Quantifying Mental Health from Social Media with Neural User Embeddings. Mental illnesses adversely affect a significant proportion of the population worldwide. However, the typical methods to estimate and characterize the prevalence of mental health conditions are time-consuming and expensive. Consequently, best-available estimates concerning the prevalence of these conditions are often years out of date. Automated approaches that supplement traditional methods with broad, aggregated information derived from social media provide a potential means of furnishing near real-time estimates at scale. These may in turn provide grist for supporting, evaluating and iteratively improving public health programs and interventions. We propose a novel approach for mental health quantification that leverages user embeddings induced from social media post histories. Recent work showed that learned user representations capture latent aspects of individuals (e.g., political leanings). This paper investigates whether these representations also correlate with mental health statuses. To this end, we induced embeddings for a set of users known to be affected by depression and post-traumatic stress disorder, and for a set of demographically matched 'control' users. We then evaluated the induced user representations with respect to: (i) their ability to capture homophilic relations with respect to mental health statuses; and (ii) their predictive performance in downstream mental health models. Our experimental results demonstrate that learned user embeddings capture relevant signals for mental health quantification.

Question: How can we quantify mental health from social media?


The CL-SciSumm Shared Task 2018: Results and Key Insights. This overview describes the official results of the CL-SciSumm Shared Task 2018 – the first medium-scale shared task on scientific document summarization in the computational linguistics (CL) domain. This year, the dataset comprised 60 annotated sets of citing and reference papers from the open access research papers in the CL domain. The Shared Task was organized as a part of the 41 Annual Conference of the Special Interest Group in Information Retrieval (SIGIR), held in Ann Arbor, USA in July 2018. We compare the participating systems in terms of two evaluation metrics. The annotated dataset and evaluation scripts can be accessed and used by the community from: https://github.com/WING-NUS/scisumm-corpus.

Question: What is the size of the dataset?


Evaluating State-of-the-Art Object Detector on Challenging Traffic Light Data. Traffic light detection (TLD) is a vital part of both intelligent vehicles and driving assistance systems (DAS). General for most TLDs is that they are evaluated on small and private datasets making it hard to determine the exact performance of a given method. In this paper we apply the state-of-the-art, real-time object detection system You Only Look Once, (YOLO) on the public LISA Traffic Light dataset available through the VIVA-challenge, which contain a high number of annotated traffic lights, captured in varying light and weather conditions.,,,,,,The YOLO object detector achieves an AUC of impressively 90.49% for daysequence1, which is an improvement of 50.32% compared to the latest ACF entry in the VIVAchallenge. Using the exact same training configuration as the ACF detector, the YOLO detector reaches an AUC of 58.3%, which is in an increase of 18.13%.

Question: What is the state-of-the-art object detector?


GRS: The green, reliability, and security of emerging machine to machine communications. Machine-to-machine communications is characterized by involving a large number of intelligent machines sharing information and making collaborative decisions without direct human intervention. Due to its potential to support a large number of ubiquitous characteristics and achieving better cost efficiency, M2M communications has quickly become a market-changing force for a wide variety of real-time monitoring applications, such as remote e-healthcare, smart homes, environmental monitoring, and industrial automation. However, the flourishing of M2M communications still hinges on fully understanding and managing the existing challenges: energy efficiency (green), reliability, and security (GRS). Without guaranteed GRS, M2M communications cannot be widely accepted as a promising communication paradigm. In this article, we explore the emerging M2M communications in terms of the potential GRS issues, and aim to promote an energy-efficient, reliable, and secure M2M communications environment. Specifically, we first formalize M2M communications architecture to incorporate three domains - the M2M, network, and application domains - and accordingly define GRS requirements in a systematic manner. We then introduce a number of GRS enabling techniques by exploring activity scheduling, redundancy utilization, and cooperative security mechanisms. These techniques hold promise in propelling the development and deployment of M2M communications applications.

Question: What is the green reliability and security of emerging machine to machine communications?


Using Syntax-Based Machine Translation to Parse English into Abstract Meaning Representation. We present a parser for Abstract Meaning Representation (AMR). We treat Englishto-AMR conversion within the framework of string-to-tree, syntax-based machine translation (SBMT). To make this work, we transform the AMR structure into a form suitable for the mechanics of SBMT and useful for modeling. We introduce an AMR-specific language model and add data and features drawn from semantic resources. Our resulting AMR parser improves upon state-of-the-art results by 7 Smatch points.

Question: How do you parse abstract meaning representation?


Corrective 3D reconstruction of lips from monocular video. In facial animation, the accurate shape and motion of the lips of virtual humans is of paramount importance, since subtle nuances in mouth expression strongly influence the interpretation of speech and the conveyed emotion. Unfortunately, passive photometric reconstruction of expressive lip motions, such as a kiss or rolling lips, is fundamentally hard even with multi-view methods in controlled studios. To alleviate this problem, we present a novel approach for fully automatic reconstruction of detailed and expressive lip shapes along with the dense geometry of the entire face, from just monocular RGB video. To this end, we learn the difference between inaccurate lip shapes found by a state-of-the-art monocular facial performance capture approach, and the true 3D lip shapes reconstructed using a high-quality multi-view system in combination with applied lip tattoos that are easy to track. A robust gradient domain regressor is trained to infer accurate lip shapes from coarse monocular reconstructions, with the additional help of automatically extracted inner and outer 2D lip contours. We quantitatively and qualitatively show that our monocular approach reconstructs higher quality lip shapes, even for complex shapes like a kiss or lip rolling, than previous monocular approaches. Furthermore, we compare the performance of person-specific and multi-person generic regression strategies and show that our approach generalizes to new individuals and general scenes, enabling high-fidelity reconstruction even from commodity video footage.

Question: How accurate is the monocular reconstruction of lips?


Types and tokens in visual processing: a double dissociation between the attentional blink and repetition blindness.. In rapid serial visual presentation tasks, correct identification of a target triggers a deficit for reporting a 2nd target appearing within 500 ms: an attentional blink (AB). A different phenomenon, termed repetition blindness (RB), refers to a deficit for the 2nd of 2 stimuli that are identical. What is the relationship between these 2 deficits? The present study obtained a double dissociation between AB and RB. AB and RB followed different time courses (Experiments 1 and 4A), increased target-distractor discriminability alleviated AB but not RB (Experiments 2 and 4A), and enhanced episodic distinctiveness of the two targets eliminated RB but not AB (Experiments 3 and 4B). The implications of the double dissociation between AB and RB for theories of visual processing are discussed.

Question: What is the relationship between the attentional blink and repetition blindness?


Distributed Data Clustering in Peer -to-Peer Networks: A Technical Review. Clustering as one of the main branches of data mining, has gained an important place in the different applied fields. On the other hand, Peer-to-Peer (P2P) networks with features such as simplicity, low cost communication, and high availability resources, have gained a worldwide popularity in the present days. In P2P network, high volumes of data are distributed between dispersed data sources. In such distributed systems, sending data to a central site is not possible, due to processing, storage and handling costs. This issue makes the need for specific methods for distributed data clustering in P2P networks. This paper makes a technical review of distributed data clustering approaches in P2P network, to understand the research trends in this area. Keywords—Peer to Peer Networks, Distributed Data Mining, Distributed Data Clustering

Question: What is the size of the canadian military?


Compact two-layer slot array antenna with SIW for 60GHz wireless applications. In a variety of microwave and millimeter-wave applications where high performance antennas are required, waveguide slot arrays have received considerable attention due to their high aperture efficiency, low side lobe levels, and low cross polarization. Resonant slot arrays usually suffer from narrow bandwidth and high cost due to high precision required in manufacturing. Furthermore, because of using standard rectangular waveguides, the antenna array is thick and heavy and is not suitable for monolithic integration with high frequency printed circuits.

Question: What is a compact two-layer slot array antenna with SIW for 60GHz wireless applications?


Facebook and texting made me do it: Media-induced task-switching while studying. Electronic communication is emotionally gratifying, but how do such technological distractions impact academic learning? The current study observed 263 middle school, high school and university students studying for 15 min in their homes. Observers noted technologies present and computer windows open in the learning environment prior to studying plus a minute-by-minute assessment of on-task behavior, off-task technology use and open computer windows during studying. A questionnaire assessed study strategies, task-switching preference, technology attitudes, media usage, monthly texting and phone calling, social networking use and grade point average (GPA). Participants averaged less than six minutes on task prior to switching most often due to technological distractions including social media, texting and preference for task-switching. Having a positive attitude toward technology did not affect being on-task during studying. However, those who preferred to task-switch had more distracting technologies available and were more likely to be off-task than others. Also, those who accessed Facebook had lower GPAs than those who avoided it. Finally, students with relatively high use of study strategies were more likely to stay on-task than other students. The educational implications include allowing students short ''technology breaks'' to reduce distractions and teaching students metacognitive strategies regarding when interruptions negatively impact learning. 2012 Elsevier Ltd. All rights reserved.

Question: How do electronic communication technologies impact academic learning?


Learning A Task-Specific Deep Architecture For Clustering. While sparse coding-based clustering methods have shown to be successful, their bottlenecks in both efficiency and scalability limit the practical usage. In recent years, deep learning has been proved to be a highly effective, efficient and scalable feature learning tool. In this paper, we propose to emulate the sparse coding-based clustering pipeline in the context of deep learning, leading to a carefully crafted deep model benefiting from both. A feed-forward network structure, named TAGnet, is constructed based on a graphregularized sparse coding algorithm. It is then trained with taskspecific loss functions from end to end. We discover that connecting deep learning to sparse coding benefits not only the model performance, but also its initialization and interpretation. Moreover, by introducing auxiliary clustering tasks to the intermediate feature hierarchy, we formulate DTAGnet and obtain a further performance boost. Extensive experiments demonstrate that the proposed model gains remarkable margins over several state-of-the-art methods.

Question: What is a feed-forward network?


Lean, six sigma and lean sigma: fads or real process improvement methods?. Purpose – The purpose of this paper is to explore if six sigma and lean are new methods, or if they are repackaged versions of previously popular methods – total quality management (TQM) and just-in-time (JIT). Design/methodology/approach – The study is based on a critical comparison of lean with JIT and six sigma with TQM, a study of the measure of the publication frequency – the number of academic articles published every year of the previous 30 years – for each topic, and a review of critical success factors (CSF) for change efforts. Findings – The more recent concepts of lean and six sigma have mainly replaced – but not necessarily added to – the concepts of JIT and TQM. lean and six sigma are essentially repackaged versions of the former, and the methods seem to follow the fad (product) life cycle. The literature offers fairly similar and rather general CSF for these methods, e.g. top management support and the importance of communication and information. What seems to be missing, however, is the need for a systemic approach to organizational change and improvement. Practical implications – A prediction is, given the fad or product life cycle phenomenon, that there will be a new method promoted soon, something perhaps already experienced with the borderline preposterous concept of lean six sigma. On the other hand, based on the gap in time between both JIT and lean, and TQM and six sigma – a gap filled by BRP/reengineering – the next method will be process oriented. This paper concludes with the discussion of the need for a process-based approach to organizational improvement efforts. Originality/value – This paper is of value in that it analyzes what lessons can be learnt from organizational change and improvement efforts. The analysis includes a comparison of CSF for any change project before discussing the need for a process (systems) perspective for successful organizational improvem

Question: What is the difference between lean and six sigma?


GraphLab: A New Framework For Parallel Machine Learning. Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insufficiently expressive while low-level tools like MPI and Pthreads leave ML experts repeatedly solving the same design challenges. By targeting common patterns in ML, we developed GraphLab, which improves upon abstractions like MapReduce by compactly expressing asynchronous iterative algorithms with sparse computational dependencies while ensuring data consistency and achieving a high degree of parallel performance. We demonstrate the expressiveness of the GraphLab framework by designing and implementing parallel versions of belief propagation, Gibbs sampling, Co-EM, Lasso and Compressed Sensing. We show that using GraphLab we can achieve excellent parallel performance on large scale real-world problems.

Question: What is the graphlab framework?


Accelerated training of conditional random fields with stochastic gradient methods. We apply Stochastic Meta-Descent (SMD), a stochastic gradient optimization method with gain vector adaptation, to the training of Conditional Random Fields (CRFs). On several large data sets, the resulting optimizer converges to the same quality of solution over an order of magnitude faster than limited-memory BFGS, the leading method reported to date. We report results for both exact and inexact inference techniques.

Question: What is a conditional random field?


The Technology Behind Personal Digital Assistants: An overview of the system architecture and key components. We have long envisioned that one day computers will understand natural language and anticipate what we need, when and where we need it, and proactively complete tasks on our behalf. As computers get smaller and more pervasive, how humans interact with them is becoming a crucial issue. Despite numerous attempts over the past 30 years to make language understanding (LU) an effective and robust natural user interface for computer interaction, success has been limited and scoped to applications that were not particularly central to everyday use. However, speech recognition and machine learning have continued to be refined, and structured data served by applications and content providers has emerged. These advances, along with increased computational power, have broadened the application of natural LU to a wide spectrum of everyday tasks that are central to a user's productivity. We believe that as computers become smaller and more ubiquitous [e.g., wearables and Internet of Things (IoT)], and the number of applications increases, both system-initiated and user-initiated task completion across various applications and web services will become indispensable for personal life management and work productivity. In this article, we give an overview of personal digital assistants (PDAs); describe the system architecture, key components, and technology behind them; and discuss their future potential to fully redefine human?computer interaction.

Question: What is the future of personal digital assistants?


Annular-ring CMUT arrays for forward-looking IVUS: transducer characterization and imaging. In this study, a 64-element, 1.15-mm diameter annular-ring capacitive micromachined ultrasonic transducer (CMUT) array was characterized and used for forward-looking intravascular ultrasound (IVUS) imaging tests. The array was manufactured using low-temperature processes suitable for CMOS electronics integration on a single chip. The measured radiation pattern of a 43 /spl times/ 140-/spl mu/m array element depicts a 40/spl deg/ view angle for forward-looking imaging around a 15-MHz center frequency in agreement with theoretical models. Pulse-echo measurements show a -10-dB fractional bandwidth of 104% around 17 MHz for wire targets 2.5 mm away from the array in vegetable oil. For imaging and SNR measurements, RF A-scan data sets from various targets were collected using an interconnect scheme forming a 32-element array configuration. An experimental point spread function was obtained and compared with simulated and theoretical array responses, showing good agreement. Therefore, this study demonstrates that annular-ring CMUT arrays fabricated with CMOS-compatible processes are capable of forward-looking IVUS imaging, and the developed modeling tools can be used to design improved IVUS imaging arrays.

Question: What is the difference between a CMUT and a CMOS?


Storytelling by the StoryCake visualization. On the issue of how to explore the complex relationships of entities in a story, Storyline visualization has proven to be a useful approach. The traditional Storyline visualization technology only applies to a linear continuous story and has the limitation of reflecting the development of the plots in a story. In this paper, we propose a hierarchical plot visualization method called StoryCake to improve this problem. First, according to the story elements, inter-sessions are divided into groups, whereby the layout of the entities will be optimized in individual groups. Then, the hierarchical relationships of entities in polar coordinates can be confirmed. Finally, we use several skills, e.g., interaction, labels and a fan-shaped visualization view, to enable people better understand how story evolves and track the hierarchical relationship more conveniently. Experimental results show that the proposed hierarchical plot visualization method in polar coordinates can reveal discontinuous events and nonlinear stories. The results also show that our algorithm presents the narrative structure and development of the story.

Question: How to explore the complex relationships of entities in a story?


Review: IT-Dependent Strategic Initiatives and Sustained Competitive Advantage: A Review and Synthesis of the Literature. The role of information systems in the creation and appropriation of economic value has a long tradition of research, within which falls the literature on the sustainability of IT-dependent competitive advantage. In this article, we formally define the notion of IT-dependent strategic initiative and use Jane Webster was the accepting senior editor for this paper. Anitesh Barua was the associate editor. Jeanne Ross, Anandhi Bharadwaj, and Paul Pavlou served as reviewers. it to frame a review of the literature on the sustainability of competitive advantage rooted in information systems use. We offer a framework that articulates both the dynamic approach to ITdependent strategic advantage currently receiving attention in the literature and the underlying drivers of sustainability. This framework models how and why the characteristics of the IT-dependent strategic initiative enable sustained competitive advantage, and how the determinants of sustainability are developed and strengthened over time. Such explanation facilitates the pre-implementation analysis of planned initiatives by innovators, as well as the post-implementation evaluation of existing initiatives so as to identify the basis of their sustainability. In carrying out this study, we examined the interdisciplinary literature on strategic information systems. Using a structured methodology, we reviewed the titles and abstracts of 648 articles drawn from information systems, strategic management, and marketing literature. We then examined and individually coded a relevant subset of 117 articles. The literature has identified four barriers to erosion of competitive advantage for ITdependent strategic initiatives and has surfaced the structural determinants of their magnitude. Previous work has also begun to theorize about the process by which these barriers to erosion evolve over time. Our r

Question: What is the difference between a strategic initiative and a strategic advantage?


Empathy decline and its reasons: a systematic review of studies with medical students and residents.. PURPOSE Empathy is a key element of patient-physician communication; it is relevant to and positively influences patients' health. The authors systematically reviewed the literature to investigate changes in trainee empathy and reasons for those changes during medical school and residency.   METHOD The authors conducted a systematic search of studies concerning trainee empathy published from January 1990 to January 2010, using manual methods and the PubMed, EMBASE, and PsycINFO databases. They independently reviewed and selected quantitative and qualitative studies for inclusion. Intervention studies, those that evaluated psychometric properties of self-assessment tools, and those with a sample size <30 were excluded.   RESULTS Eighteen studies met the inclusion criteria: 11 on medical students and 7 on residents. Three longitudinal and six cross-sectional studies of medical students demonstrated a significant decrease in empathy during medical school; one cross-sectional study found a tendency toward a decrease, and another suggested stable scores. The five longitudinal and two cross-sectional studies of residents showed a decrease in empathy during residency. The studies pointed to the clinical practice phase of training and the distress produced by aspects of the "hidden," "formal," and "informal" curricula as main reasons for empathy decline.   CONCLUSIONS The results of the reviewed studies, especially those with longitudinal data, suggest that empathy decline during medical school and residency compromises striving toward professionalism and may threaten health care quality. Theory-based investigations of the factors that contribute to empathy decline among trainees and improvement of the validity of self-assessment methods are necessary for further research.

Question: What is the difference between empathy and sympathy?


Deploying SDP for machine learning. We discuss the use in machine learning of a general type of convex optimisation problem known as semi-definite programming (SDP) [1]. We intend to argue that SDP's arise quite naturally in a variety of situations, accounting for their omnipresence in modern machine learning approaches, and we provide examples in support.

Question: What is semi-definite programming?


AVANT-GUARD: scalable and vigilant switch flow management in software-defined networks. Among the leading reference implementations of the Software Defined Networking (SDN) paradigm is the OpenFlow framework, which decouples the control plane into a centralized application. In this paper, we consider two aspects of OpenFlow that pose security challenges, and we propose two solutions that could address these concerns. The first challenge is the inherent communication bottleneck that arises between the data plane and the control plane, which an adversary could exploit by mounting a "control plane saturation attack" that disrupts network operations. Indeed, even well-mined adversarial models, such as scanning or denial-of-service (DoS) activity, can produce more potent impacts on OpenFlow networks than traditional networks. To address this challenge, we introduce an extension to the OpenFlow data plane called "connection migration", which dramatically reduces the amount of data-to-control-plane interactions that arise during such attacks. The second challenge is that of enabling the control plane to expedite both detection of, and responses to, the changing flow dynamics within the data plane. For this, we introduce "actuating triggers" over the data plane's existing statistics collection services. These triggers are inserted by control layer applications to both register for asynchronous call backs, and insert conditional flow rules that are only activated when a trigger condition is detected within the data plane's statistics module. We present Avant-Guard, an implementation of our two data plane extensions, evaluate the performance impact, and examine its use for developing more scalable and resilient SDN security services.

Question: What is the difference between a software defined network and a software defined network?


Automatic license plate recognition using extracted features. Automatic License Plate Recognition (ALPR) systems are employed for detection and recognition of license plate/number plate of vehicles. The performance of existing systems is well below the desired level. In this perspective, there is a definite need to propose a system to overcome the limitations of currently available systems. A new approach is being introduced in this paper for fast and efficient implementation of ALPR system. In this approach, the vertical edge detection algorithm is applied and removes unwanted edges by image normalization technique. The LP region is extracted by incorporating statistical and morphological image processing techniques. For character recognition, the template matching is employed for optical character recognition (OCR). The algorithm is tested on 500 real time images, which are acquired under different illumination conditions and from different scenes. Overall efficiency of the proposed method is 84.8% and the execution time is less than 0.5sec.

Question: What is the execution time of the proposed method?


Cheek defects.. The challenge surgeons face when reconstructing cheek defects varies significantly depending on the location and depth of the defect and the distensibility of the surrounding tissues. The cheek is a large aesthetic unit characterized in most areas with a convex surface and inherent transitions in color and texture. These characteristics demand the surgeon's attention to achieve superior results during reconstruction. Surgeons must also recognize the free margins of the adjacent structures, including the lower eyelid, nasal ala, and lip, to minimize distortion of these areas during healing. With these challenges in mind, this article discusses several approaches to reconstruction of various types of cheek defects.

Question: What is the difference between a cheek defect and a cheek deformity?


Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation. This paper considers additive factorial hidden Markov models, an extension to HMMs where the state factors into multiple independent chains, and the output is an additive function of all the hidden states. Although such models are very powerful, accurate inference is unfortunately difficult: exact inference is not computationally tractable, and existing approximate inference techniques are highly susceptible to local optima. In this paper we propose an alternative inference method for such models, which exploits their additive structure by 1) looking at the observed difference signal of the observation, 2) incorporating a "robust" mixture component that can account for unmodeled observations, and 3) constraining the posterior to allow at most one hidden state to change at a time. Combining these elements we develop a convex formulation of approximate inference that is computationally efficient, has no issues of local optima, and which performs much better than existing approaches in practice. The method is motivated by the problem of energy disaggregation, the task of taking a whole home electricity signal and decomposing it into its component appliances; applied to this task, our algorithm achieves state-of-the-art performance, and is able to separate many appliances almost perfectly using just the total aggregate signal.

Question: What is the approximate inference method for additive factorial HMMs?


Improving deep learning performance using random forest HTM cortical learning algorithm. Deep Learning is an artificial intelligence function that imitates the mechanisms of the human mind in processing records and developing shapes to be used in selection construction. The objective of the paper is to improve the performance of the deep learning using a proposed algorithm called RFHTMC. This proposed algorithm is a merged version from Random Forest and HTM Cortical Learning Algorithm. The methodology for improving the performance of Deep Learning depends on the concept of minimizing the mean absolute percentage error which is an indication of the high performance of the forecast procedure. In addition to the overlap duty cycle which its high percentage is an indication of the speed of the processing operation of the classifier. The outcomes depict that the proposed set of rules reduces the absolute percent errors by using half of the value. And increase the percentage of the overlap duty cycle with 15%.

Question: What is the difference between random forest and HTM cortical learning algorithm?


A difference in hypothalamic structure between heterosexual and homosexual men.. The anterior hypothalamus of the brain participates in the regulation of male-typical sexual behavior. The volumes of four cell groups in this region [interstitial nuclei of the anterior hypothalamus (INAH) 1, 2, 3, and 4] were measured in postmortem tissue from three subject groups: women, men who were presumed to be heterosexual, and homosexual men. No differences were found between the groups in the volumes of INAH 1, 2, or 4. As has been reported previously, INAH 3 was more than twice as large in the heterosexual men as in the women. It was also, however, more than twice as large in the heterosexual men as in the homosexual men. This finding indicates that INAH is dimorphic with sexual orientation, at least in men, and suggests that sexual orientation has a biological substrate.

Question: What is the anterior hypothalamus?


Empirical analysis of programming language adoption. Some programming languages become widely popular while others fail to grow beyond their niche or disappear altogether. This paper uses survey methodology to identify the factors that lead to language adoption. We analyze large datasets, including over 200,000 SourceForge projects, 590,000 projects tracked by Ohloh, and multiple surveys of 1,000-13,000 programmers.  We report several prominent findings. First, language adoption follows a power law; a small number of languages account for most language use, but the programming market supports many languages with niche user bases. Second, intrinsic features have only secondary importance in adoption. Open source libraries, existing code, and experience strongly influence developers when selecting a language for a project. Language features such as performance, reliability, and simple semantics do not. Third, developers will steadily learn and forget languages. The overall number of languages developers are familiar with is independent of age. Finally, when considering intrinsic aspects of languages, developers prioritize expressivity over correctness. They perceive static types as primarily helping with the latter, hence partly explaining the popularity of dynamic languages.

Question: What are the factors that lead to language adoption?


The CMU Pose, Illumination, and Expression Database. In the Fall of 2000 we collected a database of over 40,000 facial images of 68 people. Using the CMU 3D Room we imaged each person across 13 different poses, under 43 different illumination conditions, and with 4 different expressions. We call this the CMU Pose, Illumination, and Expression (PIE) database. We describe the imaging hardware, the collection procedure, the organization of the images, several possible uses, and how to obtain the database.

Question: What is the CMU Pose, Illumination, and Expression database?


The naturalness of reproduced high dynamic range images. The problem of visualizing high dynamic range images on the devices with restricted dynamic range has recently gained a lot of interest in the computer graphics community. Various so-called tone mapping operators have been proposed to face this issue. The field of tone mapping assumes thorough knowledge of both the objective and subjective attributes of an image. However, there no published analysis of such attributes exists so far. In this paper, we present an overview of image attributes which are used extensively in different tone mapping methods. Furthermore, we propose a scheme of relationships between these attributes, leading to the definition of an overall quality measure which we call naturalness. We present results of the subjective psychophysical testing that we have performed to prove the proposed relationship scheme. Our effort sets the stage for well-founded quality comparisons between tone mapping operators. By providing good definitions of the different attributes, comparisons, be they user-driven or fully automatic, are made possible at all.

Question: What is the relationship between the attributes of an image?


Computer Forensics Field Triage Process Model. With the proliferation of digital based evidence, the need for the timely identification, analysis and interpretation of digital evidence is becoming more crucial. In many investigations critical information is required while at the scene or within a short period of time measured in hours as opposed to days. The traditional cyber forensics approach of seizing a system(s)/media, transporting it to the lab, making a forensic image(s), and then searching the entire system for potential evidence, is no longer appropriate in some circumstances. In cases such as child abductions, pedophiles, missing or exploited persons, time is of the essence. In these types of cases, investigators dealing with the suspect or crime scene need investigative leads quickly; in some cases it is the difference between life and death for the victim(s). The Cyber Forensic Field Triage Process Model (CFFTPM) proposes an onsite or field approach for providing the identification, analysis and interpretation of digital evidence in a short time frame, without the requirement of having to take the system(s)/media back to the lab for an in-depth examination or acquiring a complete forensic image(s). The proposed model adheres to commonly held forensic principles, and does not negate the ability that once the initial field triage is concluded, the system(s)/storage media be transported back to a lab environment for a more thorough examination and analysis. The CFFTPM has been successfully used in various real world cases, Journal of Digital Forensics, Security and Law, Vol. 1(2) 20 and its investigative importance and pragmatic approach has been amply demonstrated. Furthermore, the derived evidence from these cases has not been challenged in the court proceedings where it has been introduced. The current article describes the CFFTPM in detail, discusses the model's forensic soundness, investigative support capabilities and practical considerations.

Question: What is the cyber forensic field triage process model?


Evaluation is a Dynamic Process : Moving Beyond Dual System Models. Over the past few decades, dual attitude ⁄ process ⁄ system models have emerged as the dominant framework for understanding a wide range of psychological phenomena. Most of these models characterize the unconscious and conscious mind as being built from discrete processes or systems: one that is reflexive, automatic, fast, affective, associative, and primitive, and a second that is deliberative, controlled, slow, cognitive, propositional, and more uniquely human. Although these models serve as a useful heuristic for characterizing the human mind, recent developments in social and cognitive neuroscience suggest that the human evaluative system, like most of cognition, is widely distributed and highly dynamic. Integrating these advances with current attitude theories, we review how the recently proposed Iterative Reprocessing Model can account for apparent dual systems as well as discrepancies between traditional dual system models and recent research revealing the dynamic nature of evaluation. Furthermore, we describe important implications this dynamical system approach has for various social psychological domains. For nearly a century, psychologists have sought to understand the unconscious and conscious processes that allow people to evaluate their surroundings (Allport, 1935; Freud, 1930). Building on a model of the human mind rooted in classic Greek philosophy (Annas, 2001), many contemporary psychologists have characterized the mind as possessing discrete processes or systems: one that is evolutionarily older, reflexive, automatic, fast, affective, associative, and the other that is more uniquely human, deliberative, controlled, slow, cognitive, and propositional (see Figure 1). These dual process or system models have been highly influential throughout psychology for the past three decades (Chaiken & Trope, 1999). Indeed, a dual system model of the human mind permeates research in a wide range

Question: What is the difference between dual process and dual system models?


Stress aware layout optimization. Process-induced mechanical stress is used to enhance carrier transport and achieve higher drive currents in current CMOS technologies. In this paper, we study how stress-induced performance enhancements are affected by layout properties and suggest guidelines for improving layouts so that performance gains are maximized. All MOS devices in this work include STI and nitride stress liners as sources of stress. Additionally, the PMOS devices incorporate the stress effects caused by the embedded SiGe S/D layer common in today's processes. First, we study how stress and drive current depend on layout parameters such as active area length and contact placement. We develop an intuition for the drive current dependency on these parameters and propose simple guidelines to improve a layout while considering mechanical stress effects. We then use these guidelines to improve the standard cell layouts in a 65nm industrial library. Experimental results show that we can enhance NMOS and PMOS drive currents by ~5% and ~12%, respectively, while only increasing NMOS leakage current by 1.48X and PMOS leakage current by 3.78X. By applying our guidelines to a 3-input NOR gate and a 3-input NAND gate, we are able to achieve a ~13.5% PMOS drive current improvement in the NOR gate and a ~7% NMOS drive current improvement in the NAND gate, without increasing cell area in either case

Question: How to improve layout for stress aware layout optimization?


Asynchronous Event-Based Hebbian Epipolar Geometry. Epipolar geometry, the cornerstone of perspective stereo vision, has been studied extensively since the advent of computer vision. Establishing such a geometric constraint is of primary importance, as it allows the recovery of the 3-D structure of scenes. Estimating the epipolar constraints of nonperspective stereo is difficult, they can no longer be defined because of the complexity of the sensor geometry. This paper will show that these limitations are, to some extent, a consequence of the static image frames commonly used in vision. The conventional frame-based approach suffers from a lack of the dynamics present in natural scenes. We introduce the use of neuromorphic event-based-rather than frame-based-vision sensors for perspective stereo vision. This type of sensor uses the dimension of time as the main conveyor of information. In this paper, we present a model for asynchronous event-based vision, which is then used to derive a general new concept of epipolar geometry linked to the temporal activation of pixels. Practical experiments demonstrate the validity of the approach, solving the problem of estimating the fundamental matrix applied, in a first stage, to classic perspective vision and then to more general cameras. Furthermore, this paper shows that the properties of event-based vision sensors allow the exploration of not-yet-defined geometric relationships, finally, we provide a definition of general epipolar geometry deployable to almost any visual sensor.

Question: What is the difference between frame-based and event-based vision?


German version of the Multidimensional Body-Self Relations Questionnaire - Appearance Scales (MBSRQ-AS): confirmatory factor analysis and validation.. The Multidimensional Body-Self Relations Questionnaire (MBSRQ) is a widely used questionnaire that measures body image as a multidimensional construct. The Appearance Scales (AS) of the MBSRQ (Appearance Evaluation, Appearance Orientation, Body Areas Satisfaction, Overweight Preoccupation and Self-Classified Weight) are subscales which facilitate a parsimonious assessment of appearance-related aspects of body image. The current study tested the psychometric properties and factor structure of a German translation of the MBSRQ-AS. Participants were n=230 female patients with the SCID diagnosis of an eating disorder and n=293 female healthy controls. In a confirmatory factor analysis, convincing goodness-of-fit indices emerged. The subscales of the questionnaire yielded good reliability and convergent and discriminant validity coefficients, with most items showing excellent characteristics. Like the English version, the German adaptation of the questionnaire can be recommended for a multidimensional assessment of appearance-related aspects of body image in both research and clinical practice.

Question: What is the German version of the Multidimensional Body-Self Relations Questionnaire - Appearance Scales?


A new facial expression recognition based on curvelet transform and online sequential extreme learning machine initialized with spherical clustering. In this paper, a novel algorithm is proposed for facial expression recognition by integrating curvelet transform and online sequential extreme learning machine (OSELM) with radial basis function (RBF) hidden node having optimal network architecture. In the proposed algorithm, the curvelet transform is firstly applied to each region of the face image divided into local regions instead of whole face image to reduce the curvelet coefficients too huge to classify. Feature set is then generated by calculating the entropy, the standard deviation and the mean of curvelet coefficients of each region. Finally, spherical clustering (SC) method is employed to the feature set to automatically determine the optimal hidden node number and RBF hidden node parameters of OSELM by aim of increasing classification accuracy and reducing the required time to select the hidden node number. So, the learning machine is called as OSELM-SC. It is constructed two groups of experiments: The aim of the first one is to evaluate the classification performance of OSELM-SC on the benchmark datasets, i.e., image segment, satellite image and DNA. The second one is to test the performance of the proposed facial expression recognition algorithm on the Japanese Female Facial Expression database and the Cohn-Kanade database. The obtained experimental results are compared against the state-of-the-art methods. The results demonstrate that the proposed algorithm can produce effective facial expression features and exhibit good recognition accuracy and robustness.

Question: What is the facial expression recognition based on curvelet transform and online sequential extreme learning machine initialized with spherical clustering?


Novel Concept of the Three-Dimensional Vertical FG nand Flash Memory Using the Separated-Sidewall Control Gate. Recently, we proposed a novel 3-D vertical floating gate (FG)-type nand Flash memory cell array using the separated-sidewall control gate (CG) (S-SCG). This novel cell consists of one cylindrical FG with line-type CG and S-SCG structures. For simplifying the process flow, we realized the common S-SCG lines by using the prestacked polysilicon layer, through which variable medium voltages are applied not only to control the electrically inverted S/D region but also to assist the program and erase operations. In this paper, we successfully demonstrate the normal Flash cell operation and show its superior performances in comparison with the recent 3-D FG nand cells by using the cylindrical device simulation. It is shown that the proposed cell can realize the highest CG coupling ratio, low-voltage cell operations of program with 15 V at Vth = 4V and erase with 14 V at Vth = -3V , good retention-mode electric field, and sufficient read-mode on-current margin. Moreover, the proposed S-SCG cell array can fully suppress both the interference effects and the disturbance problems at the same time by removing the direct coupling effects in the same cell string, which are the most critical problems of the recent 3-D vertical stacked cell structures. Above all, the proposed cell array has good potential for terabit 3-D vertical nand Flash cell array with highly reliable multilevel cell operation.

Question: What is the novel concept of the three-dimensional vertical floating gate (FG)-type nand flash memory using the separated-sidewall control gate (CG)?


Sentiment classification for Turkish Twitter feeds using LDA. Today, people are able to express their opinions and ideas on any subject using personal blogs or a variety of social media environments. Contents shared by users with each passing day increases the data size on the web but this situation is a complication for meaningful information extraction. Therefore, sentiment analysis studies aimed at automatically obtaining meaningful information from social media content showed a significant increase in recent years. In these studies, automatically analyzing the content of messages and identifying sentiment such as positive, neutral, negative etc. are intended. In this study, a system has been proposed for analyzing sentiment of Turkish Twitter feeds. In addition, sentiment classification which is a leading problem for sentiment analysis studies has been performed using topic modeling and the effect of modeling on results are examined. The topic model has been created using LDA (Latent Dirichlet Allocation) algorithm. Experimental results obtained with different feature extraction model and classifiers. The results show that topic modeling is more successful compared to the methods used in the previous studies. Also, sentiment classification based on topic model knowledge has been found to increase sentiment analysis success by %26 compared to our previous work.

Question: How to classify tweets?


Towards automatic generation of vulnerability-based signatures. In this paper we explore the problem of creating vulnerability signatures. A vulnerability signature matches all exploits of a given vulnerability, even polymorphic or metamorphic variants. Our work departs from previous approaches by focusing on the semantics of the program and vulnerability exercised by a sample exploit instead of the semantics or syntax of the exploit itself. We show the semantics of a vulnerability define a language which contains all and only those inputs that exploit the vulnerability. A vulnerability signature is a representation (e.g., a regular expression) of the vulnerability language. Unlike exploit-based signatures whose error rate can only be empirically measured for known test cases, the quality of a vulnerability signature can be formally quantified for all possible inputs. We provide a formal definition of a vulnerability signature and investigate the computational complexity of creating and matching vulnerability signatures. We also systematically explore the design space of vulnerability signatures. We identify three central issues in vulnerability-signature creation: how a vulnerability signature represents the set of inputs that may exercise a vulnerability, the vulnerability coverage (i.e., number of vulnerable program paths) that is subject to our analysis during signature creation, and how a vulnerability signature is then created for a given representation and coverage. We propose new data-flow analysis and novel adoption of existing techniques such as constraint solving for automatically generating vulnerability signatures. We have built a prototype system to test our techniques. Our experiments show that we can automatically generate a vulnerability signature using a single exploit which is of much higher quality than previous exploit-based signatures. In addition, our techniques have several other security applications, and thus may be of independent interest

Question: How to automatically generate vulnerability signatures?


The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?. A successful grasp requires careful balancing of the contact forces. Deducing whether a particular grasp will be successful from indirect measurements, such as vision, is therefore quite challenging, and direct sensing of contacts through touch sensing provides an appealing avenue toward more successful and consistent robotic grasping. However, in order to fully evaluate the value of touch sensing for grasp outcome prediction, we must understand how touch sensing can influence outcome prediction accuracy when combined with other modalities. Doing so using conventional model-based techniques is exceptionally difficult. In this work, we investigate the question of whether touch sensing aids in predicting grasp outcomes within a multimodal sensing framework that combines vision and touch. To that end, we collected more than 9,000 grasping trials using a two-finger gripper equipped with GelSight high-resolution tactile sensors on each finger, and evaluated visuo-tactile deep neural network models to directly predict grasp outcomes from either modality individually, and from both modalities together. Our experimental results indicate that incorporating tactile readings substantially improve grasping performance.

Question: Does touch sensing help predict grasp outcomes?


A shared " passengers & goods " city logistics system. Many strategic planning models have been developed to help decision making in city logistics. Such models do not take into account, or very few, the flow of passengers because the considered unit does not have the same nature (a person is active and a good is passive). However, it seems fundamental to gather the goods and the passengers in one model when their respective transports interact with each other. In this context, we suggest assessing a shared passengers & goods city logistics system where the spare capacity of public transport is used to distribute goods toward the city core. We model the problem as a vehicle routing problem with transfers and give a mathematical formulation. Then we propose an Adaptive Large Neighborhood Search (ALNS) to solve it. This approach is evaluated on data sets generated following a field study in the city of La Rochelle in France.

Question: How to model a shared passengers & goods city logistics system?


Close Yet Distinctive Domain Adaptation. Domain adaptation is transfer learning which aims to generalize a learning model across training and testing data with different distributions. Most previous research tackle this problem in seeking a shared feature representation between source and target domains while reducing the mismatch of their data distributions. In this paper, we propose a close yet discriminative domain adaptation method, namely CDDA, which generates a latent feature representation with two interesting properties. First, the discrepancy between the source and target domain, measured in terms of both marginal and conditional probability distribution via Maximum Mean Discrepancy is minimized so as to attract two domains close to each other. More importantly, we also design a repulsive force term, which maximizes the distances between each label dependent sub-domain to all others so as to drag different class dependent sub-domains far away from each other and thereby increase the discriminative power of the adapted domain. Moreover, given the fact that the underlying data manifold could have complex geometric structure, we further propose the constraints of label smoothness and geometric structure consistency for label propagation. Extensive experiments are conducted on 36 cross-domain image classification tasks over four public datasets. The Comprehensive results show that the proposed method consistently outperforms the state-of-the-art methods with significant margins. ∗These first two authors contributed equally.

Question: What is the difference between domain adaptation and transfer learning?


Absolute Orientation for Word Embedding Alignment. We develop a family of techniques to align word embeddings which are derived from different source datasets or created using different mechanisms (e.g., GloVe or word2vec). Our methods are simple and have a closed form to optimally rotate, translate, and scale to minimize root mean squared errors or maximize the average cosine similarity between two embeddings of the same vocabulary into the same dimensional space. Our methods extend approaches known as Absolute Orientation, which are popular for aligning objects in three-dimensions, and generalize an approach by Smith etal (ICLR 2017). We prove new results for optimal scaling and for maximizing cosine similarity. Then we demonstrate how to evaluate the similarity of embeddings from different sources or mechanisms, and that certain properties like synonyms and analogies are preserved across the embeddings and can be enhanced by simply aligning and averaging ensembles of embeddings.

Question: How to align word embeddings?


Mapping CMMI Project Management Process Areas to SCRUM Practices. Over the past years, the capability maturity model (CMM) and capability maturity model integration (CMMI) have been broadly used for assessing organizational maturity and process capability throughout the world. However, the rapid pace of change in information technology has caused increasing frustration to the heavyweight plans, specifications, and other documentation imposed by contractual inertia and maturity model compliance criteria. In light of that, agile methodologies have been adopted to tackle this challenge. The aim of our paper is to present mapping between CMMI and one of these methodologies, Scrum. It shows how Scrum addresses the Project Management Process Areas of CMMI. This is useful for organizations that have their plan-driven process based on the CMMI model and are planning to improve its processes toward agility or to help organizations to define a new project management framework based on both CMMI and Scrum practices.

Question: How to map CMMI to Scrum?


A Review of Methodological Approaches for the Design and Optimization of Wind Farms. This article presents a review of the state of the art of the Wind Farm Design and Optimization (WFDO) problem. The WFDO problem refers to a set of advanced planning actions needed to extremize the performance of wind farms, which may be composed of a few individual Wind Turbines (WTs) up to thousands of WTs. The WFDO problem has been investigated in different scenarios, with substantial differences in main objectives, modelling assumptions, constraints, and numerical solution methods. The aim of this paper is: (1) to present an exhaustive survey of the literature covering the full span of the subject, an analysis of the state-of-the-art models describing the performance of wind farms as well as its extensions, and the numerical approaches used to solve the problem; (2) to provide an overview of the available knowledge and recent progress in the application of such strategies to real onshore and offshore wind farms; and (3) to propose a comprehensive agenda for future research. OPEN ACCESS Energies 2014, 7 6931

Question: What is the state of the art of the wind farm design and optimization problem?


TwitterRank: finding topic-sensitive influential twitterers. This paper focuses on the problem of identifying influential users of micro-blogging services. Twitter, one of the most notable micro-blogging services, employs a social-networking model called "following", in which each user can choose who she wants to "follow" to receive tweets from without requiring the latter to give permission first. In a dataset prepared for this study, it is observed that (1) 72.4% of the users in Twitter follow more than 80% of their followers, and (2) 80.5% of the users have 80% of users they are following follow them back. Our study reveals that the presence of "reciprocity" can be explained by phenomenon of homophily. Based on this finding, TwitterRank, an extension of PageRank algorithm, is proposed to measure the influence of users in Twitter. TwitterRank measures the influence taking both the topical similarity between users and the link structure into account. Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank.

Question: How to measure the influence of users in Twitter?


Estimating mobile application energy consumption using program analysis. Optimizing the energy efficiency of mobile applications can greatly increase user satisfaction. However, developers lack viable techniques for estimating the energy consumption of their applications. This paper proposes a new approach that is both lightweight in terms of its developer requirements and provides fine-grained estimates of energy consumption at the code level. It achieves this using a novel combination of program analysis and per-instruction energy modeling. In evaluation, our approach is able to estimate energy consumption to within 10% of the ground truth for a set of mobile applications from the Google Play store. Additionally, it provides useful and meaningful feedback to developers that helps them to understand application energy consumption behavior.

Question: How to estimate the energy consumption of mobile applications?


Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance. Industry 4.0 can make a factory smart by applying intelligent information processing approaches, communication systems, future-oriented techniques, and more. However, the high complexity, automation, and flexibility of an intelligent factory bring new challenges to reliability and safety. Industrial big data generated by multisource sensors, intercommunication within the system and external-related information, and so on, might provide new solutions for predictive maintenance to improve system reliability. This paper puts forth attributes of industrial big data processing and actively explores industrial big data processing-based predictive maintenance. A novel framework is proposed for structuring multisource heterogeneous information, characterizing structured data with consideration of the spatiotemporal property, and modeling invisible factors, which would make the production process transparent and eventually implement predictive maintenance on facilities and energy saving in the industry 4.0 era. The effectiveness of the proposed scheme was verified by analyzing multisource heterogeneous industrial data for the remaining life prediction of key components of machining equipment.

Question: What is the remaining life of a key component of a machining equipment?


Numeric data frames and probabilistic judgments in complex real-world environments. This thesis investigates human probabilistic judgment in complex real-world settings to identify processes underpinning biases across groups which relate to numerical frames and formats. Experiments are conducted replicating real-world environments and data to test judgment performance based on framing and format. Regardless of background skills and experience, people in professional and consumer contexts show a strong tendency to perceive the world from a linear perspective, interpreting information in concrete, absolute terms and making judgments based on seeking and applying linear functions. Whether predicting sales, selecting between financial products, or forecasting refugee camp data, people use minimal cues and systematically apply additive methods amidst non-linear trends and percentage points to yield linear estimates in both rich and sparse informational contexts. Depending on data variability and temporality, human rationality and choice may be significantly helped or hindered by informational framing and format. The findings deliver both theoretical and practical contributions. Across groups and individual differences, the effects of informational format and the tendency to linearly extrapolate are connected by the bias to perceive values in concrete terms and make sense of data by seeking simple referent points. People compare and combine referents using additive methods when inappropriate and adhere strongly to defaults when applied in complex numeric environments. The practical contribution involves a framing manipulation which shows that format biases (i.e., additive processing) and optimism (i.e., associated with intertemporal effects) can be counteracted in judgments involving percentages and exponential growth rates by using absolute formats and positioning defaults in future event context information. This framing manipulation was highly effective in improving lo

Question: What is the difference between a data frame and a probabilistic judgment?


A Context-Aware Usability Model for Mobile Health Applications. Mobile healthcare is a fast growing area of research that capitalizes on mobile technologies and wearables to provide realtime and continuous monitoring and analysis of vital signs of users. Yet, most of the current applications are developed for general population without taking into consideration the context and needs of different user groups. Designing and developing mobile health applications and diaries according to the user context can significantly improve the quality of user interaction and encourage the application use. In this paper, we propose a user context model and a set of usability attributes for developing mobile applications in healthcare. The proposed model and the selected attributes are integrated into a mobile application development framework to provide user-centered and context-aware guidelines. To validate our framework, a mobile diary was implemented for patients undergoing Peritoneal Dialysis (PD) and tested with real users.

Question: What is a mobile diary?


GPU Accelerated Sub-Sampled Newton's Method. First order methods, which solely rely on gradient information, are commonly used in diverse machine learning (ML) and data analysis (DA) applications. This is attributed to the simplicity of their implementations, as well as low per-iteration computational/storage costs. However, they suffer from significant disadvantages; most notably, their performance degrades with increasing problem ill-conditioning. Furthermore, they often involve a large number of hyper-parameters, and are notoriously sensitive to parameters such as the step-size. By incorporating additional information from the Hessian, second-order methods, have been shown to be resilient to many such adversarial effects. However, these advantages of using curvature information come at the cost of higher per-iteration costs, which in "big data" regimes, can be computationally prohibitive. In this paper, we show that, contrary to conventional belief, second-order methods, when implemented appropriately, can be more efficient than first-order alternatives in many largescale ML/ DA applications. In particular, in convex settings, we consider variants of classical Newton's method in which the Hessian and/or the gradient are randomly sub-sampled. We show that by effectively leveraging the power of GPUs, such randomized Newton-type algorithms can be significantly accelerated, and can easily outperform state of the art implementations of existing techniques in popular ML/ DA software packages such as TensorFlow. Additionally these randomized methods incur a small memory overhead compared to first-order methods. In particular, we show that for million-dimensional problems, our GPU accelerated sub-sampled Newton's method achieves a higher test accuracy in milliseconds as compared with tens of seconds for first order alternatives.

Question: What is the difference between first and second order methods?


A robust, input voltage adaptive and low energy consumption level converter for sub-threshold logic. A new level converter (LC) is proposed for logic voltage shifting between sub-threshold voltage to normal high voltage. By employing 2 PMOS diodes, the LC shows good operation robustness with sub-threshold logic input. The switching delay of the proposed LC can adapt with the input logic voltage which is more suitable for power aware systems. With a simpler circuit structure, the energy consumption of the LC is smaller than that of the existing sub-threshold LC. Simulation results demonstrate the performance improvement and energy reduction of the proposed LC. Test chip was fabricated using 0.18 mum CMOS process. Measurement results show that our proposed LC can operate correctly with an input at as low as 127 mV and an output voltage at 1.8V.

Question: What is a level converter?


A 2.45 GHz harmonic suppression array antenna for rectenna application. In this paper, a 2.45 GHz harmonic suppression capability of array antenna has been presented for wireless power transfer applications. Harmonic suppression up to a fourth order of the fundamental operating frequency has been achieved by using rectangular slot Defected Ground Structure (DGS). The designed antenna uses an FR4 material having thickness 1.6 mm as a substrate which has loss tangent tanδ) 0.02. The gain and radiation efficiency are 4.5dB, 75% respectively. This antenna is suitable for rectenna applications.

Question: What is a harmonic suppression array antenna?


A High Speed Architecture for Galois/Counter Mode of Operation (GCM). In this paper we present a fully pipelined high speed hardware architecture for Galois/Counter Mode of Operation (GCM) by analyzing the data dependencies in the GCM algorithm at the architecture level. We show that GCM encryption circuit and GCM authentication circuit have similar critical path delays resulting in an efficient pipeline structure. The proposed GCM architecture yields a throughput of 34 Gbps running at 271 MHz using a 0.18 μm CMOS standard cell library.

Question: What is the throughput of the proposed architecture?


Working model of Self-driving car using Convolutional Neural Network, Raspberry Pi and Arduino. The evolution of Artificial Intelligence has served as the catalyst in the field of technology. We can now develop things which was once just an imagination. One of such creation is the birth of self-driving car. Days have come where one can do their work or even sleep in the car and without even touching the steering wheel, accelerator you will still be able to reach your target destination safely. This paper proposes a working model of self-driving car which is capable of driving from one location to the other or to say on different types of tracks such as curved tracks, straight tracks and straight followed by curved tracks. A camera module is mounted over the top of the car along with Raspberry Pi sends the images from real world to the Convolutional Neural Network which then predicts one of the following directions. i.e. right, left, forward or stop which is then followed by sending a signal from the Arduino to the controller of the remote controlled car and as a result of it the car moves in the desired direction without any human intervention.

Question: What is the working model of self-driving car?


Computer security in the real world. Most computers today are insecure because security is costly in terms of user inconvenience and foregone features, and people are unwilling to pay the price. Real-world security depends more on punishment than on locks, but it's hard to even find network attackers, much less punish them. The basic elements of security are authentication, authorization, and auditing: the gold standard. The idea of one principal speaking for another is the key to doing these uniformly across the Internet.

Question: What is the gold standard?


Review of Electromagnetic Techniques for Breast Cancer Detection. Breast cancer is anticipated to be responsible for almost 40,000 deaths in the USA in 2011. The current clinical detection techniques suffer from limitations which motivated researchers to investigate alternative modalities for the early detection of breast cancer. This paper focuses on reviewing the main electromagnetic techniques for breast cancer detection. More specifically, this work reviews the cutting edge research in microwave imaging, electrical impedance tomography, diffuse optical tomography, microwave radiometry, biomagnetic detection, biopotential detection, and magnetic resonance imaging (MRI). The goal of this paper is to provide biomedical researchers with an in-depth review that includes all main electromagnetic techniques in the literature and the latest progress in each of these techniques.

Question: What is the latest progress in each of the electromagnetic techniques?


A 6.8GHz low-profile H-plane ridged SIW horn antenna. The substrate integrated waveguide (SIW) allows the construct of some types of planar antennas which cannot be conventionally integrated on the substrate. However, owing to some technology restrictions, designed SIW horn antennas normally work in the frequency above 10 GHz. This paper proposes a 6.8GHz low-profile H-plane horn antenna based on the ridged SIW, which allows the substrates thinner than λ0/10. Far field radiation patterns are reported to reveal the good performance of proposed antenna. Comparisons among the ridged SIW horn antennas with different number of ridges are also made to shown the matching improvements.

Question: What is a ridged SIW horn antenna?


StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones. Much of the stress and strain of student life remains hidden. The StudentLife continuous sensing app assesses the day-to-day and week-by-week impact of workload on stress, sleep, activity, mood, sociability, mental well-being and academic performance of a single class of 48 students across a 10 week term at Dartmouth College using Android phones. Results from the StudentLife study show a number of significant correlations between the automatic objective sensor data from smartphones and mental health and educational outcomes of the student body. We also identify a Dartmouth term lifecycle in the data that shows students start the term with high positive affect and conversation levels, low stress, and healthy sleep and daily activity patterns. As the term progresses and the workload increases, stress appreciably rises while positive affect, sleep, conversation and activity drops off. The StudentLife dataset is publicly available on the web.

Question: What is the student lifecycle?


Q # : A Quantum Programming Language by Microsoft. The multi-paradigm quantum programming language Q# was analysed and used to study and create novel programs that were able to go beyond the capabilities of any classical program. This language was built by Microsoft to succeed LIQUi|> and is a Domain-Specific Language (DSL) that can be used within Microsoft's Quantum Development Kit (QDK). The quantum programs are run on a quantum simulator which possesses properties that a real quantum computer would from a behavioural aspect. It uses the .NET Code SDK, allowing for easy creation, building and running of quantum projects via the command line. Initially, the main features and libraries available were studied and experimented with by analysing implementations of the Quantum Teleportation and Deutsch-Jozsa algorithms. Thereafter, an algorithm to solve an arbitrary 2 × 2 matrix system of linear equations was implemented, demonstrating a theoretical quantum speed-up with time complexities of order O(logN) compared to the classical O(N) for sparse matrices. Running the algorithm for a particular matrix achieved results that were within the range of theoretical predictions. Subsequently, as an extension to the project, concepts within Quantum Game Theory were explored. This led to the Mermin-Peres Magic Square game successfully being simulated in Q#; a game where no classical winning strategy exists, yet a quantum strategy is able to win in all possible cases of the game.

Question: What is the quantum programming language Q#?


Network densification: the dominant theme for wireless evolution into 5G. This article explores network densification as the key mechanism for wireless evolution over the next decade. Network densification includes densification over space (e.g, dense deployment of small cells) and frequency (utilizing larger portions of radio spectrum in diverse bands). Large-scale cost-effective spatial densification is facilitated by self-organizing networks and intercell interference management. Full benefits of network densification can be realized only if it is complemented by backhaul densification, and advanced receivers capable of interference cancellation.

Question: What is the dominant theme for wireless evolution into 5G?


Ontogenesis of Agency in Machines : A Multidisciplinary Review. How does agency arise in biocognitive systems? Can the emergence of agency be explained by physical laws? We review these and related questions in philosophy, psychology, biology, and physics. Based on the review we ask the questions: i) Can machines have agency? and, if so, ii) How can we build machines with agency? We examine existing work in artificial intelligence and machine learning on selfmotivated, self-teaching, and self-developing systems with respect to the ontogenesis ("coming into being") of agency in computational systems. The impact of these "autogenic" systems or machine agency on science, technology, and humanity will be discussed.

Question: How can we build machines with agency?


Eigenfaces for Recognition. We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.

Question: What is the eigenface approach?


Hemisphere lens-loaded Vivaldi antenna for time domain microwave imaging of concealed objects. The hemisphere lens-loaded Vivaldi antenna for the microwave imaging applications is designed and tested in this paper. The proposed antenna is designed to work in the wide frequency band of 1–14 GHz, and is fabricated on the FR-4 substrate. The directivity of the proposed Vivaldi antenna is enhanced using a hemispherical shape dielectric lens, which is fixed on the end-fire direction of the antenna. The proposed antenna is well suited for the microwave imaging applications because of the wide frequency range and high directivity. The design of the antenna is carried out using the CST microwave studio, and various parameters such as the return loss, the radiation pattern, the directivity, and input impedance are optimized. The maximum improvement of 4.19 dB in the directivity is observed with the designed hemisphere lens. The antenna design is validated by fabricating and testing it in an anechoic environment. Finally, the designed antenna is utilized to establish a setup for measuring the scattering coefficients of various objects and structures in the frequency band of 1–14 GHz. The two-dimensional (2D) microwave images of these objects are successfully obtained in terms of the measured wide band scattering data using a novel time domain inverse scattering approach, which shows the applicability of the proposed antenna.

Question: What is the hemisphere lens-loaded Vivaldi antenna for the microwave imaging applications?


Customised Visualisations of Linked Open Data. This paper aims to tackle on Linked Open Data (LOD) customised visualisations. The work is part of an ongoing research on interfaces and tools for helping non-programmers to easily present, analyse and support sense making over large semantic dataset. The customisation is a key aspect of our work. Producing effective customised visualisations is still difficult due to the complexity of the existing tools and the limited set of options they offer, especially to those with little knowledge in LOD and semantic data models. How can we give users full control on the primitives of the visualisation and their properties, without requiring them to master Semantic Web technologies or programming languages? The paper presents a conceptual model that can be used as a reference to build tools for generating customisable infoviews. We used it to conduct a survey on existing tools in terms of customisation capabilities. Starting from the feedback collected in this phase, we will implement these customisation features into some prototypes we are currently experimenting on.

Question: What is a conceptual model?


A Secure and Efficient Uniqueness-and-Anonymity-Preserving Remote User Authentication Scheme for Connected Health Care. Connected health care has several applications including telecare medicine information system, personally controlled health records system, and patient monitoring. In such applications, user authentication can ensure the legality of patients. In user authentication for such applications, only the legal user/patient himself/herself is allowed to access the remote server, and no one can trace him/her according to transmitted data. Chang et al. proposed a uniqueness-and-anonymity-preserving remote user authentication scheme for connected health care (Chang et al., J Med Syst 37:9902, 2013). Their scheme uses the user's personal biometrics along with his/her password with the help of the smart card. The user's biometrics is verified using BioHashing. Their scheme is efficient due to usage of one-way hash function and exclusive-or (XOR) operations. In this paper, we show that though their scheme is very efficient, their scheme has several security weaknesses such as (1) it has design flaws in login and authentication phases, (2) it has design flaws in password change phase, (3) it fails to protect privileged insider attack, (4) it fails to protect the man-in-the middle attack, and (5) it fails to provide proper authentication. In order to remedy these security weaknesses in Chang et al.'s scheme, we propose an improvement of their scheme while retaining the original merit of their scheme. We show that our scheme is efficient as compared to Chang et al.'s scheme. Through the security analysis, we show that our scheme is secure against possible attacks. Further, we simulate our scheme for the formal security verification using the widely-accepted AVISPA (Automated Validation of Internet Security Protocols and Applications) tool to ensure that our scheme is secure against passive and active attacks. In addition, after successful authentication between the

Question: What is the difference between a secure and an efficient authentication scheme?


SNOW 2014 Data Challenge: Assessing the Performance of News Topic Detection Methods in Social Media. The SNOW 2014 Data Challenge aimed at creating a public benchmark and evaluation resource for the problem of topic detection in streams of social content. In particular, given a set of tweets spanning a time interval of interest, the Challenge required the extraction of the most significant news topics in short timeslots within the selected interval. Here, we provide details with respect to the Challenge definition, the data collection and evaluation process, and the results achieved by the 11 teams that participated in it, along with a concise retrospective analysis of the main conclusions and arising issues.

Question: What is the SNOW 2014 Data Challenge?


Business Intelligence and Analytics Education, and Program Development: A Unique Opportunity for the Information Systems Discipline. "Big Data," huge volumes of data in both structured and unstructured forms generated by the Internet, social media, and computerized transactions, is straining our technical capacity to manage it. More importantly, the new challenge is to develop the capability to understand and interpret the burgeoning volume of data to take advantage of the opportunities it provides in many human endeavors, ranging from science to business. Data Science, and in business schools, Business Intelligence and Analytics (BI&A) are emerging disciplines that seek to address the demands of this new era. Big Data and BI&A present unique challenges and opportunities not only for the research community, but also for Information Systems (IS) programs at business schools. In this essay, we provide a brief overview of BI&A, speculate on the role of BI&A education in business schools, present the challenges facing IS departments, and discuss the role of IS curricula and program development, in delivering BI&A education. We contend that a new vision for the IS discipline should address these challenges.

Question: What is BI&A?


Noninvasive prediction of glucose by near-infrared diffuse reflectance spectroscopy.. BACKGROUND Self-monitoring of blood glucose by diabetics is crucial in the reduction of complications related to diabetes. Current monitoring techniques are invasive and painful, and discourage regular use. The aim of this study was to demonstrate the use of near-infrared (NIR) diffuse reflectance over the 1050-2450 nm wavelength range for noninvasive monitoring of blood glucose.   METHODS Two approaches were used to develop calibration models for predicting the concentration of blood glucose. In the first approach, seven diabetic subjects were studied over a 35-day period with random collection of NIR spectra. Corresponding blood samples were collected for analyte analysis during the collection of each NIR spectrum. The second approach involved three nondiabetic subjects and the use of oral glucose tolerance tests (OGTTs) over multiple days to cause fluctuations in blood glucose concentrations. Twenty NIR spectra were collected over the 3.5-h test, with 16 corresponding blood specimens taken for analyte analysis.   RESULTS Statistically valid calibration models were developed on three of the seven diabetic subjects. The mean standard error of prediction through cross-validation was 1.41 mmol/L (25 mg/dL). The results from the OGTT testing of three nondiabetic subjects yielded a mean standard error of calibration of 1.1 mmol/L (20 mg/dL). Validation of the calibration model with an independent test set produced a mean standard error of prediction equivalent to 1.03 mmol/L (19 mg/dL).   CONCLUSIONS These data provide preliminary evidence and allow cautious optimism that NIR diffuse reflectance spectroscopy using the 1050-2450 nm wavelength range can be used to predict blood glucose concentrations noninvasively. Substantial research is still required to validate whether this technology is a viable tool for long-term home diagnostic use by diabetics.

Question: What is the difference between a calibration model and a validation model?


Coarse to Fine: Diffusing Categories in Wikipedia. Automatic taxonomy construction aims to build a categorization system without human efforts. Traditional textual pattern based methods extract hyponymy relation in raw texts. However, these methods usually yield low precision and recall. In this paper, we propose a method to automatically find diffusing attributes to a category from Wikipedia infoboxes. We use the diffusing attribute to diffuse a coarsegrained category into several fine-grained subcategories and generate a finer-grained taxonomy. Experiments show our method can find proper diffusing attributes to categories across various domains.

Question: How to find diffusing attributes to a category?


Inverse Reinforcement Learning Based Human Behavior Modeling for Goal Recognition in Dynamic Local Network Interdiction. Goal recognition is the task of inferring an agent's goals given some or all of the agent's observed actions. Among different ways of problem formulation, goal recognition can be solved as a model-based planning problem using off-theshell planners. However, obtaining accurate cost or reward models of an agent and incorporating them into the planning model becomes an issue in real applications. Towards this end, we propose an Inverse Reinforcement Learning (IRL)based opponent behavior modeling method, and apply it in the goal recognition assisted Dynamic Local Network Interdiction (DLNI) problem. We first introduce the overall framework and the DLNI problem domain of our work. After that, an IRL-based human behavior modeling method and Markov Decision Process-based goal recognition are introduced. Experimental results indicate that our learned behavior model has a higher tracking accuracy and yields better interdiction outcomes than other models.

Question: How to solve the goal recognition problem in dynamic local network interdiction?


Simultaneous Mosaicing and Tracking with an Event Camera. An event camera is a silicon retina which outputs not a sequence of video frames like a standard camera, but a stream of asynchronous spikes, each with pixel location, sign and precise timing, indicating when individual pixels record a threshold log intensity change. By encoding only image change, it offers the potential to transmit the information in a standard video but at vastly reduced bitrate, and with huge added advantages of very high dynamic range and temporal resolution. However, event data calls for new algorithms, and in particular we believe that algorithms which incrementally estimate global scene models are best placed to take full advantages of its properties. Here, we show for the first time that an event stream, with no additional sensing, can be used to track accurate camera rotation while building a persistent and high quality mosaic of a scene which is super-resolution accurate and has high dynamic range. Our method involves parallel camera rotation tracking and template reconstruction from estimated gradients, both operating on an event-by-event basis and based on probabilistic filtering.

Question: How to track a camera rotation?


Wiggling through complex traffic: Planning trajectories constrained by predictions. The vision of autonomous driving is piecewise becoming reality. Still the problem of executing the driving task in a safe and comfortable way in all possible environments, for instance highway, city or rural road scenarios is a challenging task. In this paper we present a novel approach to planning trajectories for autonomous vehicles. Hereby we focus on the problem of planning a trajectory, given a specific behavior option, e.g. merging into a specific gap at a highway entrance or a roundabout. Therefore we explicitly take arbitrary road geometry and prediction information of other traffic participants into account. We extend former contributions in this field by providing a flexible problem description and a trajectory planner without specialization to distinct classes of maneuvers beforehand. Using a carefully chosen representation of the dynamic free space, the method is capable of considering multiple lanes including the predicted dynamics of other traffic participants, while being real-time capable at the same time. The combination of those properties in one general planning method represents the novelty of the proposed method. We demonstrate the capability of our algorithm to plan safe trajectories in simulation and in real traffic in real-time.

Question: How to plan a trajectory for autonomous driving?


Affective Computing: A Review. Affective computing is currently one of the most active research topics, furthermore, having increasingly intensive attention. This strong interest is driven by a wide spectrum of promising applications in many areas such as virtual reality, smart surveillance, perceptual interface, etc. Affective computing concerns multidisciplinary knowledge background such as psychology, cognitive, physiology and computer sciences. The paper is emphasized on the several issues involved implicitly in the whole interactive feedback loop. Various methods for each issue are discussed in order to examine the state of the art. Finally, some research challenges and future directions are also discussed.

Question: What is affective computing?


Privacy-Preserving User-Auditable Pseudonym Systems. Personal information is often gathered and processed in a decentralized fashion. Examples include health records and governmental data bases. To protect the privacy of individuals, no unique user identifier should be used across the different databases. At the same time, the utility of the distributed information needs to be preserved which requires that it be nevertheless possible to link different records if they relate to the same user. Recently, Camenisch and Lehmann (CCS 15) have proposed a pseudonym scheme that addresses this problem by domain-specific pseudonyms. Although being unlinkable, these pseudonyms can be converted by a central authority (the converter). To protect the users' privacy, conversions are done blindly without the converter learning the pseudonyms or the identity of the user. Unfortunately, their scheme sacrifices a crucial privacy feature: transparency. Users are no longer able to inquire with the converter and audit the flow of their personal data. Indeed, such auditability appears to be diametral to the goal of blind pseudonym conversion. In this paper we address these seemingly conflicting requirements and provide a system where user-centric audits logs are created by the oblivious converter while maintaining all privacy properties. We prove our protocol to be UC-secure and give an efficient instantiation using novel building blocks.

Question: What is the difference between a pseudonym and a pseudonym?


AI Challenges in Human-Robot Cognitive Teaming. Among the many anticipated roles for robots in the future is that of being a human teammate. Aside from all the technological hurdles that have to be overcome with respect to hardware and control to make robots fit to work with humans, the added complication here is that humans have many conscious and subconscious expectations of their teammates – indeed, we argue that teaming is mostly a cognitive rather than physical coordination activity. This introduces new challenges for the AI and robotics community and requires fundamental changes to the traditional approach to the design of autonomy. With this in mind, we propose an update to the classical view of the intelligent agent architecture, highlighting the requirements for mental modeling of the human in the deliberative process of the autonomous agent. In this article, we outline briefly the recent efforts of ours, and others in the community, towards developing cognitive teammates along these guidelines.

Question: What is the difference between a robot and a human?


SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights. Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these "local best matches". This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.

Question: What is the difference between a feature-based and a sequence-based SLAM algorithm?


Psychopathy and the DSM-IV criteria for antisocial personality disorder.. The Axis II Work Group of the Task Force on DSM-IV has expressed concern that antisocial personality disorder (APD) criteria are too long and cumbersome and that they focus on antisocial behaviors rather than personality traits central to traditional conceptions of psychopathy and to international criteria. We describe an alternative to the approach taken in the rev. 3rd ed. of the Diagnostic and Statistical Manual of Mental Disorders (DSM-III-R; American Psychiatric Association, 1987), namely, the revised Psychopathy Checklist. We also discuss the multisite APD field trials designed to evaluate and compare four criteria sets: the DSM-III-R criteria, a shortened list of these criteria, the criteria for dyssocial personality disorder from the 10th ed. of the International Classification of Diseases (World Health Organization, 1990), and a 10-item criteria set for psychopathic personality disorder derived from the revised Psychopathy Checklist.

Question: What is the difference between the DSM-III-R and the DSM-IV?


Chunking with Support Vector Machines. We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization performance even with input data of high dimensional feature spaces. Furthermore, by the Kernel principle, SVMs can carry out training with smaller computational overhead independent of their dimensionality. We apply weighted voting of 8 SVMsbased systems trained with distinct chunk representations. Experimental results show that our approach achieves higher accuracy than previous approaches.

Question: How to chunk with support vector machines?