Cyber Security and Power System Communication—Essential Parts of a Smart Grid Infrastructure

The introduction of “smart grid” solutions imposes that cyber security and power system communication systems must be dealt with extensively. These parts together are essential for proper electricity transmission, where the information infrastructure is critical. The development of communication capabilities, moving power control systems from “islands of automation” to totally integrated computer environments, have opened up new possibilities and vulnerabilities. Since several power control systems have been procured with “openness” requirements, cyber security threats become evident. For refurbishment of a SCADA/EMS system, a separation of the operational and administrative computer systems must be obtained. The paper treats cyber security issues, and it highlights access points in a substation. Also, information security domain modeling is treated. Cyber security issues are important for “smart grid” solutions. Broadband communications open up for smart meters, and the increasing use of wind power requires a “smart grid system”.


Trustworthiness Attributes and Metrics for Engineering Trusted Internet-Based Software Systems

Trustworthiness of Internet-based software systems, apps, services and platform is a key success factor for their use and acceptance by organizations and end-users. The notion of trustworthiness, though, is subject to individual interpretation and preference, e.g., organizations require confidence about how their business critical data is handled whereas end-users may be more concerned about usability. As one main contribution, we present an extensive list of software quality attributes that contribute to trustworthiness. Those software quality attributes have been identified by a systematic review of the research literature and by analyzing two real-world use cases. As a second contribution, we sketch an approach for systematically deriving metrics to measure the trustworthiness of software system. Our work thereby contributes to better understanding which software quality attributes should be considered and assured when engineering trustworthy Internet-based software systems.


The foreign-language effect: thinking in a foreign tongue reduces decision biases.

Would you make the same decisions in a foreign language as you would in your native tongue? It may be intuitive that people would make the same choices regardless of the language they are using, or that the difficulty of using a foreign language would make decisions less systematic. We discovered, however, that the opposite is true: Using a foreign language reduces decision-making biases. Four experiments show that the framing effect disappears when choices are presented in a foreign tongue. Whereas people were risk averse for gains and risk seeking for losses when choices were presented in their native tongue, they were not influenced by this framing manipulation in a foreign language. Two additional experiments show that using a foreign language reduces loss aversion, increasing the acceptance of both hypothetical and real bets with positive expected value. We propose that these effects arise because a foreign language provides greater cognitive and emotional distance than a native tongue does.


Fixed budget quantized kernel least-mean-square algorithm

We present a quantization-based kernel least mean square (QKLMS) algorithm with a fixed memory budget. In order to deal with the growing support inherent in online kernel methods, the proposed method utilizes a growing and pruning combined technique and defines a criterion, significance, based on weighted statistical contribution of a data center. This method doesn’t need any apriori information and its computational complexity is acceptable, linear with the center number. As we show theoretically and experimentally, the introduced algorithm successfully quantifies the least ‘significant’ datum and preserves the most important ones resulting in less system error.


On Extracting Structured Knowledge from Unstructured Business Documents

Efficient management of text data is a major concern of business organizations. In this direction, we propose a novel approach to extract structured knowledge from large corpora of unstructured business documents. This knowledge is represented in the form of object instances, which are common ways of organizing the available information about entities, and are modeled here using document templates. The approach itself is based on the observation that a significant fraction of these documents are created using the cut-copy-pastemethod, and thus, it is important to factor this observation into business document analysis projects. Correspondingly, our approach solves the problem of object instance extraction in two steps, namely similarity search and then extraction of object instances from the selected documents. Early qualitative results on a couple of carefully selected document corpora indicate the effective applicability of the approach for solving an important component of the efficient text management problem.


Automated Image Annotation based on YOLOv3

A typical pedestrian protection system requires sophisticated hardware and robust detection algorithms. To solve these problems the existing systems use hybrid sensors where mono and stereo vision merged with active sensors. One of the most assuring pedestrian detection sensors is far infrared range camera. The classical pedestrian detection approach based on Histogram of oriented gradients is not robust enough to be applied in devices which consumers can trust. An application of deep neural network-based approach is able to perform with significantly higher accuracy. However, the deep learning approach requires a high number of labeled data examples. The investigation presented in this paper aimed the acceleration of pedestrian labeling in far-infrared image sequences. In order to accelerate pedestrian labeling in far-infrared camera videos, we have integrated the YOLOv3 object detector into labeling software. The verification of the pre-labeled results was around eleven times faster than manual labeling of every single frame.


STUDENTS ACCEPTANCE OF MOBILE LEARNING FOR HIGHER EDUCATION IN SAUDI ARABIA

Mobile learning is the next step in the development of distance learning. Widespread access to mobile devices and the opportunity to learn regardless of time and place make the mobile learning an important tool for lifelong learning. The research objectives are to examine the possibility of acceptance in mobile learning (m-Learning) and study main factors that affect using mLearning that focus on higher education students in Saudi Arabia. The researcher used a quantitative approach survey of 80 students. The modified acceptance framework that based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model is adopted to determine the factors that influence the students’ intention to use m-Learning. The results from statistical analysis show that the acceptance level of students on m-Learning is in the high level.


A digitally compensated 1.5 GHz CMOS/FBAR frequency reference

A temperature-compensated 1.5 GHz film bulk acoustic wave resonator (FBAR)-based frequency reference implemented in a 0.35 ¿m CMOS process is presented. The ultra-small form factor (0.79 mm × 1.72 mm) and low power dissipation (515 ¿A with 2 V supply) of a compensated FBAR oscillator present a promising alternative for the replacement of quartz crystal frequency references. The measured post-compensation frequency drift over a 0-100°C temperature range is <±10 ppm. The measured oscillator phase noise is -133 dBc/ Hz at 100 kHz offset from the 1.5 GHz carrier.


A Lightweight Virtualization Solution for Android Devices

Mobile virtualization has emerged fairly recently and is considered a valuable way to mitigate security risks on Android devices. However, major challenges in mobile virtualization include runtime, hardware, resource overhead, and compatibility. In this paper, we propose a lightweight Android virtualization solution named Condroid, which is based on container technology. Condroid utilizes resource isolation based on namespaces feature and resource control based on cgroups feature. By leveraging them, Condroid can host multiple independent Android virtual machines on a single kernel to support mutilple Android containers. Furthermore, our implementation presents both a system service sharing mechanism to reduce memory utilization and a filesystem sharing mechanism to reduce storage usage. The evaluation results on Google Nexus 5 demonstrate that Condroid is feasible in terms of runtime, hardware resource overhead, and compatibility. Therefore, we find that Condroid has a higher performance than other virtualization solutions.


Why an Intelligence Explosion is Probable

If a future Artificial Intelligence were to reach the level of human intelligence there is a possibility that it would be able to rapidly redesign itself until its own capabilities far exceeded those of human beings. We analyze the principle factors that might govern the rapidity of this ‘intelligence explosion’ process. We argue that if degree of intelligence is defined using a relatively uncontroversial measure that involves only the relative speed of thought with respect to that of the human mind, and if an intelligence explosion is defined as a thousandfold increase in speed, then there are compelling reasons to believe that none of the barriers to this process look plausible, and therefore an intelligence explosion is highly likely.


Combined Star-Delta Winding Analysis

The combined star-delta winding has been attracting the attention of industry because of its potential to improve the efficiency of electrical machines. In order to show the efficiency benefits of this type of winding, many authors have used methods to determine the improvements in winding factors, published in different papers, which have not been validated yet. Some of these methods contradict each other. Furthermore, as a demerit of this winding, a unidirectional rotation has been reported. In this paper, a set of new formulas to calculate the winding factors for combined star-delta windings is proposed. In order to check the validity of the proposed formulas, a 2-D FEM and experimental tests on a salient pole synchronous machine are presented, which allows different combined star-delta winding connections. In contrast to other publications, the measurements show that combined windings can be applied for both directions of rotation without any problems.


Examining Alignment of Frames Using Actor-Network Constructs: The Implementation of an IT Project

IT projects often cannot succeed without bridging different stakeholders’ perspectives on system design and function during implementation. These perspectives often start as competing technology frames consisting of different beliefs, interests, technology evaluation routines and artifact characteristics. For successful implementation of IT, these competing frames usually need to be stabilized into a dominant frame which is widely accepted. Prior research has offered few clues on the mechanisms that are at work in aligning competing frames. This paper uses a case study to propose mechanisms for aligning frames—namely, two actor-network theory constructs of “black-boxing” and “obligatory points of passage”—that aid in translating the competing frames into a truce frame. The examined properties of these mechanisms are described and analyzed, thereby extending the literature on technology frames by providing evidence of generic aligning mechanisms.


Swearing, Euphemisms, and Linguistic Relativity

Participants read aloud swear words, euphemisms of the swear words, and neutral stimuli while their autonomic activity was measured by electrodermal activity. The key finding was that autonomic responses to swear words were larger than to euphemisms and neutral stimuli. It is argued that the heightened response to swear words reflects a form of verbal conditioning in which the phonological form of the word is directly associated with an affective response. Euphemisms are effective because they replace the trigger (the offending word form) by another word form that expresses a similar idea. That is, word forms exert some control on affect and cognition in turn. We relate these findings to the linguistic relativity hypothesis, and suggest a simple mechanistic account of how language may influence thinking in this context.


Game-Enhanced Second Language Vocabulary Acquisition Strategies: A Systematic Review

This paper is a synthesis of 17 qualitative, quantitative, and mixed-method studies published over the last decade. The synthesis suggests that game-enhanced learning provides a set of effective strategies such as language repetitions, contextual clues, interaction with native speakers and peers, and imagery to practice and use second language vocabulary in the authentic context. Some of the strategies such as word lists, dictionaries, and vocabulary exercises are more beneficial when combined with native speakers or peers’ interactions. Due to high interactivity of games, note taking and media strategies provide less support for vocabulary learning. The lack of high quality studies and empirical data makes it difficult to draw conclusions about which video games strategies provide the most benefit. The synthesis of research identifies that generally game-enhanced practices are helpful for second language vocabulary enhancement.


Development of wearable and flexible ultrasonic sensor for skeletal muscle monitoring

A wearable and flexible ultrasonic sensor was developed for monitoring of skeletal muscle contraction. The sensor was constructed using a PVDF piezoelectric polymer film without a matching layer or backing material. Due to its lightness (less than 1 gram), thinness (200 μm) and flexibility, this sensor was wearable and enabled non-invasive and continuous muscle monitoring without restricting muscle movement, which is not feasible using a conventional handheld ultrasonic probe. The developed sensor was used to monitor muscle contractions in the index finger by ultrasonic pulse-echo measurements and in the forearm by through-transmission measurements. It was successfully demonstrated that the tissue thickness variations were measured in accordance with the muscle contraction performed.


Overcoming the Bell-Shaped Dose-Response of Cannabidiol by Using Cannabis Extract Enriched in Cannabidiol

Cannabidiol (CBD), a major constituent of Cannabis, has been shown to be a powerful anti‐in‐ flammatory and anti‐anxiety drug, without exerting a psychotropic effect. However, when given either intraperitoneally or orally as a purified product, a bell‐shaped dose‐response was observed, which limits its clinical use. In the present study, we have studied in mice the anti‐inflammatory and anti‐nociceptive activities of standardized plant extracts derived from the Cannabis sativa L., clone 202, which is highly enriched in CBD and hardly contains any psychoactive ingredients. In stark contrast to purified CBD, the clone 202 extract, when given either intraperitoneally or orally, provided a clear correlation between the anti‐inflammatory and anti‐nociceptive responses and the dose, with increasing responses upon increasing doses, which makes this plant medicine ideal for clinical uses. The clone 202 extract reduced zymosan‐induced paw swelling and pain in mice, and prevented TNFα production in vivo. It is likely that other components in the extract synergize with CBD to achieve the desired anti‐inflammatory action that may contribute to overcoming the bell‐shaped dose‐response of purified CBD. We therefore propose that Cannabis clone 202 (Avi‐ dekel) extract is superior over CBD for the treatment of inflammatory conditions.


TSC-DL: Unsupervised trajectory segmentation of multi-modal surgical demonstrations with Deep Learning

The growth of robot-assisted minimally invasive surgery has led to sizable datasets of fixed-camera video and kinematic recordings of surgical subtasks. Segmentation of these trajectories into locally-similar contiguous sections can facilitate learning from demonstrations, skill assessment, and salvaging good segments from otherwise inconsistent demonstrations. Manual, or supervised, segmentation can be prone to error and impractical for large datasets. We present Transition State Clustering with Deep Learning (TSC-DL), a new unsupervised algorithm that leverages video and kinematic data for task-level segmentation, and finds regions of the visual feature space that correlate with transition events using features constructed from layers of pre-trained image classification Deep Convolutional Neural Networks (CNNs). We report results on three datasets comparing Deep Learning architectures (AlexNet and VGG), choice of convolutional layer, dimensionality reduction techniques, visual encoding, and the use of Scale Invariant Feature Transforms (SIFT). We find that the deep architectures extract features that result in up-to a 30.4% improvement in Silhouette Score (a measure of cluster tightness) over the traditional “shallow” features from SIFT. We also present cases where TSC-DL discovers human annotator omissions. Supplementary material, data and code is available at: http://berkeleyautomation.github.io/tsc-dl/.


Effects of concurrent endurance and strength training on running economy and .VO(2) kinetics.

PURPOSE
It has been suggested that endurance training influences the running economy (CR) and the oxygen uptake (.VO(2)) kinetics in heavy exercise by accelerating the primary phase and attenuating the .VO(2) slow component. However, the effects of heavy weight training (HWT) in combination with endurance training remain unclear. The purpose of this study was to examine the influence of a concurrent HWT+endurance training on CR and the .VO(2) kinetics in endurance athletes.

METHODS
Fifteen triathletes were assigned to endurance+strength (ES) or endurance-only (E) training for 14 wk. The training program was similar, except ES performed two HWT sessions a week. Before and after the training period, the subjects performed 1) an incremental field running test for determination of .VO(2max) and the velocity associated (V(.VO2max)), the second ventilatory threshold (VT(2)); 2) a 3000-m run at constant velocity, calculated to require 25% of the difference between .VO(2max) and VT(2), to determine CR and the characteristics of the VO(2) kinetics; 3) maximal hopping tests to determine maximal mechanical power and lower-limb stiffness; 4) maximal concentric lower-limb strength measurements.

RESULTS
After the training period, maximal strength were increased (P < 0.01) in ES but remained unchanged in E. Hopping power decreased in E (P < 0.05). After training, economy (P < 0.05) and hopping power (P < 0.001) were greater in ES than in E. .VO(2max), leg hopping stiffness and the .VO(2) kinetics were not significantly affected by training either in ES or E.

CONCLUSION
Additional HWT led to improved maximal strength and running economy with no significant effects on the .VO(2) kinetics pattern in heavy exercise.


The igraph software package for complex network research

There is no other package around that satisfies all the following requirements: •Ability to handle large graphs efficiently •Embeddable into higher level environments (like R [6] or Python [7]) •Ability to be used for quick prototyping of new algorithms (impossible with “click & play” interfaces) •Platform-independent and open source igraph aims to satisfy all these requirements while possibly remaining easy to use in interactive mode as well.


Recurrence Plots of Dynamical Systems .

A new graphical tool for measuring the time constancy of dynamical systems is presented and illustrated with typical examples. In recent years a number of methods have been devised to compute dynamical parameters from time series [ 11. Such parameters are the information dimension, entropy, Liapunov exponents, dimension spectrum, etc. In all cases it is assumed that the time series is obtained from an autonomous dynamical system, i .e. the evolution equations do not contain the time explicitly. It is also assumed that the time series is much longer than the characteristic times of the dynamical system. In the present letter we present a new diagnostic tool which we call recurrence plot; this tool tests the above assumptions, and gives useful information also when they are not satisfied. As the examples will show, the information obtained from recurrence plots is often surprising, and not easily obtainable by other methods. Let x( i ) be the i-th point on the orbit describing a dynamical system in d-dimensional space, for i = 1, ..., N . The recurrence plot is an array of dots in a N x N square, where a dot is placed at (i, j) whenever x( j) is sufficiently close to x( i ) . In practice one proceeds as follows to obtain a recurrence plot from a time series {ui}. First, choosing an embedding dimension d, one constructs the d-dimensional orbit of X(i) by the method of time delays (i.e. if the ui are scalar, x(i) = (ui, u ~ + ~ , ..., u ~ + ~ ~ ) ) . Next, one chooses r(i) such that the ball of radius r(i) centred at x (i) in Rd contains a reasonable number of other points x (j) of the orbit (in the examples below, we have chosen a sequence of increasing radii and stopped when we found at least 10 neighbours). In our computation we have allowed the radius r(i) to depend on the point x ( i ) , and r(i) has been selected by a routine used in our alghorithm for the determination of the Liapunov exponents-see ref. [2]. Finally, one plots a dot at each point (i, j) for which x ( j ) is in the ball of radius r(i) centred at x( i ) . We call this picture a recurrence plot. Note that the i, j are in fact times: therefore a recurrence plot describes natural (but subtle) time correlation information. Recurrence plots tend to be fairly symmetric with respect to the diagonal i =j because if x ( i ) is close to x(j), then x ( j ) is close to x( i ) . There is, however, no complete symmetry because we do not require r(i) = r ( j ) . 974 EUROPHYSICS LETTERS Fig. 1. A scatter plot for 20000 iterates of the Henon map (x, y)+ (1 1.4x2+ y, 0 . 3 ~ ) . The embedding dimension is 8. In this figure, and in figs. 2 and 4, the diagonal is added. We shall now analyse some recurrence plots obtained from experimental or computergenerated time series. We distinguish-somewhat arbitrarily-between large-scale typology and small-scale textuye. For an autonomous dynamical system, if all the characteristic times are short compared to the length of the time series, the typology of the recurrence plot is homogeneous. This means that the overall pattern is uniformly grey although at small scale nontrivial texture may be visible (see below). An example of a homogeneous recurrence plot is provided by the Henon map, as exhibited in fig. 1. Another typology is provided by the time evolution with drif t , i .e. by dynamical systems which are not quite homogeneous, but contain adiabatically (slowly) varying parameters [3]. To get an example we started with a time series obtained from the Lorenz system, and added a term varying linearly with time (from 0 to 10 percent of the amplitude of the original signal). The corresponding recurrence plot is our fig. 2. The rich structure of this plot is due to the Lorenz system itself and not to the drift; the main effect of the drift is to make the plot somewhat paler away from the diagonal and darker near it (progressive decorrelation at large time intervals). Since the human eyehrain is not very good at seeing small variations of overall darkness, a histogram of darkness as a function of i j is given in fig. 3; this shows unquestionably the existence of a drift. Our next recurrence plot, fig. 4, shows what might be called a periodic typology; it is obtained from an experimental time series communicated by Ciliberto and described in ref. [21. This striking picture shows that-after a transient-the system goes into slow oscillations superimposed on the chaotic motion which is otherwise known to exist (2 positive characteristic exponents have been found in ref[2]). These slow oscillations (which are not quite periodic) are not easily recognizable on the original time series (and certainly have gone unrecognized in the original analysis). J.-P. ECKMANN et al.: RECURRENCE PLOTS OF DYNAMICAL SYSTEMS 975


Centrality in Social Networks Conceptual Clarification

The intuitive background for measures of structural centrality in social networks is reviewed aPzd existing measures are evaluated in terms of their consistency with intuitions and their interpretability. Three distinct intuitive conceptions of centrality are uncovered and existing measures are refined to embody these conceptions. Three measures are developed for each concept, one absolute and one relative measure of the ~entra~~t~~ of ~os~tio~ls in a network, and one relenting the degree of centralization of the entire network. The implications of these measures for the experimental study of small groups is examined.


Graph Drawing by Force-directed Placement

A graph G = (V,E) is a set V of vertices and a set E of edges, in which an edge joins a pair of vertices. 1 Normally, graphs are depicted with their vertices as points in a plane and their edges as line or curve segments connecting those points. There are different styles of representation, suited to different types of graphs or different purposes of presentation. We concentrate on the most general class of graphs: undirected graphs, drawn with straight edges. In this paper, we introduce an algorithm that attempts to produce aesthetically-pleasing, two-dimensional pictures of graphs by doing simplified simulations of physical systems. We are concerned with drawing undirected graphs according to some generally accepted aesthetic criteria: 2


SnakeFighter - Development of a Water Hydraulic Fire Fighting Snake Robot

This paper presents the SnakeFighter concept and describes the generic element within this concept in the form of a water hydraulic snake robot. Applications of a SnakeFighter system are presented with focus on fire intervention tasks. The development of a water hydraulic snake robot that demonstrates the concept is described. The robot is the first water hydraulic snake robot ever constructed. The paper identifies design challenges of a complete SnakeFighter system and describes future research on this concept


The role played by the concept of presence in validating the efficacy of a cybertherapy treatment: a literature review

The present paper considers the existing research in cybertherapy, which is a psychological therapy carried out with the use of a mediated environment, and examines the way in which the users’ sense of presence in the mediated environment can be of relevance for the validation of the intervention. With this purpose, a collection of 41 papers reporting the measurement of presence in the context of a cybertherapy treatment has been identified and examined. The general relevance of presence in cybertherapy and the measurement techniques adopted in the studies collected here are described and discussed. The way in which presence corresponds to establishing internal validity, convergent or predictive validity and external validity of a treatment is examined. In conclusion, a checklist to apply when planning a validation study is proposed, to improve the way in which presence is used.


Procedural Embedding of knowledge in Planner

Since the l a s t I JCAI , the PLANNER problem s o l v i n g fo rma l i sm has con t i nued to deve lop . Our ep ls temo lgy f o r the f ounda t i ons f o r problem s o l v i n g has been ex tended . An overv iew of the f o rma l i sm Is g iven from an I n f o r m a t i o n p rocess ing v i e w p o i n t . A s imp le example Is e x p l a i n e d us ing snapshots of the s t a t e of the problem s o l v i n g as the example I s worked, f i n a l l y , c u r r e n t a p p l i c a t i o n s f o r the fo rma l i sm are I i s ted . 1 . The S t r u c t u r a l Foundat ions o f Problem S o l v i n g We would l i k e to develop a f o u n d a t i o n f o r problem s o l v i n g analogous In some ways to the c u r r e n t l y e x i s t i n g f ounda t i ons f o r mathemat ics . Thus we need to analyze the s t r u c t u r e of f o u n d a t i o n s f o r mathemat ics . A f o u n d a t i o n f o r mathematics must p rov ide a d e f i n i t i o n a l f o rma l i sm In which mathemat ica l o b j e c t s can be d e f i n e d and t h e i r e x i s t e n c e p roved . For example set t heo ry as a f o u n d a t i o n p rov ides t h a t o b j e c t s must be b u i l t out o f s e t s . Then the re must be a deduc t i ve f o r m a l i s m In which fundamental t r u t h s can be s t a t e d and the means p rov ided to deduce a d d i t i o n a l t r u t h s f rom those a l r e a d y e s t a b l i s h e d . Cur rent mathemat ica l f ounda t i ons such as set theo ry seem q u i t e n a t u r a l and adequate f o r the vas t body o f c l a s s i c a l mathemat ics . The o b j e c t s and reason ing of most mathemat ica l domains such as a n a l y s i s and a lgebra can be e a s i l y founded on set t h e o r y . The e x i s t e n c e o f c e r t a i n a s t r o n o m i c a l l y l a r g e c a r d i n a l s poses some problems f o r set t h e o r e t i c f o u n d a t i o n s . However, the problems posed seem to be of p r a c t i c a l Importance on l y t o c e r t a i n ca tego ry t h e o r i s t s . Foundat ions o f mathemat ics have devoted a g rea t deal o f a t t e n t i o n to the problems o f c o n s i s t e n c y and comple teness. The problem of cons i s t ency Is Impor tant s i nce I f the f ounda t i ons are i n c o n s i s t e n t then any fo rmu la whatsoever may be deduced thus t r i v i a l i z i n g the f o u n d a t i o n s . Semantics f o r f ounda t i ons o f mathemat ics are d e f i n e d model 167 t h e o r e t i c a l l y In terms o f the n o t i o n o f s a t i s f i a b i l i t y . The problem o f completeness is t h a t , f o r a f o u n d a t i o n of mathematics to be I n t u i t i v e l y s a t i s f a c t o r y a l l the t r u e fo rmu las shou ld be proveab le s ince a f o u n d a t i o n mathemat ics alms to be a theory of mathemat ica l t r u t h . S i m i l a r fundamental ques t i ons must be faced by a f o u n d a t i o n f o r problem s o l v i n g . However t he re are some impor tant d i f f e r e n c e s s ince a f o u n d a t i o n of problem s o l v i n g alms more to be a theory of a c t i o n s and purposes than a theory of mathemat ica l t r u t h . A f o u n d a t i o n f o r problem s o l v i n g must s p e c i f y a g o a l o r l e n t e d f o rma l i sm In which problems can be s t a t e d . Fur thermore the re must be a f o rma l i sm f o r s p e c i f y i n g the a l l o w a b l e methods o f s o l u t i o n of p rob lems. As p a r t o f the d e f i n i t i o n o f the f o r m a l i s m s , the f o l l o w i n g elements must be d e f i n e d : the data s t r u c t u r e , the c o n t r o l s t r u c t u r e , and the p r i m i t i v e p rocedures . The problem of what are a l l o w a b l e data s t r u c t u r e s f o r f a c t s about the wo r l d Immediate ly a r i s e s . Being a theory of a c t i o n s , a f o u n d a t i o n f o r problem s o l v i n g must c o n f r o n t the problem of change: How can account be taken of the changing s i t u a t i o n In the wor ld? In o rder f o r the re to be problem s o l v i n g , the re must be a problem s o l v e r . A f o u n d a t i o n f o r problem s o l v i n g must cons ide r how much knowledge and what k ind of knowledge problem s o l v e r s can have about themse lves . In c o n t r a s t to the f o u n d a t i o n o f mathemat ics , the semant ics f o r a f o u n d a t i o n f o r problem s o l v i n g should be d e f i n e d In terms of p r o p e r t i e s of p rocedures . We would l i k e to see mathemat ica l I n v e s t i g a t i o n s on the adequacy of the f o u n d a t i o n s f o r problem s o l v i n g p rov ided by PLANNER. In chap te r C of the d i s s e r t a t i o n , we have made the beg inn ings of one k ind of such an I n v e s t i g a t i o n . To be more s p e c i f i c a f o u n d a t i o n f o r problem s o l v i n g must concern I t s e l f w i t h the f o l l o w i n g complex o f t o p i c s . PROCEDURAL EMBEDDING: How can " r e a l w o r l d " knowledge be e f f e c t i v e l y embedded In p rocedures . What are good ways to express problem s o l u t i o n methods and how can p lans f o r the s o l u t i o n of problems be fo rmu la ted? GENERALIZED COMPILATION: What are good methods f o r t r a n s f o r m i n g h igh l e v e l g o a l o r l e n t e d language in to s p e c i f i c e f f i c i e n t a l g o r i t h m s .


A high gain multi-port bidirectional non-isolated DC-DC converter for renewable integration

A topology for bidirectional non-isolated multiport DC-DC converter is proposed in this paper. The energy generation from the renewable sources such as wind and solar aie intermittent in nature. A battery port is mandatory for this purpose. Hence, a high gain multiport bidirectional converter for DC micro-grid application with high conversion efficiency, reduced size, and low cost is proposed. The bidirectional power flow capability through a feedback back path to charge a battery from the load capacitor is also provided. Therefore, the battery is not only charged from the input source but also from the load capacitor. The number of outputs are independent from each other and the battery is charged from the second output. Steady state operation and theoretical analysis of the converter proposed is elaborated. To validate the performance and theoretical concepts, it is simulated in MATLAB/Simulink software. The results of simulation verify the appropriate performance of proposed topology.


Solid state circuit breaker protection devices for DC microgrid in review

The advent of alternating current (AC) is due to its ability to transmit power over long distances from transformers, does not mark the end of utilizing direct current (DC). The development of renewable energy sources is because of high usage of DC because the generated power is directed to the main grid for conversion into electric power and the source of energy cannot tolerate high voltage. Fault detection is a major problem that arises when an excess amount of current flows in the circuit leading to the necessity of having circuit breakers. Circuit breakers are made of light materials, which creates a problem for dealing with high voltage DC applications with the protection of circuit breakers have become popularized, and different papers have outlined various studies on the effectiveness of Solid State Circuit Breakers (SSCBs) protection for a direct current microgrid. This paper reviews the benefits and shortfalls of The Wide bandgap (WBG) SSCBs and its application with photovoltaic (PV) generators.


Patients' Experience After a Fall and Their Perceptions of Fall Prevention: A Qualitative Study.

An exploratory descriptive study was conducted to explore the perspectives of patients who had fallen in the hospital; 100 patients were interviewed. An inductive content analysis approach was adopted. Six themes emerged: Apathetic toward falls, self-blame behavior, reluctance to impose on busy nurses, negative feelings toward nurses, overestimating own ability, and poor retention of information. Patients often downplayed the risks of falls and were reluctant to call for help.


Annual research review: re-thinking the classification of autism spectrum disorders.

BACKGROUND
The nosology of autism spectrum disorders (ASD) is at a critical point in history as the field seeks to better define dimensions of social-communication deficits and restricted/repetitive behaviors on an individual level for both clinical and neurobiological purposes. These different dimensions also suggest an increasing need for quantitative measures that accurately map their differences, independent of developmental factors such as age, language level and IQ.

METHOD
Psychometric measures, clinical observation as well as genetic, neurobiological and physiological research from toddlers, children and adults with ASD are reviewed.

RESULTS
The question of how to conceptualize ASDs along dimensions versus categories is discussed within the nosology of autism and the proposed changes to the DSM-5 and ICD-11. Differences across development are incorporated into the new classification frameworks.

CONCLUSIONS
It is crucial to balance the needs of clinical practice in ASD diagnostic systems, with neurobiologically based theories that address the associations between social-communication and restricted/repetitive dimensions in individuals. Clarifying terminology, improving description of the core features of ASD and other dimensions that interact with them and providing more valid and reliable ways to quantify them, both for research and clinical purposes, will move forward both practice and science.


Efficacy of botulinum toxin A in upper limb function of hemiplegic patients

Botulinum toxin A has been reported to reduce spasticity and increase the comfort of hemiplegic patients. The aim of this study was to assess the efficacy of the treatment on disability, especially in manual activities, and to attempt to identify predictive factors of improvement. Twenty patients (mean age: 54.4 years; M: 14; right hemiplegia: 12) were included, with a delay of at least three months after unilateral hemispheric stroke. Botulinum toxin A (BOTOX®) was injected into the arm adductors (8 cases), forearm flexors (17 cases), pronators, wrist and finger flexors (20 cases), with a total dose of 200 to 300 U. Examination (day 1 and 15, month 2 and 5) consisted of spasticity assessment (modified Ashworth scale), muscle strength, passive range of motion (goniometry), and pain, followed by functional tests, especially the Rivermead Motor Assessment (RMA) and Nine-hole Peg Test (NHPT). Performance in daily living was assessed with the Functional Independence Measure (FIM), and an original analysis of hand grasp, grip and pinches used in domestic activities (9 items), and of comfort of patients and caregivers. Significant reduction in spasticity was observed on the elbow flexors, pronators, wrist and fingers flexors, especially at day 15 (mean 0.90 to 1 point), with wide variations in effect. Muscle strength was increased in wrist and fingers extensors, with concomitant increase in the opening of the thumb to index finger space. There was no effect on the NHPT requiring distal manipulation, but the RMA, which especially concerned picking up and releasing a tennis ball, showed significant improvement. Furthermore, use of the upper limb in daily living increased, particularly for internal grasping of objects, and for grasping by the top, transporting and releasing of objects. Patients and caregivers reported facilitation in dressing, and in proximal and distal care of the upper limb. The global flexor position of the limb improved. Adverse reactions were rare and mostly consisted of transitory pain during injection. The improvement in the RMA was better explained by the quality of the initial motor command on distal prehension (positive correlation with motor strength), and that in hand using in domestic activities by a lower level of spasticity on pronators and wrist flexors (negative correlations with spasticity). Conversely, the severity of the motor deficit (negative correlations with motor strength) and a high level of spasticity before injection (positive correlations with spasticity) mostly explained the improvement in comfort. In conclusion, botulinum toxin A is efficient in improving hand use in patients with relatively preserved distal motricity, and in increasing comfort in patients with severe global disorders.


Use of Virtual Reality Feedback for Patients with Chronic Neck Pain and Kinesiophobia

This study examined how individuals with and without neck pain performed exercises under the influence of altered visual feedback in virtual reality. Chronic neck pain (n = 9) and asymptomatic (n = 10) individuals were recruited for this cross-sectional study. Participants performed head rotations while receiving programmatically manipulated visual feedback from a head-mounted virtual reality display. The main outcome measure was the control-display gain (ratio between actual head rotation angle and visual rotation angle displayed) recorded at the just-noticeable difference. Actual head rotation angles were measured for different gains. Detection of the manipulated visual feedback was affected by gain. The just-noticeable gain for asymptomatic individuals, below and above unity gain, was 0.903 and 1.159, respectively. Head rotation angle decreased or increased 5.45° for every 0.1 increase or decrease in gain, respectively. The just-noticeable gain for chronic pain individuals, below unity gain, was 0.950. The head rotation angle increased 4.29° for every 0.1 decrease in gain. On average, chronic pain individuals reported that neck rotation was feasible for 84% of the unity gain trials, 66% of the individual just-noticeable difference trials, and 50% of the “nudged” just-noticeable difference trials. This research demonstrated that virtual reality may be useful for promoting the desired outcome of increased range of motion in neck rehabilitation exercises by altering visual feedback.


Detailed Garment Recovery from a Single-View Image

Most recent garment capturing techniques rely on acquiring multiple views of clothing, which may not always be readily available, especially in the case of pre-existing photographs from the web. As an alternative, we propose a method that is able to compute a rich and realistic 3D model of a human body and its outfits from a single photograph with little human interaction. Our algorithm is not only able to capture the global shape and geometry of the clothing, it can also extract small but important details of cloth, such as occluded wrinkles and folds. Unlike previous methods using full 3D information (i.e. depth, multi-view images, or sampled 3D geometry), our approach achieves detailed garment recovery from a single-view image by using statistical, geometric, and physical priors and a combination of parameter estimation, semantic parsing, shape recovery, and physicsbased cloth simulation. We demonstrate the effectiveness of our algorithm by re-purposing the reconstructed garments for virtual try-on and garment transfer applications, as well as cloth animation for digital characters.


SEMI-AUTOMATIC EXTRACTION OF RIBBON ROADS FORM HIGH RESOLUTION REMOTELY SENSED IMAGERY BY COOPERATION BETWEEN ANGULAR TEXTURE SIGNATURE AND TEMPLATE MATCHING

Road tracking is a promising technique to increase the efficiency of road mapping. In this paper an improved road tracker, based on cooperation between angular texture signature and template matching, is presented. Our tracker uses parabola to model the road trajectory and to predict the position of next road centreline point. It employs angular texture signature to get the exact moving direction of current road centreline point, and moves forward one predefined step along the direction to reach a new position, and then uses curvature change to verify the new added road point whether right enough. We also build compactness of angular texture signature polygon to check whether the angular texture signature is suitable to be used to go on tracking. When angular texture signature fails, least squares template matching is then employed instead. Cooperation between angular texture signature and template matching can reliably extract continuous and homogenous ribbon roads on high resolution remotely sensed imagery. ∗ Corresponding author


Female Pattern Hair Loss: a clinical and pathophysiological review*

Female Pattern Hair Loss or female androgenetic alopecia is the main cause of hair loss in adult women and has a major impact on patients' quality of life. It evolves from the progressive miniaturization of follicles that lead to a subsequent decrease of the hair density, leading to a non-scarring diffuse alopecia, with characteristic clinical, dermoscopic and histological patterns. In spite of the high frequency of the disease and the relevance of its psychological impact, its pathogenesis is not yet fully understood, being influenced by genetic, hormonal and environmental factors. In addition, response to treatment is variable. In this article, authors discuss the main clinical, epidemiological and pathophysiological aspects of female pattern hair loss.


Learning Fine-Grained Knowledge about Contingent Relations between Everyday Events

Much of the user-generated content on social media is provided by ordinary people telling stories about their daily lives. We develop and test a novel method for learning fine-grained common-sense knowledge from these stories about contingent (causal and conditional) relationships between everyday events. This type of knowledge is useful for text and story understanding, information extraction, question answering, and text summarization. We test and compare different methods for learning contingency relation, and compare what is learned from topic-sorted story collections vs. general-domain stories. Our experiments show that using topicspecific datasets enables learning finergrained knowledge about events and results in significant improvement over the baselines. An evaluation on Amazon Mechanical Turk shows 82% of the relations between events that we learn from topic-sorted stories are judged as contingent.


Distributional Semantic Models of Attribute Meaning in Adjectives and Nouns

Attributes such as size, weight or color are at the core of conceptualization, i.e., the formal representation of entities or events in the real world. In natural language, formal attributes find their counterpart in attribute nouns which can be used in order to generalize over individual properties (e.g., big or small in case of size, blue or red in case of color). In order to ascribe such properties to entities or events, adjectivenoun phrases are a very frequent linguistic pattern (e.g., a blue shirt, a big lion). In these constructions, attribute meaning is conveyed only implicitly, i.e., without being overtly realized at the phrasal surface. This thesis is about modeling attribute meaning in adjectives and nouns in a distributional semantics framework. This implies the acquisition of meaning representations for adjectives, nouns and their phrasal combination from corpora of natural language text in an unsupervised manner, without tedious handcrafting or manual annotation efforts. These phrase representations can be used to predict implicit attribute meaning from adjective-noun phrases – a problem which will be referred to as attribute selection throughout this thesis. The approach to attribute selection proposed in this thesis is framed in structured distributional models. We model adjective and noun meanings as distinct semantic vectors in the same semantic space spanned by attributes as dimensions of meaning. Based on these word representations, we make use of vector composition operations in order to construct a phrase representation from which the most prominent attribute(s) being expressed in the compositional semantics of the adjective-noun phrase can be selected by means of an unsupervised selection function. This approach not only accounts for the linguistic principle of compositionality that underlies adjective-noun phrases, but also avoids inherent sparsity issues that result from the fact that the relationship between an adjective, a noun and a particular attribute is rarely explicitly observed in corpora. The attribute models developed in this thesis aim at a reconciliation of the conflict between specificity and sparsity in distributional semantic models. For this purpose, we compare various instantiations of attribute models capitalizing on pattern-based and dependency-based distributional information as well as attribute-specific latent topics induced from a weakly supervised adaptation of Latent Dirichlet Allocation. Moreover, we propose a novel framework of distributional enrichment in order to enhance structured vector representations by incorporating additional lexical information from complementary distributional sources. In applying distributional enrichment to distributional attribute models, we follow the idea to augment structured


What exactly is viral marketing ?

So what is viral marketing? In 1997, when we first coined the term in a Netscape newsletter (www.dfj.com/viralmarketing), we used several examples to illustrate the phenomenon, defined loosely as “network-enhanced word of mouth.” Its original inspiration came from the adoption pattern of Hotmail, the Webbased email service that launched in 1996. Tim Draper persuaded Hotmail to include a promotional pitch with a clickable URL in every message sent by a Hotmail user. Therein lay one of the critical elements of viral marketing: every customer becomes an involuntary salesperson simply by using the product.


Plan-Based Dialogue Management in a Physics Tutor

This paper describes an application of APE (the Atlas Planning Engine), an integrated planning and execution system at the heart of the Atlas dialogue management system. APE controls a mixedinitiative dialogue between a human user and a host system, where turns in the ‘conversation’ may include graphical actions and/or written text. APE has full unification and can handle arbitrarily nested discourse constructs, making it more powerful than dialogue managers based on finitestate machines. We illustrate this work by describing Atlas-Andes, an intelligent tutoring system built using APE with the Andes physics tutor as the host.


Intelligent Unmanned Air Vehicle Flight Systems

This paper describes an intelligent autonomous airborne flight capability that is being used as a test bed for future technology development. The unmanned air vehicles (UAVs) fly under autonomous control of both an onboard computer and an autopilot. The onboard computer provides the mission control and runs the autonomous Intelligent Controller (IC) software while the autopilot controls the vehicle navigation and flight control. The autonomous airborne flight system is described in detail. An IC architecture directly applicable to the design of unmanned vehicles is also presented. The UAVs may operate independently or in cooperation with one another to carry out a specified mission. The intelligent UAV flight system is used to evaluate and study autonomous UAV control as well as multi-vehicle collaborative control.


Relationship between smartphone addiction and physical activity in Chinese international students in Korea

BACKGROUND AND AIMS
Excessive usage of smartphones may induce social problems, such as depression and impairment of social and emotional functioning. Moreover, its usage can impede physical activity, but the relationship between smartphone addiction and physical activity is obscure. Therefore, we examined the relationship and the impact of excessive smartphone use on physical activity.

METHODS
This study collected data through the structured questionnaire consisting of general characteristics, the number and hours of smartphone usage, and the Smartphone Addiction Proneness Scale (SAPS) from 110 Chinese international students in Korea. The body composition and physical activity, such as the total daily number of steps and consumed calories, were measured.

RESULTS
In this study, high-risk smartphone users showed less physical activity, such as the total number of steps taken and the average consumed calories per day. Moreover, their body composition, such as muscle mass and fat mass, was significantly different. Among these factors, the hours of smartphone use revealed the proportional relationship with smartphone addiction (β = 0.209, p = 0.026), while the average number of walking steps per day showed a significant reverse proportional tendency in participants with smartphone addiction (β = -0.883, p < 0.001).

CONCLUSIONS
Participants with smartphone addiction were less likely to walk for each day. Namely, smartphone addiction may negatively influence physical health by reducing the amount of physical activity, such as walking, resulting in an increase of fat mass and a decrease of muscle mass associated with adverse health consequences.


Snakes and Strings: New Robotic Components for Rescue Operations

The Japanese government is establishing an International Rescue Complex to promote research and development of key technologies for realization of practical search-and-rescue robots, anticipating for future large-scale earthquakes and other catastrophic disasters. This paper proposes a new paradigm called “snakes and strings”, for developing practical mobile robot systems that may be useful in such situations. “Snakes” stands for snake-like robots, which can skillfully move among the debris of the collapsed buildings. “Strings”, on the other hand, means robotic systems using strings or tethers, such as proposed in the “hyper-tether” research [9]. Theters can continuously supply energy, accomplish reliable communication link, and also exhibit high traction force. This paper will present many new mechanical implementations of snake-like robots developed in our lab., and also explain in detail the new paradigm.


The Traveling Salesman Problem for Cubic Graphs

We show how to find a Hamiltonian cycle in a graph of degree at most three with n vertices, in time O(2) ≈ 1.260 and linear space. Our algorithm can find the minimum weight Hamiltonian cycle (traveling salesman problem), in the same time bound. We can also count or list all Hamiltonian cycles in a degree three graph in time O(2) ≈ 1.297. We also solve the traveling salesman problem in graphs of degree at most four, by randomized and deterministic algorithms with runtime O((27/4)) ≈ 1.890 and O((27/4+ ǫ)) respectively. Our algorithms allow the input to specify a set of forced edges which must be part of any generated cycle. Our cycle listing algorithm shows that every degree three graph has O(2) Hamiltonian cycles; we also exhibit a family of graphs with 2 Hamiltonian cycles per graph. Article Type Communicated by Submitted Revised Regular paper J. S. B. Mitchell April 2004 Work supported in part by NSF grant CCR-9912338. A preliminary version of this paper appeared at the 8th Annual Workshop on Algorithms and Data Structures,


Overcoming the Brittleness Bottleneck using Wikipedia: Enhancing Text Categorization with Encyclopedic Knowledge

When humans approach the task of text categorization, they interpret the specific wording of the document in the much larger context of their background knowledge and experience. On the other hand, state-of-the-art information retrieval systems are quite brittle—they traditionally represent documents as bags of words, and are restricted to learning from individual word occurrences in the (necessarily limited) training set. For instance, given the sentence “Wal-Mart supply chain goes real time”, how can a text categorization system know that Wal-Mart manages its stock with RFID technology? And having read that “Ciprofloxacin belongs to the quinolones group”, how on earth can a machine know that the drug mentioned is an antibiotic produced by Bayer? In this paper we present algorithms that can do just that. We propose to enrich document representation through automatic use of a vast compendium of human knowledge—an encyclopedia. We apply machine learning techniques to Wikipedia, the largest encyclopedia to date, which surpasses in scope many conventional encyclopedias and provides a cornucopia of world knowledge. Each Wikipedia article represents a concept, and documents to be categorized are represented in the rich feature space of words and relevant Wikipedia concepts. Empirical results confirm that this knowledge-intensive representation brings text categorization to a qualitatively new level of performance across a diverse collection of datasets.


The Generative Lexicon

In this paper, I will discuss four major topics relating to current research in lexical semantics: methodology, descriptive coverage, adequacy of the representation, and the computational usefulness of representations. In addressing these issues, I will discuss what I think are some of the central problems facing the lexical semantics community, and suggest ways of best approaching these issues. Then, I will provide a method for the decomposition of lexical categories and outline a theory of lexical semantics embodying a notion of cocompositionality and type coercion, as well as several levels of semantic description, where the semantic load is spread more evenly throughout the lexicon. I argue that lexical decomposition is possible if it is performed generatively. Rather than assuming a fixed set of primitives, I will assume a fixed number of generative devices that can be seen as constructing semantic expressions. I develop a theory of Qualia Structure, a representation language for lexical items, which renders much lexical ambiguity in the lexicon unnecessary, while still explaining the systematic polysemy that words carry. Finally, I discuss how individual lexical structures can be integrated into the larger lexical knowledge base through a theory of lexical inheritance. This provides us with the necessary principles of global organization for the lexicon, enabling us to fully integrate our natural language lexicon into a conceptual whole.


Centroid-Based Document Classification: Analysis and Experimental Results

In recent years we have seen a tremendous growth in the volume of text documents available on the Internet, digital libraries, news sources, and company-wide intrane ts. Automatic text categorization, which is the task of assigning text documents to pre-specified classes (topics o r themes) of documents, is an important task that can help both in organizing as well as in finding information on these h uge resources. Text categorization presents unique challenges due to the large number of attributes present in t he data set, large number of training samples, and attribute dependencies. In this paper we focus on a simple linear-time centroid-based document classification algorithm, that despite its simplicity and robust performance, has not been extensively studied and analyzed. Our extensive experiments show that this centroid-based classifier consi stently and substantially outperforms other algorithms su ch as Naive Bayesian, k-nearest-neighbors, and C4.5, on a wide range of datasets. O ur analysis shows that the similarity measure used by the centroid-based scheme allows it to class ify new document based on how closely its behavior matches the behavior of the documents belonging to differen t classes, as measured by the average similarity between the documents. This matching allows it to dynamically adjus t for classes with different densities. Furthermore, our analysis shows that the similarity measure of the centroidbased scheme accounts for dependencies between the terms in the different classes. We believe that this feature is the reason why it consistently outperforms other classifiers th at cannot take these dependencies into account.


Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language

This article presents a measure of semantic similarity in an is-a taxonomy based on the notion of shared information content. Experimental evaluation against a benchmark set of human similarity judgments demonstrates that the measure performs better than the traditional edge-counting approach. The article presents algorithms that take advantage of taxonomic similarity in resolving syntactic and semantic ambiguity, along with experimental results demonstrating their e ectiveness.


INTRODUCTION
Evidence was recently provided for vaginal orgasm, orgasm triggered purely by penile-vaginal intercourse (PVI), being associated with better psychological functioning. Common sex education and sexual medicine approaches might undermine vaginal orgasm benefits.

AIMS
To examine the extent to which women's vaginal orgasm consistency is associated with (i) being told in childhood or adolescence that the vagina was the important zone for inducing female orgasm; (ii) how well they focus mentally on vaginal sensations during PVI; (iii) greater PVI duration; and (iv) preference for above-average penis length.

METHODS
  In a representative sample of the Czech population, 1,000 women reported their vaginal orgasm consistency (from never to almost every time; only 21.9% never had a vaginal orgasm), estimates of their typical foreplay and PVI durations, what they were told in childhood and adolescence was the important zone for inducing female orgasm, their degree of focus on vaginal sensations during PVI, and whether they were more likely to orgasm with a longer than average penis.

MAIN OUTCOME MEASURES
The association of vaginal orgasm consistency with the predictors noted above.

RESULTS
Vaginal orgasm consistency was associated with all hypothesized correlates. Multivariate analysis indicated the most important predictors were being educated that the vagina is important for female orgasm, being mentally focused on vaginal sensations during PVI, and in some analyses duration of PVI (but not foreplay) and preferring a longer than average penis.

CONCLUSIONS
Focusing attention on penile-vaginal sensation supports vaginal orgasm and the myriad benefits thereof. Brody S, and Weiss P. Vaginal orgasm is associated with vaginal (not clitoral) sex education, focusing mental attention on vaginal sensations, intercourse duration, and a preference for a longer penis.


Schedulability Analysis for Tasks with Static and Dynamic Offsets

In this paper we present an extension to current schedulability analysis techniques for periodic tasks with offsets, scheduled under a preemptive fixed priority scheduler. Previous techniques allowed only static offsets restricted to being smaller than the task periods. With the extension presented in this paper, we eliminate this restriction and we allow both static and dynamic offsets. The most significant application of this extension is in the analysis of multiprocessor and distributed systems. We show that we can achieve a significant increase of the maximum schedulable utilization by using the new technique, as opposed to using previously known worst-case analysis techniques for distributed systems. ___________________ This work has been supported in part by the Comisión Interministerial de Ciencia y Tecnología of the Spanish Government, under grant number TAP97-892


Knowledge Technologies

Several technologies are emerging that provide new ways to capture, store, present and use knowledge. This book is the first to provide a comprehensive introduction to five of the most important of these technologies: Knowledge Engineering, Knowledge Based Engineering, Knowledge Webs, Ontologies and Semantic Webs. For each of these, answers are given to a number of key questions (What is it? How does it operate? How is a system developed? What can it be used for? What tools are available? What are the main issues?). The book is aimed at students, researchers and practitioners interested in Knowledge Management, Artificial Intelligence, Design Engineering and Web Technologies. During the 1990s, Nick worked at the University of Nottingham on the application of AI techniques to knowledge management and on various knowledge acquisition projects to develop expert systems for military applications. In 1999, he joined Epistemics where he worked on numerous knowledge projects and helped establish knowledge management programmes at large organisations in the engineering, technology and legal sectors. He is author of the book"Knowledge Acquisition in Practice", which describes a step-by-step procedure for acquiring and implementing expertise. He maintains strong links with leading research organisations working on knowledge technologies, such as knowledge-based engineering, ontologies and semantic technologies.


Hotplug or Ballooning: A Comparative Study on Dynamic Memory Management Techniques for Virtual Machines

In virtualization environments, static memory allocation for virtual machines (VMs) can lead to severe service level agreement (SLA) violations or inefficient use of memory. Dynamic memory allocation mechanisms such as ballooning and memory hotplug were proposed to handle the dynamics of memory demands. However, these mechanisms so far have not been quantitatively or comparatively studied. In this paper, we first develop a runtime system called U-tube, which provides a framework to adopt memory hotplug or ballooning for dynamic memory allocation. We then implement fine-grained memory hotplug in Xen. We demonstrate the effectiveness of U-tube for dynamic memory management through two case studies: dynamic memory balancing and memory overcommitment. With these two case studies, we make a quantitative comparison between memory hotplug and ballooning. The experiments show that there is no absolute winner for different scenarios. Our findings can be very useful for practitioners to choose the suitable dynamic memory management techniques in different scenarios.


The big five personality dimensions and entrepreneurial status: a meta-analytical review.

In this study, the authors used meta-analytical techniques to examine the relationship between personality and entrepreneurial status. Personality variables used in previous studies were categorized according to the five-factor model of personality. Results indicate significant differences between entrepreneurs and managers on 4 personality dimensions such that entrepreneurs scored higher on Conscientiousness and Openness to Experience and lower on Neuroticism and Agreeableness. No difference was found for Extraversion. Effect sizes for each personality dimension were small, although the multivariate relationship for the full set of personality variables was moderate (R = .37). Considerable heterogeneity existed for all of the personality variables except Agreeableness, suggesting that future research should explore possible moderators of the personality-entrepreneurial status relationship.


A Twitter Tale of Three Hurricanes: Harvey, Irma, and Maria

People increasingly use microblogging platforms such as Twitter during natural disasters and emergencies. Research studies have revealed the usefulness of the data available on Twitter for several disaster response tasks. However, making sense of social media data is a challenging task due to several reasons such as limitations of available tools to analyze high-volume and high-velocity data streams. This work presents an extensive multidimensional analysis of textual and multimedia content from millions of tweets shared on Twitter during the three disaster events. Specifically, we employ various Artificial Intelligence techniques from Natural Language Processing and Computer Vision fields, which exploit different machine learning algorithms to process the data generated during the disaster events. Our study reveals the distributions of various types of useful information that can inform crisis managers and responders as well as facilitate the development of future automated systems for disaster management.


Integral imaging using color multiplexed holographic optical element

We propose a novel integral imaging system using holographic optical element (HOE) which can provide multi-color 3-dimensional (3D) images. The optical properties of a lens array are recorded on a photopolymer using color multiplexing method with two different wavelength lasers (473 nm and 532 nm). The lens array HOE recorded on the photopolymer functions as 3D imaging HOE for integral imaging instead of the conventional lens array or concave mirror array. Recording scheme for color multiplexed HOE is presented and the proposed system is experimentally verified.


Gaming frequency and academic performance

There are numerous claims that playing computer and video games may be educationally beneficial, but there has been little formal investigation into whether or not the frequency of exposure to such games actually affects academic performance. This paper explores the issue by analysing the relationships between gaming frequency – measured as the amount of time undergraduate students spend playing games in their free time – and their academic performance as measured by their examination marks. Using a sample of 713 students, correlation analyses between gaming frequency and examination performance were conducted for students of varying gaming frequency, study discipline, gender, and general attitudes towards gaming and study. The results reveal that examination marks are in fact negatively correlated with gaming frequency – i.e. frequent gamers generally achieve lower marks than less frequent gamers.


Co-management: concepts and methodological implications.

Co-management, or the joint management of the commons, is often formulated in terms of some arrangement of power sharing between the State and a community of resource users. In reality, there often are multiple local interests and multiple government agencies at play, and co-management can hardly be understood as the interaction of a unitary State and a homogeneous community. An approach focusing on the legal aspects of co-management, and emphasizing the formal structure of arrangements (how governance is configured) runs the risk of neglecting the functional side of co-management. An alternative approach is to start from the assumption that co-management is a continuous problem-solving process, rather than a fixed state, involving extensive deliberation, negotiation and joint learning within problem-solving networks. This presumption implies that co-management research should preferably focus on how different management tasks are organized and distributed concentrating on the function, rather than the structure, of the system. Such an approach has the effect of highlighting that power sharing is the result, and not the starting point, of the process. This kind of research approach might employ the steps of (1) defining the social-ecological system under focus; (2) mapping the essential management tasks and problems to be solved; (3) clarifying the participants in the problem-solving processes; (4) analyzing linkages in the system, in particular across levels of organization and across geographical space; (5) evaluating capacity-building needs for enhancing the skills and capabilities of people and institutions at various levels; and (6) prescribing ways to improve policy making and problem-solving.


A model of neuronal responses in visual area MT

Electrophysiological studies indicate that neurons in the middle temporal (MT) area of the primate brain are selective for the velocity of visual stimuli. This paper describes a computational model of MT physiology, in which local image velocities are represented via the distribution of MT neuronal responses. The computation is performed in two stages, corresponding to neurons in cortical areas V1 and MT. Each stage computes a weighted linear sum of inputs, followed by rectification and divisive normalization. V1 receptive field weights are designed for orientation and direction selectivity. MT receptive field weights are designed for velocity (both speed and direction) selectivity. The paper includes computational simulations accounting for a wide range of physiological data, and describes experiments that could be used to further test and refine the model.


Text2Scene: Generating Abstract Scenes from Textual Descriptions

In this paper, we propose an end-to-end model that learns to interpret natural language describing a scene to generate an abstract pictorial representation. The pictorial representations generated by our model comprise the spatial distribution and attributes of the objects in the described scene. Our model uses a sequence-to-sequence network with a double attentive mechanism and introduces a regularization strategy. These scene representations can be sampled from our model similarly as in languagegeneration models. We show that the proposed model, initially designed to handle the generation of cartoon-like pictorial representations in the Abstract Scenes Dataset, can also handle, under minimal modifications, the generation of semantic layouts corresponding to real images in the COCO dataset. Human evaluations using a visual entailment task show that pictorial representations generated with our full model can entail at least one out of three input visual descriptions 94% of the times, and at least two out of three 62% of the times for each image.


Preference for Nature in Urbanized Societies : Stress , Restoration , and the Pursuit of Sustainability

Urbanicity presents a challenge for the pursuit of sustainability. High settlement density may offer some environmental, economic, and social advantages, but it can impose psychological demands that people find excessive. These demands of urban life have stimulated a desire for contact with nature through suburban residence, leading to planning and transportation practices that have profound implications for the pursuit of sustainability. Some might dismiss people’s desire for contact with nature as the result of an anti-urban bias in conjunction with a romantic view of nature. However, research in environmental psychology suggests that people’s desire for contact with nature serves an important adaptive function, namely, psychological restoration. Based on this insight, we offer a perspective on an underlying practical challenge: designing communities that balance settlement density with satisfactory access to nature experience. We discuss research on four issues: how people tend to believe that nature is restorative; how restoration needs and beliefs shape environmental preferences; how well people actually achieve restoration in urban and natural environments; and how contact with nature can promote health. In closing, we consider urban nature as a design option that promotes urban sustainability.


Generation from Abstract Meaning Representation using Tree Transducers

Language generation from purely semantic representations is a challenging task. This paper addresses generating English from the Abstract Meaning Representation (AMR), consisting of re-entrant graphs whose nodes are concepts and edges are relations. The new method is trained statistically from AMRannotated English and consists of two major steps: (i) generating an appropriate spanning tree for the AMR, and (ii) applying tree-tostring transducers to generate English. The method relies on discriminative learning and an argument realization model to overcome data sparsity. Initial tests on held-out data show good promise despite the complexity of the task. The system is available open-source as part of JAMR at: http://github.com/jflanigan/jamr


JMASM Algorithms and Code Pseudo-Random Number Generators for Vector Processors and Multicore Processors

There is a lack of good pseudo-random number generators capable of utilizing the vector processing capabilities and multiprocessing capabilities of modern computers. A suitable generator must have a feedback path long enough to fit the vector length or permit multiple instances with different parameter sets. The risk that multiple random streams from identical generators have overlapping subsequences can be eliminated by combining two different generators. Combining two generators can also increase randomness by remedying weaknesses caused by the need for mathematical tractability. Larger applications need higher precision. The article discusses hitherto ignored imprecisions caused by quantization errors in the application of generators with prime modulus and when generating uniformly distributed integers with an arbitrary interval length. A C++ software package that overcomes all these problems is offered. The RANVEC1 code combines a Mersenne Twister variant and a multiply-with-carry generator to produce vector output. It is suitable for processors with vector sizes up to 512 bits. Some theorists have argued that there is no theoretical proof that the streams from different generators are statistically independent. The article contends that the request for such a proof misunderstands the nature of the problem, and that the mathematical tractability that would allow such a proof would also defeat it. This calls for a more fundamental philosophical discussion of the status of proofs in relation to deterministic pseudo-random sequences.


Mining Frequent Patterns in Data Streams at Multiple Time Granularities

Although frequent-pattern mining has been widely studied and used, it is challenging to extend it to data streams. Compared to mining from a static transaction data set, the streaming case has far more information to track and far greater complexity to manage. Infrequent items can become frequent later on and hence cannot be ignored. The storage structure needs to be dynamically adjusted to reflect the evolution of itemset frequencies over time. In this paper, we propose computing and maintaining all the frequent patterns (which is usually more stable and smaller than the streaming data) and dynamically updating them with the incoming data streams. We extended the framework to mine time-sensitive patterns with approximate support guarantee. We incrementally maintain tilted-time windows for each pattern at multiple time granularities. Interesting


The psychology of globalization.

The influence of globalization on psychological functioning is examined. First, descriptions of how globalization is occurring in various world regions are presented. Then the psychological consequences of globalization are described, with a focus on identity issues. Specifically, it is argued that most people worldwide now develop a bicultural identity that combines their local identity with an identity linked to the global culture; that identity confusion may be increasing among young people in non-Western cultures as a result of globalization; that some people join self-selected cultures to maintain an identity that is separate from the global culture; and that a period of emerging adulthood increasingly extends identity explorations beyond adolescence, through the mid- to late twenties.


Three stages of emotional word processing: an ERP study with rapid serial visual presentation.

Rapid responses to emotional words play a crucial role in social communication. This study employed event-related potentials to examine the time course of neural dynamics involved in emotional word processing. Participants performed a dual-target task in which positive, negative and neutral adjectives were rapidly presented. The early occipital P1 was found larger when elicited by negative words, indicating that the first stage of emotional word processing mainly differentiates between non-threatening and potentially threatening information. The N170 and the early posterior negativity were larger for positive and negative words, reflecting the emotional/non-emotional discrimination stage of word processing. The late positive component not only distinguished emotional words from neutral words, but also differentiated between positive and negative words. This represents the third stage of emotional word processing, the emotion separation. Present results indicated that, similar with the three-stage model of facial expression processing; the neural processing of emotional words can also be divided into three stages. These findings prompt us to believe that the nature of emotion can be analyzed by the brain independent of stimulus type, and that the three-stage scheme may be a common model for emotional information processing in the context of limited attentional resources.


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Adversarial Deep Learning for Robust Detection of Binary Encoded Malware

Malware is constantly adapting in order to avoid detection. Model-based malware detectors, such as SVM and neural networks, are vulnerable to so-called adversarial examples which are modest changes to detectable malware that allows the resulting malware to evade detection. Continuous-valued methods that are robust to adversarial examples of images have been developed using saddle-point optimization formulations. We are inspired by them to develop similar methods for the discrete, e.g. binary, domain which characterizes the features of malware. A specific extra challenge of malware is that the adversarial examples must be generated in a way that preserves their malicious functionality. We introduce methods capable of generating functionally preserved adversarial malware examples in the binary domain. Using the saddle-point formulation, we incorporate the adversarial examples into the training of models that are robust to them. We evaluate the effectiveness of the methods and others in the literature on a set of Portable Execution (PE) files. Comparison prompts our introduction of an online measure computed during training to assess general expectation of robustness.


Syntax-aware Neural Machine Translation Using CCG

Neural machine translation (NMT) models are able to partially learn syntactic information from sequential lexical information. Still, some complex syntactic phenomena such as prepositional phrase attachment are poorly modeled. This work aims to answer two questions: 1) Does explicitly modeling source or target language syntax help NMT? 2) Is tight integration of words and syntax better than multitask training? We introduce syntactic information in the form of CCG supertags either in the source as an extra feature in the embedding, or in the target, by interleaving the target supertags with the word sequence. Our results on WMT data show that explicitly modeling syntax improves machine translation quality for English↔German, a high-resource pair, and for English↔Romanian, a lowresource pair and also several syntactic phenomena including prepositional phrase attachment. Furthermore, a tight coupling of words and syntax improves translation quality more than multitask training.


Modern Recommender Systems: from Computing Matrices to Thinking with Neurons

Starting with the Netflix Prize, which fueled much recent progress in the field of collaborative filtering, recent years have witnessed rapid development of new recommendation algorithms and increasingly more complex systems, which greatly differ from their early content-based and collaborative filtering systems. Modern recommender systems leverage several novel algorithmic approaches: from matrix factorization methods and multi-armed bandits to deep neural networks. In this tutorial, we will cover recent algorithmic advances in recommender systems, highlight their capabilities, and their impact. We will give many examples of industrial-scale recommender systems that define the future of the recommender systems area. We will discuss related evaluation issues, and outline future research directions. The ultimate goal of the tutorial is to encourage the application of novel recommendation approaches to solve problems that go beyond user consumption and to further promote research in the intersection of recommender systems and databases.


Transparent operation of Kronecker product based full dimension MIMO to exploit 2D antenna array

A concept of full-dimension MIMO (FD-MIMO) adopting 2-dimensional (2D) antenna array has been proposed with intent to cope with practical challenges of massive MIMO systems. In this paper, we investigate an FD-MIMO operation that can be transparently applied with current 3GPP LTE specifications, and propose a novel method that facilitates an accurate estimation of channel quality indicator (CQI) at base station (BS). We first introduce a feedback scheme in which horizontal and vertical dimension channel information are separately reported. To further improve accuracy of modulation and coding scheme (MCS) selection to maximize system throughput, we propose an enhanced feedback scheme that is helpful to more accurate CQI estimation at the BS. In the evaluation results, we show performance improvement of the proposed scheme over the conventional MIMO scheme.


Medical Image Analysis: Progress over Two Decades and the Challenges Ahead

ÐThe analysis of medical images has been woven into the fabric of the Pattern Analysis and Machine Intelligence (PAMI) community since the earliest days of these Transactions. Initially, the efforts in this area were seen as applying pattern analysis and computer vision techniques to another interesting dataset. However, over the last two to three decades, the unique nature of the problems presented within this area of study have led to the development of a new discipline in its own right. Examples of these include: the types of image information that are acquired, the fully three-dimensional image data, the nonrigid nature of object motion and deformation, and the statistical variation of both the underlying normal and abnormal ground truth. In this paper, we look at progress in the field over the last 20 years and suggest some of the challenges that remain for the years to come. Index TermsÐMedical imaging, medical image analysis, computer vision, image segmentation, image registration, nonrigid motion, deformable contours, image-guided surgery.


Dynamic Programming for Detecting, Tracking, and Matching Deformable Contours

Abstruct-The problem of segmenting an image into separate regions and tracking them over time is one of the most significant problems in vision. Tenopoulos et al have proposed an approach to detect the contour regions of complex shapes, assuming a user selected initial contour not very far from the desired solution. We propose to further explore the information provided by the user’s selected points and applyan optimal method to detect contours which allows a segmentation of the image. The method is based on dynamic programming @P), and applies to a wide variety of shapes. It is exact and not iterative. We also consider a multiscale approach capable of speeding up the algorithm by a factor of 20, although at the expense of losing the guaranteed optimality characteristic. The problem of tracking and matching these contours is addressed. For tracking, the final contour obtained at one frame is sampled and used as initial points for the next frame. Then, the same DP process is applied. For matching, a novel strategy is proposed where the solution is a smooth displacement field in which unmatched regions are allowed while cross vectors are not. The algorithm is again based on DP and the optimal solution is guaranteed. We have demonstrated the algorithms on natural objects in a large spectrum of applications, including interactive segmentation and automatic tracking of the regions of interest in medical images.


Dynamic programming algorithm optimization for spoken word recognition

This paper reports on an optimum dynamic programming (DP) based time-normalization algorithm for spoken word recognition. First, a general principle of time-normalization i s given using timewarping function. Then, two time-normalized distance definitions, d e d symmetric and asymmetric forms, are derived from the principle. These two forms are compared with each other through theoretical discussions and experimental studies. The symmetric form algorithm superiority is established. A new technique, called slope constraint, is successfully introduced, in which the warping function slope is restricted so as to improve discrimination between words in different categories. The effective slope constraint characteristic is qualitatively analyzed, and the optimum slope constraint condition is determined through experiments. The optimized algorithm is then extensively subjected to experimentat comparison with various DP-algorithms, previously applied to spoken word recognition by different research groups. The experiment shows that the present algorithm gives no more than about twothirds errors, even compared to the best conventional algorithm. I


Humour Research: State of Art

Humour is a multi-disciplinary field of research. People have been working on humour in many fields of research like psychology, philosophy and linguistics, sociology and literature. Especially in the context of computer science (or Artificial Intelligence) humour research aims at modeling humour in a computationally tractable way. Having computational models of humour allows interface designers to have the computer generate and interpret humour when interacting with users. There are many situations in human-human interaction where humour plays an important role in keeping the conversation going. Making use of the so-called CASA paradigm (Computers Are Social Actors) we may expect that a similar role can be played in human-computer interaction. In this report we survey current humour research with the aim to identify useful theories that can be applied in the human-computer interaction context. We focus on the following subjects: humour theories, related humour research, linguistic aspects of humour, computational aspects of humour, applications and resources. 1 University of Twente, Center of Telematics and Information Technology, Technical Report CTIT-02-34, September 2002, 24 pp.


2.4 GHz micro-strip patch antenna array with suppressed sidelobes

In this paper, we present the design and simulated results for a patch antenna array at 2.4 GHz. The antenna consists of one linear array with six series-fed patch antennas. In radar and smart-antenna applications it is very important to use antennas with high gain and low sidelobes in order to avoid the interference and to increase the gain of antenna array. The aim of this research is to design an antenna array that has suppressed sidelobes as much as possible in azimuth by using Kaiser-Bessel amplitude coefficients. Our designed antenna has a gain of 15.1 dB with an angular width of 14.6° at a frequency of 2.4 GHz. The measured 10 dB return loss bandwidth equals 18.7 MHz. Patch antenna array is analyzed using CST MICROWAVE STUDIO 2014.


Distinguishing Between Natural and Computer-Generated Images Using Convolutional Neural Networks

Distinguishing between natural images (NIs) and computer-generated (CG) images by naked human eyes is difficult. In this paper, we propose an effective method based on a convolutional neural network (CNN) for this fundamental image forensic problem. Having observed the rather limited performance of training existing CCNs from scratch or fine-tuning pre-trained network, we design and implement a new and appropriate network with two cascaded convolutional layers at the bottom of a CNN. Our network can be easily adjusted to accommodate different sizes of input image patches while maintaining a fixed depth, a stable structure of CNN, and a good forensic performance. Considering the complexity of training CNNs and the specific requirement of image forensics, we introduce the so-called local-to-global strategy in our proposed network. Our CNN derives a forensic decision on local patches, and a global decision on a full-sized image can be easily obtained via simple majority voting. This strategy can also be used to improve the performance of existing methods that are based on hand-crafted features. Experimental results show that our method outperforms existing methods, especially in a challenging forensic scenario with NIs and CG images of heterogeneous origins. Our method also has good robustness against typical post-processing operations, such as resizing and JPEG compression. Unlike previous attempts to use CNNs for image forensics, we try to understand what our CNN has learned about the differences between NIs and CG images with the aid of adequate and advanced visualization tools.


Content-based retrieval of logo and trademarks in unconstrained color image databases using Color Edge Gradient Co-occurrence Histograms

In this paper, we present an algorithm that extends the Color Edge Co-occurrence Histogram (CECH) object detection scheme on compound color objects, for the retrieval of logos and trademarks in unconstrained color image databases. We introduce more accurate information to the CECH, by virtue of incorporating color edge detection using vector order statistics. This produces a more accurate representation of edges in color images, as compared to the simple color pixel difference classification of edges seen with the CECH. Our proposed method is thus reliant on edge gradient information, and so we call it the Color Edge Gradient Co-occurrence Histogram (CEGCH). We also introduce a color quantization method based in the hue–saturation–value (HSV) color space, illustrating that it is a more suitable scheme of quantization for image retrieval, compared to the color quantization scheme introduced with the CECH. Experimental results demonstrate that the CEGCH and the HSV color quantization scheme is insensitive to scaling, rotation, and partial deformations, and outperforms the use of the CECH in image retrieval, with higher precision and recall. We also perform experiments on a closely related algorithm based on the Color Co-occurrence Histogram (CCH) and demonstrate that our algorithm is also superior in comparison, with higher precision and recall. 2009 Elsevier Inc. All rights reserved.


Limiting privacy breaches in privacy preserving data mining

There has been increasing interest in the problem of building accurate data mining models over aggregate data, while protecting privacy at the level of individual records. One approach for this problem is to randomize the values in individual records, and only disclose the randomized values. The model is then built over the randomized data, after first compensating for the randomization (at the aggregate level). This approach is potentially vulnerable to privacy breaches: based on the distribution of the data, one may be able to learn with high confidence that some of the randomized records satisfy a specified property, even though privacy is preserved on average.In this paper, we present a new formulation of privacy breaches, together with a methodology, "amplification", for limiting them. Unlike earlier approaches, amplification makes it is possible to guarantee limits on privacy breaches without any knowledge of the distribution of the original data. We instantiate this methodology for the problem of mining association rules, and modify the algorithm from [9] to limit privacy breaches without knowledge of the data distribution. Next, we address the problem that the amount of randomization required to avoid privacy breaches (when mining association rules) results in very long transactions. By using pseudorandom generators and carefully choosing seeds such that the desired items from the original transaction are present in the randomized transaction, we can send just the seed instead of the transaction, resulting in a dramatic drop in communication and storage cost. Finally, we define new information measures that take privacy breaches into account when quantifying the amount of privacy preserved by randomization.


Minimum elastic bounding box algorithm for dimension detection of 3D objects: a case of airline baggage measurement

Motivated by the interference of appendages in airline baggage dimension detection using three-dimensional (3D) point cloud, a minimum elastic bounding box (MEBB) algorithm for dimension detection of 3D objects is developed. The baggage dimension measurements using traditional bounding box method or shape fitting method can cause large measurements due to the interference of appendages. Starting from the idea of ‘enclosing’, an elastic bounding box model with the deformable surface is established. On the basis of using principal component analysis to obtain the main direction of the bounding box, the elastic rules for deformable surfaces are developed so as to produce a large elastic force when it comes into contact with the main body part and to produce a small elastic force when it comes into contact with the appendages part. The airline baggage measurement shows how to use MEBB for dimension detection, especially for the processing of isotropic density distribution, the elasticity computing and the adaptive adjustment of elasticity. Results on typical baggage samples, comparisons to other methods, and error distribution experiments with different algorithm parameters show that the authors’ method can reliably obtain the size of the main body part of the object under the interference of appendages.


A systematic literature review: Information security culture

Human behavior inside organizations is considered the main threat to organizations. Moreover, in information security the human element consider the most of weakest link in general. Therefore it is crucial to create an information security culture to protect the organization's assets from inside and to influence employees' security behavior. This paper focuses on identifying the definitions and frameworks for establishing and maintaining information security culture inside organizations. It presents work have been done to conduct a systematic literature review of papers published on information security culture from 2003 to 2016. The review identified 68 papers that focus on this area, 18 of which propose an information security culture framework. An analysis of these papers indicate there is a positive relationship between levels of knowledge and how employees behave. The level of knowledge significantly affects information security behavior and should be considered as a critical factor in the effectiveness of information security culture and in any further work that is carried out on information security culture. Therefore, there is a need for more studies to identity the security knowledge that needs to be incorporated into organizations and to find instances of best practice for building an information security culture within organizations.