how it is used to detect time series outliers. We have introduced a new application for the DBSCAN algorithm by adapting it to detect outliers in natural gas flow. The performance of DBSCAN is compared with GasDay’s existing technique. Five data sets from temperature-sensitive operating areas with identified outliers and 1000 data sets with synthetic outliers are used in the evaluation process. The 1000 synthetic data sets are prepared using the same empirical distribution as one of the identified data set. This work indicates that DBSCAN has shown some improvement in detecting outliers over GasDays existing technique and merits further exploration.


title: 3D printing and metalization methodology for high dielectric resonator waveguide microwave filters

abstract: This paper presents a novel approach to manufacture and metalize a standard waveguide microwave filter, with a high dielectric resonator inserted in it. As a first step, a set of 3D-printed ABS plastic filaments have been measured, to establish their electrical properties and suitability for microwave design. Subsequently, a new commercial silver conductive lacquer has been tested to metalize a standard hollow WR-90 waveguide, printed by Fused Deposition Modeling (FDM) technique. This lacquer furnishes acceptable conductivity and proper shielding of the electromagnetic fields inside the structure, operating among microwave frequencies. In parallel, a special thin pellet, chemically synthesized, has been investigated and fabricated. This small cylindrical rod behaves like a high dielectric resonator, that has been employed to design a highly selective waveguide microwave filter, paving the road to the implementation of a novel class of microwave components and systems.


title: Consistency in Non-Transactional Distributed Storage Systems

abstract: Over the years, different meanings have been associated to the word consistency in the distributed systems community. While in the ’80s “consistency” typically meant strong consistency, later defined also as linearizability, in recent years, with the advent of highly available and scalable systems, the notion of “consistency” has been at the same time both weakened and blurred. In this paper we aim to fill the void in literature, by providing a structured and comprehensive overview of different consistency notions that appeared in distributed systems, and in particular storage systems research, in the last four decades. We overview more than 50 different consistency notions, ranging from linearizability to eventual and weak consistency, defining precisely many of these, in particular where the previous definitions were ambiguous. We further provide a partial order among different consistency predicates, ordering them by their semantic “strength”, which we believe will reveal useful in future research. Finally, we map the consistency semantics to different practical systems and research prototypes. The scope of this paper is restricted to non-transactional semantics, i.e., those that apply to single storage object operations. As such, our paper complements the existing surveys done in the context of transactional, database consistency semantics. ar X iv :1 51 2. 00 16 8v 4 [ cs .D C ] 1 2 A pr 2 01 6


TOWARDS THE UNIFICATION OF CRITICAL SUCCESS FACTORS FOR ERP IMPLEMENTATIONS


Simulations of voltage sags in an industrial installation featuring a large induction motor


title: Power System Control and Stability

abstract: Preface.Part I: Introduction.Chapter 1: Power System Stability.Chapter 2: The Elementary Mathematical Model.Chapter 3: System Response to Small Disturbances.Part II: The Electromagnetic Torque.Chapter 4: The Synchronous Machine.Chapter 5: The Simulation of Synchronous Machines.Chapter 6: Linear Models of the Synchronous Machine.Chapter 7: Excitation Systems.Chapter 8: Effect of Excitation on Stability.Chapter 9: Multimachine Systems with Constant Impedance Loads.Part III: The Mechanical Torque Power System Control and Stability.Chapter 10: Speed Governing.Chapter 11: Steam Turbine Prime Movers.Chapter 12: Hydraulic Turbine Prime Movers.Chapter 13: Combustion Turbine and Combined-Cycle Power Plants.Appendix A: Trigonometric Identities for Three-Phase Systems.Appendix B: Some Computer Methods for Solving Differential Equations.Appendix C: Normalization.Appendix D: Typical System Data.Appendix E: Excitation Control System Definitions.Appendix F: Control System Components.Appendix G: Pressure Control Systems.Appendix H: The Governor Equations.Appendix I: Wave Equations for a Hydraulic Conduit.Appendix J: Hydraulic Servomotors.Index.


title: Outlier Detection and Trend Detection: Two Sides of the Same Coin

abstract: Outlier detection is commonly defined as the process of finding unusual, rare observations in a large data set, without prior knowledge of which objects to look for. Trend detection is the task of finding some unexpected change in some quantity, such as the occurrence of certain topics in a textual data stream. Many established outlier detection methods are designed to search for low-density objects in a static data set of vectors in Euclidean space. For trend detection, high volume events are of interest and the data set is constantly changing. These two problems appear to be very different at first. However, they also have obvious similarities. For example, trends and outliers likewise are supposed to be rare occurrences. In this paper, we discuss the close relationship of these tasks. We call to action to investigate this further, to carry over insights, ideas, and algorithms from one domain to the other.


title: Neural PID Control of Robot Manipulators With Application to an Upper Limb Exoskeleton

abstract: In order to minimize steady-state error with respect to uncertainties in robot control, proportional-integral-derivative (PID) control needs a big integral gain, or a neural compensator is added to the classical proportional-derivative (PD) control with a large derivative gain. Both of them deteriorate transient performances of the robot control. In this paper, we extend the popular neural PD control into neural PID control. This novel control is a natural combination of industrial linear PID control and neural compensation. The main contributions of this paper are semiglobal asymptotic stability of the neural PID control and local asymptotic stability of the neural PID control with a velocity observer which are proved with standard weight training algorithms. These conditions give explicit selection methods for the gains of the linear PID control. An experimental study on an upper limb exoskeleton with this neural PID control is addressed.


title: Cycle-borrow digital valley current control of buck DC-DC converter

abstract: The digital double loop current control become the promising technique for digital switching DC-DC converters. In this paper a novel current control technique, named cycle-borrow control is proposed and studied, which has faster loop transient response than the time delay control, and its requirement to loop processing speed can be relaxed as compared with the deadbeat current control. The cycle-borrow valley current control laws of buck DC-DC converters with four different pulse width modulations are derived, and their stabilities are analyzed. A simple digital slope compensation (DSC) method which doesn't need any extra slope compensation circuit is proposed to eliminate the sub-harmonic oscillations of cycle-borrow control. The simulation results show that, the transient response performance of dual-edge modulations is better than that of single-edge modulations. The cycle-borrow control technique is especially benefit to high-frequency digitally controlled switching DC-DC converters.


title: QBoost: Predicting quantiles with boosting for regression and binary classification

abstract: In the framework of functional gradient descent/ascent, this paper proposes Quantile Boost (QBoost) algorithms which predict quantiles of the interested response for regression and binary classification. Quantile Boost Regression performs gradient descent in functional space to minimize the objective function used by quantile regression (QReg). In the classification scenario, the class label is defined via a hidden variable, and the quantiles of the class label are estimated by fitting the corresponding quantiles of the hidden variable. An equivalent form of the definition of quantile is introduced, whose smoothed version is employed as the objective function, and then maximized by functional gradient ascent to obtain the Quantile Boost Classification algorithm. Extensive experimentation and detailed analysis show that QBoost performs better than the original QReg and other alternatives for regression and binary classification. Furthermore, QBoost is capable of solving problems in high dimensional space and is more robust to noisy predictors.


title: K-means Algorithm Based on Particle Swarm Optimization Algorithm for Anomaly Intrusion Detection

abstract: K-means as a clustering algorithm has been studied in intrusion detection. However, with the deficiency of global search ability it is not satisfactory. Particle swarm optimization (PSO) is one of the evolutionary computation techniques based on swarm intelligence, which has high global search ability. So K-means algorithm based on PSO (PSO-KM) is proposed in this paper. Experiment over network connection records from KDD CUP 1999 data set was implemented to evaluate the proposed method. A Bayesian classifier was trained to select some fields in the data set. The experimental results clearly showed the outstanding performance of the proposed method.


title: Detection of Anchor Points for 3D Face Veri.cation

abstract: This paper outlines methods to detect key anchor points in 3D face scanner data. These anchor points can be used to estimate the pose and then match the test image to a 3D face model. We present two algorithms for detecting face anchor points in the context of face verification; One for frontal images and one for arbitrary pose. We achieve 99% success in finding anchor points in frontal images and 86% success in scans with large variations in pose and changes in expression. These results demonstrate the challenges in 3D face recognition under arbitrary pose and expression. We are currently working on robust ?tting algorithms to localize more precisely the anchor points for arbitrary pose images.


Quantum machine learning


title: Security Analysis of PHP Bytecode Protection Mechanisms

abstract: PHP is the most popular scripting language for web applications. Because no native solution to compile or protect PHP scripts exists, PHP applications are usually shipped as plain source code which is easily understood or copied by an adversary. In order to prevent such attacks, commercial products such as ionCube, Zend Guard, and Source Guardian promise a source code protection. ::: ::: In this paper, we analyze the inner working and security of these tools and propose a method to recover the source code by leveraging static and dynamic analysis techniques. We introduce a generic approach for decompilation of obfuscated bytecode and show that it is possible to automatically recover the original source code of protected software. As a result, we discovered previously unknown vulnerabilities and backdoors in 1 million lines of recovered source code of 10 protected applications.


title: Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?

abstract: In this paper, we evaluate the generalization power of deep features (ConvNets) in two new scenarios: aerial and remote sensing image classification. We evaluate experimentally ConvNets trained for recognizing everyday objects for the classification of aerial and remote sensing images. ConvNets obtained the best results for aerial images, while for remote sensing, they performed well but were outperformed by low-level color descriptors, such as BIC. We also present a correlation analysis, showing the potential for combining/fusing different ConvNets with other descriptors or even for combining multiple ConvNets. A preliminary set of experiments fusing ConvNets obtains state-of-the-art results for the well-known UCMerced dataset.


title: On the non-trivial generalization of Dynamic Time Warping to the multi-dimensional case

abstract: In the last decade, Dynamic Time Warping (DTW) has emerged as the distance measure of choice for virtually all time series data mining applications. This is the result of significant progress in improving DTW’s efficiency, and multiple empirical studies showing that DTW-based classifiers at least equal the accuracy of all their rivals across dozens of datasets. Thus far, most of the research has considered only the one-dimensional case, with practitioners generalizing to the multi-dimensional case in one of two ways. In general, it appears the community believes either that the two ways are equivalent, or that the choice is irrelevant. In this work, we show that this is not the case. The two most commonly used multidimensional DTW methods can produce different classifications, and neither one dominates over the other. This seems to suggest that one should learn the best method for a particular application. However, we will show that this is not necessary; a simple, principled rule can be used on a case-by-case basis to predict which of the two methods we should give credence to. Our method allows us to ensure that classification results are at least as accurate as the better of the two rival methods, and in many cases, our method is strictly more accurate. We demonstrate our ideas with the most extensive set of multi-dimensional time series classification experiments ever attempted. Keywords—Dynamic Time Warping; Classification


The effect of horizontal database table partitioning on query performance.


title: The research on traffic sign recognition based on deep learning

abstract: With the further and faster urbanization, here come the advent and development of intelligent public transportation system. The identification of the traffic signs, as a key component of the intelligent transit system, has a prospect of widespread use in self-driving and driver assistance system. The paper focuses on the classification and identification of traffic signs based on the researches on traffic signs home and abroad which also include the status quo, technological problems and development tendency. Combined with the significant features of the shape and internal structure of the traffic signs, three new feature-based algorithms on road signs identification have been implemented, including image pretreatment, feature extraction and classifier. The feature extraction work of traffic signs is conducted with the algorithms using an integration of Deep Boltzmann Machines and Canonical Correlation Analysis. Compared with the algorithms based on HOG and LBP feature extraction, the DBM-CCA has higher accuracy.


title: Multi-frequency CPW bow-tie aperture antenna with rectangular and U shape metal strips

abstract: This paper presents a new design for aperture bowtie antenna with added rectangular and U shape metal strips to function at new different frequencies: 8.2, 9.25 and 10.5 GHz. The advantage of the proposed design is to use the original antenna shape and its dimensions to achieve the multiple frequencies. The proposed design consists of adding 2 U shape metal strips of similar sizes as well as 4 rectangular metal strips of different sizes for the original antenna. HFSS software is used in the simulation of the antenna in order to obtain reflection coefficients, radiation patterns, impedances and surface current distributions.


title: Denial of Service in Sensor Networks

abstract: Sensor networks hold the promise of facilitating large-scale, real-time data processing in complex environments, helping to protect and monitor military, environmental, safety-critical, or domestic infrastructures and resources, Denial-of-service attacks against such networks, however, may permit real world damage to public health and safety. Without proper security mechanisms, networks will be confined to limited, controlled environments, negating much of the promise they hold. The limited ability of individual sensor nodes to thwart failure or attack makes ensuring network availability more difficult. To identify denial-of-service vulnerabilities, the authors analyzed two effective sensor network protocols that did not initially consider security. These examples demonstrate that consideration of security at design time is the best way to ensure successful network deployment.


title: Actor-Oriented Programming for Wireless Sensor Networks

abstract: Wireless sensor networks is an emerging area of embedded systems that has the potential to revolutionize our lives at home and at work, with wide-ranging applications, including environmental monitoring and conservation, manufacturing and industrial control, business asset management, seismic and structural monitoring, transportation, health care, and home automation. Building sensor networks today requires piecing together a variety of hardware and software components, each with different design methodologies and tools, making it a challenging and error-prone process. ::: In this dissertation, I advocate using an actor-oriented approach to designing, generating, programming, and simulating wireless sensor network applications. Actor-oriented programming provides a common high-level language that unifies the programming interface between the operating system, node-centric, middleware, and macroprogramming layers of a sensor network application. ::: This dissertation presents the TinyGALS (Globally Asynchronous, Locally Synchronous) programming model, which is built on the TinyOS programming model. TinyGALS is implemented in the galsC programming language, which provides constructs to systematically build concurrent tasks called actors. The galsC compiler toolsuite provides high-level type checking and code generation facilities and allows developers to deploy actor-oriented programs on actual sensor node hardware. ::: This dissertation then describes Viptos (Visual Ptolemy and TinyOS), a joint modeling and design environment for wireless networks and sensor node software. Viptos is built on Ptolemy II, an actor-oriented graphical modeling and simulation environment for embedded systems, and TOSSIM, an interrupt-level discrete-event simulator for TinyOS networks. ::: This dissertation also presents methods for using higher-order actors with various metaprogramming and generative programming techniques that enable wireless sensor network application developers to create high-level, parameterizable descriptions and automatically generate sensor network simulation scenarios from these models. ::: All of the tools I developed and describe in this dissertation are open-source and freely available on the web. The networked embedded computing community can use these tools and the knowledge shared in this dissertation to improve the way we program wireless sensor networks.


title: Title

abstract: In this article, we provide a basic introduction to CMOS image-sensor technology, design and performance limits and present recent developments and future directions in this area. We also discuss image-sensor operation and describe the most popular CMOS image-sensor architectures. We note the main non-idealities that limit CMOS image sensor performance, and specify several key performance measures. One of the most important advantages of CMOS image sensors over CCDs is the ability to integrate sensing with analog and digital processing down to the pixel level. Finally, we focus on recent developments and future research directions that are enabled by pixel-level processing, the applications of which promise to further improve CMOS image sensor performance and broaden their applicability beyond current markets.


[Slipped capital femoral epiphysis].


title: Efficient Deduplication Techniques for Modern Backup Operation

abstract: In this work, we focus on optimizing the deduplication system by adjusting the pertinent factors in fingerprint lookup and chunking, the factors which we identify as the key ingredients of efficient deduplication. For efficient fingerprint lookup, we propose fingerprint management scheme called LRU-based Index Partitioning. For efficient chunking, we propose Incremental Modulo-K(INC-K) algorithm which is optimized Rabin's algorithm where we significantly reduce the number of arithmetic operations exploiting the algebraic nature of modulo arithmetic. LRU-based Index Partitioning uses the notion of tablet and enforces access locality of the fingerprint lookup in storing fingerprints. We maintain tablets with LRU manner to exploit temporal locality of the fingerprint lookup. To preserve access correlation across the tablets, we apply prefetching in maintaining tablet list. We propose Context-aware chunking to maximize chunking speed and deduplication ratio. We develop prototype backup system and performed comprehensive analysis on various factors and their relationship: average chunk size, chunking speed, deduplication ratio, tablet management algorithms, and overall backup speed. By increasing the average chunk size from 4 KB to 10 KB, chunking time increases by 34.3 percent, deduplication ratio decreases by 0.66 percent and the overall backup speed increases by 50 percent (from 51.4 MB/sec to 77.8 MB/sec).


Psychophysiological assessment of emotions


title: Geometric approximation via coresets

abstract: The paradigm of coresets has recently emerged as a powerful tool for efficiently approximating various extent measures of a point set P . Using this paradigm, one quickly computes a small subset Q of P , called a coreset, that approximates the original set P and and then solves the problem on Q using a relatively inefficient algorithm. The solution for Q is then translated to an approximate solution to the original point set P . This paper describes the ways in which this paradigm has been successfully applied to various optimization and extent measure problems.


title: Performance Analysis of Heterogeneous Networks With Interference Cancellation

abstract: The performance of heterogeneous networks (HetNets) is investigated with both direct and relay-aided transmission strategies in a macrocell. We first propose an interference cancelation scheme at the macro base station on the basis of canceling cross-tier interferences from the macro base station to small-cell users. Then, expressions of the probability density function (PDF) and cumulative distribution function (CDF) of all receivers in both macro and small cells are derived, and the closed-form overall outage probability, average bit error rate (BER), and spectral efficiency of the network are obtained. Based on the derivations, we propose two adaptive relay-aided transmission schemes with instantaneous and statistical channel state information (CSI) to further improve the spectral efficiency. We show that the SNR boost at the small-cell mobile station comes from the cancelation of the cross-layer interference by the macro base station. Therefore, the performance of the small cell is improved greatly with interference cancelation, while the overall performance of the HetNet is also ameliorated. The numerical results finally show the validity of our analysis.


title: Personalizing Similar Product Recommendations in Fashion E-commerce

abstract: In fashion e-commerce platforms, product discovery is one of the key components of a good user experience. There are numerous ways using which people find the products they desire. Similar product recommendations is one of the popular modes using which users find products that resonate with their intent. Generally these recommendations are not personalized to a specific user. Traditionally, collaborative filtering based approaches have been popular in the literature for recommending non-personalized products given a query product. Also, there has been focus on personalizing the product listing for a given user. In this paper, we marry these approaches so that users will be recommended with personalized similar products. Our experimental results on a large fashion e-commerce platform (Myntra) show that we can improve the key metrics by applying personalization on similar product recommendations.


title: A low-power ultra-fast capacitor-less LDO with advanced dynamic push-pull techniques

abstract: A current-efficent, fully integrated low-dropout regulator(LDO) with improved load transient responses for system-on-chips(SoC) is presented in this paper. It makes use of high bandwidth common-gate amplifier and slew-rate enhancement circuit(SRE) triggered by voltage spikes to improve output voltage spike and response time of the LDO greatly. The proposed circuit has been implemented in a 0.35µm standard CMOS process and occupies an active chip area of 0.057mm2. Experimental results show that it can deliver 100mA load current at 150mV dropout voltage. It only consumes 8µA quiescent current and is able to recover within 0.2µs even under the maximum load current change. Consequently, a low-power and ultra-fast capacitor-less LDO can be achieved.


Secondary Design: A Case of Behavioral Design Science Research


title: Multi-step classification approaches to cumulative citation recommendation

abstract: Knowledge bases have become indispensable sources of information. It is therefore critical that they rely on the latest information available and get updated every time new facts surface. Knowledge base acceleration (KBA) systems seek to help humans expand knowledge bases like Wikipedia by automatically recommending edits based on incoming content streams. A core step in this process is that of identifying relevant content, i.e., filtering documents that would imply modifications to the attributes or relations of a given target entity. We propose two multi-step classification approaches for this task that consist of two and three binary classification steps, respectively. Both methods share the same initial component, which is concerned with the identification of entity mentions in documents, while subsequent steps involve identification of documents being relevant and/or central to a given entity. Using the evaluation platform of the TREC 2012 KBA track and a rich feature set developed for this particular task, we show that both approaches deliver state-of-the-art performance.


title: Energy-accuracy trade-off for continuous mobile device location

abstract: Mobile applications often need location data, to update locally relevant information and adapt the device context. While most smartphones do include a GPS receiver, it's frequent use is restricted due to high battery drain. We design and prototype an adaptive location service for mobile devices, a-Loc, that helps reduce this battery drain. Our design is based on the observation that the required location accuracy varies with location, and hence lower energy and lower accuracy localization methods, such as those based on WiFi and cell-tower triangulation, can sometimes be used. Our method automatically determines the dynamic accuracy requirement for mobile search-based applications. As the user moves, both the accuracy requirements and the location sensor errors change. A-Loc continually tunes the energy expenditure to meet the changing accuracy requirements using the available sensors. A Bayesian estimation framework is used to model user location and sensor errors. Experiments are performed with Android G1 and AT&T Tilt phones, on paths that include outdoor and indoor locations, using war-driving data from Google and Microsoft. The experiments show that a-Loc not only provides significant energy savings, but also improves the accuracy achieved, because it uses multiple sensors.


title: A Review of Data Mining Techniques for Result Prediction in Sports

abstract: In the current world, sports produce considerable statistical information about each player, team, games, and seasons. Traditional sports science believed science to be owned by experts, coaches, team managers, and analyzers. However, sports organizations have recently realized the abundant science available in their data and sought to take advantage of that science through the use of data mining techniques. Sports data mining assists coaches and managers in result prediction, player performance assessment, player injury prediction, sports talent identification, and game strategy evaluation. The present study reviews previous research on data mining systems to predict sports results and evaluates the advantages and disadvantages of each system.


title: Antidepressant and anxiolytic potential of the multimodal antidepressant vortioxetine (Lu AA21004) assessed by behavioural and neurogenesis outcomes in mice

abstract: Vortioxetine (Lu AA21004) is an investigational novel antidepressant with multimodal activity that functions as a 5-HT3, 5-HT7 and 5-HT1D receptor antagonist, 5-HT1B receptor partial agonist, 5-HT1A receptor agonist and inhibitor of the 5-HT transporter in vitro. Here we explore its anxiolytic and antidepressant potential in adult mice. Vortioxetine was assessed in BalB/cJ@RJ mice using the open-field and forced-swim tests (acute: p.o. 1 h, repeated: daily p.o. 21 days), and in 129S6/SvEvTac mice using the novelty suppressed feeding paradigm (acute: p.o. 1 h, sustained: daily p.o. 14 or 21 days). Fluoxetine and diazepam were controls. Acute and repeated dosing of vortioxetine produced more pronounced anxiolytic- and antidepressant-like activities than fluoxetine. Vortioxetine significantly increased cell proliferation and cell survival and stimulated maturation of immature granule cells in the subgranular zone of the dentate gyrus of the hippocampus after 21 days of treatment. After 14 days, a high dose of vortioxetine increased dendritic length and the number of dendrite intersections, suggesting that vortioxetine accelerates the maturation of immature neurons. Vortioxetine displays an antidepressant and anxiolytic profile following repeated administration associated with increased neurogenesis at several stages. Vortioxetine effects were observed at low levels of 5-HT transporter occupancy, suggesting an alternative mechanism of action to 5-HT reuptake inhibition.


The native races of the Russian empire


title: Moving Technology Leaders up the Influence Curve

abstract: This article records an interview by the author with Brian Donovan, a prominent executive coach specializing in working with information technology (IT) professionals. The interview highlights the knowledge and experience he has gained and applied in his work, and provides insights into the career development of information systems (IS) leaders aspiring to the ranks of top management. Based on these insights, the article describes the four main implications for IS leadership identified by the author. 1