title: Beyond citation analysis: a model for assessment of research impact

abstract: Question: Is there a means of assessing researchimpact beyond citation analysis?Setting: The case study took place at the WashingtonUniversity School of Medicine Becker MedicalLibrary.Method: This case study analyzed the research studyprocess to identify indicators beyond citation countthat demonstrate research impact.Main Results: The authors discovered a number ofindicators that can be documented for assessment ofresearch impact, as well as resources to locateevidence of impact. As a result of the project, theauthors developed a model for assessment of researchimpact, the Becker Medical Library Model forAssessment of Research.Conclusion: Assessment of research impact usingtraditional citation analysis alone is not a sufficienttool for assessing the impact of research findings, andit is not predictive of subsequent clinical applicationsresulting in meaningful health outcomes. The BeckerModel can be used by both researchers and librariansto document research impact to supplement citationanalysis.INTRODUCTIONA traditional method of assessing research impact,citation analysis is performed by examining anindividual publication and assessing how often ithas been cited, if ever, by subsequent publications [1].It is a tool for gauging the extent of a publication’sinfluence in the literature and for tracking theadvancement of knowledge with the inherent as-sumption that significant publications will demon-strate a high citation count [2–4]. While citationanalysis is subject to some flaws, such as self-citingand reciprocal citing by colleagues [5, 6], it is acceptedas a standard tool for assessing the merits of apublication.In May 2007, a principal investigator from theOcular Hypertension Treatment Study (OHTS) [7]requested a citation analysis of OHTS articles afterviewing a poster by Sieving, ‘‘The Impact of NEI-Funded Multi-Center Trials: Bibliometric Indicationsof Dissemination, Acceptance and Implementation ofTrial Findings’’ [8], which had been presented at the2007 meeting of the Association for Research in Visionand Ophthalmology. The authors performed a cita-tion analysis of twenty-six OHTS peer-reviewedarticles using the SCOPUS, Web of Science, andEssential Science Indicators databases.Of the twenty-six journal articles, several demon-strated high rates of citation. That is, they were oftencited by subsequent publications. In some instances,the citations exceeded baseline citation rates as notedon Essential Science Indicators, a database thatassesses intellectual impact by analyzing the citationrate for a publication against other publications in aparticular area of research or multiple areas ofresearch.The high citation rates for the selected journalarticles sparked the authors’ interest in furtherinvestigation to determine why these articles werecited so often by other peer-reviewed journal articles.Was this indicative of significant findings that mighthave resulted in clinical outcomes? If so, what werethose outcomes and how could they be revealed?What other evidence of research impact could theauthors discover by going beyond citation analysis?A cursory search of the Google and Yahoo searchengines using key terms related to OHTS (i.e., openangle glaucoma, pachymetry, central corneal thick-ness, ocular hypertension) and the title of the study(i.e., OHTS, Ocular Hypertension Treatment Study)yielded findings such as practice guidelines, continu-ing education guidelines, curriculum guidelines,insurance coverage documents, and quality measuresguidelines that noted OHTS as supporting documen-tation. Further analysis using other resources, such asgovernment websites, revealed additional evidence ofresearch impact attributed to OHTS findings. Thesematerials are not usually indexed by databases, norare they consistently noted as cited-by publications.Given the depth of evidence of research impact thatwas not revealed from citation analysis alone, theauthors decided to perform a more systematic andcomprehensive evaluation.METHODOLOGYThe project methodology beyond the initial citationanalysis was neither a tidy nor linear process. Theauthors consulted with research investigators, clini-cians, and other librarians to gain insight about theresearch process in both bench and clinical studies inorder to identify tangible outcome indicators that canbe documented and quantified for assessment ofresearch impact. Indicators are defined as specific,concrete examples that demonstrate research impactas a result of a research finding or output. Examplesof tangible indicators include new or ancillaryresearch studies, research findings used as supporting


title: Diagnosis and treatment of acute uncomplicated cystitis.

abstract: Urinary tract infections are the most common bacterial infections in women. Most urinary tract infections are acute uncomplicated cystitis. Identifiers of acute uncomplicated cystitis are frequency and dysuria in an immunocompetent woman of childbearing age who has no comorbidities or urologic abnormalities. Physical examination is typically normal or positive for suprapubic tenderness. A urinalysis, but not urine culture, is recommended in making the diagnosis. Guidelines recommend three options for first-line treatment of acute uncomplicated cystitis: fosfomycin, nitrofurantoin, and trimethoprim/sulfamethoxazole (in regions where the prevalence of Escherichia coli resistance does not exceed 20 percent). Beta-lactam antibiotics, amoxicillin/clavulanate, cefaclor, cefdinir, and cefpodoxime are not recommended for initial treatment because of concerns about resistance. Urine cultures are recommended in women with suspected pyelonephritis, women with symptoms that do not resolve or that recur within two to four weeks after completing treatment, and women who present with atypical symptoms.


title: Design of Manchester II bi-phase encoder for MIL-STD-1553 protocol

abstract: Data bus standards are inevitable in all electronic systems. In avionics systems the medium used for data transfer should be highly reliable and communication should be done with a minimum amount of hardware. MIL-STD-1553 is one such data bus standard which is mainly used in military avionics applications for data transfer between critical systems in an aircraft. It is a bidirectional, digital time division multiplexed data bus which eliminates the use of point-to-point wiring. 1553 uses Manchester II bi-phase encoding scheme. Compared to other encoding schemes Manchester coding has some advantages like easy error detection, high noise immunity etc. Even with more bandwidth requirement 1553 protocol uses this encoding because of its low error rate. In this paper we discuss the design of a Manchester II bi-phase encoder and decoder for the 1553 data bus system. With the presented method, the encoder and decoder is modeled as state machine in HDL. This is explicitly designed for encoding and decoding the words of 1553 data bus protocol.


title: Jointly Learning Word Embeddings and Latent Topics

abstract: Word embedding models such as Skip-gram learn a vector-space representation for each word, based on the local word collocation patterns that are observed in a text corpus. Latent topic models, on the other hand, take a more global view, looking at the word distributions across the corpus to assign a topic to each word occurrence. These two paradigms are complementary in how they represent the meaning of word occurrences. While some previous works have already looked at using word embeddings for improving the quality of latent topics, and conversely, at using latent topics for improving word embeddings, such "two-step'' methods cannot capture the mutual interaction between the two paradigms. In this paper, we propose STE, a framework which can learn word embeddings and latent topics in a unified manner. STE naturally obtains topic-specific word embeddings, and thus addresses the issue of polysemy. At the same time, it also learns the term distributions of the topics, and the topic distributions of the documents. Our experimental results demonstrate that the STE model can indeed generate useful topic-specific word embeddings and coherent latent topics in an effective and efficient way.


title: Architectural layout planning using genetic algorithms

abstract: This paper presents a model which generates architectural layout for a single flat having regular shaped spaces; Bedroom, Bathroom, Kitchen, Balcony, Living and Dining Room. Using constraints at two levels; Topological (Adjacency, Compactness, Vaastu, and Open and Closed face constraints) and Dimensional (Length to Width ratio constraint), Genetic Algorithms have been used to generate the topological arrangement of spaces in the layout and further if required, feasibility have been dimensionally analyzed. Further easy evacuation form the selected layout in case of adversity has been proposed using Dijkstra's Algorithm. Later the proposed model has been tested for efficiency using various test cases. This paper also presents a classification and categorization of various problems of space planning.


title: Software Architecture Risk Containers

abstract: Our motivation is to determine whether risks such as implementation error-proneness can be isolated into three types of containers at design time. This paper identifies several container candidates in other research that fit the risk container concept. Two industrial case studies were used to determine which of three container types tested is most effective at isolating and predicting at design time the risk of implementation error-proneness. We found that Design Rule Containers were more effective than Use Case and Resource Containers.


title: Support vector machines in remote sensing : A review

abstract: A wide range of methods for analysis of airborne- and satellite-derived imagery continues to be proposed and assessed. In this paper, we review remote sensing implementations of support vector machines (SVMs), a promising machine learning methodology. This review is timely due to the exponentially increasing number of works published in recent years. SVMs are particularly appealing in the remote sensing field due to their ability to generalize well even with limited training samples, a common limitation for remote sensing applications. However, they also suffer from parameter assignment issues that can significantly affect obtained results. A summary of empirical results is provided for various applications of over one hundred published works (as of April, 2010). It is our hope that this survey will provide guidelines for future applications of SVMs and possible areas of algorithm enhancement.


title: CatchTartan: Representing and Summarizing Dynamic Multicontextual Behaviors

abstract: Representing and summarizing human behaviors with rich contexts facilitates behavioral sciences and user-oriented services. Traditional behavioral modeling represents a behavior as a tuple in which each element is one contextual factor of one type, and the tensor-based summaries look for high-order dense blocks by clustering the values (including timestamps) in each dimension. However, the human behaviors are multicontextual and dynamic: (1) each behavior takes place within multiple contexts in a few dimensions, which requires the representation to enable non-value and set-values for each dimension; (2) many behavior collections, such as tweets or papers, evolve over time. In this paper, we represent the behavioral data as a two-level matrix (temporal-behaviors by dimensional-values) and propose a novel representation for behavioral summary called Tartan that includes a set of dimensions, the values in each dimension, a list of consecutive time slices and the behaviors in each slice. We further develop a propagation method CatchTartan to catch the dynamic multicontextual patterns from the temporal multidimensional data in a principled and scalable way: it determines the meaningfulness of updating every element in the Tartan by minimizing the encoding cost in a compression manner. CatchTartan outperforms the baselines on both the accuracy and speed. We apply CatchTartan to four Twitter datasets up to 10 million tweets and the DBLP data, providing comprehensive summaries for the events, human life and scientific development.


The heritage of Apelles : studies in the art of the Renaissance


title: Eight Years of Rider Measurement in the Android Malware Ecosystem: Evolution and Lessons Learned

abstract: Despite the growing threat posed by Android malware, the research community is still lacking a comprehensive view of common behaviors and trends exposed by malware families active on the platform. Without such view, the researchers incur the risk of developing systems that only detect outdated threats, missing the most recent ones. In this paper, we conduct the largest measurement of Android malware behavior to date, analyzing over 1.2 million malware samples that belong to 1.2K families over a period of eight years (from 2010 to 2017). We aim at understanding how the behavior of Android malware has evolved over time, focusing on repackaging malware. In this type of threats different innocuous apps are piggybacked with a malicious payload (rider), allowing inexpensive malware manufacturing. One of the main challenges posed when studying repackaged malware is slicing the app to split benign components apart from the malicious ones. To address this problem, we use differential analysis to isolate software components that are irrelevant to the campaign and study the behavior of malicious riders alone. Our analysis framework relies on collective repositories and recent advances on the systematization of intelligence extracted from multiple anti-virus vendors. We find that since its infancy in 2010, the Android malware ecosystem has changed significantly, both in the type of malicious activity performed by the malicious samples and in the level of obfuscation used by malware to avoid detection. We then show that our framework can aid analysts who attempt to study unknown malware families. Finally, we discuss what our findings mean for Android malware detection research, highlighting areas that need further attention by the research community.


title: Functional assessment of a Multigrasp Myoelectric prosthesis: An amputee case study

abstract: This work presents a functional assessment of the Vanderbilt Multigrasp Hand prosthesis and Multigrasp Myoelectric Control method which the authors have previously described. In the study, a transradial amputee utilized the prosthetic system to perform the Southampton Hand Assessment Procedure (SHAP), which involves manipulation tasks designed to simulate the activities of daily living. The results of the study indicate 81% restoration of typical hand function and compare favorably to recently published SHAP results for commercially available single-grasp and multigrasp prosthetic hands. A video of the assessment is included as supplementary material.


title: Neuroticism, marital interaction, and the trajectory of marital satisfaction.

abstract: Theories of how initially satisfied marriages deteriorate or remain stable over time have been limited by a failure to distinguish between key facets of change. The present study defines the trajectory of marital satisfaction in terms of 2 separate parameters-(a) the initial level of satisfaction and (b) the rate of change in satisfaction over time-and seeks to estimate unique effects on each of these parameters with variables derived from intrapersonal and interpersonal models of marriage. Sixty newlywed couples completed measures of neuroticism, were observed during a marital interaction, and provided reports of marital satisfaction every 6 months for 4 years. Neuroticism was associated with initial levels of marital satisfaction but had no additional effects on rates of change. Behavior during marital interaction predicted rates of change in marital satisfaction but was not associated with initial levels.


title: Learning to Map Sentences to Logical Form

abstract: One of the classical goals of research in artificial intelligence is to construct systems that automatically recover the meaning of natural language text. Machine learning methods hold significant potential for addressing many of the challenges involved with these systems. This book describes a series of new techniques for learning to map sentences to logical form --- lambda-calculus representations of their meanings.


title: Massively parallel single-nucleus RNA-seq with DroNc-seq

abstract: DroNc-seq enables low-cost, high-throughput single-nucleus RNA-seq of tissues that are archived or difficult to dissociate, such as post-mortem human brain.


title: ZVS investigation of llc converters based on FHA assumptions

abstract: While resonant converters have been gaining much attention within the recent years, their exact mathematical description is rather complicated. Thus, simplified calculation methods are very attractive, e.g. the First Harmonic Approximation (FHA). Although the steady state solution of these simplified methods is usually quite acceptable and close to the exact solution, the prediction of the ZVS characteristic is either neglected or treated to simple. The ZVS characteristic is typically directly related to the voltage transfer function resulting in a too optimistic estimation of the operation range obtaining ZVS. Therefore, this paper focuses on a more accurate investigation of the resonant LLC converter's ZVS behavior and proposes two approximation methods, which are also based on simplified FHA assumptions allowing a fast estimation of different converter designs.


title: An optimal design of printed log-periodic antenna for L-band EMC applications

abstract: This paper proposes a 12-dipole printed log-periodic dipole array antenna (PLPDA) for L-band electromagnetic compatibility (EMC) applications. The antenna proposed is designed to operate in the frequency range of 0.8 GHz to 2.5 GHz. The proposed antenna design is the result of optimization performed using the Trusted Region Framework (TRF) algorithm. The low values of the S-parameter (S) of the optimized PLPDA antenna design suggest good matching. The antenna also provides satisfactory realized gain between 4.5 dBi to 6.3 dBi along with good directional characteristics in its operating frequency range.


Latch-free Synchronization in Database Systems: Silver Bullet or Fool's Gold?


title: Deep Neural Decision Forests

abstract: We present a novel approach to enrich classification trees with the representation learning ability of deep (neural) networks within an end-to-end trainable architecture. We combine these two worlds via a stochastic and differentiable decision tree model, which steers the formation of latent representations within the hidden layers of a deep network. The proposed model differs from conventional deep networks in that a decision forest provides the final predictions and it differs from conventional decision forests by introducing a principled, joint and global optimization of split and leaf node parameters. Our approach compares favourably to other state-of-the-art deep models on a large-scale image classification task like ImageNet.


title: User privacy risk calculator

abstract: User privacy is becoming an issue on the Internet due to common data breaches and various security threats. Services tend to require private user data in order to provide more personalized content and users are typically unaware of potential risks to their privacy. This paper proposes a risk calculator based on a feedforward neural network that will provide users with an ability to calculate risks to their privacy. The proposed calculator is evaluated on a set of real world example scenarios. Furthermore, to give more insight into privacy issues, each estimated risk is explained by several real life scenarios that might happen if the observed parameters are obtained by an attacker. In turn, this should raise user awareness and knowledge about privacy issues on the Internet.


title: Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints

abstract: As neural networks continue their reach into nearly every aspect of software operations, the details of those networks become an increasingly sensitive subject. Even those that deploy neural networks embedded in physical devices may wish to keep the inner working of their designs hidden -- either to protect their intellectual property or as a form of protection from adversarial inputs. The specific problem we address is how, through heavy system stack, given noisy and imperfect memory traces, one might reconstruct the neural network architecture including the set of layers employed, their connectivity, and their respective dimension sizes. Considering both the intra-layer architecture features and the inter-layer temporal association information introduced by the DNN design empirical experience, we draw upon ideas from speech recognition to solve this problem. We show that off-chip memory address traces and PCIe events provide ample information to reconstruct such neural network architectures accurately. We are the first to propose such accurate model extraction techniques and demonstrate an end-to-end attack experimentally in the context of an off-the-shelf Nvidia GPU platform with full system stack. Results show that the proposed techniques achieve a high reverse engineering accuracy and improve the one's ability to conduct targeted adversarial attack with success rate from 14.6\%$\sim$25.5\% (without network architecture knowledge) to 75.9\% (with extracted network architecture).


title: Sampling-based algorithms for optimal motion planning

abstract: United States. Air Force Office of Scientific Research (Michigan/AFRL Collaborative Center in Control Science Grant FA 8650-07-2-3744)


title: Light Absorption and Energy Transfer in the Antenna Complexes of Photosynthetic Organisms

abstract: The process of photosynthesis is initiated by the capture of sunlight by a network of light-absorbing molecules (chromophores), which are also responsible for the subsequent funneling of the excitation energy to the reaction centers. Through evolution, genetic drift, and speciation, photosynthetic organisms have discovered many solutions for light harvesting. In this review, we describe the underlying photophysical principles by which this energy is absorbed, as well as the mechanisms of electronic excitation energy transfer (EET). First, optical properties of the individual pigment chromophores present in light-harvesting antenna complexes are introduced, and then we examine the collective behavior of pigment-pigment and pigment-protein interactions. The description of energy transfer, in particular multichromophoric antenna structures, is shown to vary depending on the spatial and energetic landscape, which dictates the relative coupling strength between constituent pigment molecules. In the latter half of the article, we focus on the light-harvesting complexes of purple bacteria as a model to illustrate the present understanding of the synergetic effects leading to EET optimization of light-harvesting antenna systems while exploring the structure and function of the integral chromophores. We end this review with a brief overview of the energy-transfer dynamics and pathways in the light-harvesting antennas of various photosynthetic organisms.


title: High-power density hybrid converter topologies for low-power Dc-Dc SMPS

abstract: This paper gives a review of several emerging dc-dc converter topologies that combine capacitor-based and inductive converters in single hybrid converter structures. It is shown that, compared to the conventional topologies, the hybrid buck converters allow for a drastic reduction of the inductive components while minimizing switching losses and improving the overall power processing efficiency. Therefore, the hybrid converters result in a higher power density. As examples, buck with merged capacitive divider, a two-phase interleaved buck, and a differential buck-based multi-output power module for mobile applications are shown. The presented converters have up to four times smaller inductor volume and, at the same time, about 12% lower losses.


title: Computers that care: investigating the effects of orientation of emotion exhibited by an embodied computer agent

abstract: Embodied computer agents are becoming an increasingly popular human-computer interaction technique. Often, these agents are programmed with the capacity for emotional expression. This dissertation investigates the psychological effect of emotion in agents upon users. In particular, two types of emotion were evaluated: self-oriented emotion and other-oriented, empathic emotion. Empathic emotion was found to lead to more positive ratings of the agent by users, consistent with the hypothesis that empathic emotion is inferred to reflect caring for the user. No such effect was found for the presence of self-oriented emotion. ::: Experiment 1 compared an agent exhibiting both self-oriented and empathic emotion to an agent exhibiting self-oriented emotion only in a 2 (empathic emotion: absent vs. present) by 2 (gender dyad: male vs. female) between-subjects experiment (N = 48). Significant main effects demonstrated that the presence of empathic emotion increased users' ratings of the agent's likability, trustworthiness, and caring. In addition, caring was found to be a significant mediator of the effect of empathic emotion on both likability and trustworthiness. ::: To ensure that the effects of empathic emotion were not due to emotion more generally, and to investigate the potential moderating effect of self-oriented emotion, Experiment 2 additionally investigated the effect of self-oriented emotion in a 2 (self-oriented emotion: absent vs. present) by 2 (empathic emotion: absent vs. present) by 2 (gender dyad: male vs. female) between-subjects experiment (N = 96). Empathic emotion was found to have a significant effect on the agent's likability, trustworthiness, and perceived caring, as well as on the user's felt support, independent of the presence of self-oriented emotion. The presence of self-oriented emotion was found to have no such effects. Implications for the design of embodied computer agents are discussed and directions for future research are suggested.


title: Hierarchical Recurrent Neural Encoder for Video Representation with Application to Captioning

abstract: Recently, deep learning approach, especially deep Convolutional Neural Networks (ConvNets), have achieved overwhelming accuracy with fast processing speed for image classification. Incorporating temporal structure with deep ConvNets for video representation becomes a fundamental problem for video content analysis. In this paper, we propose a new approach, namely Hierarchical Recurrent Neural Encoder (HRNE), to exploit temporal information of videos. Compared to recent video representation inference approaches, this paper makes the following three contributions. First, our HRNE is able to efficiently exploit video temporal structure in a longer range by reducing the length of input information flow, and compositing multiple consecutive inputs at a higher level. Second, computation operations are significantly lessened while attaining more non-linearity. Third, HRNE is able to uncover temporal transitions between frame chunks with different granularities, i.e., it can model the temporal transitions between frames as well as the transitions between segments. We apply the new method to video captioning where temporal information plays a crucial role. Experiments demonstrate that our method outperforms the state-of-the-art on video captioning benchmarks. Notably, even using a single network with only RGB stream as input, HRNE beats all the recent systems which combine multiple inputs, such as RGB ConvNet plus 3D ConvNet.


title: Multidimensional SVM to include the samples of the derivatives in the reconstruction of a function

abstract: In this paper we propose a multidimensional regression estimation algorithm for estimating a function from its first derivatives. The proposed method is extended to introduce the information about the function itself and higher order derivatives. The proposed algorithm is able to exploit the dependency between the output variables to provide a better estimation of the function and it guarantees that the estimated derivatives belong to the same function. The method has been validated by synthetic test functions and it has been used to model a MESFET transistor including intermodulation distortion characterization, where the approximation of the derivatives of the characteristic function is mandatory.


title: Maternal age as a source of variation in the ability of an aphid to produce dispersing forms

abstract: Apterous parthenogentic females of the pea aphid,Acyrthosiphon pisum (Harris), begin to produce alate offspring soon after they have been subjected to crowding.


title: Vibrotactile stimulus frequency optimization for the haptic BCI prototype

abstract: The paper presents results from a psychophysical study conducted to optimize vibrotactile stimuli delivered to subject finger tips in order to evoke the somatosensory responses to be utilized next in a haptic brain computer interface (hBCI) paradigm. We also present the preliminary EEG evoked responses for the chosen stimulating frequency. The obtained results confirm our hypothesis that the hBCI paradigm concept is valid and it will allow for rapid stimuli presentation in order to improve information-transfer-rate (ITR) of the BCI.


title: Variational free energy and the Laplace approximation

abstract: This note derives the variational free energy under the Laplace approximation, with a focus on accounting for additional model complexity induced by increasing the number of model parameters. This is relevant when using the free energy as an approximation to the log-evidence in Bayesian model averaging and selection. By setting restricted maximum likelihood (ReML) in the larger context of variational learning and expectation maximisation (EM), we show how the ReML objective function can be adjusted to provide an approximation to the log-evidence for a particular model. This means ReML can be used for model selection, specifically to select or compare models with different covariance components. This is useful in the context of hierarchical models because it enables a principled selection of priors that, under simple hyperpriors, can be used for automatic model selection and relevance determination (ARD). Deriving the ReML objective function, from basic variational principles, discloses the simple relationships among Variational Bayes, EM and ReML. Furthermore, we show that EM is formally identical to a full variational treatment when the precisions are linear in the hyperparameters. Finally, we also consider, briefly, dynamic models and how these inform the regularisation of free energy ascent schemes, like EM and ReML.


title: Using Inter-Sentence Diverse Beam Search to Reduce Redundancy in Visual Storytelling

abstract: Visual storytelling includes two important parts: coherence between the story and images as well as the story structure. For image to text neural network models, similar images in the sequence would provide close information for story generator to obtain almost identical sentence. However, repeatedly narrating same objects or events will undermine a good story structure. In this paper, we proposed an inter-sentence diverse beam search to generate a more expressive story. Comparing to some recent models of visual storytelling task, which generate story without considering the generated sentence of the previous picture, our proposed method can avoid generating identical sentence even given a sequence of similar pictures.


title: Energy and Delay Efficient Routing Protocol (EDERP) for Threshold based Cluster Head Selection in Heterogeneous WSN

abstract: In recent era of ubiquitous computing the internet of things and sensor networks are researched widely. The deployment of the wireless sensor networks in the harsh environments ascends issues associated with delay clustering approaches, packet drop, delay, energy, link quality, mobility and coverage. Various research studies are proposing routing protocols clustering algorithm with research goal for reduction in terms of energy and delay. This paper focuses on delay and energy by introducing threshold based scheme. Furthermore energy and delay efficient routing protocol is proposed for cluster head selection in the heterogeneous wireless sensor networks. We have introduced delay and energy based adaptive threshold scheme in this paper to solve this problem. Furthermore this study presents new routing algorithm which contains energy and delay and velocity threshold based cluster-head election scheme. The cluster head is selected according to distance, velocity and energy where probability is set for the residual energy. The nodes are classified into normal, advanced and herculean levels. This paper presents new routing protocol named as energy and delay efficient routing protocol (EDERP). The MATLAB is used for simulation and comparison of the routing protocol with other protocols. The simulations results indicate that this protocol is effective regarding delay and energy.


Linguistic Cues to Deception and Perceived Deception in Interview Dialogues


THU_NGN at SemEval-2018 Task 2: Residual CNN-LSTM Network with Attention for English Emoji Prediction


Descriptive study of female roller derby athletes' beliefs about risk factors for injury in roller derby


DiMeX: A Text Mining System for Mutation-Disease Association Extraction


title: Optimized active-clamp circuit design for an isolated full-bridge current-fed DC-DC converter

abstract: This paper presents a new method for the capacitance selection of an active-clamp circuit to enhance the performance of an isolated current-fed full-bridge DC-DC converter. The developed approach can reduce the circulating energy through the bridge switches using an appropriate choice of the resonant frequency between the transformer leakage inductance and the clamp capacitance in relation to the switching frequency. Thus, current and the voltage stresses can be reduced and the converter efficiency improved. It has also been demonstrated that the use of a voltage-doubling output rectifier enhances the performance further. The optimized active-clamp circuit has been implemented on a 1.2 kW isolated current-fed full-bridge DC-DC converter and test results have confirmed the claimed improvements.


The Socialbot Network : When Bots Socialize for Fame & Money


title: Hoaxy: A Platform for Tracking Online Misinformation

abstract: Massive amounts of misinformation have been observed to spread in uncontrolled fashion across social media. Examples include rumors, hoaxes, fake news, and conspiracy theories. At the same time, several journalistic organizations devote significant efforts to high-quality fact checking of online claims. The resulting information cascades contain instances of both accurate and inaccurate information, unfold over multiple time scales, and often reach audiences of considerable size. All these factors pose challenges for the study of the social dynamics of online news sharing. Here we introduce Hoaxy, a platform for the collection, detection, and analysis of online misinformation and its related fact-checking efforts. We discuss the design of the platform and present a preliminary analysis of a sample of public tweets containing both fake news and fact checking. We find that, in the aggregate, the sharing of fact-checking content typically lags that of misinformation by 10-20 hours. Moreover, fake news are dominated by very active users, while fact checking is a more grass-roots activity. With the increasing risks connected to massive online misinformation, social news observatories have the potential to help researchers, journalists, and the general public understand the dynamics of real and fake news sharing.


title: How to Learn a Model Checker

abstract: We show how machine-learning techniques, particularly neural networks, offer a very effective and highly efficient solution to the approximate model-checking problem for continuous and hybrid systems, a solution where the general-purpose model checker is replaced by a model-specific classifier trained by sampling model trajectories. To the best of our knowledge, we are the first to establish this link from machine learning to model checking. Our method comprises a pipeline of analysis techniques for estimating and obtaining statistical guarantees on the classifier's prediction performance, as well as tuning techniques to improve such performance. Our experimental evaluation considers the time-bounded reachability problem for three well-established benchmarks in the hybrid systems community. On these examples, we achieve an accuracy of 99.82% to 100% and a false-negative rate (incorrectly predicting that unsafe states are not reachable from a given state) of 0.0007 to 0. We believe that this level of accuracy is acceptable in many practical applications and we show how the approximate model checker can be made more conservative by tuning the classifier through further training and selection of the classification threshold.


title: Intelligent power allocation and load management of more electric aircraft

abstract: Intelligent power allocation and load management systems have been playing an increasingly important role in aircrafts whose electrical network systems are getting more and more complex. Load shedding used to be the main means of aircraft power management. But the increasing number of electrical components and the emphasis of safety and human comfort call for more resilient power management. In this paper we present a novel power allocation and scheduling formulation which aims for minimum load shedding and optimal generator operational profiles. The problem is formulated as a mixed integer quadratic programming (MIQP) problem and solved by CPLEX optimization tool.


title: Air-to-Dielectric-Filled Two-Hole Substrate-Integrated Waveguide Directional Coupler

abstract: A two-hole directional coupler consisting of an air-filled substrate-integrated waveguide (AFSIW) coupled to a dielectric-filled substrate-integrated waveguide (SIW) is introduced and studied for the first time. The design of the proposed two-hole coupler, based on different dielectric-loaded SIWs and on multilayer printed circuit board process, is presented in detail with its theoretical foundation. The proposed coupler is of interest for monitoring high-performance, self-packaged, and low-cost millimeter-wave substrate integrated systems based on AFSIW. For demonstration purposes, a forward coupler operating in the Ka-band satellite uplink frequency band (27 to 31 GHz) has been designed and fabricated. In its frequency range of operation, it achieves measured insertion loss, coupling, and isolation of 0.28 ± 0.15 dB, 20.8 ± 0.24 dB, and 46.76 ± 3.92 dB, respectively.


The role of visual scenes in spoken language comprehension : evidence from eye-tracking


title: A Leaf Vein Extraction Method Based On Snakes Technique

abstract: Leaf vein extraction is a key step of modeling plant organs and living plant recognition. An efficient leaf vein extraction method is proposed in this paper by combining snakes technique with cellular neural networks (CNN). The active contours technique based on CNN provide high flexibility and control for the contour dynamics of the snakes. This approach has the advantage of applying a priori knowledge, puts similar characteristics from both the implicit and parametric models, to improve the precise and robustness of the segmentation. The experimental results have also shown that the proposed method can obtain satisfactory results of leaf segmentation (to extract leaf veins and outlines), for the subsequent modeling leaf and leaf recognition


title: CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes

abstract: The CoNLL-2012 shared task involved predicting coreference in three languages -- English, Chinese and Arabic -- using OntoNotes data. It was a follow-on to the English-only task organized in 2011. Until the creation of the OntoNotes corpus, resources in this subfield of language processing have tended to be limited to noun phrase coreference, often on a restricted set of entities, such as ACE entities. OntoNotes provides a large-scale corpus of general anaphoric coreference not restricted to noun phrases or to a specified set of entity types and covering multiple languages. OntoNotes also provides additional layers of integrated annotation, capturing additional shallow semantic structure. This paper briefly describes the OntoNotes annotation (coreference and other layers) and then describes the parameters of the shared task including the format, pre-processing information, evaluation criteria, and presents and discusses the results achieved by the participating systems. Being a task that has a complex evaluation history, and multiple evalation conditions, it has, in the past, been difficult to judge the improvement in new algorithms over previously reported results. Having a standard test set and evaluation parameters, all based on a resource that provides multiple integrated annotation layers (parses, semantic roles, word senses, named entities and coreference) that could support joint models, should help to energize ongoing research in the task of entity and event coreference.


title: Guidelines for using human event-related potentials to study cognition : Recording standards and publication criteria

abstract: Event-related potentials ~ERPs! recorded from the human scalp can provide important information about how the human brain normally processes information and about how this processing may go awry in neurological or psychiatric disorders. Scientists using or studying ERPs must strive to overcome the many technical problems that can occur in the recording and analysis of these potentials. The methods and the results of these ERP studies must be published in a way that allows other scientists to understand exactly what was done so that they can, if necessary, replicate the experiments. The data must then be analyzed and presented in a way that allows different studies to be compared readily. This paper presents guidelines for recording ERPs and criteria for publishing the results.


title: eBird: a human/computer learning network for biodiversity conservation and research

abstract: In this paper we describe eBird, a citizen science project that takes advantage of human observational capacity and machine learning methods to explore the synergies between human computation and mechanical computation. We call this model a Human/Computer Learning Network, whose core is an active learning feedback loop between humans and machines that dramatically improves the quality of both, and thereby continually improves the effectiveness of the network as a whole. Human/Computer Learning Networks leverage the contributions of a broad recruitment of human observers and processes their contributed data with Artificial Intelligence algorithms leading to a computational power that far exceeds the sum of the individual parts.


title: SimLand: a prototype to simulate land conversion through the integrated GIS and CA with AHP-derived transition rules

abstract: This paper presents a prototype of a simulation model based on cellular automata (CA), and multicriteria evaluation (MCE) and integrated with GIS. Specifically, a method, analytical hierarchy process (AHP), of MCE is used here to derive behaviour-oriented rules of transition in CA. A ‘tight' integration strategy is adopted, which means that the modules of MCE and CA are written in the C programming language and built within ARC/INFO GIS. Designed to run on a workstation Unix SimL and fully utilizes the graphical user interface (GUI),which allows the modelto be driven by menusand automate the simulation of land conversion in the urban-rural fringe. The combination of three elements, GIS, CA, and MCE, has several advantages: visualization of decision-making, easier access to spatial information, and the more realistic definition of transition rules in CA.


title: A comparative study of data mining techniques for credit scoring in banking

abstract: Credit is becoming one of the most important incomes of banking. Past studies indicate that the credit risk scoring model has been better for Logistic Regression and Neural Network. The purpose of this paper is to conduct a comparative study on the accuracy of classification models and reduce the credit risk. In this paper, we use data mining of enterprise software to construct four classification models, namely, decision tree, logistic regression, neural network and support vector machine, for credit scoring in banking. We conduct a systematic comparison and analysis on the accuracy of 17 classification models for credit scoring in banking. The contribution of this paper is that we use different classification methods to construct classification models and compare classification models accuracy, and the evidence demonstrates that the support vector machine models have higher accuracy rates and therefore outperform past classification methods in the context of credit scoring in banking.


title: Reconfigurable helical antenna based on an origami structure for wireless communication system

abstract: This paper presents a reconfigurable axial mode helical antenna which could be folded and unfolded using rigidly foldable origami structure to work at different resonant frequencies for kinds of wireless systems. Measured results show that the folded state could operate at both 1.82 GHz and 2.14 GHz. And the measured operating frequency for the unfolded state is 860 MHz. The frequency reconfigurability up to 1.28 GHz was achieved, and the height of the folded state could be reduced to 17% of the height of the unfolded state.


title: Correlated Nonideal Effects of Dark and Light I–V Characteristics in a-Si/c-Si Heterojunction Solar Cells

abstract: a-Si/c-Si (amorphous Silcon/crystalline Silicon) heterojunction solar cells exhibit several distinctive dark and light I-V nonideal features. The dark I-V of these cells exhibits unusually high ideality factors at low forward-bias and the occurrence of a “knee” at medium forward-bias. Nonidealities under illumination, such as the failure of superposition and the occurrence of an “S-type” curve, are also reported in these cells. However, the origin of these nonidealities and how the dark I-V nonidealities manifest themselves under illumination, and vice versa, have not been clearly and consistently explained in the current literature. In this study, a numerical framework is used to interpret the origin of the dark I-V nonidealities, and a novel simulation technique is developed to separate the photo-current and the contact injection current components of the light I-V. Using this technique, the voltage dependence of photo-current is studied to explain the failure of the superposition principle and the origin of the S-type light I-V characteristics. The analysis provides a number of insights into the correlations between the dark I-V and the light I-V. Finally, using the experimental results from this study and from the current literature, it is shown that these nonideal effects indeed affect the dark I-V and the light I-V in a predictable manner.


title: Tied Kronecker Product Graph Models to Capture Variance in Network Populations

abstract: Much of the past work on mining and modeling networks has focused on understanding the observed properties of single example graphs. However, in many real-life applications it is important to characterize the structure of populations of graphs. In this work, we analyze the distributional properties of probabilistic generative graph models (PGGMs) for network populations. PGGMs are statistical methods that model the network distribution and match common characteristics of real-world networks. Specifically, we show that most PGGMs cannot reflect the natural variability in graph properties observed across multiple networks because their edge generation process assumes independence among edges. Then, we propose the mixed Kronecker Product Graph Model (mKPGM), a scalable generalization of KPGMs that uses tied parameters to increase the variability of the sampled networks, while preserving the edge probabilities in expectation. We compare mKPGM to several other graph models. The results show that learned mKPGMs accurately represent the characteristics of real-world networks, while also effectively capturing the natural variability in network structure.


title: Circularly-Polarized Wide Slot Antenna Loaded With a Parasitic Patch

abstract: A design is described of a broadband circularly-polarized (CP) slot antenna. A conventional annular-ring slot antenna is first analyzed, and it is found that two adjacent CP modes can be simultaneously excited through the proximity coupling of an L-shaped feed line. By tuning the dimensions of this L-shaped feed line, the two CP modes can be coupled together and a broad CP bandwidth is thus formed. The design method is also valid when the inner circular patch of the annular-ring slot antenna is vertically raised from the ground plane. In this case, the original band-limited ring slot antenna is converted into a wide-band structure that is composed of a circular wide slot and a parasitic patch, and consequently the CP bandwidth is further enhanced. For the patch-loaded wide slot antenna, its key parameters are investigated to show how to couple the two CP modes and achieve impedance matching. The effects of the distance between the parasitic patch and wide slot on the CP bandwidth and antenna gain are also presented and discussed in details.


title: Next-Generation Sequencing Platforms

abstract: Automated DNA sequencing instruments embody an elegant interplay among chemistry, engineering, software, and molecular biology and have built upon Sanger's founding discovery of dideoxynucleotide sequencing to perform once-unfathomable tasks. Combined with innovative physical mapping approaches that helped to establish long-range relationships between cloned stretches of genomic DNA, fluorescent DNA sequencers produced reference genome sequences for model organisms and for the reference human genome. New types of sequencing instruments that permit amazing acceleration of data-collection rates for DNA sequencing have been developed. The ability to generate genome-scale data sets is now transforming the nature of biological inquiry. Here, I provide an historical perspective of the field, focusing on the fundamental developments that predated the advent of next-generation sequencing instruments and providing information about how these instruments work, their application to biological research, and the newes...


title: An ultra low power system architecture for sensor network applications

abstract: Recent years have seen a burgeoning interest in embedded wireless sensor networks with applications ranging from habitat monitoring to medical applications. Wireless sensor networks have several important attributes that require special attention to device design. These include the need for inexpensive, long-lasting, highly reliable devices coupled with very low performance requirements. Ultimately, the "holy grail" of this design space is a truly untethered device that operates off of energy scavenged from the ambient environment. In this paper, we describe an application-driven approach to the architectural design and implementation of a wireless sensor device that recognizes the event-driven nature of many sensor-network workloads. We have developed a full-system simulator for our sensor node design to verify and explore our architecture. Our simulation results suggest one to two orders of magnitude reduction in power dissipation over existing commodity-based systems for an important class of sensor network applications. We are currently in the implementation stage of design, and plan to tape out the first version of our system within the next year.


6 Energy Management for Intelligent Buildings


title: Mapping the zoonotic niche of Ebola virus disease in Africa

abstract: Ebola virus disease (EVD) is a complex zoonosis that is highly virulent in humans. The largest recorded outbreak of EVD is ongoing in West Africa, outside of its previously reported and predicted niche. We assembled location data on all recorded zoonotic transmission to humans and Ebola virus infection in bats and primates (1976-2014). Using species distribution models, these occurrence data were paired with environmental covariates to predict a zoonotic transmission niche covering 22 countries across Central and West Africa. Vegetation, elevation, temperature, evapotranspiration, and suspected reservoir bat distributions define this relationship. At-risk areas are inhabited by 22 million people; however, the rarity of human outbreaks emphasises the very low probability of transmission to humans. Increasing population sizes and international connectivity by air since the first detection of EVD in 1976 suggest that the dynamics of human-to-human secondary transmission in contemporary outbreaks will be very different to those of the past.


title: QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies

abstract: In 2003, the QUADAS tool for systematic reviews of diagnostic accuracy studies was developed. Experience, anecdotal reports, and feedback suggested areas for improvement; therefore, QUADAS-2 was developed. This tool comprises 4 domains: patient selection, index test, reference standard, and flow and timing. Each domain is assessed in terms of risk of bias, and the first 3 domains are also assessed in terms of concerns regarding applicability. Signalling questions are included to help judge risk of bias. The QUADAS-2 tool is applied in 4 phases: summarize the review question, tailor the tool and produce review-specific guidance, construct a flow diagram for the primary study, and judge bias and applicability. This tool will allow for more transparent rating of bias and applicability of primary diagnostic accuracy studies.


title: A Hybrid Approach for Predicting Aging-Related Failures of Software Systems

abstract: Software aging is a chronic process that is hidden under system monitoring until a system failure occurs. Aging related failures (ARFs) are the result of a variety of complex factors. Therefore, how to precisely predict the ARFs for a running software system is a challenge problem. Previous studies typically predict ARFs by means of predicting the time to resource exhaustion (TTE), which adopts resource data as aging indicators to predict when the resource data achieve the preset threshold. However, the practical effect of prior approaches are far from satisfactory due to lack of effective aging indicators and difficult to set accurate threshold. In this paper, we propose a hybrid approach, which combines model and measurements to construct a probabilistic aging indicator. The aging indicator is a multifactorial aging indicator that is more effective than traditional ones. Moreover, the hybrid approach is threshold-free in ARFs prediction. We evaluate the hybrid approach in Data caching system and Media streaming system, the results show that the hybrid approach can achieve high precision and recall for ARFs prediction. Compared to previous approaches, our approach increases the prediction precision and recall significantly.


title: Improving energy efficiency of DRAM by exploiting half page row access

abstract: DRAM energy is an important component to optimize in modern computing systems. One outstanding source of DRAM energy is the energy to fetch data stored on cells to the row buffer, which occurs during two DRAM operations, row activate and refresh. This work exploits previously proposed half page row access, modifying the wordline connections within a bank to halve the number of cells fetched to the row buffer, to save energy in both cases. To accomplish this, we first change the data wire connections in the sub-array to reduce the cost of row buffer overfetch in multi-core systems which yields a 12% energy savings on average and a slight performance improvement in quad-core systems. We also propose charge recycling refresh, which reuses charge left over from a prior half page refresh to refresh another half page. Our charge recycling scheme is capable of reducing both auto- and self-refresh energy, saving more than 15% of refresh energy at 85°C, and provides even shorter refresh cycle time. Finally, we propose a refresh scheduling scheme that can dynamically adjust the number of charge recycled half pages, which can save up to 30% of refresh energy at 85°C.


B LACK BOX VARIATIONAL INFERENCE FOR STATE SPACE MODELS


A study of attention-based neural machine translation model on Indian languages.


title: A microbenchmark to study GPU performance models

abstract: Basic microarchitectural features of NVIDIA GPUs have been stable for a decade, and many analytic solutions were proposed to model their performance. We present a way to review, systematize, and evaluate these approaches by using a microbenchmark. In this manner, we produce a brief algebraic summary of key elements of selected performance models, identify patterns in their design, and highlight their previously unknown limitations. Also, we identify a potentially superior method for estimating performance based on classical work.


title: Instrumentation and sampling strategies for cooperative concurrency bug isolation

abstract: Fixing concurrency bugs (or "crugs") is critical in modern software systems. Static analyses to find crugs such as data races and atomicity violations scale poorly, while dynamic approaches incur high run-time overheads. Crugs manifest only under specific execution interleavings that may not arise during in-house testing, thereby demanding a lightweight program monitoring technique that can be used post-deployment. We present Cooperative Crug Isolation (CCI), a low-overhead instrumentation framework to diagnose production-run failures caused by crugs. CCI tracks specific thread interleavings at run-time, and uses statistical models to identify strong failure predictors among these. We offer a varied suite of predicates that represent different trade-offs between complexity and fault isolation capability. We also develop variant random sampling strategies that suit different types of predicates and help keep the run-time overhead low. Experiments with 9 real-world bugs in 6 non-trivial C applications show that these schemes span a wide spectrum of performance and diagnosis capabilities, each suitable for different usage scenarios.


title: High-frequency modeling and signal integrity analysis of high-density silicone rubber socket

abstract: In this paper, we propose and analyze an equivalent RLGC model of silicone rubber socket with single-ended signaling. A silicone rubber socket consists of highly dense metal powders in elastic silicone rubber. When the silicone rubber is compressed, metal powders form a column which corresponds to a pad of package. Thus, it can be modeled as a pair of cylinders. We have successfully verified the proposed model using a 3D electromagnetic (EM) solver in frequency domain and the eye diagram measurement in time domain. As a result, the verified model can be used to determine whether socket is applicable to the test system in the simulation level concurrently with offering physical insight and reducing time spent 3D EM simulating socket.


title: Intrusion detection systems in Internet of Things: A literature review

abstract: The Internet of Things (IoT) is a new model that integrates physical objects and Internet and became one of the principal technological evolutions of computing. It is estimated that a trillion of physical objects will be connected to the Internet until 2022. The low accessibility and the lack of interoperability of many of these devices in a vast heterogenous landscape will make it very hard to design specific security measures and apply specific security mechanism. Moreover, IoT networks still exposed and vulnerable to attacks aimed to disrupt the network. Therefore, additional security tools specific to IoT are needed. Intrusion Detection System (IDS) could fulfill this purpose. In this paper, we present a literature review on the IDS in IoT topic, mainly focusing on the current state of research by examining the literature, identifying current trends and presenting open issues and future directions.


title: Smart governance through bigdata: Digital transformation of public agencies

abstract: Bigdata is a potential instrument to transform traditional governance into smart governance. There are a long debate and discussion on the application of big data for the transformation of traditional public administration to modern and smart public administration in the academician, researchers, and policymakers. This study aims to explore the suitability and applicability of big data for smart governance of public agencies. A systematic review of literature and meta-analysis method is employed with various levels of scales and indicators. Literature survey shows that a number of models have been developed to explain smart governance but systematic research on the suitability and applicability of big data for smart governance of public agencies is still lacking. This article argues that the application of big data for smart governance in the public sector can increase the efficiency of the public agencies fastest public service delivery, enhancing transparency, reducing public hassle and helping to the become a smart agency. This paper further argues that implementation of big data for smart governance has a significant role in timely, error-free, appropriate and cost-effective service delivery to citizens which leads to the sustainable economic development of a country. The findings suggest that every public-sector agency should be brought under smart governance which should be a fully promoted under big data technologies for easy access, transparent and accountable, and hassle-free public agencies.


title: Triangle Generative Adversarial Networks

abstract: A Triangle Generative Adversarial Network ($\Delta$-GAN) is developed for semi-supervised cross-domain joint distribution matching, where the training data consists of samples from each domain, and supervision of domain correspondence is provided by only a few paired samples. $\Delta$-GAN consists of four neural networks, two generators and two discriminators. The generators are designed to learn the two-way conditional distributions between the two domains, while the discriminators implicitly define a ternary discriminative function, which is trained to distinguish real data pairs and two kinds of fake data pairs. The generators and discriminators are trained together using adversarial learning. Under mild assumptions, in theory the joint distributions characterized by the two generators concentrate to the data distribution. In experiments, three different kinds of domain pairs are considered, image-label, image-image and image-attribute pairs. Experiments on semi-supervised image classification, image-to-image translation and attribute-based image generation demonstrate the superiority of the proposed approach.


title: Open source social media intelligence for enabling government applications: extended abstract

abstract: Open-source social media intelligence (OSSMInt) is a field comprising of techniques and applications to analyze and mine open-source social media data for extracting actionable information and useful insights. The focus of the work presented in this paper is on novel applications and techniques of OSSMInt in the government sector. I propose and develop several novel use-cases and applications around OSSMInt for government and broadly divide them into three categories: identification, prediction, and response applications. In particular, I present solutions, tools and techniques for analyzing data from micro-blogging website to analyze citizen complaints and grievances in the public sector [response]. The research presented in this paper also describes my work on analyzing data from Twitter micro-blogging website to early forecast a civil unrest and protest [prediction]. Furthermore, I build various applications around identification and detection that are useful for the government and security analysts. I demonstrate the application of OSSMInt for identifying religious conflicts within society by mining public opinions on Tumblr website and fill the gaps of offline surveys. The study presented in this paper propose solutions for enabling law enforcement agencies to detect, prevent and combat online radicalization and extremism (content, users, and communities) by mining data from Tumblr, Twitter and YouTube websites [identification]. Furthermore, I also propose and build an application for detecting secret message exchanged in an adversarial communication and capture the obfuscated terms in messages. The central component of my proposed solution approach is the application of information retrieval and machine learning based techniques and algorithms. The study consists of experimenting with a diverse range of machine learning algorithms such as unsupervised, semi-supervised and supervised learning (k-NN, SVM, naive Bayes, random forest and decision tree) based algorithms. Based on the experimental results, I observe that there are several untapped opportunities as well as technical challenges in exploiting open-source social media data by the government for intelligence gathering. Furthermore, social media is a rich source of information for building security informatics based applications by mining the content, users, links and relationships on social media platforms.


title: Polymer-Based Additive Manufacturing: Historical Developments, Process Types and Material Considerations

abstract: 3D printing is a manufacturing technique where parts are built in a layer-by-layer fashion. The earliest embodiments were based on generating successive layers of solidified materials at the air liquid interface through exposure with actinic radiation and the lowering of the growing object into a vat of photoactive material. One of the earliest concepts was by Otto Munz in 1956 where he describes “photo-glyph recording”. However, many today consider Charles Hull the father of 3D printing as it was his patent that coined the term “stereolithography” and was the first to commercialize a 3D printing apparatus.


title: What is Usability? A Characterization based on ISO 9241-11 and ISO/IEC 25010

abstract: Abstract. According to Brooke [3] “Usability does not exist in anyabsolute sense; it can only be deﬁned with reference to particular con-texts.” That is, one cannot speak of usability without specifying what that particular usability is characterized by. Driven by the feedback ofa reviewer at an international conference, I explore in which way onecan precisely specify the kind of usability they are investigating in agiven setting. Finally, I come up with a formalism that deﬁnes usabil-ity as a quintuple comprising the elements level of usability metrics,product, users, goals and context of use. Providing concrete values forthese elements then constitutes the investigated type of usability. Theuse of this formalism is demonstrated in two case studies. 1 Introduction In 2014, I submitted a research paper about a concept called Usability-based SplitTesting 1 to a web engineering conference [10]. My evaluation involved a ques-tionnaire that asked for ratings of diﬀerent factors of usability based on a novelusability instrument speciﬁcally developed for web interfaces [11]. This instru-ment comprises the items


title: Two-Factor Authentication Resilient to Server Compromise Using Mix-Bandwidth Devices

abstract: Two-factor authentication (TFA), enabled by hardware tokens and personal devices, is gaining momentum. The security of TFA schemes relies upon a human-memorable password p drawn from some implicit dictionary D and a t-bit device-generated one-time PIN z. Compared to password-only authentication, TFA reduces the probability of adversary’s online guessing attack to 1/(|D| ∗ 2) (and to 1/2 if the password p is leaked). However, known TFA schemes do not improve security in the face of offline dictionary attacks, because an adversary who compromises the service and learns a (salted) password hash can still recover the password with O(|D|) amount of effort. This password might be reused by the user at another site employing password-only authentication. We present a suite of efficient novel TFA protocols which improve upon password-only authentication by a factor of 2 with regards to both the online guessing attack and the offline dictionary attack. To argue the security of the presented protocols, we first provide a formal treatment of TFA schemes in general. The TFA protocols we present enable utilization of devices that are connected to the client over several channel types, formed using manual PIN entry, visual QR code capture, wireless communication (Bluetooth or WiFi), and combinations thereof. Utilizing these various communication settings we design, implement, and evaluate the performance of 13 different TFA mechanisms, and we analyze them with respect to security, usability (manual effort needed beyond typing a password), and deployability (need for additional hardware or software), showing consistent advantages over known TFA schemes.


title: CApRI: CAche-conscious data reordering for irregular codes

abstract: Caches play a critical role in today's computer systems and optimizing their performance has been a critical objective in the last couple of decades. Unfortunately, compared to a plethora of work in software and hardware directed code/data optimizations, much less effort has been spent in understanding the fundamental characteristics of data access patterns exhibited by application programs and their interaction with the underlying cache hardware. Therefore, in general it is hard to reason about cache behavior of a program running on a target system. Motivated by this observation, we first set up a "locality model" that can help us determine the theoretical bounds of the cache misses caused by irregular data accesses. We then explain how this locality model can be used for different data locality optimization purposes. After that, based on our model, we propose a data reordering (data layout reorganization) scheme that can be applied after any existing data reordering schemes for irregular applications to improve cache performance by further reducing the cache misses. We evaluate the effectiveness of our scheme using a set of 8 programs with irregular data accesses, and show that it brings significant improvements over the state-of-the-art on two commercial multicore machines.


title: Contextual anomaly detection framework for big sensor data

abstract: The ability to detect and process anomalies for Big Data in real-time is a difficult task. The volume and velocity of the data within many systems makes it difficult for typical algorithms to scale and retain their real-time characteristics. The pervasiveness of data combined with the problem that many existing algorithms only consider the content of the data source; e.g. a sensor reading itself without concern for its context, leaves room for potential improvement. The proposed work defines a contextual anomaly detection framework. It is composed of two distinct steps: content detection and context detection. The content detector is used to determine anomalies in real-time, while possibly, and likely, identifying false positives. The context detector is used to prune the output of the content detector, identifying those anomalies which are considered both content and contextually anomalous. The context detector utilizes the concept of profiles, which are groups of similarly grouped data points generated by a multivariate clustering algorithm. The research has been evaluated against two real-world sensor datasets provided by a local company in Brampton, Canada. Additionally, the framework has been evaluated against the open-source Dodgers dataset, available at the UCI machine learning repository, and against the R statistical toolbox.


title: Flexibly-bounded Rationality and Marginalization of Irrationality Theories for Decision Making

abstract: In this paper the theory of flexibly-bounded rationality which is an extension to the theory of bounded rationality is revisited. Rational decision making involves using information which is almost always imperfect and incomplete together with some intelligent machine which if it is a human being is inconsistent to make decisions. In bounded rationality, this decision is made irrespective of the fact that the information to be used is incomplete and imperfect and that the human brain is inconsistent and thus this decision that is to be made is taken within the bounds of these limitations. In the theory of flexibly-bounded rationality, advanced information analysis is used, the correlation machine is applied to complete missing information and artificial intelligence is used to make more consistent decisions. Therefore flexibly-bounded rationality expands the bounds within which rationality is exercised. Because human decision making is essentially irrational, this paper proposes the theory of marginalization of irrationality in decision making to deal with the problem of satisficing in the presence of irrationality.


title: A language modeling framework for resource selection and results merging

abstract: Statistical language models have been proposed recently for several information retrieval tasks, including the resource selection task in distributed information retrieval. This paper extends the language modeling approach to integrate resource selection, ad-hoc searching, and merging of results from different text databases into a single probabilistic retrieval model. This new approach is designed primarily for Intranet environments, where it is reasonable to assume that resource providers are relatively homogeneous and can adopt the same kind of search engine. Experiments demonstrate that this new, integrated approach is at least as effective as the prior state-of-the-art in distributed IR.


title: A what-and-where fusion neural network for recognition and tracking of multiple radar emitters

abstract: A neural network recognition and tracking system is proposed for classification of radar pulses in autonomous Electronic Support Measure systems. Radar type information is considered with position-specific information from active emitters in a scene. Type-specific parameters of the input pulse stream are fed to a neural network classifier trained on samples of data collected in the field. Meanwhile, a clustering algorithm is used to separate pulses from different emitters according to position-specific parameters of the input pulse stream. Classifier responses corresponding to different emitters are separated into tracks, or trajectories, one per active emitter, allowing for more accurate identification of radar types based on multiple views of emitter data along each emitter trajectory. Such a What-and-Where fusion strategy is motivated by a similar subdivision of labor in the brain. The fuzzy ARTMAP neural network is used to classify streams of pulses according to radar type using their functional parameters. Simulation results obtained with a radar pulse data set indicate that fuzzy ARTMAP compares favorably to several other approaches when performance is measured in terms of accuracy and computational complexity. Incorporation into fuzzy ARTMAP of negative match tracking (from ARTMAP-IC) facilitated convergence during training with this data set. Other modifications improved classification of data that include missing input pattern components and missing training classes. Fuzzy ARTMAP was combined with a bank of Kalman filters to group pulses transmitted from different emitters based on their position-specific parameters, and with a module to accumulate evidence from fuzzy ARTMAP responses corresponding to the track defined for each emitter. Simulation results demonstrate that the system provides a high level of performance on complex, incomplete and overlapping radar data.


title: Blockchain for the IoT: Opportunities and Challenges

abstract: Blockchain technology has been transforming the financial industry and has created a new crypto-economy in the last decade. The foundational concepts such as decentralized trust and distributed ledger are promising for distributed, and large-scale Internet of Things (IoT) applications. However, the applications of Blockchain beyond cryptocurrencies in this domain are few and far between because of the lack of understanding and inherent architectural challenges. In this paper, we describe the opportunities for applications of blockchain for the IoT and examine the challenges involved in architecting Blockchain-based IoT applications.


title: A strong baseline for question relevancy ranking

abstract: The best systems at the SemEval-16 and SemEval-17 community question answering shared tasks -- a task that amounts to question relevancy ranking -- involve complex pipelines and manual feature engineering. Despite this, many of these still fail at beating the IR baseline, i.e., the rankings provided by Google's search engine. We present a strong baseline for question relevancy ranking by training a simple multi-task feed forward network on a bag of 14 distance measures for the input question pair. This baseline model, which is fast to train and uses only language-independent features, outperforms the best shared task systems on the task of retrieving relevant previously asked questions.


title: Spectral-Pruning: Compressing deep neural network via spectral analysis

abstract: The model size of deep neural network is getting larger and larger to realize superior performance in complicated tasks. This makes it difficult to implement deep neural network in small edge-computing devices. To overcome this problem, model compression methods have been gathering much attention. However, there have been only few theoretical back-grounds that explain what kind of quantity determines the compression ability. To resolve this issue, we develop a new theoretical frame-work for model compression, and propose a new method called {\it Spectral-Pruning} based on the theory. Our theoretical analysis is based on the observation such that the eigenvalues of the covariance matrix of the output from nodes in the internal layers often shows rapid decay. We define "degree of freedom" to quantify an intrinsic dimensionality of the model by using the eigenvalue distribution and show that the compression ability is essentially controlled by this quantity. Along with this, we give a generalization error bound of the compressed model. Our proposed method is applicable to wide range of models, unlike the existing methods, e.g., ones possess complicated branches as implemented in SegNet and ResNet. Our method makes use of both "input" and "output" in each layer and is easy to implement. We apply our method to several datasets to justify our theoretical analyses and show that the proposed method achieves the state-of-the-art performance.


title: RFID tag positioning with the aid of an active electronically-steered array

abstract: Thanks to the nowadays widespread use of radio frequency identification (RFID), applications have emerged concerning positioning techniques for passive RFID tags. Most of the approaches so far deliver low positioning precision and require moving parts (mechanical antenna steering), which limits their application. Herein, we introduce an RFID tag positioning system utilizing an active electronically-steered array, where the positioning methodology is based on the principles of radar systems. We thoroughly present the main attributes of the system with the aid of an FEM (finite element method) simulation model and investigate the system performance with far-field tests. The demonstrated positing precision of less than 40 mm can be helpful in applications like mobile robot localization and the automated handling of packaged goods.


title: Protein kinase A, not Epac, suppresses hedgehog activity and regulates glucocorticoid sensitivity in acute lymphoblastic leukemia cells.

abstract: Cyclic AMP synergizes strongly with glucocorticoids (GC) to induce apoptosis in normal or malignant lymphoid cells. We examined the individual roles that cAMP-dependent protein kinase (PKA) and Epac (exchange protein directly activated by cAMP), two intracellular cAMP receptors, play in this synergistic effect. Our studies demonstrate that PKA is responsible for the observed synergism with GC, whereas Epac exerts a weak antagonistic effect against GC-induced apoptosis. We find that endogenous PKA activity is higher in the GC-sensitive clone than in the GC-resistant clone. In the GC-sensitive clone, higher PKA activity is associated with lower Hedgehog (Hh) activity. Moreover, inhibition of Hh activity by Hh pathway-specific inhibitors leads to cell cycle arrest and apoptosis in CEM (human acute lymphoblastic leukemia, T lineage) cells, and the GC-sensitive clone is more sensitive to Hh inhibition. These results suggest that Hh activity is critical for leukemia cell growth and survival and that the level of Hh activity is in part responsible for the synergism between cAMP and GC.


title: Managing Expectations: Technology Tensions among Parents and Teens

abstract: Extensive scholarship has investigated technology use among families. Existing work has focused primarily on parentsâ reactions to and restrictions of their childrenâ s technology use; here, we explore the underlying tensions surrounding technology use in the home. We draw on historical perspectives of adolescence and family life to better understand technologyâ s impact on present-day parents and teens. Through an interview study with 18 parent-child pairs (19 parents; 23 children, ages 10-17), we find a number of technology tensions, including 1) parentsâ underestimation of childrenâ s technology use; 2) childrenâ s perception that parents only tell them which behaviors to avoid; 3) both parentsâ and childrenâ s poor adherence to household technology rules; and 4) parentsâ and childrenâ s desire for mutual attention. We argue that the use of personal devices introduces distinctive challenges into modern family life, due to the limited visibility (or practical obscurity) of personal device use, expectations of constant connectivity, and overly-romanticized notions of family time. We consider the historical evolution of both teenage and family life, and conclude that consistent and realistic expectations around work, attention, and adolescence may help families better manage household technology use.


title: Effect of Black Walnut ( Tetracarpidium conophorum ) Leaf Extract on the Reproductive Organ of Male Albino Rats

abstract: This study determined the effect of Tetracarpidium conophorum (black walnut) leaf extract on the male reproductive organs of albino rats. The effects of the leaf extracts were determined on the Epididymal sperm concentration, Testicular histology, and on testosterone concentration in the rat serum by a micro plate enzyme immunoassay (Testosterone assay). A total of sixteen (16) male albino wistar rats were divided into four (1, 2, 3 and 4) groups of four rats each. Group 1 served as the control and was fed with normal diet only, while groups 2, 3 and 4 were fed with 200, 400 and 600 mg/kg body weight (BW) of the extract for a period of two weeks. The Epididymal sperm concentration were not significantly affected (p>0.05) across the groups. The level of testosterone for the treatment groups 2 and 4 showed no significant difference (p>0.05) compared to the control while group 4 showed significant increase compared to that of the control (p<0.05). Pathologic changes were observed in testicular histology across the treatment groups. Robust seminiferous tubular lumen containing sperm cells and increased production of Leydig cells and Sertoli cells were observed across different treatment groups compared to that of the control.


Facilitating Object Detection and Recognition through Eye Gaze


title: An approach of feature selection using graph-theoretic heuristic and hill climbing

abstract: Search-based methods that use matrix- or vector-based representations of the dataset are commonly employed to solve the problem of feature selection. These methods are more generalized and easy to apply. Recently, a set of algorithms have started using graph-based representation of the dataset instead of the traditional representations. These methods require additional modelling as the dataset needs to be represented as a graph. However, graph-based methods help in visualizing inter-feature relationship based on which graph-theoretic principles can be applied to identify good-quality feature subsets. A combination of the graph-based representation with traditional search techniques has the potential to increase model performance as well as interpretability. As per literature study, there is hardly any method which combines these approaches. In this paper, we have proposed a feature selection algorithm, which represents the dataset as a graph and then uses maximal independent sets and minimal vertex covers to improve traditional hill climbing search. The proposed method produces statistically significant improvement over (i) hill climbing, (ii) standard search-based methods and (iii) pure graph-based methods.


title: Design of Lightweight Authentication and Key Agreement Protocol for Vehicular Ad Hoc Networks

abstract: Due to the widespread popularity in both academia and industry, vehicular ad hoc networks (VANETs) have been used in a wide range of applications starting from intelligent transportation to e-health and itinerary planning. This paper proposes a new decentralized lightweight authentication and key agreement scheme for VANETs. In the proposed scheme, there are three types of mutual authentications: 1) between vehicles; 2) between vehicles and their respective cluster heads; and 3) between cluster heads and their respective roadside units. Apart from these authentications, the proposed scheme also maintains secret keys between roadside units for their secure communications. The rigorous formal and informal security analysis shows that the proposed scheme is capable to defend various malicious attacks. Moreover, the ns-2 simulation demonstrates the practicability of the proposed scheme in VANET environment.


title: Innovation, trade, and economic welfare: Contrasts between petrochemicals and semiconductors

abstract: Abstract Since the early 1980s, the corpus of international trade theory has been broadened to include new theories of international trade based on “special assumptions”. While these models are often highly specific, they are starting to yield outcomes that appear to be more general, and which comport with what industry analysts report is going on in international markets, especially for manufactured goods. Further progress can be made if these models are able to take into account intellectual property and firm organization. This paper suggests how such factors impact policy analysis.


title: Combinatorial auctions for supply chain formation

abstract: Supply chain formation presents difficult coordination issues for distributed negotiation protocols. Agents must simultaneously negotiate production relationships at multiple levels, with important interdependencies among inputs and outputs at each level. Combinatorial auctions address this problem by global optimization over expressed offers to engage in compound exchanges. A one-shot combinatorial auction that optimizes the reported value of the bids results in optimal allocations with truthful bids. But autonomous self-interested agents have an incentive to bid strategically in an attempt to gain extra surplus. We investigate a particular combinatorial protocol consisting of a one-shot auction and a strategic bidding policy. We experimentally analyze the efficiency and producer surplus obtained in five networks, and compare this performance to that of a distributed, progressive auction protocol with non-strategic bidding. We find that producers can sometimes gain significantly by bidding strategically. However, when the available surplus is small relative to the consumers’ values, the producers’ strategic behavior may prevent the supply chain from forming at all, resulting in zero gains for all agents. We examine the robustness of the combinatorial protocol by investigating agent incentives to deviate, identifying quasi-equilibrium behavior for an example network.


title: A novel dsign of UWB microstrip high-pass filter

abstract: This paper presents a ultra-wideband (UWB) micro-strip structure high-pass filter with multi-stubs. The proposed filter was designed using a combination of 4 short-circuited stubs and an open-circuited stub in the form of micro-strip lines. The short-circuited stubs are to realize a high-pass filter with a bad band rejection. In order to achieve a steep cutoff, a transmission zero can be added thus an open-circuited stub is used. The passband is 5–19 GHz. The insertion loss is greater than −2dB and the return loss is less than −10dB, while the suppression of the modified filter is better than 30 dB below 4.2GHz.


title: Special Issue on Data Mining in Finance c ○ World Scientific Publishing Company IMPROVED OPTION PRICING USING ARTIFICIAL NEURAL NETWORKS AND BOOTSTRAP METHODS

abstract: A hybrid neural network is used to predict the difference between the conventional option-pricing model and observed intraday option prices for stock index option futures. Confidence intervals derived with bootstrap methods are used in a trading strategy that only allows trades outside the estimated range of spurious model fits to be executed. Whilst hybrid neural network option pricing models can improve predictions they have bias. The hybrid option-pricing bias can be reduced with bootstrap methods. A modified bootstrap predictor is indexed by a parameter that allows the predictor to range from a pure bootstrap predictor, to a hybrid predictor, and finally the bagging predictor. The modified bootstrap predictor outperforms the hybrid and bagging predictors. Greatly improved performance was observed on the boundary of the training set and where only sparse training data exists. Finally, bootstrap bias estimates were studied.


Introduction to radar systems third edition [Book Review]


title: Grading fruits and vegetables using RGB-D images and convolutional neural network

abstract: This paper presents a method for grading fruits and vegetables by means of using RGB-D (RGB and depth) images and convolutional neural network (CNN). Here, we focus on grading according to the size of objects. First, the method transforms positions of pixels in RGB image so that the center of the object in 3D space is placed at the position equidistant from the focal point by means of using the corresponding depth image. Then, with the transformed RGB images involving equidistant objects, the method uses CNN for learning to classify the objects or fruits and vegetables in the images for grading according to the size, where the CNN is structured for achieving both size sensitivity for grading and shift invariance for reducing position error involved in images. By means of numerical experiments, we show the effectiveness and the analysis of the present method.


title: Persistence-based segmentation of deformable shapes

abstract: In this paper, we combine two ideas: persistence-based clustering and the Heat Kernel Signature (HKS) function to obtain a multi-scale isometry invariant mesh segmentation algorithm. The key advantages of this approach is that it is tunable through a few intuitive parameters and is stable under near-isometric deformations. Indeed the method comes with feedback on the stability of the number of segments in the form of a persistence diagram. There are also spatial guarantees on part of the segments. Finally, we present an extension to the method which first detects regions which are inherently unstable and segments them separately. Both approaches are reasonably scalable and come with strong guarantees. We show numerous examples and a comparison with the segmentation benchmark and the curvature function.


title: Converting relational to graph databases

abstract: Graph Database Management Systems provide an effective and efficient solution to data storage in current scenarios where data are more and more connected, graph models are widely used, and systems need to scale to large data sets. In this framework, the conversion of the persistent layer of an application from a relational to a graph data store can be convenient but it is usually an hard task for database administrators. In this paper we propose a methodology to convert a relational to a graph database by exploiting the schema and the constraints of the source. The approach supports the translation of conjunctive SQL queries over the source into graph traversal operations over the target. We provide experimental results that show the feasibility of our solution and the efficiency of query answering over the target database.


title: Hierarchical Graph Representation Learning with Differentiable Pooling

abstract: Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction. However, current GNN methods are inherently flat and do not learn hierarchical representations of graphs---a limitation that is especially problematic for the task of graph classification, where the goal is to predict the label associated with an entire graph. Here we propose DiffPool, a differentiable graph pooling module that can generate hierarchical representations of graphs and can be combined with various graph neural network architectures in an end-to-end fashion. DiffPool learns a differentiable soft cluster assignment for nodes at each layer of a deep GNN, mapping nodes to a set of clusters, which then form the coarsened input for the next GNN layer. Our experimental results show that combining existing GNN methods with DiffPool yields an average improvement of 5-10% accuracy on graph classification benchmarks, compared to all existing pooling approaches, achieving a new state-of-the-art on four out of five benchmark datasets.


title: Design and Validation of a Mobile Robot for Power Line Inspection and Maintenance

abstract: This paper presents the LineScout Technology, a mobile teleoperated robot for power line inspection and maintenance. Optimizing several geometric parameters achieved a compact design that was successfully tested over many line configurations and obstacle sequences. An overview of the technology is presented, including a description of the control strategy, followed by a section focusing on key aspects of the prototype thorough validation. Working on live lines, up to 735 kV and 1,000 A, means that the technology must be robust to electromagnetic interference. The third generation prototype, tested in laboratory and in field conditions, is now ready to undertake inspection pilot projects.


Parameter Server for Distributed Machine Learning


title: Wives giving care to husbands with Alzheimer's disease: A process of interpretive caring

abstract: Wives giving care to spouses with dementia are a particularly vulnerable segment of the caregiving population. In this article a grounded theory study of 20 such wives is described, with their experiences explained as a process of interpretive caring. Wives began the process by either seeing changes in their husbands or recognizing changes in their work. Following this, the wives moved on to a phase of drawing inferences about what they observed and then took over their husbands' roles and responsibilities. These changes prompted the wives to rewrite identities for their husbands that incorporated the dementia and to rewrite identities for themselves to reflect their new roles, abilities, and strengths. Finally, the wives set about constructing a new daily life to sustain both partners. This process is neutral and allows for positive aspects of caring to be considered along with grief and frustration. © 2002 Wiley Periodicals, Inc. Res Nurs Health 25:307–316, 2002


title: The Application of Data Mining to Build Classification Model for Predicting Graduate Employment

abstract: Data mining has been applied in various areas because of its ability to rapidly analyze vast amounts of data. This study is to build the Graduates Employment Model using classification task in data mining, and to compare several of data-mining approaches such as Bayesian method and the Tree method. The Bayesian method includes 5 algorithms, including AODE, BayesNet, HNB, NaviveBayes, WAODE. The Tree method includes 5 algorithms, including BFTree, NBTree, REPTree, ID3, C4.5. The experiment uses a classification task in WEKA, and we compare the results of each algorithm, where several classification models were generated. To validate the generated model, the experiments were conducted using real data collected from graduate profile at the Maejo University in Thailand. The model is intended to be used for predicting whether a graduate was employed, unemployed, or in an undetermined situation.


title: Mechanical reasoning by mental simulation

abstract: Abstract Recent studies have provided evidence for mental simulation as a strategy in mechanical reasoning. This type of reasoning can be dissociated from reasoning based on descriptive knowledge in that it depends on different abilities and memory stores, is expressed more easily in gesture than in language, exhibits analog properties, and can result in correct inferences in situations where people do not have correct descriptive knowledge. Although it is frequently accompanied by imagery, mental simulation is not a process of inspecting a holistic visual image in the ‘mind's eye'. Mental simulations are constructed piecemeal, include representations of non-visible properties and can be used in conjunction with non-imagery processes, such as task decomposition and rule-based reasoning.