title: Real-time DDoS attack detection for Cisco IOS using NetFlow

abstract: Flow-based DDoS attack detection is typically performed by analysis applications that are installed on or close to a flow collector. Although this approach allows for easy deployment, it makes detection far from real-time and susceptible to DDoS attacks for the following reasons. First, the fact that the flow export process is timeout-based and that flow collectors typically provide data to analysis applications in chunks, can result in detection delays in the order of several minutes. Second, by the nature of flow export, attack traffic may be amplified by the flow export process if the original packets are small enough and are part of small flows. We have shown in a previous work how to perform DDoS attack detection on a flow exporter instead of a flow collector, i.e., close to the data source and in a real-time fashion, which however required access to a fully-extendible flow monitoring infrastructure. In this work, we investigate whether it is possible to operate the same detection system on a widely deployed networking platform: Cisco IOS. Since our ultimate goal is to identify besides the presence of an attack also attackers and targets, we rely on NetFlow. In this context, we present our DDoS attack detection prototype that has shown to generate a constant load on the underlying platform — even under attacks — underlining that DDoS attack detection can be performed on a Cisco Catalyst 6500 in production networks, if enough spare capacity is available.


title: Study of Channel Characteristics for Galvanic-Type Intra-Body Communication Based on a Transfer Function from a Quasi-Static Field Model

abstract: Intra-Body Communication (IBC), which modulates ionic currents over the human body as the communication medium, offers a low power and reliable signal transmission method for information exchange across the body. This paper first briefly reviews the quasi-static electromagnetic (EM) field modeling for a galvanic-type IBC human limb operating below 1 MHz and obtains the corresponding transfer function with correction factor using minimum mean square error (MMSE) technique. Then, the IBC channel characteristics are studied through the comparison between theoretical calculations via this transfer function and experimental measurements in both frequency domain and time domain. High pass characteristics are obtained in the channel gain analysis versus different transmission distances. In addition, harmonic distortions are analyzed in both baseband and passband transmissions for square input waves. The experimental results are consistent with the calculation results from the transfer function with correction factor. Furthermore, we also explore both theoretical and simulation results for the bit-error-rate (BER) performance of several common modulation schemes in the IBC system with a carrier frequency of 500 kHz. It is found that the theoretical results are in good agreement with the simulation results.


title: Mining Business Opportunities from Location-based Social Networks

abstract: Urbanization's rapid progress has modernized a large number of human beings' lives. This urbanization progress is accompanied by the increase of a variety of shops (e.g., restaurants and fitness centers) to meet the increasing citizens, which means business opportunities for the investors. Nevertheless, it is difficult for the investors to catch such opportunities because opening what kind of business at which place is not easy to decide. In this paper, we take this challenge and define the business opportunity mining problem, which recommends new business categories at a partitioned business district. Specifically, we exploit the data from location-based social networks (LBSNs) to mine the business opportunities, guiding the business owners to open new commercial shops in certain categories at a particular area. First, we define the properties of a business district and propose a greedy algorithm to partition a city into different districts. Next, we propose an embedding model to learn latent representations of categories, which captures the functional correlations among business categories. Furthermore, we propose a ranking model based on the pairwise loss to recommend categories for a specific district. Finally, we conduct experiments on Yelp data, and experimental results show that our proposed method outperforms the baseline methods and resolves the problem well.


title: Flexible time management in data stream systems

abstract: Continuous queries in a Data Stream Management System (DSMS) rely on time as a basis for windows on streams and for defining a consistent semantics for multiple streams and updatable relations. The system clock in a centralized DSMS provides a convenient and well-behaved notion of time, but often it is more appropriate for a DSMS application to define its own notion of time---its own clock(s), sequence numbers, or other forms of ordering and times-tamping. Flexible application-defined time poses challenges to the DSMS, since streams may be out of order and uncoordinated with each other, they may incur latency reaching the DSMS, and they may pause or stop. We formalize these challenges and specify how to generate heartbeats so that queries can be evaluated correctly and continuously in an application-defined time domain. Our heartbeat generation algorithm is based on parameters capturing skew between streams, unordering within streams, and latency in streams reaching the DSMS. We also describe how to estimate these parameters at run-time, and we discuss how heartbeats can be used for processing continuous queries.


title: Sentiment Analysis in Twitter with Lightweight Discourse Analysis

abstract: We propose a lightweight method for using discourse relations for polarity detection of tweets . This method is targeted towards the web-based appli cations that deal with noisy, unstructured text, like the tweets, and cannot afford to use heavy linguistic resource s like parsing due to frequent failure of the parsers to handle noisy dat a. Most of the works in micro-blogs, like Twitter, use a bag-of-words model that ignores the discours e particles like but, since, although etc. In this work, we show how the discourse relations like the connectives and conditionals can be used to incorporate discourse information in any bag-of-words model, to improve sentiment classification accuracy. We also probe the influenc e of the semantic operators like modals and negations on the discourse relations that affect the sentime nt of a sentence. Discourse relations and corresponding rules are identified with minimal processing - just a list look up. We first give a linguistic description of the various discourse r elations which leads to conditions in rules and features in SVM. We show that our discourse-based bag-of-words model performs well in a noisy medium ( Twitter ), where it performs better than an existing Twitte r-based application. Furthermore, we show that our approach is beneficia l to structured reviews as well, where we achieve a better accuracy than a state-of-the-art s ystem in the travel review domain. Our system compares favorably with the state-of-the-art system s and has the additional attractiveness of being less resource intensive.


Factored Deep Convolutional Neural Networks for Noise Robust Speech Recognition.


title: AMRoute: Adhoc Multicast Routing Protocol

abstract: The Ad hoc Multicast Routing protocol (AMRoute) presents a novel approach for robust IP Multicast in mobile ad hoc networks by exploiting user-multicast trees and dynamic logical cores. It creates a bidirectional, shared tree for data distribution using only group senders and receivers as tree nodes. Unicast tunnels are used as tree links to connect neighbors on the user-multicast tree. Thus, AMRoute does not need to be supported by network nodes that are not interested/capable of multicast, and group state cost is incurred only by group senders and receivers. Also, the use of tunnels as tree links implies that tree structure does not need to change even in case of a dynamic network topology, which reduces the signaling traffic and packet loss. Thus AMRoute does not need to track network dynamics; the underlying unicast protocol is solely responsible for this function. AMRoute does not require a specific unicast routing protocol; therefore, it can operate seamlessly over separate domains with different unicast protocols. Certain tree nodes are designated by AMRoute as logical cores, and are responsible for initiating and managing the signaling component of AMRoute, such as detection of group members and tree setup. Logical cores differ significantly from those in CBT and PIM-SM, since they are not a central point for data distribution and can migrate dynamically among member nodes. Simulation results (using ns-2) demonstrate that AMRoute signaling traffic remains at relatively low level for typical group sizes. The results also indicate that group members receive a high proportion of data multicast by senders, even in the case of a highly dynamic network.


title: Learning to Transfer

abstract: Transfer learning borrows knowledge from a source domain to facilitate learning in a target domain. Two primary issues to be addressed in transfer learning are what and how to transfer. For a pair of domains, adopting different transfer learning algorithms results in different knowledge transferred between them. To discover the optimal transfer learning algorithm that maximally improves the learning performance in the target domain, researchers have to exhaustively explore all existing transfer learning algorithms, which is computationally intractable. As a trade-off, a sub-optimal algorithm is selected, which requires considerable expertise in an ad-hoc way. Meanwhile, it is widely accepted in educational psychology that human beings improve transfer learning skills of deciding what to transfer through meta-cognitive reflection on inductive transfer learning practices. Motivated by this, we propose a novel transfer learning framework known as Learning to Transfer (L2T) to automatically determine what and how to transfer are the best by leveraging previous transfer learning experiences. We establish the L2T framework in two stages: 1) we first learn a reflection function encrypting transfer learning skills from experiences; and 2) we infer what and how to transfer for a newly arrived pair of domains by optimizing the reflection function. Extensive experiments demonstrate the L2T's superiority over several state-of-the-art transfer learning algorithms and its effectiveness on discovering more transferable knowledge.


title: DQ METRICS: A NOVEL APPROACH TO QUANTIFY TIMELINESS AND ITS APPLICATION IN CRM (Completed Paper) Topic Category: IQ Concepts; IQ Assessment

abstract: The importance of customer relationship management (CRM) has increased during the last decade. Thus information systems and particularly the quality of customer data are highly relevant for conducting CRM efficiently. Due to the importance of using up-to-date data, this paper analyzes how the data quality dimension timeliness can be quantified. Based on several requirements (e.g. normalization and interpretability) as well as a literature review it proposes a procedure for developing metrics for timeliness that can be for instance adapted to the specific characteristics of the customer data attribute considered. A case study demonstrates the application of our metric by presenting a business case for a CRM campaign of a major mobile services provider.


title: Fast depth estimation using semi-global matching and adaptive stripe-based optimization

abstract: Depth estimation is one of the essential components in many computer vision applications such as 3D scene reconstruction and stereo-based object detection. In such applications, the overall quality of the system highly depends on the quality of depth maps. Recently, several depth estimation methods based on semi-global matching (SGM) have been proposed because SGM provides a good trade-off between runtime and accuracy in depth estimation. In addition, non-parametric matching costs have drawn a lot of attention because they tolerate all radiometric distortions. In this paper, a depth estimation method based on the Census transform with adaptive window patterns and stripe-based optimization has been proposed. For finding the most accurate depth value, adaptive length optimization paths via multiple stripes are used. A modified cross-based cost aggregation technique is proposed which adaptively creates the shape of the cross for each pixel distinctly. In addition, a depth refinement algorithm is proposed which fills the holes of the estimated depth map using the surrounding background depth pixels and sharpens the object boundaries by applying a trilateral filter to the generated depth map. The trilateral filter uses the curvature of pixels as well as texture and depth information to sharpen the edges. Simulation results indicate that the proposed methods enhance the quality of depth maps while reducing the computational complexity compared to the existing SGM-based depth estimation methods.


title: Blood pressure estimation from pulse wave velocity measured on the chest

abstract: Recently, monitoring of blood pressure fluctuation in the daily life is focused on in the hypertension care area to predict the risk of cardiovascular and cerebrovascular disease events. In this paper, in order to propose an alternative system to the existed ambulatory blood pressure monitoring (ABPM) sphygmomanometer, we have developed a prototype of small wearable device consisting of electrocardiogram (ECG) and photopelthysmograph (PPG) sensors. In addition, it was examined whether blood pressure can be estimated based on pulse wave transit time (PWTT) only by attaching that device on the surface of the chest. We indicated that our system could also sense tendency of time-dependent change of blood pressure by measuring pulse of vessel over the sternum while its propagation distance is short.


title: Benefits and challenges of three cloud computing service models

abstract: Cloud computing can be defined as the use of new or existing computing hardware and virtualization technologies to form a shared infrastructure that enables web-based value added services. The three predominant service models are infrastructure, platform, and software-asa-service. Infrastructure-as-a-Service (IaaS) can be defined as the use of servers, storage, and virtualization to enable utility like services for users. Security is a big concern within IaaS, especially considering that the rest of the cloud service models run on top of the infrastructure and related layers. Platform-as-a-Service (PaaS) providers offer access to APIs, programming languages and development middleware which allows subscribers to develop custom applications without installing or configuring the development environment. Software-as-a-Service (SaaS) gives subscribed or pay-peruse users access to software or services which reside in the cloud and not on the user's device. Understanding the cloud service models is critical in determining if cloud services or hosting are an appropriate business solution, and if so, which model best balances the level of control required versus reduced hardware, configuration, and maintenance costs. Cloud computing offers many benefits to organizations; it has enabled collaboration amongst disparate communities and workgroups, and has overcome challenges that have plagued existing business solutions. However, the security, privacy, and integrity of the cloud are of prime importance and there are many challenges that exist. At the present time there seems to be a lot of momentum behind the adoption of cloud computing despite these. This may simply be a trend, an indication that society truly wants their data to be available whenever from anywhere, or a sign that few understand the associated risks.


title: A method to build usable directory system using 3D web-based simulation and directory mode

abstract: A directory system is an indispensable and inseparable facility for a campus building. Numerous studies have been conducted on developing a building directory system for public visitors, however, to the best of our knowledge, little is known on a directory system for a campus building using 3D web-based simulation technology. This research paper presents a method to develop a building directory system which adopted 3D web-based simulation and conventional directory mode for each user interface. The system was developed using the user-center system design method which comprises of some iterative cyclesthat are aimed to achieved a usable design from user perspective. Although the system was primarily built based on the Joseph Wibowo Center Campus building as the model, the adopted technique is applicable to meet requirement of the various target building. The usability evaluation conformance test has shown promising results that the model was accepted by potential users.


title: Review on high utility itemset mining algorithms

abstract: Finding interesting patterns in the database is an important research area in the field of data mining. Association Rule Mining (ARM) finds the items that go together. It finds out the association between items. Frequent Itemset Mining (FIM) finds out the itemset that occur frequently in the database. But this approach misses out the profit and the quantity of item purchased. This is addressed in High Utility Itemset Mining (HUIM). HUIM find the profit generating itemset in the database. Many algorithms have been proposed in this field in the recent years. This paper focuses on reviewing the existing state of art algorithms to create a path for the future research in the area of high utility itemset mining.


title: Taking a lexical approach to teaching: principles and problems

abstract: After briefly reviewing some of the reasons why the traditional grammar/vocabulary distinction is no longer adequate, this article describes two key principles which are claimed to be at the core of teaching according to a lexical approach. There are, however, a number of major difficulties which necessarily co-occur alongside any attempted classroom implementation. Having discussed how these difficulties may be overcome, the article closes by conceding that there is still much work to be done before the approach can hope to become more fully integrated into the mainstream ELT coursebook.


title: Smart Agents: When Lower Search Costs for Quality Information Increase Price Sensitivity

abstract: Recent consumer research suggests that lowering search costs for quality information reduces consumer price sensitivity by creating greater perceived differentiation among brands (e.g., Kaul and Wittink 1995; Lynch and Ariely 2000). We argue that lowering quality search costs by smart agents can have the opposite effect on differentiation and price sensitivity. Smart agents screen through a universe of alternatives, recommending only a handful well-matched to the customer's quality preferences. In this research, we ask and answer the following questions: In markets in which price and quality are uncorrelated, will the use of screening agents increase or decrease prices paid compared to searching from an unordered list of options? Will increasing the size of the store's underlying assortment increase or decrease prices paid when options have been screened on quality? In markets where higher priced goods have higher quality, will the use of screening agents increase or decrease prices paid and quality selected? Experiments 1 and 2 test the effect of quality screening when price and quality are uncorrelated. We then present an analytic model for markets in which price and quality are correlated. We deduce that ordering can cause price and quality to increase or decrease depending on the slope of the price-quality relationship in comparison with the relative importance of price in the utility function. We find support for this model in experiment 3. Copyright 2003 by the University of Chicago.


title: Growth in a Time of Debt

abstract: We study economic growth and inflation at different levels of government and external debt. Our analysis is based on new data on forty-four countries spanning about two hundred years. The dataset incorporates over 3,700 annual observations covering a wide range of political systems, institutions, exchange rate arrangements, and historic circumstances. Our main findings are: First, the relationship between government debt and real GDP growth is weak for debt/GDP ratios below a threshold of 90 percent of GDP. Above 90 percent, median growth rates fall by one percent, and average growth falls considerably more. We find that the threshold for public debt is similar in advanced and emerging economies. Second, emerging markets face lower thresholds for external debt (public and private)--which is usually denominated in a foreign currency. When external debt reaches 60 percent of GDP, annual growth declines by about two percent; for higher levels, growth rates are roughly cut in half. Third, there is no apparent contemporaneous link between inflation and public debt levels for the advanced countries as a group (some countries, such as the United States, have experienced higher inflation when debt/GDP is high.) The story is entirely different for emerging markets, where inflation rises sharply as debt increases.


A Survey on Various Security Threats and Classification of Malware Attacks , Vulnerabilities and Detection Techniques


title: Modified Lawn Weed Detection: Utilization of Edge-Color Based SVM and Grass-Model Based Blob Inspection Filterbank

abstract: We propose a lawn weed detection method modified from our previous work, i.e., Bayesian classifier based method. The proposed method employs features calculated from not only the edge-strength of weed/lawn textures but also color information of RGB. Instead of using Bayesian classifier, we exploit more sophisticated classifier, i.e., support-vector machine, for detecting weeds. After weed detection, the proposed method uses noise blob inspection for removing misclassified weed areas. The inspection process is based on a bank of directional filters modeled from characteristics of the edge of grass blade. Experimental results show that the performance of the proposed method outperforms the compared methods.


Applying Brand Equity Theory to Understand Consumer Opinion in Social Media.


title: Combination of singular value decomposition and K-means clustering methods for topic detection on Twitter

abstract: Online social media are growing very rapidly in recent years, such as Twitter. Even the interaction and communication in the social media can reflect on the events of the real world. This causes the value of the information increasing significantly. However, the huge amount of the information requires a method of automatically detecting topics, one of which is the K-means Clustering. Moreover, the large dimensions of data become obstacles. So, we used singular value decomposition (SVD) to reduce the dimension of the data prior to the learning process using the K-means Clustering. The accuracy of the combination of SVD and K-means Clustering methods showed comparative results, while the computation time required is likely to be faster than the method of K-means Clustering without any reduction in advance.


title: Real user evaluation of spoken dialogue systems using Amazon Mechanical Turk

abstract: This paper describes a framework for evaluation of spoken dialogue systems. Typically, evaluation of dialogue systems is performed in a controlled test environment with carefully selected and instructed users. However, this approach is very demanding. An alternative is to recruit a large group of users who evaluate the dialogue systems in a remote setting under virtually no supervision. Crowdsourcing technology, for example Amazon Mechanical Turk (AMT), provides an efficient way of recruiting subjects. This paper describes an evaluation framework for spoken dialogue systems using AMT users and compares the obtained results with a recent trial in which the systems were tested by locally recruited users. The results suggest that the use of crowdsourcing technology is feasible and it can provide reliable results. Copyright © 2011 ISCA.


title: Automated Personalized Feedback in Introductory Java Programming MOOCs

abstract: Currently, there is a "boom" in introductory programming courses to help students develop their computational thinking skills. Providing timely, personalized feedback that makes students reflect about what and why they did correctly or incorrectly is critical in such courses. However, the limited number of instructors and the great volume of submissions instructors need to assess, especially in Massive Open Online Courses (MOOCs), prove this task a challenge. One solution is to hire graders or create peer discussions among students, however, feedback may be too general, incomplete or even incorrect. Automatic techniques focus on: a) Functional testing, in which feedback usually does not sufficiently guide novices, b) Software verification to find code bugs, which may confuse novices since these tools usually skip true errors or produce false errors, and c) Comparing using reference solutions, in which a large amount of reference solutions or pre-existing correct submissions are usually required. This paper presents a semantic-aware technique to provide personalized feedback that aims to mimic an instructor looking for code snippets in student submissions. These snippets are modeled as subgraph patterns with natural language feedback attached to them. Submissions are transformed into extended program dependence graphs combining control and data flows. We leverage subgraph matching techniques to compute the adequate personalized feedback. Also, constraints correlating patterns allow performing fine-grained assessments. We have evaluated our method on several introductory programming assignments and a large number of submissions. Our technique delivered personalized feedback in milliseconds using a small set of patterns, which makes it appealing in real-world settings.


title: Image recognition based on deep learning

abstract: Deep learning is a multilayer neural network learning algorithm which emerged in recent years. It has brought a new wave to machine learning, and making artificial intelligence and human-computer interaction advance with big strides. We applied deep learning to handwritten character recognition, and explored the two mainstream algorithm of deep learning: the Convolutional Neural Network (CNN) and the Deep Belief NetWork (DBN). We conduct the performance evaluation for CNN and DBN on the MNIST database and the real-world handwritten character database. The classification accuracy rate of CNN and DBN on the MNIST database is 99.28% and 98.12% respectively, and on the real-world handwritten character database is 92.91% and 91.66% respectively. The experiment results show that deep learning does have an excellent feature learning ability. It don't need to extract features manually. Deep learning can learn more nature features of the data.


title: COOPERATION BETWEEN DISTRIBUTED AGENTS THROUGH SELF-ORCAMSATION Luc Steels

abstract: The paper uses a behavior-based approach to tackle the problem of cooperation between distributed agents. It focuses on the use of self-organisation and dissipative structures to establish emergent functionality. Other principles such as randomness the subsumption architecture, and the gradient field are also used. Results of computational experiments are presented. >


title: Biomimetics: its practice and theory

abstract: Biomimetics, a name coined by Otto Schmitt in the 1950s for the transfer of ideas and analogues from biology to technology, has produced some significant and successful devices and concepts in the past 50 years, but is still empirical. We show that TRIZ, the Russian system of problem solving, can be adapted to illuminate and manipulate this process of transfer. Analysis using TRIZ shows that there is only 12% similarity between biology and technology in the principles which solutions to problems illustrate, and while technology solves problems largely by manipulating usage of energy, biology uses information and structure, two factors largely ignored by technology.


title: A Corpus with Multi-Level Annotations of Patients, Interventions and Outcomes to Support Language Processing for Medical Literature

abstract: We present a corpus of 5,000 richly annotated abstracts of medical articles describing clinical randomized controlled trials. Annotations include demarcations of text spans that describe the Patient population enrolled, the Interventions studied and to what they were Compared, and the Outcomes measured (the `PICO' elements). These spans are further annotated at a more granular level, e.g., individual interventions within them are marked and mapped onto a structured medical vocabulary. We acquired annotations from a diverse set of workers with varying levels of expertise and cost. We describe our data collection process and the corpus itself in detail. We then outline a set of challenging NLP tasks that would aid searching of the medical literature and the practice of evidence-based medicine.


title: QBE: QLearning-Based Exploration of Android Applications

abstract: Android applications are used extensively around the world. Many of these applications contain potential crashes. Black-box testing of Android applications has been studied over the last decade to detect these crashes. In this paper, we propose QLearning-Based Exploration (QBE), a fully automated black-box testing methodology, which explores GUI actions using a well-known reinforcement learning technique called QLearning. QBE performs automata learning to obtain a model of the AUT, and generates replayable test suites. Specifically, QBE learns from a set of existing applications the kinds of actions that are most useful in order to reach a particular objective such as detecting crashes or increasing activity coverage. To the best of our knowledge, ours is the first machine learning based approach in Android GUI Testing. We conduct experiments on a test set of 100 AUTs obtained from the commonly used F-Droid benchmarks to show the effectiveness of QBE. We show that QBE performs better than all compared black-box tools in terms of activity coverage and number of distinct detected crashes. We make QBE and our experimental data available online.


title: Analysis of blood vessel topology by cubical homology

abstract: We segment and topologically classify brain vessel data obtained from magnetic resonance angiography (MRA). The segmentation is done adaptively and the classification by means of cubical homology, i.e. the computation of homology groups. In this way the number of connected components; (measured by H/sub 0/), the tunnels (given by H/sub 1/) and the voids (given by H/sub 2/) are determined, resulting in a topological characterization of the blood vessels.


Web-Based Ontology Languages and its Based Description Logics


title: Constraint-Aware Deep Neural Network Compression

abstract: Deep neural network compression has the potential to bring modern resource-hungry deep networks to resource-limited devices. However, in many of the most compelling deployment scenarios of compressed deep networks, the operational constraints matter: for example, a pedestrian detection network on a self-driving car may have to satisfy a latency constraint for safe operation. We propose the first principled treatment of deep network compression under operational constraints. We formulate the compression learning problem from the perspective of constrained Bayesian optimization, and introduce a cooling (annealing) strategy to guide the network compression towards the target constraints. Experiments on ImageNet demonstrate the value of modelling constraints directly in network compression.


title: Keep Your Friends Close and Your Facebook Friends Closer: A Multiplex Network Approach to the Analysis of Offline and Online Social Ties

abstract: Social media allow for an unprecedented amount of interaction between people online. A fundamental aspect of human social behavior, however, is the tendency of people to ::: associate themselves with like-minded individuals, forming ::: homogeneous social circles both online and offline. In this ::: work, we apply a new model that allows us to distinguish ::: between social ties of varying strength, and to observe evidence of homophily with regards to politics, music, health, ::: residential sector & year in college, within the online and ::: offline social network of 74 college students. We present a ::: multiplex network approach to social tie strength, here applied to mobile communication data - calls, text messages, ::: and co-location, allowing us to dimensionally identify relationships by considering the number of communication channels utilized between students. We find that strong social ties ::: are characterized by maximal use of communication channels, while weak ties by minimal use. We are able to identify ::: 75% of close friendships, 90% of weaker ties, and 90% of ::: Facebook friendships as compared to reported ground truth. ::: We then show that stronger ties exhibit greater profile similarity than weaker ones. Apart from high homogeneity in social ::: circles with respect to political and health aspects, we observe ::: strong homophily driven by music, residential sector and year ::: in college. Despite Facebook friendship being highly dependent on residence and year, exposure to less homogeneous ::: content can be found in the online rather than the offline social circles of students, most notably in political and music ::: aspects.


title: Coalescing HDFS Blocks to Avoid Recurring YARN Container Overhead

abstract: Hadoop clusters have been transitioning from a dedicated cluster environment to a shared cluster environment. This trend has resulted in the YARN container abstraction that isolates computing tasks from physical resources. With YARN containers, Hadoop has expanded to support various distributed frameworks. However, it has been reported that Hadoop tasks suffer from a significant overhead of container relaunch. In order to reduce the container overhead without making significant changes to the existing YARN framework, we propose leveraging the input split, which is the logical representation of physical HDFS blocks. Our assorted block coalescing scheme combines multiple HDFS blocks and creates large input splits of various sizes, reducing the number of containers and their initialization overhead. Our experimental study shows the assorted block coalescing scheme reduces the container overhead by a large margin while it achieves good load balance and job scheduling fairness without impairing the degree of overlap between map phase and reduce phase.


title: AI-Based Game Design : Enabling New Playable Experiences

abstract: With the current set of design tools and methods available to game designers, vast portions of the space of possible games are not currently reachable. In the past, technological advances such as i ...


title: An Inverse-Kinematics Table-Based Solution of a Humanoid Robot Finger With Nonlinearly Coupled Joints

abstract: This paper presents a novel approach toward the inverse kinematics solution of the humanoid robot fingers with nonlinearly coupled joints, a challenging problem existing for years. Under an assumption that the coupled joint angles of the finger are the same, we first derive an approximate closed-form solution of the finger's inverse kinematics. Then, utilizing the approximate solution as the ancillary variable, we propose to solve the finger joint angles from this approximate solution rather than the fingertip position. Through analyzing properties of the approximate solution, it can be known that the coupled joint angles in the approximate solution play the most important role in the derivation of inverse kinematics. In practical implementation, a 1-D lookup table and the linear interpolation to the approximate solution are used to calculate the accurate joint angles. Simulation and experimental results demonstrate effectiveness of the proposed inverse kinematics method.


title: Coronary Artery Spasm During Radiofrequency Ablation of a Left Lateral Accessory Pathway

abstract: A 16-year-old female patient with Wolff-Parkinson-White syndrome and supraventricular tachycardia underwent radiofrequency (RF) catheter ablation of an accessory pathway in the left lateral area. During RF ablation she developed reversible ST segment elevation secondary to coronary artery spasm. Coronary angiography demonstrated the ablation catheter in close proximity to the circumflex coronary artery, with no evidence of coronary artery injury. Subsequently, conduction by way of the accessory pathway was successfully eliminated with cryoablation with no further coronary spasm or injury.


title: Predicting Insider Threat Risks through Linguistic Analysis of Electronic Communication

abstract: Organizations face growing risks from malicious or careless insiders. An insider threat may take many forms, including disgruntled workers, individuals under financial stress or intentional acts of espionage. Waiting for threats to manifest may leave an organization open to liability, hurt morale and in extreme cases lead to physical harm of others. However, predicting who may pose the greatest risk is challenging. Legal and economic concerns make direct psychological examinations challenging, while reliance upon supervisor or co-worker assessments may lead to unfounded accusations. This research investigates the potential for active monitoring of electronic communications as a method that may identify problems early, allowing for proactive mitigation through coaching, assistance programs and where warranted, termination. Research has found correlations between word use and behavior. This research demonstrates that subtle but measurable differences in the frequency of common words found in electronic communication may provide clues about potential insider threat risks.


title: Enzymatic characterization ofBacilli from food packaging paper and board machines

abstract: Aerobic spore-forming bacteria were found dominant in the microflora of food packaging paper and board. Twenty-five strains of bacteria belonging to the genusBacillus were isolated from these paper and board machines, papermaking chemicals, and final products of papermaking. Nineteen strains were analyzed for production of α-amylase, α-glucosidase, glucoamylase, pullulanase, β-glucanase, carboxymethyl cellulase, and caseinase, and also for resistance towards industrial biocides. pH and temperature optima for the activity of the enzymes were determined. All strains were found to produce one or more of the enzymes studied. The amylolytic enzymes of most strains had high temperature optima for activity. Vegetative cells of all strains were found very resistant towards the different commercial slimicides used in paper and board mills. This property together with the ability to survive through the dry end of the machine to the final board and paper, and the production of enzymes degrading papermaking chemicals makes these bacteria potentially harmful in paper and board mills.


title: Tangible VR: Diegetic Tangible Objects for Virtual Reality Narratives

abstract: We present a system for diegetic tangible objects in virtual reality (VR) narratives. The system integrates a custom-designed sensor unit, built with low-cost off-the-shelf hardware, to track objects in VR and to support a variety of custom-made and found tangibles. In its current form, the sensor unit tracks the objects' orientation and supports the authoring of specifically designed interactions for each tangible object. We contribute our design rationale, sensor unit, and four proof of concept prototypes, including a cube, a stuffed animal, a treasure chest, and a wooden boat, demonstrating how we leverage passive and active haptics to create a closer link between real and virtual worlds. For developers and users of VR, we expand interaction possibilities to include the physical characteristics of tangible objects. For the field of tangible narratives, we expand the current use of diegetic objects.


Principles Of Esthetic Evaluation For Anterior Teeth


title: Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems

abstract: Many problems in signal processing and statistical inference involve finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ) error term combined with a sparseness-inducing regularization term. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), wavelet-based deconvolution, and compressed sensing are a few well-known examples of this approach. This paper proposes gradient projection (GP) algorithms for the bound-constrained quadratic programming (BCQP) formulation of these problems. We test variants of this approach that select the line search parameters in different ways, including techniques based on the Barzilai-Borwein method. Computational experiments show that these GP approaches perform well in a wide range of applications, often being significantly faster (in terms of computation time) than competing methods. Although the performance of GP methods tends to degrade as the regularization term is de-emphasized, we show how they can be embedded in a continuation scheme to recover their efficient practical performance.


title: Independency relationships and learning algorithms for singly connected networks

abstract: Graphical structures such as Bayesian networks or Markov networks are very useful tools for representing irrelevance or independency relationships, and they may be used to ec ciently perform reasoning tasks. Singly connected networks are important speci® c cases where there is no more than one undirected path connecting each pair of variables. The aim of this paper is to investigate the kind of properties that a dependency model must verify in order to be equivalent to a singly connected graph structure, as a way of driving automated discovery and construction of singly connected networks in data. The main results are the characterizations of those dependency models which are isomorphic to singly connected graphs (either via the d-separation criterion for directed acyclic graphs or via the separation criterion for undirected graphs), as well as the development of ec cient algorithms for learning singly connected graph representations of dependency models.


title: A fully homomorphic encryption scheme with better key size

abstract: Fully homomorphic encryption is faced with two problems now. One is candidate fully homomorphic encryption schemes are few. Another is that the efficiency of fully homomorphic encryption is a big question. In this paper, we propose a fully homomorphic encryption scheme based on LWE, which has better key size. Our main contributions are: (1) According to the binary-LWE recently, we choose secret key from binary set and modify the basic encryption scheme proposed in Linder and Peikert in 2010. We propose a fully homomorphic encryption scheme based on the new basic encryption scheme. We analyze the correctness and give the proof of the security of our scheme. The public key, evaluation keys and tensored ciphertext have better size in our scheme. (2) Estimating parameters for fully homomorphic encryption scheme is an important work. We estimate the concert parameters for our scheme. We compare these parameters between our scheme and Bra12 scheme. Our scheme have public key and private key that smaller by a factor of about logq than in Bra12 scheme. Tensored ciphertext in our scheme is smaller by a factor of about log2q than in Bra12 scheme. Key switching matrix in our scheme is smaller by a factor of about log3q than in Bra12 scheme.


title: Identification of a pathway for intelligible speech in the left temporal lobe.

abstract: It has been proposed that the identification of sounds, including species-specific vocalizations, by primates depends on anterior projections from the primary auditory cortex, an auditory pathway analogous to the ventral route proposed for the visual identification of objects. We have identified a similar route in the human for understanding intelligible speech. Using PET imaging to identify separable neural subsystems within the human auditory cortex, we used a variety of speech and speech-like stimuli with equivalent acoustic complexity but varying intelligibility. We have demonstrated that the left superior temporal sulcus responds to the presence of phonetic information, but its anterior part only responds if the stimulus is also intelligible. This novel observation demonstrates a left anterior temporal pathway for speech comprehension.


title: Synopsis of the genus Salix (Salicaceae) in southern Africa

abstract: One species of Salix , S. mucronata Thunb. (= S . subserrata Willd.), with five subspecies, is recognized as indigenous to the southern African region. Problems of delimitation in the genus in southern Africa are discussed, and a key to the indigenous and exotic taxa is presented. The synonymy of the subspecies is presented, with leaf silhouettes and a distribution map of each. The following new combinations are made: S. mucronata subsp. hirsuta (Thunb.) Immelman, S. mucronata subsp. capensis (Thunb.) Immelman, 5. mucronata subsp. woodii (Seemen) Immelman and S. mucronata subsp. wilmsi i (Seemen) Immelman.


title: Credit Card Fraud Detection Using AdaBoost and Majority Voting

abstract: Credit card fraud is a serious problem in financial services. Billions of dollars are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card data owing to confidentiality issues. In this paper, machine learning algorithms are used to detect credit card fraud. Standard models are first used. Then, hybrid methods which use AdaBoost and majority voting methods are applied. To evaluate the model efficacy, a publicly available credit card data set is used. Then, a real-world credit card data set from a financial institution is analyzed. In addition, noise is added to the data samples to further assess the robustness of the algorithms. The experimental results positively indicate that the majority voting method achieves good accuracy rates in detecting fraud cases in credit cards.


title: Characterisation of forward stutter in the AmpFlSTR® SGM Plus® PCR

abstract: Abstract PCR amplification of tetrameric short tandem repeats (STRs) can lead to Taq enzyme slippage and artefact products typically one repeat unit less in size than the parent STR. These back stutter or n −4 amplification products are low-level relative to the amplification of the parent STR but are widely seen in the forensic community where tetrameric STRs are employed in the generation of DNA profiles. To aid the interpretation of DNA mixtures where minor contributor(s) might be present in comparable amounts to the back stutter products, the typical amounts of back stutter generated have been well characterised and guidelines for interpretation are in place. However, further artefacts thought to be Taq enzyme slippage leading to products with one repeat unit greater than the parent sequence ( n +4 or forward stutter) or two repeats less ( n −8 or double back stutter) also occur, but these have not been well characterised despite their potential influence in mixture interpretations. Here we present findings with respect to these additional artefacts from a study of 10,000 alleles and include guidelines for interpretation.


title: CoMine : Efficient Mining of Correlated Patterns

abstract: Association rule mining often generates a huge number of rules, but a majority of them either are redundant or do not reflect the true correlation relationship among data objects. We re-examine this problem and show that two interesting measures, all-confidence (denoted as /spl alpha/) and coherence (denoted as /spl gamma/), both disclose genuine correlation relationships and can be computed efficiently. Moreover, we propose two interesting algorithms, CoMine(/spl alpha/) and CoMine(/spl gamma/), based on extensions of a pattern-growth methodology. Our performance study shows that the CoMine algorithms have high performance in comparison with their Apriori-based counterpart algorithms.


title: Using the Optical Mouse Sensor as a Two-Euro Counterfeit Coin Detector

abstract: In this paper, the sensor of an optical mouse is presented as a counterfeit coin detector applied to the two-Euro case. The detection process is based on the short distance image acquisition capabilities of the optical mouse sensor where partial images of the coin under analysis are compared with some partial reference coin images for matching. Results show that, using only the vision sense, the counterfeit acceptance and rejection rates are very similar to those of a trained user and better than those of an untrained user.


title: Online vehicle detection using Haar-like, LBP and HOG feature based image classifiers with stereo vision preselection

abstract: Environment sensing is an essential property for autonomous cars. With the help of sensors, nearby objects can be detected and localized. Furthermore, the creation of an accurate model of the surroundings is crucial for high-level planning. In this paper, we focus on vehicle detection based on stereo camera images. While stereoscopic computer vision is applied to localize objects in the environment, the objects are then identified by image classifiers. We implemented and evaluated several algorithms from image based pattern recognition in our autonomous car framework, using HOG-, LBP-, and Haar-like features. We will present experimental results using real traffic data with focus on classification accuracy and execution times.


title: A framework for the validation of privacy protection solutions in video surveillance

abstract: The issue of privacy protection in video surveillance has drawn a lot of interest lately. However, thorough performance analysis and validation is still lacking, especially regarding the fulfillment of privacy-related requirements. In this paper, we put forward a framework to assess the capacity of privacy protection solutions to hide distinguishing facial information and to conceal identity. We then conduct rigorous experiments to evaluate the performance of face recognition algorithms applied to images altered by privacy protection techniques. Results show the ineffectiveness of naive privacy protection techniques such as pixelization and blur. Conversely, they demonstrate the effectiveness of more sophisticated scrambling techniques to foil face recognition.


title: Active Learning with Unbalanced Classes and Example-Generation Queries

abstract: Machine learning in real-world high-skew domains is difficult, because traditional strategies for crowdsourcing labeled training examples are ineffective at locating the scarce minority-class examples. For example, both random sampling and traditional active learning (which reduces to random sampling when just starting) will most likely recover very few minority-class examples. To bootstrap the machine learning process, researchers have proposed tasking the crowd with finding or generating minority-class examples, but such strategies have their weaknesses as well. They are unnecessarily expensive in well-balanced domains, and they often yield samples from a biased distribution that is unrepresentative of the one being learned.This paper extends the traditional active learning framework by investigating the problem of intelligently switching between various crowdsourcing strategies for obtaining labeled training examples in order to optimally train a classifier. We start by analyzing several such strategies (e.g., annotate an example, generate a minority-class example, etc.), and then develop a novel, skew-robust algorithm, called MB-CB, for the control problem. Experiments show that our method outperforms state-of-the-art GL-Hybrid by up to 14.3 points in F1 AUC, across various domains and class-frequency settings.


title: Copilot - a coprocessor-based kernel runtime integrity monitor

abstract: Copilot is a coprocessor-based kernel integrity monitor for commodity systems. Copilot is designed to detect malicious modifications to a host's kernel and has correctly detected the presence of 12 real-world rootkits, each within 30 seconds of their installation with less than a 1% penalty to the host's performance. Copilot requires no modifications to the protected host's software and can be expected to operate correctly even when the host kernel is thoroughly compromised - an advantage over traditional monitors designed to run on the host itself.


title: A Kalman filtering approach to multispectral image classification and detection of changes in signature abundance

abstract: Subpixel detection and classification are important in identification and quantification of multicomponent mixtures in remotely sensed data, such as multispectral/hyperspectral images. A recently proposed orthogonal subspace projection (OSP) has shown some success in Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperspectral Digital Imagery Collection Experiment (HYDICE) data. However, like most techniques, OSP has its own constraints. One inherent limitation is that the number of signatures to be classified cannot be greater than that of spectral bands. Owing to this limitation, OSP may not perform well for multispectral imagery as it does for hyperspectral imagery. This phenomenon is observed by three-band Satellite Pour l'Observation de la Terra (SPOT) data because of an insufficient number of spectral bands compared to the number of materials to be classified. Further, most approaches proposed for multispectral and hyperspectral image analysis, including OSP, operate on a pixel by pixel basis. In this case, a general assumption is made on the fact that the image data are stationary and pixel independent. Unfortunately, this may be true for laboratory data, but not for real data, due to varying atmospheric and scattering effects. In this paper, a Kalman filtering approach is presented that overcomes the aforementioned problems. In addition to the observation process described by a linear mixture model, a Kalman filter utilizes an abundance state equation to model the nonstationary nature in signature abundance. As a result, the signature abundance can be estimated and updated recursively by the Kalman filter and an abrupt change in signature abundance can be detected via the abundance state equation.


title: A social recommender mechanism for location-based group commerce

abstract: With the rapid growth of social media platforms, numerous group commerce websites, which exploit both the advantages of price discounts and experience value, have emerged. Moreover, the popularity of sophisticated mobile devices brings great commercial opportunities for local store to gain publicity. In this research, considering user preference, geographic convenience, and friends’ influence, a group-coupon recommender system is proposed for promoting location-sensitive products. The results of experiments conducted on Facebook indicate that the proposed mechanism could accurately recommend products and satisfactorily provide a companion list of to customers, significantly increasing willingness to purchase by taking advantage of the power of social influence.


title: Someone to watch over me

abstract: Traditional security mechanisms are part of a larger socio-technical system involving the people and organizations that use them. Yet, those security mechanisms rarely take this social context and social processes into account. In this paper we propose to make security more social, by integrating community oversight into security mechanisms. Like a neighborhood watch, community oversight can provide additional information as more people are able to detect anomalies and problems, as well as foster greater awareness and social norms of security-related behaviors. We describe this new paradigm, several scenarios of use, and the sets of issues involved in implementing this approach.


Pairwise Relation Classification with Mirror Instances and a Combined Convolutional Neural Network


title: 2007 ) A micro-spatial analysis of the demographic and criminogenic environment of drug markets

abstract: AbstractFew studies have combined factors related to social disorganisation and factors related to opportunity theory at the microspatial level, with most studies aggregating to the block group or census tract. This study disaggregates block group census data in and around the vicinity of locations believed to encourage outdoor drug markets in Philadelphia, Pennsylvania, USA. A location quotient analysis finds that drug arrests cluster within a block or two of many suspected crime generators/attractors. Results from a zero-inflated Poisson model suggest some land use variables and factors associated with social disorganisation can help predict the location and size of drug markets; however, at the city level only retail alcohol outlets remained significant when combined with the social disorganisation-related variables, as a predictor of drug markets. The article discusses a number of potential reasons for these findings.


title: Geometry Synthesis on Surfaces Using Field-Guided Shape Grammars

abstract: We show how to model geometric patterns on surfaces. We build on the concept of shape grammars to allow the grammars to be guided by a vector or tensor field. Our approach affords greater artistic freedom in design and enables the use of grammars to create patterns on manifold surfaces. We show several application examples in visualization, anisotropic tiling of mosaics, and geometry synthesis on surfaces. In contrast to previous work, we can create patterns that adapt to the underlying surface rather than distorting the geometry with a texture parameterization. Additionally, we are the first to model patterns with a global structure thanks to the ability to derive field-guided shape grammars on surfaces.


Facebook Profiles Reflect Actual Personality, Not Self-Idealization


Adalimumab maintains remission of Crohn's disease after up to 4 years of treatment: data from CHARM and ADHERE


title: Synchronization of Parallel Single-Phase Inverters With Virtual Oscillator Control

abstract: A method to synchronize and control a system of parallel single-phase inverters without communication is presented. Inspired by the phenomenon of synchronization in networks of coupled oscillators, we propose that each inverter be controlled to emulate the dynamics of a nonlinear dead-zone oscillator. As a consequence of the electrical coupling between inverters, they synchronize and share the load in proportion to their ratings. We outline a sufficient condition for global asymptotic synchronization and formulate a methodology for controller design such that the inverter terminal voltages oscillate at the desired frequency, and the load voltage is maintained within prescribed bounds. We also introduce a technique to facilitate the seamless addition of inverters controlled with the proposed approach into an energized system. Experimental results for a system of three inverters demonstrate power sharing in proportion to power ratings for both linear and nonlinear loads.


title: Non-invasive blood oxygen saturation monitoring for neonates using reflectance pulse oximeter

abstract: Blood oxygen saturation is one of the key parameters for health monitoring of premature infants at the neonatal intensive care unit (NICU). In this paper, we propose and demonstrate a design of a wearable wireless blood saturation monitoring system. Reflectance pulse oxymeter based on Near Infrared Spectroscopy (NIRS) techniques are applied for enhancing the flexibility of measurements at different locations on the body of the neonates and the compatibility to be integrated into a non-invasive monitoring platform, such as a neonatal smart jacket. Prototypes with the reflectance sensors embedded in soft fabrics are built. The thickness of device is minimized to optimize comfort. To evaluate the performance of the prototype, experiments on the premature babies were carried out at NICU of Maxima Medical Centre (MMC) in Veldhoven, the Netherlands. The results show that the heart rate and SpO 2 measured by the proposed design are corresponding to the readings of the standard monitor.


title: Optimal machine operation planning for construction by Contour Crafting

abstract: Abstract Contour Crafting is an emerging technology that uses robotics to construct free form building structures by repeatedly laying down layers of material such as concrete. The Contour Crafting technology scales up automated additive fabrication from building small industrial parts to constructing buildings. Optimal machine operation planning for Contour Crafting benefits the technology by increasing the efficiency of construction, especially for complicated structures. The research reported here has aimed at providing a systematic solution for improving the overall Contour Crafting system efficiency in building custom-designed buildings. An approach is first presented to find the optimal machine operation plan for the single nozzle Contour Crafting system. Other approaches are then presented to determine collision-free operation plans for machines with multiple nozzles. The models developed incorporate physical constraints as well as some practical construction issues.


title: Can we use minor finger knuckle images to identify humans?

abstract: Biometric identification using finger knuckle imaging has generated lot of promises with interesting applications in forensics and remote biometrics. Prior efforts in the biometrics literature have only investigated the ‘major’ finger knuckle patterns that are formed on the finger surface joining proximal phalanx and middle phalanx bones. This paper investigates the possible use of ‘minor’ finger knuckle patterns which are formed on the finger surface joining distal phalanx and middle phalanx bones. The ‘minor’ or ‘upper’ finger knuckle patterns can either be used as independent biometric patterns or employed to improve the performance from the major finger knuckle patterns. This paper investigates a completely automated approach for the ‘minor’ finger knuckle identification by developing steps of region of interest segmentation, image normalization, enhancement and robust matching to accommodate image variations. Comparative experimental results are presented for matching the normalized ‘minor’ finger knuckle images using LBP, ILBP and 1D log Gabor filter. The efforts to develop automated ‘minor’ finger knuckle patterns achieve promising results, with 1.04% equal error rate on the database of 202 subjects, and illustrate its simultaneous use to improve the performance for conventional finger knuckle identification.


title: A New Planar Artificial Transmission Line and Its Applications to a Miniaturized Butler Matrix

abstract: A miniaturized quadrature hybrid coupler, a rat-race coupler, and a 4 times 4 Butler matrix based on a newly proposed planar artificial transmission line are presented in this paper for application in ultra-high-frequency (UHF) radio-frequency identification (RFID) systems. This planar artificial transmission line is composed of microstrip quasi-lumped elements and their discontinuities and is capable of synthesizing microstrip lines with various characteristic impedances and electrical lengths. At the center frequency of the UHF RFID system, the occupied sizes of the proposed quadrature hybrid and rat-race couplers are merely 27% and 9% of those of the conventional designs. The miniaturized couplers demonstrate well-behaved wideband responses with no spurious harmonics up to two octaves. The measured results reveal excellent agreement with the simulations. Additionally, a 4 times 4 Butler matrix, which may occupy a large amount of circuit area in conventional designs, has been successfully miniaturized with the help of the proposed artificial transmission line. The circuit size of the Butler matrix is merely 21% of that of a conventional design. The experimental results show that the proposed Butler matrix features good phase control, nearly equal power splitting, and compact size and is therefore applicable to the reader modules in various RFID systems.


title: Deep Neural Networks for Query Expansion using Word Embeddings

abstract: Query expansion is a method for alleviating the vocabulary mismatch problem present in information retrieval tasks. Previous works have shown that terms selected for query expansion by traditional pseudo-relevance feedback methods such as mixture model are not always helpful to the retrieval process. In this paper, we show that this is also true for more recently proposed embedding-based query expansion methods. We then introduce an artificial neural network classifier, which uses term word embeddings as input, to predict the usefulness of query expansion terms. Experiments on four TREC newswire and web collections show that using terms selected by the classifier for expansion significantly improves retrieval performance compared to competitive baselines. The results are also shown to be more robust than the baselines.


title: Playing games for security: An efficient exact algorithm for solving bayesian stackelberg games

abstract: In a class of games known as Stackelberg games, one agent (the leader) must commit to a strategy that can be observed by the other agent (the follower or adversary) before the adversary chooses its own strategy. We consider Bayesian Stackelberg games, in which the leader is uncertain about the types of adversary it may face. Such games are important in security domains, where, for example, a security agent (leader) must commit to a strategy of patrolling certain areas, and a robber (follower) has a chance to observe this strategy over time before choosing its own strategy of where to attack. This paper presents an efficient exact algorithm for finding the optimal strategy for the leader to commit to in these games. This algorithm, DOBSS, is based on a novel and compact mixed-integer linear programming formulation. Compared to the most efficient algorithm known previously for this problem, DOBSS is not only faster, but also leads to higher quality solutions, and does not suffer from problems of infeasibility that were faced by this previous algorithm. Note that DOBSS is at the heart of the ARMOR system that is currently being tested for security scheduling at the Los Angeles International Airport.


title: CRePE: context-related policy enforcement for android

abstract: Most of the research work for enforcing security policies on smartphones considered coarse-grained policies, e.g. either to allow an application to run or not. In this paper we present CRePE, the first system that is able to enforce fine-grained policies, e.g. that vary while an application is running, that also depend on the context of the smartphone. A context can be defined by the status of some variables (e.g. location, time, temperature, noise, and light), the presence of other devices, a particular interaction between the user and the smartphone, or a combination of these. CRePE allows context-related policies to be defined either by the user or by trusted third parties. Depending on the authorization, third parties can set a policy on a smartphone at any moment or just when the phone is within a particular context, e.g. within a building, or a plane.


A Unified Open-Circuit-Voltage Model of Lithium-ion Batteries for State-of-Charge Estimation and State-of-Health Monitoring I


title: Big data maturity model for Malaysian zakat institutions to embark on big data initiatives

abstract: Organizations worldwide are dealing with immense growth of data at a huge speed hence making it difficult to manage. The main difficulty in handling such large amount of data is due to the five big data concepts involving volume, velocity, variety, veracity and value which increases rapidly in comparison to the computing resources. One of the arms of the government which holds the responsibility of managing the almsgiving or zakat is currently facing similar issues with the growing amount of data on zakat payers, zakat distribution and various zakat recipients. The overwhelming amount of data must be carefully managed by the zakat institution and the appointed zakat fund trustee. Zakat data resides in massive, unstructured information, largely in various forms of numeric, text and imagery. Hence, there exist a need for zakat institutions to have better data management mechanism namely to coordinate and govern data between the zakat institution and zakat fund trustee offices. This study proposes a big data maturity model to gauge the readiness of zakat institutions to embark into the big data evolution. The proposed model also aims to provide the zakat institution with more structured processes of managing the high volume of data in order to provide better reporting transparency, making well-informed decisions and instill better trust from the Islamic community on better zakat management and distribution.


title: Validation of LiDAR point clouds for classification of high-value crops using geometric-and reflectance-based extraction algorithm

abstract: This study provides an experimental analysis of high-value crop classification using geometric and reflectance-based feature extraction algorithms which can be used to validate extensive classification. The main goal of this study is to identify the class based on the 3D reconstruction of small scale LiDAR scanner and RGB images. The reference used in this study was obtained from the initially classified classes of Mapua-Phil LiDAR2 Project. In validating high-value crops, the classification results were compared to the results obtained using the reference data. The validation procedures involve comparing the locations of previously classified crops and analyzing the crop cycle in the tested fields. The proposed methodology used to identify specific class based on the geometric feature alone are found to be acceptable with an average accuracy of 92.5% while the reflectance based classification alone provides an average accuracy of 90%.


title: Analysis of Near-Field Shielding Effectiveness for the SiP Module

abstract: In this letter, near-field scanning technology is utilized to analyze the shielding effectiveness (SE) of the system-in-package (SiP) module at the frequency of 1–6 GHz. Different definitions of the near-field SE are explored and analyzed. The SE is defined by using values of the radiated magnetic near-field from the SiP module, including the maximum value, the spatial averaged value, and the value at the same observation point, respectively. The SE defined by the spatial averaged value is a reasonable method to represent the overall SE of a well-shielded SiP module especially at low frequencies. Besides, a comparison of the SE between the reverberation chamber measurement and averaged magnetic near field is carried on. Good consistency is obtained, which confirms the reliability and efficiency of the near-field scanning method.


A framework for scalable real-time anomaly detection over voluminous, geospatial data streams


title: The importance of being modest

abstract: In this chapter, we consider modesty and its importance. We begin by defining modesty, proceed to argue that being modest is hard work, and then lay out some reasons why this is so. Next, we make the case that modesty correlates with, and may even cause, several desirable outcomes—intrapersonal, interpersonal, and group. We conclude by attempting to reconcile the discrepancies between two empirical literatures, one suggesting that modesty entails social and mental health benefits, the other suggesting that self-enhancement does.


title: Multilevel inverters for electric vehicle applications

abstract: This paper presents multilevel inverters as an application for all-electric vehicle (EV) and hybrid-electric vehicle (HEV) motor drives. Diode-clamped inverters and cascaded H-bridge inverters: (1) can generate near-sinusoidal voltages with only fundamental frequency switching; (2) have almost no electromagnetic interference (EMI) and common-mode voltage; and (3) make an EV more accessible/safer and open wiring possible for most of an EV's power system. This paper explores the benefits and discusses control schemes of the cascade inverter for use as an EV motor drive or a parallel HEV drive and the diode-clamped inverter as a series HEV motor drive. Analytical, simulated, and experimental results show the superiority of these multilevel inverters for this new niche.


Polarity Lexicon Building: to what Extent Is the Manual Effort Worth?


RESTful Web Services: Principles, Patterns, Emerging Technologies


title: The spatial and temporal distribution characteristics of summer precipitation in China from 1960 to 2012

abstract: Based on the summer monthly precipitation data of 310 stations from 1960 to 2012 in 8 regions of China,the spatial and temporal distribution characteristics of summer precipitation are analyzed by trend features index,Mann-Kendall mutation testing and spatial interpolation.Results show that in spatial and temporal,the summer precipitation of the mid-lower reaches of the Yangtze River,south China,northwest China and Tibetan Plateau showed an increasing trend.The summer precipitation of the mid-lower reaches of the Yangtze River and the west of northwest China showed a remarkable increasing trend and the summer precipitation of the two regions increased most in 1990s.The summer precipitation of northeast China,north China,Tibetan and southwest China showed a decreasing trend and the summer precipitation of northeast China and north China showed a remarkable decreasing trend and it decreased mostfrom 2000 to 2012in northeast China and it decreased most in 1980 sin north China.The summer precipitation increased most in Anhui province.It increased least in Shandong,Hebei,Shanxi,Yunnan and Sichuan province.The summer precipitation variation of the mid-lower reaches of the Yangtze River and the west of northwest China reached to the level of mutations.The abrupt points were 1986 and 1989.The summer precipitation showed the variation from the less to the more.The weakening of southeast summer monsoon may have an important role in the process of summer precipitation reducing in north China,northeast China and in the process of summer precipitation increasing in the Yangtze River and south China.The summer precipitation reduction of southwest China was related to the weakening of southwest summer monsoon.The strengthening of westerlies convective activity may have a leading effect on the process of summer precipitation increasing in northwest China.The terrain of Tibetan Plateau is complex and the summer precipitation of Tibetan Plateau is mainly influenced by the terrain.


CBSE as Novel Approach for IDS


title: Miniaturized Dipole antenna using Koch fractal technique for wearable application

abstract: In this paper, Koch iteration Technique is applied to a Dipole antenna to get second iteration version of Koch fractal antenna. The performance properties of Koch fractal Dipole for 450MHZ have been primarily indicated to its fractal geometry. The Koch geometry is used to reduce the overall size of the antenna. The size of designed Antenna is reduced around 32% compared to conventional dipole at designed frequency. The Koch fractal antenna is designed, fabricated and characterized on a wearable (jeans) substrate. Simulations are done in CST MICROWAVE STUDIO.


title: FloSIS: a highly scalable network flow capture system for fast retrieval and storage efficiency

abstract: Network packet capture performs essential functions in network management such as attack analysis, network troubleshooting, and performance debugging. As the network edge bandwidth exceeds 10 Gbps, the demand for scalable packet capture and retrieval is rapidly increasing. However, existing software-based packet capture systems neither provide high performance nor support flow-level indexing for fast query response. This would either prevent important packets from being stored or make it too slow to retrieve relevant flows. ::: ::: In this paper, we present FloSIS, a highly scalable, software-based flow storing and indexing system. Flo-SIS is characterized as the following three aspects. First, it exercises full parallelism in multiple CPU cores and disks at all stages of packet processing. Second, it constructs two-stage flow-level indexes, which helps minimize expensive disk access for user queries. It also stores the packets in the same flow at a contiguous disk location, which maximizes disk read throughput. Third, we optimize storage usage by flow-level content deduplication at real time. Our evaluation shows that FloSIS on a dual octa-core CPU machine with 24 HDDs achieves 30 Gbps of zero-drop performance with real traffic, consuming only 0.25% of the space for indexing.


title: Detecting sinkhole attack and selective forwarding attack in wireless sensor networks

abstract: Security in Wireless Sensor Networks (WSNs) is especially challenging and quite different from traditional network security mechanisms. There are two major reasons. Firstly, there are severe constraints on these devices namely their minimal energy, computational and communicational capabilities. Secondly, there is an additional risk of physical attacks such as node capture and tampering. Moreover, cryptography based techniques alone are insufficient to secure WSNs [1]. Hence, intrusion detection techniques must be designed to detect the attacks. Further, these techniques should be lightweight because of resource-constrained nature of WSNs [2]. In this paper, we present a new approach of robust and lightweight solution for detecting the Sinkhole attack and the Selective Forwarding attack based on Received Signal Strength Indicator (RSSI) readings of messages. The proposed solution needs collaboration of some Extra Monitor (EM) node apart from the ordinary nodes. We use RSSI value from four EM nodes to determine the position of all sensor nodes which the Base Station (BS) is origin position (0,0). Later, we use this information as weight from the BS. Another functions of EM nodes are eavesdropper and monitor all traffics, in order to detect the Selective Forwarding attack in the network. Our solution is lightweight in the sense that monitor nodes were not loaded any ordinary nodes or BS and not cause a communication overhead.


title: An iterative image registration technique with an application to stereo vision

abstract: Image registration finds a variety of applications in computer vision. Unfortunately, traditional image registration techniques tend to be costly. We present a new image registration technique that makes use of the spatial intensity gradient of the images to find a good match using a type of Newton-Raphson iteration. Our technique is taster because it examines far fewer potential matches between the images than existing techniques Furthermore, this registration technique can be generalized to handle rotation, scaling and shearing. We show how our technique can be adapted tor use in a stereo vision system.


title: An Effective , Low-Cost Measure of Semantic Relatedness Obtained from Wikipedia Links

abstract: This paper describes a new technique for obtaining measures of semantic relatedness. Like other recent approaches, it uses Wikipedia to provide structured world knowledge about the terms of interest. Our approach is unique in that it does so using the hyperlink structure of Wikipedia rather than its category hierarchy or textual content. Evaluation with manually defined measures of semantic relatedness reveals this to be an effective compromise between the ease of computation of the former approach and the accuracy of the latter.


title: Content based image retrieval with LIRe

abstract: LIRe (Lucene Image Retrieval) is an open source library for content based image retrieval. Besides providing multiple common and state of the art retrieval mechanisms it allows for easy use on multiple platforms. LIRe is actively used for research, teaching and commercial applications. Due to its modular nature it can be used on process level (e.g. index images and search) as well as on image feature level. Developers and researchers can easily extend and modify LIRe to adapt it to their needs.


Deep Reinforcement Learning for Robotic Manipulation


title: EN2: Graphics GPU Ray Tracing – Comparative Study of Ray-Triangle Intersection Algorithms

abstract: I present a comparative study on GPU ray tracing implemented for two different types of ray-triangle intersection algorithms used with BVH (Bounding Volume Hierarchy) spatial data structure evaluated for performance on three static scenes. I study how number of triangles placed in a BVH leaf node affects rendering performance. I propose GPU-optimized SIMD ray-triangle intersection method evaluated on GPU for path-tracing and compare it's performance with plain Moller-Trumbore and Unit Triangle intersection methods.


Controlled and Automatic Human Information Processing: 1. Detection, Search, and Attention.


title: Dependency and its Relationship to the Assessment of Care Needs of Elderly People

abstract: After experience in commerce and the health service Ian Gibbs has worked in higher education for the last fifteen years. At present he is a research fellow in the Social Policy Research Unit at the University of York. Jonathan Bradshaw is Professor of Social Policy and Head of the Department of Social Policy and Social Work at the University of York. He was until 1987 Director of the Social Policy Research Unit.


title: SemStim at the LOD-RecSys 2014 Challenge

abstract: SemStim is a graph-based recommendation algorithm which is based on Spreading Activation and adds targeted activation and duration constraints. SemStim is not aected by data sparsity, the cold-start prob- lem or data quality issues beyond the linking of items to DBpedia. The overall results show that the performance of SemStim for the diversity task of the challenge is comparable to the other participants, as it took 3rd place out of 12 participants with 0.0413 F1@20 and 0.476 ILD@20. In addition, as SemStim has been designed for the requirements of cross-domain rec- ommendations with dierent target and source domains, this shows that SemStim can also provide competitive single-domain recommendations.


title: Membrane cholesterol modulates the cytolytic mechanism of myotoxin II, a Lys49 phospholipase A2 homologue from the venom of Bothrops asper

abstract: Lys49 phospholipase A2 (PLA2) homologues present in crotalid snake venoms lack enzymatic activity, yet they induce skeletal muscle necrosis by a membrane permeabilizing mechanism whose details are only partially understood. The present study evaluated the effect of altering the membrane cholesterol content on the cytolytic activity of myotoxin II, a Lys49 PLA2 isolated from the venom of Bothrops asper, using the myogenic cell line C2C12 as a model target. Cell membrane cholesterol depletion by methyl-β-cyclodextrin (MβCD) treatment enhanced the cytolytic action of myotoxin II, as well as of its bioactive C-terminal synthetic peptide p115-129. Conversely, cell membrane cholesterol enrichment by preformed cholesterol-MβCD complexes reduced the cytolytic effect of myotoxin II. The toxic actions of myotoxin I, a catalytically active PLA2 from the same venom, as well as of the cytolytic peptide melittin from bee venom, also increased in cholesterol-depleted cells. Although physical and functional changes resulting from variations in membrane cholesterol are complex, these findings suggest that membrane fluidity could be a relevant parameter to explain the observed modulation of the cytolytic mechanism of myotoxin II, possibly influencing bilayer penetration. In concordance, the cytolytic effect of myotoxin II decreased in direct proportion to lower temperature, a physical factor that affects membrane fluidity. In conclusion, physicochemical properties that depend on membrane cholesterol content significantly influence the cytolytic mechanism of myotoxin II, reinforcing the concept that the primary site of action of Lys49 PLA2 myotoxins is the plasma membrane. Copyright © 2011 John Wiley & Sons, Ltd.


title: Utilizing a Bluetooth remote lock system for a smartphone

abstract: With the advent of mobile devices and the convergence of wireless technologies and the Internet, both the content and the quality of research in this field are subject to regular change. A variety of state-of-the-art computing devices that are compatible with each other have been produced. These devices have the ability to interact with people. This is also known as pervasive computing. Particularly, as smartphones have recently become one of the most popular devices worldwide, various convenient applications are being released. Smartphones available today not only provide the ordinary internal processes such as dialing or receiving phone calls, sending text messages, and doing mobile banking, but also increasingly control various other devices that are part of our daily lives. In effect, this means that through smartphone applications, we can remotely control a variety of external devices such as televisions, projectors for presentations, computers, and even cars. The research in this paper is based on the evolving technological possibilities of using smartphone applications to control external devices. This paper presents the design and implementation of a remote lock system using wireless communication on a smartphone. In this context, remote lock system refers to a lock system that can be controlled remotely by a dedicated Android application. Every smartphone is equipped with Bluetooth which makes this technology possible. The application proposed in this paper uses the existing Bluetooth function on Android smartphones to open and manage locks. The users' lock information can be stored and managed in real time in the database via a server that is built and managed by a server manager. Even if users forget the password of the lock, our proposed lock system can guide them to retrieve it easily, and a user manual is included to help users navigate the system. This system also provides a variety of management functions such as adding, deleting, modifying, and purchasing the user's own locks.


title: Bilateral Back-Projection for Single Image Super Resolution

abstract: In this paper, a novel algorithm for single image super resolution is proposed. Back-projection [1] can minimize the reconstruction error with an efficient iterative procedure. Although it can produce visually appealing result, this method suffers from the chessboard effect and ringing effect, especially along strong edges. The underlining reason is that there is no edge guidance in the error correction process. Bilateral filtering can achieve edge-preserving image smoothing by adding the extra information from the feature domain. The basic idea is to do the smoothing on the pixels which are nearby both in space domain and in feature domain. The proposed bilateral back-projection algorithm strives to integrate the bilateral filtering into the back-projection method. In our approach, the back-projection process can be guided by the edge information to avoid across-edge smoothing, thus the chessboard effect and ringing effect along image edges are removed. Promising results can be obtained by the proposed bilateral back-projection method efficiently.


title: Low-temperature intermediates to oxygen reduction reaction catalysts based on amine-modified metal-loaded carbons. An XPS and ss-NMR investigation

abstract: Abstract Carbon functionalization is a major subject of interest in a number of project applications. Herein we report results on the characterization of nitrogen- and metal-loaded (Me = Fe, Co) carbon derivatives from low-T reaction steps before they are converted to catalysts for electrochemical oxygen reduction by later high-T treatments. The aim is to shed light on the state of carbon and carbon-bonded moieties before thermal modifications take place during any chosen high-T treatment. Though necessary for end catalyst activation, such thermal treatments make difficult to establish a relation between the starting reactants and finally obtained catalysts. Of interest to the paper are 13 C, 15 N solid-state NMR (ss-NMR) and high-resolution X-ray Photoelectron Spectroscopy (XPS) results on a commercial carbon that was reacted first with aliphatic di- and tri-amines and then with Fe, Co ions in room-T water. Data from natural abundance ss- 15 N NMR in combination with XPS analysis were found especially relevant to assess that, in the adopted conditions, amines preferentially bind to carbon by creating alkylimino functional groups, which spontaneously form hydrous surface metal complexes with soluble Fe and Co ions. A chemical model is thus proposed for metal coordination in such C–N species.


title: Analysis and Implementation of a Single-Stage Flyback PV Microinverter With Soft Switching

abstract: This paper presents a novel zero-voltage switching (ZVS) approach to a grid-connected single-stage flyback inverter. The soft-switching of the primary switch is achieved by allowing negative current from the grid side through bidirectional switches placed on the secondary side of the transformer. Basically, the negative current discharges the metal-oxide-semiconductor field-effect transistor's output capacitor, thereby allowing turn on of the primary switch under zero voltage. To optimize the amount of reactive current required to achieve ZVS, a variable-frequency control scheme is implemented over the line cycle. In addition, the bidirectional switches on the secondary side of the transformer have ZVS during the turn- on times. Therefore, the switching losses of the bidirectional switches are negligible. A 250-W prototype has been implemented to validate the proposed scheme. Experimental results confirm the feasibility and superior performance of the converter compared with the conventional flyback inverter.


title: Understanding Online Payment Method Choice: An Eye-tracking Study

abstract: Due to the impact of online payment on the development of e-commerce, this article seeks to deepen the current understanding about the determinants of online payment method choice. Based on an extensive literature review, we identified perceived trustworthiness of the seller and perceived product uncertainty as major determinants, and we theorize that product type, product price, and product description are antecedents of product uncertainty. In our theoretical framework, we model perceived trustworthiness of the seller and perceived product uncertainty as independent variables, which are hypothesized to predict the dependent variable, namely online payment method choice (credit card, debit card, or cash on delivery). Moreover, we define payment method characteristics (e.g., information security) and buyer characteristics (e.g., trust propensity, online shopping experience) as control variables. Also, we describe a laboratory experiment in which we test our theoretical framework. Considering the recent calls for the use of neurobiological and physiological approaches to advance information systems (IS) theorizing (see, for example, www.NeuroIS.org), we suggest using eye-tracking data to complement traditional data sources, particularly those captured through survey research. Specifically, we propose that eye- tracking data can be used to measure product uncertainty, a major predictor of online payment method choice, which is associated with unconscious and automatic information processing that cannot be articulated easily through self-reports.


title: Shortcut-Stacked Sentence Encoders for Multi-Domain Inference

abstract: We present a simple sequential sentence encoder for multi-domain natural language inference. Our encoder is based on stacked bidirectional LSTM-RNNs with shortcut connections and fine-tuning of word embeddings. The overall supervised model uses the above encoder to encode two input sentences into two vectors, and then uses a classifier over the vector combination to label the relationship between these two sentences as that of entailment, contradiction, or neural. Our Shortcut-Stacked sentence encoders achieve strong improvements over existing encoders on matched and mismatched multi-domain natural language inference (top non-ensemble single-model result in the EMNLP RepEval 2017 Shared Task (Nangia et al., 2017)). Moreover, they achieve the new state-of-the-art encoding result on the original SNLI dataset (Bowman et al., 2015).


MCDM method combining QFD with TOPSIS for knowledge anagement system selection from the user ’ s perspective in ntuitionistic fuzzy environment


title: Enhanced CSI Acquisition for FDD Multi-User Massive MIMO Systems

abstract: Massive multiple-input multiple-output (MIMO) is envisioned to meet the growing spectral efficiency (SE) requirement in next generation cellular systems. However, it has been recognized that the unaffordable channel state information reference signal (CSI-RS) overhead required for acquiring CSI is a major challenge in frequency division duplexing (FDD) massive MIMO systems. To tackle the challenge of this CSI-RS overhead limitation, certain attempts, which have been designed for the scenarios with dispersive locations of users and sparse nature of channels, suffer from inaccurate channel estimation when the nature of channels for different users becomes highly correlated and rich. In this paper, we propose an enhanced CSI acquisition scheme for FDD multi-user massive MIMO systems, which exploits beamformed CSI-RS transmission mechanism to reduce the CSI-RS overhead. We present two limited feedback algorithms which provide different tradeoffs between system performance and CSI feedback overhead, where the multistage CSI acquisition strategy is adopted to avoid prohibitive computational complexity and amount of feedback requirement. The simulation results show that our CSI acquisition scheme, without additional CSI-RS overhead and enormous CSI feedback overhead, achieves higher SE performance as compared to conventional scheme.


title: Domain Adaptation Extreme Learning Machines for Drift Compensation in E-nose Systems

abstract: This paper addresses an important issue, known as sensor drift that behaves a nonlinear dynamic property in electronic nose (E-nose), from the viewpoint of machine learning. Traditional methods for drift compensation are laborious and costly due to the frequent acquisition and labeling process for gases samples recalibration. Extreme learning machines (ELMs) have been confirmed to be efficient and effective learning techniques for pattern recognition and regression. However, ELMs primarily focus on the supervised, semi-supervised and unsupervised learning problems in single domain (i.e. source domain). To our best knowledge, ELM with cross-domain learning capability has never been studied. This paper proposes a unified framework, referred to as Domain Adaptation Extreme Learning Machine (DAELM), which learns a robust classifier by leveraging a limited number of labeled data from target domain for drift compensation as well as gases recognition in E-nose systems, without loss of the computational efficiency and learning ability of traditional ELM. In the unified framework, two algorithms called DAELM-S and DAELM-T are proposed for the purpose of this paper, respectively. In order to percept the differences among ELM, DAELM-S and DAELM-T, two remarks are provided. Experiments on the popular sensor drift data with multiple batches collected by E-nose system clearly demonstrate that the proposed DAELM significantly outperforms existing drift compensation methods without cumbersome measures, and also bring new perspectives for ELM.


title: Automatic music generation by true random numbers for Turkish makams

abstract: In this paper, we describe a cloud hosted — mobile first system which generates music for Turkish makams, driven by hardware generated true random numbers. Random numbers are fed into system for determining the notes and note lengths of the song in chosen makam and tempo with chosen instruments and percussion. This system can generate completely random or musical compositions according to user preferences, even though its aim is not imitating the past genius composers and compose perfectly musical pieces. The experimental results show that this composition model can produce completely random or slightly artistic music having characteristics of Turkish makam music, hence introduce inspiration and entertainment to Turkish makam music enthusiasts.


title: Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review

abstract: Species knowledge is essential for protecting biodiversity. The identification of plants by conventional keys is complex, time consuming, and due to the use of specific botanical terms frustrating for non-experts. This creates a hard to overcome hurdle for novices interested in acquiring species knowledge. Today, there is an increasing interest in automating the process of species identification. ::: The availability and ubiquity of relevant technologies, such as, digital cameras and mobile devices, the remote access to databases, new techniques in image processing and pattern recognition let the idea of automated species identification become reality. This paper is the first systematic literature review with the aim of a thorough analysis and comparison of primary studies on computer vision approaches for plant species identification. We identified 120 peer-reviewed studies, selected through a multi-stage process, published in the last 10 years (2005–2015). After a careful analysis of these studies, we describe the applied methods categorized according to the studied plant organ, and the studied features, i.e., shape, texture, color, margin, and vein structure. Furthermore, we compare methods based on classification accuracy achieved on publicly available datasets. Our results are relevant to researches in ecology as well as computer vision for their ongoing research. The systematic and concise overview will also be helpful for beginners in those research fields, as they can use the comparable analyses of applied methods as a guide in this complex activity.