Query: A Study of Automatic Speech Recognition in Noisy Classroom Environments for Automated Dialog Analysis

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Tactical Language and Culture Training Systems: Using AI to Teach Foreign Languages and Cultures <=SEP=> 72 AI MAGAZINE The Tactical Language and Culture Training System (TLCTS) helps people quickly acquire functional skills in foreign languages and cultures. It includes interactive lessons that focus on particular communicative skills and interactive games that apply those skills. Heavy emphasis is placed on spoken communication: learners must learn to speak the foreign language to complete the lessons and play the games. It focuses on the language and cultural skills needed to accomplish particular types of tasks and gives learners rich, realistic opportunities to practice achieving those tasks. Several TLCTS courses have been developed so far. Tactical Iraqi, Tactical Pashto, and Tactical French are in widespread use by U.S. marines and soldiers, and increasingly by military service members in other countries. Additional courses are being developed for use by business executives, workers for nongovernmental organizations, and high school and college students. While precise numbers are impossible to obtain (we do not control copies made by the U.S. government), over 40,000 and as many as 60,000 people have trained so far with TLCTS courses. More than 1000 people download copies of TLCTS courses each month, either for their own use or to set up computer language labs and redistribute copies to students. Just one training site, the military advisor training center at Fort Riley, Kansas, trains approximately 10,000 people annually. Artificial intelligence technologies play multiple essential functions in TLCTS. Speech is the primary input modality, so automated speech recognition tailored to foreign language learners is essential. TLCTS courses are populated with “virtual humans” that engage in dialogue with learners. AI techniques are used to model the decision processes of the virtual humans and to support the generation of their behavior. This makes it possible to give learners extensive conversational practice. Learner modeling software continually monitors each learner’s application of communication skills to estimate the learner’s

Sphinx-4: a flexible open source framework for speech recognition <=SEP=> Sphinx-4 is a flexible, modular and pluggable framework to he lp foster new innovations in the core research of hidden Mark ov model (HMM) recognition systems. The design of Sphinx-4 is b a ed on patterns that have emerged from the design of past sys tems as well as new requirements based on areas that researchers c urr ntly want to explore. To exercise this framework, and to pr vide researchers with a ”research-ready” system, Sphinx-4 also includes several implementations of both simple and stateofhe-art techniques. The framework and the implementations are all f reely available via open source.

My science tutor: A conversational multimedia virtual tutor for elementary school science <=SEP=> This article describes My Science Tutor (MyST), an intelligent tutoring system designed to improve science learning by students in 3rd, 4th, and 5th grades (7 to 11 years old) through conversational dialogs with a virtual science tutor. In our study, individual students engage in spoken dialogs with the virtual tutor Marni during 15 to 20 minute sessions following classroom science investigations to discuss and extend concepts embedded in the investigations. The spoken dialogs in MyST are designed to scaffold learning by presenting open-ended questions accompanied by illustrations or animations related to the classroom investigations and the science concepts being learned. The focus of the interactions is to elicit self-expression from students. To this end, Marni applies some of the principles of Questioning the Author, a proven approach to classroom conversations, to challenge students to think about and integrate new concepts with prior knowledge to construct enriched mental models that can be used to explain and predict scientific phenomena. In this article, we describe how spoken dialogs using Automatic Speech Recognition (ASR) and natural language processing were developed to stimulate students' thinking, reasoning and self explanations. We describe the MyST system architecture and Wizard of Oz procedure that was used to collect data from tutorial sessions with elementary school students. Using data collected with the procedure, we present evaluations of the ASR and semantic parsing components. A formal evaluation of learning gains resulting from system use is currently being conducted. This paper presents survey results of teachers' and children's impressions of MyST.

The architecture of the Festival speech synthesis system <=SEP=> We describe a new formalism for storing linguistic data in a text to speech system. Linguistic entities such as words and phones are stored as feature structures in a general object called an linguistic item. Items are configurable at run time and via the feature structure can contain arbitrary information. Linguistic relations are used to store the relationship between items of the same linguistic type. Relations can take any graph structure but are commonly trees or lists. Utterance structures contain all the items and relations contained in a single utterance. We first describe the design goals when building a synthesis architecture, and then describe some problems with previous architectures. We then discuss our new formalism in general along with the implementation details and consequences of our approach.

Finite-State Transducers in Language and Speech Processing <=SEP=> Finite-state machines have been used in various domains of natural language processing. We consider here the use of a type of transducers that supports very efficient programs: sequential transducers. We recall classical theorems and give new ones characterizing sequential string-tostring transducers. Transducers that output weights also play an important role in language and speech processing. We give a specific study of string-to-weight transducers, including algorithms for determinizing and minimizing these transducers very efficiently, and characterizations of the transducers admitting determinization and the corresponding algorithms. Some applications of these algorithms in speech recognition are described and illustrated.

Measuring benchmark similarity using inherent program characteristics <=SEP=> This paper proposes a methodology for measuring the similarity between programs based on their inherent microarchitecture-independent characteristics, and demonstrates two applications for it: 1) finding a representative subset of programs from benchmark suites and 2) studying the evolution of four generations of SPEC CPU benchmark suites. Using the proposed methodology, we find a representative subset of programs from three popular benchmark suites - SPEC CPU2000, MediaBench, and MiBench. We show that this subset of representative programs can be effectively used to estimate the average benchmark suite IPC, L1 data cache miss-rates, and speedup on 11 machines with different ISAs and microarchitectures - this enables one to save simulation time with little loss in accuracy. From our study of the similarity between the four generations of SPEC CPU benchmark suites, we find that, other than a dramatic increase in the dynamic instruction count and increasingly poor temporal data locality, the inherent program characteristics have more or less remained unchanged

On Probabilistic Analysis of Disagreement in Synchronous Consensus Protocols <=SEP=> This paper presents a probabilistic analysis of disagreement for a family of simple synchronous consensus algorithms aimed at solving the 1-of-n selection problem in presence of unrestricted communication failures. In this problem, a set of n nodes are to select one common value among n proposed values. There are two possible outcomes of each node's selection process: decide to select a value or abort. We have disagreement if some nodes select the same value while other nodes decide to abort. Previous research has shown that it is impossible to guarantee agreement among the nodes subjected to an unbounded number of message losses. Our aim is to find decision algorithms for which the probability of disagreement is as low as possible. In this paper, we investigate two different decision criteria, one optimistic and one pessimistic. We assume two communication failure models, symmetric and asymmetric. For symmetric communication failures, we present the closed-form expressions for the probability of disagreement. For asymmetric failures, we analyse the algorithm using a probabilistic model checking tool. Our results show that the choice of decision criterion significantly influences the probability of disagreement for the 1-of-n selection algorithm. The optimistic decision criterion shows a lower probability of disagreement compare to the pessimistic one when the probability of message loss is less than 30% to 70%. On the other hand, the optimistic decision criterion has in general a higher maximum probability of disagreement compared to the pessimistic criterion.

Accurate dB-Linear Variable Gain Amplifier With Gain Error Compensation <=SEP=> This paper describes use of a novel exponential approximation for designing dB-linear variable gain amplifiers (VGAs). The exponential function is accurately generated using a simple error-compensation technique. The dB-linear gain is controlled linearly by the gate voltage, resulting in a simple and robust VGA. The proposed dB-linear VGA fabricated in a 65-nm CMOS process achieves a total variable gain range of 76 dB and dB-linear range greater than 50 dB with ±0.5-dB gain error. Under a 1.2-V supply voltage, the current consumption of the VGA is 1.8 mA and that of the output buffer is 1.4 mA. The input-referred in-band noise density is 3.5 nV/√{Hz} and the in-band OIP3 is 11.5 dBm. Due to the very simple circuit topology, the total active area of the VGA and the output buffer is extremely small, 0.01 mm2.

Fair and balanced?: bias in bug-fix datasets <=SEP=> Software engineering researchers have long been interested in where and why bugs occur in code, and in predicting where they might turn up next. Historical bug-occurence data has been key to this research. Bug tracking systems, and code version histories, record when, how and by whom bugs were fixed; from these sources, datasets that relate file changes to bug fixes can be extracted. These historical datasets can be used to test hypotheses concerning processes of bug introduction, and also to build statistical bug prediction models. Unfortunately, processes and humans are imperfect, and only a fraction of bug fixes are actually labelled in source code version histories, and thus become available for study in the extracted datasets. The question naturally arises, are the bug fixes recorded in these historical datasets a fair representation of the full population of bug fixes? In this paper, we investigate historical data from several software projects, and find strong evidence of systematic bias. We then investigate the potential effects of "unfair, imbalanced" datasets on the performance of prediction techniques. We draw the lesson that bias is a critical problem that threatens both the effectiveness of processes that rely on biased datasets to build prediction models and the generalizability of hypotheses tested on biased data.

Combined Effects of Attention and Motivation on Visual Task Performance: Transient and Sustained Motivational Effects <=SEP=> We investigated how the brain integrates motivational and attentional signals by using a neuroimaging paradigm that provided separate estimates for transient cue- and target-related signals, in addition to sustained block-related responses. Participants performed a Posner-type task in which an endogenous cue predicted target location on 70% of trials, while motivation was manipulated by varying magnitude and valence of a cash incentive linked to task performance. Our findings revealed increased detection performance (d') as a function of incentive value. In parallel, brain signals revealed that increases in absolute incentive magnitude led to cue- and target-specific response modulations that were independent of sustained state effects across visual cortex, fronto-parietal regions, and subcortical regions. Interestingly, state-like effects of incentive were observed in several of these brain regions, too, suggesting that both transient and sustained fMRI signals may contribute to task performance. For both cue and block periods, the effects of administering incentives were correlated with individual trait measures of reward sensitivity. Taken together, our findings support the notion that motivation improves behavioral performance in a demanding attention task by enhancing evoked responses across a distributed set of anatomical sites, many of which have been previously implicated in attentional processing. However, the effect of motivation was not simply additive as the impact of absolute incentive was greater during invalid than valid trials in several brain regions, possibly because motivation had a larger effect on reorienting than orienting attentional mechanisms at these sites.

A benchmark for semantic image segmentation <=SEP=> Though quite a few image segmentation benchmark datasets have been constructed, there is no suitable benchmark for semantic image segmentation. In this paper, we construct a benchmark for such a purpose, where the ground-truths are generated by leveraging the existing fine granular ground-truths in Berkeley Segmentation Dataset (BSD) as well as using an interactive segmentation tool for new images. We also propose a percept-tree-based region merging strategy for dynamically adapting the ground-truth for evaluating test segmentation. Moreover, we propose a new evaluation metric that is easy to understand and compute, and does not require boundary matching. Experimental results show that, compared with BSD, the generated ground-truth dataset is more suitable for evaluating semantic image segmentation, and the conducted user study demonstrates that the proposed evaluation metric matches user ranking very well.

Final Project : Entangled Photons and Quantum Teleportation <=SEP=> This paper contains an overview of the concept of photon entanglement and discusses generating EPR photon pairs and performing Bell state measurements on them. The procedure of quantum teleportation, or transferring the quantum state of a particle onto another particle, is described in detail. The experimental progress in performing such operations is reviewed in relationship to the developing field of quantum communication.

Generating Keyword Queries for Natural Language Queries to Alleviate Lexical Chasm Problem <=SEP=> In recent years, the task of reformulating natural language queries has received considerable attention from both industry and academic communities. Because of the lexical chasm problem between natural language queries and web documents, if we directly use natural language queries as inputs for retrieval, the results are usually unsatisfactory. In this work, we formulated the task as a translation problem to convert natural language queries into keyword queries. Since the nature language queries users input are diverse and multi-faceted, general encoder-decoder models cannot effectively handle low-frequency words and out-of-vocabulary words. We propose a novel encoder-decoder method with two decoders: the pointer decoder firstly extracts query terms directly from the source text via copying mechanism, then the generator decoder generates query terms using two attention modules simultaneously considering the source text and extracted query terms. For evaluation and training, we also proposed a semi-automatic method to construct a large-scale dataset about natural language query-keyword query pairs. Experimental results on this dataset demonstrated that our model could achieve better performance than the previous state-of-the-art methods.

Vehicle tracking system for intelligent and connected vehicle based on radar and V2V fusion <=SEP=> The environment perception plays a significantly role in intelligent vehicles and the advanced driver assistance system (ADAS), which enhances the driving safety and convenience. Target tracking is one of the key technologies of environment perception. The on-board sensors such as cameras and radar are commonly used for target tracking while they have limitations in terms of detection range and angle of view. One way to overcome the perception limitations of on-board ranging sensors by incorporating the vehicle-to-vehicle (V2V) communication. This paper proposes a vehicle tracking system which fuse the radar and V2V information to improve the target tracking accuracy. The proposed system integrates the radar, GPS and DSRC communication equipment. The GPS and radar are utilized to obtain its own position information and the position information of nearby vehicles. The proposed system also resolves the problem of data association in multiple target tracking measurements by other connected vehicles' identity information. With the association measurements, a Kalman filter is used to improve the accuracy of target tracking. An assessment of tracking system in real road environment shows that the proposed fusion approach for target tracking can reduce the data association error and improve the vehicle target tracking accuracy.

Correlated discrete data generation using adversarial training <=SEP=> Generative Adversarial Networks (GAN) have shown great promise in tasks like synthetic image generation, image inpainting, style transfer, and anomaly detection. However, generating discrete data is a challenge. This work presents an adversarial training based correlated discrete data (CDD) generation model. It also details an approach for conditional CDD generation. The results of our approach are presented over two datasets; job-seeking candidates skill set (private dataset) and MNIST (public dataset). From quantitative and qualitative analysis of these results, we show that our model performs better as it leverages inherent correlation in the data, than an existing model that overlooks correlation.

Prior Knowledge, Level Set Representations & Visual Grouping <=SEP=> In this paper, we propose a level set method for shape-driven object extraction. We introduce a voxel-wise probabilistic level set formulation to account for prior knowledge. To this end, objects are represented in an implicit form. Constraints on the segmentation process are imposed by seeking a projection to the image plane of the prior model modulo a similarity transformation. The optimization of a statistical metric between the evolving contour and the model leads to motion equations that evolve the contour toward the desired image properties while recovering the pose of the object in the new image. Upon convergence, a solution that is similarity invariant with respect to the model and the corresponding transformation are recovered. Promising experimental results demonstrate the potential of such an approach.

Project Success : A Multidimensional Strategic Concept <=SEP=> This article presents projects as powerful strategic weapons, initiated to create economic value and competitive advantage. It suggests that project managers are the new strategic leaders, who must take on total responsibility for project business results. Defining and assessing project success is therefore a strategic management concept, which should help align project efforts with the shortand long-term goals of the organization. While this concept seems simple and intuitive, there is very little agreement in previous studies as to what really constitutes project success. Traditionally, projects were perceived as successful when they met time, budget, and performance goals. However, many would agree that there is more to project success than meeting time and budget. The object of this study was to develop a multidimensional framework for assessing project success, showing how different dimensions mean different things to different stakeholders at different times and for different projects. Given the complexity of this question, a combination of qualitative and quantitative methods and two data sets were used. The analysis identified four major distinct success dimensions: (1) project efficiency, (2) impact on the customer, (3) direct business and organizational success, and (4) preparing for the future. The importance of the dimensions varies according to time and the level of technological uncertainty involved in the project. The article demonstrates how these dimensions should be addressed during the project’s definition, planning, and execution phases, and provides a set of guidelines for project managers and senior managers, as well as suggestions for further research. c 2002 Elsevier Science Ltd. All rights reserved.

Adaptive control of systems in cascade with saturation <=SEP=> Some systems may be approximated as block cascades structures where each block of the cascade can be approximately feedback linearized. In such systems, the states of the lower subsystem blocks affect the dynamics of the upper subsystems. In generating an inverse for a given block, a subset of its lower subsystem states can be treated as virtual actuators in addition to actual direct actuation that may be available. The desired virtual actuator signal arising from the upper subsystem's inverse now appears as a command to the lower subsystem. This paper introduces an adaptive element that is capable of canceling modeling errors arising due to feedback linearization. It also introduces reference models that include a pseudocontrol hedging signal which protects the adaptive element from lower subsystem's dynamics.

A survey of hardware Trojan threat and defense <=SEP=> Hardware Trojans (HTs) can be implanted in security-weak parts of a chip with various means to steal the internal sensitive data or modify original functionality, which may lead to huge economic losses and great harm to society. Therefore, it is very important to analyze the specific HT threats existing in the whole life cycle of integrated circuits (ICs), and perform protection against hardware Trojans. In this paper, we elaborate an IC market model to illustrate the potential HT threats faced by the parties involved in the model. Then we categorize the recent research advances in the countermeasures against HT attacks. Finally, the challenges and prospects for HT defense are illuminated. & 2016 Elsevier B.V. All rights reserved.

Online 3D acquisition and model integration <=SEP=> This paper presents a system which yields complete 3D models in a fast and inexpensive way. The major building blocks are a high-speed structured light range scanner and a registration module. The former generates ’raw’ range data whereas the latter performs a fast registration (ICP) and renders the partially integrated model as a preview of the final result. As the scanner uses only a single image to make a reconstruction, it is possible to scan while the object is moved. The use of an adaptive projection pattern gives a more robust behaviour. This allows the system to deal more easily with complicated geometry and texture than most other systems. During the scanning process the model is built up incrementally and rendered on the screen. This real-time visual feedback allows the user to check the current state of the 3D model. Holes or other flaws can be detected during the acquisition process itself. This speeds up the model building process, and solves indirectly the problem of view planning. The whole real-time pipeline comprising acquisition, merging and visualization only uses off-the-shel f hardware. A regular desktop PC connected to a camera and LCD projector is turned into a high-speed scanner and modeler. The current implementation has a throughput of approx. 5 fps.

CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases <=SEP=> How can we enable computers to automatically answer questions like “Who created the character Harry Potter”? Carefully built knowledge bases provide rich sources of facts. However, it remains a challenge to answer factoid questions raised in natural language due to numerous expressions of one question. In particular, we focus on the most common questions — ones that can be answered with a single fact in the knowledge base. We propose CFO, a Conditional Focused neuralnetwork-based approach to answering factoid questions with knowledge bases. Our approach first zooms in a question to find more probable candidate subject mentions, and infers the final answers with a unified conditional probabilistic framework. Powered by deep recurrent neural networks and neural embeddings, our proposed CFO achieves an accuracy of 75.7% on a dataset of 108k questions – the largest public one to date. It outperforms the current state of the art by an absolute margin of 11.8%.

Forensics examination of volatile system data using virtual introspection <=SEP=> While static examination of computer systems is an important part of many digital forensics investigations, there are often important system properties present only in volatile memory that cannot be effectively recovered using static analysis techniques, such as offline hard disk acquisition and analysis. An alternative approach, involving the live analysis of target systems to uncover this volatile data, presents significant risks and challenges to forensic investigators as observation techniques are generally intrusive and can affect the system being observed. This paper provides a discussion of live digital forensics analysis through virtual introspection and presents a suite of virtual introspection tools developed for Xen (VIX tools). The VIX tools suite can be used for unobtrusive digital forensic examination of volatile system data in virtual machines, and addresses a key research area identified in the virtualization in digital forensics research agenda [22].

Better ϵ-Dependencies for Offline Approximate Nearest Neighbor Search, Euclidean Minimum Spanning Trees, and ϵ-Kernels <=SEP=> Recently, Arya, da Fonseca, and Mount [STOC 2011, SODA 2012] made notable progress in improving the &epsis;-dependencies in the space/query-time tradeoffs for (1 + &epsis;)-factor approximate nearest neighbor search in fixed-dimensional Euclidean spaces. However, &epsis;-dependencies in the preprocessing time were not considered, and so their data structures cannot be used to derive faster algorithms for offline proximity problems. Known algorithms for many such problems, including approximate bichromatic closest pair (BCP) and approximate Euclidean minimum spanning trees (EMST), typically have factors near (1/&epsis;)d/2±O(1) in the running time when the dimension d is a constant.
 We describe a technique that breaks the (1/&epsis;)d/2 barrier and yields new results for many well-known proximity problems, including:
 • an O((1/&epsis;)d/3+O(1) n)-time randomized algorithm for approximate BCP,
 • an O((1/&epsis;)d/3+O(1) n log n)-time algorithm for approximate EMST, and
 • an O(n log n + (1/&epsis;)d/3+O(1) n)-time algorithm to answer n approximate nearest neighbor queries on n points.
 Using additional bit-packing tricks, we can shave off the log n factor for EMST, and even move most of the &epsis;-factors to a sublinear term.
 The improvement arises from a new time bound for exact "discrete Voronoi diagrams", which were previously used in the construction of &epsis;-kernels (or extent-based coresets), a well-known tool for another class of fundamental problems. This connection leads to more results, including:
 • a streaming algorithm to maintain an approximate diameter in O((1/&epsis;)d/3+O(1)) time per point using O((1/&epsis;)d/2+O(1)) space, and
 • a streaming algorithm to maintain an &epsis;-kernel in O((1/&epsis;)d/4+O(1)) time per point using O((1/&epsis;)d/2+O(1)) space.

Automatic Calcium Scoring in Low-Dose Chest CT Using Deep Neural Networks With Dilated Convolutions <=SEP=> Heavy smokers undergoing screening with low-dose chest CT are affected by cardiovascular disease as much as by lung cancer. Low-dose chest CT scans acquired in screening enable quantification of atherosclerotic calcifications and thus enable identification of subjects at increased cardiovascular risk. This paper presents a method for automatic detection of coronary artery, thoracic aorta, and cardiac valve calcifications in low-dose chest CT using two consecutive convolutional neural networks. The first network identifies and labels potential calcifications according to their anatomical location and the second network identifies true calcifications among the detected candidates. This method was trained and evaluated on a set of 1744 CT scans from the National Lung Screening Trial. To determine whether any reconstruction or only images reconstructed with soft tissue filters can be used for calcification detection, we evaluated the method on soft and medium/sharp filter reconstructions separately. On soft filter reconstructions, the method achieved F1 scores of 0.89, 0.89, 0.67, and 0.55 for coronary artery, thoracic aorta, aortic valve, and mitral valve calcifications, respectively. On sharp filter reconstructions, the F1 scores were 0.84, 0.81, 0.64, and 0.66, respectively. Linearly weighted kappa coefficients for risk category assignment based on per subject coronary artery calcium were 0.91 and 0.90 for soft and sharp filter reconstructions, respectively. These results demonstrate that the presented method enables reliable automatic cardiovascular risk assessment in all low-dose chest CT scans acquired for lung cancer screening.

Lifelong Machine Learning Systems: Beyond Learning Algorithms <=SEP=> Lifelong Machine Learning, or LML, considers systems that can learn many tasks from one or more domains over its lifetime. The goal is to sequentially retain learned knowledge and to selectively transfer that knowledge when learning a new task so as to develop more accurate hypotheses or policies. Following a review of prior work on LML, we propose that it is now appropriate for the AI community to move beyond learning algorithms to more seriously consider the nature of systems that are capable of learning over a lifetime. Reasons for our position are presented and potential counter-arguments are discussed. The remainder of the paper contributes by defining LML, presenting a reference framework that considers all forms of machine learning, and listing several key challenges for and benefits from LML research. We conclude with ideas for next steps to advance the field.

A Random-Finite-Set Approach to Bayesian SLAM <=SEP=> This paper proposes an integrated Bayesian frame work for feature-based simultaneous localization and map building (SLAM) in the general case of uncertain feature number and data association. By modeling the measurements and feature map as random finite sets (RFSs), a formulation of the feature-based SLAM problem is presented that jointly estimates the number and location of the features, as well as the vehicle trajectory. More concisely, the joint posterior distribution of the set-valued map and vehicle trajectory is propagated forward in time as measurements arrive, thereby incorporating both data association and feature management into a single recursion. Furthermore, the Bayes optimality of the proposed approach is established. A first-order solution, which is coined as the probability hypothesis density (PHD) SLAM filter, is derived, which jointly propagates the posterior PHD of the map and the posterior distribution of the vehicle trajectory. A Rao-Blackwellized (RB) implementation of the PHD-SLAM filter is proposed based on the Gaussian-mixture PHD filter (for the map) and a particle filter (for the vehicle trajectory). Simulated and experimental results demonstrate the merits of the proposed approach, particularly in situations of high clutter and data association ambiguity.

Scalp dermoscopy of androgenetic alopecia in Asian people. <=SEP=> Although dermoscopy is used mainly for diagnosing pigmented skin lesions, this device has been reported to be useful in observing alopecia areata and frontal fibrosing alopecia. Herein, we investigated the dermoscopic features and their incidence of androgenetic alopecia (AGA; n = 50 men) and female AGA (FAGA; n = 10 women) in Asian people. More than 20% hair diameter diversity (HDD), which reportedly is an early sign of AGA and corresponds to hair follicle miniaturization, was observed in the affected area of all AGA and FAGA cases, suggesting that HDD is an essential feature to diagnose AGA and FAGA. Peripilar signs, corresponding to perifollicular pigmentation, were seen in 66% (33/50) of AGA and 20% (2/10) of FAGA women. This incidence in the present study was lower than previously reported in white subjects possibly because the Asian skin color conceals slight peripilar pigmentation. Yellow dots were observed in 26% (13/50) of AGA and 10% (1/10) of FAGA cases and the number of yellow dots in AGA and FAGA was limited to 10 on the overall hair loss area. Yellow dots possibly indicate the coincidence of AGA and enlargement of the sebaceous glands caused by common end-organ hypersensitivity to androgen. In conclusion, dermoscopy is useful to diagnose AGA and FAGA and provides insights into the pathogenesis of AGA.

Abstractive Summarization of Reddit Posts with Multi-level Memory Networks <=SEP=> ive Summarization of Reddit Posts with Multi-level Memory Networks Byeongchang Kim Hyunwoo Kim Gunhee Kim Department of Computer Science and Engineering & Center for Superintelligence Seoul National University, Seoul, Korea {byeongchang.kim,hyunwoo.kim}@vision.snu.ac.kr gunhee@snu.ac.kr http://vision.snu.ac.kr/projects/reddit-tifu

FARMER: a novel approach to file access correlation mining and evaluation reference model for optimizing peta-scale file system performance <=SEP=> File correlation, which refers to a relationship among related files that can manifest in the form of their common access locality (temporal and/or spatial), has become an increasingly important consideration for performance enhancement in peta-scale storage systems. Previous studies on file correlations mainly concern with two aspects of files: file access sequence and semantic attribute. Based on mining with regard to these two aspects of file systems, various strategies have been proposed to optimize the overall system performance. Unfortunately, all of these studies consider either file access sequences or semantic attribute information separately and in isolation, thus unable to accurately and effectively mine file correlations, especially in large-scale distributed storage systems.
 This paper introduces a novel File Access coRrelation Mining and Evaluation Reference model (FARMER) for optimizing petascale file system performance that judiciously considers both file access sequences and semantic attributes simultaneously to evaluate the degree of file correlations by leveraging the Vector Space Model (VSM) technique adopted from the Information Retrieval field. We extract the file correlation knowledge from some typical file system traces using FARMER, and incorporate FARMER into a real large-scale object-based storage system as a case study to dynamically infer file correlations and evaluate the benefits and costs of a FARMER-enabled prefetching algorithm for the metadata servers under real file system workloads. Experimental results show that FARMER can mine and evaluate file correlations more accurately and effectively. More significantly, the FARMER-enabled prefetching algorithm is shown to reduce the metadata operations latency by approximately 24-35% when compared to a state-of-the-art metadata prefetching algorithm and a commonly used replacement policy.

A Risk Management Approach to the "Insider Threat" <=SEP=> Recent surveys indicate that the financial impact and operating losses due to insider intrusions are increasing. But these studies often disagree on what constitutes an “insider;” indeed, many define it only implicitly. In theory, appropriate selection of, and enforcement of, properly specified security policies should prevent legitimate users from abusing their access to computer systems, information, and other resources. However, even if policies could be expressed precisely, the natural mapping between the natural language expression of a security policy, and the expression of that policy in a form that can be implemented on a computer system or network, creates gaps in enforcement. This paper defines “insider” precisely, in terms of these gaps, and explores an access-based model for analyzing threats that include those usually termed “insider threats.” This model enables an organization Matt Bishop Matt Bishop, Dept. of Computer Science, University of California at Davis e-mail: bishop@cs.