Guide to Health Informatics

From the very earliest moments in the modern history of the computer, scientists have dreamed of creating an " electronic brain ". Of all the modern technological quests, this search to create artificially intelligent (AI) computer systems has been one of the most ambitious and, not surprisingly, controversial. It also seems that very early on, scientists and doctors alike were captivated by the potential such a technology might have in medicine (e.g. Ledley and Lusted, 1959). With intelligent computers able to store and process vast stores of knowledge, the hope was that they would become perfect 'doctors in a box', assisting or surpassing clinicians with tasks like diagnosis. With such motivations, a small but talented community of computer scientists and healthcare professionals set about shaping a research program for a new discipline called Artificial Intelligence in Medicine (AIM). These researchers had a bold vision of the way AIM would revolutionise medicine, and push forward the frontiers of technology. AI in medicine at that time was a largely US-based research community. Work originated out of a number of campuses, including MIT-Tufts, Pittsburgh, Stanford and Rutgers (e. The field attracted many of the best computer scientists and by any measure their output in the first decade of the field remains a remarkable achievement. In reviewing this new field in 1984, Clancey and Shortliffe provided the following definition: 'Medical artificial intelligence is primarily concerned with the construction of AI programs that perform diagnosis and make therapy recommendations. Unlike medical applications based on other programming methods, such as purely statistical and probabilistic methods, medical AI programs are based on symbolic models of disease entities and their relationship to patient factors and clinical manifestations.' Much has changed since then, and today the importance of diagnosis as a task requiring computer support in routine clinical situations receives much less emphasis (Durinck et al., 1994). The strict focus on the medical setting has now broadened across the healthcare spectrum, and instead of AIM systems, it is more typical to describe them as clinical decision support systems (CDSS). Intelligent systems today are thus found supporting medication prescribing, in clinical laboratories and educational settings, for clinical surveillance, or in data-rich areas like the intensive care setting. While there certainly have been ongoing challenges in developing such systems, they actually have proven their reliability and accuracy on repeated occasions (Shortliffe, 1987). Much of the difficulty experienced in introducing them has …


A sock puppet detection algorithm on virtual spaces

On virtual spaces, some individuals use multiple usernames or copycat/forge other users (usually called ‘‘sock puppet’’) to communicate with others. Those sock puppets are fake identities through which members of Internet community praise or create the illusion of support for the product or one’s work, pretending to be a different person. A fundamental problem is how to identify these sock puppets. In this paper, we propose a sock puppet detection algorithm which combines authorship-identification techniques and link analysis. Firstly, we propose an interesting social network model in which links between two IDs are built if they have similar attitudes to most topics that both of them participate in; then, the edges are pruned according a hypothesis test, which consider the impact of their writing styles; finally, the link-based community detection for pruned network is performed. Compared to traditional methods, our approach has three advantages: (1) it conforms to the practical meanings of sock puppet community; (2) it can be applied in online situation; (3) it increases the efficiency of link analysis. In the experimental work, we evaluate our method using real datasets and compared our approach with several previous methods; the results have proved above advantages. 2012 Elsevier B.V. All rights reserved.


EdgeChain: An Edge-IoT Framework and Prototype Based on Blockchain and Smart Contracts

The emerging Internet of Things (IoT) is facing significant scalability and security challenges. On the one hand, IoT devices are “weak” and need external assistance. Edge computing provides a promising direction addressing the deficiency of centralized cloud computing in scaling massive number of devices. On the other hand, IoT devices are also relatively “vulnerable” facing malicious hackers due to resource constraints. The emerging blockchain and smart contracts technologies bring a series of new security features for IoT and edge computing. In this paper, to address the challenges, we design and prototype an edge-IoT framework named “EdgeChain” based on blockchain and smart contracts. The core idea is to integrate a permissioned blockchain and the internal currency or “coin” system to link the edge cloud resource pool with each IoT device’ account and resource usage, and hence behavior of the IoT devices. EdgeChain uses a credit-based resource management system to control how much resource IoT devices can obtain from edge servers, based on pre-defined rules on priority, application types and past behaviors. Smart contracts are used to enforce the rules and policies to regulate the IoT device behavior in a non-deniable and automated manner. All the IoT activities and transactions are recorded into blockchain for secure data logging and auditing. We implement an EdgeChain prototype and conduct extensive experiments to evaluate the ideas. The results show that while gaining the security benefits of blockchain and smart contracts, the cost of integrating them into EdgeChain is within a reasonable and acceptable range.


"Now, i have a body": uses and social norms for mobile remote presence in the workplace

As geographically distributed teams become increasingly common, there are more pressing demands for communication work practices and technologies that support distributed collaboration. One set of technologies that are emerging on the commercial market is mobile remote presence (MRP) systems, physically embodied videoconferencing systems that remote workers use to drive through a workplace, communicating with locals there. Our interviews, observations, and survey results from people, who had 2-18 months of MRP use, showed how remotely-controlled mobility enabled remote workers to live and work with local coworkers almost as if they were physically there. The MRP supported informal communications and connections between distributed coworkers. We also found that the mobile embodiment of the remote worker evoked orientations toward the MRP both as a person and as a machine, leading to formation of new usage norms among remote and local coworkers.


Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

The goal of precipitation nowcasting is to predict the future rainfall intensity in a local region over a relatively short period of time. Very few previous studies have examined this crucial and challenging weather forecasting problem from the machine learning perspective. In this paper, we formulate precipitation nowcasting as a spatiotemporal sequence forecasting problem in which both the input and the prediction target are spatiotemporal sequences. By extending the fully connected LSTM (FC-LSTM) to have convolutional structures in both the input-to-state and state-to-state transitions, we propose the convolutional LSTM (ConvLSTM) and use it to build an end-to-end trainable model for the precipitation nowcasting problem. Experiments show that our ConvLSTM network captures spatiotemporal correlations better and consistently outperforms FC-LSTM and the state-of-theart operational ROVER algorithm for precipitation nowcasting.


Combining Instance-Based and Model-Based Learning

This paper concerns learning tasks that require the prediction of a continuous value rather than a discrete class. A general method is presented that allows predictions to use both instance-based and model-based learning. Results with three approaches to constructing models and with eight datasets demonstrate improvements due to the composite method.


An Analysis of Transformations

In the analysis of data it is often assumed that observations y,, y,, ...,y, are independently normally distributed with constant variance and with expectations specified by a model linear in a set of parameters 0. In this paper we make the less restrictive assumption that such a normal, homoscedastic, linear model is appropriate after some suitable transformation has been applied to the y's. Inferences about the transformation and about the parameters of the linear model are made by computing the likelihood function and the relevant posterior distribution. The contributions of normality, homoscedasticity and additivity to the transformation are separated. The relation of the present methods to earlier procedures for finding transformations is discussed. The methods are illustrated with examples.


Deep Learning for Time Series Modeling CS 229 Final Project Report

Demand forecasting is crucial to electricity providers because their ability to produce energy exceeds their ability to store it. Excess demand can cause “brown outs,” while excess supply ends in waste. In an industry worth over $1 trillion in the U.S. alone [1], almost 9% of GDP [2], even marginal improvements can have a huge impact. Any plan toward energy efficiency should include enhanced utilization of existing production. Energy loads provide an interesting topic for Machine Learning techniques due to the availability of large datasets that exhibit fundamental nonlinear patterns. Using data from the Kaggle competition “Global Energy Forecasting Competition 2012 Load Forecasting” [3] we sought to use deep learning architectures to predict energy loads across different network grid areas, using only time and temperature data. Data included hourly demand for four and a half years from 20 different geographic regions, and similar hourly temperature readings from 11 zones. For most of our analysis we focused on short term load forecasting because this will aide online operational scheduling.


When moderation is mediated and mediation is moderated.

Procedures for examining whether treatment effects on an outcome are mediated and/or moderated have been well developed and are routinely applied. The mediation question focuses on the intervening mechanism that produces the treatment effect. The moderation question focuses on factors that affect the magnitude of the treatment effect. It is important to note that these two processes may be combined in informative ways, such that moderation is mediated or mediation is moderated. Although some prior literature has discussed these possibilities, their exact definitions and analytic procedures have not been completely articulated. The purpose of this article is to define precisely both mediated moderation and moderated mediation and provide analytic strategies for assessing each.


Compact ultra-wide stopband lowpass filter using transformed stepped impedance hairpin resonator

A compact microstrip lowpass filter (LPF) with ultra-wide stopband using transformed stepped impedance hairpin resonator is proposed. The transformed resonator consists of a stepped impedance hairpin resonator and an embedded hexagon stub loaded coupled-line structure. Without enlarging the size, the embedded structure is introduced to get a broad stopband. A prototype LPF has been simulated, fabricated and measured, and the measurements are in good agreement with simulations. The implemented lowpass filter exhibits an ultra-wide stopband up to 12.01fc with a rejection level of 14 dB. In addition, the proposed filter features a size of 0.071λg× 0.103λg, where λg is the waveguide length at the cutoff frequency 1.45 GHz.


Are Coherence Protocol States Vulnerable to Information Leakage?

Most commercial multi-core processors incorporate hardware coherence protocols to support efficient data transfers and updates between their constituent cores. While hardware coherence protocols provide immense benefits for application performance by removing the burden of software-based coherence, we note that understanding the security vulnerabilities posed by such oft-used, widely-adopted processor features is critical for secure processor designs in the future. In this paper, we demonstrate a new vulnerability exposed by cache coherence protocol states. We present novel insights into how adversaries could cleverly manipulate the coherence states on shared cache blocks, and construct covert timing channels to illegitimately communicate secrets to the spy. We demonstrate 6 different practical scenarios for covert timing channel construction. In contrast to prior works, we assume a broader adversary model where the trojan and spy can either exploit explicitly shared read-only physical pages (e.g., shared library code), or use memory deduplication feature to implicitly force create shared physical pages. We demonstrate how adversaries can manipulate combinations of coherence states and data placement in different caches to construct timing channels. We also explore how adversaries could exploit multiple caches and their associated coherence states to improve transmission bandwidth with symbols encoding multiple bits. Our experimental results on commercial systems show that the peak transmission bandwidths of these covert timing channels can vary between 700 to 1100 Kbits/sec. To the best of our knowledge, our study is the first to highlight the vulnerability of hardware cache coherence protocols to timing channels that can help computer architects to craft effective defenses against exploits on such critical processor features.


Seahawk: Stack Overflow in the IDE

Services, such as Stack Overflow, offer a web platform to programmers for discussing technical issues, in form of Question and Answers (Q&A). Since Q&A services store the discussions, the generated crowd knowledge can be accessed and consumed by a large audience for a long time. Nevertheless, Q&A services are detached from the development environments used by programmers: Developers have to tap into this crowd knowledge through web browsers and cannot smoothly integrate it into their workflow. This situation hinders part of the benefits of Q&A services. To better leverage the crowd knowledge of Q&A services, we created Seahawk, an Eclipse plugin that supports an integrated and largely automated approach to assist programmers using Stack Overflow. Seahawk formulates queries automatically from the active context in the IDE, presents a ranked and interactive list of results, lets users import code samples in discussions through drag & drop and link Stack Overflow discussions and source code persistently as a support for team work. Video Demo URL: http://youtu.be/DkqhiU9FYPI


Cooperative Control for A Hybrid Rehabilitation System Combining Functional Electrical Stimulation and Robotic Exoskeleton

Functional electrical stimulation (FES) and robotic exoskeletons are two important technologies widely used for physical rehabilitation of paraplegic patients. We developed a hybrid rehabilitation system (FEXO Knee) that combined FES and an exoskeleton for swinging movement control of human knee joints. This study proposed a novel cooperative control strategy, which could realize arbitrary distribution of torque generated by FES and exoskeleton, and guarantee harmonic movements. The cooperative control adopted feedfoward control for FES and feedback control for exoskeleton. A parameter regulator was designed to update key parameters in real time to coordinate FES controller and exoskeleton controller. Two muscle groups (quadriceps and hamstrings) were stimulated to generate active torque for knee joint in synchronization with torque compensation from exoskeleton. The knee joint angle and the interactive torque between exoskeleton and shank were used as feedback signals for the control system. Central pattern generator (CPG) was adopted that acted as a phase predictor to deal with phase confliction of motor patterns, and realized synchronization between the two different bodies (shank and exoskeleton). Experimental evaluation of the hybrid FES-exoskeleton system was conducted on five healthy subjects and four paraplegic patients. Experimental results and statistical analysis showed good control performance of the cooperative control on torque distribution, trajectory tracking, and phase synchronization.


Uniform circular array with integrated microstrip tapered baluns

This paper presents the design, simulation, and measurement results of a 4-element uniform circular array (UCA) using dipole antenna elements with integrated microstrip tapered baluns to convert between balanced and unbalanced signals. These baluns are compact, integrated into the array support structure, and can provide a balanced signal over a relatively wide bandwidth. Simulation results are shown for the amplitude and phase balance of the balun from 1.6 GHz to 2.2 GHz. The array was fabricated and its manifold was measured in an anechoic chamber at a frequency of 1.7 GHz. The gain and phase responses of the array are presented and are compared to a simulated ideal dipole UCA in free-space.


N-Party Encrypted Diffie-Hellman Key Exchange Using Different Passwords

We consider the problem of password-authenticated group Diffie-Hellman key exchange among N parties, N−1 clients and a singleserver, using different passwords. Most password-authenticated key exchange schemes in the literature have focused on an authenticated key exchange using a shared password between a client and a server. With a rapid change in modern communication environment such as ad-hoc networks and ubiquitous computing, it is necessary to construct a secure end-to-end channel between clients, which is a quite different paradigm from the existing ones. To achieve this end-to-end security, only a few schemes of three-party setting have been presented where two clients exchange a key using their own passwords with the help of a server. However, up until now, no formally treated and round efficient protocols which enable group members to generate a common session key with clients’ distinct passwords have been suggested. In this paper we securely and efficiently extend three-party case to Nparty case with a formal proof of security. Two provably secure N-party EKE protocols are suggested; N-party EKE-U in the unicast network and N-party EKE-M in the multicast network. The proposed N-party EKE-M is provable secure and provides forward secrecy. Especially, the scheme is of constant-round, hence scalable and practical.


Iterative unified clustering in big data

We propose a novel iterative unified clustering algorithm for data with both continuous and categorical variables, in the big data environment. Clustering is a well-studied problem and finds several applications. However, none of the big data clustering works discuss the challenge of mixed attribute datasets, with both categorical and continuous attributes. We study an application in the health care domain namely Case Based Reasoning (CBR), which refers to solving new problems based on solutions to similar past problems. This is particularly useful when there is a large set of clinical records with several types of attributes, from which similar patients need to be identified. We go one step further and include the genomic components of patient records to enhance the CBR discovery. Thus, our contributions in this paper spans across the big data algorithmic research and a key contribution to the domain of heath care information technology research. First, our clustering algorithm deals with both continuous and categorical variables in the data; second, our clustering algorithm is iterative where it finds the clusters which are not well formed and iteratively drills down to form well defined clusters at the end of the process; third we provide a novel approach to CBR across clinical and genomic data. Our research has implications for clinical trials and facilitating precision diagnostics in large and heterogeneous patient records. We present extensive experimental results to show the efficacy or our approach.


PLEdestrians: A Least-Effort Approach to Crowd Simulation

We present a new algorithm for simulating large-scale crowds at interactive rates based on the Principle of Least Effort. Our approach uses an optimization method to compute a biomechanically energy-efficient, collision-free trajectory that minimizes the amount of effort for each heterogeneous agent in a large crowd. Moreover, the algorithm can automatically generate many emergent phenomena such as lane formation, crowd compression, edge and wake effects ant others. We compare the results from our simulations to data collected from prior studies in pedestrian and crowd dynamics, and provide visual comparisons with real-world video. In practice, our approach can interactively simulate large crowds with thousands of agents on a desktop PC and naturally generates a diverse set of emergent behaviors.


Android taint flow analysis for app sets

One approach to defending against malicious Android applications has been to analyze them to detect potential information leaks. This paper describes a new static taint analysis for Android that combines and augments the FlowDroid and Epicc analyses to precisely track both inter-component and intra-component data flow in a set of Android applications. The analysis takes place in two phases: given a set of applications, we first determine the data flows enabled individually by each application, and the conditions under which these are possible; we then build on these results to enumerate the potentially dangerous data flows enabled by the set of applications as a whole. This paper describes our analysis method, implementation, and experimental results.


An Optimization Approach for Utilizing Cloud Services for Mobile Devices in Cloud Environment

Abstract. Mobile cloud computing has emerged aiming at assisting mobile devices in processing computationally or data intensive tasks using cloud resources. This paper presents an optimization approach for utilizing cloud services for mobile client in mobile cloud, which considers the benefit of both mobile device users and cloud datacenters. The mobile cloud service provisioning optimization is conducted in parallel under the deadline, budget and energy expenditure constraint. Mobile cloud provider runs multiple VMs to execute the jobs for mobile device users, the cloud providers want to maximize the revenue and minimize the electrical cost. The mobile device user gives the suitable payment to the cloud datacenter provider for available cloud resources for optimize the benefit. The paper proposes a distributed optimization algorithm for utilizing cloud services for mobile devices. The experiment is to test convergence of the proposed algorithm and also compare it with other related work. The experiments study the impacts of job arrival rate, deadline and mobility speeds on energy consumption ratio, execution success ratio, resource allocation efficiency and cost. The experiment shows that the proposed algorithm outperforms other related work in terms of some performance metrics such as allocation efficiency.


Inner Product Similarity Search using Compositional Codes

This paper addresses the nearest neighbor search problem under inner product similarity and introduces a compact code-based approach. The idea is to approximate a vector using the composition of several elements selected from a source dictionary and to represent this vector by a short code composed of the indices of the selected elements. The inner product between a query vector and a database vector is efficiently estimated from the query vector and the short code of the database vector. We show the superior performance of the proposed group M -selection algorithm that selects M elements from M source dictionaries for vector approximation in terms of search accuracy and efficiency for compact codes of the same length via theoretical and empirical analysis. Experimental results on large-scale datasets (1M and 1B SIFT features, 1M linear models and Netflix) demonstrate the superiority of the proposed approach.


Lexical input as related to children's vocabulary acquisition: effects of sophisticated exposure and support for meaning.

A corpus of nearly 150,000 maternal word-tokens used by 53 low-income mothers in 263 mother-child conversations in 5 settings (e.g., play, mealtime, and book readings) was studied. Ninety-nine percent of maternal lexical input consisted of the 3,000 most frequent words. Children's vocabulary performance in kindergarten and later in 2nd grade related more to the occurrence of sophisticated lexical items than to quantity of lexical input overall. Density of sophisticated words heard and the density with which such words were embedded in helpful or instructive interactions, at age 5 at home, independently predicted over a third of the variance in children's vocabulary performance in both kindergarten and 2nd grade. These two variables, with controls for maternal education, child nonverbal IQ, and amount of child's talk produced during the interactive settings, at age 5, predicted 50% of the variance in children's 2nd-grade vocabulary.


Automotive radar target characterization from 22 to 29 GHz and 76 to 81 GHz

The radar signatures of automotive targets were measured from 22 to 29 GHz and 76 to 81 GHz inside an anechoic chamber using a vector network analyzer. Radar cross section maps of a sedan, truck, van, pedestrian, motorcycle, and bicycle as a function of effective radar sensor bandwidth, center frequency, and polarization are presented.


A model for smart agriculture using IoT

Climate changes and rainfall has been erratic over the past decade. Due to this in recent era, climate-smart methods called as smart agriculture is adopted by many Indian farmers. Smart agriculture is an automated and directed information technology implemented with the IOT (Internet of Things). IOT is developing rapidly and widely applied in all wireless environments. In this paper, sensor technology and wireless networks integration of IOT technology has been studied and reviewed based on the actual situation of agricultural system. A combined approach with internet and wireless communications, Remote Monitoring System (RMS) is proposed. Major objective is to collect real time data of agriculture production environment that provides easy access for agricultural facilities such as alerts through Short Massaging Service (SMS) and advices on weather pattern, crops etc.


"It Won't Happen To Me!": Self-Disclosure in Online Social Networks

Despite the considerable amount of self-disclosure in Online Social Networks (OSN), the motivation behind this phenomenon is still little understood. Building on the Privacy Calculus theory, this study fills this gap by taking a closer look at the factors behind individual self-disclosure decisions. In a Structural Equation Model with 237 subjects we find Perceived Enjoyment and Privacy Concerns to be significant determinants of information revelation. We confirm that the privacy concerns of OSN users are primarily determined by the perceived likelihood of a privacy violation and much less by the expected damage. These insights provide a solid basis for OSN providers and policy-makers in their effort to ensure healthy disclosure levels that are based on objective rationale rather than subjective misconceptions.


Finite-time posture control of a unicycle robot

This paper deals with the problem of posture control for a unicycle (single-wheel) robot by designing and analyzing a finite-time posture control strategy. The unicycle robot consists of a lower rotating wheel, an upper rotating disk, and a robot body. The rotating wheel enables the unicycle robot to move forward and backward to obtain pitch (longitudinal) balance. The rotating disk is used for obtaining roll (lateral) balance. Therefore, the unicycle robot can be viewed as a mobile inverted pendulum for the pitch axis and a reaction-wheel pendulum for the roll axis. The dynamics models for unicycle robots at roll and pitch axes are derived using the Lagrange equation. According to the dynamics models, two finite-time controller are designed, which includes the finite-time roll controller and the finite-time pitch controller. Moreover, the stability on the unicycle robot with the proposed finite-time posture control strategy is also analyzed. Finally, simulations are worked out to illustrate the effectiveness of the finite-time posture control strategy for the unicycle robot at roll and pitch axes.


Road Traffic Accidents Analysis in Mexico City through Crowdsourcing Data and Data Mining Techniques

Road traffic accidents are among the principal causes of traffic congestion, causing human losses, damages to health and the environment, economic losses and material damages. Studies about traditional road traffic accidents in urban zones represents very high inversion of time and money, additionally, the result are not current. However, nowadays in many countries, the crowdsourced GPS based traffic and navigation apps have emerged as an important source of information to low cost to studies of road traffic accidents and urban congestion caused by them. In this article we identified the zones, roads and specific time in the CDMX in which the largest number of road traffic accidents are concentrated during 2016. We built a database compiling information obtained from the social network known as Waze. The methodology employed was Discovery of knowledge in the database (KDD) for the discovery of patterns in the accidents reports. Furthermore, using data mining techniques with the help of Weka. The selected algorithms was the Maximization of Expectations (EM) to obtain the number ideal of clusters for the data and k-means as a grouping method. Finally, the results were visualized with the Geographic Information System QGIS. Keywords—Data mining, K-means, road traffic accidents, Waze, Weka.


Using Program Analysis to Improve Database Applications

Applications that interact with database management systems (DBMSs) are ubiquitous. Such database applications are usually hosted on an application server and perform many small accesses over the network to a DBMS hosted on the database server to retrieve data for processing. For decades, the database and programming systems research communities have worked on optimizing such applications from different perspectives: database researchers have built highly efficient DBMSs, and programming systems researchers have developed specialized compilers and runtime systems for hosting applications. However, there has been relatively little work that optimizes database applications by considering these specialized systems in combination and looking for optimization opportunities that span across them. In this article, we highlight three projects that optimize database applications by looking at both the programming system and the DBMS in a holistic manner. By carefully revisiting the interface between the DBMS and the application, and by applying a mix of declarative database optimization and modern program analysis techniques, we show that a speedup of multiple orders of magnitude is possible in real-world applications.


Exploring Models and Data for Remote Sensing Image Caption Generation

Inspired by recent development of artificial satellite, remote sensing images have attracted extensive attention. Recently, notable progress has been made in scene classification and target detection. However, it is still not clear how to describe the remote sensing image content with accurate and concise sentences. In this paper, we investigate to describe the remote sensing images with accurate and flexible sentences. First, some annotated instructions are presented to better describe the remote sensing images considering the special characteristics of remote sensing images. Second, in order to exhaustively exploit the contents of remote sensing images, a large-scale aerial image data set is constructed for remote sensing image caption. Finally, a comprehensive review is presented on the proposed data set to fully advance the task of remote sensing caption. Extensive experiments on the proposed data set demonstrate that the content of the remote sensing image can be completely described by generating language descriptions. The data set is available at https://github.com/201528014227051/RSICD_optimal.


Hybrid Multi-Mode Narrow-Frame Antenna for WWAN/LTE Metal-Rimmed Smartphone Applications

A hybrid multi-mode narrow-frame antenna for WWAN/LTE metal-rimmed smartphone applications is proposed in this paper. The ground clearance is only 5 mm × 45 mm, which is promising for narrow-frame smartphones. The metal rim with a small gap is connected to the system ground by three grounded patches. This proposed antenna can excite three coupled-loop modes and one slot mode. By incorporating these four modes, the proposed antenna can provide coverage for GSM850/900, DCS/PCS/UMTS2100, and LTE2300/2500 operations. Detailed design considerations of the proposed antenna are described, and both experimental and simulated results are also presented.


An integrated fuzzy MCDM approach for supplier evaluation and selection

A fuzzy multi-criteria group decision making approach that makes use of quality function deployment (QFD), fusion of fuzzy information and 2-tuple linguistic representation model is developed for supplier selection. The proposed methodology seeks to establish the relevant supplier assessment criteria while also considering the impacts of inner dependence among them. Two interrelated house of quality matrices are constructed, and fusion of fuzzy information and 2-tuple linguistic representation model are employed to compute the weights of supplier selection criteria and subsequently the ratings of suppliers. The proposed method is apt to manage non-homogeneous information in a decision setting with multiple information sources. The decision framework presented in this paper employs ordered weighted averaging (OWA) operator, and the aggregation process is based on combining information by means of fuzzy sets on a basic linguistic term set. The proposed framework is illustrated through a case study conducted in a private hospital in Istanbul.


Automatic Semantic Face Recognition Nawaf

Recent expansion in surveillance systems has motivated research in soft biometrics that enable the unconstrained recognition of human faces. Comparative soft biometrics show superior recognition performance than categorical soft biometrics and have been the focus of several studies which have highlighted their ability for recognition and retrieval in constrained and unconstrained environments. These studies, however, only addressed face recognition for retrieval using human generated attributes, posing a question about the feasibility of automatically generating comparative labels from facial images. In this paper, we propose an approach for the automatic comparative labelling of facial soft biometrics. Furthermore, we investigate unconstrained human face recognition using these comparative soft biometrics in a human labelled gallery (and vice versa). Using a subset from the LFW dataset, our experiments show the efficacy of the automatic generation of comparative facial labels, highlighting the potential extensibility of the approach to other face recognition scenarios and larger ranges of attributes.


Emergency situation awareness from twitter for crisis management

This paper describes ongoing work with the Australian Government to detect, assess, summarise, and report messages of interest for crisis coordination published by Twitter. The developed platform and client tools, collectively termed the Emergency Situation Awareness - Automated Web Text Mining (ESA-AWTM) system, demonstrate how relevant Twitter messages can be identified and utilised to inform the situation awareness of an emergency incident as it unfolds.
 A description of the ESA-AWTM platform is presented detailing how it may be used for real life emergency management scenarios. These scenarios are focused on general use cases to provide: evidence of pre-incident activity; near-real-time notification of an incident occurring; first-hand reports of incident impacts; and gauging the community response to an emergency warning. Our tools have recently been deployed in a trial for use by crisis coordinators.


Avoiding and Treating Blindness From Fillers: A Review of the World Literature.

BACKGROUND
As the popularity of soft tissue fillers increases, so do the reports of adverse events. The most serious complications are vascular in nature and include blindness.

OBJECTIVE
To review the cases of blindness after filler injection, to highlight key aspects of the vascular anatomy, and to discuss prevention and management strategies.

METHODS
A literature review was performed to identify all the cases of vision changes from filler in the world literature.

RESULTS
Ninety-eight cases of vision changes from filler were identified. The sites that were high risk for complications were the glabella (38.8%), nasal region (25.5%), nasolabial fold (13.3%), and forehead (12.2%). Autologous fat (47.9%) was the most common filler type to cause this complication, followed by hyaluronic acid (23.5%). The most common symptoms were immediate vision loss and pain. Most cases of vision loss did not recover. Central nervous system complications were seen in 23.5% of the cases. No treatments were found to be consistently successful in treating blindness.

CONCLUSION
Although the risk of blindness from fillers is rare, it is critical for injecting physicians to have a firm knowledge of the vascular anatomy and to understand key prevention and management strategies.


Filler Rhinoplasty Evaluated by Anthropometric Analysis.

BACKGROUND
There are no reports of objectively evaluating the efficacy of filler rhinoplasty by anthropometric techniques.

OBJECTIVE
To objectively demonstrate the effectiveness of filler rhinoplasty by anthropometric analysis.

MATERIALS AND METHODS
A total of 242 patients who revisited the clinic within 2 months of undergoing hyaluronic acid filler rhinoplasty were analyzed based on the injection site, injected volume, and the change in anthropometry.

RESULTS
Among the 242 patients, 112 (46.3%) were in the nasal dorsum augmentation group, 8 (3.3%) were in the tip rotation group, and 122 (50.4%) were in the whole nose augmentation group. Average injection volume was 1 ± 0.4 mL for nasal dorsum and 0.9 ± 0.3 mL for tip rotation, whereas 1.6 ± 0.5 mL was used for whole nose augmentation. On follow-up, the radix height, nasofrontal angle, and nasolabial angle (NLA) had increased by 78.3%, 5.7 ± 4.1°, and 9.4 ± 4.5°, respectively, whereas the modified nasofacial angle had decreased by 1.9 ± 2.9°. Three cases (1.2%) of vascular complications were encountered.

CONCLUSION
Filler rhinoplasty is a simple and effective treatment modality producing outcomes comparable with surgical augmentation rhinoplasty. Among various anthropometric measurements, the nasal radix height was the most useful for evaluating dorsum augmentation, whereas the NLA was the best for nasal tip rotation.


The anatomical origin and course of the angular artery regarding its clinical implications.

BACKGROUND
The purposes of this study were to determine the morphological features and conceptualize the anatomical definition of the angular artery (AA) as an aid to practical operations in the clinical field.

MATERIALS AND METHODS
Thirty-one hemifaces from 17 Korean cadavers and 26 hemifaces from 13 Thai cadavers were dissected.

RESULTS
The topography of the AA was classified into 4 types according to its course: Type I (persistent pattern), in which the AA traverses the lateral side of the nose (11%); Type II (detouring pattern), in which the AA traverses the cheek and tear trough area (18%); Type III (alternative pattern), in which the AA traverses the medial canthal area through a branch of the ophthalmic artery (22.8%); and Type IV (latent pattern), in which the AA is absent (26.3%).

CONCLUSION
The findings of this study will contribute toward improved outcomes for cosmetic surgery involving the injection of facial filler by enhancing the understanding of AA anatomy.


Ischemic oculomotor nerve palsy and skin necrosis caused by vascular embolization after hyaluronic acid filler injection: a case report.

Hyaluronic acid filler injection is widely used for soft tissue augmentation. However, there can be disastrous complications by direct vascular embolization. We present a case of ischemic oculomotor nerve palsy and skin necrosis after hyaluronic acid filler injection on glabellar.blepharoptosis, exotropia and diplopia developed suddenly after the injection, and skin necrosis gradually occurred. Symptoms and signs of oculomotor nerve palsy continuously improved with steroid therapy. Skin defects healed with minimal scars through intensive wound care.Percutaneous filler injection of periorbital areas should be performed carefully by experienced surgeons, and the possibility of embolization should be considered promptly if symptoms develop.


Complications of injectable fillers, part I.

Dermal filling has rapidly become one of the most common procedures performed by clinicians worldwide. The vast majority of treatments are successful and patient satisfaction is high. However, complications, both mild and severe, have been reported and result from injection of many different types of dermal fillers. In this Continuing Medical Education review article, the author describes common technical errors, the signs and symptoms of both common and rare complications, and management of sequelae in clear, easily adaptable treatment algorithms.


Developing a Research Agenda for Human-Centered Data Science

The study and analysis of large and complex data sets offer a wealth of insights in a variety of applications. Computational approaches provide researchers access to broad assemblages of data, but the insights extracted may lack the rich detail that qualitative approaches have brought to the understanding of sociotechnical phenomena. How do we preserve the richness associated with traditional qualitative methods while utilizing the power of large data sets? How do we uncover social nuances or consider ethics and values in data use? These and other questions are explored by human-centered data science, an emerging field at the intersection of human-computer interaction (HCI), computer-supported cooperative work (CSCW), human computation, and the statistical and computational techniques of data science. This workshop, the first of its kind at CSCW, seeks to bring together researchers interested in human-centered approaches to data science to collaborate, define a research agenda, and form a community.


MEASURING THE SIMILARITY OF TWO IMAGE SEQUENCES

We propose a novel similarity measure of two image sequences based on shapeme histograms. The idea of shapeme histogram has been used for single image/texture recognition, but is used here to solve the sequence-tosequence matching problem. We develop techniques to represent each sequence as a set of shapeme histograms, which captures different variations of the object appearances within the sequence. These shapeme histograms are computed from the set of 2D invariant features that are stable across multiple images in the sequence, and therefore minimizes the effect of both background clutter, and 2D pose variations. We define sequence similarity measure as the similarity of the most similar pair of images from both sequences. This definition maximizes the chance of matching between two sequences of the same object, because it requires only part of the sequences being similar. We also introduce a weighting scheme to conduct an implicit feature selection process during the matching of two shapeme histograms. Experiments on clustering image sequences of tracked objects demonstrate the efficacy of the proposed method.


Design and implementation of a 1.3 kW, 7-level flying capacitor multilevel AC-DC converter with power factor correction

This work presents a 1.3 kW, single phase, AC-DC converter with power factor correction based on a 7-level flying capacitor multilevel converter. The topology features a tradeoff of active circuit complexity for dramatic reduction on the magnetic component size, while maintaining a high efficiency. In this work, we demonstrate these features through theoretical analysis as well as a hardware prototype. It has been experimentally demonstrated that the prototype can operate at an universal AC input from 90–230 VRMS and frequencies from 47–63 Hz with an output voltage of 400 V, achieving a box power density of 1.21 W/cm3 (19.8 W/in3) and a peak efficiency of 97.6%. This prototype is the first successful demonstration of a 7-level flying capacitor multilevel boost topology as an AC-DC converter with fully implemented digital PFC control and self-powered start-up from a universal AC input.


Gender Identity and Adjustment in Middle Childhood

Gender identity is a central construct in many accounts of psychosocial development, yet it has been defined in diverse ways. Kohlberg (1966) and Zucker et al. (1993) viewed gender identity as knowing that one is a member of one sex rather than the other; Kagan (1964) regarded gender identity as the degree to which one perceives the self as conforming to cultural stereotypes for one's gender; Bem (1981) saw gender identity as the degree to which one internalizes societal pressures for gender conformity; Green (1974) and Spence (1985) viewed gender identity as a fundamental sense of acceptance of, and of belonging to, one's gender. It is conceivable that all of the foregoing (and still other) conceptualizations of gender identity have merit but that different varieties or facets of gender identity serve different psychological functions or affect adjustment in different ways. Thus, it may be fruitful to regard gender identity as a multidimensional construct and to define gender identity as the collection of thoughts and feelings one has about one's gender category and one's membership in it. A recent study by Egan and Perry (2001) was built on this premise. Egan and Perry proposed that gender identity is composed of five major components: (a) membership knowledge (knowledge of membership in a gender category); (b) gender typicality (the degree to which one feels one is a typical member of one's gender category); (c) gender contentedness (the degree to which one is happy with one's gender assignment); (d) felt pressure for gender conformity (the degree to which one feels pressure from parents, peers, and self for conformity to gender stereotypes); and (e) intergroup bias (the extent to which one believes one's own sex is superior to the other). Egan and Perry (2001) measured the last four of these components of gender identity in preadolescent children and found the components to be relatively independent, to be fairly stable over a school year, and to relate to adjustment (i.e., self-esteem and peer acceptance) in different ways. Gender typicality and gender contentedness were favorably related to adjustment, whereas felt pressure and intergroup bias were negatively associated with adjustment. Links between the gender identity constructs and the adjustment indexes remained significant when children's perceptions of self-efficacy for a wide variety of sex-typed activities were statistically controlled. This suggests that the gender identity constructs carry implications for adjustment beyond self-perceptions of specific sex-linked competencies. The purposes of the …


Load Forecasting via Deep Neural Networks

Nowadays, electricity plays a vital role in national economic and social development. Accurate load forecasting can help power companies to secure electricity supply and scheduling and reduce wastes since electricity is difficult to store. In this paper, we propose a novel Deep Neural Network architecture for short term load forecasting. We integrate multiple types of input features by using appropriate neural network components to process each of them. We use Convolutional Neural Network components to extract rich features from historical load sequence and use Recurrent Components to model the implicit dynamics. In addition, we use Dense layers to transform other types of features. Experimental results on a large data set containing hourly loads of a North China city show the superiority of our method. Moreover, the proposed method is quite flexible and can be applied to other time series prediction tasks. c © 2017 The Authors. Published by Elsevier B.V. Selectio and/or peer-review under responsibility of ITQM2017.


3D-Printing of Meso-structurally Ordered Carbon Fiber/Polymer Composites with Unprecedented Orthotropic Physical Properties

Here we report the first example of a class of additively manufactured carbon fiber reinforced composite (AMCFRC) materials which have been achieved through the use of a latent thermal cured aromatic thermoset resin system, through an adaptation of direct ink writing (DIW) 3D-printing technology. We have developed a means of printing high performance thermoset carbon fiber composites, which allow the fiber component of a resin and carbon fiber fluid to be aligned in three dimensions via controlled micro-extrusion and subsequently cured into complex geometries. Characterization of our composite systems clearly show that we achieved a high order of fiber alignment within the composite microstructure, which in turn allows these materials to outperform equivalently filled randomly oriented carbon fiber and polymer composites. Furthermore, our AM carbon fiber composite systems exhibit highly orthotropic mechanical and electrical responses as a direct result of the alignment of carbon fiber bundles in the microscale which we predict will ultimately lead to the design of truly tailorable carbon fiber/polymer hybrid materials having locally programmable complex electrical, thermal and mechanical response.


Ten-year research update review: child sexual abuse.

OBJECTIVE To provide clinicians with current information on prevalence, risk factors, outcomes, treatment, and prevention of child sexual abuse (CSA). To examine the best-documented examples of psychopathology attributable to CSA. METHOD Computer literature searches of and for key words. All English-language articles published after 1989 containing empirical data pertaining to CSA were reviewed. RESULTS CSA constitutes approximately 10% of officially substantiated child maltreatment cases, numbering approximately 88,000 in 2000. Adjusted prevalence rates are 16.8% and 7.9% for adult women and men, respectively. Risk factors include gender, age, disabilities, and parental dysfunction. A range of symptoms and disorders has been associated with CSA, but depression in adults and sexualized behaviors in children are the best-documented outcomes. To date, cognitive-behavioral therapy (CBT) of the child and a nonoffending parent is the most effective treatment. Prevention efforts have focused on child education to increase awareness and home visitation to decrease risk factors. CONCLUSIONS CSA is a significant risk factor for psychopathology, especially depression and substance abuse. Preliminary research indicates that CBT is effective for some symptoms, but longitudinal follow-up and large-scale "effectiveness" studies are needed. Prevention programs have promise, but evaluations to date are limited.


Appropriateness of plantar pressure measurement devices: a comparative technical assessment.

Accurate plantar pressure measurements are mandatory in both clinical and research contexts. Differences in accuracy, precision and reliability of the available devices have prevented so far the onset of standardization processes or the definition of reliable reference datasets. In order to comparatively assess the appropriateness of the most used pressure measurement devices (PMD) on-the-market, in 2006 the Institute the author is working for approved a two-year scientific project aimed to design, validate and implement dedicated testing methods for both in-factory and on-the field assessment. A first testing phase was also performed which finished in December 2008. Five commercial PMDs using different technologies-resistive, elastomer-based capacitive, air-based capacitive-were assessed and compared with respect to absolute pressure measurements, hysteresis, creep and COP estimation. The static and dynamic pressure tests showed very high accuracy of capacitive, elastomer-based technology (RMSE<0.5%), and quite a good performance of capacitive, air-based technology (RMSE<5%). High accuracy was also found for the resistive technology by TEKSCAN (RMSE<2.5%), even though a complex ad hoc calibration was necessary.


Finite Precision Error Analysis of Neural Network Hardware Implementations

29 computation. On the other hand, for network learning, at least 14-16 bits of precision must be used for the weights to avoid having the training process divert too much from the trajectory of the high precision computation. References [1] D. Hammerstrom. A VLSI architecture for high-performance, low cost, on-chip learning. Figure 10: The average squared dierences between the desired and actual outputs of the XOR problem after the network converges. The paper is devoted to the derivation of the nite precision error analysis techniques for neural network implementations, especially analysis of the back-propagation learning of MLP's. This analysis technique is proposed to be more versatile and to prepare the ground for a wider variety of neural network algorithms: recurrent neural networks, competitive learning networks, and etc. All these networks share similar computational mechanisms as those used in back-propagation learning. For the forward retrieving operations, it is shown that 8-bit weights are sucient to maintain the same performance as using high precision 27 ture of the XOR problem, a soft convergence is good enough for the termination of training. Therefore, at the predicted point of 12-13 bits of weights, the squared dierence curve dives. Another interesting observation is worthwhile to mention: the total nite precision error in a single iteration of weight updating is mainly generated in the nal jamming operators in the computation of the output delta, hidden delta, and weight update. Therefore, even though it is required to have at least 13 to 16 bits assigned to the computation of the weight update and stored as the total weight value, the number of weight bits in the computation of forward retrieving and hidden delta steps of learning can be as low as 8 bits without excessive degradation of learning convergence and accuracy.


Rotation Invariant Multi-View Color Face Detection Based on Skin Color and Adaboost Algorithm

As the training of Adaboost is complicated or does not work well in the multi-view face detection with large plane-rotated angle, this paper proposes a rotation invariant multi-view color face detection method combining skin color segmentation and multi-view Adaboost algorithm. First the possible face region is fast detected by skin color table, and the skin-color background adhesion of face is separated by color clustering. After region merging, the candidate face region is received. Then the face direction in plane is calculated by K-L transform, and finally the candidate face region corrected by rotating is scanned by multi-view Adaboost classifier to locate the face accurately. The experiments show that the method can effectively detect the plane large-angle multi-view face image which the conventional Adaboost can not do. It can be effectively applied to the cases of multi-view and multi-face image with complex background.


Amorphous Silicon Solar Vivaldi Antenna

An ultrawideband solar Vivaldi antenna is proposed. Cut from amorphous silicon cells, it maintains a peak power at 4.25 V, which overcomes a need for lossy power management components. The wireless communications device can yield solar energy or function as a rectenna for dual-source energy harvesting. The solar Vivaldi performs with 0.5-2.8 dBi gain from 0.95-2.45 GHz , and in rectenna mode, it covers three bands for wireless energy scavenging.


Automating metadata generation: the simple indexing interface

In this paper, we focus on the development of a framework for automatic metadata generation. The first step towards this framework is the definition of an Application Programmer Interface (API), which we call the Simple Indexing Interface (SII). The second step is the definition of a framework for implementation of the SII. Both steps are presented in some detail in this paper. We also report on empirical evaluation of the metadata that the SII and supporting framework generated in a real-life context.


Four common anatomic variants that predispose to unfavorable rhinoplasty results: a study based on 150 consecutive secondary rhinoplasties.

A retrospective study was conducted of 150 consecutive secondary rhinoplasty patients operated on by the author before February of 1999, to test the hypothesis that four anatomic variants (low radix/low dorsum, narrow middle vault, inadequate tip projection, and alar cartilage malposition) strongly predispose to unfavorable rhinoplasty results. The incidences of each variant were compared with those in 50 consecutive primary rhinoplasty patients. Photographs before any surgery were available in 61 percent of the secondary patients; diagnosis in the remaining individuals was made from operative reports, physical diagnosis, or patient history. Low radix/low dorsum was present in 93 percent of the secondary patients and 32 percent of the primary patients; narrow middle vault was present in 87 percent of the secondary patients and 38 percent of the primary patients; inadequate tip projection was present in 80 percent of the secondary patients and 31 percent of the primary patients; and alar cartilage malposition was present in 42 percent of the secondary patients and 18 percent of the primary patients. In the 150-patient secondary group, the most common combination was the triad of low radix, narrow middle vault, and inadequate tip projection (40 percent of patients). The second largest group (27 percent) had shared all four anatomic points before their primary rhinoplasties. Seventy-eight percent of the secondary patients had three or all four anatomic variants in some combination; each secondary patient had at least one of the four traits; 99 percent had two or more. Seventy-eight percent of the primary patients had at least two variants, and 58 percent had three or more. Twenty-two percent of the primary patients had none of the variants and therefore would presumably not be predisposed to unfavorable results following traditional reduction rhinoplasty. This study supports the contention that four common anatomic variants, if unrecognized, are strongly associated with unfavorable results following primary rhinoplasty. It is important for all surgeons performing rhinoplasty to recognize these anatomic variants to avoid the unsatisfactory functional and aesthetic sequelae that they may produce by making their correction a deliberate part of each preoperative surgical plan.


Categorization of Anomalies in Smart Manufacturing Systems to Support the Selection of Detection Mechanisms

An important issue in anomaly detection in smart manufacturing systems is the lack of consistency in the formal definitions of anomalies, faults, and attacks. The term anomaly is used to cover a wide range of situations that are addressed by different types of solutions. In this letter, we categorize anomalies in machines, controllers, and networks along with their detection mechanisms, and unify them under a common framework to aid in the identification of potential solutions. The main contribution of the proposed categorization is that it allows the identification of gaps in anomaly detection in smart manufacturing systems.


Multimodal Machine Learning: A Survey and Taxonomy

Our experience of the world is multimodal - we see objects, hear sounds, feel texture, smell odors, and taste flavors. Modality refers to the way in which something happens or is experienced and a research problem is characterized as multimodal when it includes multiple such modalities. In order for Artificial Intelligence to make progress in understanding the world around us, it needs to be able to interpret such multimodal signals together. Multimodal machine learning aims to build models that can process and relate information from multiple modalities. It is a vibrant multi-disciplinary field of increasing importance and with extraordinary potential. Instead of focusing on specific multimodal applications, this paper surveys the recent advances in multimodal machine learning itself and presents them in a common taxonomy. We go beyond the typical early and late fusion categorization and identify broader challenges that are faced by multimodal machine learning, namely: representation, translation, alignment, fusion, and co-learning. This new taxonomy will enable researchers to better understand the state of the field and identify directions for future research.


NCDawareRank: a novel ranking method that exploits the decomposable structure of the web

Research about the topological characteristics of the hyperlink graph has shown that Web possesses a nested block structure, indicative of its innate hierarchical organization. This crucial observation opens the way for new approaches that can usefully regard Web as a Nearly Completely Decomposable(NCD) system; In recent years, such approaches gave birth to various efficient methods and algorithms that exploit NCD from a computational point of view and manage to considerably accelerate the extraction of the PageRank vector. However, very little have been done towards the qualitative exploitation of NCD.
 In this paper we propose NCDawareRank, a novel ranking method that uses the intuition behind NCD to generalize and refine PageRank. NCDawareRank considers both the link structure and the hierarchical nature of the Web in a way that preserves the mathematically attractive characteristics of PageRank and at the same time manages to successfully resolve many of its known problems, including Web Spamming Susceptibility and Biased Ranking of Newly Emerging Pages. Experimental results show that NCDawareRank is more resistant to direct manipulation, alleviates the problems caused by the sparseness of the link graph and assigns more reasonable ranking scores to newly added pages, while maintaining the ability to be easily implemented on a large-scale and in a computationally efficient manner.


Motion Detection Techniques Using Optical Flow

Motion detection is very important in image processing. One way of detecting motion is using optical flow. Optical flow cannot be computed locally, since only one independent measurement is available from the image sequence at a point, while the flow velocity has two components. A second constraint is needed. The method used for finding the optical flow in this project is assuming that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image. This technique is later used in developing software for motion detection which has the capability to carry out four types of motion detection. The motion detection software presented in this project also can highlight motion region, count motion level as well as counting object numbers. Many objects such as vehicles and human from video streams can be recognized by applying optical flow technique. Keywords—Background modeling, Motion detection, Optical flow, Velocity smoothness constant, motion trajectories.


What ’ s good for the goose is good for the GANder Comparing Generative Adversarial Networks for NLP

Generative Adversarial Nets (GANs), which use discriminators to help train a generative model, have been successful particularly in computer vision for generating images. However, there are many restrictions in its applications to natural language tasks–mainly, it is difficult to back-propagate through discrete-value random variables. Yet recent publications have applied GAN with promising results. Sequence GAN (Yu et al. 2017) introduces a solution by modeling the data generator as a reinforcement learning (RL) policy to overcome the generator differentiation problem, with the RL reward signals produced by the discriminator after it judges complete sequences. However, problems with this model persist, as the GAN training objective is inherently unstable, producing a large variation of results that make it difficult to fool the discriminator. Maximum-Likelihood Augmented Discrete GAN (Che at al. 2017) suggests a new low-variance objective for the generator, using a normalized reward signal from the discriminator that corresponds to log-likelihood. Our project explores both proposed implementations: we produce experimental results on both synthetic and real-world discrete datasets to explore the effectiveness of GAN over strong baselines.


Design and implementation of intrusion detection system using convolutional neural network for DoS detection

Nowadays, network is one of the essential parts of life, and lots of primary activities are performed by using the network. Also, network security plays an important role in the administrator and monitors the operation of the system. The intrusion detection system (IDS) is a crucial module to detect and defend against the malicious traffics before the system is affected. This system can extract the information from the network system and quickly indicate the reaction which provides real-time protection for the protected system. However, detecting malicious traffics is very complicating because of their large quantity and variants. Also, the accuracy of detection and execution time are the challenges of some detection methods. In this paper, we propose an IDS platform based on convolutional neural network (CNN) called IDS-CNN to detect DoS attack. Experimental results show that our CNN based DoS detection obtains high accuracy at most 99.87%. Moreover, comparisons with other machine learning techniques including KNN, SVM, and Naïve Bayes demonstrate that our proposed method outperforms traditional ones.


Is multiagent deep reinforcement learning the answer or the question? A brief survey

Deep reinforcement learning (DRL) has achieved outstanding results in recent years. This has led to a dramatic increase in the number of applications and methods. Recent works have explored learning beyond single-agent scenarios and have considered multiagent scenarios. Initial results report successes in complex multiagent domains, although there are several challenges to be addressed. In this context, first, this article provides a clear overview of current multiagent deep reinforcement learning (MDRL) literature. Second, it provides guidelines to complement this emerging area by (i) showcasing examples on how methods and algorithms from DRL and multiagent learning (MAL) have helped solve problems in MDRL and (ii) providing general lessons learned from these works. We expect this article will help unify and motivate future research to take advantage of the abundant literature that exists in both areas (DRL and MAL) in a joint effort to promote fruitful research in the multiagent community.


Identifiable Phenotyping using Constrained Non-Negative Matrix Factorization

This work proposes a new algorithm for automated and simultaneous phenotyping of multiple co–occurring medical conditions, also referred as comorbidities, using clinical notes from the electronic health records (EHRs). A basic latent factor estimation technique of non-negative matrix factorization (NMF) is augmented with domain specific constraints to obtain sparse latent factors that are anchored to a fixed set of chronic conditions. The proposed anchoring mechanism ensures a one-to-one identifiable and interpretable mapping between the latent factors and the target comorbidities. Qualitative assessment of the empirical results by clinical experts suggests that the proposed model learns clinically interpretable phenotypes while being predictive of 30 day mortality. The proposed method can be readily adapted to any non-negative EHR data across various healthcare institutions.


Integrating perceived playfulness into expectation-confirmation model for web portal context

This paper investigated the value of including "playfulness" in expectation-confirmation theory (ECT) when studying continued use of a web site. Original models examined cognitive beliefs and effects that influence a person's intention to continue to use an information system. Here, an extended ECT model (with an additional relationship between perceived playfulness and satisfaction) was shown to provide a better fit than a simple path from perceived usefulness to satisfaction. The results indicated that perceived playfulness, confirmation to satisfaction, and perceived usefulness all contributed significantly to the users' intent to reuse a web site. Thus, we believe that the extended ECT model is an appropriate tool for the study of web site effects.


Survey of review spam detection using machine learning techniques

Online reviews are often the primary factor in a customer’s decision to purchase a product or service, and are a valuable source of information that can be used to determine public opinion on these products or services. Because of their impact, manufacturers and retailers are highly concerned with customer feedback and reviews. Reliance on online reviews gives rise to the potential concern that wrongdoers may create false reviews to artificially promote or devalue products and services. This practice is known as Opinion (Review) Spam, where spammers manipulate and poison reviews (i.e., making fake, untruthful, or deceptive reviews) for profit or gain. Since not all online reviews are truthful and trustworthy, it is important to develop techniques for detecting review spam. By extracting meaningful features from the text using Natural Language Processing (NLP), it is possible to conduct review spam detection using various machine learning techniques. Additionally, reviewer information, apart from the text itself, can be used to aid in this process. In this paper, we survey the prominent machine learning techniques that have been proposed to solve the problem of review spam detection and the performance of different approaches for classification and detection of review spam. The majority of current research has focused on supervised learning methods, which require labeled data, a scarcity when it comes to online review spam. Research on methods for Big Data are of interest, since there are millions of online reviews, with many more being generated daily. To date, we have not found any papers that study the effects of Big Data analytics for review spam detection. The primary goal of this paper is to provide a strong and comprehensive comparative study of current research on detecting review spam using various machine learning techniques and to devise methodology for conducting further investigation.


Incorporating Structural Alignment Biases into an Attentional Neural Translation Model

Neural encoder-decoder models of machine translation have achieved impressive results, rivalling traditional translation models. However their modelling formulation is overly simplistic, and omits several key inductive biases built into traditional models. In this paper we extend the attentional neural translation model to include structural biases from word based alignment models, including positional bias, Markov conditioning, fertility and agreement over translation directions. We show improvements over a baseline attentional model and standard phrase-based model over several language pairs, evaluating on difficult languages in a low resource setting.


Neural Turing Machines

We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-toend, allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples.


Deep Neural Networks in Machine Translation: An Overview

Deep neural networks (DNNs) are widely used in machine translation (MT). This article gives an overview of DNN applications in various aspects of MT.


Syntactically Guided Neural Machine Translation

We investigate the use of hierarchical phrase-based SMT lattices in end-to-end neural machine translation (NMT). Weight pushing transforms the Hiero scores for complete translation hypotheses, with the full translation grammar score and full ngram language model score, into posteriors compatible with NMT predictive probabilities. With a slightly modified NMT beam-search decoder we find gains over both Hiero and NMT decoding alone, with practical advantages in extending NMT to very large input and output vocabularies.


Facing facts: neuronal mechanisms of face perception.

The face is one of the most important stimuli carrying social meaning. Thanks to the fast analysis of faces, we are able to judge physical attractiveness and features of their owners' personality, intentions, and mood. From one's facial expression we can gain information about danger present in the environment. It is obvious that the ability to process efficiently one's face is crucial for survival. Therefore, it seems natural that in the human brain there exist structures specialized for face processing. In this article, we present recent findings from studies on the neuronal mechanisms of face perception and recognition in the light of current theoretical models. Results from brain imaging (fMRI, PET) and electrophysiology (ERP, MEG) show that in face perception particular regions (i.e. FFA, STS, IOA, AMTG, prefrontal and orbitofrontal cortex) are involved. These results are confirmed by behavioral data and clinical observations as well as by animal studies. The developmental findings reviewed in this article lead us to suppose that the ability to analyze face-like stimuli is hard-wired and improves during development. Still, experience with faces is not sufficient for an individual to become an expert in face perception. This thesis is supported by the investigation of individuals with developmental disabilities, especially with autistic spectrum disorders (ASD).


Differential neuronal plasticity in mouse hippocampus associated with various periods of enriched environment during postnatal development

Enriched environment (EE) is characterized by improved conditions for enhanced exploration, cognitive activity, social interaction and physical exercise. It has been shown that EE positively regulates the remodeling of neural circuits, memory consolidation, long-term changes in synaptic strength and neurogenesis. However, the fine mechanisms by which environment shapes the brain at different postnatal developmental stages and the duration required to induce such changes are still a matter of debate. In EE, large groups of mice were housed in bigger cages and were given toys, nesting materials and other equipment that promote physical activity to provide a stimulating environment. Weaned mice were housed in EE for 4, 6 or 8 weeks and compared with matched control mice that were raised in a standard environment. To investigate the differential effects of EE on immature and mature brains, we also housed young adult mice (8 weeks old) for 4 weeks in EE. We studied the influence of onset and duration of EE housing on the structure and function of hippocampal neurons. We found that: (1) EE enhances neurogenesis in juvenile, but not young adult mice; (2) EE increases the number of synaptic contacts at every stage; (3) long-term potentiation (LTP) and spontaneous and miniature activity at the glutamatergic synapses are affected differently by EE depending on its onset and duration. Our study provides an integrative view of the role of EE during postnatal development in various mechanisms of plasticity in the hippocampus including neurogenesis, synaptic morphology and electrophysiological parameters of synaptic connectivity. This work provides an explanation for discrepancies found in the literature about the effects of EE on LTP and emphasizes the importance of environment on hippocampal plasticity.


Flash memory cells-an overview

The aim of this paper is to give a thorough overview of Flash memory cells. Basic operations and charge-injection mechanisms that are most commonly used in actual Flash memory cells are reviewed to provide an understanding of the underlying physics and principles in order to appreciate the large number of device structures, processing technologies, and circuit designs presented in the literature. New cell structures and architectural solutions have been surveyed to highlight the evolution of the Flash memory technology, oriented to both reducing cell size and upgrading product functions. The subject is of extreme interest: new concepts involving new materials, structures, principles, or applications are being continuously introduced. The worldwide semiconductor memory market seems ready to accept many new applications in fields that are not specific to traditional nonvolatile memories.


On the Global Convergence of Majorization Minimization Algorithms for Nonconvex Optimization Problems

In this paper, we study the global convergence of majorization minimization (MM) algorithms for solving nonconvex regularized optimization problems. MM algorithms have received great attention in machine learning. However, when applied to nonconvex optimization problems, the convergence of MM algorithms is a challenging issue. We introduce theory of the KurdykaLojasiewicz inequality to address this issue. In particular, we show that many nonconvex problems enjoy the KurdykaLojasiewicz property and establish the global convergence result of the corresponding MM procedure. We also extend our result to a well known method that called CCCP (concave-convex procedure).


An Overview on Mobile Data Mining

In early days the mobile phones are considered to perform only telecommunication operation. This scenario of mobile phones as communication devices changed with the emergence of a new class of mobile devices called the smart phones. These smart phones in addition to being used as a communication device are capable of doing things that a computer does. In recent times the smart phone are becoming more and more powerful in both computing and storage aspects. The data generated by the smart phone provide a means to get new knowledge about various aspects like usage, movement of the user etc. This paper provides an introduction to Mobile Data Mining and its types.


Cognitive Neuroscience : implications for education ?

Research into the functioning of the human brain, particularly during the past decade, has greatly enhanced our understanding of cognitive behaviours which are fundamental to education: learning, memory, intelligence, emotion. Here, we argue the case that research findings from cognitive neuroscience hold implications for educational practice. In doing so we advance a bio-psycho-social position that welcomes multi-disciplinary perspectives on current educational challenges. We provide some examples of research implications which support conventional pedagogic wisdom, and others which are novel and perhaps counter-intuitive. As an example, we take a model of adaptive plasticity that relies on stimulus reinforcement and examine possible implications for pedagogy and curriculum depth. In doing so, we reject some popular but over-simplistic applications of neuroscience to education. In sum, the education profession could benefit from embracing rather than ignoring cognitive neuroscience. Moreover, educationists should be actively contributing to the research agenda of future brain research.


Bayesian Reinforcement Learning in Factored POMDPs

Bayesian approaches provide a principled solution to the explorationexploitation trade-off in Reinforcement Learning. Typical approaches, however, either assume a fully observable environment or scale poorly. This work introduces the Factored Bayes-Adaptive POMDP model, a framework that is able to exploit the underlying structure while learning the dynamics in partially observable systems. We also present a belief tracking method to approximate the joint posterior over state and model variables, and an adaptation of the Monte-Carlo Tree Search solution method, which together are capable of solving the underlying problem near-optimally. Our method is able to learn efficiently given a known factorization or also learn the factorization and the model parameters at the same time. We demonstrate that this approach is able to outperform current methods and tackle problems that were previously infeasible.


Training techniques to improve endurance exercise performances.

In previously untrained individuals, endurance training improves peak oxygen uptake (VO2peak), increases capillary density of working muscle, raises blood volume and decreases heart rate during exercise at the same absolute intensity. In contrast, sprint training has a greater effect on muscle glyco(geno)lytic capacity than on muscle mitochondrial content. Sprint training invariably raises the activity of one or more of the muscle glyco(geno)lytic or related enzymes and enhances sarcolemmal lactate transport capacity. Some groups have also reported that sprint training transforms muscle fibre types, but these data are conflicting and not supported by any consistent alteration in sarcoplasmic reticulum Ca2+ ATPase activity or muscle physicochemical H+ buffering capacity. While the adaptations to training have been studied extensively in previously sedentary individuals, far less is known about the responses to high-intensity interval training (HIT) in already highly trained athletes. Only one group has systematically studied the reported benefits of HIT before competition. They found that >or=6 HIT sessions, was sufficient to maximally increase peak work rate (W(peak)) values and simulated 40 km time-trial (TT(40)) speeds of competitive cyclists by 4 to 5% and 3.0 to 3.5%, respectively. Maximum 3.0 to 3.5% improvements in TT(40) cycle rides at 75 to 80% of W(peak) after HIT consisting of 4- to 5-minute rides at 80 to 85% of W(peak) supported the idea that athletes should train for competition at exercise intensities specific to their event. The optimum reduction or 'taper' in intense training to recover from exhaustive exercise before a competition is poorly understood. Most studies have shown that 20 to 80% single-step reductions in training volume over 1 to 4 weeks have little effect on exercise performance, and that it is more important to maintain training intensity than training volume. Progressive 30 to 75% reductions in pool training volume over 2 to 4 weeks have been shown to improve swimming performances by 2 to 3%. Equally rapid exponential tapers improved 5 km running times by up to 6%. We found that a 50% single-step reduction in HIT at 70% of W(peak) produced peak approximately 6% improvements in simulated 100 km time-trial performances after 2 weeks. It is possible that the optimum taper depends on the intensity of the athletes' preceding training and their need to recover from exhaustive exercise to compete. How the optimum duration of a taper is influenced by preceding training intensity and percentage reduction in training volume warrants investigation.


Multi-View Inpainting for Image-Based Scene Editing and Rendering

We propose a method to remove objects such as people and cars from multi-view urban image datasets, enabling free-viewpoint IBR in the edited scenes. Our method combines information from multi-view 3D reconstruction with image inpainting techniques, by formulating the problem as an optimization of a global patch-based objective function. We use Image-Based Rendering (IBR) techniques to reproject information from neighboring views, and 3D multi-view stereo reconstruction to perform multiview coherent initialization for inpainting of pixels not filled by reprojection. Our algorithm performs multi-view consistent inpainting for color and 3D by blending reprojections with patch-based image inpainting. We run our algorithm on casually captured datasets, and Google StreetViewdata, removing objects cars, people and pillars, showing that our approach produces results of sufficient quality for free-viewpoint IBR on "cleaned up" scenes, as well as IBR scene editing, such as limited motion of real objects.


Habanero-Java: the new adventures of old X10

In this paper, we present the Habanero-Java (HJ) language developed at Rice University as an extension to the original Java-based definition of the X10 language. HJ includes a powerful set of task-parallel programming constructs that can be added as simple extensions to standard Java programs to take advantage of today's multi-core and heterogeneous architectures. The language puts a particular emphasis on the usability and safety of parallel constructs. For example, no HJ program using async, finish, isolated, and phaser constructs can create a logical deadlock cycle. In addition, the future and data-driven task variants of the async construct facilitate a functional approach to parallel programming. Finally, any HJ program written with async, finish, and phaser constructs that is data-race free is guaranteed to also be deterministic.
 HJ also features two key enhancements that address well known limitations in the use of Java in scientific computing --- the inclusion of complex numbers as a primitive data type, and the inclusion of array-views that support multidimensional views of one-dimensional arrays. The HJ compiler generates standard Java class-files that can run on any JVM for Java 5 or higher. The HJ runtime is responsible for orchestrating the creation, execution, and termination of HJ tasks, and features both work-sharing and work-stealing schedulers. HJ is used at Rice University as an introductory parallel programming language for second-year undergraduate students. A wide variety of benchmarks have been ported to HJ, including a full application that was originally written in Fortran 90. HJ has a rich development and runtime environment that includes integration with DrJava, the addition of a data race detection tool, and service as a target platform for the Intel Concurrent Collections coordination language


BigBench: towards an industry standard benchmark for big data analytics

There is a tremendous interest in big data by academia, industry and a large user base. Several commercial and open source providers unleashed a variety of products to support big data storage and processing. As these products mature, there is a need to evaluate and compare the performance of these systems.
 In this paper, we present BigBench, an end-to-end big data benchmark proposal. The underlying business model of BigBench is a product retailer. The proposal covers a data model and synthetic data generator that addresses the variety, velocity and volume aspects of big data systems containing structured, semi-structured and unstructured data. The structured part of the BigBench data model is adopted from the TPC-DS benchmark, which is enriched with semi-structured and unstructured data components. The semi-structured part captures registered and guest user clicks on the retailer's website. The unstructured data captures product reviews submitted online. The data generator designed for BigBench provides scalable volumes of raw data based on a scale factor. The BigBench workload is designed around a set of queries against the data model. From a business prospective, the queries cover the different categories of big data analytics proposed by McKinsey. From a technical prospective, the queries are designed to span three different dimensions based on data sources, query processing types and analytic techniques.
 We illustrate the feasibility of BigBench by implementing it on the Teradata Aster Database. The test includes generating and loading a 200 Gigabyte BigBench data set and testing the workload by executing the BigBench queries (written using Teradata Aster SQL-MR) and reporting their response times.


Colostrum avoidance, prelacteal feeding and late breast-feeding initiation in rural Northern Ethiopia.

OBJECTIVE
To identify specific cultural and behavioural factors that might be influenced to increase colostrum feeding in a rural village in Northern Ethiopia to improve infant health.

DESIGN
Background interviews were conducted with six community health workers and two traditional birth attendants. A semi-structured tape-recorded interview was conducted with twenty mothers, most with children under the age of 5 years. Variables were: parental age and education; mother's ethnicity; number of live births and children's age; breast-feeding from birth through to weaning; availability and use of formula; and descriptions of colostrum v. other stages of breast milk. Participant interviews were conducted in Amharic and translated into English.

SETTING
Kossoye, a rural Amhara village with high prevalence rates of stunting: inappropriate neonatal feeding is thought to be a factor.

SUBJECTS
Women (20-60 years of age) reporting at least one live birth (range: 1-8, mean: ∼4).

RESULTS
Colostrum (inger) and breast milk (yetut wotet) were seen as different substances. Colostrum was said to cause abdominal problems, but discarding a portion was sufficient to mitigate this effect. Almost all (nineteen of twenty) women breast-fed and twelve (63 %) reported ritual prelacteal feeding. A majority (fifteen of nineteen, 79 %) reported discarding colostrum and breast-feeding within 24 h of birth. Prelacteal feeding emerged as an additional factor to be targeted through educational intervention.

CONCLUSIONS
To maximize neonatal health and growth, we recommend culturally tailored education delivered by community health advocates and traditional health practitioners that promotes immediate colostrum feeding and discourages prelacteal feeding.


High gain antipodal tapered slot antenna With sine-shaped corrugation and fermi profile substrate slotted cut-out for MMW 5G

A high gain, high-efficiency antipodal tapered slot antenna with sine-shaped corrugation and a Fermi profile substrate cut-out has been developed for 5G millimeter wave (MMW) communications. A parametric study of a new substrate cutout with Fermi profile is demonstrated to reduce the sidelobe level at the E-plane and H-plane as well as to increase antenna efficiency by an average of 88% over a band of 20-40 GHz. A low-cost printed circuit board (PCB) is processed simply with a CNC Milling machine to fabricate the proposed antenna with Fermi profile substrate cut-out. The measured reflection coefficient is found to be less than -14 dB over a frequency range of 20-40 GHz. Furthermore, the measured gain of the proposed antenna is 17 dB at 30 GHz and the measured radiation pattern and gain is almost constant within the wide bandwidth from 30-40 GHz. Therefore, this antenna is proposed for use in an H-plane array structure such as for point-to-point communication systems, a switched-beam system.


The staircase to terrorism: a psychological exploration.

To foster a more in-depth understanding of the psychological processes leading to terrorism, the author conceptualizes the terrorist act as the final step on a narrowing staircase. Although the vast majority of people, even when feeling deprived and unfairly treated, remain on the ground floor, some individuals climb up and are eventually recruited into terrorist organizations. These individuals believe they have no effective voice in society, are encouraged by leaders to displace aggression onto out-groups, and become socialized to see terrorist organizations as legitimate and out-group members as evil. The current policy of focusing on individuals already at the top of the staircase brings only short-term gains. The best long-term policy against terrorism is prevention, which is made possible by nourishing contextualized democracy on the ground floor.


Reducing gender bias in word embeddings

Word embedding is a popular framework that represents text data as vectors of real numbers. These vectors capture semantics in language, and are used in a variety of natural language processing and machine learning applications. Despite these useful properties, word embeddings derived from ordinary language corpora necessarily exhibit human biases [6]. We measure direct and indirect gender bias for occupation word vectors produced by the GloVe word embedding algorithm [9], then modify this algorithm to produce an embedding with less bias to mitigate amplifying the bias in downstream applications utilizing this embedding.


Text Mining of Tweet for Sentiment Classification and Association with Stock Prices

In present days, the social media and networking act as one of the key platforms for sharing information and opinions. Many people share ideas, express their view points and opinions on various topic of their interest. Social media text has rich information about the companies, their products and various services offered by them. In this research we focus exploring the association of sentiments of social media text and stock prices of a company. The tweets of several company has been extracted and performed sentiment classification using Naïve Bayes classifier and SVM classifier. To perform the classification, N-gram based feature vectors are constructed using important words of tweets. Further, the pattern of association between number of tweets which are positive or negative and stock prices has been explored. Motivated by such an association, the features related to tweets such as number of positive, negative, neutral tweets and total number of tweets are used to predict the stock market status using Support Vector Machine classifier.


History and State of the Art in Commercial Electric Ship Propulsion, Integrated Power Systems, and Future Trends

Electric propulsion has emerged as one of the most efficient propulsion arrangements for several vessel types over the last decades. Even though examples can be found in the history at the end of 19th century, and further into the 20th century, the modern use of electric propulsion started in the 1980s along with the development of semiconductor switching devices to be used in high power drives (dc drives and later ac-to-ac drives). This development opened up for full rpm control of propellers and thrusters, and thereby enabling a simplification of the mechanical structure. However, the main reason for using electric propulsion in commercial ship applications is the potential for fuel savings compared to equivalent mechanical alternatives, except for icebreakers where the performance of an electric powered propeller is superior to a combustion engine powered propeller. The fuel saving potential lies within the fact that the applicable vessels have a highly varying operation profile and are seldom run at full power. This favors the power plant principle in which electric power can be produced at any time with optimum running of prime movers, e.g., diesel engines, by turning on and off units depending on the power demand for propulsion and other vessel loads. Icebreakers were among the first vessels to take advantage of this technology later followed by cruise vessel, and the offshore drilling vessels operating with dynamic positioning (DP). The converter technology was rapidly developing and soon the dc drives were replaced with ac drives. In the same period electric propulsion emerged as basic standard for large cruise liners, and DP operated drilling vessels, but also found its way into other segments as shuttle tankers, ferries, and other special vessels. At the same time podded propulsion were introduced, where the electric motor was mounted directly on the propeller shaft in a submerged 360 $^{\circ}$ steerable pod, adding better efficiency, improved maneuvering, and reduced installation space/cost to the benefits of electric propulsion. The future trends are now focusing on further optimization of efficiency by allowing multiple energy sources, independent operation of individual power producers, and energy storage for various applications, such as power back up, peak shaving, or emission free operation (short voyages).


Shining the Floodlights on Mobile Web Tracking — A Privacy Survey

We present the first published large-scale study of mobile web tracking. We compare tracking across five physical and emulated mobile devices with one desktop device as a benchmark. Our crawler is based on FourthParty; however, our architecture avoids clearing state which has the benefit of continual observation of (and by) third-parties. We confirm many intuitive predictions and report a few surprises. The lists of top third-party domains across different categories devices are substantially similar; we found surprisingly few mobile-specific ad networks. The use of JavaScript by tracking domains increases gradually as we consider more powerful devices. We also analyze cookie longevity by device. Finally, we analyze a curious phenomenon of cookies that are used to store information about the user’s browsing history on the client. Mobile tracking appears to be an under-researched area, and this paper is only a first step. We have made our code and data available at http://webtransparency.org/ for others to build on.