title: Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry

abstract: Retaining customers is one of the most critical challenges in the maturing mobile telecommunications service industry. Using customer transaction and billing data, this study investigates determinants of customer churn in the Korean mobile telecommunications service market. Results indicate that call quality-related factors influence customer churn; however, customers participating in membership card programs are also more likely to churn, which raises questions about program effectiveness. Furthermore, heavy users also tend to churn. In order to analyze partial and total defection, this study defines changes in a customer’s status from active use (using the service on a regular basis) to non-use (deciding not to use it temporarily without having churned yet) or suspended (being suspended by the service provider) as partial defection and from active use to churn as total defection. Thus, mediating effects of a customer’s partial defection on the relationship between the churn determinants and total defection are analyzed and their implications are discussed. Results indicate that some churn determinants influence customer churn, either directly or indirectly through a customer’s status change, or both; therefore, a customer’s status change explains the relationship between churn determinants and the probability of churn.


title: Research issues on product lifecycle management and information tracking using smart embedded systems

abstract: Abstract PROMISE will develop appropriate technology, including product lifecycle models, product embedded information devices with associated firmware and software components and tools for decision making based on data gathered through a product lifecycle. This is done to enable and exploit the seamless flow, tracing and updating of information about a product, after its delivery to the customer and up to its final destiny (deregistration, decommissioning) and back to the designer and producer. The breakthrough contribution of PROMISE, in the long term, is to allow information flow management to go beyond the customer, to close the product lifecycle information loops, and to enable the seamless e-transformation of product lifecycle information to knowledge. The PROMISE R&D implementation plan includes fundamental and applied research activities in the disciplines of information systems modelling, smart embedded systems, short and long distance wireless communication technologies, data management and modelling, design for X and adaptive production management for beginning of life (BOL), statistical methods for predictive maintenance for middle of life (MOL) and planning and management of product end of life (EOL). After a general description of the PROMISE project, we present an example of the issues to be addressed in it. It consists of defining a data structure for recording maintainability data during the maintenance operations in order to use them in design for maintainability.


title: Hit Song Science is Not Yet a Science

abstract: We describe a large-scale experiment aiming at validating the hypothesis that the popularity of music titles can be predicted from global acoustic or human features. We use a 32.000 title database with 632 manually-entered labels per title including 3 related to the popularity of the title. Our experiment uses two audio feature sets, as well as the set of all the manually-entered labels but the popularity ones. The experiment shows that some subjective labels may indeed be reasonably well-learned by these techniques, but not popularity. This contradicts recent and sustained claims made in the MIR community and in the media about the existence of “Hit Song Science”.


Aspect Browser: Tool Support for Managing Dispersed Aspects


title: Low-resolution face recognition: a review

abstract: Low-resolution face recognition (LR FR) aims to recognize faces from small size or poor quality images with varying pose, illumination, expression, etc. It has received much attention with increasing demands for long distance surveillance applications, and extensive efforts have been made on LR FR research in recent years. However, many issues in LR FR are still unsolved, such as super-resolution (SR) for face recognition, resolution-robust features, unified feature spaces, and face detection at a distance, although many methods have been developed for that. This paper provides a comprehensive survey on these methods and discusses many related issues. First, it gives an overview on LR FR, including concept description, system architecture, and method categorization. Second, many representative methods are broadly reviewed and discussed. They are classified into two different categories, super-resolution for LR FR and resolution-robust feature representation for LR FR. Their strategies and advantages/disadvantages are elaborated. Some relevant issues such as databases and evaluations for LR FR are also presented. By generalizing their performances and limitations, promising trends and crucial issues for future research are finally discussed.


title: An efficient modified grey wolf optimizer with Lévy flight for optimization tasks

abstract: Abstract The grey wolf optimizer (GWO) is a new efficient population-based optimizer. The GWO algorithm can reveal an efficient performance compared to other well-established optimizers. However, because of the insufficient diversity of wolves in some cases, a problem of concern is that the GWO can still be prone to stagnation at local optima. In this article, an improved modified GWO algorithm is proposed for solving either global or real-world optimization problems. In order to boost the efficacy of GWO, Levy flight (LF) and greedy selection strategies are integrated with the modified hunting phases. LF is a class of scale-free walks with randomly-oriented steps according to the Levy distribution. In order to investigate the effectiveness of the modified Levy-embedded GWO (LGWO), it was compared with several state-of-the-art optimizers on 29 unconstrained test beds. Furthermore, 30 artificial and 14 real-world problems from CEC2014 and CEC2011 were employed to evaluate the LGWO algorithm. Also, statistical tests were employed to investigate the significance of the results. Experimental results and statistical tests demonstrate that the performance of LGWO is significantly better than GWO and other analyzed optimizers.


RefineNet : MultiPath Refinement Networks with Identity Mappings for High-Resolution Semantic Segmentation


title: A Single-Chip CMOS UHF RFID Reader Transceiver for Chinese Mobile Applications

abstract: A novel single-chip 860–960MHz band UHF RFID reader transceiver IC is fabricated in 0.18µm CMOS technology. The transceiver consists of a compact high-linearity low-noise-figure RF front-end, a programmable analog baseband for Rx path; and an image reject filter, a PGA, a switchable up-conversion modulator and a driver amplifier for Tx path. The 3-bit 3rd-order DSM fractional-N frequency synthesizer with optimized all-band phase noise performance is integrated to implement Rx/Tx frequency translation. The transceiver IC has a die area of 4mm×4mm. It achieves IIP3 of 13dBm, sensitivity of −75dBm in listen-before-talk (LBT) mode and −66dBm in normal mode in the presence of −4.4dBm self-jammer for the backscatter modulation while drawing 112mA from 3.3V power supply.


title: Very High Resolution Spaceborne SAR Tomography in Urban Environment

abstract: Synthetic aperture radar tomography (TomoSAR) extends the synthetic aperture principle into the elevation direction for 3-D imaging. It uses stacks of several acquisitions from slightly different viewing angles (the elevation aperture) to reconstruct the reflectivity function along the elevation direction by means of spectral analysis for every azimuth-range pixel. The new class of meter-resolution spaceborne SAR systems (TerraSAR-X and COSMO-Skymed) offers a tremendous improvement in tomographic reconstruction of urban areas and man-made infrastructure. The high resolution fits well to the inherent scale of buildings (floor height, distance of windows, etc.). This paper demonstrates the tomographic potential of these SARs and the achievable quality on the basis of TerraSAR-X spotlight data of urban environment. A new Wiener-type regularization to the singular-value decomposition method-equivalent to a maximum a posteriori estimator-for TomoSAR is introduced and is extended to the differential case (4-D, i.e., space-time). Different model selection schemes for the estimation of the number of scatterers in a resolution cell are compared and proven to be applicable in practice. Two parametric estimation algorithms of the scatterers' elevation and their velocities are evaluated. First 3-D and 4-D reconstructions of an entire building complex (including its radar reflectivity) with very high level of detail from spaceborne SAR data by pixelwise TomoSAR are presented.


title: The Edinburgh Twitter Corpus

abstract: We describe the first release of our corpus of 97 million Twitter posts. We believe that this data will prove valuable to researchers working in social media, natural language processing, large-scale data processing, and similar areas.


title: Electrically small omni-directional antenna of circular polarization

abstract: A compact omni-directional antenna of circular polarization (CP) is presented for 2.4 GHz WLAN access-point applications. The antenna consists of four bended monopoles and a feeding network simultaneously exciting these four monopoles. The electrical size of the CP antenna is only λ 0 /5×λ 0 /5×λ 0 /13. The impedance bandwidth (|S 11 |<−10 dB) is 3.85% (2.392 GHz to 2.486 GHz) and the axial ratio in the azimuth plane is lower than 0.5 dB in the operating band.


title: 7. CONSTRUCTIVISM: IMPLICATIONS FOR THE DESIGN AND DELIVERY OF INSTRUCTION

abstract: Constructivism! The increase in frequency with which this word appears in the discourse of educational research, theory, and policy is truly remarkable. Unfortunately much of the discussion is at the level of slogan and cliche even bromide. "Students should construct their own knowledge" is being reverentially chanted throughout the halls of many a school/college/department of education these days, and any approach that is other than constructivist is characterized as promoting passive, rote, and sterile learning. For example, consider Rogoff's (1994) description of what she calls the adult-run model of how learning occurs:


Intrusion Detection Systems


title: Neighboring in Netville : How the Internet Supports Community and Social Capital in a Wired Suburb

abstract: What is the Internet doing to local community? Analysts have debated about whether the Internet is weakening community by leading people away from meaningful in-person contact; transforming community by creating new forms of community online; or enhancing community by adding a new means of connecting with existing relationships. They have been especially concerned that the globe-spanning capabilities of the Internet can limit local involvements. Survey and ethnographic data from a “wired suburb” near Toronto show that high-speed, always-on access to the Internet, coupled with a local online discussion group, transforms and enhances neighboring. The Internet especially supports increased contact with weaker ties. In comparison to nonwired residents of the same suburb, more neighbors are known and chatted with, and they are more geographically dispersed around the suburb. Not only did the Internet support neighboring, it also facilitated discussion and mobilization around local issues.


title: Efficient Parallel Binary Operations on Homomorphic Encrypted Real Numbers

abstract: A number of homomorphic encryption application areas could be better enabled if there existed a general solution for combining sufficiently expressive logical and numerical circuit primitives. This paper examines accelerating binary operations on real numbers suitable for somewhat homomorphic encryption. A parallel solution based on SIMD can be used to efficiently perform combined addition, subtraction and comparison-based operations on packed binary operands in a single step. The result maximises computational efficiency, memory space usage and minimises multiplicative circuit depth. General application and performance of these accelerated binary primitives are demonstrated in a number of case studies, including min-max and sorting operations.


1 . 2 Named Entity Recognition Task


title: Soccer video segmentation: Referee and player detection

abstract: This paper presents a player segmentation methodology for videos of soccer games. This methodology includes some low and high level video processing algorithms, such as dominant color region detection, referee and player identification. Experimental results have shown the efficiency of the methodology in locating and segmenting referee and players.


title: The Effect of Web Characteristics on Relationship Building

abstract: The purpose of this article is to examine the relationship between Web characteristics and perceptions toward relational components. A 2 (low interactivity and high interactivity) x 2 (text-oriented and multimedia-oriented) between-group experiment was designed with 197 participants to investigate this question. The outcomes showed that interactivity has significant effects on relationship building. However, the interaction effects between interactivity and medium arrangement suggest that nonessential arrangement of interactivity and multimedia did not enhance positive perceptions of relationships with the organizations. Interactivity, however, showed that the main effect, multimedia orientation, did not. The application of interactivity on the Web was discussed for better relationship building.


title: The ChoicePoint Dilemma: How Data Brokers Should Handle the Privacy of Personal Information

abstract: Before 2005, data broker ChoicePoint suffered fraudulent access to its databases that exposed thousands of customers' personal information. We examine Choice-Point's data breach, explore what went wrong from the perspective of consumers, executives, policy, and IT systems, and offer recommendations for the future.


title: Confabulation based sentence completion for machine reading

abstract: Sentence completion and prediction refers to the capability of filling missing words in any incomplete sentences. It is one of the keys to reading comprehension, thus making sentence completion an indispensible component of machine reading. Cogent confabulation is a bio-inspired computational model that mimics the human information processing. The building of confabulation knowledge base uses an unsupervised machine learning algorithm that extracts the relations between objects at the symbolic level. In this work, we propose performance improved training and recall algorithms that apply the cogent confabulation model to solve the sentence completion problem. Our training algorithm adopts a two-level hash table, which significantly improves the training speed, so that a large knowledge base can be built at relatively low computation cost. The proposed recall function fills missing words based on the sentence context. Experimental results show that our software can complete trained sentences with 100% accuracy. It also gives semantically correct answers to more than two thirds of the testing sentences that have not been trained before.


title: Potential pitfalls of process modeling: part A

abstract: Purpose – This paper summarizes typical pitfalls as they can be observed in larger process modeling projects.Design/methodology/approach – The identified pitfalls have been derived from a series of focus groups and semi‐structured interviews with business process analysts and managers of process management and modeling projects.Findings – The paper provides a list of typical characteristics of unsuccessful process modeling. It covers six pitfalls related to strategy and governance (1‐3) and the involved stakeholders (4‐6). Further issues related to tools and related requirements (7‐10), the practice of modeling (11‐16), the way we design to‐be models (17‐19), and how we deal with success of modeling and maintenance issues (19‐21) will be discussed in the second part of this paper.Research limitations/implications – This paper is a personal viewpoint, and does not report on the outcomes of a structured qualitative research project.Practical implications – The provided list of total 22 pitfalls increases th...


title: Spatial Clockwork Recurrent Neural Network for Muscle Perimysium Segmentation

abstract: Accurate segmentation of perimysium plays an important role in early diagnosis of many muscle diseases because many diseases contain different perimysium inflammation. However, it remains as a challenging task due to the complex appearance of the perymisum morphology and its ambiguity to the background area. The muscle perimysium also exhibits strong structure spanned in the entire tissue, which makes it difficult for current local patch-based methods to capture this long-range context information. In this paper, we propose a novel spatial clockwork recurrent neural network (spatial CW-RNN) to address those issues. Specifically, we split the entire image into a set of non-overlapping image patches, and the semantic dependencies among them are modeled by the proposed spatial CW-RNN. Our method directly takes the 2D structure of the image into consideration and is capable of encoding the context information of the entire image into the local representation of each patch. Meanwhile, we leverage on the structured regression to assign one prediction mask rather than a single class label to each local patch, which enables both efficient training and testing. We extensively test our method for perimysium segmentation using digitized muscle microscopy images. Experimental results demonstrate the superiority of the novel spatial CW-RNN over other existing state of the arts.


title: Deep Optical Flow Estimation Via Multi-Scale Correspondence Structure Learning

abstract: As an important and challenging problem in computer vision, learning based optical flow estimation aims to discover the intrinsic correspondence structure between two adjacent video frames through statistical learning. Therefore, a key issue to solve in this area is how to effectively model the multi-scale correspondence structure properties in an adaptive end-to-end learning fashion. Motivated by this observation, we propose an end-to-end multi-scale correspondence structure learning (MSCSL) approach for optical flow estimation. In principle, the proposed MSCSL approach is capable of effectively capturing the multi-scale inter-image-correlation correspondence structures within a multi-level feature space from deep learning. Moreover, the proposed MSCSL approach builds a spatial Conv-GRU neural network model to adaptively model the intrinsic dependency relationships among these multi-scale correspondence structures. Finally, the above procedures for correspondence structure learning and multi-scale dependency modeling are implemented in a unified end-to-end deep learning framework. Experimental results on several benchmark datasets demonstrate the effectiveness of the proposed approach.


title: Compact Subnanosecond Pulse Generator Using Avalanche Transistors for Cell Electroperturbation Studies

abstract: Research on the electroperturbation effects of ultrashort high field pulses in cancer cells requires subnanosecond rise time, high voltage pulses delivered to low impedance biological loads. Here we present a compact solid-state pulse generator developed for this application. The pulse is generated by switching a chain of avalanche transistors configured as a tapered transmission line from high voltage to ground. The system features a built in 1400:1 capacitively compensated resistive voltage divider. The divider, with a 3 dB point at 910 MHz, overcomes challenges in the direct measurement of the high frequency components of the output pulse. The generator is capable of producing a 0.8 ns rise time, 1.3 ns wide, 1.1 kV pulse into a 50 Omega load at a maximum repetition rate of 200 kHz. Techniques to implement physical layouting strategies to achieve subnanosecond rise times are outlined. Problems faced in integrating the subnanosecond pulse generator with a biological load are discussed. This pulse generator will be used in experiments aimed at electromanipulation of intracellular biomolecular structures.


title: Emotion Detection in Text: a Review

abstract: In recent years, emotion detection in text has become more popular due to its vast potential applications in marketing, political science, psychology, human-computer interaction, artificial intelligence, etc. Access to a huge amount of textual data, especially opinionated and self-expression text also played a special role to bring attention to this field. In this paper, we review the work that has been done in identifying emotion expressions in text and argue that although many techniques, methodologies, and models have been created to detect emotion in text, there are various reasons that make these methods insufficient. Although, there is an essential need to improve the design and architecture of current systems, factors such as the complexity of human emotions, and the use of implicit and metaphorical language in expressing it, lead us to think that just re-purposing standard methodologies will not be enough to capture these complexities, and it is important to pay attention to the linguistic intricacies of emotion expression.


title: GamePad: A Learning Environment for Theorem Proving

abstract: In this paper, we introduce a system called GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant. Interactive theorem provers such as Coq enable users to construct machine-checkable proofs in a step-by-step manner. Hence, they provide an opportunity to explore theorem proving with human supervision. We use GamePad to synthesize proofs for a simple algebraic rewrite problem and train baseline models for a formalization of the Feit-Thompson theorem. We address position evaluation (i.e., predict the number of proof steps left) and tactic prediction (i.e., predict the next proof step) tasks, which arise naturally in tactic-based theorem proving.


title: Ask No More: Deciding when to guess in referential visual dialogue

abstract: Our goal is to explore how the abilities brought in by a dialogue manager can be included in end-to-end visually grounded conversational agents. We make initial steps towards this general goal by augmenting a task-oriented visual dialogue model with a decision-making component that decides whether to ask a follow-up question to identify a target referent in an image, or to stop the conversation to make a guess. Our analyses show that adding a decision making component produces dialogues that are less repetitive and that include fewer unnecessary questions, thus potentially leading to more efficient and less unnatural interactions.


title: Comparison of two intraoral scanners based on three-dimensional surface analysis

abstract: BackgroundThis in vivo study evaluated the difference of two well-known intraoral scanners used in dentistry, namely iTero (Align Technology) and TRIOS (3Shape).MethodsThirty-two participants underwent intraoral scans with TRIOS and iTero scanners, as well as conventional alginate impressions. The scans obtained with the two intraoral scanners were compared with each other and were also compared with the corresponding model scans by means of three-dimensional surface analysis. The average differences between the two intraoral scans on the surfaces were evaluated by color-mapping. The average differences in the three-dimensional direction between each intraoral scans and its corresponding model scan were calculated at all points on the surfaces.ResultsThe average differences between the two intraoral scanners were 0.057 mm at the maxilla and 0.069 mm at the mandible. Color histograms showed that local deviations between the two scanners occurred in the posterior area. As for difference in the three-dimensional direction, there was no statistically significant difference between two scanners.ConclusionsAlthough there were some deviations in visible inspection, there was no statistical significance between the two intraoral scanners.


title: CRQA: Crowd-Powered Real-Time Automatic Question Answering System

abstract: Modern search engines have made dramatic progress in answering questions about facts, such as those that might be retrieved or directly inferred from a knowledge base. However, many other real user questions are more complex, such as requests for opinions, explanations, instructions or advice for a particular situation, and are still largely beyond the competence of the computer systems. As conversational agents become more popular, QA systems are increasingly expected to handle such complex questions, and to do so in (nearly) real-time, as the searcher is unlikely to wait longer than a minute or two for an answer. One way to overcome some of the challenges in complex question answering is crowdsourcing. We explore two ways crowdsourcing can assist a question answering system that operates in (near) real time: by providing answer validation, which could be used to filter or re-rank the candidate answers, and by creating the answer candidates directly. In this paper we present CRQA, a crowd-powered, near real-time automatic question answering system for complex informational tasks, that incorporates a crowdsourcing module for augmenting and validating the candidate answers. The crowd input, obtained in real-time, is integrated into CRQA via a learning-to-rank model, to select the final system answer. Our large-scale experiments, performed on a live stream of real users questions, show that even within a one minute time limit, CRQA can produce answers of high quality. The returned answers are judged to be significantly better compared to the automatic system alone, and even are often preferred to answers posted days later in the original community question answering site. Our findings can be useful for developing hybrid human-computer systems for automatic question answering and conversational agents.


title: State-of-the-Art Survey on In-Vehicle Network Communication (CAN-Bus) Security and Vulnerabilities

abstract: Nowadays with the help of advanced technology, modern vehicles are not only made up of mechanical devices but also consist of highly complex electronic devices and connections to the outside world. There are around 70 Electronic Control Units (ECUs) in modern vehicle which are communicating with each other over the standard communication protocol known as Controller Area Network (CAN-Bus) that provides the communication rate up to 1Mbps. There are different types of in-vehicle network protocol and bus system namely Controlled Area Network (CAN), Local Interconnected Network (LIN), Media Oriented System Transport (MOST), and FlexRay. Even though CAN-Bus is considered as de-facto standard for in-vehicle network communication, it inherently lacks the fundamental security features by design like message authentication. This security limitation has paved the way for adversaries to penetrate into the vehicle network and do malicious activities which can pose a dangerous situation for both driver and passengers. In particular, nowadays vehicular networks are not only closed systems, but also they are open to different external interfaces namely Bluetooth, GPS, to the outside world. Therefore, it creates new opportunities for attackers to remotely take full control of the vehicle. The objective of this research is to survey the current limitations of CAN-Bus protocol in terms of secure communication and different solutions that researchers in the society of automotive have provided to overcome the CAN-Bus limitation on different layers.


title: Detecting alpha spindle events in EEG time series using adaptive autoregressive models

abstract: BackgroundRhythmic oscillatory activity is widely observed during a variety of subject behaviors and is believed to play a central role in information processing and control. A classic example of rhythmic activity is alpha spindles, which consist of short (0.5-2 s) bursts of high frequency alpha activity. Recent research has shown that alpha spindles in the parietal/occipital area are statistically related to fatigue and drowsiness. These spindles constitute sharp changes in the underlying statistical properties of the signal. Our hypothesis is that change point detection models can be used to identify the onset and duration of spindles in EEG. In this work we develop an algorithm that accurately identifies sudden bursts of narrowband oscillatory activity in EEG using techniques derived from change point analysis. Our motivating example is detection of alpha spindles in the parietal/occipital areas of the brain. Our goal is to develop an algorithm that can be applied to any type of rhythmic oscillatory activity of interest for accurate online detection.MethodsIn this work we propose modeling the alpha band EEG time series using discounted autoregressive (DAR) modeling. The DAR model uses a discounting rate to weigh points measured further in the past less heavily than points more recently observed. This model is used together with predictive loss scoring to identify periods of EEG data that are statistically significant.ResultsOur algorithm accurately captures changes in the statistical properties of the alpha frequency band. These statistical changes are highly correlated with alpha spindle occurrences and form a reliable measure for detecting alpha spindles in EEG. We achieve approximately 95% accuracy in detecting alpha spindles, with timing precision to within approximately 150 ms, for two datasets from an experiment of prolonged simulated driving, as well as in simulated EEG. Sensitivity and specificity values are above 0.9, and in many cases are above 0.95, for our analysis.ConclusionModeling the alpha band EEG using discounted AR models provides an efficient method for detecting oscillatory alpha activity in EEG. The method is based on statistical principles and can generally be applied to detect rhythmic activity in any frequency band or brain region.


title: Analysis of complex contagions in random multiplex networks

abstract: We study the diffusion of influence in random multiplex networks where links can be of r different types, and, for a given content (e.g., rumor, product, or political view), each link type is associated with a content-dependent parameter ci in [0,∞] that measures the relative bias type i links have in spreading this content. In this setting, we propose a linear threshold model of contagion where nodes switch state if their "perceived" proportion of active neighbors exceeds a threshold τ. Namely a node connected to mi active neighbors and ki-mi inactive neighbors via type i links will turn active if ∑cimi/∑ciki exceeds its threshold τ. Under this model, we obtain the condition, probability and expected size of global spreading events. Our results extend the existing work on complex contagions in several directions by (i) providing solutions for coupled random networks whose vertices are neither identical nor disjoint, (ii) highlighting the effect of content on the dynamics of complex contagions, and (iii) showing that content-dependent propagation over a multiplex network leads to a subtle relation between the giant vulnerable component of the graph and the global cascade condition that is not seen in the existing models in the literature.


title: Inhibition of arachidonic acid metabolism and its implication on cell proliferation and tumour-angiogenesis.

abstract: Arachidonic acid (AA) and its metabolites have recently generated a heightened interest due to growing evidence of their significant role in cancer biology. Thus, inhibitors of the AA cascade, first and foremost COX inhibitors, which have originally been of interest in the treatment of inflammatory conditions and certain types of cardiovascular disease, are now attracting attention as an arsenal against cancer. An increasing number of investigations support their role in cancer chemoprevention, although the precise molecular mechanisms that link levels of AA, and its metabolites, with cancer progression have still to be elucidated. This article provides an overview of the AA cascade and focuses on the roles of its inhibitors and their implication in cancer treatment. In particular, emphasis is placed on the inhibition of cell proliferation and neo-angiogenesis through inhibition of the enzymes COX-2, 5-LOX and CYP450. Downstream effects of inhibition of AA metabolites are analysed and the molecular mechanisms of action of a selected number of inhibitors of catalytic pathways reviewed. Lastly, the benefits of dietary omega-3 fatty acids and their mechanisms of action leading to reduced cancer risk and impeded cancer cell growth are mentioned. Finally, a proposal is put forward, suggesting a novel and integrated approach in viewing the molecular mechanisms and complex interactions responsible for the involvement of AA metabolites in carcinogenesis and the protective effects of omega-3 fatty acids in inflammation and tumour prevention.


title: Oscillations in the Alpha Band (9–12 Hz) Increase with Memory Load during Retention in a Short-term Memory Task

abstract: To study the role of brain oscillations in working memory, we recorded the scalp electroencephalogram (EEG) during the retention interval of a modified Sternberg task. A power spectral analysis of the EEG during the retention interval revealed a clear peak at 9−12 Hz, a frequency in the alpha band (8−13 Hz). In apparent conflict with previous ideas according to which alpha band oscillations represent brain ‘idling’, we found that the alpha peak systematically increased with the number of items held in working memory. The enhancement was prominent over the posterior and bilateral central regions. The enhancement over posterior regions is most likely explained by the well known alpha rhythm produced close to the parietal-occipital fissure, whereas the lateral enhancement could be explained by sources in somato-motor cortex. A time-frequency analysis revealed that the enhancement was present throughout the last 2.5 s of the 2.8 s retention interval and that alpha power rapidly diminished following the probe. The load dependence and the tight temporal regulation of alpha provide strong evidence that the alpha generating system is directly or indirectly linked to the circuits responsible for working memory. Although a clear peak in the theta band (5−8 Hz) was only detectable in one subject, other lines of evidence indicate that theta occurs and also has a role in working memory. Hypotheses concerning the role of alpha band activity in working memory are discussed.


title: Content-preserving warps for 3D video stabilization

abstract: We describe a technique that transforms a video from a hand-held video camera so that it appears as if it were taken with a directed camera motion. Our method adjusts the video to appear as if it were taken from nearby viewpoints, allowing 3D camera movements to be simulated. By aiming only for perceptual plausibility, rather than accurate reconstruction, we are able to develop algorithms that can effectively recreate dynamic scenes from a single source video. Our technique first recovers the original 3D camera motion and a sparse set of 3D, static scene points using an off-the-shelf structure-from-motion system. Then, a desired camera path is computed either automatically (e.g., by fitting a linear or quadratic path) or interactively. Finally, our technique performs a least-squares optimization that computes a spatially-varying warp from each input video frame into an output frame. The warp is computed to both follow the sparse displacements suggested by the recovered 3D structure, and avoid deforming the content in the video frame. Our experiments on stabilizing challenging videos of dynamic scenes demonstrate the effectiveness of our technique.


title: 7-Level asymmetrical hybrid cascaded multilevel converter topology for traction system

abstract: Electrified traction system connected to the medium voltage high power achieve great attention due to its high efficiency. However the conventional topologies used in electrified traction system causes power quality issues such as high harmonics distortion, low power factor on ac line due to the non linear nature of the load. To address the said issues, this paper presents a topology of 7-level asymmetrical cascaded converter based on the series connection of cascaded module of five levels converter (CM), with H-bridge cell to meet the demand of traction application with improved power factor at minimum harmonics distortion. The proposed topology consists of a single dc source and two dc-link capacitors to obtain the desire level of output with minimum power switches. Harmonics compensation is achieved by providing H-bridge cell and cascaded module with a gating signal at fundamental frequency and high switching frequency respectively. Working principle of proposed topology, mathematical analysis, controlled PWM strategy, stability problem related to dc-link capacitor voltage, possible means of balancing and results has been fully investigated with the help of simulation.


title: Blockchains and Smart Contracts for the Internet of Things

abstract: Motivated by the recent explosion of interest around blockchains, we examine whether they make a good fit for the Internet of Things (IoT) sector. Blockchains allow us to have a distributed peer-to-peer network where non-trusting members can interact with each other without a trusted intermediary, in a verifiable manner. We review how this mechanism works and also look into smart contracts—scripts that reside on the blockchain that allow for the automation of multi-step processes. We then move into the IoT domain, and describe how a blockchain-IoT combination: 1) facilitates the sharing of services and resources leading to the creation of a marketplace of services between devices and 2) allows us to automate in a cryptographically verifiable manner several existing, time-consuming workflows. We also point out certain issues that should be considered before the deployment of a blockchain network in an IoT setting: from transactional privacy to the expected value of the digitized assets traded on the network. Wherever applicable, we identify solutions and workarounds. Our conclusion is that the blockchain-IoT combination is powerful and can cause significant transformations across several industries, paving the way for new business models and novel, distributed applications.


title: Comparison of pure and ‘‘Latinized’ ’ centroidal Voronoi tessellation

abstract: Abstract A recently developed centroidal Voronoi tessellation (CVT) sampling method is investigated here to assess its suitability for use in statistical sampling applications. CVT efficiently generates a highly uniform distribution of sample points over arbitrarily shaped M -dimensional parameter spaces. On several 2-D test problems CVT has recently been found to provide exceedingly effective and efficient point distributions for response surface generation. Additionally, for statistical function integration and estimation of response statistics associated with uniformly distributed random-variable inputs (uncorrelated), CVT has been found in initial investigations to provide superior points sets when compared against latin-hypercube and simple-random Monte Carlo methods and Halton and Hammersley quasi-random sequence methods. In this paper, the performance of all these sampling methods and a new variant (“Latinized” CVT) are further compared for non -uniform input distributions. Specifically, given uncorrelated normal inputs in a 2-D test problem, statistical sampling efficiencies are compared for resolving various statistics of response: mean, variance, and exceedence probabilities.


title: Prediction of seismic slope stability through combination of particle swarm optimization and neural network

abstract: One of the main concerns in geotechnical engineering is slope stability prediction during the earthquake. In this study, two intelligent systems namely artificial neural network (ANN) and particle swarm optimization (PSO)---ANN models were developed to predict factor of safety (FOS) of homogeneous slopes. Geostudio program based on limit equilibrium method was utilized to obtain 699 FOS values with different conditions. The most influential factors on FOS such as slope height, gradient, cohesion, friction angle and peak ground acceleration were considered as model inputs in the present study. A series of sensitivity analyses were performed in modeling procedures of both intelligent systems. All 699 datasets were randomly selected to 5 different datasets based on training and testing. Considering some model performance indices, i.e., root mean square error, coefficient of determination (R2) and value account for (VAF) and using simple ranking method, the best ANN and PSO---ANN models were selected. It was found that the PSO---ANN technique can predict FOS with higher performance capacities compared to ANN. R2 values of testing datasets equal to 0.915 and 0.986 for ANN and PSO---ANN techniques, respectively, suggest the superiority of the PSO---ANN technique.


title: Parallel $(k)$-Clique Community Detection on Large-Scale Networks

abstract: The analysis of real-world complex networks has been the focus of recent research. Detecting communities helps in uncovering their structural and functional organization. Valuable insight can be obtained by analyzing the dense, overlapping, and highly interwoven k-clique communities. However, their detection is challenging due to extensive memory requirements and execution time. In this paper, we present a novel, parallel k-clique community detection method, based on an innovative technique which enables connected components of a network to be obtained from those of its subnetworks. The novel method has an unbounded, user-configurable, and input-independent maximum degree of parallelism, and hence is able to make full use of computational resources. Theoretical tight upper bounds on its worst case time and space complexities are given as well. Experiments on real-world networks such as the Internet and the World Wide Web confirmed the almost optimal use of parallelism (i.e., a linear speedup). Comparisons with other state-of-the-art k-clique community detection methods show dramatic reductions in execution time and memory footprint. An open-source implementation of the method is also made publicly available.


title: Using Software Dependencies and Churn Metrics to Predict Field Failures: An Empirical Case Study

abstract: Commercial software development is a complex task that requires a thorough understanding of the architecture of the software system. We analyze the Windows Server 2003 operating system in order to assess the relationship between its software dependencies, churn measures and post-release failures. Our analysis indicates the ability of software dependencies and churn measures to be efficient predictors of post-release failures. Further, we investigate the relationship between the software dependencies and churn measures and their ability to assess failure-proneness probabilities at statistically significant levels.


title: Microcontroller Based Home Security System with GSM Technology

abstract: In this paper, design and implement of a microcontroller based home security system with GSM technology have been presented and analyzed. Two microcontrollers with other peripheral devices which include Light Emitting Diode (LED), Liquid Crystal Display (LCD), Buzzer and Global System for Mobile Communication (GSM) Module are responsible for reliable operation of the proposed security system. In addition, a mobile phone is interfaced with microcontroller through a Bluetooth device in order to control the system. Moreover, a manual keypad is another way to lock or unlock the system. A Compiler Code Vision AVR is used to design a program that controls the system along with maintaining all security functions. The designed program is applied in Proteus Software for simulation. At last, the results of practical circuit show the proper functions and also verify the reliable security within reasonable cost.


title: Gradient-based Hyperparameter Optimization through Reversible Learning

abstract: Tuning hyperparameters of learning algorithms is hard because gradients are usually unavailable. We compute exact gradients of cross-validation performance with respect to all hyperparameters by chaining derivatives backwards through the entire training procedure. These gradients allow us to optimize thousands of hyperparameters, including step-size and momentum schedules, weight initialization distributions, richly parameterized regularization schemes, and neural network architectures. We compute hyperparameter gradients by exactly reversing the dynamics of stochastic gradient descent with momentum.


title: Belt: An Unobtrusive Touch Input Device for Head-worn Displays

abstract: Belt is a novel unobtrusive input device for wearable displays that incorporates a touch surface encircling the user's hip. The wide input space is leveraged for a horizontal spatial mapping of quickly accessible information and applications. We discuss social implications and interaction capabilities for unobtrusive touch input and present our hardware implementation and a set of applications that benefit from the quick access time. In a qualitative user study with 14 participants we found out that for short interactions (2-4 seconds), most of the surface area is considered as appropriate input space, while for longer interactions (up to 10 seconds), the front areas above the trouser pockets are preferred.


title: Transmission Characteristics of a Twisted Radio Wave Based on Circular Traveling-Wave Antenna

abstract: A traveling-wave circular loop antenna with its phase distribution along the circle of $\bm{l}\bm{\varphi}$ is demonstrated to generate the twisted radio wave. Theoretical formulations of its radiation fields are deduced in the cylindrical coordinate system and used to analyze its transmission characteristics, such as the radiation patterns, the evolution of the polarization states, and the angular momentum modes including the orbital angular momentum (OAM) modes and the spin angular momentum (SAM) modes. The prototype of a practical configuration for such antenna is proposed and conducted to validate the theoretical results. This theoretical analysis is applicable not only to radio waves but also to the whole electromagnetic spectrum.


title: Multifunctional Coating Based on Hyaluronic Acid and Dopamine Conjugate for Potential Application on Surface Modification of Cardiovascular Implanted Devices

abstract: Surface modification by conjugating biomolecules has been widely proved to enhance biocompatibility of cardiovascular implanted devices. Here, we aimed at developing a multifunctional surface that not only provides good hemocompatibility but also functions well in inducing desirable vascular cell–material interaction. In the present work, the multicoatings of hyaluronic acid (HA) and dopamine (PDA) were prepared onto 316L stainless steel (316L SS) via chemical conjugation (Michael addition, Schiff base reaction, and electrostatic adsorption). The results of platelet adhesion and activation and the whole blood tests indicated that the HA/PDA coatings obtained better hemocompatibility compared with the bare 316L SS and HA or PDA immobilized on 316L SS. The HA/PDA coatings also inhibited the proliferation of smooth muscle cells and adhesion/activation of macrophages effectively, whereas not all the HA/PDA coatings improved surface endothelialization rapidly and the effects of the multifunctional coatings on ...


title: Wider or Deeper: Revisiting the ResNet Model for Visual Recognition

abstract: Abstract The community has been going deeper and deeper in designing one cutting edge network after another, yet some works are there suggesting that we may have gone too far in this dimension. Some researchers unravelled a residual network into an exponentially wider one, and assorted the success of residual networks to fusing a large amount of relatively shallow models. Since some of their early claims are still not settled, we in this paper dig more on this topic, i.e., the unravelled view of residual networks. Based on that, we try to find a good compromise between the depth and width. Afterwards, we walk through a typical pipeline of developing a deep-learning-based algorithm. We start from a group of relatively shallow networks, which perform as well or even better than the current (much deeper) state-of-the-art models on the ImageNet classification dataset. Then, we initialize fully convolutional networks (FCNs) using our pre-trained models, and tune them for semantic image segmentation. Results show that the proposed networks, as pre-trained features, can boost existing methods a lot. Even without exhausting the sophistical techniques to improve the classic FCN model, we achieve comparable results with the best performers on four widely-used datasets, i.e., Cityscapes, PASCAL VOC, ADE20k and PASCAL-Context. The code and pre-trained models are released for public access 1 .


title: Observer based fuzzy LMI regulator for stabilization and tracking control of an aeropendulum

abstract: This paper describes the Takagi-Sugeno fuzzy modeling and output feedback control design of an aero-pendulum. The equilibrium states of reduced order nonlinear plant model are translated to the origin and the resulting model is transformed to TS fuzzy model using sector nonlinearity method. Observer based fuzzy regulator is then designed for a set of operating points using linear matrix inequality (LMI) techniques where the scheduling variable is assumed to be measurable. MATLAB simulations are performed to validate the designed regulator in stabilizing and reference tracking of aero-pendulum.


title: Plastibell circumcision supported by a calcium-alginate fibre dressing to reduce bleeding

abstract: Objective ::: To assess the benefit of a new technique of a Plastibell circumcision supported by a ventral calcium-alginate fibre dressing, to reduce bleeding from the frenular vessels, the commonest complication of Plastibell circumcision.


Named Entity Recognition and Hashtag Decomposition to Improve the Classification of Tweets.


title: A Time-Efficient Cascade for Real-Time Object Detection: With applications for the visually impaired

abstract: Real-time object detection is essential for many computer vision applications. Many rapid detection algorithms are based on using cascades of tests. But existing design criteria for cascades either ignore the time complexity of the tests or make over-simplified assumptions about them. This paper gives a criterion for designing a time-efficient cascade that explicitly takes into account the time complexity of tests (as evaluated by computer run time) including the time for pre-processing. We design a greedy algorithm to minimize this criterion (noting that the full problem is NP-complete). Finally, we illustrate our method on the task of text detection in city scenes. This gives a text detection algorithm that runs at 0.025 seconds per 320×240 image, which is equivalent to 40 frames per second. This is a speed up factor of 2.5 compared to our previous text detector. It gives a realtime system which can be used for applications to help the blind and visually impaired.


title: ELECTRONIC COMMUNICATIONS OF EASST The ParMol package for frequent subgraph mining

abstract: Mining for frequent subgraphs in a graph database has become a popular topic in the last years. Algorithms to solve this problem are used in chemoinformatics to find common molecular fragments in a database of molecules represented as two-dimensional graphs. However, the search process in arbitrary graph structures includes costly graph and subgraph isomorphism tests. In our ParMol package we have implemented four of the most popular frequent subgraph miners using a common infrastructure: MoFa, gSpan, FFSM, and Gaston. Besides the pure re-implementation, we have added additional functionality to some of the algorithms like parallel search, mining directed graphs, and mining in one big graph instead of a graph database. Also a 2D-visualizer for molecules has been integrated.


MARSSx 86 : A Full System Simulator for x 86 CPUs


title: Human Action Recognition Using Improved Salient Dense Trajectories

abstract: Human action recognition in videos is a topic of active research in computer vision. Dense trajectory (DT) features were shown to be efficient for representing videos in state-of-the-art approaches. In this paper, we present a more effective approach of video representation using improved salient dense trajectories: first, detecting the motion salient region and extracting the dense trajectories by tracking interest points in each spatial scale separately and then refining the dense trajectories via the analysis of the motion saliency. Then, we compute several descriptors (i.e., trajectory displacement, HOG, HOF, and MBH) in the spatiotemporal volume aligned with the trajectories. Finally, in order to represent the videos better, we optimize the framework of bag-of-words according to the motion salient intensity distribution and the idea of sparse coefficient reconstruction. Our architecture is trained and evaluated on the four standard video actions datasets of KTH, UCF sports, HMDB51, and UCF50, and the experimental results show that our approach performs competitively comparing with the state-of-the-art results.


title: Evolving hierarchical temporal memory-based trading models

abstract: We explore the possibility of using the genetic algorithm to optimize trading models based on the Hierarchical Temporal Memory (HTM) machine learning technology. Technical indicators, derived from intraday tick data for the E-mini S&P 500 futures market (ES), were used as feature vectors to the HTM models. All models were configured as binary classifiers, using a simple buy-and-hold trading strategy, and followed a supervised training scheme. The data set was partitioned into multiple folds to enable a modified cross validation scheme. Artificial Neural Networks (ANNs) were used to benchmark HTM performance. The results show that the genetic algorithm succeeded in finding predictive models with good performance and generalization ability. The HTM models outperformed the neural network models on the chosen data set and both technologies yielded profitable results with above average accuracy.


title: What motivates chinese consumers to adopt FinTech services: a regulatory focus theory

abstract: FinTech is a newly emerged service that combines innovative financial services and latest mobile technology. Since this service is new, there is a lack of study that investigates consumer behavior in adopting the service. Founded on Regulatory Focus Theory, our study aims to explain two different factors that motivate the adoption of FinTech in China. We plan to collect data from university students who used Alipay and Tenpay (two of the leading FinTech companies in China). The findings from our study will be valuable to FinTech companies in strategizing and promoting usage of their services.


title: Data-driven fiber tractography with neural networks

abstract: Elaborate expert modeling has been the predominant approach to fiber tractography. It attempts to invert the measurement process of diffusion-weighted MRI to reconstruct fibers. We present a purely data-driven neural network regression model for fiber tractography. The model sequentially takes as input a local block of data and the incoming direction of the fiber. From this input, the neural network predicts the outgoing direction. The training data can be provided by either automatic or human supervision. On both real, and synthetic data we observe that our model produces smoother and more accurate fibers than its supervisor. The performance of the model is scored with the Tractometer tool, where it consistently improves the supervisor baseline as well as the state-of-the-art in data-driven tractography. We show that our approach benefits from additional data, which can be incorporated easily, even from different supervisors. In experiments, the model is robust to noise and variation in the data, while being simple to use.


title: Revisiting code ownership and its relationship with software quality in the scope of modern code review

abstract: Code ownership establishes a chain of responsibility for modules in large software systems. Although prior work uncovers a link between code ownership heuristics and software quality, these heuristics rely solely on the authorship of code changes. In addition to authoring code changes, developers also make important contributions to a module by reviewing code changes. Indeed, recent work shows that reviewers are highly active in modern code review processes, often suggesting alternative solutions or providing updates to the code changes. In this paper, we complement traditional code ownership heuristics using code review activity. Through a case study of six releases of the large Qt and OpenStack systems, we find that: (1) 67%--86% of developers did not author any code changes for a module, but still actively contributed by reviewing 21%--39% of the code changes, (2) code ownership heuristics that are aware of reviewing activity share a relationship with software quality, and (3) the proportion of reviewers without expertise shares a strong, increasing relationship with the likelihood of having post-release defects. Our results suggest that reviewing activity captures an important aspect of code ownership, and should be included in approximations of it in future studies.


title: Artificial neural network development by means of a novel combination of grammatical evolution and genetic algorithm

abstract: Abstract The most important problems with exploiting artificial neural networks (ANNs) are to design the network topology, which usually requires an excessive amount of expert’s effort, and to train it. In this paper, a new evolutionary-based algorithm is developed to simultaneously evolve the topology and the connection weights of ANNs by means of a new combination of grammatical evolution (GE) and genetic algorithm (GA). GE is adopted to design the network topology while GA is incorporated for better weight adaptation. The proposed algorithm needs to invest a minimal expert’s effort for customization and is capable of generating any feedforward ANN with one hidden layer. Moreover, due to the fact that the generalization ability of an ANN may decrease because of overfitting problems, the algorithm utilizes a novel adaptive penalty approach to simplify ANNs generated through the evolution process. As a result, it produces much simpler ANNs that have better generalization ability and are easy to implement. The proposed method is tested on some real world classification datasets, and the results are statistically compared against existing methods in the literature. The results indicate that our algorithm outperforms the other methods and provides the best overall performance in terms of the classification accuracy and the number of hidden neurons. The results also present the contribution of the proposed penalty approach in the simplicity and generalization ability of the generated networks.


title: Brain MRI super-resolution using 3D generative adversarial networks

abstract: In this work we propose an adversarial learning approach to generate high resolution MRI scans from low resolution images. The architecture, based on the SRGAN model, adopts 3D convolutions to exploit volumetric information. For the discriminator, the adversarial loss uses least squares in order to stabilize the training. For the generator, the loss function is a combination of a least squares adversarial loss and a content term based on mean square error and image gradients in order to improve the quality of the generated images. We explore different solutions for the upsampling phase. We present promising results that improve classical interpolation, showing the potential of the approach for 3D medical imaging super-resolution. Source code available at https://github.com/imatge-upc/3D-GAN-superresolution


title: A vectorization framework for constant and linear gradient filled regions

abstract: Linear gradients are commonly applied in non-photographic artwork for shading and other artistic effects. It is sometimes necessary to generate a vector graphics form of raster images comprising such artwork with the expectation to obtain a simple output and plug it into a traditional workflow, to be further edited and arranged. Many such workflows support only linear gradients and our goal is to generate a standard vector form of the image that can fit such workflow. This vectorization process should be automatic with minimal user intervention. We present a simple image vectorization algorithm that detects regions of linear gradient in potentially noisy images and reconstructs the vector definition on the basis of that information. It uses a novel interval gradient optimization scheme to derive large regions of uniform gradient. We also demonstrate the technique on noisy and hand-drawn portraits.


title: IMPROVED BAT ALGORITHM FOR OPTIMUM DESIGN OF LARGE-SCALE TRUSS STRUCTURES

abstract: Deterring the optimum design of large-scale structures is a difficult task. Great number of design variables, largeness of the search space and controlling great number of design constraints are major preventive factors in performing optimum design of large-scale truss structures in a reasonable time. Meta-heuristic algorithms are known as one of the useful tools to deal with these problems. This paper presents an improved bat algorithm for optimizing large-scale structures. The capability of the algorithm is examined by comparing the resulting design parameters and structural weight with those of other methods from literature.


title: An open platform for rapid-prototyping protection and control schemes with IEC 61850

abstract: Communications is becoming increasingly important to the operation of protection and control schemes. Although offering many benefits, using standards-based communications, particularly IEC 61850, in the course of the research and development of novel schemes can be complex. This paper describes an open-source platform which enables the rapid prototyping of communications-enhanced schemes. The platform automatically generates the data model and communications code required for an intelligent electronic device to implement a publisher-subscriber generic object-oriented substation event and sampled-value messaging. The generated code is tailored to a particular system configuration description (SCD) file, and is therefore extremely efficient at runtime. It is shown here how a model-centric tool, such as the open-source Eclipse Modeling Framework, can be used to manage the complexity of the IEC 61850 standard, by providing a framework for validating SCD files and by automating parts of the code generation process. The flexibility and convenience of the platform are demonstrated through a prototype of a real-time, fast-acting load-shedding scheme for a low-voltage microgrid network. The platform is the first open-source implementation of IEC 61850 which is suitable for real-time applications, such as protection, and is therefore readily available for research and education.


title: A Bivalent Chromatin Structure Marks Key Developmental Genes in Embryonic Stem Cells

abstract: Summary The most highly conserved noncoding elements (HCNEs) in mammalian genomes cluster within regions enriched for genes encoding developmentally important transcription factors (TFs). This suggests that HCNE-rich regions may contain key regulatory controls involved in development. We explored this by examining histone methylation in mouse embryonic stem (ES) cells across 56 large HCNE-rich loci. We identified a specific modification pattern, termed "bivalent domains," consisting of large regions of H3 lysine 27 methylation harboring smaller regions of H3 lysine 4 methylation. Bivalent domains tend to coincide with TF genes expressed at low levels. We propose that bivalent domains silence developmental genes in ES cells while keeping them poised for activation. We also found striking correspondences between genome sequence and histone methylation in ES cells, which become notably weaker in differentiated cells. These results highlight the importance of DNA sequence in defining the initial epigenetic landscape and suggest a novel chromatin-based mechanism for maintaining pluripotency.


title: Optimal Placement and Configuration of Roadside Units in Vehicular Networks

abstract: In this paper, we propose a novel optimization framework for Roadside Unit (RSU) deployment and configuration in a vehicular network. We formulate the problem of placement of RSUs and selecting their configurations (e.g. power level, types of antenna and wired/wireless back haul network connectivity) as a linear program. The objective function is to minimize the total cost to deploy and maintain the network of RSU's. A user specified constraint on the minimum coverage provided by the RSU is also incorporated into the optimization framework. Further, the framework also supports the option of specifying selected regions of higher importance such as locations of frequently occurring accidents and incorporating constraints requiring stricter coverage in those areas. Simulation results are presented to demonstrate the feasibility of deployment on the campus map of Southern Methodist University (SMU). The efficiency and scalability of the optimization procedure for large scale problems are also studied and results shows that optimization over an area with the size of Cambridge, Massachusetts is completed in under 2 minutes. Finally, the effects of variation in several key parameters on the resulting design are studied.


title: Spoofing Face Recognition With 3D Masks

abstract: Spoofing is the act of masquerading as a valid user by falsifying data to gain an illegitimate access. Vulnerability of recognition systems to spoofing attacks (presentation attacks) is still an open security issue in biometrics domain and among all biometric traits, face is exposed to the most serious threat, since it is particularly easy to access and reproduce. In this paper, many different types of face spoofing attacks have been examined and various algorithms have been proposed to detect them. Mainly focusing on 2D attacks forged by displaying printed photos or replaying recorded videos on mobile devices, a significant portion of these studies ground their arguments on the flatness of the spoofing material in front of the sensor. However, with the advancements in 3D reconstruction and printing technologies, this assumption can no longer be maintained. In this paper, we aim to inspect the spoofing potential of subject-specific 3D facial masks for different recognition systems and address the detection problem of this more complex attack type. In order to assess the spoofing performance of 3D masks against 2D, 2.5D, and 3D face recognition and to analyze various texture-based countermeasures using both 2D and 2.5D data, a parallel study with comprehensive experiments is performed on two data sets: the Morpho database which is not publicly available and the newly distributed 3D mask attack database.


title: Broadband CPW-Fed Circularly Polarized Square Slot Antenna With Inverted-L Strips for UWB Applications

abstract: This letter presents a new design for a circularly polarized square slot antenna (CPSSA). The proposed single-layer antenna composed of a square ground plane embedded with two unequal-size inverted-L strips around two opposite corners is capable of generating a resonant mode for exciting two orthogonal E vectors. In this model, compared to the previous CPSSA structures, the impedance bandwidth and the axial ratio bandwidth of antenna are increased, which is four times wider than the impedance bandwidth of previous similar designs. The designed CPSS antenna with size 60 × 60 × 0.8 mm3 operates over the frequency band between 2.67 and 13 GHz (4.74:1, 132%) for VSWR <; 2, exhibiting a 32.2% (4.9-6.9 GHz, 1.5:1) circular polarization (CP) bandwidth. Throughout this letter, the improvement process of the axial ratio (AR) and S\\ properties are presented and discussed in detail.


title: Cyberbullying: The Challenge to Define

abstract: Abstract Cyberbullying is a reality of the digital age. To address this phenomenon, it becomes imperative to understand exactly what cyberbullying is. Thus, establishing a workable and theoretically sound definition is essential. This article contributes to the existing literature in relation to the definition of cyberbullying. The specific elements of repetition, power imbalance, intention, and aggression, regarded as essential criteria of traditional face-to-face bullying, are considered in the cyber context. It is posited that the core bullying elements retain their importance and applicability in relation to cyberbullying. The element of repetition is in need of redefining, given the public nature of material in the online environment. In this article, a clear distinction between direct and indirect cyberbullying is made and a model definition of cyberbullying is offered. Overall, the analysis provided lends insight into how the essential bullying elements have evolved and should apply in our parallel...


title: Robust Non-negative Graph Embedding: Towards noisy data, unreliable graphs, and noisy labels

abstract: Non-negative data factorization has been widely used recently. However, existing techniques, such as Non-negative Graph Embedding (NGE), often suffer from noisy data, unreliable graphs, and noisy labels, which are commonly encountered in real-world applications. To address these issues, in this paper, we propose a Robust Non-negative Graph Embedding (RNGE) framework. The joint sparsity in both graph embedding and reconstruction endues the robustness of RNGE. We develop an elegant multiplicative updating solution that can solve RNGE efficiently and prove the convergence rigourously. RNGE is robust to unreliable graphs, as well as both sample and label noises in training data. Moreover, RNGE provides a general formulation such that all the algorithms unified with the graph embedding framework can be easily extended to obtain their robust non-negative solutions. We conduct extensive experiments on four real-world datasets and compared the proposed RNGE to NGE and other representative non-negative data factorization and subspace learning methods. The experimental results demonstrate the effectiveness and robustness of RNGE.


title: CA4IOT Context Awareness for Internet of Things

abstract: Internet of Things (IoT) will connect billions of sensors deployed around the world together. This will create an ideal opportunity to build a sensing-as-a-service platform. Due to large number of sensor deployments, there would be number of sensors that can be used to sense and collect similar information. Further, due to advances in sensor hardware technology, new methods and measurements will be introduced continuously. In the IoT paradigm, selecting the most appropriate sensors which can provide relevant sensor data to address the problems at hand among billions of possibilities would be a challenge for both technical and non-technical users. In this paper, we propose the Context Awareness for Internet of Things (CA4IOT) architecture to help users by automating the task of selecting the sensors according to the problems/tasks at hand. We focus on automated configuration of filtering, fusion and reasoning mechanisms that can be applied to the collected sensor data streams using selected sensors. Our objective is to allow the users to submit their problems, so our proposed architecture understands them and produces more comprehensive and meaningful information than the raw sensor data streams generated by individual sensors.


title: Fair, G2- and C2-continuous circle splines for the interpolation of sparse data points

abstract: This paper presents a carefully chosen curve blending scheme between circles, which is based on angles, rather than point positions. The result is a class of circle splines that robustly produce fair-looking G 2 -continuous curves without any cusps or kinks, even through rather challenging, sparse sets of interpolation points. With a simple reparameterization the curves can also be made C 2 -continuous. The same


title: Automated image quality assessment for camera-captured OCR

abstract: Camera-captured optical character recognition (OCR) is a challenging area because of artifacts introduced during image acquisition with consumer-domain hand-held and Smart phone cameras. Critical information is lost if the user does not get immediate feedback on whether the acquired image meets the quality requirements for OCR. To avoid such information loss, we propose a novel automated image quality assessment method that predicts the degree of degradation on OCR. Unlike other image quality assessment algorithms which only deal with blurring, the proposed method quantifies image quality degradation across several artifacts and accurately predicts the impact on OCR error rate. We present evaluation results on a set of machine-printed document images which have been captured using digital cameras with different degradations.


title: REA-DSL: Business Model Driven Data-Engineering

abstract: An accounting information system (AIS) manages data about a company's financial and economic status. The contribution of this paper is closing the gap between the languages used by business domain experts and IT-experts in analyzing the relevant data. A well accepted approach for an accountability infrastructure is the Resource-Event-Agent (REA) ontology. Although REA has been based on well-established concepts of the accounting theory, its representation has not been intuitive to domain experts. In previous work, we developed the REA-DSL, a dedicated and easy-to-understand graphical domain specific modeling language for the REA ontology. Evidently, a model-driven approach requires to transform the REA-DSL artifacts to code. In this paper we present the transformation of the REA-DSL to a relational database for AIS. This approach offers the advantage that a domain expert verifies the relevant data in an "accounting language", whereas the IT expert is able to work with traditional data base structures.


title: Organisational change management: A critical review

abstract: It can be argued that the successful management of change is crucial to any organisation in order to survive and succeed in the present highly competitive and continuously evolving business environment. However, theories and approaches to change management currently available to academics and practitioners are often contradictory, mostly lacking empirical evidence and supported by unchallenged hypotheses concerning the nature of contemporary organisational change management. The purpose of this article is, therefore, to provide a critical review of some of the main theories and approaches to organisational change management as an important first step towards constructing a new framework for managing change. The article concludes with recommendations for further research.


title: Pretty in pink: The early development of gender-stereotyped colour preferences

abstract: Parents commonly dress their baby girls in pink and their baby boys in blue. Although there is research showing that children prefer the colour blue to other colours (regardless of gender), there is no evidence that girls actually have a special preference for the colour pink. This is the focus of the current investigation. In a large cross-sectional study, children aged 7 months to 5 years were offered eight pairs of objects and asked to choose one. In every pair, one of the objects was always pink. By the age of 2, girls chose pink objects more often than boys did, and by the age of 2.5, they had a significant preference for the colour pink over other colours. At the same time, boys showed an increasing avoidance of pink. These results thus reveal that sex differences in young children's preference for the colour pink involves both an increasing attraction to pink by young girls and a growing avoidance of pink by boys.


Mapping Ontologies Using Ontologies: Cross-lingual Semantic Role Information Transfer.


title: Auto-Meta: Automated Gradient Based Meta Learner Search

abstract: Fully automating machine learning pipelines is one of the key challenges of current artificial intelligence research, since practical machine learning often requires costly and time-consuming human-powered processes such as model design, algorithm development, and hyperparameter tuning. In this paper, we verify that automated architecture search synergizes with the effect of gradient-based meta learning. We adopt the progressive neural architecture search \cite{liu:pnas_google:DBLP:journals/corr/abs-1712-00559} to find optimal architectures for meta-learners. The gradient based meta-learner whose architecture was automatically found achieved state-of-the-art results on the 5-shot 5-way Mini-ImageNet classification problem with $74.65\%$ accuracy, which is $11.54\%$ improvement over the result obtained by the first gradient-based meta-learner called MAML \cite{finn:maml:DBLP:conf/icml/FinnAL17}. To our best knowledge, this work is the first successful neural architecture search implementation in the context of meta learning.


title: Optimum Design of Traveling-Wave SIW Slot Array Antennas

abstract: This paper presents an optimum design method based on the Method of Least Squares (MLS) for the traveling-wave substrate integrated waveguide (SIW) slot array antenna. Elliot's design formulas for dielectric filled waveguide including external and internal mutual coupling are used for the design by means of the equivalent waveguide. In order to achieve an efficient antenna, design formulas are extended to nonuniformly spaced slots. An error function consisting of four terms is formulated and then minimized with respect to the design parameters namely lengths, offsets, spacings and excitations of slots using combination of genetic algorithm and conjugate gradient method (GA-CG). A linear slot array at $X$ -band is designed, simulated and fabricated for verification. Results of the proposed method, simulation software and measurement are in good agreement.


title: A hybrid recommendation approach using LDA and probabilistic matrix factorization

abstract: Recommender systems provide users with suggestions and selections. Hybrid approaches which combine the neighborhood-based methods and the model-based methods have become popular when building collaborative filtering recommenders, but similarity is established between users/items only by rating information which is just numerical value and does not contain any semantic information, leading to the loss of flexibility. To address this problem, a probabilistic matrix factorization recommendation approach fusing neighborhood selection based on Latent Dirichlet Allocation is proposed. In the proposed approach, users’ and items’ neighbors are selected through users’ interests distribution and items’ attributes distribution. Then the approach incorporates similarity matrix into probabilistic matrix factorization to obtain users’ feature vectors and items’ feature vectors to make recommendations. The experimental results show that our approach is effective to improve recommendation performance and to solve data sparsity problem.


title: A Classifier to Detect Abnormality in CT Brain Images

abstract:  Abstract— Medical images are among important data sources available, since these images are usually used by physicians to detect different diseases. Extracting features from brain CT images helps in building a machine classifier that able to classify new brain images without human interference. In this paper, we used a data set of 25 CT brain images with different diagnoses, and built a decision tree classifier that is able to predict general abnormality in human brain. The preprocessing uses the three stages described by Peng et al with modifications. The process of feature extraction was mainly to identify the regions of interest and extract analytical data from those regions. The model was evaluated using hold out method and N-fold evaluation. The results showed that the classifier is able to detect abnormality, even with a small training data set.


title: A novel mm-Wave phased array antenna with 360° coverage for 5G smartphone applications

abstract: In this paper, a novel capacitive coupled patch antenna array capable of providing 360° coverage is proposed. The design and performance of the antenna element is discussed. The proposed antenna design is positioned in the mobile phone chassis as a set of 4 sub-arrays each with 12 antenna elements to provide high gain around 27 dBi with each sub-array providing 90° coverage. The antenna array covers the frequency range of 24–28 GHz which is a promising band for future 5G based smartphone services. The antenna array performance is evaluated. The proposed antenna exhibits a uniform radiation pattern with a stable gain when integrated with the mobile phone chassis.


SENTIMENT ANALYSIS AND CLASSIFICATION USING LEXICON-BASED APPROACH AND ADDRESSING POLARITY SHIFT PROBLEM


title: Graphene-Fiber-Based Supercapacitors Favor N-Methyl-2-pyrrolidone/Ethyl Acetate as the Spinning Solvent/Coagulant Combination

abstract: One-dimensional flexible fiber supercapacitors (FSCs) have attracted great interest as promising energy-storage units that can be seamlessly incorporated into textiles via weaving, knitting, or braiding. The major challenges in this field are to develop tougher and more efficient FSCs with a relatively easy and scalable process. Here, we demonstrate a wet-spinning process to produce graphene oxide (GO) fibers from GO dispersions in N-methyl-2-pyrrolidone (NMP), with ethyl acetate as the coagulant. Upon chemical reduction of GO, the resulting NMP-based reduced GO (rGO) fibers (rGO@NMP-Fs) are twice as high in the surface area and toughness but comparable in tensile strength and conductivity as that of the water-based rGO fibers (rGO@H2O-Fs). When assembled into parallel FSCs, rGO@NMP-F-based supercapacitors (rGO@NMP-FSCs) offered a specific capacitance of 196.7 F cm–3 (147.5 mF cm–2), five times higher than that of rGO@H2O-F-based supercapacitors (rGO@H2O-FSCs) and also higher than most existing wet-spun r...


title: Perception of Ethical Behaviour among Business Studies Students

abstract: The research on business students’ attitudes toward some ethical aspects is presented. Although the research dealt with students’ (future business persons’) perceptions of managerial ethics in general, the part of the research that focused on some of the ethically questionable students’ practices (e. g. cheating) is presented in the paper.


title: An Application of Recurrent Nets to Phone Probability Estimation

abstract: This paper presents an application of recurrent networks for phone probability estimation in large vocabulary speech recognition. The need for efficient exploitation of context information is discussed; a role for which the recurrent net appears suitable. An overview of early developments of recurrent nets for phone recognition is given along with the more recent improvements that include their integration with Markov models. Recognition results are presented for the DARPA TIMIT and Resource Management tasks, and it is concluded that recurrent nets are competitive with traditional means for performing phone probability estimation. >


title: A New Microfluidics-Based Droplet Dispenser for ICPMS

abstract: In this work, a novel droplet microfluidic sample introduction system for inductively coupled plasma mass spectrometry (ICPMS) is proposed and characterized. The cheap and disposable microfluidic chip generates droplets of an aqueous sample in a stream of perfluorohexane (PFH), which is also used to eject them as a liquid jet. The aqueous droplets remain intact during the ejection and can be transported into the ICP with >50% efficiency. The transport is realized via a custom-built system, which includes a membrane desolvator necessary for the PFH vapor removal. The introduction system presented here can generate highly monodisperse droplets in the size range of 40–60 μm at frequencies from 90 to 300 Hz. These droplets produced very stable signals with a relative standard deviation (RSD) comparable to the one achieved with a commercial droplet dispenser. Using the current system, samples with a total volume of <1 μL can be analyzed. Moreover, the capabilities of the setup for introduction and quantitative...


title: Mean-Variance Optimization in Markov Decision Processes

abstract: We consider finite horizon Markov decision processes under performance measures that involve both the mean and the variance of the cumulative reward. We show that either randomized or history-based policies can improve performance. We prove that the complexity of computing a policy that maximizes the mean reward under a variance constraint is NP-hard for some cases, and strongly NP-hard for others. We finally offer pseudopoly-nomial exact and approximation algorithms.


STDP-based spiking deep convolutional neural networks for object recognition


title: Millimeter-Wave High Radiation Efficiency Planar Waveguide Series-Fed Dielectric Resonator Antenna (DRA) Array: Analysis, Design, and Measurements

abstract: A low cost general architecture for a substrate integrated waveguide (SIW) series-fed dielectric resonator antenna (DRA) array, formed by two different slot polarizations, is proposed. In addition, a novel, simple, and generic transmission line (T.L.) circuit model, along with a fast and generic formulation for the new linear array antenna, is developed. The model can be used for reflection coefficient and radiation pattern (gain) calculations. The experimental data from two linear array modules, operating at the millimeter-wave band, are used to verify the simulated results of HFSS and the proposed model results. The measured radiation pattern for a 4 t 1 SIW-DRA array demonstrates a broadside beam with a radiated gain of 11.70 dB over an operating impedance bandwidth of 4.70%. Moreover, the simulated radiation efficiency is more than 90%.


title: WUW - wear Ur world: a wearable gestural interface

abstract: Information is traditionally confined to paper or digitally to a screen. In this paper, we introduce WUW, a wearable gestural interface, which attempts to bring information out into the tangible world. By using a tiny projector and a camera mounted on a hat or coupled in a pendant like wearable device, WUW sees what the user sees and visually augments surfaces or physical objects the user is interacting with. WUW projects information onto surfaces, walls, and physical objects around us, and lets the user interact with the projected information through natural hand gestures, arm movements or interaction with the object itself.


title: Deciphering Signatures of Mutational Processes Operative in Human Cancer

abstract: The genome of a cancer cell carries somatic mutations that are the cumulative consequences of the DNA damage and repair processes operative during the cellular lineage between the fertilized egg and the cancer cell. Remarkably, these mutational processes are poorly characterized. Global sequencing initiatives are yielding catalogs of somatic mutations from thousands of cancers, thus providing the unique opportunity to decipher the signatures of mutational processes operative in human cancer. However, until now there have been no theoretical models describing the signatures of mutational processes operative in cancer genomes and no systematic computational approaches are available to decipher these mutational signatures. Here, by modeling mutational processes as a blind source separation problem, we introduce a computational framework that effectively addresses these questions. Our approach provides a basis for characterizing mutational signatures from cancer-derived somatic mutational catalogs, paving the way to insights into the pathogenetic mechanism underlying all cancers.


title: Catching The Drift: Probabilistic Content Models, With Applications To Generation And Summarization

abstract: We consider the problem of modeling the content structure of texts within a specic domain, in terms of the topics the texts address and the order in which these topics appear. We rst present an effective knowledge-lean method for learning content models from unannotated documents, utilizing a novel adaptation of algorithms for Hidden Markov Models. We then apply our method to two complementary tasks: information ordering and extractive summarization. Our experiments show that incorporating content models in these applications yields substantial improvement over previously-proposed methods.


title: Personality in the context of social networks

abstract: There is great interest in environmental effects on the development and evolution of animal personality traits. An important component of an individual's environment is its social environment. However, few studies look beyond dyadic relationships and try to place the personality of individuals in the context of a social network. Social network analysis provides us with many new metrics to characterize the social fine-structure of populations and, therefore, with an opportunity to gain an understanding of the role that different personalities play in groups, communities and populations regarding information or disease transmission or in terms of cooperation and policing of social conflicts. The network position of an individual is largely a consequence of its interactive strategies. However, the network position can also shape an individual's experiences (especially in the case of juveniles) and therefore can influence the way in which it interacts with others in future. Finally, over evolutionary time, the social fine-structure of animal populations (as quantified by social network analysis) can have important consequences for the evolution of personalities—an approach that goes beyond the conventional game-theoretic analyses that assumed random mixing of individuals in populations.


title: Modeling biochemical pathways in the gene ontology

abstract: The concept of a biological pathway, an ordered sequence of molecular transformations, is used to collect and represent molecular knowledge for a broad span of organismal biology. Representations of biomedical pathways typically are rich but idiosyncratic presentations of organized knowledge about individual pathways. Meanwhile, biomedical ontologies and associated annotation files are powerful tools that organize molecular information in a logically rigorous form to support computational analysis. The Gene Ontology (GO), representing Molecular Functions, Biological Processes and Cellular Components, incorporates many aspects of biological pathways within its ontological representations. Here we present a methodology for extending and refining the classes in the GO for more comprehensive, consistent and integrated representation of pathways, leveraging knowledge embedded in current pathway representations such as those in the Reactome Knowledgebase and MetaCyc. With carbohydrate metabolic pathways as a use case, we discuss how our representation supports the integration of variant pathway classes into a unified ontological structure that can be used for data comparison and analysis.


title: Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data

abstract: This paper presents the first, 15-PetaFLOP Deep Learning system for solving scientific pattern classification problems on contemporary HPC architectures. We develop supervised convolutional architectures for discriminating signals in high-energy physics data as well as semi-supervised architectures for localizing and classifying extreme weather in climate data. Our Intelcaffe-based implementation obtains $\sim$2TFLOP/s on a single Cori Phase-II Xeon-Phi node. We use a hybrid strategy employing synchronous node-groups, while using asynchronous communication across groups. We use this strategy to scale training of a single model to $\sim$9600 Xeon-Phi nodes; obtaining peak performance of 11.73-15.07 PFLOP/s and sustained performance of 11.41-13.27 PFLOP/s. At scale, our HEP architecture produces state-of-the-art classification accuracy on a dataset with 10M images, exceeding that achieved by selections on high-level physics-motivated features. Our semi-supervised architecture successfully extracts weather patterns in a 15TB climate dataset. Our results demonstrate that Deep Learning can be optimized and scaled effectively on many-core, HPC systems.


title: Performance evaluation in obstacle avoidance

abstract: No quantitative procedure currently exists to evaluate the obstacle avoidance capabilities of robotic systems. Such an evaluation method is not only needed to compare different avoidance methods, but also to determine the operational limits of autonomous systems. This work proposes an evaluation framework which can find such limits. The framework comprises two types of tests: detection tests and avoidance tests. For each type, both environment and performance metrics need to be defined. For detection tests such metrics are well known, but for avoidance tests such metrics are not readily available. Therefore a new set of metrics is proposed. The framework is applied to a UAV that uses stereo vision to detect obstacles. Three different avoidance methods are compared in environments of varying difficulty.


Data Management in Analytical Customer Relationship Management


title: Necrotizing pneumonia in infants.

abstract: Community-acquired bacterial pneumonias generally have a good prognosis, given a good response to the antibiotic treatment applied, and complications such as pleural effusion, empyema, abscess and necrotizing pneumonia with pneumatocele formation (cavitary necrosis) are rare. Although cavitary necrosis is manifested as a severe disease, most children show complete recovery even without surgical treatment and have normal chest radiographs at long term. A case is presented of an immunocompetent infant that developed necrotizing pneumonia with pneumatocele formation during treatment of bacterial pneumonia. Conservative treatment led to complete regression of necrotic cavities and resulted in normal chest radiography finding 2.5 months of the occurrence of pneumatoceles.


title: Multimodal theater: extending low fidelity paper prototyping to multimodal applications

abstract: Low-fidelity paper prototyping has proven to be a useful technique for designing graphical user interfaces [1]. Wizard of Oz prototyping for other input modalities, such as speech, also has a long history [2]. Yet to surface are guidelines for low-fidelity prototyping of multimodal applications, those that use multiple and sometimes simultaneous combination of different input types. This paper describes our recent research in low fidelity, multimodal, paper prototyping and suggest guidelines to be used by future designers of multimodal applications.


Vault: Fast Bootstrapping for Cryptocurrencies


title: A Deep Learning Based Behavioral Approach to Indoor Autonomous Navigation

abstract: We present a semantically rich graph representation for indoor robotic navigation. Our graph representation encodes: semantic locations such as offices or corridors as nodes, and navigational behaviors such as enter office or cross a corridor as edges. In particular, our navigational behaviors operate directly from visual inputs to produce motor controls and are implemented with deep learning architectures. This enables the robot to avoid explicit computation of its precise location or the geometry of the environment, and enables navigation at a higher level of semantic abstraction. We evaluate the effectiveness of our representation by simulating navigation tasks in a large number of virtual environments. Our results show that using a simple sets of perceptual and navigational behaviors, the proposed approach can successfully guide the way of the robot as it completes navigational missions such as going to a specific office. Furthermore, our implementation shows to be effective to control the selection and switching of behaviors.


title: Hormone therapy for inmates: a metonym for transgender rights.

abstract: The issue of hormone therapy for transgender inmates, while seemingly limited in importance, is one that involves issues of greater importance for the transgender community. The greatest issue at the heart of the matter is the legal argument that is traditionally used to gain access to hormone therapy: the Eighth Amendment. The Eighth Amendment prohibits deliberate indifference to the medical needs of inmates. Traditionally, transgender inmates have gained access to hormone therapy by appealing to the DSM-IV's classification of Gender Identity Disorder (GID) as a mental illness, and by establishing that prison officials' failure to provide hormone therapy constitutes deliberate indifference to a serious medical need. However, appeal to GID is a double-edged sword: while it allows access to hormone therapy, it does so by describing transgender individuals as somehow sick or infirm. This description is at odds with the transgender community's conceptualization of itself. This Note seeks to square the legal arguments for provision of hormone therapy to transgender inmates with the philosophical backdrop that shapes the transgender rights movement by using Plyler v. Doe as a model. This Note argues that access to hormone therapy by transgender inmates involves the intersection of a quasi-fundamental right with a quasi-suspect class. By utilizing such an argument, the transgender community is not bound by the negative expressive effect that the law may have in describing it as infirm or deficient.