title: Human selection of mnemonic phrase-based passwords

abstract: Textual passwords are often the only mechanism used to authenticate users of a networked system. Unfortunately, many passwords are easily guessed or cracked. In an attempt to strengthen passwords, some systems instruct users to create mnemonic phrase-based passwords. A mnemonic password is one where a user chooses a memorable phrase and uses a character (often the first letter) to represent each word in the phrase.In this paper, we hypothesize that users will select mnemonic phrases that are commonly available on the Internet, and that it is possible to build a dictionary to crack mnemonic phrase-based passwords. We conduct a survey to gather user-generated passwords. We show the majority of survey respondents based their mnemonic passwords on phrases that can be found on the Internet, and we generate a mnemonic password dictionary as a proof of concept. Our 400,000-entry dictionary cracked 4% of mnemonic passwords; in comparison, a standard dictionary with 1.2 million entries cracked 11% of control passwords. The user-generated mnemonic passwords were also slightly more resistant to brute force attacks than control passwords. These results suggest that mnemonic passwords may be appropriate for some uses today. However, mnemonic passwords could become more vulnerable in the future and should not be treated as a panacea.


title: Wrapper Maintenance: A Machine Learning Approach

abstract: The proliferation of online information sources has led to an increased use of wrappers for extracting data from Web sources. While most of the previous research has focused on quick and efficient generation of wrappers, the development of tools for wrapper maintenance has received less attention. This is an important research problem because Web sources often change in ways that prevent the wrappers from extracting data correctly. We present an efficient algorithm that learns structural information about data from positive examples alone. We describe how this information can be used for two wrapper maintenance applications: wrapper verification and reinduction. The wrapper verification system detects when a wrapper is not extracting correct data, usually because the Web source has changed its format. The reinduction algorithm automatically recovers from changes in the Web source by identifying data on Web pages so that a new wrapper may be generated for this source. To validate our approach, we monitored 27 wrappers over a period of a year. The verification algorithm correctly discovered 35 of the 37 wrapper changes, and made 16 mistakes, resulting in precision of 0.73 and recall of 0.95. We validated the reinduction algorithm on ten Web sources. We were able to successfully reinduce the wrappers, obtaining precision and recall values of 0.90 and 0.80 on the data extraction task.


title: Application of combined electric- and magnetic-conductor ground planes for antenna performance enhancement

abstract: This paper investigates the application of three different ground planes for antenna performance enhancement. They are the conventional perfect electric conductor (PEC), the perfect magnetic conductor (PMC), and their combination. A half-wavelength dipole in free space is considered as the reference case, and its performance over these ground planes is investigated and compared. It is shown that by using a combined PMC-PEC ground plane, one can achieve a better performance. Next, a microstrip patch antenna is considered as the source and is placed over a high-impedance surface (HIS) or an artificial magnetic conductor (AMC). Here too, adding a PEC ground plane on the periphery of the HIS or AMC improves the antenna gain significantly. As well, the superiority of AMC ground planes, consisting of grounded dielectric slabs loaded with periodic metallic patches without shorting pins (vias), is investigated in detail. Simulation results show that use of such artificial ground planes can enhance the microstrip patch antenna gain and bandwidth remarkably. These enhancements are confirmed by measurement, and high gains of 10.4 dBi and input impedance bandwidths of 28% are measured.


title: A Biomimetic Elastic Cable Driven Quadruped Robot: The RoboCat

abstract: State of the art legged robots, such as the Honda’s series of bipedal robots ending in the latest advanced walking robot ASIMO, and the series of bipedal robots of Waseda University including the latest advanced robot WABIAN, employ joint-mount motors, which simplifies the analysis/design and traces the route for an effective control system, but results in legs that are heavy and bulky. Cable-driven robots overcome this shortcoming by allowing the motors to be mounted on or near the torso, thereby reducing the weight and inertia of the legs, resulting in lower overall weight and power consumption. To facilitate analysis and design, typical cable-driven robots use non-stretchable cables, which require at least n+1 motors for an n Degree-of-Freedom (DoF) joint. Therefore, for a robot with N joints, at least N additional motors are needed comparing to joint-mount motor drives. Moreover, the drive train of both joint-mount and cable-driven designs are rigid, which cannot effectively absorb ground impact shocks nor transfer potential energy to kinetic energy and vice versa when the robot is in motion, as biologic animals do. In this paper we present the design and test of a cat-size quadruped robot called RoboCat, which employs stretchable elastic cable-driven joints as inspired by biological quadruped animals. Although it complicates kinematics and dynamics analysis and design, the elastic cables allow n motors to be used for an n-DoF joint, thereby eliminating N motors for a robot with N joints comparing to non-stretchable cables, further realizing the weight and power savings of the cable driven design. Moreover, the elastic cable driven joints not only effectively absorb ground contact shock, but also effectively transfer potential and kinetic energy during walking or running, thereby improving the robot motion performance and energy efficiency. In the paper we will discuss the kinematics and dynamics analysis of elastic cable driven joints, implementation of elastic cable-driven joints on the Ohio University RoboCat, and control.Copyright © 2011 by ASME


title: Image-Based Crack Detection for Real Concrete Surfaces

abstract: In this paper, we introduce a novel image-based approach to detect cracks in concrete surfaces. Crack detection is important for the inspection, diagnosis, and maintenance of concrete structures. However, conventional image-based approaches cannot achieve precise detection since the image of the concrete surface contains various types of noise due to different causes such as concrete blebs, stain, insufficient contrast, and shading. In order to detect the cracks with high fidelity, we assume that they are composed of thin interconnected textures and propose an image-based percolation model that extracts a continuous texture by referring to the connectivity of brightness and the shape of the percolated region, depending on the length criterion of the scalable local image processing techniques. Additionally, noise reduction based on the percolation model is proposed. We evaluated the validity of the proposed technique by using precision recall and receiver operating characteristic (ROC) analysis by means of some experiments with actual concrete surface images. Copyright © 2007 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.


title: Containment of misinformation spread in online social networks

abstract: With their blistering expansions in recent years, popular on-line social sites such as Twitter, Facebook and Bebo, have become some of the major news sources as well as the most effective channels for viral marketing nowadays. However, alongside these promising features comes the threat of misinformation propagation which can lead to undesirable effects, such as the widespread panic in the general public due to faulty swine flu tweets on Twitter in 2009. Due to the huge magnitude of online social network (OSN) users and the highly clustered structures commonly observed in these kinds of networks, it poses a substantial challenge to efficiently contain viral spread of misinformation in large-scale social networks. In this paper, we focus on how to limit viral propagation of misinformation in OSNs. Particularly, we study a set of problems, namely the β1T -- Node Protectors, which aims to find the smallest set of highly influential nodes whose decontamination with good information helps to contain the viral spread of misinformation, initiated from the set I, to a desired ratio (1 − β) in T time steps. In this family set, we analyze and present solutions including inapproximability result, greedy algorithms that provide better lower bounds on the number of selected nodes, and a community-based heuristic method for the Node Protector problems. To verify our suggested solutions, we conduct experiments on real world traces including NetHEPT, NetHEPT_WC and Facebook networks. Empirical results indicate that our methods are among the best ones for hinting out those important nodes in comparison with other available methods.


title: Bayesian Probabilistic Matrix Factorization with Social Relations and Item Contents for recommendation

abstract: Abstract Recommendation systems have received great attention for their commercial value in today's online business world. However, most recommendation systems encounter the data sparsity problem and the cold-start problem. To improve recommendation accuracy in this circumstance, additional sources of information about the users and items should be incorporated in recommendation systems. In this paper, we modify the model in Bayesian Probabilistic Matrix Factorization, and propose two recommendation approaches fusing social relations and item contents with user ratings in a novel way. The proposed approach is computationally efficient and can be applied to trust-aware or content-aware recommendation systems with very large dataset. Experimental results on three real world datasets show that our method gets more accurate recommendation results with faster converging speed than other matrix factorization based methods. We also verify our method in cold-start settings, and our method gets more accurate recommendation results than the compared approaches.


title: Predicting crime using Twitter and kernel density estimation

abstract: Abstract Twitter is used extensively in the United States as well as globally, creating many opportunities to augment decision support systems with Twitter-driven predictive analytics. Twitter is an ideal data source for decision support: its users, who number in the millions, publicly discuss events, emotions, and innumerable other topics; its content is authored and distributed in real time at no charge; and individual messages (also known as tweets) are often tagged with precise spatial and temporal coordinates. This article presents research investigating the use of spatiotemporally tagged tweets for crime prediction. We use Twitter-specific linguistic analysis and statistical topic modeling to automatically identify discussion topics across a major city in the United States. We then incorporate these topics into a crime prediction model and show that, for 19 of the 25 crime types we studied, the addition of Twitter data improves crime prediction performance versus a standard approach based on kernel density estimation. We identify a number of performance bottlenecks that could impact the use of Twitter in an actual decision support system. We also point out important areas of future work for this research, including deeper semantic analysis of message content, temporal modeling, and incorporation of auxiliary data sources. This research has implications specifically for criminal justice decision makers in charge of resource allocation for crime prevention. More generally, this research has implications for decision makers concerned with geographic spaces occupied by Twitter-using individuals.


title: A Survey on Synchrophasor Data Quality and Cybersecurity Challenges, and Evaluation of their Interdependencies

abstract: Synchrophasor devices guarantee situation awareness for real-time monitoring and operational visibility of smart grid. With their widespread implementation, significant challenges have emerged, especially in communication, data quality and cybersecurity. The existing literature treats these challenges as separate problems, when in reality, they have a complex interplay. This paper conducts a comprehensive review of quality and cybersecurity challenges for synchrophasors, and identifies the interdependencies between them. It also summarizes different methods used to evaluate the dependency and surveys how quality checking methods can be used to detect potential cyberattacks. This paper serves as a starting point for researchers entering the fields of synchrophasor data analytics and security.


title: Learning Attractor Dynamics for Generative Memory

abstract: A central challenge faced by memory systems is the robust retrieval of a stored pattern in the presence of interference due to other stored patterns and noise. A theoretically well-founded solution to robust retrieval is given by attractor dynamics, which iteratively clean up patterns during recall. However, incorporating attractor dynamics into modern deep learning systems poses difficulties: attractor basins are characterised by vanishing gradients, which are known to make training neural networks difficult. In this work, we avoid the vanishing gradient problem by training a generative distributed memory without simulating the attractor dynamics. Based on the idea of memory writing as inference, as proposed in the Kanerva Machine, we show that a likelihood-based Lyapunov function emerges from maximising the variational lower-bound of a generative memory. Experiments shows it converges to correct patterns upon iterative retrieval and achieves competitive performance as both a memory model and a generative model.


title: Optimal Local Basis: A Reinforcement Learning Approach for Face Recognition

abstract: This paper presents a novel learning approach for Face Recognition by introducing Optimal Local Basis. Optimal local bases are a set of basis derived by reinforcement learning to represent the face space locally. The reinforcement signal is designed to be correlated to the recognition accuracy. The optimal local bases are derived then by finding the most discriminant features for different parts of the face space, which represents either different individuals or different expressions, orientations, poses, illuminations, and other variants of the same individual. Therefore, unlike most of the existing approaches that solve the recognition problem by using a single basis for all individuals, our proposed method benefits from local information by incorporating different bases for its decision. We also introduce a novel classification scheme that uses reinforcement signal to build a similarity measure in a non-metric space. ::: ::: Experiments on AR, PIE, ORL and YALE databases indicate that the proposed method facilitates robust face recognition under pose, illumination and expression variations. The performance of our method is compared with that of Eigenface, Fisherface, Subclass Discriminant Analysis, and Random Subspace LDA methods as well.


title: Charm: an efficient algorithm for closed itemset mining

abstract: The set of frequent closed itemsets uniquely determines the exact frequency of all itemsets, yet it can be orders of magnitude smaller than the set of all frequent itemsets. In this paper we present CHARM, an efficient algorithm for mining all frequent closed itemsets. It enumerates closed sets using a dual itemset-tidset search tree, using an efficient hybrid search that skips many levels. It also uses a technique called diffsets to reduce the memory footprint of intermediate computations. Finally it uses a fast hash-based approach to remove any “non-closed” sets found during computation. An extensive experimental evaluation on a number of real and synthetic databases shows that CHARM significantly outperforms previous methods. It is also linearly scalable in the number of transactions.


title: A Fully Dynamic Universal Accumulator

abstract: A dynamic universal accumulator is an accumulator that allows one to efficiently compute both membership and nonmembership witnesses in a dynamic way. It was first defined and instantiated by Li et al., based on the Strong RSA problem, building on the dynamic accumulator of Camenisch and Lysyanskaya. We revisit their construction and show that it does not provide efficient witness computation in certain cases and, thus, is only achieving the status of a partially dynamic universal accumulator. In particular, their scheme is not equipped with an efficient mechanism to produce non-membership witnesses for a new element, whether a newly deleted element or an element which occurs for the first time. We construct the first fully dynamic universal accumulator based on the Strong RSA assumption, building upon the construction of Li et al., by providing a new proof structure for the non-membership witnesses. In a fully dynamic universal accumulator, we require that not only one can always create a membership witness without having to use the accumulated set for a newly added element, but also one can always create non-membership witnesses for a new element, whether a newly deleted element or an element which occurs for the first time, i.e., a newcomer who is not a member, without using the accumulated set.


title: Modeling Response Time In Digital Human Communication

abstract: Our daily lives increasingly involve interactions with other individuals via different communication channels, such as email, text messaging, and social media. In this paper we focus on the problem of modeling and predicting how long it takes an individual to respond to an incoming communication event, such as receiving an email or a text. In particular, we explore the effect on response times of an individual’s temporal pattern of activity, such as circadian and weekly patterns which are typically present in individual data. A probabilistic time-warping approach is used, considering linear time to be a transformation of “effective time,” where the transformation is a function of an individual’s activity rate. We apply this transformation of time to two different types of temporal event models, the first for modeling response times directly, and the second for modeling event times via a Hawkes process. We apply our approach to two different sets of real-world email histories. The experimental results clearly indicate that the transformation-based approach produces systematically better models and predictions compared to simpler methods that ignore circadian and weekly patterns. Current technology allows us to collect large quantities of time-stamped individual-level event data characterizing our “digital behavior” in contexts such as texting, email activity, microblogging, social media interactions, and more — and the volume and variety of this type of data is continually increasing. The resulting time-series of events are rich in behavioral information about our daily lives. Tools for obtaining and visualizing such information are becoming increasingly popular, such as the ability to download your entire email history for mail applications such as Gmail, and various software packages for tracking personal fitness using data from devices such as Fitbit. This paper is focused on modeling the temporal aspects of how an individual (also referred to as the “ego”) responds to others, given a sequence of timestamped events (e.g. communication messages via email or text). What can we learn from the way we respond to others? Are there systematic Currently employed at Google. The research described in this paper was conducted while the author was a graduate student at UC Irvine.


title: Post-ranking query suggestion by diversifying search results

abstract: Query suggestion refers to the process of suggesting related queries to search engine users. Most existing researches have focused on improving the relevance of suggested queries. In this paper, we introduce the concept of diversifying the content of the search results from suggested queries while keeping the suggestion relevant. Our framework first retrieves a set of query candidates from search engine logs using random walk and other techniques. We then re-rank the suggested queries by ranking them in the order which maximizes the diversification function that measures the difference between the original search results and the results from suggested queries. The diversification function we proposed includes features like ODP category, URL and domain similarity and so on. One important outcome from our research which contradicts with most existing researches is that, with the increase of suggestion relevance, the similarity between the queries actually decreases. Experiments are conducted on a large set of human-labeled data, which is randomly sampled from a commercial search engine's log. Results indicate that the post-ranking framework significantly improves the relevance of suggested queries by comparing to existing models.


title: Choosing Qualitative Research: A Primer for Technology Education Researchers

abstract: A number of writers have commented on the dearth of substantive research within the field of technology education, and point to the expansion of its research agenda as a means of strengthening the discipline. Waetjen, in his call for good research in technology education, states that “the plea is to use experimental type research as much as possible” (1992, p. 30). Interestingly, the three areas of research need outlined in his essay would all lend themselves to alternative methodologies, including qualitative methodologies. More recently, others have called for an expansion in the types of research methods used. Of the 220 reports included in Zuga’s review of technology education-related research (1994), only 16 are identified as having used qualitative methods, and Zuga notes that many of those studies were conducted outside the United States. Johnson (1995) suggests that technology educators “engage in research that probes for deeper understanding rather than examining surface features.” He notes that qualitative methodologies are powerful tools for enhancing our understanding of teaching and learning, and that they have “gained increasing acceptance in recent years” (p. 4). There are compelling reasons for the selection of qualitative methodologies within the educational research arena, yet many people remain unfamiliar with these methods. Researchers trained in the use of quantitative designs face real challenges when called upon to use or teach qualitative research (Stallings, 1995). There is, however, a growing body of literature devoted to qualitative research in education, some of which is synthesized here. The goals of this article are to elaborate on the reasons for choosing qualitative methodologies, and to provide a basic introduction to the features of this type of research.


title: Automatic detection of fracture in femur bones using image processing

abstract: The femur is the enlarged and the vigorous bone in the human body, ranging from the hip to the knee. This bone is responsible for the creation of Red Blood Cell in the body. Since this bone is a major part of the body, a method is proposed through this paper to visualize and classify deformities for locating fractures in the femur through image processing techniques. The input image is preprocessed to highlight the domain of interest. In the process, the foreground which is the major domain of interest is figured out by suppressing the background details. The mathematical morphological techniques are used for these operations. With the help of basic morphological operations, the foreground is highlighted and edge detection is used to highlight the objects in the foreground. The processed image is classified using the support vector machine (SVM) to distinguish fractured and unfractured sides of the bone.


title: A computational approach to the analysis and generation of emotion in text

abstract: Sentiment analysis is a field of computational linguistics involving identification, extraction, and classification of opinions, sentiments, and emotions expressed in natural language. Sentiment classification algorithms aim to identify whether the author of a text has a positive or a negative opinion about a topic. One of the main indicators which help to detect the opinion are the words used in the texts. Needless to say, the sentiments expressed in the texts also depend on the syntactic structure and the discourse context. Supervised machine learning approaches to sentiment classification were shown to achieve good results. Classifying texts by emotions requires finer-grained analysis than sentiment classification. ::: In this thesis, we explore the task of emotion and mood classification for blog postings. We propose a novel approach that uses the hierarchy of possible moods to achieve better results than a standard flat classification approach. We also show that using sentiment orientation features improves the performance of classification. We used the LiveJournal blog corpus as a dataset to train and evaluate our method. ::: Another contribution of this work is extracting paraphrases for emotion terms based on the six basics emotions proposed by Ekman (happiness, anger, sadness, disgust, surprise, fear). Paraphrases are different ways to express the same information. Algorithms to extract and automatically identify paraphrases are of interest from both linguistic and practical points of view. Our paraphrase extraction method is based on a bootstrapping algorithms that starts with seed words. Unlike in previous work, our algorithm does not need a parallel corpus. ::: In Natural Language Generation (NLG), paraphrasing is employed to create more varied and natural text. In our research, we extract paraphrases for emotions, with the goal of using them to automatically generate emotional texts (such as friendly or hostile texts) for conversations between intelligent agents and characters in educational games. ::: Nowadays, online services are popular in many disciplines such as: e-learning, interactive games, educational games, stock market, chat rooms and so on. NLG methods can be used in order to generate more interesting and normal texts for such applications. Generating text with emotions is one of the contributions of our work. In the last part of this thesis, we give an overview of NLG from an applied system's points of view. We discuss when NLG techniques can be used; we explained the requirements analysis and specification of NLG systems. We also, describe the main NLG tasks of content determination, discourse planning, sentence aggregation, lexicalization, referring expression generation, and linguistic realisation. Moreover, we describe our Authoring Tool that we developed in order to allow writers without programming skills to automatically generate texts for educational games. ::: We develop an NLG system that can generate text with different emotions. To do this, we introduce our pattern-based model for generation. We show our model starts with initial patterns, then constructs extended patterns from which we choose "final'' patterns that are suitable for generating emotion sentences. A user can generate sentences to express the desired emotions by using our patterns. Alternatively, the user can use our Authoring Tool to generate sentences with emotions. Our acquired paraphrases will be employed by the tool in order to generate more varied outputs.


title: Review of Farm Management Information Systems (FMIS)

abstract: There have been considerable advancements in the field of MIS over the years, and it continues to grow and develop in response to the changing needs of the business and marketing environment. Professionals and academicians are contributing and serving the field of MIS through the dissemination of their knowledge and ideas in professional journals. Thus, changes and trends that likely have an impact on MIS concepts, processes, and implementation can be determined by reviewing the articles published in journals. Content of the articles published in journals can also give us an idea about the types of research and themes that are popular during a given period. To see the evolutionary change in the field of MIS, the present study examined the content of articles published in business and marketing journals. New York Science Journal, 2010;3(5): • …


title: Balancing and Velocity Control of a Unicycle Robot Based on the Dynamic Model

abstract: This paper presents a dynamic-model-based control scheme for the balancing and velocity control of a unicycle robot. Unicycle robot motion consists of a pitch that is controlled by an in-wheel motor and a roll that is controlled by a reaction wheel pendulum. The unicycle robot lacks an actuator for yaw-axis control, which makes the derivation of the dynamics relatively simple although it may limit the motion control. The Euler-Lagrange equation is applied to derive the dynamic equations of the unicycle robot to implement dynamic speed control. To achieve real-time speed control, a sliding-mode control and a nonzero set-point linear quadratic regulator (LQR) are utilized to guarantee stability while maintaining the desired speed-tracking performance. In the roll controller, a sigmoid-function-based sliding-mode controller has been adopted to minimize switching-function chattering. An LQR controller has been implemented for pitch control to drive the unicycle robot to follow the desired velocity trajectory in real time using the state variables of pitch angle, angular velocity, wheel angle, and angular velocity. The control performance of the two control systems using a single dynamic model has been experimentally demonstrated.


title: Abstract Meaning Representation for Multi-Document Summarization

abstract: Generating an abstract from a collection of documents is a desirable capability for many real-world applications. However, abstractive approaches to multi-document summarization have not been thoroughly investigated. This paper studies the feasibility of using Abstract Meaning Representation (AMR), a semantic representation of natural language grounded in linguistic theory, as a form of content representation. Our approach condenses source documents to a set of summary graphs following the AMR formalism. The summary graphs are then transformed to a set of summary sentences in a surface realization step. The framework is fully data-driven and flexible. Each component can be optimized independently using small-scale, in-domain training data. We perform experiments on benchmark summarization datasets and report promising results. We also describe opportunities and challenges for advancing this line of research.


title: Optical Single-Sideband WDM Nyquist 32-QAM SC-FDE Transmission System with Direct Detection

abstract: 125 Gbit/s optical single side band (OSSB) Square-32QAM wavelength-division-multiplexed direct detection over 75.1km SSMF transmission has been successfully demonstrated with a subcarrier frequency band of 2.9 GHz and a channel frequency grid of 12.5GHz.


Addressing the Curse of Imbalanced Training Sets: One-Sided Selection


title: Detecting stairs and pedestrian crosswalks for the blind by RGBD camera

abstract: A computer vision-based wayfinding and navigation aid can improve the mobility of blind and visually impaired people to travel independently. In this paper, we develop a new framework to detect and recognize stairs and pedestrian crosswalks using a RGBD camera. Since both stairs and pedestrian crosswalks are featured by a group of parallel lines, we first apply Hough transform to extract the concurrent parallel lines based on the RGB channels. Then, the Depth channel is employed to further recognize pedestrian crosswalks, upstairs, and downstairs using support vector machine (SVM) classifiers. Furthermore, we estimate the distance between the camera and stairs for the blind users. The detection and recognition results on our collected dataset demonstrate that the effectiveness and efficiency of our proposed framework.


title: Functionalized Single Graphene Sheets Derived from Splitting Graphite Oxide

abstract: A process is described to produce single sheets of functionalized graphene through thermal exfoliation of graphite oxide. The process yields a wrinkled sheet structure resulting from reaction sites involved in oxidation and reduction processes. The topological features of single sheets, as measured by atomic force microscopy, closely match predictions of first-principles atomistic modeling. Although graphite oxide is an insulator, functionalized graphene produced by this method is electrically conducting.


title: Modeling of axial flux permanent-magnet machines

abstract: In modeling axial field machines, three-dimensional (3-D) finite-element method (FEM) models are required in accurate computations. However, 3-D FEM analysis is generally too time consuming in industrial use. In order to evaluate the performance of the axial flux machine rapidly, an analytical design program that uses quasi-3-D computation is developed. In this paper the main features of the developed program are illustrated. Results given by the program are verified with two-dimensional and 3-D finite element computations and measurements. According to the results, it is possible to evaluate the performance of the surface-mounted axial flux PM machine with reasonable accuracy via an analytical model using quasi-3-D computation.


title: Suspicious News Detection Using Micro Blog Text

abstract: We present a new task, suspicious news detection using micro blog text. This task aims to support human experts to detect suspicious news articles to be verified, which is costly but a crucial step before verifying the truthfulness of the articles. Specifically, in this task, given a set of posts on SNS referring to a news article, the goal is to judge whether the article is to be verified or not. For this task, we create a publicly available dataset in Japanese and provide benchmark results by using several basic machine learning techniques. Experimental results show that our models can reduce the cost of manual fact-checking process.


title: Passive returning mechanism for twisted string actuators

abstract: The twisted string actuator is an actuator that is gaining popularity in various engineering and robotics and applications. However, the fundamental limitation of actuators of this type is the uni-directional action, meaning that the actuator can contract but requires external power to return to its initial state. This paper proposes 2 novel passive extension mechanisms based on buckling effect to solve the uni-directional issue of the twisted string actuator. The proposed mechanisms are mechanically simple and compact and provide a nearly-constant extension force throughout the operation range. The constant force can fully extend the twisted string actuator with minimal loss of force during contraction. The designed mechanisms are evaluated in a series of practical tests, and their performances are compared and discussed.


title: Pointwise Prediction and Sequence-Based Reranking for Adaptable Part-of-Speech Tagging

abstract: This paper proposes an accurate method for part-of-speech (POS) tagging that is highly domain-adaptable. The method is based on an assumption that the POS transition tendencies do not depend on domains, and has the following three characteristics: (1) it is trainable from partially annotated data, (2) it uses efficiently trainable pointwise POS taggers to allow for active learning, and (3) is more accurate than the pointwise or sequence-based POS taggers. The proposed method estimates POS tags by stacking pointwise and sequence-based predictors. In the experiments we deal with the joint problem of word segmentation and POS tagging in Japanese. We show that our proposed stacking process improves over pointwise and sequence-based methods (hidden Markov models and conditional random fields) both in the general domain and the target domain. In addition we show the learning curve in a domain adaptation scenario. The result shows that our method outperforms state-of-the-art methods in the same domain as the training data and is better than them in domain adaptation situations as well.


title: A comparision between steganography software tools

abstract: The process of hiding data in a medium that is insignificant, ordinary and not a secret is referred to as Steganography. This medium may be an image, text, video, audio, pdf, etc. The main advantage of Steganography is the fact that data is hidden in such a way that attackers find it hard to figure out exactly what the medium that carries the hidden message is. This is because the data is hidden in a medium that is not suspicious. Steganography tools take input images that can embed data like text or images inside of them. These tools then hide the text or image in the input image to create an output image. This image although looks similar to the input image, contains secret text or image information. In this paper, we will compare various tools that offer different algorithms for Steganography. We will input the same images and hide an image in them and see the different output images that are produced by the tools and compare them.


title: Effects of a weighted vest on attention to task and self-stimulatory behaviors in preschoolers with pervasive developmental disorders.

abstract: The American Journal of Occupational Therapy 629 Objective. This study examined the effectiveness of using a weighted vest for increasing attention to a fine motor task and decreasing self-stimulatory behaviors in preschool children with pervasive developmental disorders (PDD). Method. Using an ABA single-subject design, the duration of attention to task and selfstimulatory behaviors and the number of distractions were measured in five preschool children with PDD over a period of 6 weeks. Results. During the intervention phase, all participants displayed a decrease in the number of distractions and an increase in the duration of focused attention while wearing the weighted vest. All but 1 participant demonstrated a decrease in the duration of self-stimulatory behaviors while wearing a weighted vest; however, the type of self-stimulatory behaviors changed and became less selfabusive for this child while she wore the vest. During the intervention withdrawal phase, 3 participants experienced an increase in the duration of self-stimulatory behaviors, and all participants experienced an increase in the number of distractions and a decrease in the duration of focused attention. The increase or decrease, however, never returned to baseline levels for these behaviors. Conclusion. The findings suggest that for these 5 children with PDD, the use of a weighted vest resulted in an increase in attention to task and decrease in self-stimulatory behaviors. The most consistent improvement observed was the decreased number of distractions. Additional research is necessary to build consensus about the effectiveness of wearing a weighted vest to increase attention to task and decrease self-stimulatory behaviors for children with PDD.


title: COUNT Forest: CO-Voting Uncertain Number of Targets Using Random Forest for Crowd Density Estimation

abstract: This paper presents a patch-based approach for crowd density estimation in public scenes. We formulate the problem of estimating density in a structured learning framework applied to random decision forests. Our approach learns the mapping between patch features and relative locations of all objects inside each patch, which contribute to generate the patch density map through Gaussian kernel density estimation. We build the forest in a coarse-to-fine manner with two split node layers, and further propose a crowdedness prior and an effective forest reduction method to improve the estimation accuracy and speed. Moreover, we introduce a semi-automatic training method to learn the estimator for a specific scene. We achieved state-of-the-art results on the public Mall dataset and UCSD dataset, and also proposed two potential applications in traffic counts and scene understanding with promising results.


title: Nighttime Haze Removal with Glow and Multiple Light Colors

abstract: This paper focuses on dehazing nighttime images. Most existing dehazing methods use models that are formulated to describe haze in daytime. Daytime models assume a single uniform light color attributed to a light source not directly visible in the scene. Nighttime scenes, however, commonly include visible lights sources with varying colors. These light sources also often introduce noticeable amounts of glow that is not present in daytime haze. To address these effects, we introduce a new nighttime haze model that accounts for the varying light sources and their glow. Our model is a linear combination of three terms: the direct transmission, airlight and glow. The glow term represents light from the light sources that is scattered around before reaching the camera. Based on the model, we propose a framework that first reduces the effect of the glow in the image, resulting in a nighttime image that consists of direct transmission and airlight only. We then compute a spatially varying atmospheric light map that encodes light colors locally. This atmospheric map is used to predict the transmission, which we use to obtain our nighttime scene reflection image. We demonstrate the effectiveness of our nighttime haze model and correction method on a number of examples and compare our results with existing daytime and nighttime dehazing methods' results.


title: ITS Sequences and Phylogenetic Relationships of Fallopia multiflora from Different Areas Based on Evidences

abstract: In order to resolve the relationships among Fallopia multiflora populations,ITS (internal transcribed spacer) sequences of 10 populations were used to analyze their sequence divergence and to construct phylogenetic tree in this paper.Polygonum aviculare were used as the outgroup.Some conclusions could be achieved as follows (1)the sequence of ITS1 in 10 populations was 195 bp in length,and the G+C content varied from 69.23% to 72.31%.The sequence of ITS2 was 189 bp.The G+C content varied from77.25% to 80.95%.The genetic distances varied from 0.001 75 to 0.049 45;(2)Ten populations of F.multiflora can be classified as two clades naturally according to their cluster dendrogram.The population from Tianyang was an independent branch,so the kinship between Tianyan and others were far.The other populations were clustered together;(3)The population from Tianyang was regarded as varietas of F.multiflora because of the population being the farest according to the cluster analysis,its genetic identity amounting to 0.049 45,its different in morphological characteristics and chemical constituents.


Learning to Represent Review with Tensor Decomposition for Spam Detection


CFIXX : Object Type Integrity for C + + Virtual


title: met*: A Method For Discriminating Metonymy And Metaphor By Computer

abstract: The met* method distinguishes selected examples of metonymy from metaphor and from literalness and anomaly in short English sentences. In the met* method, literalness is distinguished because it satisfies contextual constraints that the nonliteral others all violate. Metonymy is discriminated from metaphor and anomaly in a way that [1] supports Lakoff and Johnson's (1980) view that in metonymy one entity stands for another whereas in metaphor one entity is viewed as another, [2] permits chains of metonymies (Reddy 1979), and [3] allows metonymies to co-occur with instances of either literalness, metaphor, or anomaly. Metaphor is distinguished from anomaly because the former contains a relevant analogy, unlike the latter. The met* method is part of Collative Semantics, a semantics for natural language processing, and has been implemented in a computer program called meta5. Some examples of meta5's analysis of metaphor and metonymy are given. The met* method is compared with approaches from artificial intelligence, linguistics, philosophy, and psychology.


title: TOPICAL REVIEW A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces

abstract: Objective: Most current Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately 10 years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. The time is therefore ripe for an updated review of EEG classification algorithms for BCIs. Approach: We surveyed the BCI and machine learning literature from 2007 to 2017 to identify the new classification approaches that have been investigated to design BCIs. We synthesize these studies in order to present such algorithms, to report how they were used for BCIs, what were the outcomes, and to identify their pros and cons. Main results: We found that the recently designed classification algorithms for EEG-based BCIs can be divided into four main categories: adaptive classifiers, matrix and tensor classifiers, transfer learning and deep learning, plus a few other miscellaneous classifiers. Among these, adaptive classifiers were demonstrated to be generally superior to static ones, even with unsupervised adaptation. Transfer learning can also prove useful although the benefits of transfer learning remain unpredictable. Riemannian geometry-based methods have reached state-of-the-art performances on multiple BCI problems and deserve to be explored more thoroughly, along with tensor-based methods. Shrinkage linear discriminant analysis and random forests also appear particularly useful for small training samples settings. On the other hand, deep learning methods have not yet shown convincing improvement over state-of-the-art BCI methods. Significance: This paper provides a comprehensive overview of the modern classification algorithms used in EEG-based BCIs, presents the principles of these Review of Classification Algorithms for EEG-based BCI 2 methods and guidelines on when and how to use them. It also identifies a number of challenges to further advance EEG classification in BCI.


title: An Efficient Tree Classifier Ensemble-Based Approach for Pedestrian Detection

abstract: Classification-based pedestrian detection systems (PDSs) are currently a hot research topic in the field of intelligent transportation. A PDS detects pedestrians in real time on moving vehicles. A practical PDS demands not only high detection accuracy but also high detection speed. However, most of the existing classification-based approaches mainly seek for high detection accuracy, while the detection speed is not purposely optimized for practical application. At the same time, the performance, particularly the speed, is primarily tuned based on experiments without theoretical foundations, leading to a long training procedure. This paper starts with measuring and optimizing detection speed, and then a practical classification-based pedestrian detection solution with high detection speed and training speed is described. First, an extended classification/detection speed metric, named feature-per-object (fpo), is proposed to measure the detection speed independently from execution. Then, an fpo minimization model with accuracy constraints is formulated based on a tree classifier ensemble, where the minimum fpo can guarantee the highest detection speed. Finally, the minimization problem is solved efficiently by using nonlinear fitting based on radial basis function neural networks. In addition, the optimal solution is directly used to instruct classifier training; thus, the training speed could be accelerated greatly. Therefore, a rapid and accurate classification-based detection technique is proposed for the PDS. Experimental results on urban traffic videos show that the proposed method has a high detection speed with an acceptable detection rate and a false-alarm rate for onboard detection; moreover, the training procedure is also very fast.


title: Socially-Driven Learning-Based Prefetching in Mobile Online Social Networks

abstract: Mobile online social networks (OSNs) are emerging as the popular mainstream platform for information and content sharing among people. In order to provide the quality of experience support for mobile OSN services, in this paper, we propose a socially-driven learning-based framework, namely Spice , for the media content prefetching to reduce the access delay and enhance mobile user’s satisfaction. Through a large-scale data-driven analysis over real-life mobile Twitter traces from over 17 000 users during a period of five months, we reveal that the social friendship has a great impact on user’s media content click behavior. To capture this effect, we conduct the social friendship clustering over the set of user’s friends, and then develop a cluster-based Latent Bias Model for socially-driven learning-based prefetching prediction. We then propose a usage-adaptive prefetching scheduling scheme by taking into account that different users may possess heterogeneous patterns in the mobile OSN app usage. We comprehensively evaluate the performance of Spice framework using trace-driven emulations on smartphones. Evaluation results corroborate that the Spice can achieve superior performance, with an average 80.6% access delay reduction at the low cost of cellular data and energy consumption. Furthermore, by enabling users to offload their machine learning procedures to a cloud server, our design can achieve up to a factor of 1000 speed-up over the local data training execution on smartphones.


The great outdoors? Exploring the mental health benefits of natural environments


title: Creation of a Digital Business Model Builder A Concept to Simulate a Digital Twin of a Business Model and Its Imperative Nature

abstract: Digitization forces industries to adapt to changing market conditions and consumer behavior. Exponential advances in technology, increased consumer power and sharpened competition imply that companies are facing the menace of commoditization. To sustainably succeed in the market, obsolete business models have to be adapted and new business models can be developed. Differentiation and unique selling propositions through innovation as well as holistic stakeholder engagement help companies to master the transformation. To enable companies and start-ups facing the implications of digital change, a tool was created and designed specifically for this demand: the Business Model Builder. This paper investigates the process of transforming the Business Model Builder into a software-supported digitized version. The digital twin allows companies to simulate the iterative adjustment of business models to constantly changing market conditions as well as customer needs on an ongoing basis. The user can modify individual variables, understand interdependencies and see the impact on the result of the business case, i.e. earnings before interest and taxes (EBIT) or economic value added (EVA). The simulation of a business models accordingly provides the opportunity to generate a dynamic view of the business model where any changes of input variables are considered in the result, the business case. Thus, functionality, feasibility and profitability of a business model can be reviewed, tested and validated in the digital simulation tool.


title: Ontology-Based Data Quality Management for Data Streams

abstract: Data Stream Management Systems (DSMS) provide real-time data processing in an effective way, but there is always a tradeoff between data quality (DQ) and performance. We propose an ontology-based data quality framework for relational DSMS that includes DQ measurement and monitoring in a transparent, modular, and flexible way. We follow a threefold approach that takes the characteristics of relational data stream management for DQ metrics into account. While (1) Query Metrics respect changes in data quality due to query operations, (2) Content Metrics allow the semantic evaluation of data in the streams. Finally, (3) Application Metrics allow easy user-defined computation of data quality values to account for application specifics. Additionally, a quality monitor allows us to observe data quality values and take counteractions to balance data quality and performance. The framework has been designed along a DQ management methodology suited for data streams. It has been evaluated in the domains of transportation systems and health monitoring.


title: Reviewing the transport domain: an evolutionary bibliometrics and network analysis

abstract: Tracing the trajectory of scientific fields has been recognized by informaticians, nonetheless, little effort has been dedicated to understanding the evolution of the fast-moving research field of transport, quantitatively and qualitatively. This paper identifies intellectual turning points and emerging trends in the area of transport. Using bibliometric methods, co-keyword networks, journal co-citation networks, highly cited categories, and country and institute networks are detected, visualized and discussed. To conduct this analysis, all publications (35,712) in 23 top journals in the field of transport are extracted from the Institute for Scientific Information (Web of Science). The output of this article could be a valuable source for academics and practitioners working in the field of transport planning and those who work in the areas having a strong relationship with transport issues including mathematicians, economics, operation research, management and geography.


title: Ultra-wideband antipodal vivaldi antenna array with Wilkinson power divider feeding network

abstract: In this paper, a two element antipodal Vivaldi antenna array in combination with an ultra-wideband (UWB) Wilkinson power divider feeding network is presented. The antennas are placed in a stacked configuration to provide more gain and to limit cross talk between transmit and receive antennas in radar applications. Each part can be realized with standard planar technology. For a single antenna element, a comparison in performance is done between standard FR4 and a Rogers® RO3003 substrate. The antenna spacing is obtained using the parametric optimizer of CST MWS®. The performance of the power divider and the antenna array is analyzed by means of simulations and measurements.


title: Geo-Conquesting: Competitive Locational Targeting of Mobile Promotions

abstract: As consumers spend more time on their mobile devices, a focal retailer's natural approach is to target potential customers in close proximity to its own location. Yet focal (own) location targeting may cannibalize profits on inframarginal sales. This study demonstrates the effectiveness of competitive locational targeting, the practice of promoting to consumers near a competitor's location. The analysis is based on a randomized field experiment in which mobile promotions were sent to customers at three similar shopping areas (competitive, focal, and benchmark locations). The results show that competitive locational targeting can take advantage of heightened demand that a focal retailer would not otherwise capture. Competitive locational targeting produced increasing returns to promotional discount depth, whereas targeting the focal location produced decreasing returns to deep discounts, indicating saturation effects and profit cannibalization. These findings are important for marketers, who can use compet...


title: The North Cyprus conference sector: establishing a competitive advantage.

abstract: North Cyprus had recently positioned itself as a conference destination. Given external and internal factors such as political isolation and the over dependency on casino tourism, policy makers have recognized the importance of the conference sector in creating sustainable growth for North Cyprus's tourism-driven economy. Increasingly, Turkish conference organizers are choosing North Cyprus as a destination to host conferences. As such, Turkish conference organizers were questioned via an Internet-based questionnaire to determine attributes considered important within the conference destination selection process. An Importance-Performance Analysis (IPA) was performed; the findings identified that North Cyprus was perceived as a high performer with reference to: ease of accessibility, distance of destination, conference venue and accommodation price, safety and security, climate, hospitality, quality, quantity, and availability of hotel rooms. However, in relation to North Cyprus, a unique application of IPA identified macro- and microdestination attributes that North Cyprus should improve on: transportation within destination, availability of technological resources, the range of conference venues, conference staff training, destination's marketing activities, and quality of local restaurants. The findings provided implications for destination managers in terms of branding, as well as conference venue and accommodation providers in terms of targeting conference organizers more effectively and promoting North Cyprus to conference sector stakeholders.


title: Current Established Risk Assessment Methodologies and Tools

abstract: The technology behind information systems evolves at an exponential rate, while at the same time becoming more and more ubiquitous. This brings with it an implicit rise in the average complexity of systems as well as the number of external interactions. In order to allow a proper assessment of the security of such (sub)systems, a whole arsenal of methodologies, methods and tools have been developed in recent years. However, most security auditors commonly use a very small subset of this collection, that best suits their needs. This thesis aims at uncovering the differences and limitations of the most common Risk Assessment frameworks, the conceptual models that support them, as well as the tools that implement them. This is done in order to gain a better understanding of the applicability of each method and/or tool and suggest guidelines to picking the most suitable one.


title: Stock Replenishment and Shipment Scheduling for Vendor-Managed Inventory Systems

abstract: Vendor-managed inventory (VMI) is a supply-chain initiative where the supplier is authorized to manage inventories of agreed-upon stock-keeping units at retail locations. The benefits of VMI are well recognized by successful retail businesses such as Wal-Mart. In VMI, distortion of demand information (known as bullwhip effect) transferred from the downstream supply-chain member (e.g., retailer) to the upstream member (e.g., supplier) is minimized, stockout situations are less frequent, and inventory-carrying costs are reduced. Furthermore, a VMI supplier has the liberty of controlling the downstream resupply decisions rather than filling orders as they are placed. Thus, the approach offers a framework for synchronizing inventory and transportation decisions.In this paper, we present an analytical model for coordinating inventory and transportation decisions in VMI systems. Although the coordination of inventory and transportation has been addressed in the literature, our particular problem has not been explored previously. Specifically, we consider a vendor realizing a sequence of random demands from a group of retailers located in a given geographical region. Ideally, these demands should be shipped immediately. However, the vendor has the autonomy of holding small orders until anagreeable dispatch time with the expectation that an economical consolidated dispatch quantity accumulates. As a result, the actual inventory requirements at the vendor are partly dictated by the parameters of the shipment-release policy in use. We compute the optimum replenishment quantity and dispatch frequency simultaneously. We develop a renewaltheoretic model for the case of Poisson demands, and present analytical results.


title: Self-Configuring and Optimizing Mobile Ad Hoc Networks

abstract: We present he design and implementation of a working system that enables self-configuration and self-optimization in mobile ad hoc networks (MANETs) by exploiting context awareness and cross-layer design principles, combined with simple network management policies and network programmability. We propose the collaborative management of the MANET through a proactively constructed body of nodes in order to overcome the inherently dynamic nature of MANETs


title: Artificial intelligence for monitoring and supervisory control of process systems

abstract: Complex processes involve many process variables, and operators faced with the tasks of monitoring, control, and diagnosis of these processes often find it difficult to effectively monitor the process data, analyse current states, detect and diagnose process anomalies, or take appropriate actions to control the processes. The complexity can be rendered more manageable provided important underlying trends or events can be identified based on the operational data (Rengaswamy and Venkatasubramanian, 1992. An Integrated Framework for Process Monitoring, Diagnosis, and Control Using Knowledge-based Systems and Neural Networks. IFAC, Delaware, USA, pp. 49-54.). To assist plant operators, decision support systems that incorporate artificial intelligence (AI) and non-AI technologies have been adopted for the tasks of monitoring, control, and diagnosis. The support systems can be implemented based on the data-driven, analytical, and knowledge-based approach (Chiang et al., 2001. Fault Detection and Diagnosis in Industrial Systems. Springer, London, Great Britain). This paper presents a literature survey on intelligent systems for monitoring, control, and diagnosis of process systems. The main objectives of the survey are first, to introduce the data-driven, analytical, and knowledge-based approaches for developing solutions in intelligent support systems, and secondly, to present research efforts of four research groups that have done extensive work in integrating the three solutions approaches in building intelligent systems for monitoring, control and diagnosis. The four main research groups include the Laboratory of Intelligent Systems in Process Engineering (LISPE) at Massachusetts Institute of Technology, the Laboratory for Intelligent Process Systems (LIPS) at Purdue University, the Intelligent Engineering Laboratory (IEL) at the University of Alberta, and the Department of Chemical Engineering at University of Leeds. The paper also gives some comparison of the integrated approaches, and suggests their strengths and weaknesses.


THE RELATIONSHIP BETWEEN ONLINE RATING, HOTEL STAR CATEGORY AND ROOM PRICING POWER


title: A large-scale study of programming languages and code quality in GitHub

abstract: What is the effect of programming languages on software quality? This question has been a topic of much debate for a very long time. In this study, we gather a very large data set from GitHub (728 projects, 63 million SLOC, 29,000 authors, 1.5 million commits, in 17 languages) in an attempt to shed some empirical light on this question. This reasonably large sample size allows us to use a mixed-methods approach, combining multiple regression modeling with visualization and text analytics, to study the effect of language features such as static versus dynamic typing and allowing versus disallowing type confusion on software quality. By triangulating findings from different methods, and controlling for confounding effects such as team size, project size, and project history, we report that language design does have a significant, but modest effect on software quality. Most notably, it does appear that disallowing type confusion is modestly better than allowing it, and among functional languages, static typing is also somewhat better than dynamic typing. We also find that functional languages are somewhat better than procedural languages. It is worth noting that these modest effects arising from language design are overwhelmingly dominated by the process factors such as project size, team size, and commit size. However, we caution the reader that even these modest effects might quite possibly be due to other, intangible process factors, for example, the preference of certain personality types for functional, static languages that disallow type confusion.


title: Contextual Relationship-based Activity Segmentation on an Event Stream in the IoT Environment with Multi-user Activities

abstract: The human activity recognition in the IoT environment plays the central role in the ambient assisted living, where the human activities can be represented as a concatenated event stream generated from various smart objects. From the concatenated event stream, each activity should be distinguished separately for the human activity recognition to provide services that users may need. In this regard, accurately segmenting the entire stream at the precise boundary of each activity is indispensable high priority task to realize the activity recognition. Multiple human activities in an IoT environment generate varying event stream patterns, and the unpredictability of these patterns makes them include redundant or missing events. In dealing with this complex segmentation problem, we figured out that the dynamic and confusing patterns cause major problems due to: inclusive event stream, redundant events, and shared events. To address these problems, we exploited the contextual relationships associated with the activity status about either ongoing or terminated/started. To discover the intrinsic relationships between the events in a stream, we utilized the LSTM model by rendering it for the activity segmentation. Then, the inferred boundaries were revised by our validation algorithm for a bit shifted boundaries. Our experiments show the surprising result of high accuracy above 95%, on our own testbed with various smart objects. This is superior to the prior works that even do not assume the environment with multi-user activities, where their accuracies are slightly above 80% in their test environment. It means that our work is feasible enough to be applied in the IoT environment.


Context-Sensitive Lexicon Features for Neural Sentiment Analysis


title: Wavelet-based statistical features for distinguishing mitotic and non-mitotic cells in breast cancer histopathology

abstract: To diagnose breast cancer (BCa), the number of mitotic cells present in tissue sections is an important parameter to examine and grade breast biopsy specimen. The differentiation of mitotic from non-mitotic cells in breast histopathological images is a crucial step for automatical mitosis detection. This work aims at improving the accuracy of mitosis classification by characterizing objects of interest (tissue cells) in wavelet based multi-resolution representations that better capture the statistical features having mitosis discrimination. A dual-tree complex wavelet transform (DT-CWT) is performed to decompose the image patches into multi-scale forms. Five commonly-used statistical features are extracted on each wavelet subband. Since both mitotic and non-mitotic cells appear as small objects with a large variety of shapes in the images, characterization of mitosis is a challenging problem. The inter-scale dependencies of wavelet coefficients allow extraction of important texture features within the cells that are more likely to appear at all different scales. The wavelet-based statistical features were evaluated on a dataset containing 327 mitotic and 406 non-mitotic cells via a support vector machine classifier in iterative cross-validation. The quantitative results showed that our DT-CWT based approach achieved superior classification performance with the accuracy of 87.94%, sensitivity of 86.80%, specificity of 89.89%, and the area under the curve (AUC) value of 0.94.


title: Stock Direction Forecasting Techniques: An Empirical Study Combining Machine Learning System with Market Indicators in the Indian Context

abstract: Stock price movement prediction has been one of the most challenging issues in finance since the time immemorial. Many researchers in past have carried out extensive studies with the intention of investigating the approaches that uncover the hidden information in stock market data. As a result of which, Artificial Intelligence and data mining techniques have come to the forefront because of their ability to map non-linear data. The study encapsulates market indicators with AI techniques to generate useful extracts to improve decisions under conditions of uncertainty. Three approaches (fundamental model, technical indicators model and hybrid model) have been tested using the standalone and integrated machine learning algorithms viz. SVM, ANN, GA-SVM, and GA-ANN and the results of all the three approaches have been compared in the four above mentioned methods. The core objective of this paper is to identify an approach from the above mentioned algorithms that best predicts the Indian stocks price movement. It is observed from the results that the use of GA significantly increases the accuracy of ANN and that the use of technical analysis with SVM and ANN is well suited for Indian stocks and can help investors and traders maximize their quarterly profits.


title: Effects of robot-assisted activity for elderly people and nurses at a day service center

abstract: Mental commit robots have been developed to provide psychological, physiological, and social effects in human beings through physical interaction. The appearance of these robots is designed to emulate real animals such as a cat or a seal. The seal robot was developed especially for therapeutic applications. Seal robots have been applied to assisting the activity of the elderly at a day service center. In order to investigate the effects of seal robots on elderly people, their mood was evaluated using face scales (which express moods by identifying the appropriate facial illustration) and questionnaires. Changes in reaction to stress in the elderly was measured using urinary tests. In addition, stress of nursing staff was investigated. Their mental state was evaluated using a "burnout scale." The day service center was provided with seal robots for five weeks. As a result, the feelings of elderly people improved by the interaction with the robots. Urinary tests showed that their ability to overcome stress were also improved. Moreover, the stress levels of the nursing staff decreased because the elderly people required less supervision when interacting with the robots. Consequently, the seal robots were judged to be useful at institutions for the elderly, such as the day service center.


title: GenoGuard: Protecting Genomic Data against Brute-Force Attacks

abstract: Secure storage of genomic data is of great and increasing importance. The scientific community's improving ability to interpret individuals' genetic materials and the growing size of genetic database populations have been aggravating the potential consequences of data breaches. The prevalent use of passwords to generate encryption keys thus poses an especially serious problem when applied to genetic data. Weak passwords can jeopardize genetic data in the short term, but given the multi-decade lifespan of genetic data, even the use of strong passwords with conventional encryption can lead to compromise. We present a tool, called Geno Guard, for providing strong protection for genomic data both today and in the long term. Geno Guard incorporates a new theoretical framework for encryption called honey encryption (HE): it can provide information-theoretic confidentiality guarantees for encrypted data. Previously proposed HE schemes, however, can be applied to messages from, unfortunately, a very restricted set of probability distributions. Therefore, Geno Guard addresses the open problem of applying HE techniques to the highly non-uniform probability distributions that characterize sequences of genetic data. In Geno Guard, a potential adversary can attempt exhaustively to guess keys or passwords and decrypt via a brute-force attack. We prove that decryption under any key will yield a plausible genome sequence, and that Geno Guard offers an information-theoretic security guarantee against message-recovery attacks. We also explore attacks that use side information. Finally, we present an efficient and parallelized software implementation of Geno Guard.


title: The Relationship between Relaxation Techniques and Sport Performance

abstract: Anxiety, as negative emotion, affect perceptions in sports competitions, where a large majority of athletes consider anxiety to be debilitative towards performance, which may result in decreases in performance. Several researches on anxiety among athletes indicated that athletes take drugs to combat anxiety and enhance performance. There are many researches indicating that relaxation techniques benefits athletes by enhancing self-confidence, concentration, performance, reducing anxiety and stress, blood pressure as well as muscle tense. The aim of this research was to find the correlation between different kinds of relaxation techniques and sports performance. The sample consisted of 122 athletes. The sample was drawn from athletes who competed in Sports between University (MASUM). The Questionnaire of Imagery, The Questionnaire of Meditation, The Questionnaire of Progressive Muscle Relaxation and The Questionnaire of Techniques of Breathing, was used in this study. Besides that, Sports Performance Questionnaire also has been used in this study to correlate the relationship between the relaxation techniques and performance of athletes. The result showed that there are positive correlation between imagery and sports performance, meditation and sports performance, progressive muscle relaxation and sports performance, breathing techniques and sports performance. Sport psychologist and coaches should use these findings to practice various kinds of relaxation techniques on athletes. Practicing relaxation techniques can reduce the number of athletes taking drugs to reduce anxiety and enhance performance. For sport industry, relaxation techniques product can be sold through the form of CDs, VCDs, DVDs and audio.


title: Towards a Capability Model for Big Data Analytics

abstract: Big data analytics is becoming a veritable source of competitive advantageas it helps companies to better understand their business environment and to create or improve their products and services accordingly. However, big data analytics also poses challenges to organizations with respect to establishing the required capabilities. Building upon a design science research approach and the Work System Theory as a kernel theory, we identify several capabilities necessary to leverage the potential of big data analytics. To achieve this goal, we conducted 16 interviews with experts from an IT-strategy consulting firm. We furthermore organize the identified capabilities into a coherent model. The resulting capability model consists of eight capability groups that contain 34 capabilities. It provides a basis to systematically develop the necessary capabilities for the adoption und strategic usage of big data analytics.


title: The Matthew effect in empirical data

abstract: The Matthew effect describes the phenomenon that in societies, the rich tend to get richer and the potent even more powerful. It is closely related to the concept of preferential attachment in network science, where the more connected nodes are destined to acquire many more links in the future than the auxiliary nodes. Cumulative advantage and success-breads-success also both describe the fact that advantage tends to beget further advantage. The concept is behind the many power laws and scaling behaviour in empirical data, and it is at the heart of self-organization across social and natural sciences. Here, we review the methodology for measuring preferential attachment in empirical data, as well as the observations of the Matthew effect in patterns of scientific collaboration, socio-technical and biological networks, the propagation of citations, the emergence of scientific progress and impact, career longevity, the evolution of common English words and phrases, as well as in education and brain development. We also discuss whether the Matthew effect is due to chance or optimization, for example related to homophily in social systems or efficacy in technological systems, and we outline possible directions for future research.


title: Multiband Printed Monopole Antennas Loaded With OCSRRs for PANs and WLANs

abstract: Multiband printed monopole antennas loaded with open complementary split-ring resonators (OCSRRs) are presented. The OCSRRs, modeled as parallel LC resonant tanks, act as high-impedance elements at their resonance frequencies, and different effective λ/4 sections can be achieved in the monopole by placing them at proper locations. Thus, the first working frequency is related to the length of the monopole, while the additional bands are controlled by the resonance frequencies of the OCSRRs. Moreover, the proposed antennas present monopolar radiation characteristics at all the operation bands. Two prototypes are designed, manufactured and measured: 1) a single-loaded OCSRR dual-band printed monopole antenna covering the Bluetooth and IEEE 802.11a/b/g/n bands (2.40-2.48 and 5.15-5.80 GHz, respectively); and 2) a triband prototype based on the same design, but with an additional OCSRR designed to also cover the IEEE 802.11y frequency band (3.65-3.70 GHz). Both antennas are printed on a single-layer of a low-cost substrate, resulting in very compact designs.


title: Educational game design for online education

abstract: The use of educational games in learning environments is an increasingly relevant trend. The motivational and immersive traits of game-based learning have been deeply studied in the literature, but the systematic design and implementation of educational games remain an elusive topic. In this study some relevant requirements for the design of educational games in online education are analyzed, and a general game design method that includes adaptation and assessment features is proposed. Finally, a particular implementation of that design is described in light of its applicability to other implementations and environments.


title: Effects of Caffeine Ingestion on Exercise Testing: A Meta-Analysis

abstract: This study used the meta-analytic approach to examine the effects of caffeine ingestion on exercise testing. Forty double-blind studies with 76 effect sizes (ES) met the inclusion criteria. The type of exercise test was classified as endurance, graded, or short-term. In comparison with placebo, caffeine improved test outcome by 12.3% (95% CI, 9.1 to 15.4), which was equivalent to an overall ES of 0.41 (95% CI, 0.31 to 0.51). Endurance exercise significantly improved test outcome (P < 0.05) more than either graded or short-term exercise. When exercise protocol was examined, time-to-exhaustion (Tlim) protocols had a significantly greater (P < 0.05) ES than either the graded or the non-Tlim protocol(s). The results from this meta-analysis confirm the ergogenic effects of caffeine, particularly for endurance testing that use Tlim protocols.


title: Blending Scrum practices and CMMI project management process areas

abstract: Software development organizations that have been employing capability maturity models, such as SW-CMM or CMMI for improving their processes are now increasingly interested in the possibility of adopting agile development methods. In the context of project management, what can we say about Scrum’s alignment with CMMI? The aim of our paper is to present the mapping between CMMI and the agile method Scrum, showing major gaps between them and identifying how organizations are adopting complementary practices in their projects to make these two approaches more compliant. This is useful for organizations that have a plan-driven process based on the CMMI model and are planning to improve the agility of processes or to help organizations to define a new project management framework based on both CMMI and Scrum practices.


Pairs Trading, Convergence Trading, Cointegration


title: FireCaffe: Near-Linear Acceleration of Deep Neural Network Training on Compute Clusters

abstract: Long training times for high-accuracy deep neural networks (DNNs) impede research into new DNN architectures and slow the development of high-accuracy DNNs. In this paper we present FireCaffe, which successfully scales deep neural network training across a cluster of GPUs. We also present a number of best practices to aid in comparing advancements in methods for scaling and accelerating the training of deep neural networks. The speed and scalability of distributed algorithms is almost always limited by the overhead of communicating between servers; DNN training is not an exception to this rule. Therefore, the key consideration here is to reduce communication overhead wherever possible, while not degrading the accuracy of the DNN models that we train. Our approach has three key pillars. First, we select network hardware that achieves high bandwidth between GPU servers -- Infiniband or Cray interconnects are ideal for this. Second, we consider a number of communication algorithms, and we find that reduction trees are more efficient and scalable than the traditional parameter server approach. Third, we optionally increase the batch size to reduce the total quantity of communication during DNN training, and we identify hyperparameters that allow us to reproduce the small-batch accuracy while training with large batch sizes. When training GoogLeNet and Network-in-Network on ImageNet, we achieve a 47x and 39x speedup, respectively, when training on a cluster of 128 GPUs.


title: Improving Generalization with Active Learning

abstract: Active learning differs from “learning from examples” in that the learning algorithm assumes at least some control over what part of the input domain it receives information about. In some situations, active learning is provably more powerful than learning from examples alone, giving better generalization for a fixed number of training examples. ::: ::: In this article, we consider the problem of learning a binary concept in the absence of noise. We describe a formalism for active concept learning called selective sampling and show how it may be approximately implemented by a neural network. In selective sampling, a learner receives distribution information from the environment and queries an oracle on parts of the domain it considers “useful.” We test our implementation, called an SG-network, on three domains and observe significant improvement in generalization.


title: Event Identification in Social Networks

abstract: Social networks enable users to freely communicate with each other and share their recent news, ongoing activities or views about different topics. As a result, they can be seen as a potentially viable source of information to understand the current emerging topics/events. The ability to model emerging topics is a substantial step to monitor and summarize the information originating from social sources. Applying traditional methods for event detection which are often proposed for processing large, formal and structured documents, are less effective, due to the short length, noisiness and informality of the social posts. Recent event detection techniques address these challenges by exploiting the opportunities behind abundant information available in social networks. This article provides an overview of the state of the art in event detection from social networks.


title: Painting with Bob: assisted creativity for novices

abstract: Current digital painting tools are primarily targeted at professionals and are often overwhelmingly complex for use by novices. At the same time, simpler tools may not invoke the user creatively, or are limited to plain styles that lack visual sophistication. There are many people who are not art professionals, yet would like to partake in digital creative expression. Challenges and rewards for novices differ greatly from those for professionals. In this paper, we leverage existing works in Creativity and Creativity Support Tools (CST) to formulate design goals specifically for digital art creation tools for novices. We implemented these goals within a digital painting system, called Painting with Bob. We evaluate the efficacy of the design and our prototype with a user study, and we find that users are highly satisfied with the user experience, as well as the paintings created with our system.


title: Traditional Host based Intrusion Detection Systems' Challenges in Cloud Computing

abstract: Cloud computing is one of the hottest topics in IT today. It can help enterprises improve the creation and delivery of IT solutions by allowing them to access services more flexibly and cost-effectively. Security concerns in the cloud environment are the main obstacles in cloud adoption. As clouds are distributed in nature, it becomes an easy target for the intruders to exploit the vulnerabilities of the network. Intrusion Detection Systems are one of the key components for securing computing infrastructures. But there are some issues in applying traditional intrusion detection systems on the cloud. This paper discusses each of the three cloud models, their security risks and covers the study of intrusion detection system implemented in cloud environment for dealing with various security issues related to cloud, and offers solutions for it.


title: Learn to Classify and Count: A Unified Framework for Object Classification and Counting

abstract: In this paper, we focus on the problem of object classification and counting in natural scenes. The existing methods of instance detection can be utilized to classify and count. These works depend on the bounding box annotations, which requires time-consuming human labeling effort. Meanwhile, traditional methods of object counting are mostly based on the class-insensitive density map, therefore they are only able to count one specific category. We propose a unified framework for object classification and counting to overcome these drawbacks of existing works. We employ a two-stream convolutional neural network (CNN), which consists of classification and counting branches. Considering the correlation between classification and counting tasks, we propose a novel correlation loss function to coordinate representations learned by both two branches of network. Without hand-labeled bounding box annotations as supervision information, our end-to-end model can recognize the category of objects in the image and count object numbers per category. The proposed method is evaluated on PASCAL VOC 2007 dataset in multi-label classification and object counting tasks. We have achieved improved performance on both two tasks, which shows the effectiveness of our proposed method.


title: ConnNet: A Long-Range Relation-Aware Pixel-Connectivity Network for Salient Segmentation

abstract: Salient segmentation aims to segment out attention-grabbing regions, a critical yet challenging task and the foundation of many high-level computer vision applications. It requires semantic-aware grouping of pixels into salient regions and benefits from the utilization of global multi-scale contexts to achieve good local reasoning. Previous works often address it as two-class segmentation problems utilizing complicated multi-step procedures, including refinement networks and complex graphical models. We argue that semantic salient segmentation can instead be effectively resolved by reformulating it as a simple yet intuitive pixel-pair-based connectivity prediction task. Following the intuition that salient objects can be naturally grouped via semantic-aware connectivity between neighboring pixels, we propose a pure Connectivity Net (ConnNet). ConnNet predicts the connectivity probabilities of each pixel with its neighboring pixels by leveraging multi-level cascade contexts embedded in the image and long-range pixel relations. We investigate our approach on two tasks, namely, salient object segmentation and salient instance-level segmentation, and illustrate that consistent improvements can be obtained by modeling these tasks as connectivity instead of binary segmentation tasks for a variety of network architectures. We achieve the state-of-the-art performance, outperforming or being comparable to existing approaches while reducing inference time due to our less complex approach.


title: LaRED: a large RGB-D extensible hand gesture dataset

abstract: We present the LaRED, a Large RGB-D Extensible hand gesture Dataset, recorded with an Intel's newly-developed short range depth camera. This dataset is unique and differs from the existing ones in several aspects. Firstly, the large volume of data recorded: 243, 000 tuples where each tuple is composed of a color image, a depth image, and a mask of the hand region. Secondly, the number of different classes provided: a total of 81 classes (27 gestures in 3 different rotations). Thirdly, the extensibility of dataset: the software used to record and inspect the dataset is also available, giving the possibility for future users to increase the number of data as well as the number of gestures. Finally, in this paper, some experiments are presented to characterize the dataset and establish a baseline as the start point to develop more complex recognition algorithms. The LaRED dataset is publicly available at: http://mclab.citi.sinica.edu.tw/dataset/lared/lared.html.


Selection Techniques in Case Study Research : A Menu of Qualitative and Quantitative


title: Political polarization in social media: Analysis of the “Twitter political field” in Japan

abstract: There is an ongoing debate about whether the Internet is like a public sphere or an echo chamber. Among many forms of social media, Twitter is one of the most crucial online places for political debate. Most of the previous studies focus on the formal structure of the Twitter political field, such as its homophilic tendency, or otherwise limit the analysis to a few topics. In order to explore whether Twitter functions as an echo chamber in general, however, we have to investigate not only the structure but also the contents of Twitter's political field. Accordingly, we conducted both large-scale social network analysis and natural language processing. We firstly applied a community detection method to the reciprocal following network in Twitter and found five politically distinct communities in the field. We further examined dominant topics discussed therein by employing a topic model in analyzing the content of the tweets, and we found that a topic related to xenophobia is circulated solely in right-wing communities. To our knowledge, this is the first study to address echo chambers in Japanese Twitter political field and to examine the formal structure and the contents of tweets with the combination of large-scale social network analysis and natural language processing.


title: Parsing And Question Classification For Question Answering

abstract: This paper describes machine learning based parsing and question classification for question answering. We demonstrate that for this type of application, parse trees have to be semantically richer and structurally more oriented towards semantics than what most treebanks offer. We empirically show how question parsing dramatically improves when augmenting a semantically enriched Penn treebank training corpus with an additional question treebank.


title: A high-efficiency runtime reconfigurable IP for CNN acceleration on a mid-range all-programmable SoC

abstract: Convolutional Neural Networks (CNNs) are a nature-inspired model, extensively employed in a broad range of applications in computer vision, machine learning and pattern recognition. The CNN algorithm requires execution of multiple layers, commonly called convolution layers, that involve application of 2D convolution filters of different sizes over a set of input image features. Such a computation kernel is intrinsically parallel, thus significantly benefits from acceleration on parallel hardware. In this work, we propose an accelerator architecture, suitable to be implemented on mid-to high-range FPGA devices, that can be re-configured at runtime to adapt to different filter sizes in different convolution layers. We present an accelerator configuration, mapped on a Xilinx Zynq XC-Z7045 device, that achieves up to 120 GMAC/s (16 bit precision) when executing 5×5 filters and up to 129 GMAC/s when executing 3×3 filters, consuming less than 10W of power, reaching more than 97% DSP resource utilizazion at 150MHz operating frequency and requiring only 16B/cycle I/O bandwidth.


title: Intelligence Measure of Cognitive Radios with Learning Capabilities

abstract: Cognitive radio (CR) is considered as a key enabling technology for dynamic spectrum access to improve spectrum efficiency. Although the CR concept was invented with the core idea of realizing "cognition", the research on measuring CR cognition capabilities and intelligence is largely open. Deriving the intelligence capabilities of CR not only can lead to the development of new CR technologies, but also makes it possible to better configure the networks by integrating CRs with different intelligence capabilities in a more cost- efficient way. In this paper, for the first time, we propose a data-driven methodology to quantitatively analyze the intelligence factors of the CR with learning capabilities. The basic idea of our methodology is to run various tests on the CR in different spectrum environments under different settings and obtain various performance results on different metrics. Then we apply factor analysis on the performance results to identify and quantize the intelligence capabilities of the CR. More specifically, we present a case study consisting of sixty three different types of CRs. CRs are different in terms of learning-based dynamic spectrum access strategies, number of sensors, sensing accuracy, and processing speed. Based on our methodology, we analyze the intelligence capabilities of the CRs through extensive simulations. Four intelligence capabilities are identified for the CRs through our analysis, which comply with the nature of the tested algorithms.


title: 3D cost aggregation with multiple minimum spanning trees for stereo matching.

abstract: Cost aggregation is one of the key steps in the stereo matching problem. In order to improve aggregation accuracy, we propose a cost-aggregation method that can embed minimum spanning tree (MST)-based support region filtering into PatchMatch 3D label search rather than aggregating on fixed size patches. However, directly combining PatchMatch label search and MST filtering is not straightforward, due to the extremely high complexity. Thus, we develop multiple MST structures for cost aggregation on plenty of 3D labels, and design the tree-level random search strategy to find possible 3D labels of each pixel. Extensive experiments show that our method reaches higher accuracy than the other existing cost-aggregation and global-optimization methods such as the 1D MST, the PatchMatch and the PatchMatch Filter, and currently ranks first on the Middlebury 3.0 benchmark.


title: CiteSeerX: AI in a digital library search engine

abstract: CiteSeerX is a digital library search engine that provides access to more than 4 million academic documents with nearly a million users and millions of hits per day. Artificial intelligence (AI) technologies are used in many components of CiteSeerX e.g. to accurately extract metadata, intelligently crawl the web, and ingest documents. We present key AI technologies used in the following components: document classification and deduplication, document and citation clustering, automatic metadata extraction and indexing, and author disambiguation. These AI technologies have been developed by CiteSeerX group members over the past 5-6 years. We also show the usage status, payoff, development challenges, main design concepts, and deployment and maintenance requirements. While it is challenging to rebuild a system like CiteSeerX from scratch, many of these AI technologies are transferable to other digital libraries and/or search engines.


title: Oxidative stress triggers the preferential assembly of base excision repair complexes on open chromatin regions

abstract: How DNA repair machineries detect and access, within the context of chromatin, lesions inducing little or no distortion of the DNA structure is a poorly understood process. Removal of oxidized bases is initiated by a DNA glycosylase that recognises and excises the damaged base, initiating the base excision repair (BER) pathway. We show that upon induction of 8-oxoguanine, a mutagenic product of guanine oxidation, the mammalian 8-oxoguanine DNA glycosylase OGG1 is recruited together with other proteins involved in BER to euchromatin regions rich in RNA and RNA polymerase II and completely excluded from heterochromatin. The underlying mechanism does not require direct interaction of the protein with the oxidized base, however, the release of the protein from the chromatin fraction requires completion of repair. Inducing chromatin compaction by sucrose results in a complete but reversible inhibition of the in vivo repair of 8-oxoguanine. We conclude that after induction of oxidative DNA damage, the DNA glycosylase is actively recruited to regions of open chromatin allowing the access of the BER machinery to the lesions, suggesting preferential repair of active chromosome regions.