Tactical Language and Culture Training Systems: Using AI to Teach Foreign Languages and Cultures

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

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

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

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

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

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

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.


Combined Effects of Attention and Motivation on Visual Task Performance: Transient and Sustained Motivational Effects

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.


Final Project : Entangled Photons and Quantum Teleportation

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.


Vehicle tracking system for intelligent and connected vehicle based on radar and V2V fusion

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.


Prior Knowledge, Level Set Representations & Visual Grouping

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

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.


CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases

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

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

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

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

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.


Scalp dermoscopy of androgenetic alopecia in Asian people.

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

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

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"

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.


Geodesic flow kernel for unsupervised domain adaptation

In real-world applications of visual recognition, many factors - such as pose, illumination, or image quality - can cause a significant mismatch between the source domain on which classifiers are trained and the target domain to which those classifiers are applied. As such, the classifiers often perform poorly on the target domain. Domain adaptation techniques aim to correct the mismatch. Existing approaches have concentrated on learning feature representations that are invariant across domains, and they often do not directly exploit low-dimensional structures that are intrinsic to many vision datasets. In this paper, we propose a new kernel-based method that takes advantage of such structures. Our geodesic flow kernel models domain shift by integrating an infinite number of subspaces that characterize changes in geometric and statistical properties from the source to the target domain. Our approach is computationally advantageous, automatically inferring important algorithmic parameters without requiring extensive cross-validation or labeled data from either domain. We also introduce a metric that reliably measures the adaptability between a pair of source and target domains. For a given target domain and several source domains, the metric can be used to automatically select the optimal source domain to adapt and avoid less desirable ones. Empirical studies on standard datasets demonstrate the advantages of our approach over competing methods.


SIFT Flow: Dense Correspondence across Scenes and Its Applications

While image alignment has been studied in different areas of computer vision for decades, aligning images depicting different scenes remains a challenging problem. Analogous to optical flow, where an image is aligned to its temporally adjacent frame, we propose SIFT flow, a method to align an image to its nearest neighbors in a large image corpus containing a variety of scenes. The SIFT flow algorithm consists of matching densely sampled, pixelwise SIFT features between two images while preserving spatial discontinuities. The SIFT features allow robust matching across different scene/object appearances, whereas the discontinuity-preserving spatial model allows matching of objects located at different parts of the scene. Experiments show that the proposed approach robustly aligns complex scene pairs containing significant spatial differences. Based on SIFT flow, we propose an alignment-based large database framework for image analysis and synthesis, where image information is transferred from the nearest neighbors to a query image according to the dense scene correspondence. This framework is demonstrated through concrete applications such as motion field prediction from a single image, motion synthesis via object transfer, satellite image registration, and face recognition.


Unsupervised Visual Domain Adaptation Using Subspace Alignment

In this paper, we introduce a new domain adaptation (DA) algorithm where the source and target domains are represented by subspaces described by eigenvectors. In this context, our method seeks a domain adaptation solution by learning a mapping function which aligns the source subspace with the target one. We show that the solution of the corresponding optimization problem can be obtained in a simple closed form, leading to an extremely fast algorithm. We use a theoretical result to tune the unique hyper parameter corresponding to the size of the subspaces. We run our method on various datasets and show that, despite its intrinsic simplicity, it outperforms state of the art DA methods.


Avoiding Confusing Features in Place Recognition

We seek to recognize the place depicted in a query image using a database of “street side” images annotated with geolocation information. This is a challenging task due to changes in scale, viewpoint and lighting between the query and the images in the database. One of the key problems in place recognition is the presence of objects such as trees or road markings, which frequently occur in the database and hence cause significant confusion between different places. As the main contribution, we show how to avoid features leading to confusion of particular places by using geotags attached to database images as a form of supervision. We develop a method for automatic detection of image-specific and spatially-localized groups of confusing features, and demonstrate that suppressing them significantly improves place recognition performance while reducing the database size. We show the method combines well with the state of the art bag-of-features model including query expansion, and demonstrate place recognition that generalizes over wide range of viewpoints and lighting conditions. Results are shown on a geotagged database of over 17K images of Paris downloaded from Google Street View.


Sherpa : Hyperparameter Optimization for Machine Learning Models

Sherpa is a free open-source hyperparameter optimization library for machine learning models. It is designed for problems with computationally expensive iterative function evaluations, such as the hyperparameter tuning of deep neural networks. With Sherpa, scientists can quickly optimize hyperparameters using a variety of powerful and interchangeable algorithms. Additionally, the framework makes it easy to implement custom algorithms. Sherpa can be run on either a single machine or a cluster via a grid scheduler with minimal configuration. Finally, an interactive dashboard enables users to view the progress of models as they are trained, cancel trials, and explore which hyperparameter combinations are working best. Sherpa empowers machine learning researchers by automating the tedious aspects of model tuning and providing an extensible framework for developing automated hyperparameter-tuning strategies. Its source code and documentation are available at https://github.com/LarsHH/sherpa and https://parameter-sherpa.readthedocs.io/, respectively. A demo can be found at https://youtu.be/L95sasMLgP4. 1 Existing Hyperparameter Optimization Libraries Hyperparameter optimization algorithms for machine learning models have previously been implemented in software packages such as Spearmint [15], HyperOpt [2], Auto-Weka 2.0 [9], and Google Vizier [5] among others. Spearmint is a Python library based on Bayesian optimization using a Gaussian process. Hyperparameter exploration values are specified using the markup language YAML and run on a grid via SGE and MongoDB. Overall, it combines Bayesian optimization with the ability for distributed training. HyperOpt is a hyperparameter optimization framework that uses MongoDB to allow parallel computation. The user manually starts workers which receive tasks from the HyperOpt instance. It offers the use of Random Search and Bayesian optimization based on a Tree of Parzen Estimators. Auto-WEKA 2.0 implements the SMAC [6] algorithm for automatic model selection and hyperparameter optimization within the WEKA machine learning framework. It provides a graphical user interface and supports parallel runs on a single machine. It is meant to be accessible for novice users and specifically targets the problem of choosing a model. Auto-WEKA is related to Auto-Sklearn [4] and Auto-Net [11] which specifically focus on tuning Scikit-Learn models and fully-connected 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada. Table 1: Comparison to Existing Libraries Spearmint Auto-WEKA HyperOpt Google Vizier Sherpa Early Stopping No No No Yes Yes Dashboard/GUI Yes Yes No Yes Yes Distributed Yes No Yes Yes Yes Open Source Yes Yes Yes No Yes # of Algorithms 2 1 2 3 5 neural networks in Lasagne, respectively. Auto-WEKA, Auto-Sklearn, and Auto-Net focus on an end-to-end automatic approach. This makes it easy for novice users, but restricts the user to the respective machine learning library and the models it implements. In contrast our work aims to give the user more flexibility over library, model and hyper-parameter optimization algorithm selection. Google Vizier is a service provided by Google for its cloud machine learning platform. It incorporates recent innovation in Bayesian optimization such as transfer learning and provides visualizations via a dashboard. Google Vizier provides many key features of a current hyperparameter optimization tool to Google Cloud users and Google engineers, but is not available in an open source version. A similar situation occurs with other cloud based platforms like Microsoft Azure Hyperparameter Tuning 1 and Amazon SageMaker’s Hyperparameter Optimization 2. 2 Need for a new library The field of machine learning has experienced massive growth over recent years. Access to open source machine learning libraries such as Scikit-Learn [14], Keras [3], Tensorflow [1], PyTorch [13], and Caffe [8] allowed research in machine learning to be widely reproduced by the community making it easy for practitioners to apply state of the art methods to real world problems. The field of hyperparameter optimization for machine learning has also seen many innovations recently such as Hyperband [10], Population Based Training [7], Neural Architecture Search [17], and innovation in Bayesian optimization such as [16]. While the basic implementation of some of these algorithms can be trivial, evaluating trials in a distributed fashion and keeping track of results becomes cumbersome which makes it difficult for users to apply these algorithms to real problems. In short, Sherpa aims to curate implementations of these algorithms while providing infrastructure to run these in a distributed way. The aim is for the platform to be scalable from usage on a laptop to a computation grid.


Avoiding pathologies in very deep networks

Choosing appropriate architectures and regularization strategies of deep networks is crucial to good predictive performance. To shed light on this problem, we analyze the analogous problem of constructing useful priors on compositions of functions. Specifically, we study the deep Gaussian process, a type of infinitely-wide, deep neural network. We show that in standard architectures, the representational capacity of the network tends to capture fewer degrees of freedom as the number of layers increases, retaining only a single degree of freedom in the limit. We propose an alternate network architecture which does not suffer from this pathology. We also examine deep covariance functions, obtained by composing infinitely many feature transforms. Lastly, we characterize the class of models obtained by performing dropout on Gaussian processes.


Future directions for behavioral information security research

Information Security (InfoSec) research is far reaching and includes many approaches to deal with protecting and mitigating threats to the information assets and technical resources available within computer based systems. Although a predominant weakness in properly securing information assets is the individual user within an organization, much of the focus of extant security research is on technical issues. The purpose of this paper is to highlight future directions for Behavioral InfoSec research, which is a newer, growing area of research. The ensuing paper presents information about challenges currently faced and future directions that Behavioral InfoSec researchers should explore. These areas include separating insider deviant behavior from insider misbehavior, approaches to understanding hackers, improving information security compliance, cross-cultural Behavioral InfoSec research, and data collection and measurement issues in Behavioral InfoSec research. a 2012 Elsevier Ltd. All rights reserved.


Kitchen Units for Food Units for Table . . . Past Future Predictor

In many computer vision applications, machines will need to reason beyond the present, and predict the future. This task is challenging because it requires leveraging extensive commonsense knowledge of the world that is difficult to write down. We believe that a promising resource for efficiently obtaining this knowledge is through the massive amounts of readily available unlabeled video. In this paper, we present a large scale framework that capitalizes on temporal structure in unlabeled video to learn to anticipate both actions and objects in the future. The key idea behind our approach is that we can train deep networks to predict the visual representation of images in the future. We experimentally validate this idea on two challenging “in the wild” video datasets, and our results suggest that learning with unlabeled videos significantly helps forecast actions and anticipate objects.


Development and initial validation of a short three-dimensional inventory of character strengths

Character strength is described as a positive and organized pattern of emotions, thoughts, and behaviors. It serves as a schema that organizes categories of information toward the self, others, and the world, and provides the self-aware knowledge that facilitates the pursuit of goals, values, and ethical principles. Recent research has suggested that three reliable factors emerge from the measures of character strengths: caring, inquisitiveness, and self-control. The goal of this paper is to develop a psychometrically sound short measure of character strength. The questions were addressed in two studies using two independent samples: a cross-cultural (i.e., 518 Asians and 556 Westerners) sample, and a cross-population (i.e., 175 community participants and 171 inpatients) sample in China. Findings from the exploratory and confirmatory factor analysis suggested a cross-cultural three-factor model of character strength that could be measured by the Three-dimensional Inventory of Character Strengths (TICS). A multigroup confirmatory factor analysis further indicated that the number of factors and factor loadings was invariant in the medical and community samples. This result indicated that the brief inventory could be applied to a medical context. Internal reliability, content validity, and predictive validity were good, although the predictive validity of the three character strengths for psychological symptoms in the medical sample was more modest than that in the community sample. TICS is expected to be used for screening populations at risk, and a tool to aid mental health professionals in group-based treatment/intervention planning. It also should be noted that this short inventory should be used with caution for individual decision making.


Active learning strategies for rating elicitation in collaborative filtering: A system-wide perspective

The accuracy of collaborative-filtering recommender systems largely depends on three factors: the quality of the rating prediction algorithm, and the quantity and quality of available ratings. While research in the field of recommender systems often concentrates on improving prediction algorithms, even the best algorithms will fail if they are fed poor-quality data during training, that is, garbage in, garbage out. Active learning aims to remedy this problem by focusing on obtaining better-quality data that more aptly reflects a user's preferences. However, traditional evaluation of active learning strategies has two major flaws, which have significant negative ramifications on accurately evaluating the system's performance (prediction error, precision, and quantity of elicited ratings). (1) Performance has been evaluated for each user independently (ignoring system-wide improvements). (2) Active learning strategies have been evaluated in isolation from unsolicited user ratings (natural acquisition).
 In this article we show that an elicited rating has effects across the system, so a typical user-centric evaluation which ignores any changes of rating prediction of other users also ignores these cumulative effects, which may be more influential on the performance of the system as a whole (system centric). We propose a new evaluation methodology and use it to evaluate some novel and state-of-the-art rating elicitation strategies. We found that the system-wide effectiveness of a rating elicitation strategy depends on the stage of the rating elicitation process, and on the evaluation measures (MAE, NDCG, and Precision). In particular, we show that using some common user-centric strategies may actually degrade the overall performance of a system. Finally, we show that the performance of many common active learning strategies changes significantly when evaluated concurrently with the natural acquisition of ratings in recommender systems.


DeeperBind: Enhancing prediction of sequence specificities of DNA binding proteins

Transcription factors (TFs) are macromolecules that bind to cis-regulatory specific sub-regions of DNA promoters and initiate transcription. Finding the exact location of these binding sites (aka motifs) is important in a variety of domains such as drug design and development. To address this need, several in vivo and in vitro techniques have been developed so far that try to characterize and predict the binding specificity of a protein to different DNA loci. The major problem with these techniques is that they are not accurate enough in prediction of the binding affinity and characterization of the corresponding motifs. As a result, downstream analysis is required to uncover the locations where proteins of interest bind. Here, we propose DeeperBind, a long short term recurrent convolutional network for prediction of protein binding specificities with respect to DNA probes. DeeperBind can model the positional dynamics of probe sequences and hence reckons with the contributions made by individual sub-regions in DNA sequences, in an effective way. Moreover, it can be trained and tested on datasets containing varying-length sequences. We apply our pipeline to the datasets derived from protein binding microarrays (PBMs), an in-vitro high-throughput technology for quantification of protein-DNA binding preferences, and present promising results. To the best of our knowledge, this is the most accurate pipeline that can predict binding specificities of DNA sequences from the data produced by high-throughput technologies through utilization of the power of deep learning for feature generation and positional dynamics modeling.


A Survey of Challenges and Applications of Wireless Body Area Network (WBAN) and Role of a Virtual Doctor Server in Existing Architecture

Wireless body area network has gained much interest and became emerging technology at health service facilities due to its wide range of utility and vital role to improve the human health. In this research paper, we are conducting a comprehensive survey of wireless Body Area Network (WBAN) and also introducing a virtual doctor server (VDS) in existing WBAN architecture. Existing architecture of WBAN consists of: wireless sensor, wireless actuator node , wireless central unit and wireless Personal Device (PD). Personal Digital Assistant (PDA) or smart phone can be used as PD. Based on the existing architecture mentioned above, we propose a design concept for a virtual doctor server (VDS) to support various patient health care services. VDS will keep the historical data about the patient, generate the daily tips and advices for him, call the doctor or emergency squad if required and can provide first aid assistance instructions on patient or any of his close relative's PDA's.


Opinion Target Extraction Using Partially-Supervised Word Alignment Model

Mining opinion targets from online reviews is an important and challenging task in opinion mining. This paper proposes a novel approach to extract opinion targets by using partially-supervised word alignment model (PSWAM). At first, we apply PSWAM in a monolingual scenario to mine opinion relations in sentences and estimate the associations between words. Then, a graph-based algorithm is exploited to estimate the confidence of each candidate, and the candidates with higher confidence will be extracted as the opinion targets. Compared with existing syntax-based methods, PSWAM can effectively avoid parsing errors when dealing with informal sentences in online reviews. Compared with the methods using alignment model, PSWAM can capture opinion relations more precisely through partial supervision from partial alignment links. Moreover, when estimating candidate confidence, we make penalties on higherdegree vertices in our graph-based algorithm in order to decrease the probability of the random walk running into the unrelated regions in the graph. As a result, some errors can be avoided. The experimental results on three data sets with different sizes and languages show that our approach outperforms state-of-the-art methods.


Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model

Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information such as dependency graph or constituent trees. In this paper, we first propose to use the syntactic graph to represent three types of syntactic information, i.e., word order, dependency and constituency features. We further employ a graph-to-sequence model to encode the syntactic graph and decode a logical form. Experimental results on benchmark datasets show that our model is comparable to the state-ofthe-art on Jobs640, ATIS and Geo880. Experimental results on adversarial examples demonstrate the robustness of the model is also improved by encoding more syntactic information.


Wireless Optical Communication for Underwater Applications

Wireless optical communication can be presented as an alternative to acoustic modems for scenarios where high speed, moderate distances, lower power and less complex communication systems are desired. Wireless communication is much more feasible solution to the problem of communicating with robotic vehicles. In this paper, wireless optical communication for the underwater criteria is designed and analysed. The focus of the paper is to construct light emitting diode based links at low cost with faster data rate of 1Mbps, wirelessly under the water. The choice of using LEDs instead of Lasers was largely economic. However, the underwater environment can be very challenging optically and many of the advantages that lasers have in terms of beam quality can be rapidly degraded by scattering and turbulence. Keyword: Moderate distance, Wireless optical, Super bright blue LED.


Integrated Pedestrian and Direction Classification Using a Random Decision Forest

For analysing the behaviour of pedestrians in a scene, it is common practice that pedestrian localization, classification, and tracking are conducted consecutively. The direction of a pedestrian, being part of the pose, implies the future path. This paper proposes novel Random Decision Forests (RDFs) to simultaneously classify pedestrians and their directions, without adding an extra module for direction classification to the pedestrian classification module. The proposed algorithm is trained and tested on the TUD multi-view pedestrian and Daimler Mono Pedestrian Benchmark data-sets. The proposed integrated RDF classifiers perform comparable to pedestrian or direction trained separated RDF classifiers. The integrated RDFs yield results comparable to those of state-of-the-art and baseline methods aiming for pedestrian classification or body direction classification, respectively.


A C-band microwave rectifier based on harmonic termination and with input filter removed

This paper presents a C-band rectifier employing a third harmonic termination which is realized by a λg/12 short-ended microstrip transmission line. The proposed harmonic termination balances the capacitive impedance of the diode at the fundamental frequency and blocks harmonic current of the diode. The input low-pass filter or matching circuit is removed and the bandwidth of rectifier is enhanced. Theoretical analysis and experiments are carried out. The results show that the proposed topology can realize high efficiency with a wide input frequency range. The fabricated rectifier has a compact dimension of 14mm by 22mm. A maximum RF-DC conversion efficiency of 75.6% at 13 dBm input power is observed. The measured efficiency remains above 70% with the operating frequency form 5.2 GHz to 6 GHz.


Magnetic resonance imaging in pediatric appendicitis: a systematic review

Magnetic resonance imaging for the evaluation of appendicitis in children has rapidly increased recently. This change has been primarily driven by the desire to avoid CT radiation dose. This meta-analysis reviews the diagnostic performance of MRI for pediatric appendicitis and discusses current knowledge of cost-effectiveness. We used a conservative Haldane correction statistical method and found pooled diagnostic parameters including a sensitivity of 96.5% (95% confidence interval [CI]: 94.3–97.8%), specificity of 96.1% (95% CI: 93.5–97.7%), positive predictive value of 92.0% (95% CI: 89.3–94.0%) and negative predictive value of 98.3% (95% CI: 97.3–99.0%), based on 11 studies. Assessment of patient outcomes associated with MRI use at two institutions indicates that time to antibiotics was 4.7 h and 8.2 h, time to appendectomy was 9.1 h and 13.9 h, and negative appendectomy rate was 3.1% and 1.4%, respectively. Alternative diagnoses were present in ~20% of cases, most commonly adnexal cysts and enteritis/colitis. Regarding technique, half-acquisition single-shot fast spin-echo (SSFSE) pulse sequences are crucial. While gadolinium-enhanced T1-weighted pulse sequences might be helpful, any benefit beyond non-contrast MRI has not been confirmed. Balanced steady-state free precession (SSFP) sequences are generally noncontributory. Protocols do not need to exceed five sequences; four-sequence protocols are commonly utilized. Sedation generally is not indicated; patients younger than 5 years might be attempted based on the child’s ability to cooperate. A comprehensive pediatric cost-effectiveness analysis that includes both direct and indirect costs is needed.


Energy disaggregation meets heating control

Heating control is of particular importance, since heating accounts for the biggest amount of total residential energy consumption. Smart heating strategies allow to reduce such energy consumption by automatically turning off the heating when the occupants are sleeping or away from home. The present context or occupancy state of a household can be deduced from the appliances that are currently in use. In this study we investigate energy disaggregation techniques to infer appliance states from an aggregated energy signal measured by a smart meter. Since most household devices have predictable energy consumption, we propose to use the changes in aggregated energy consumption as features for the appliance/occupancy state classification task. We evaluate our approach on real-life energy consumption data from several households, compare the classification accuracy of various machine learning techniques, and explain how to use the inferred appliance states to optimize heating schedules.


Anisotropic material properties of fused deposition modeling ABS

Rapid Prototyping (RP) technologies provide the ability to fabricate initial prototypes from various model materials. Stratasys Fused Deposition Modeling (FDM) is a typical RP process that can fabricate prototypes out of ABS plastic. To predict the mechanical behavior of FDM parts, it is critical to understand the material properties of the raw FDM process material, and the effect that FDM build parameters have on anisotropic material properties. This paper characterizes the properties of ABS parts fabricated by the FDM 1650. Using a Design of Experiment (DOE) approach, the process parameters of FDM, such as raster orientation, air gap, bead width, color, and model temperature were examined. Tensile strengths and compressive strengths of directionally fabricated specimens were measured and compared with injection molded FDM ABS P400 material. For the FDM parts made with a 0.003 inch overlap between roads, the typical tensile strength ranged between 65 and 72 percent of the strength of injection molded ABS P400. The compressive strength ranged from 80 to 90 percent of the injection molded FDM ABS. Several build rules for designing FDM parts were formulated based on experimental results.


Modulation, control and capacitor voltage balancing of alternate arm modular multilevel converter with DC fault blocking capability

This paper provides an overview of DC side fault tolerance issues of VSC based HVDC system and the need for fault tolerant converters. The working principle and DC fault ride through capability of recently introduced Alternate Arm Modular Multilevel Converter(AAMMC) has been discussed. The capacitor voltage balancing issues of AAMMC is analyzed and a novel scheme for balancing capacitor voltages of the wave shaping circuit is presented in this paper. The voltage balancing of capacitors of wave shaping circuits in the arm is done by introducing an overlap period during zero voltage period. Using the proposed scheme, the magnitude and direction of the current during the overlap period can be controlled by varying the switching pattern. It helps in charging or discharging of the submodule capacitors to bring them to their reference value. At the end of the overlap period, the arm current is brought to zero before opening the director switch so as to avoid the spike across the arm inductor. The efficacy of the proposed control scheme has been validated using simulation study done in PSCAD/EMTDC.


Upright orientation of man-made objects

Humans usually associate an upright orientation with objects, placing them in a way that they are most commonly seen in our surroundings. While it is an open challenge to recover the functionality of a shape from its geometry alone, this paper shows that it is often possible to infer its upright orientation by analyzing its geometry. Our key idea is to reduce the two-dimensional (spherical) orientation space to a small set of orientation candidates using functionality-related geometric properties of the object, and then determine the best orientation using an assessment function of several functional geometric attributes defined with respect to each candidate. Specifically we focus on obtaining the upright orientation for man-made objects that typically stand on some flat surface (ground, floor, table, etc.), which include the vast majority of objects in our everyday surroundings. For these types of models orientation candidates can be defined according to static equilibrium. For each candidate, we introduce a set of discriminative attributes linking shape to function. We learn an assessment function of these attributes from a training set using a combination of Random Forest classifier and Support Vector Machine classifier. Experiments demonstrate that our method generalizes well and achieves about 90% prediction accuracy for both a 10-fold cross-validation over the training set and a validation with an independent test set.


Holistic 3D Scene Parsing and Reconstruction from a Single RGB Image

We propose a computational framework to jointly parse a single RGB image and reconstruct a holistic 3D configuration composed by a set of CAD models using a stochastic grammar model. Specifically, we introduce a Holistic Scene Grammar (HSG) to represent the 3D scene structure, which characterizes a joint distribution over the functional and geometric space of indoor scenes. The proposed HSG captures three essential and often latent dimensions of the indoor scenes: i) latent human context, describing the affordance and the functionality of a room arrangement, ii) geometric constraints over the scene configurations, and iii) physical constraints that guarantee physically plausible parsing and reconstruction. We solve this joint parsing and reconstruction problem in an analysis-by-synthesis fashion, seeking to minimize the differences between the input image and the rendered images generated by our 3D representation, over the space of depth, surface normal, and object segmentation map. The optimal configuration, represented by a parse graph, is inferred using Markov chain Monte Carlo (MCMC), which efficiently traverses through the non-differentiable solution space, jointly optimizing object localization, 3D layout, and hidden human context. Experimental results demonstrate that the proposed algorithm improves the generalization ability and significantly outperforms prior methods on 3D layout estimation, 3D object detection, and holistic scene understanding.


Automated reference resolution in legal texts

This paper investigates the task of reference resolution in the legal domain. This is a new interesting task in Legal Engineering research. The goal is to create a system which can automatically detect references and then extracts their referents. Previous work limits itself to detect and resolve references at the document targets. In this paper, we go a step further in trying to resolve references to sub-document targets. Referents extracted are the smallest fragments of texts in documents, rather than the entire documents that contain the referenced texts. Based on analyzing the characteristics of reference phenomena in legal texts, we propose a four-step framework to deal with the task: mention detection, contextual information extraction, antecedent candidate extraction, and antecedent determination. We also show how machine learning methods can be exploited in each step. The final system achieves 80.06 % in the F1 score for detecting references, 85.61 % accuracy for resolving them, and 67.02 % in the F1 score for the end-to-end setting task on the Japanese National Pension Law corpus.


SMART LOW VOLTAGE AC SOLID STATE CIRCUIT BREAKERS FOR SMART GRIDS

The solid state circuit breaker (SSCB) is a device used in the power system in order to provide protection when a short circuit or fault current occurs. The objective of this paper is to study and implement a smart prototype of SSCB for smart grids. The presented SSCB is controlled through the current/time characteristics of that used in the conventional mechanical circuit breakers in addition to limit the high fault current levels (fault current limiter) especially with the proliferation of the distributed generation and the associated fault current level increase. In this paper, the principle of operation of the mechanical circuit breakers (MCB) and their classifications are introduced, andswitches used in the design of SSCBs are presented. Simulation of SSCB is carried out to study its feasibility and performance in the case of various operating conditions. Then, ahardware prototype of SSCB using IGBTs devices is constructed and tested to elucidate the proposed


ECNU: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity Estimation

This paper reports our submissions to semantic textual similarity task, i.e., task 2 in Semantic Evaluation 2015. We built our systems using various traditional features, such as string-based, corpus-based and syntactic similarity metrics, as well as novel similarity measures based on distributed word representations, which were trained using deep learning paradigms. Since the training and test datasets consist of instances collected from various domains, three different strategies of the usage of training datasets were explored: (1) use all available training datasets and build a unified supervised model for all test datasets; (2) select the most similar training dataset and separately construct a individual model for each test set; (3) adopt multi-task learning framework to make full use of available training sets. Results on the test datasets show that using all datasets as training set achieves the best averaged performance and our best system ranks 15 out of 73.


Learning with Noisy Labels

In this paper, we theoretically study the problem of binary classification in the presence of random classification noise — the learner, instead of seeing the true labels, sees labels that have independently been flipped with some small probability. Moreover, random label noise is class-conditional — the flip probability depends on the class. We provide two approaches to suitably modify any given surrogate loss function. First, we provide a simple unbiased estimator of any loss, and obtain performance bounds for empirical risk minimization in the presence of iid data with noisy labels. If the loss function satisfies a simple symmetry condition, we show that the method leads to an efficient algorithm for empirical minimization. Second, by leveraging a reduction of risk minimization under noisy labels to classification with weighted 0-1 loss, we suggest the use of a simple weighted surrogate loss, for which we are able to obtain strong empirical risk bounds. This approach has a very remarkable consequence — methods used in practice such as biased SVM and weighted logistic regression are provably noise-tolerant. On a synthetic non-separable dataset, our methods achieve over 88% accuracy even when 40% of the labels are corrupted, and are competitive with respect to recently proposed methods for dealing with label noise in several benchmark datasets.


Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation

Recent advances in semantic image segmentation have mostly been achieved by training deep convolutional neural networks (CNNs). We show how to improve semantic segmentation through the use of contextual information, specifically, we explore 'patch-patch' context between image regions, and 'patch-background' context. For learning from the patch-patch context, we formulate Conditional Random Fields (CRFs) with CNN-based pairwise potential functions to capture semantic correlations between neighboring patches. Efficient piecewise training of the proposed deep structured model is then applied to avoid repeated expensive CRF inference for back propagation. For capturing the patch-background context, we show that a network design with traditional multi-scale image input and sliding pyramid pooling is effective for improving performance. Our experimental results set new state-of-the-art performance on a number of popular semantic segmentation datasets, including NYUDv2, PASCAL VOC 2012, PASCAL-Context, and SIFT-flow. In particular, we achieve an intersection-overunion score of 78:0 on the challenging PASCAL VOC 2012 dataset.


The Cityscapes Dataset for Semantic Urban Scene Understanding

Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes. To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations, 20 000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark.


SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network [1] . The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature map(s). Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to perform non-linear upsampling. This eliminates the need for learning to upsample. The upsampled maps are sparse and are then convolved with trainable filters to produce dense feature maps. We compare our proposed architecture with the widely adopted FCN [2] and also with the well known DeepLab-LargeFOV [3] , DeconvNet [4] architectures. This comparison reveals the memory versus accuracy trade-off involved in achieving good segmentation performance. SegNet was primarily motivated by scene understanding applications. Hence, it is designed to be efficient both in terms of memory and computational time during inference. It is also significantly smaller in the number of trainable parameters than other competing architectures and can be trained end-to-end using stochastic gradient descent. We also performed a controlled benchmark of SegNet and other architectures on both road scenes and SUN RGB-D indoor scene segmentation tasks. These quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures. We also provide a Caffe implementation of SegNet and a web demo at http://mi.eng.cam.ac.uk/projects/segnet/.


Long-Time Exposure to Violent Video Games Does Not Show Desensitization on Empathy for Pain: An fMRI Study

As a typical form of empathy, empathy for pain refers to the perception and appraisal of others' pain, as well as the corresponding affective responses. Numerous studies investigated the factors affecting the empathy for pain, in which the exposure to violent video games (VVGs) could change players' empathic responses to painful situations. However, it remains unclear whether exposure to VVG influences the empathy for pain. In the present study, in terms of the exposure experience to VVG, two groups of participants (18 in VVG group, VG; 17 in non-VVG group, NG) were screened from nearly 200 video game experience questionnaires. And then, the functional magnetic resonance imaging data were recorded when they were viewing painful and non-painful stimuli. The results showed that the perception of others' pain were not significantly different in brain regions between groups, from which we could infer that the desensitization effect of VVGs was overrated.


An Augmented Reality System for Astronomical Observations

Anyone who has gazed through the eyepiece of an astronomical telescope knows that, with the exception of the Moon and the planets, extra-solar astronomical objects are disappointing to observe visually. This is mainly due to their low surface brightness, but also depends on the visibility, sky brightness and telescope aperture. We propose a system which projects images of astronomical objects (with focus on nebulae and galaxies), animations and additional information directly into the eyepiece view of an astronomical telescope. As the telescope orientation is queried continuously, the projected image is adapted in real-time to the currently visible field of view. For projection, a custom-built video projection module with high contrast and low maximum luminance value was developed. With this technology visitors to public observatories have the option to experience the richness of faint astronomical objects while directly looking at them through a telescope.


A gateway system for an automotive system: LIN, CAN, and FlexRay

In the automotive industry, the usage of microcontrollers has been increasing rapidly. Many mechanical parts of automobiles are being replaced with microcontrollers, and these microcontrollers are controlling various parts of the automobile, so communications between microcontrollers must be reliable. Until now, several protocols for automotive communications have been introduced, and LIN, CAN, and FlexRay are the most widely known protocols. Different vendors of automobiles use different protocols and each protocol possess different features. This paper presents a network gateway system between LIN, low-speed CAN, high-speed CAN, and FlexRay.


ECG-Cryptography and Authentication in Body Area Networks

Wireless body area networks (BANs) have drawn much attention from research community and industry in recent years. Multimedia healthcare services provided by BANs can be available to anyone, anywhere, and anytime seamlessly. A critical issue in BANs is how to preserve the integrity and privacy of a person's medical data over wireless environments in a resource efficient manner. This paper presents a novel key agreement scheme that allows neighboring nodes in BANs to share a common key generated by electrocardiogram (ECG) signals. The improved Jules Sudan (IJS) algorithm is proposed to set up the key agreement for the message authentication. The proposed ECG-IJS key agreement can secure data commnications over BANs in a plug-n-play manner without any key distribution overheads. Both the simulation and experimental results are presented, which demonstrate that the proposed ECG-IJS scheme can achieve better security performance in terms of serval performance metrics such as false acceptance rate (FAR) and false rejection rate (FRR) than other existing approaches. In addition, the power consumption analysis also shows that the proposed ECG-IJS scheme can achieve energy efficiency for BANs.


An anomaly-based network intrusion detection system using Deep learning

Recently, anomaly-based intrusion detection techniques are valuable methodology to detect both known as well as unknown/new attacks, so they can cope with the diversity of the attacks and the constantly changing nature of network attacks. There are many problems need to be considered in anomaly-based network intrusion detection system (NIDS), such as ability to adapt to dynamic network environments, unavailability of labeled data, false positive rate. This paper, we use Deep learning techniques to implement an anomaly-based NIDS. These techniques show the sensitive power of generative models with good classification, capabilities to deduce part of its knowledge from incomplete data and the adaptability. Our experiments with KDDCup99 network traffic connections show that our work is effective to exact detect in anomaly-based NIDS and classify intrusions into five groups with the accuracy based on network data sources.


Smarter City : Smart Energy Grid based on Blockchain Technology

The improvement of the Quality of Life (QoL) and the enhancement of the Quality of Services (QoS) represent the main goal of every city evolutionary process. It is possible making cities smarter promoting innovative solutions by use of Information and Communication Technology (ICT) for collecting and analysing large amounts of data generated by several sources, such as sensor networks, wearable devices, and IoT devices spread among the city. The integration of different technologies and different IT systems, needed to build smart city applications and services, remains the most challenge to overcome. In the Smart City context, this paper intends to investigate the Smart Environment pillar, and in particular the aspect related to the implementation of Smart Energy Grid for citizens in the urban context. The innovative characteristic of the proposed solution consists of using the Blockchain technology to join the Grid, exchanging information, and buy/sell energy between the involved nodes (energy providers and private citizens), using the Blockchain granting ledger. Keywords— Information Technology, Smart City, Digital Revolution, Digital Innovation, Blockchain, Smart Energy Grid, Machine Learning.


Social interaction based video recommendation: Recommending YouTube videos to facebook users

Online videos, e.g., YouTube videos, are important topics for social interactions among users of online social networking sites (OSN), e.g., Facebook. This opens up the possibility of exploiting video-related user social interaction information for better video recommendation. Towards this goal, we conduct a case study of recommending YouTube videos to Facebook users based on their social interactions. We first measure social interactions related to YouTube videos among Facebook users. We observe that the attention a video attracts on Facebook is not always well-aligned with its popularity on YouTube. Unpopular videos on YouTube can become popular on Facebook, while popular videos on YouTube often do not attract proportionally high attentions on Facebook. This finding motivates us to develop a simple top-k video recommendation algorithm that exploits user social interaction information to improve the recommendation accuracy for niche videos, that are globally unpopular, but highly relevant to a specific user or user group. Through experiments on the collected Facebook traces, we demonstrate that our recommendation algorithm significantly outperforms the YouTube-popularity based video recommendation algorithm as well as a collaborative filtering algorithm based on user similarities.


TextLuas: Tracking and Visualizing Document and Term Clusters in Dynamic Text Data

For large volumes of text data collected over time, a key knowledge discovery task is identifying and tracking clusters. These clusters may correspond to emerging themes, popular topics, or breaking news stories in a corpus. Therefore, recently there has been increased interest in the problem of clustering dynamic data. However, there exists little support for the interactive exploration of the output of these analysis techniques, particularly in cases where researchers wish to simultaneously explore both the change in cluster structure over time and the change in the textual content associated with clusters. In this paper, we propose a model for tracking dynamic clusters characterized by the evolutionary events of each cluster. Motivated by this model, the TextLuas system provides an implementation for tracking these dynamic clusters and visualizing their evolution using a metro map metaphor. To provide overviews of cluster content, we adapt the tag cloud representation to the dynamic clustering scenario. We demonstrate the TextLuas system on two different text corpora, where they are shown to elucidate the evolution of key themes. We also describe how TextLuas was applied to a problem in bibliographic network research.


Signal processing of range detection for SFCW radars using Matlab and GNU radio

Development of radar technology is now rapidly. One of them is Step Frequency Continuous Wave Radar (SFCW Radar). SFCW radar can be used for various purposes. SFCW radar consists of antenna, control unit, signal processing unit. The radar advantages compared to the other radar are this radar is easier to implement and more widely within the range of radar. In this research that will be done is design SFCW radar. There are several stages to be performed in this research. At first, the simulation will be done using Matlab®. This simulation will determine the parameters needed to obtain an appropriate resolution. The second, simulations is performed by using GNU radio. This simulation will be adjusted to the parameters that have been previously designed by Matlab®. Both of the results will be analyzed. Input signal is complex. Simulation results for 5kHz-1.285MHz will display in graph. Graph will show location of the object in 5 m, 1km, and 1.1km depend on initial design.


Weather classification with deep convolutional neural networks

In this paper, we study weather classification from images using Convolutional Neural Networks (CNNs). Our approach outperforms the state of the art by a huge margin in the weather classification task. Our approach achieves 82.2% normalized classification accuracy instead of 53.1% for the state of the art (i.e., 54.8% relative improvement). We also studied the behavior of all the layers of the Convolutional Neural Networks, we adopted, and interesting findings are discussed.


Image Deblurring via Enhanced Low-Rank Prior

Low-rank matrix approximation has been successfully applied to numerous vision problems in recent years. In this paper, we propose a novel low-rank prior for blind image deblurring. Our key observation is that directly applying a simple low-rank model to a blurry input image significantly reduces the blur even without using any kernel information, while preserving important edge information. The same model can be used to reduce blur in the gradient map of a blurry input. Based on these properties, we introduce an enhanced prior for image deblurring by combining the low rank prior of similar patches from both the blurry image and its gradient map. We employ a weighted nuclear norm minimization method to further enhance the effectiveness of low-rank prior for image deblurring, by retaining the dominant edges and eliminating fine texture and slight edges in intermediate images, allowing for better kernel estimation. In addition, we evaluate the proposed enhanced low-rank prior for both the uniform and the non-uniform deblurring. Quantitative and qualitative experimental evaluations demonstrate that the proposed algorithm performs favorably against the state-of-the-art deblurring methods.


10 Internet Addiction and Its Cognitive Behavioral Therapy


Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms

Over the last decade, we have observed a revolution in brain structural and functional Connectomics. On one hand, we have an ever-more detailed characterization of the brain's white matter structural connectome. On the other, we have a repertoire of consistent functional networks that form and dissipate over time during rest. Despite the evident spatial similarities between structural and functional connectivity, understanding how different time-evolving functional networks spontaneously emerge from a single structural network requires analyzing the problem from the perspective of complex network dynamics and dynamical system's theory. In that direction, bottom-up computational models are useful tools to test theoretical scenarios and depict the mechanisms at the genesis of resting-state activity. Here, we provide an overview of the different mechanistic scenarios proposed over the last decade via computational models. Importantly, we highlight the need of incorporating additional model constraints considering the properties observed at finer temporal scales with MEG and the dynamical properties of FC in order to refresh the list of candidate scenarios.


Spider Monkey Optimization algorithm for numerical optimization

Swarm intelligence is one of the most promising area for the researchers in the field of numerical optimization. Researchers have developed many algorithms by simulating the swarming behavior of various creatures like ants, honey bees, fish, birds and the findings are very motivating. In this paper, a new approach for numerical optimization is proposed by modeling the foraging behavior of spider monkeys. Spider monkeys have been categorized as fission–fusion social structure based animals. The animals which follow fission– fusion social systems, split themselves from large to smaller groups and vice-versa based on the scarcity or availability of food. The proposed swarm intelligence approach is named as Spider Monkey Optimization (SMO) algorithm and can broadly be classified as an algorithm inspired by intelligent foraging behavior of fission–fusion social structure based animals.


Modelling Domain Relationships for Transfer Learning on Retrieval-based Question Answering Systems in E-commerce

Nowadays, it is a heated topic for many industries to build automatic question-answering (QA) systems. A key solution to these QA systems is to retrieve from a QA knowledge base the most similar question of a given question, which can be reformulated as a paraphrase identification (PI) or a natural language inference (NLI) problem. However, most existing models for PI and NLI have at least two problems: They rely on a large amount of labeled data, which is not always available in real scenarios, and they may not be efficient for industrial applications. In this paper, we study transfer learning for the PI and NLI problems, aiming to propose a general framework, which can effectively and efficiently adapt the shared knowledge learned from a resource-rich source domain to a resource-poor target domain. Specifically, since most existing transfer learning methods only focus on learning a shared feature space across domains while ignoring the relationship between the source and target domains, we propose to simultaneously learn shared representations and domain relationships in a unified framework. Furthermore, we propose an efficient and effective hybrid model by combining a sentence encoding-based method and a sentence interaction-based method as our base model. Extensive experiments on both paraphrase identification and natural language inference demonstrate that our base model is efficient and has promising performance compared to the competing models, and our transfer learning method can help to significantly boost the performance. Further analysis shows that the inter-domain and intra-domain relationship captured by our model are insightful. Last but not least, we deploy our transfer learning model for PI into our online chatbot system, which can bring in significant improvements over our existing system. Finally, we launch our new system on the chatbot platform Eva in our E-commerce site AliExpress.


Generation of circularly polarized conical beam pattern using toroidal helical knot antenna

A novel circularly polarized antenna with a conical beam radiation pattern is presented. It consists of a feeding probe and a (1, 6) helical torus knot as a polarizer. Measured results shows that, the antenna has an impedance bandwidth of 19.67% and axial ratio bandwidth of 15.7%. With the RHCP gain of about 6 dBic at 3.5 GHz. Toroidal helical knot antenna is mechanically simple to fabricate using additive manufacturing technology. The proposed prototype is suitable for mounting on vehicles to facilitate communication with geostationary satellites.


Towards deep learning with segregated dendrites

Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful higher-order representations-the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the morphology of neocortical pyramidal neurons.


Introduction to E-commerce Learning Objectives

● To understand the complexity of e-commerce and its many facets. ● To explore how e-business and e-commerce fit together. ● To identify the impact of e-commerce. ● To recognise the benefits and limitations of e-commerce. ● To use classification frameworks for analysing e-commerce. ● To identify the main barriers to the growth and development of e-commerce in organisations.  Even today, some considerable time after the so called 'dot com/Internet revolution', electronic commerce (e-commerce) remains a relatively new, emerging and constantly changing area of business management and information technology. There has been and continues to be much publicity and discussion about e-commerce. Library catalogues and shelves are filled with books and articles on the subject. However, there remains a sense of confusion, suspicion and misunderstanding surrounding the area, which has been exacerbated by the different contexts in which electronic commerce is used, coupled with the myriad related buzzwords and acronyms. This book aims to consolidate the major themes that have arisen from the new area of electronic commerce and to provide an understanding of its application and importance to management. In order to understand electronic commerce it is important to identify the different terms that are used, and to assess their origin and usage. According to the editor-in-chief of International Journal of Electronic Commerce , Vladimir Zwass, 'Electronic commerce is sharing business information , maintaining business relationships and conducting business transactions by means of telecommunications networks'. 1 He maintains that in its purest form, electronic commerce has existed for over 40 years, originating from the electronic transmission of messages during the Berlin airlift in 1948. 2 From this, electronic data interchange (EDI) was the next stage of e-commerce development. In the 1960s a cooperative effort between industry groups produced a first attempt at common electronic data formats. The formats, however, were only for purchasing, transportation and finance data, and were used primarily for intra-industry transactions. It was not until the late 1970s that work began for national Electronic Data Interchange (EDI) standards, which developed well into the early 1990s. EDI is the electronic transfer of a standardised business transaction between a sender and receiver computer, over some kind of private network or value added network (VAN). Both sides would have to have the same application software and the data would be exchanged in an extremely rigorous format. In sectors such as retail, automotive, defence and heavy manufacturing, EDI was …


Development of personality in early and middle adulthood: set like plaster or persistent change?

Different theories make different predictions about how mean levels of personality traits change in adulthood. The biological view of the Five-factor theory proposes the plaster hypothesis: All personality traits stop changing by age 30. In contrast, contextualist perspectives propose that changes should be more varied and should persist throughout adulthood. This study compared these perspectives in a large (N = 132,515) sample of adults aged 21-60 who completed a Big Five personality measure on the Internet. Conscientiousness and Agreeableness increased throughout early and middle adulthood at varying rates; Neuroticism declined among women but did not change among men. The variety in patterns of change suggests that the Big Five traits are complex phenomena subject to a variety of developmental influences.


Nature over nurture: temperament, personality, and life span development.

Temperaments are often regarded as biologically based psychological tendencies with intrinsic paths of development. It is argued that this definition applies to the personality traits of the five-factor model. Evidence for the endogenous nature of traits is summarized from studies of behavior genetics, parent-child relations, personality structure, animal personality, and the longitudinal stability of individual differences. New evidence for intrinsic maturation is offered from analyses of NEO Five-Factor Inventory scores for men and women age 14 and over in German, British, Spanish, Czech, and Turkish samples (N = 5,085). These data support strong conceptual links to child temperament despite modest empirical associations. The intrinsic maturation of personality is complemented by the culturally conditioned development of characteristic adaptations that express personality; interventions in human development are best addressed to these.


A power primer.

One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.


Gender differences in personality: a meta-analysis.

Four meta-analyses were conducted to examine gender differences in personality in the literature (1958-1992) and in normative data for well-known personality inventories (1940-1992). Males were found to be more assertive and had slightly higher self-esteem than females. Females were higher than males in extraversion, anxiety, trust, and, especially, tender-mindedness (e.g., nurturance). There were no noteworthy sex differences in social anxiety, impulsiveness, activity, ideas (e.g., reflectiveness), locus of control, and orderliness. Gender differences in personality traits were generally constant across ages, years of data collection, educational levels, and nations.


Personality and Performance at the Beginning of the New Millennium : What Do We Know and Where Do We Go Next ?

As we begin the new millennium, it is an appropriate time to examine what we have learned about personality-performance relationships over the past century and to embark on new directions for research. In this study we quantitatively summarize the results of 15 prior meta-analytic studies that have investigated the relationship between the Five Factor Model (FFM) personality traits and job performance. Results support the previous findings that conscientiousness is a valid predictor across performance measures in all occupations studied. Emotional stability was also found to be a generalizable predictor when overall work performance was the criterion, but its relationship to specific performance criteria and occupations was less consistent than was conscientiousness. Though the other three Big Five traits (extraversion, openness and agreeableness) did not predict overall work performance, they did predict success in specific occupations or relate to specific criteria. The studies upon which these results are based comprise most of the research that has been conducted on this topic in the past century. Consequently, we call for a moratorium on meta-analytic studies of the type reviewed in our study and recommend that researchers embark on a new research agenda designed to further our understanding of personalityperformance linkages.


3D printing bone models extracted from medical imaging data

Additive manufacturing in the form of 3D printing is becoming more common with the rise of low-cost hobby machines which makes research in the field of medicine more accessible. This article explores and experiments with methods of producing three dimensional replicas of body parts as education material from the medical imaging data of Computed Tomography and Magnetic Resonance Imaging.


A novel redis security extension for NoSQL database using authentication and encryption

Redis is a new generation NoSQL database. Redis in its simplest form is a key value pair based data system. It supports all the data structures like variables, Linked list, arrays, strings, and queues. However unlike the conventional databases, Redis does not provide enough security for the data. Anyone can get the value if the key is known because the data is stored in the form of key value pair. Therefore such a database is unsuitable for enterprise and most practical application data. In this paper, the work is carried out to add immense security to a Redis system using following: a) Authentication Service b) Encryption Services c) Security to persistent data d) Security to blob data (multimedia data for images). The Encryption algorithm plays a very important role in the field of Database Management System. Here we make use of AES algorithm because the AES algorithm consumes least Encryption and Decryption time in comparison with RSA and DES [12]. The principle of the work is that a separate Key is created in the database whose value is an encrypted data, encrypted by symmetric key cryptography using AES. This data contains all other key values being concatenated and encrypted. Once a query is generated, first the extraction of independent data entities are made, followed by decryption of data using symmetric key. We also design a UI system to demonstrate the capabilities of the system with and without the security implementation. The result of our work shows that adding the security extension does not increase the overhead by much in terms of system resources and latency. We also extend the key-value based system to be able to store binary image data which is also stored in the encrypted pattern.


SPICE Models to Analyze Radiated and Conducted Susceptibilities of Shielded Coaxial Cables

This paper presents simulation program with integrated circuit emphasis (SPICE) models for the analyses of the radiated and conducted susceptibilities of shielded coaxial cables. Derived from the transmission-line equations of shielded cables, these models can be directly used for the time-domain and frequency-domain analyses, and connected to nonlinear and time-varying loads with the models already available in SPICE. In the proposed models, the usual discretization of shielded cables is not needed, and both the transfer impedance and admittance have been taken into account. Results obtained with these new models are in a good agreement with those obtained by using other approaches.