title: The emotional force of swearwords and taboo words in the speech of multilinguals

abstract: This paper investigates the perception of emotional force of swearwords and taboo words (S-T words) among 1039 multilinguals. It is based on data drawn from a large database collected through a web questionnaire on bilingualism and emotions. t-Tests revealed that the perceived emotional force of S-T words is highest in the L1 and gradually lower in languages learned subsequently. Self-reported L1 attriters were found to judge S-T words in their L1 to be less powerful than those who are still dominant in their L1. Participants who learned their language(s) in a naturalistic—or partly naturalistic—context gave higher ratings on emotional force of S-T words in that language than instructed language learners. Self-rated proficiency in a language and frequency of use of language significantly predicted perception of emotional force of S-T words. Age of onset of learning was found to only predict perception of emotional force of S-T words in the L2.


title: Empirical analysis: stock market prediction via extreme learning machine

abstract: How to predict stock price movements based on quantitative market data modeling is an attractive topic. In front of the market news and stock prices that are commonly believed as two important market data sources, how to extract and exploit the hidden information within the raw data and make both accurate and fast predictions simultaneously becomes a challenging problem. In this paper, we present the design and architecture of our trading signal mining platform that employs extreme learning machine (ELM) to make stock price prediction based on those two data sources concurrently. Comprehensive experimental comparisons between ELM and the state-of-the-art learning algorithms, including support vector machine (SVM) and back-propagation neural network (BP-NN), have been undertaken on the intra-day tick-by-tick data of the H-share market and contemporaneous news archives. The results have shown that (1) both RBF ELM and RBF SVM achieve higher prediction accuracy and faster prediction speed than BP-NN; (2) the RBF ELM achieves similar accuracy with the RBF SVM and (3) the RBF ELM has faster prediction speed than the RBF SVM. Simulations of a preliminary trading strategy with the signals are conducted. Results show that strategy with more accurate signals will make more profits with less risk.


title: A phenomenology of human-electricity relations

abstract: This paper investigates the philosophical question of how we can experience energy with the aim of informing the design of future ways of experiencing energy by means of technology. Four human-technology relations formulated by philosopher of technology Don Ihde are presented. Each is then developed in the context of electrical interactive technologies. In conclusion these human-electricity and human-technology relations are employed in order to interpret current work related to energy and sustainability within HCI and point to future work in these areas.


Fake Account Detection in Twitter Based on Minimum Weighted Feature set


title: What were the Tweets about? Topical Associations between Public Events and Twitter Feeds

abstract: Social media channels such as Twitter have emerged as platforms for crowds to respond to public and televised events such as speeches and debates. However, the very large volume of responses presents challenges for attempts to extract sense from them. In this work, we present an analytical method based on joint statistical modeling of topical influences from the events and associated Twitter feeds. The model enables the auto-segmentation of the events and the characterization of tweets into two categories: (1) episodic tweets that respond specifically to the content in the segmentsof the events, and (2) steady tweets that respond generally about the events. By applying our method to two large sets of tweets in response to President Obama's speech on the Middle East in May 2011 and a Republican Primary debate in September 2011, we present what these tweets were about. We also reveal the nature and magnitude of the influences of the event on the tweets over its timeline. In a user study, we further show that users find the topics and the episodic tweets discovered by our method to be of higher quality and more interesting as compared to the state-of-the-art, with improvements in the range of 18-41%.


title: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection

abstract: A unified deep neural network, denoted the multi-scale CNN (MS-CNN), is proposed for fast multi-scale object detection. The MS-CNN consists of a proposal sub-network and a detection sub-network. In the proposal sub-network, detection is performed at multiple output layers, so that receptive fields match objects of different scales. These complementary scale-specific detectors are combined to produce a strong multi-scale object detector. The unified network is learned end-to-end, by optimizing a multi-task loss. Feature upsampling by deconvolution is also explored, as an alternative to input upsampling, to reduce the memory and computation costs. State-of-the-art object detection performance, at up to 15 fps, is reported on datasets, such as KITTI and Caltech, containing a substantial number of small objects.


title: A practical integration of automatic generation control and Demand Response

abstract: For a power grid to operate properly, electrical frequency must be continuously maintained close to its nominal value. Increasing penetration of distributed generation, such as solar and wind generation, introduces fluctuations in active power while also reducing the natural inertial response of the electricity grid, creating reliability concerns. While frequency regulation has traditionally been achieved by controlling generators, the control of Demand Response (DR) resources has been recognized in recent smart grid literature as an efficient means for providing additional regulation capability. To this end, several control methodologies have been proposed recently, but various features of these proposals make their practical implementations difficult. In this paper, we propose a new control algorithm that facilitates optimal frequency regulation through direct control of both generators and DR, while addressing several issues that prevent practical implementation of other proposals. In particular, i) our algorithm is ideal for control over a large, low-bandwidth network as communication and measurement is only required every 2 seconds, ii) it enables DR resources to recover energy lost during system transients, and iii) it allows the market to immediately respond to disturbances through feedback of the system frequency. We demonstrate the viability of our approach through dynamic simulations on a 118-bus grid model.


title: A Survey of Paraphrasing and Textual Entailment Methods

abstract: Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often similar. Both kinds of methods are useful, at least in principle, in a wide range of natural language processing applications, including question answering, summarization, text generation, and machine translation. We summarize key ideas from the two areas by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.


title: Beauty in a smile: the role of medial orbitofrontal cortex in facial attractiveness

abstract: The attractiveness of a face is a highly salient social signal, influencing mate choice and other social judgements. In this study, we used event-related functional magnetic resonance imaging (fMRI) to investigate brain regions that respond to attractive faces which manifested either a neutral or mildly happy face expression. Attractive faces produced activation of medial orbitofrontal cortex (OFC), a region involved in representing stimulus-reward value. Responses in this region were further enhanced by a smiling facial expression, suggesting that the reward value of an attractive face as indexed by medial OFC activity is modulated by a perceiver directed smile.


title: SAO network analysis of patents for technology trends identification: a case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells

abstract: This paper suggests a method for Subject---Action---Object (SAO) network analysis of patents for technology trends identification by using the concept of function. The proposed method solves the shortcoming of the keyword-based approach to identification of technology trends, i.e., that it cannot represent how technologies are used or for what purpose. The concept of function provides information on how a technology is used and how it interacts with other technologies; the keyword-based approach does not provide such information. The proposed method uses an SAO model and represents "key concept" instead of "key word". We present a procedure that formulates an SAO network by using SAO models extracted from patent documents, and a method that applies actor network theory to analyze technology implications of the SAO network. To demonstrate the effectiveness of the SAO network this paper presents a case study of patents related to Polymer Electrolyte Membrane technology in Proton Exchange Membrane Fuel Cells.


title: High efficiency CMOS rectifier circuit with self-Vth-cancellation and power regulation functions for UHF RFIDs

abstract: High efficiency CMOS rectifier circuit for UHF RFID applications has been developed. The rectifier utilizes self Vth cancellation (SVC) scheme in which threshold voltage of MOSFETs is cancelled by applying gate bias voltage generated by output voltage of the rectifier itself. Very simple circuit configuration and no power dissipation feature of the scheme enable excellent power conversion efficiency (PCE) especially in small RF input power conditions. At higher RF input power conditions, PCE of the rectifier automatically decreases. This is the built-in self-power-regulation function. Proposed SVC CMOS rectifier has been fabricated with 0.35 mum CMOS process and the measured performance has been compared with other types of rectifiers. The SVC CMOS rectifier achieves 29% PCE at -9.9 dBm RF input power condition. This PCE is larger than ever reported rectifiers under the condition.


title: Toward privacy-assured and searchable cloud data storage services

abstract: Cloud computing is envisioned as the next generation architecture of IT enterprises, providing convenient remote access to massively scalable data storage and application services. While this outsourced storage and computing paradigm can potentially bring great economical savings for data owners and users, its benefits may not be fully realized due to wide concerns of data owners that their private data may be involuntarily exposed or handled by cloud providers. Although end-to-end encryption techniques have been proposed as promising solutions for secure cloud data storage, a primary challenge toward building a full-fledged cloud data service remains: how to effectively support flexible data utilization services such as search over the data in a privacy-preserving manner. In this article, we identify the system requirements and challenges toward achieving privacy-assured searchable outsourced cloud data services, especially, how to design usable and practically efficient search schemes for encrypted cloud storage. We present a general methodology for this using searchable encryption techniques, which allows encrypted data to be searched by users without leaking information about the data itself and users? queries. In particular, we discuss three desirable functionalities of usable search operations: supporting result ranking, similarity search, and search over structured data. For each of them, we describe approaches to design efficient privacy-assured searchable encryption schemes, which are based on several recent symmetric-key encryption primitives. We analyze their advantages and limitations, and outline the future challenges that need to be solved to make such secure searchable cloud data service a reality.


3 D differential phase contrast microscopy


title: Next Gen PCFG Password Cracking

abstract: Passwords continue to remain an important authentication technique. The probabilistic context-free grammar-based password cracking system of Weir et al. was an important addition to dictionary-based password cracking approaches. In this paper, we show how to substantially improve upon this system by systematically adding keyboard patterns and multiword patterns (two or more words in the alphabetic part of a password) to the context-free grammars used in the probabilistic password cracking. Our results on cracking multiple data sets show that by learning these new classes of patterns, we can achieve up to 22% improvement over the original system. In this paper, we also define metrics to help analyze and improve attack dictionaries. Using our approach to improving the dictionary, we achieve an additional improvement of $\sim 33$ % by increasing the coverage of a standard attack dictionary. Combining both approaches, we can achieve a 55% improvement over the previous system. Our tests were done over fairly long password guessing sessions (up to 85 billion) and thus show the uniform effectiveness of our techniques for long cracking sessions.


Summarization using Neural Networks and Rhetorical Structure Theory


title: Computer-aided design of analog and mixed-signal integrated circuits

abstract: This survey presents an overview of recent advances in the state of the art for computer-aided design (CAD) tools for analog and mixed-signal integrated circuits (ICs). Analog blocks typically constitute only a small fraction of the components on mixed-signal ICs and emerging systems-on-a-chip (SoC) designs. But due to the increasing levels of integration available in silicon technology and the growing requirement for digital systems to communicate with the continuous-valued external world, there is a growing need for CAD tools that increase the design productivity and improve the quality of analog integrated circuits. This paper describes the motivation and evolution of these tools and outlines progress on the various design problems involved: simulation and modeling, symbolic analysis, synthesis and optimization, layout generation, yield analysis and design centering, and test. This paper summarizes the problems for which viable solutions are emerging and those which are still unsolved.


title: Fusion of doppler radar and geometric attributes for motion estimation of extended objects

abstract: A prime requirement for autonomous driving is a fast and reliable estimation of the motion state of dynamic objects in the ego-vehicle’s surroundings. An instantaneous approach for extended objects based on two Doppler radar sensors has recently been proposed. In this paper, that approach is augmented by prior knowledge of the object’s heading angle and rotation center. These properties can be determined reliably by state-ofthe- art methods based on sensors such as LIDAR or cameras. The information fusion is performed utilizing an appropriate measurement model, which directly maps the motion state in the Doppler velocity space. This model integrates the geometric properties. It is used to estimate the object’s motion state using a linear regression. Additionally, the model allows a straightforward calculation of the corresponding variances. The resulting method shows a promising accuracy increase of up to eight times greater than the original approach.


An Exploration of Dropout with LSTMs


title: The effect of multimedia annotation modes on l2 vocabulary acquisition: a comparative study

abstract: One aspect of second language teaching via multimedia to have received attention over the past few years is the impact of glossing individual vocabulary words through different modalities. This study examines which of the image modalities--dynamic video or still picture--is more effective in aiding vocabulary acquisition. The participants, 30 ESL students, were introduced to a hypermedia-learning program, designed by the researcher for reading comprehension. The program provides users reading a narrative English text with a variety of glosses or annotations for words in the form of printed text, graphics, video, and sound, all of which are intended to aid in the understanding and learning of unknown words. A within-subject design was used in this study with 30 participants being measured under three conditions: printed text definition alone, printed text definition coupled with still pictures, and printed text definition coupled with video clips. In order to assess the efficacy of each mode, a vocabulary test was designed and administered to participants after they had read the English narrative. Two types of tests were administered: recognition and production. In addition, a face-to-face interview was conducted, and questionnaires were distributed. Results of the both tests were analyzed using analysis of variance procedures. The investigation has yielded the conclusion that a video clip is more effective in teaching unknown vocabulary words than a still picture. Among the suggested factors that explain such a result are that video better builds a mental image, better creates curiosity leading to increased concentration, and embodies an advantageous combination of modalities (vivid or dynamic image, sound, and printed text).


title: RiskVis: Supply chain visualization with risk management and real-time monitoring

abstract: With increased complexity, supply chain networks (SCNs) of modern era face higher risks and lower efficiency due to limited visibility. Hence, there is an immediate need to provide end-to-end supply chain visibility for efficient management of complex supply chains. This paper proposes a visualization scheme based on multi-hierarchical modular design and develops a supply chain visualization platform with risk management and real-time monitoring, named RiskVis, for realizing better Supply Chain Risk Management (SCRM). A Supply Chain Visualizer (SCV) with a graphical visualization platform is mounted as a part of a SCRM management decision-making dashboard and it provides senior management a clearer view of supply chain operations in a local/regional/global setting. The platform not only displays spatio-temporal connectivity patterns of entities in a supply chain; it also accommodates real-time risk-related data collection and risk monitoring. The proposed platform offers the flexibility to be customized based on the user's requirements - to process and store the supply chain data in the server, visualize the supply chain data, network map, risk alert, and other information needed for SCRM. Supply chain decision makers can deploy it on the desktop or embed it into the company's enterprise applications in a front office environment for better managing risks of their supply chains.


title: Know Abnormal, Find Evil: Frequent Pattern Mining for Ransomware Threat Hunting and Intelligence

abstract: Emergence of crypto-ransomware has significantly changed the cyber threat landscape. A crypto ransomware removes data custodian access by encrypting valuable data on victims' computers and requests a ransom payment to reinstantiate custodian access by decrypting data. Timely detection of ransomware very much depends on how quickly and accurately system logs can be mined to hunt abnormalities and stop the evil. In this paper we first setup an environment to collect activity logs of 517 Locky ransomware samples, 535 Cerber ransomware samples and 572 samples of TeslaCrypt ransomware. We utilize Sequential Pattern Mining to find Maximal Frequent Patterns (MFP) of activities within different ransomware families as candidate features for classification using J48, Random Forest, Bagging and MLP algorithms. We could achieve 99% accuracy in detecting ransomware instances from goodware samples and 96.5% accuracy in detecting family of a given ransomware sample. Our results indicate usefulness and practicality of applying pattern mining techniques in detection of good features for ransomware hunting. Moreover, we showed existence of distinctive frequent patterns within different ransomware families which can be used for identification of a ransomware sample family for building intelligence about threat actors and threat profile of a given target.


title: Rethinking main memory OLTP recovery

abstract: Fine-grained, record-oriented write-ahead logging, as exemplified by systems like ARIES, has been the gold standard for relational database recovery. In this paper, we show that in modern high-throughput transaction processing systems, this is no longer the optimal way to recover a database system. In particular, as transaction throughputs get higher, ARIES-style logging starts to represent a non-trivial fraction of the overall transaction execution time.


title: Path-Level Network Transformation for Efficient Architecture Search

abstract: We introduce a new function-preserving transformation for efficient neural architecture search. This network transformation allows reusing previously trained networks and existing successful architectures that improves sample efficiency. We aim to address the limitation of current network transformation operations that can only perform layer-level architecture modifications, such as adding (pruning) filters or inserting (removing) a layer, which fails to change the topology of connection paths. Our proposed path-level transformation operations enable the meta-controller to modify the path topology of the given network while keeping the merits of reusing weights, and thus allow efficiently designing effective structures with complex path topologies like Inception models. We further propose a bidirectional tree-structured reinforcement learning meta-controller to explore a simple yet highly expressive tree-structured architecture space that can be viewed as a generalization of multi-branch architectures. We experimented on the image classification datasets with limited computational resources (about 200 GPU-hours), where we observed improved parameter efficiency and better test results (97.70% test accuracy on CIFAR-10 with 14.3M parameters and 74.6% top-1 accuracy on ImageNet in the mobile setting), demonstrating the effectiveness and transferability of our designed architectures.


title: Discrete Chirp-Fourier Transform and Its Application to Chirp Rate Estimation

abstract: The discrete Fourier transform (DFT) has found tremendous applications in almost all fields, mainly because it can be used to match the multiple frequencies of a stationary signal with multiple harmonics. In many applications, wideband and nonstationary signals, however, often occur. One of the typical examples of such signals is chirp-type signals that are usually encountered in radar signal processing, such as synthetic aperture radar (SAR) and inverse SAR imaging. Due to the motion of a target, the radar return signals are usually chirps, and their chirp rates include the information about the target, such as the location and the velocity. In this paper, we study discrete chirp-Fourier transform (DCFT), which is analogous to the DFT. Besides the multiple frequency matching similar to the DFT, the DCFT can be used to match the multiple chirp rates in a chirp-type signal with multiple chirp components. We show that when the signal length N is prime, the magnitudes of all the sidelobes of the DCFT of a quadratic chirp signal are 1, whereas the magnitude of the mainlobe of the DCFT is /spl radic/N. With this result, an upper bound for the number of the detectable chirp components using the DCFT is provided in terms of signal length and signal and noise powers. We also show that the N-point DCFT performs optimally when N is a prime.


title: Robust Speech Recognition using DNN-HMM Acoustic Model Combining Noise-aware training with Spectral Subtraction

abstract: Recently, acoustic models based on deep neural notworks (DNNs) have been introduced and showed dramatic improvements over acoustic models based on GMM in a variety of tasks. In this paper, we considered the improvement of noise robustness of DNN. Inspired by Missing Feature Theory and static noise aware training, we proposed an approach that uses a noise-suppressed acoustic feature and estimated noise information as input of DNN. We used simple Spectral Subtraction as noise-suppression. As noise estimation, we used estimation per utterance or frame. In noisy speech recognition experiments, we compared the proposed method with other methods and the proposed method showed the superior performance than the other approaches. For noise estimation per utterance with log Mel Filterbank, we obtained 28.6% word error rate reduction compared with multi condition training, 5.9% reduction compared with noise adaptive training. Index Terms: speech recognition, deep neural network, noise robustness, adaptive training, noise aware training, Spectral Subtraction, Missing Feature Theory


title: How do we know the minds of others? Domain-specificity, simulation, and enactive social cognition

abstract: In what ways, and to what extent, is social cognition distinguished from cognition in general? And how do data from cognitive neuroscience speak to this question? I review recent findings that argue social cognition may indeed be specialized, and at multiple levels. One particularly interesting respect in which social cognition differs from the rest of cognition is in its close interaction with the social environment. We actively probe other people in order to make inferences about what is going on in their minds (e.g., by asking them questions, and directing our gaze onto them), and we use the minds of other people as a collective resource. Experiments from our own laboratory point to the amygdala as one structure that is critically involved in such processes.


title: Neural networks for computer-aided diagnosis in medicine

abstract: This survey makes an overview of the most recent applications on the neural networks for the computer-aided medical diagnosis (CAMD) over the past decade. CAMD can facilitate the automation of decision making, extraction and visualization of complex characteristics for clinical diagnosis purposes. Over the past decade, neural networks have attained considerable research interest and are widely employed to complex CAMD systems in diverse clinical application domains, such as detecting diseases, classification of diseases, testing the compatibility of new drugs, etc. Overall, this paper reviews the state-of-the-art of neural networks for CAMD. It helps the readers understand the topic of neural networks for CAMD by summarizing the findings addressed in recent academic papers as well as presenting a few open issues of developing the research on this topic.


title: MusicMood: Predicting the mood of music from song lyrics using machine learning

abstract: Sentiment prediction of contemporary music can have a wide-range of applications in modern society, for instance, selecting music for public institutions such as hospitals or restaurants to potentially improve the emotional well-being of personnel, patients, and customers, respectively. In this project, music recommendation system built upon on a naive Bayes classifier, trained to predict the sentiment of songs based on song lyrics alone. The experimental results show that music corresponding to a happy mood can be detected with high precision based on text features obtained from song lyrics.


title: Central Pattern Generators

abstract: duction to today’s discussion. I will start with definitions. Central pattern generators (CPG) are neuron ensembles whose members cooperate to generate a motor program. The term motor program identifies the timeand target-oriented output activity of the ensemble, which serves as a command to muscles. On receipt of the command, the muscle contracts and relaxes in a particular coordinated sequence, which constitutes the motor pattern. The word “pattern” is familiar to English speakers, but in Russian is unclear and dissonant... [Translator’s note: Sakharov is referring to the fact that the English word pattern has been Russified for the phrase central pattern generator rather than using the usual Russian word.] O. Yu. Orlov. “Template” could be used instead. D. A. Sakharov. Yes, thank you. A graphic example of such a “template” is provided by locomotor gaits: gallop/ canter, trot, amble, and step. Each motor template has its program and each program has its generating ensemble, a CPG. A CPG is often defined as a neural network which has become assimilated. The correct term is “neuron ensemble.” The concept “network” refers to a montage system, i.e., the synaptic organization. However, the means by which neurons are organized into a pattern-generating set is not entirely clear, and we will discuss this. It is convenient to work with motor rhythms, though behavioral templates do not have to be cyclic. The function of central generators should not be reduced to rhythm generators, as is sometimes done. Interest in this subject is increased every time a smoothly progressing study comes up against revolutionary events. Thus, attempts to show that behavioral templates persist after complete deafferentation of a motor center were longlasting but unsuccessful. It was only in 1960 that a successful experiment on crabs was reported [53], this being followed by one in locusts [84]. These two studies overcame Neuroscience and Behavioral Physiology, Vol. 45, No. 1, January, 2015


title: Software defined air interface: a framework of 5G air interface

abstract: The anticipated 1000 fold traffic growth in 2020 and the explosion of new services and applications including the massive Internet of Things, tactile internet, etc. pose tough challenges for the next generation mobile network. A soft end-toend solution from the core network to the radio access network, as well as the air interface is envisioned as the key to meet the extremely stringent demands. This paper presents our perspective of the 5G air interface and raise the concept of software defined air interface (SDAI) as a framework of 5G air interface. Instead of a global optimized air interface for the previous communication generations, SDAI is envisioned to bring agility and heighten the efficiency into the design and implementation of the building blocks of the air interface. The fundamental building blocks and the related parameters including spectrum, bandwidth, multiple access schemes, waveform, and so on, are all tunable such that the air interface can be optimized to each individual application scenario. To clarify the adaptation mechanism of SDAI, we provide a case study on the link adaptation between the sparse code multiple access (SCMA) scheme and the orthogonal frequency division multiplexing access (OFDMA) from energy efficiency and spectral efficiency co-design perspective.


title: Design of a wheel-propeller-leg integrated amphibious robot

abstract: The operation capabilities of robot in the amphibious environments (such as shallow water fields, surf zones, and beaches) are critical for military and civilian. In this paper, we introduce a novel amphibious robot with wheel-propeller-leg integrated driving devices, developed by Shenyang Institute of Automation, which can realize both crawling locomotion on the ground and swimming locomotion in the water without changing its driving devices. This paper describes the design of the overall robot structure, the design of the novel driving devices, and the design of the embedded control system, respectively. All the driving devices of the robot are driven by independent motor, thus the amphibious robot can conveniently switch its locomotion modes according to the operational environments. The embedded control system is a distributed control system based on CAN bus, which makes it is easy to expend sensors and devices for the robot in the future. Finally, the hydrodynamic performances of the wheel-propeller device are analyzed by using CFX hydrodynamic calculation software, and some primary experiments have been done for verifying the fundamental locomotion functions of the robot.


title: Efficient multi-instance learning for activity recognition from time series data using an auto-regressive hidden Markov model

abstract: Activity recognition from sensor data has spurred a great deal of interest due to its impact on health care. Prior work on activity recognition from multivariate time series data has mainly applied supervised learning techniques which require a high degree of annotation effort to produce training data with the start and end times of each activity. In order to reduce the annotation effort, we present a weakly supervised approach based on multi-instance learning. We introduce a generative graphical model for multi-instance learning on time series data based on an auto-regressive hidden Markov model. Our model has a number of advantages, including the ability to produce both bag and instance-level predictions as well as an efficient exact inference algorithm based on dynamic programming.


title: Unsupervised representation learning of structured radio communication signals

abstract: We explore unsupervised representation learning of radio communication signals in raw sampled time series representation. We demonstrate that we can learn modulation basis functions using convolutional autoencoders and visually recognize their relationship to the analytic bases used in digital communications. We also propose and evaluate quantitative met- rics for quality of encoding using domain relevant performance metrics.


title: Analysis and Enhancement of Wikification for Microblogs with Context Expansion

abstract: Disambiguation to Wikipedia (D2W) is the task of linking mentions of concepts in text to their corresponding Wikipedia entries. Most previous work has focused on linking terms in formal texts (e.g. newswire) to Wikipedia. Linking terms in short informal texts (e.g. tweets) is difficult for systems and humans alike as they lack a rich disambiguation context. We first evaluate an existing Twitter dataset as well as the D2W task in general. We then test the effects of two tweet context expansion methods, based on tweet authorship and topic-based clustering, on a state-of-the-art D2W system and evaluate the results.


title: DNA damage response--a double-edged sword in cancer prevention and cancer therapy.

abstract: Genomic stability depends on an efficient DNA damage repair system to keep the chromosomes intact. Unrepaired DNA damage not only causes cell cycle arrest, apoptosis, but also accumulates genome mutations. DNA damage response (DDR) exhibits a critical function on the protection against human cancer, as indicated by the high predisposition to cancer of individuals with germ-line mutations in DDR genes. However, a defective DNA repair is liked intimately with the unchecked proliferation and the intrinsic resistance to clinical DNA-damaging agents. Therefore, abrogation of specific proteins in DNA damage repair pathways is a promising strategy for developing targeted cancer treatments. It may sound paradoxical to inhibit DDR pathway for sensitization of clinical therapy because cancer promotion and malignant transformation are aided by deficient DNA repair pathways. Actually, DDR acts as a positive guardian of genomic stability to prevent from tumorigenesis. On the other hand, DDR also performs as a negative saboteur to resist chemo- and radiotherapy. In this regard, DDR functions as "a double-edged sword" in cancer prevention and cancer therapy. The defective DDR that makes cancer cells of high mutability should alternatively provide therapeutic opportunities that confer the lethality to cancer cells without harming normal cells.


title: Structured Image Segmentation using Kernelized Features

abstract: Most state-of-the-art approaches to image segmentation formulate the problem using Conditional Random Fields. These models typically include a unary term and a pairwise term, whose parameters must be carefully chosen for optimal performance. Recently, structured learning approaches such as Structured SVMs (SSVM) have made it possible to jointly learn these model parameters. However, they have been limited to linear kernels, since more powerful non-linear kernels cause the learning to become prohibitively expensive. In this paper, we introduce an approach to "kernelize" the features so that a linear SSVM framework can leverage the power of non-linear kernels without incurring the high computational cost. We demonstrate the advantages of this approach in a series of image segmentation experiments on the MSRC data set as well as 2D and 3D datasets containing imagery of neural tissue acquired with electron microscopes.


title: Geometric Consistency for Self-Supervised End-to-End Visual Odometry

abstract: With the success of deep learning based approaches in tackling challenging problems in computer vision, a wide range of deep architectures have recently been proposed for the task of visual odometry (VO) estimation. Most of these proposed solutions rely on supervision, which requires the acquisition of precise ground-truth camera pose information, collected using expensive motion capture systems or high-precision IMU/GPS sensor rigs. In this work, we propose an unsupervised paradigm for deep visual odometry learning. We show that using a noisy teacher, which could be a standard VO pipeline, and by designing a loss term that enforces geometric consistency of the trajectory, we can train accurate deep models for VO that do not require ground-truth labels. We leverage geometry as a self-supervisory signal and propose"Composite Transformation Constraints (CTCs)", that automatically generate supervisory signals for training and enforce geometric consistency in the VO estimate. We also present a method of characterizing the uncertainty in VO estimates thus obtained. To evaluate our VO pipeline, we present exhaustive ablation studies that demonstrate the efficacy of end-to-end, self-supervised methodologies to train deep models for monocular VO. We show that leveraging concepts from geometry and incorporating them into the training of a recurrent neural network results in performance competitive to supervised deep VO methods.


title: Markov Chain Sampling Methods for Dirichlet Process Mixture Models

abstract: Abstract This article reviews Markov chain methods for sampling from the posterior distribution of a Dirichlet process mixture model and presents two new classes of methods. One new approach is to make Metropolis—Hastings updates of the indicators specifying which mixture component is associated with each observation, perhaps supplemented with a partial form of Gibbs sampling. The other new approach extends Gibbs sampling for these indicators by using a set of auxiliary parameters. These methods are simple to implement and are more efficient than previous ways of handling general Dirichlet process mixture models with non-conjugate priors.


Research on an Open-Source Software Platform for Autonomous Driving Systems


A Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators


title: An Introduction to Variational Methods for Graphical Models

abstract: This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields). We present a number of examples of graphical models, including the QMR-DT database, the sigmoid belief network, the Boltzmann machine, and several variants of hidden Markov models, in which it is infeasible to run exact inference algorithms. We then introduce variational methods, which exploit laws of large numbers to transform the original graphical model into a simplified graphical model in which inference is efficient. Inference in the simpified model provides bounds on probabilities of interest in the original model. We describe a general framework for generating variational transformations based on convex duality. Finally we return to the examples and demonstrate how variational algorithms can be formulated in each case.


title: CogNet: a cognitive complete knowledge network system

abstract: The benefits of using cognitive information at the physical layer, as in cognitive radios, are many. In this article, we propose CogNet -- a cognitive complete knowledge network system -- which makes use of a large amount of information that can be gained from the experience of each node to improve the overall network and user performance. CogNet gathers, processes, analyzes, and stores information available through a variety of devices and protocols to build an omnipresent, distributed repository that holds the spatiotemporal, network-experience information. The inexpensiveness and plentifulness of storage resources and increasing processing power in handheld devices help accelerate the development of CogNet-like systems. Our contribution in this article is the proposal of the architecture and of the communication elements, as well as a transport layer application of CogNet as a proof of concept for possible application scenarios of such a system. We also present performance evaluation of CogTCP, the CogNetenabled TCP, which exploits the transport layer experience of other nodes for improved performance. From our experiments, we found that the use of cognitive information is very useful for networking.


title: BinaryConnect: Training Deep Neural Networks with binary weights during propagations

abstract: Deep Neural Networks (DNN) have achieved state-of-the-art results in a wide range of tasks, with the best results obtained with large training sets and large models. In the past, GPUs enabled these breakthroughs because of their greater computational speed. In the future, faster computation at both training and test time is likely to be crucial for further progress and for consumer applications on low-power devices. As a result, there is much interest in research and development of dedicated hardware for Deep Learning (DL). Binary weights, i.e., weights which are constrained to only two possible values (e.g. -1 or 1), would bring great benefits to specialized DL hardware by replacing many multiply-accumulate operations by simple accumulations, as multipliers are the most space and power-hungry components of the digital implementation of neural networks. We introduce BinaryConnect, a method which consists in training a DNN with binary weights during the forward and backward propagations, while retaining precision of the stored weights in which gradients are accumulated. Like other dropout schemes, we show that BinaryConnect acts as regularizer and we obtain near state-of-the-art results with BinaryConnect on the permutation-invariant MNIST, CIFAR-10 and SVHN.


title: The TOPHITS Model for Higher-Order Web Link Analysis∗

abstract: As the size of the web increases, it becomes more and more important to analyze link structure while also considering context. Multilinear algebra provides a novel tool for incorporating anchor text and other information into the authority computation used by link analysis methods such as HITS. Our recently proposed TOPHITS method uses a higher-order analogue of the matrix singular value decomposition called the PARAFAC model to analyze a three-way representation of web data. We compute hubs and authorities together with the terms that are used in the anchor text of the links between them. Adding a third dimension to the data greatly extends the applicability of HITS because the TOPHITS analysis can be performed in advance and offline. Like HITS, the TOPHITS model reveals latent groupings of pages, but TOPHITS also includes latent term information. In this paper, we describe a faster mathematical algorithm for computing the TOPHITS model on sparse data, and Web data is used to compare HITS and TOPHITS. We also discuss how the TOPHITS model can be used in queries, such as computing context-sensitive authorities and hubs. We describe different query response methodologies and present experimental results.


title: A New MRI Grading System for Cervical Foraminal Stenosis Based on Axial T2-Weighted Images

abstract: OBJECTIVE ::: The purpose of this study was to evaluate the reliability of a new magnetic resonance imaging (MRI) grading system for cervical neural foraminal stenosis (NFS). ::: ::: ::: MATERIALS AND METHODS ::: Cervical NFS at bilateral C4/5, C5/6, and C6/7 was classified into the following three grades based on the T2-weighted axial images: Grade 0 = absence of NFS, with the narrowest width of the neural foramen greater than the width of the extraforaminal nerve root (EFNR); Grade 1 = the narrowest width of the neural foramen the same or less than (but more than 50% of) the width of the EFNR; Grade 2 = the width of the neural foramen the same or less than 50% of the width of the EFNR. The MRIs of 96 patients who were over 60 years old (M:F = 50:46; mean age 68.4 years; range 61-86 years) were independently analyzed by seven radiologists. Interobserver and intraobserver agreements were analyzed using the percentage agreement, kappa statistics, and intraclass correlation coefficient (ICC). ::: ::: ::: RESULTS ::: For the distinction among the three individual grades at all six neural foramina, the ICC ranged from 0.68 to 0.73, indicating fair to good reproducibility. The percentage agreement ranged from 60.2% to 70.6%, and the kappa values (κ = 0.50-0.58) indicated fair to moderate agreement. The percentages of intraobserver agreement ranged from 85.4% to 93.8% (κ = 0.80-0.92), indicating near perfect agreement. ::: ::: ::: CONCLUSION ::: The new MRI grading system shows sufficient interobserver and intraobserver agreement to reliably assess cervical NFS.


Kratos: Discovering Inconsistent Security Policy Enforcement in the Android Framework


title: Theory of Rumour Spreading in Complex Social Networks

abstract: We introduce a general stochastic model for the spread of rumours, and derive mean-field equations that describe the dynamics of the model on complex social networks (in particular those mediated by the Internet). We use analytical and numerical solutions of these equations to examine the threshold behavior and dynamics of the model on several models of such networks: random graphs, uncorrelated scale-free networks and scale-free networks with assortative degree correlations. We show that in both homogeneous networks and random graphs the model exhibits a critical threshold in the rumour spreading rate below which a rumour cannot propagate in the system. In the case of scale-free networks, on the other hand, this threshold becomes vanishingly small in the limit of infinite system size. We find that the initial rate at which a rumour spreads is much higher in scale-free networks than in random graphs, and that the rate at which the spreading proceeds on scale-free networks is further increased when assortative degree correlations are introduced. The impact of degree correlations on the final fraction of nodes that ever hears a rumour, however, depends on the interplay between network topology and the rumour spreading rate. Our results show that scale-free social networks are prone to the spreading of rumours, just as they are to the spreading of infections. They are relevant to the spreading dynamics of chain emails, viral advertising and large-scale information dissemination algorithms on the Internet.


title: The Application-View Model of the International Standard IEC 61850

abstract: The introduction of IEC 61850 as the de facto SA standard for the communication between devices in a substation and the related system requirements has made it possible and justifiable to integrate station Intelligent Electronic Devices (IEDs) on a high-speed peer-to-peer communication network (Ethernet) through standardization. IEC 61850 presents a set of standard object-oriented (OO) data models to describe the processes to be implemented and controlled in a substation and a set of service models for the interactions between devices in a substation and the transfer of all sorts of IED data. IEC 61850 proposes the concept of standardizing IED data using data objects referred to as logical nodes. This makes it possible to achieve the ldquoplug and playrdquo capability so that information and commands can be shared on a single network independent from any implementation. In this paper, the authors describe the standard's application-view model and present the use of OO methodology and techniques for the implementation of the Logical Node (LN) application-view data model of the standard. The target audience for this paper includes power system protection and automation engineers and technicians as well as research personnel who have at least a basic understanding of the IEC 61850 international standard and other technology mechanisms addressed in this paper.


title: Rotor Eddy-Current Losses Reduction in an Axial Flux Permanent-Magnet Machine

abstract: A novel rotor structure for a tooth-coil-winding (concentrated winding) open-slot axial-flux permanent-magnet (PM) machine that reduces the eddy currents and increases the main flux linkage is proposed in this paper. A composite-body-based rotor assembly and a steel-laminated layer inserted on top of the magnets are used to reduce eddy-current losses in the PMs. A prototype of an axial flux generator for a heavy-duty vehicle following this structure is optimized, analyzed and measured. The tooth coil windings produce high-amplitude harmonics in the air gap and the large slot openings reduce the rotor flux. Consequently, a rotor structure that decreases the amount of harmonics travelling through the magnets is required. Steel laminations covering the magnets have positive effects on both phenomena. Two-dimensional and three-dimensional finite-element analysis is used to verify and optimize the geometry of the laminations. Computations are verified by experimental measurements of a 100 kW test machine prototype.


title: Issues in data fusion for healthcare monitoring

abstract: Pervasive healthcare monitoring using body sensors and wireless sensor networks is a rapidly growing area in healthcare monitoring applications. Several issues arise in these systems, such as complex distributed data processing, data fusion, unreliable data communication, and uncertainty of data analysis in order to successfully monitor patients in real time. In this paper, we introduce some of the important issues in healthcare monitoring with focus on software problems such as reliability, network robustness, and context awareness. We describe related works in data filtering, data fusion, and data analysis then we suggest new architecture for handling data cleaning, data fusion, and context and knowledge generation using multi-tiered communication and a triadic hierarchical class analysis approach. The proposed architecture is called "Pervasive Healthcare Architecture" and we discuss how it can be applied to a particular monitoring scenario.


title: Author self-citations in the field of ecology

abstract: Ecologists writing research articles frequently cite their own papers. Self-citations are frequent in science, but the reasons behind abnormally high rates of self-citations are questionable. My goals were to assess the prevalence of author self-citations and to identify the combination of attributes that best predict high levels of self-citations in ecology articles. I searched 643 articles from 9 different ecology journals of various impact factors for synchronous (i.e., within reference lists) and diachronous (i.e., following publication) self-citations, using the Web of Science online database. I assessed the effect of the number of authors, pages, and references/citations, the proportion of diachronous/synchronous self-citations, and the impact factor, on the proportion of synchronous and diachronous self-citations separately. I compared various candidate models made of these covariates using Akaike's Information Criterion. On average, ecologists made 6.0 synchronous self-citations (12.8% of references), and 2.5 diachronous self-citations (25.5% of citations received 2.8 to 4.5 years after publication) per article. The best predictor of the proportion of synchronous self-citations was the number of authors. My study is the first to report recidivism in the inclusion of self-citations by researchers, i.e., the proportion of diachronous self-citations was best explained by the proportion of synchronous self-citations. The proportion of self-citations also increased with the number of pages and the impact factor of ecology journals, and decreased with the number of references/citations. Although a lot of variance remained unexplained, my study successfully showed regularities in the propensity of ecologists to include self-citations in their research articles.


title: Bayesian optimization for predicting rare internal failures in manufacturing processes

abstract: Modern manufacturing processes are highly instrumented to capture data at every step along the assembly lines. Such fine-grained data can help manufacturers perform quality control by predicting which manufactured products are at risk of being defective. A predictive analytics system that identifies internal failures in products before they are shipped can result in significant savings for the manufacturer and result in better customer satisfaction. However, predicting internal failures in manufactured products is a challenging machine learning task due to two reasons: (i) the rarity of such failures in modern manufacturing processes, and (ii) the failure of traditional machine learning algorithms to optimize non-convex metrics such as Matthew's Correlation Coefficient (MCC) used to measure performance on imbalanced datasets. In this paper, we present “ImbalancedBayesOpt”, a meta-optimization algorithm that directly maximizes MCC by learning optimal weights for the positive and negative classes. We use Gradient Boosting Machine (GBM) as the base classifier for our meta-optimization algorithm due to its competitive performance on machine learning prediction tasks. Our quantitative evaluation suggests that “ImbalancedBayesOpt” can significantly improve the classification performance of base classifiers on severely imbalanced high-dimensional datasets.


title: An Alternative to NCD for Large Sequences, Lempel-Ziv Jaccard Distance

abstract: The Normalized Compression Distance (NCD) has been used in a number of domains to compare objects with varying feature types. This flexibility comes from the use of general purpose compression algorithms as the means of computing distances between byte sequences. Such flexibility makes NCD particularly attractive for cases where the right features to use are not obvious, such as malware classification. However, NCD can be computationally demanding, thereby restricting the scale at which it can be applied. We introduce an alternative metric also inspired by compression, the Lempel-Ziv Jaccard Distance (LZJD). We show that this new distance has desirable theoretical properties, as well as comparable or superior performance for malware classification, while being easy to implement and orders of magnitude faster in practice.


title: Promoting Self-regulated Learning of Brazilian Preservice Student Teachers: Results of an Intervention Program

abstract: Self-regulation is the process by which individuals monitor, control and reflect on their learning. Self-regulated students have motivational, metacognitive, affective and behavioral characteristics that enhance their learning. As the importance of self-regulated learning is well acknowledged by research nowadays, the aim of the present study is to examine the effectiveness of an innovative course designed to promote self-regulated learning among Brazilian preservice student teachers. The innovative approach was developed in the format of a program of intervention based heavily on self-reflection. The content involved student exposure to self-reflexive activities, lectures on the self-regulated learning framework, and theoretical tasks aimed at fostering self-regulation of students in a double perspective: as a student and as a future teacher. The efficacy of the approach was tested by comparison with both the results of students who had taken a course with theoretical content only and those who had not taken any course at all. The sample consisted of 109 students in four different freshman classes in a Teacher Education Program in a Brazilian public university in an inner city in the state of Sao Paulo. The research was conducted using a quasi-experimental design with three stages: pretest, intervention and post-test. The classes were randomly assigned to experimental and control conditions as follows: an experimental group involving intervention, an experimental group exposed to theory and two control groups not taking the course. Before and after the intervention program all the participants responded to the Learning and Study Strategies Inventory and the Self-efficacy for Self-regulated Learning scales. Overall, the results showed that the intervention program format had a positive impact in enhancing student self-regulation. Moreover, students in both the experimental groups reported both higher gains in self-efficacy for self-regulated learning scores and an increase in employment of learning strategies when compared to the control groups.


title: Unsupervised Transductive Domain Adaptation

abstract: Supervised learning with large scale labeled datasets and deep layered models has made a paradigm shift in diverse areas in learning and recognition. However, this approach still suffers generalization issues under the presence of a domain shift between the training and the test data distribution. In this regard, unsupervised domain adaptation algorithms have been proposed to directly address the domain shift problem. In this paper, we approach the problem from a transductive perspective. We incorporate the domain shift and the transductive target inference into our framework by jointly solving for an asymmetric similarity metric and the optimal transductive target label assignment. We also show that our model can easily be extended for deep feature learning in order to learn features which are discriminative in the target domain. Our experiments show that the proposed method significantly outperforms state-of-the-art algorithms in both object recognition and digit classification experiments by a large margin.


title: Analysis and Design of a New AC–DC Single-Stage Full-Bridge PWM Converter With Two Controllers

abstract: Single-phase power factor correction (PFC) ac-dc converters are widely used in the industry for ac-dc power conversion from single phase ac-mains to an isolated output dc voltage. Typically, for high-power applications, such converters use an ac-dc boost input converter followed by a dc-dc full-bridge converter. A new ac-dc single-stage high-power universal PFC ac input full-bridge, pulse-width modulated converter is proposed in this paper. The converter can operate with an excellent input power factor, continuous input and output currents, and a non-excessive intermediate dc bus voltage and has reduced number of semiconductor devices thus presenting a cost-effective novel solution for such applications. In this paper, the operation of the proposed converter is explained, a steady-state analysis of its operation is performed, and the results of the analysis are used to develop a procedure for its design. The operation of the proposed converter is confirmed with results obtained from an experimental prototype.


title: Cloud Robotics: A Framework Towards Cloud-enabled Multi-robotics Survivability

abstract: The emergence of cloud computing has transformed the potential of robotics by enabling multi-robotic teams to fulfil complex tasks in the cloud. This paradigm is known as "cloud robotics" and relieves robots from hardware and software limitations as large amounts of available resources and parallel computing capabilities are available in the cloud. A major challenge however, currently faced in cloud robotics is the inherent problem of cloud disconnection which would result in a robot not being able to fulfil certain tasks. This work serves to assist with the challenge of disconnection in cloud robotics by proposing a survivable cloud multi-robotics (SCMR) framework for heterogeneous environments. The SCMR framework leverages the combination of a virtual ad hoc network formed by the robot-to-robot communication and a physical cloud infrastructure formed by the robot-to-cloud communications. The Quality of Service (QoS) on the SCMR framework is tested and validated by determining the optimal energy utilization and Time of Response (ToR) on drivability analysis with and without cloud connection. The experimental results demonstrate that the proposed framework is feasible for current multi-robotic applications and shows the survivability aspect of the framework in instances of cloud disconnection.


title: Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO Collaborative Project on Early Detection of Persons with Harmful Alcohol Consumption-II

abstract: The Alcohol Use Disorders Identification Test (AUDIT) has been developed from a six-country WHO collaborative project as a screening instrument for hazardous and harmful alcohol consumption. It is a 10-item questionnaire which covers the domains of alcohol consumption, drinking behaviour, and alcoholrelated problems. Questions were selected from a 150-item assessment schedule (which was administered to 1888 persons attending representative primary health care facilities) on the basis of their representativeness for these conceptual domains and their perceived usefulness for intervention. Responses to each question are scored from 0 to 4, giving a maximum possible score of 40. Among those diagnosed as having hazardous or harmful alcohol use, 92% had an AUDIT score of 8 or more, and 94% of those with non-hazardous consumption had a score of less than 8. A UDIT provides a simple method of early detection of hazardous and harmful alcohol use in primary health care settings and is the first instrument of its type to be derived on the basis of a cross-national study.


Neuropathies : Essential oils show promising results in the fight against symptoms


title: Trapping of highly charged ions with an electrostatic ion trap

abstract: In the current contribution, we present our first results with an electrostatic ion trap which has been constructed at LKB (Laboratoire Kastler Brossel) in collaboration with the Weizmann institute. The electrostatic ion trap is composed of two coaxial electrostatic mirrors, working for charged particles in the same way as a Fabry-Perot interferometer works for light in the optical regime. The trap has been attached to the beamline of SIMPA (Source d'ions Multichargs de Paris) an Electron Cyclotron Resonance Ion Source (ECRIS) at LKB (Laboratoire Kastler Brossel) and INSP (Institut des NanoSciences de Paris). The trap is foreseen to be applied for various studies on highly charged ions; as measurements of the life times of metastable states, mass spectroscopy etc. First results for trapping highly-charged ions are presented and simulations in order to explain the observed behavior are discussed.


title: Connectionist reinforcement learning for intelligent unit micro management in StarCraft

abstract: Real Time Strategy Games are one of the most popular game schemes in PC markets and offer a dynamic environment that involves several interacting agents. The core strategies that need to be developed in these games are unit micro management, building order, resource management, and the game main tactic. Unfortunately, current games only use scripted and fixed behaviors for their artificial intelligence (AI), and the player can easily learn the counter measures to defeat the AI. In this paper, we describe a system based on neural networks that controls a set of units of the same type in the popular game StarCraft. Using the neural networks, the units will either choose a unit to attack or evade from the battlefield. The system uses reinforcement learning combined with neural networks using online Sarsa and neural-fitted Sarsa, both with a short term memory reward function. We also present an incremental learning method for training the units for larger scenarios involving more units using trained neural networks on smaller scenarios. Additionally, we developed a novel sensing system to feed the environment data to the neural networks using separate vision grids. The simulation results show superior performance against the human-made AI scripts in StarCraft.


title: Parametric drive of a toroidal MEMS rate integrating gyroscope demonstrating < 20 PPM scale factor stability

abstract: In this paper, we report parametric drive of a MEMS rate integrating gyroscope for reduction of drifts induced by drive electronics. A Toroidal Ring Gyroscope (TRG) with 100k Q-factor at 1760 µm diameter was fabricated in epitaxial silicon encapsulation (EpiSeal) process. Device was operated in rate integrating mode using a FPGA based control system. In contrast to the conventional amplitude control architecture, the central star electrode of the gyroscope, connected to a single drive channel was utilized for amplitude control of both modes. Due to the parametric pumping effect, energy added to each (x and y) mode is proportional to the existing amplitude of the respective mode. As a result, errors associated with finding the orientation of the standing wave and x-y drive gain drift are bypassed. Compared to conventional x-y drive architecture, as high as 14× improvement in scale factor stability was observed with parametric pumping, resulting in better than 20 ppm scale factor stability without any compensation or temperature stabilization.


title: Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces.

abstract: We propose a novel and fast multiscale feature detection and description approach that exploits the benefits of nonlinear scale spaces. Previous attempts to detect and describe features in nonlinear scale spaces such as KAZE [1] and BFSIFT [6] are highly time consuming due to the computational burden of creating the nonlinear scale space. In this paper we propose to use recent numerical schemes called Fast Explicit Diffusion (FED) [3, 4] embedded in a pyramidal framework to dramatically speed-up feature detection in nonlinear scale spaces. In addition, we introduce a Modified-Local Difference Binary (M-LDB) descriptor that is highly efficient, exploits gradient information from the nonlinear scale space, is scale and rotation invariant and has low storage requirements. Our features are called Accelerated-KAZE (A-KAZE) due to the dramatic speed-up introduced by FED schemes embedded in a pyramidal framework.


An Insight Extraction System on BioMedical Literature with Deep Neural Networks


title: Accelerating Neural Transformer via an Average Attention Network

abstract: With parallelizable attention networks, the neural Transformer is very fast to train. However, due to the auto-regressive architecture and self-attention in the decoder, the decoding procedure becomes slow. To alleviate this issue, we propose an average attention network as an alternative to the self-attention network in the decoder of the neural Transformer. The average attention network consists of two layers, with an average layer that models dependencies on previous positions and a gating layer that is stacked over the average layer to enhance the expressiveness of the proposed attention network. We apply this network on the decoder part of the neural Transformer to replace the original target-side self-attention model. With masking tricks and dynamic programming, our model enables the neural Transformer to decode sentences over four times faster than its original version with almost no loss in training time and translation performance. We conduct a series of experiments on WMT17 translation tasks, where on 6 different language pairs, we obtain robust and consistent speed-ups in decoding.


title: Knowledge-based Question Answering by Jointly Generating, Copying and Paraphrasing

abstract: With the development of large-scale knowledge bases, people are building systems which give simple answers to questions based on consolidate facts. In this paper, we focus on simple questions, which ask about only a subject and relation in the knowledge base. Observing that certain parts of a question usually overlap with names of its corresponding subject and relation in the knowledge base, we argue that a question is formed by a mixture of copying and generation. To model that, we propose a sequence-to-sequence (seq2seq) architecture which encodes a candidate subject-relation pair and decodes it into the given question, where the decoding probability is used to select the best candidate. In our decoder, the copying mode points the subject or relation and duplicates its name, while the generating mode summarizes the meaning of the subject-relation pair and produces a word to smooth the question. Realizing that although sometimes a subject or relation is pointed, different names or keywords might be used, we also incorporate a paraphrasing mode to supplement the copying mode using an automatically mined lexicon. Extensive experiments on the largest dataset exhibit our better performance compared with the state-of-the-art methods.


title: Firefly Monte Carlo: Exact MCMC with Subsets of Data

abstract: Markov chain Monte Carlo (MCMC) is a popular tool for Bayesian inference. However, MCMC cannot be practically applied to large data sets because of the prohibitive cost of evaluating every likelihood term at every iteration. Here we present Firefly Monte Carlo (FlyMC) MCMC algorithm with auxiliary variables that only queries the likelihoods of a subset of the data at each iteration yet simulates from the exact posterior distribution. FlyMC is compatible with modern MCMC algorithms, and only requires a lower bound on the per-datum likelihood factors. In experiments, we find that FlyMC generates samples from the posterior more than an order of magnitude faster than regular MCMC, allowing MCMC methods to tackle larger datasets than were previously considered feasible.


Development of Autonomous Underwater Vehicles ; A brief overview


title: Efficacy and Safety of a Hyaluronic Acid Filler to Correct Aesthetically Detracting or Deficient Features of the Asian Nose: A Prospective, Open-Label, Long-Term Study

abstract: BACKGROUND ::: There is increasing interest among patients and plastic surgeons for alternatives to rhinoplasty, a common surgical procedure performed in Asia. ::: ::: ::: OBJECTIVES ::: To evaluate the safety, efficacy, and longevity of a hyaluronic acid filler in the correction of aesthetically detracting or deficient features of the Asian nose. ::: ::: ::: METHODS ::: Twenty-nine carefully screened Asian patients had their noses corrected with the study filler (Juvéderm VOLUMA [Allergan plc, Dublin, Ireland] with lidocaine injectable gel), reflecting individualized treatment goals and utilizing a standardized injection procedure, and were followed for over 12 months. ::: ::: ::: RESULTS ::: A clinically meaningful correction (≥1 grade improvement on the Assessment of Aesthetic Improvement Scale) was achieved in 27 (93.1%) patients at the first follow-up visit. This was maintained in 28 (96.6%) patients at the final visit, based on the independent assessments of a central non-injecting physician and the patients. At this final visit, 23 (79.3%) patients were satisfied or very satisfied with the study filler and 25 (86.2%) would recommend it to others. In this small series of patients, there were no serious adverse events (AEs), with all treatment-related AEs being mild to moderate, transient injection site reactions, unrelated to the study filler. ::: ::: ::: CONCLUSIONS ::: Using specific eligibility criteria, individualized treatment goals, and a standardized injection procedure, the study filler corrected aesthetically detracting or deficient features of the Asian nose, with the therapeutic effects lasting for over 12 months, consistent with a high degree of patient satisfaction. This study supports the safety and efficacy of this HA filler for specific nose augmentation procedures in selected Asian patients. LEVEL OF EVIDENCE 3: Therapeutic.


title: Experiments on the Indonesian plagiarism detection using latent semantic analysis

abstract: Plagiarism is an important task since its number is increasing and the plagiarism technique is getting difficult. It means that there is not only literal plagiarism but also intelligence plagiarism. In order to handle the intelligence plagiarism, we employed latent semantic analysis (LSA) as the term-document representation. The LSA was used in the Heuristic Retrieval (HR) component and Detailed Analysis (DA) component. We conducted several experiments to compare the token type, the text segmentation and the threshold value. The test data were prepared manually from the available Indonesian paper corpus. Experimental results showed that the LSA outperformed the VSM (Vector Space Model), especially in test cases with intelligence plagiarism.


title: Trust, but Verify! Better Entity Linking through Automatic Verification

abstract: We introduce automatic verification as a post-processing step for entity linking (EL). ::: The proposed method \emph{trusts} EL system results collectively, by assuming entity mentions are mostly linked correctly, in order to create a semantic profile of the given text using geospatial and temporal information, as well as fine-grained entity types. ::: This profile is then used to automatically \emph{verify} each linked mention individually, i.e., to predict whether it has been linked correctly or not. ::: Verification allows leveraging a rich set of global and pairwise features that would be prohibitively expensive for EL systems employing global inference. ::: Evaluation shows consistent improvements across datasets and systems. In particular, when applied to state-of-the-art systems, our method yields an absolute improvement in linking performance of up to 1.7\,$F1$ on AIDA/CoNLL'03 and up to 2.4\,$F1$ on the English TAC KBP 2015 TEDL dataset.


title: A SimPLR method for routability-driven placement

abstract: Highly-optimized placements may lead to irreparable routing congestion due to inadequate models of modern interconnect stacks and the impact of partial routing obstacles. Additional challenges in routability-driven placement include scalability to large netlists and limiting the complexity of software integration. Addressing these challenges, we develop lookahead routing to give the placer advance, firsthand knowledge of trouble spots, not distorted by crude congestion models. We also extend global placement to (i) spread cells apart in congested areas, and (ii) move cells together in less-congested areas to ensure short, routable interconnects and moderate runtime. While previous work adds isolated steps to global placement, our SIMultaneous PLace-and-Route tool SimPLR integrates a layer- and via-aware global router into a leading-edge, force-directed placer. The complexity of integration is mitigated by careful design of simple yet effective optimizations. On the ISPD 2011 Contest Benchmark Suite, with the official evaluation protocol, SimPLR outperforms every contestant on every benchmark.


Visualizing and Understanding Stochastic Depth Networks


title: Multiview Stereo via Volumetric Graph-Cuts and Occlusion Robust Photo-Consistency

abstract: This paper presents a volumetric formulation for the multiview stereo problem which is amenable to a computationally tractable global optimization using Graph-cuts. Our approach is to seek the optimal partitioning of 3D space into two regions labeled as "object" and "empty" under a cost functional consisting of the following two terms: 1) A term that forces the boundary between the two regions to pass through photo-consistent locations; and 2) a ballooning term that inflates the "object" region. To take account of the effect of occlusion on the first term, we use an occlusion robust photo-consistency metric based on normalized cross correlation, which does not assume any geometric knowledge about the reconstructed object. The globally optimal 3D partitioning can be obtained as the minimum cut solution of a weighted graph.


title: Learning a Part-Based Pedestrian Detector in a Virtual World

abstract: Detecting pedestrians with on-board vision systems is of paramount interest for assisting drivers to prevent vehicle-to-pedestrian accidents. The core of a pedestrian detector is its classification module, which aims at deciding if a given image window contains a pedestrian. Given the difficulty of this task, many classifiers have been proposed during the last 15 years. Among them, the so-called (deformable) part-based classifiers, including multiview modeling, are usually top ranked in accuracy. Training such classifiers is not trivial since a proper aspect clustering and spatial part alignment of the pedestrian training samples are crucial for obtaining an accurate classifier. In this paper, the authors first perform automatic aspect clustering and part alignment by using virtual-world pedestrians, i.e., human annotations are not required. Second, the authors use a mixture-of-parts approach that allows part sharing among different aspects. Third, these proposals are integrated in a learning framework, which also allows incorporating real-world training data to perform domain adaptation between virtual- and real-world cameras. Overall, the obtained results on four popular on-board data sets show that the proposal clearly outperforms the state-of-the-art deformable part-based detector known as latent support vector machine.


title: Learning Visual Servoing with Deep Features and Fitted Q-Iteration

abstract: Visual servoing involves choosing actions that move a robot in response to observations from a camera, in order to reach a goal configuration in the world. Standard visual servoing approaches typically rely on manually designed features and analytical dynamics models, which limits their generalization capability and often requires extensive application-specific feature and model engineering. In this work, we study how learned visual features, learned predictive dynamics models, and reinforcement learning can be combined to learn visual servoing mechanisms. We focus on target following, with the goal of designing algorithms that can learn a visual servo using low amounts of data of the target in question, to enable quick adaptation to new targets. Our approach is based on servoing the camera in the space of learned visual features, rather than image pixels or manually-designed keypoints. We demonstrate that standard deep features, in our case taken from a model trained for object classification, can be used together with a bilinear predictive model to learn an effective visual servo that is robust to visual variation, changes in viewing angle and appearance, and occlusions. A key component of our approach is to use a sample-efficient fitted Q-iteration algorithm to learn which features are best suited for the task at hand. We show that we can learn an effective visual servo on a complex synthetic car following benchmark using just 20 training trajectory samples for reinforcement learning. We demonstrate substantial improvement over a conventional approach based on image pixels or hand-designed keypoints, and we show an improvement in sample-efficiency of more than two orders of magnitude over standard model-free deep reinforcement learning algorithms. Videos are available at this http URL .


title: Relative anterior spinal overgrowth in adolescent idiopathic scoliosis—result of disproportionate endochondral-membranous bone growth?

abstract: We undertook a comparative study of magnetic resonance imaging (MRI) vertebral morphometry of thoracic vertebrae of girls with adolescent idiopathic thoracic scoliosis (AIS) and age and gender-matched normal subjects, in order to investigate abnormal differential growth of the anterior and posterior elements of the thoracic vertebrae in patients with scoliosis. Previous studies have suggested that disproportionate growth of the anterior and posterior columns may contribute to the development of AIS. Whole spine MRI was undertaken on 83 girls with AIS between the age of 12 and 14 years, and Cobb's angles of between 20 degrees and 90 degrees, and 22 age-matched controls. Multiple measurements of each thoracic vertebra were obtained from the best sagittal and axial MRI cuts. Compared with the controls, the scoliotic spines had longer vertebral bodies between T1 and T12 in the anterior column and shorter pedicles with a larger interpedicular distance in the posterior column. The differential growth between the anterior and the posterior elements of each thoracic vertebra in the patients with AIS was significantly different from that in the controls (p < 0.01). There was also a significant positive correlation between the scoliosis severity score and the ratio of differential growth between the anterior and posterior columns for each thoracic vertebra (p < 0.01). Compared with age-matched controls, the longitudinal growth of the vertebral bodies in patients with AIS is disproportionate and faster and mainly occurs by endochondral ossification. In contrast, the circumferential growth by membranous ossification is slower in both the vertebral bodies and pedicles.


title: What makes a convincing argument? Empirical analysis and detecting attributes of convincingness in Web argumentation

abstract: This article tackles a new challenging task in computational argumentation. Given a pair of two arguments to a certain controversial topic, we aim to directly assess qualitative properties of the arguments in order to explain why one argument is more convincing than the other one. We approach this task in a fully empirical manner by annotating 26k explanations written in natural language. These explanations describe convincingness of arguments in the given argument pair, such as their strengths or flaws. We create a new crowd-sourced corpus containing 9,111 argument pairs, multi-labeled with 17 classes, which was cleaned and curated by employing several strict quality measures. We propose two tasks on this data set, namely (1) predicting the full label distribution and (2) classifying types of flaws in less convincing arguments. Our experiments with feature-rich SVM learners and Bidirectional LSTM neural networks with convolution and attention mechanism reveal that such a novel fine-grained analysis of Web argument convincingness is a very challenging task. We release the new UKPConvArg2 corpus and software under permissive licenses to the research community.


title: Algebraic Clustering of Affine Subspaces

abstract: Subspace clustering is an important problem in machine learning with many applications in computer vision and pattern recognition. Prior work has studied this problem using algebraic, iterative, statistical, low-rank and sparse representation techniques. While these methods have been applied to both linear and affine subspaces, theoretical results have only been established in the case of linear subspaces. For example, algebraic subspace clustering (ASC) is guaranteed to provide the correct clustering when the data points are in general position and the union of subspaces is transversal. In this paper we study in a rigorous fashion the properties of ASC in the case of affine subspaces. Using notions from algebraic geometry, we prove that the homogenization trick, which embeds points in a union of affine subspaces into points in a union of linear subspaces, preserves the general position of the points and the transversality of the union of subspaces in the embedded space, thus establishing the correctness of ASC for affine subpaces.


title: Motivated Reinforcement Learning

abstract: The standard reinforcement learning view of the involvement of neuromodulatory systems in instrumental conditioning includes a rather straightforward conception of motivation as prediction of sum future reward. Competition between actions is based on the motivating characteristics of their consequent states in this sense. Substantial, careful, experiments reviewed in Dickinson & Balleine,12,13 into the neurobiology and psychology of motivation shows that this view is incomplete. In many cases, animals are faced with the choice not between many different actions at a given state, but rather whether a single response is worth executing at all. Evidence suggests that the motivational process underlying this choice has different psychological and neural properties from that underlying action choice. We describe and model these motivational systems, and consider the way they interact.


title: The Next-Generation Sequencing Revolution and Its Impact on Genomics

abstract: Genomics is a relatively new scientific discipline, having DNA sequencing as its core technology. As technology has improved the cost and scale of genome characterization over sequencing's 40-year history, the scope of inquiry has commensurately broadened. Massively parallel sequencing has proven revolutionary, shifting the paradigm of genomics to address biological questions at a genome-wide scale. Sequencing now empowers clinical diagnostics and other aspects of medical care, including disease risk, therapeutic identification, and prenatal testing. This Review explores the current state of genomics in the massively parallel sequencing era.


title: IoT based smart health care system using CNT electrodes (for continuous ECG monitoring)

abstract: As the world population is growing at a rapid pace, the demand for better health care facilities is increasing steadily. It has been observed that many health problems are unpredictable; especially heart diseases; hence it is important to monitor heart continuously. Earlier it was not always possible to monitor the patient continuously and for longer durations, but thanks to the latest developments in the domain of Information and Communications Technology (ICT), that facilitate the acquirement, transmission, interpretation and reception of various bio-signals, from permanent or mobile sites. The developments in the sphere of sensing technologies, wireless communication, embedded systems, nano-electronics, etc make it feasible to build intelligent systems to monitor bio-signals especially heart signals (ECG) of the patient; and further with the help of necessary protocols, the information can be promptly sent to the doctor, hospitals, etc. Thanks to the concept of Internet-of-Things (IoT), which provides necessary help in emergency situations. Nanomaterials are now being used in a big way because of their several advantages over the conventional materials. So, getting together the concept of IoT and nanomaterials will restructure the field of health-care, promising economic, technological and social benefits.


title: If, When, and How to Perform Lane Change Maneuvers on Highways

abstract: Advanced driver assistance systems or highly automated driving systems for lane change maneuvers are expected to enhance highway traffic safety, transport efficiency, and driver comfort. To extend the capability of current advanced driver assistance systems, and eventually progress to highly automated highway driving, the task of automatically determine if, when, and how to perform a lane change maneuver, is essential. This paper thereby presents a low-complexity lane change maneuver algorithm which determines whether a lane change maneuver is desirable, and if so, selects an appropriate inter-vehicle traffic gap and time instance to perform the maneuver, and calculates the corresponding longitudinal and lateral control trajectory. The ability of the proposed lane change maneuver algorithm to make appropriate maneuver decisions and generate smooth and safe lane change trajectories in various traffic situations is demonstrated by simulation and experimental results.


title: Broadband design of substrate integrated waveguide to stripline interconnect

abstract: A broadband design of substrate integrated waveguide (SIW) to stripline interconnects is presented for the first time. The transition shows a wideband performance and, contrary to microstrip and coplanar waveguide circuitry, interconnects two planar transmission-line media that are both capable of medium-level power handling capabilities. Over a bandwidth of 18 GHz to 28 GHz (43.4 percent), the single interconnect shows a worst-case return loss of 24 dB and maximum insertion losses of 0.44 dB. For a back-to-back connection, the return loss reduces to 19 dB and insertion loss increases to 0.87 dB. All dimensional parameters are specified, and the design is validated by two commercially available field-solver packages. Moreover, field plots are presented that highlight the step-by-step mode conversion from SIW to stripline.


title: Mindfulness-based stress reduction for healthy individuals: A meta-analysis

abstract: Abstract Background An increasing number of mindfulness-based stress reduction (MBSR) studies are being conducted with nonclinical populations, but very little is known about their effectiveness. Objective To evaluate the efficacy, mechanisms of actions, and moderators of MBSR for nonclinical populations. Data sources A systematic review of studies published in English journals in Medline, CINAHL or Alt HealthWatch from the first available date until September 19, 2014. Study selection Any quantitative study that used MBSR as an intervention, that was conducted with healthy adults, and that investigated stress or anxiety. Results A total of 29 studies ( n = 2668) were included. Effect-size estimates suggested that MBSR is moderately effective in pre–post analyses ( n = 26; Hedge's g = .55; 95% CI [.44, .66], p n = 18; Hedge's g = .53; 95% CI [.41, .64], p Conclusions MBSR is moderately effective in reducing stress, depression, anxiety and distress and in ameliorating the quality of life of healthy individuals; however, more research is warranted to identify the most effective elements of MBSR.


title: Performance Comparisons Between 7-nm FinFET and Conventional Bulk CMOS Standard Cell Libraries

abstract: FinFET devices have been proposed as a promising substitute for conventional bulk CMOS-based devices at the nanoscale due to their extraordinary properties such as improved channel controllability, a high on / off current ratio, reduced short-channel effects, and relative immunity to gate line-edge roughness. This brief builds standard cell libraries for the advanced 7-nm FinFET technology, supporting multiple threshold voltages and supply voltages. The circuit synthesis results of various combinational and sequential circuits based on the presented 7-nm FinFET standard cell libraries forecast 10× and 1000× energy reductions on average in a superthreshold regime and 16× and 3000× energy reductions on average in a near-threshold regime as compared with the results of the 14-nm and 45-nm bulk CMOS technology nodes, respectively.


title: An approach to modeling Internet of Things based smart built environments

abstract: Smart built environments enhanced with technology can improve the lives of individuals, groups, and the broader community. We describe an approach to modeling IOT-based smart built environments that uses a large-scale virtual environment where a building model is aligned with the physical space. This approach takes advantages of affordances and embodied cognition in a large physical space to model user interaction with built spaces. The built space contains ‘smart objects’ with embedded sensors/actuators/controllers (e.g., kitchen appliances). A ‘smart object’ has the corresponding virtual object in the virtual environment. A case study (FutureHAUS) demonstrates the proposed approach.


Depth Estimation from Single Image Using CNN-Residual Network


title: An OFDMA based multiple access protocol with QoS guarantee for next generation WLAN

abstract: To provide better QoS guarantee for the next generation WLAN, IEEE 802.11ax task group is founded in March 2014. As a promising technology to accommodate multiple nodes concurrent transmissions in dense deployment scenario, orthogonal frequency division multiple access (OFDMA) will be adopted in IEEE 802.11ax with great possibility. In this paper, an OFDMA based multiple access protocol with QoS guarantee is proposed for the next generation WLAN. Firstly, a redundant access mechanism is given to increase the access success probability of the video traffic where the video stations can concurrently send multiple RTS packets in multiple subchannels. Secondly, a priority based resource allocation scheme is presented to let AP allocate more resources to the video stations. Simulation results show that our protocol outperforms the existing OFDMA based multiple access for IEEE 802.11ax (OMAX) protocol in terms of delay and delay jitter of video traffic in dense deployment scenario.


title: Development of polyimide flexible tactile sensor skin

abstract: We present the development of a polyimide-based two-dimensional tactile sensing array realized using a novel inverted fabrication technique. Thermal silicon oxide or Pyrex® substrates are treated such that their surfaces are OH group terminated, allowing good adhesion between such substrates and a spun-on polyimide film during processing through what are suspected to be hydrogen bonds that can be selectively broken when release is desired. The release of the continuous polyimide film is rapidly accomplished by breaking these bonds. This process results in robust, low-cost and continuous polymer-film devices. The developed sensor skin contains an array of membrane-based tactile sensors (taxels). Micromachined thin-film metal strain gauges are positioned on the edges of polyimide membranes. The change in resistance from each strain gauge resulting from normal forces applied to tactile bumps on the top of the membranes is used to image force distribution. Response of an individual taxel is characterized. The effective gauge factor of the taxels is found to be approximately 1.3. Sensor array output is experimentally obtained. The demonstrated devices are robust enough for direct contact with humans, everyday objects and contaminants without undue care.


title: Enter the Matrix: A Virtual World Approach to Safely Interruptable Autonomous Systems

abstract: Robots and autonomous systems that operate around humans will likely always rely on kill switches that stop their execution and allow them to be remote-controlled for the safety of humans or to prevent damage to the system. It is theoretically possible for an autonomous system with sufficient sensor and effector capability and using reinforcement learning to learn that the kill switch deprives it of long-term reward and learn to act to disable the switch or otherwise prevent a human operator from using the switch. This is referred to as the big red button problem. We present a technique which prevents a reinforcement learning agent from learning to disable the big red button. Our technique interrupts the agent or robot by placing it in a virtual simulation where it continues to receive reward. We illustrate our technique in a simple grid world environment.


title: A Compact Dual-Polarized MIMO Antenna With Distinct Diversity Performance for UWB Applications

abstract: In this letter, a simple compact dual-polarized ultrawideband (UWB) multiple-input–multiple-output (MIMO) antenna is presented with the circularly polarized band for WiMAX (5.5 GHz) and WLAN (5, 5.2 GHz) applications. The antenna parameters such as reflection coefficient, diversity performance, gain, axial ratio, and isolation have been studied on a fabricated prototype of the proposed antenna. The 3 dB axial-ratio bandwidth (ARBW) of 34.38% along with isolation between two ports has been achieved by introducing a rectangular vertical arm (I-parameter) in the ground plane. To enhance the isolation and –10 dB impedance bandwidth, L-shaped radiators have been incorporated in the rectangular feeds. The –10 dB impedance bandwidth of the proposed antenna is 112.05% with isolation >17 dB. The envelope correlation coefficient is less than 0.01 (using S -parameter) and less than 0.15 (using far-field radiation pattern) in the entire operational bandwidth. The gain of the proposed antenna varies from 1.2 to 4.9 dBi. The performance of the proposed antenna makes it a suitable candidate for use in portable UWB applications as well as in wireless communication systems.


title: Implementation of WiFi-based single phase smart meter for Internet of Things (IoT)

abstract: With the advent of Internet and computational era, not only opportunity to send and receive data between humans, but also among the devices without human control over it. This is known as Internet of Things (IoTs) concept which can be applied for solving the growing issue of power/energy management. A solution is a cheap and easy to implement and manage energy monitoring system for our daily usage of electric power. In order to overcome the human errors, manual labor and cost reducing in energy consumption with more efficiency for the power management system, in this paper, we focus mainly on IoT's energy monitoring. The proposed design is to implement a very low cost wireless sensor network and protocol for smart energy and web application capable of automatically reading the unit and sending the data automatically for the power users to view their current energy meter reading. By using this system, the users will be aware of the electricity usage in his/her home to reduce the power wastage and cost of consumption. The system consists of a digital energy meter, ESP8266 WiFi module and web applications for management system. The ESP8266 WiFi module will be embedded into the meter and implement the TCP/IP protocol for the communications between the meter and web application. The experimental results show that the proposed system works very well with efficiency, and it is feasible to implement in practical applications for very low cost-build automatic energy meter reading.