Query: Could We Issue Driving Licenses to Autonomous Vehicles?

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Model-Agnostic Interpretability of Machine Learning <=SEP=> Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the predictions, and in more intuitive user interfaces. Thus, interpretability has become a vital concern in machine learning, and work in the area of interpretable models has found renewed interest. In some applications, such models are as accurate as non-interpretable ones, and thus are preferred for their transparency. Even when they are not accurate, they may still be preferred when interpretability is of paramount importance. However, restricting machine learning to interpretable models is often a severe limitation. In this paper we argue for explaining machine learning predictions using model-agnostic approaches. By treating the machine learning models as blackbox functions, these approaches provide crucial flexibility in the choice of models, explanations, and representations, improving debugging, comparison, and interfaces for a variety of users and models. We also outline the main challenges for such methods, and review a recently-introduced model-agnostic explanation approach (LIME) that addresses these challenges.

Safety Verification of Deep Neural Networks <=SEP=> Deep neural networks have achieved impressive experimental results in image classification, but can surprisingly be unstable with respect to adversarial perturbations, that is, minimal changes to the input image that cause the network to misclassify it. With potential applications including perception modules and end-to-end controllers for self-driving cars, this raises concerns about their safety. We develop a novel automated verification framework for feed-forward multi-layer neural networks based on Satisfiability Modulo Theory (SMT). We focus on image manipulations, such as scratches or changes to camera angle or lighting conditions, and define safety for an image classification decision in terms of invariance of the classification with respect to manipulations of the original image within a region of images that are close to it. We enable exhaustive search of the region by employing discretisation, and propagate the analysis layer by layer. Our method works directly with the network code and, in contrast to existing methods, can guarantee that adversarial examples, if they exist, are found for the given region and family of manipulations. If found, adversarial examples can be shown to human testers and/or used to fine-tune the network. We implement the techniques using Z3 and evaluate them on state-of-the-art networks, including regularised and deep learning networks. We also compare against existing techniques to search for adversarial examples and estimate network robustness.

Deep Reinforcement Learning framework for Autonomous Driving <=SEP=> Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes. Despite its perceived utility, it has not yet been successfully applied in automotive applications. Motivated by the successful demonstrations of learning of Atari games and Go by Google DeepMind, we propose a framework for autonomous driving using deep reinforcement learning. This is of particular relevance as it is difficult to pose autonomous driving as a supervised learning problem due to strong interactions with the environment including other vehicles, pedestrians and roadworks. As it is a relatively new area of research for autonomous driving, we provide a short overview of deep reinforcement learning and then describe our proposed framework. It incorporates Recurrent Neural Networks for information integration, enabling the car to handle partially observable scenarios. It also integrates the recent work on attention models to focus on relevant information, thereby reducing the computational complexity for deployment on embedded hardware. The framework was tested in an open source 3D car racing simulator called TORCS. Our simulation results demonstrate learning of autonomous maneuvering in a scenario of complex road curvatures and simple interaction of other vehicles. INTRODUCTION A robot car that drives autonomously is a long-standing goal of Artificial Intelligence. Driving a vehicle is a task that requires high level of skill, attention and experience from a human driver. Although computers are more capable of sustained attention and focus than humans, fully autonomous driving requires a level of intelligence that surpasses that achieved so far by AI agents. The tasks involved in creating an autonomous driving agent can be divided into 3 categories, as shown in Figure 1: 1) Recognition: Identifying components of the surrounding environment. Examples of this are pedestrian detection, traffic sign recognition, etc. Although far from trivial, recognition is a relatively easy task nowadays thanks to advances in Deep Learning (DL) algorithms, which have reached human level recognition or above at several object detection and classification problems [8] [2]. Deep learning models are able to learn complex feature representations from raw input data, omitting the need for handcrafted features [15] [2] [7]. In this regard, Convolutional Neural Networks (CNNs) are probably the most successful deep learning model, and have formed the basis of every winning entry on the ImageNet challenge since AlexNet [8]. This success has been replicated in lane & vehicle detection for autonomous driving [6]. 2) Prediction: It is not enough for an autonomous driving agent to recognize its environment; it must also be able to build internal models that predict the future states of the environment. Examples of this class of problem include building a map of the environment or tracking an object. To be able to predict the future, it is important to integrate past information. As such, Recurrent Neural Networks (RNNs) are essential to this class of problem. Long-Short Term Memory (LSTM) networks [5] are one such category of RNN that have been used in end-to-end scene labeling systems [14]. More recently, RNNs have also been used to improve object tracking performance in the DeepTracking model [13]. 3) Planning: The generation of an efficient model that incorporates recognition and prediction to plan the future sequence of driving actions that will enable the vehicle to navigate successfully. Planning is the hardest task of the three. The difficulty lies in integrating the ability of the model to understand the environment (recognition) and its dynamics (prediction) in a way that enables it to plan the future actions such that it avoids unwanted situations (penalties) and drives safely to its destination (rewards). Figure 1: High level autonomous driving tasks The Reinforcement Learning (RL) framework [17] [20] has been used for a long time in control tasks. The mixture of RL with DL was pointed out to be one of the most promising approaches to achieve human-level control in [9]. In [12] and [11] this humanlevel control was demonstrated on Atari games using the Deep Q Networks (DQN) model, in which RL is responsible for the planning part while DL is responsible for the representation learning part. Later, RNNs were integrated in the mixture to account for partial observable scenarios [4]. Autonomous driving requires the integration of information ar X iv :1 70 4. 02 53 2v 1 [ st at .M L ] 8 A pr 2 01 7 from multiple sensors. Some of them are low dimensional, like LIDAR, while others are high dimensional, like cameras. It is noteworthy in this particular example, however, that although raw camera images are high dimensional, the useful information needed to achieve the autonomous driving task is of much lower dimension. For example, the important parts of the scene that affect driving decisions are limited to the moving vehicle, free space on the road ahead, the position of kerbs, etc. Even the fine details of vehicles are not important, as only their spatial location is truly necessary for the problem. Hence the memory bandwidth for relevant information is much lower. If this relevant information can be extracted, while the other non-relevant parts are filtered out, it would improve both the accuracy and efficiency of autonomous driving systems. Moreover, this would reduce the computation and memory requirements of the system, which are critical constraints on embedded systems that will contain the autonomous driving control unit. Attention models are a natural fit for such an information filtering process. Recently, these models were successfully deployed for image recognition in [23] and [10], wherein RL was mixed with RNNs to obtain the parts of the image to attend to. Such models are easily extended and integrated to the DQN [11] and Deep Recurrent Q Networks (DRQN) [4] models. This integration was performed in [16]. The success of attention models drives us to propose them for the extraction of low level information from the raw sensory information to perform autonomous driving. In this paper, we propose a framework for an end-end autonomous driving model that takes in raw sensor inputs and outputs driving actions. The model is able to handle partially observable scenarios. Moreover, we propose to integrate the recent advances in attention models in order to extract only relevant information from the received sensors data, thereby making it suitable for real-time embedded systems. The main contributions of this paper: 1) presenting a survey of the recent advances of deep reinforcement learning and 2) introducing a framework for endend autonomous driving using deep reinforcement learning to the automotive community. The rest of the paper is divided into two parts. The first part provides a survey of deep reinforcement learning algorithms, starting with the traditional MDP framework and Q-learning, followed by the the DQN, DRQN and Deep Attention Recurrent Q Networks (DARQN). The second part of the paper describes the proposed framework that integrates the recent advances in deep reinforcement learning. Finally, we conclude and suggest directions for future work. REVIEW OF REINFORCEMENT LEARNING For a comprehensive overview of reinforcement learning, please refer to the second edition of Rich Sutton’s textbook [18]. We provide a short review of important topics in this section. The Reinforcement Learning framework was formulated in [17] as a model to provide the best policy an agent can follow (best action to take in a given state), such that the total accumulated rewards are maximized when the agent follows that policy from the current and until a terminal state is reached. Motivation for RL Paradigm Driving is a multi-agent interaction problem. As a human driver, it is much easier to keep within a lane without any interaction with other cars than to change lanes in heavy traffic. The latter is more difficult because of the inherent uncertainty in behavior of other drivers. The number of interacting vehicles, their geometric configuration and the behavior of the drivers could have large variability and it is challenging to design a supervised learning dataset with exhaustive coverage of all scenarios. Human drivers employ some sort of online reinforcement learning to understand the behavior of other drivers such as whether they are defensive or aggressive, experienced or in-experienced, etc. This is particularly useful in scenarios which need negotiation, namely entering a roundabout, navigating junctions without traffic lights, lane changes during heavy traffic, etc. The main challenge in autonomous driving is to deal with corner cases which are unexpected even for a human driver, like recovering from being lost in an unknown area without GPS or dealing with disaster situations like flooding or appearance of a sinkhole on the ground. The RL paradigm models uncharted territory and learns from its own experience by taking actions. Additionally, RL may be able to handle non-differentiable cost functions which can create challenges for supervised learning problems. Currently, the standard approach for autonomous driving is to decouple the system into isolated sub-problems, typically supervised-learning-like object detection, visual odometry, etc and then having a post processing layer to combine all the results of the previous steps. There are two main issues with this approach: Firstly, the sub-problems which are solved may be more difficult than autonomous driving. For example, one might be solving object detection by semantic segmentation which is both challenging and unnecessary. Human drivers don’t detect and classify all visible objects while driving, only the most relevant ones. Secondly, the isolated sub-problems may not combine coherently to achieve

The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation <=SEP=> This report surveys the landscape of potential security threats from malicious uses of AI, and proposes ways to better forecast, prevent, and mitigate these threats. After analyzing the ways in which AI may influence the threat landscape in the digital, physical, and political domains, we make four high-level recommendations for AI researchers and other stakeholders. We also suggest several promising areas for further research that could expand the portfolio of defenses, or make attacks less effective or harder to execute. Finally, we discuss, but do not conclusively resolve, the long-term equilibrium of attackers and defenders.

Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks <=SEP=> Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their behavior. We present a novel, scalable, and efficient technique for verifying properties of deep neural networks (or providing counter-examples). The technique is based on the simplex method, extended to handle the non-convex Rectified Linear Unit (ReLU ) activation function, which is a crucial ingredient in many modern neural networks. The verification procedure tackles neural networks as a whole, without making any simplifying assumptions. We evaluated our technique on a prototype deep neural network implementation of the next-generation airborne collision avoidance system for unmanned aircraft (ACAS Xu). Results show that our technique can successfully prove properties of networks that are an order of magnitude larger than the largest networks verified using existing methods.

Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-to-End Learning from Demonstration <=SEP=> We propose a technique for multi-task learning from demonstration that trains the controller of a low-cost robotic arm to accomplish several complex picking and placing tasks, as well as non-prehensile manipulation. The controller is a recurrent neural network using raw images as input and generating robot arm trajectories, with the parameters shared across the tasks. The controller also combines VAE-GAN-based reconstruction with autoregressive multimodal action prediction. Our results demonstrate that it is possible to learn complex manipulation tasks, such as picking up a towel, wiping an object, and depositing the towel to its previous position, entirely from raw images with direct behavior cloning. We show that weight sharing and reconstruction-based regularization substantially improve generalization and robustness, and training on multiple tasks simultaneously increases the success rate on all tasks.

Mechanical design of the Wheel-Leg hybrid mobile robot to realize a large wheel diameter <=SEP=> In this paper, a new category of the wheel-leg hybrid robot is presented. The proposed mechanism can compose large wheel diameter compared with the previous hybrid robot to realize a greater ability to climb obstacles. A prototype model of one Wheel-Leg module of the proposed robot mechanism has been developed to illustrate the concept. Actual design and mode changing experiment with a test mechanical module is also presented. Basic movement tests and a test of the basic properties of the rotational fingertip are also shown. The Basic configurations of wheel-leg retractable is considered well. The integrated mode is also described.

Development of GaN HEMT based High Power High Efficiency Distributed Power Amplifier for Military Applications <=SEP=> Implementing wide bandgap GaN HEMT device into broadband distributed power amplifier creates a tremendous opportunity for RF designers to develop high power high efficiency very broadband power amplifiers for military applications. Several prototypes of 10-40 W GaN based distributed power amplifiers, including MMIC distributed PA, are currently under the development at Rockwell Collins, Inc. In this paper, we will discuss the results of a 10 W distributed power amplifier with the maximum power output of more than 40 dBm and a power-added efficiency of 30-70% over the bandwidth of 20-2000 MHz.

IDENTIFICATION OF FAILURE MECHANISMS TO ENHANCE PROGNOSTIC OUTCOMES <=SEP=> Predicting the reliability of a system in its actual life-cycle conditions and estimating its time to failure is helpful in decision making to mitigate system risks. There are three approaches to prognostics: the physics-of-failure approach, the data-driven approach, and the fusion approach. A key requirement in all these approaches is identification of the appropriate parameter(s) to monitor to gather data that can be used to assess impending failure. This paper presents a physics-of-failure approach, which uses failure modes, mechanisms and effects analysis (FMMEA) to enhance prognostics planning and implementation. This paper also presents the fusion approach to prognostics and the applicability of FMMEA to this approach. An example case of generating FMMEA information and using that to identify appropriate parameters to monitor is presented.

A survey on Vehicular Cloud Computing and its security <=SEP=> Vehicular networking has a significant advantages in the today era. It provides desirable features and some specific applications such as efficient traffic management, road safety and infotainment. The vehicle consists of comparatively more communication systems such as on-board computing device, storage and computing power, GPS etc. to provide Intelligent Transportation System (ITS). The new hybrid technology known as Vehicular Cloud Computing (VCC) has great impact on the ITS by using the resources of vehicles such as GPS, storage, internet and computing power for instant decision making and sharing information on the cloud. Moreover, the paper not only present the concept of vehicular cloud but also provide a brief overview on the applications, security issues, threats and security solution for the VCC.

Tabu Search - Part II <=SEP=> Full terms and conditions of use: http://pubsonline.informs.org/page/terms-and-conditions This article may be used only for the purposes of research, teaching, and/or private study. Commercial use or systematic downloading (by robots or other automatic processes) is prohibited without explicit Publisher approval, unless otherwise noted. For more information, contact permissions@informs.org. The Publisher does not warrant or guarantee the article’s accuracy, completeness, merchantability, fitness for a particular purpose, or non-infringement. Descriptions of, or references to, products or publications, or inclusion of an advertisement in this article, neither constitutes nor implies a guarantee, endorsement, or support of claims made of that product, publication, or service. © 1990 INFORMS

Marahel : A Language for Constructive Level Generation <=SEP=> Marahel is a language and framework for constructive generation of 2D tile-based game levels. It is developed with the dual aim of making it easier to build level generators for game developers, and to help solving the general level generation problem by creating a generator space that can be searched using evolution. We describe the different sections of the level generators, and show examples of generated maps from 5 different generators. We analyze their expressive range on three dimensions: percentage of empty space, number of isolated elements, and cell-wise entropy of empty space. The results show that generators that have starkly different output from each other can easily be defined in Marahel.

Right nulled GLR parsers <=SEP=> The right nulled generalized LR parsing algorithm is a new generalization of LR parsing which provides an elegant correction to, and extension of, Tomita's GLR methods whereby we extend the notion of a reduction in a shift-reduce parser to include right nulled items. The result is a parsing technique which runs in linear time on LR(1) grammars and whose performance degrades gracefully to a polynomial bound in the presence of nonLR(1) rules. Compared to other GLR-based techniques, our algorithm is simpler and faster.

One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities <=SEP=> The softmax representation of probabilities for categorical variables plays a prominent role in modern machine learning with numerous applications in areas such as large scale classification, neural language modeling and recommendation systems. However, softmax estimation is very expensive for large scale inference because of the high cost associated with computing the normalizing constant. Here, we introduce an efficient approximation to softmax probabilities which takes the form of a rigorous lower bound on the exact probability. This bound is expressed as a product over pairwise probabilities and it leads to scalable estimation based on stochastic optimization. It allows us to perform doubly stochastic estimation by subsampling both training instances and class labels. We show that the new bound has interesting theoretical properties and we demonstrate its use in classification problems.

Object Recognition using 3D SIFT in Complex CT Volumes <=SEP=> The automatic detection of objects within complex volumetric imagery is becoming of increased interest due to the use of dual energy Computed Tomography (CT) scanners as an aviation security deterrent. These devices produce a volumetric image akin to that encountered in prior medical CT work but in this case we are dealing with a complex multi-object volumetric environment including significant noise artefacts. In this work we look at the application of the recent extension to the seminal SIFT approach to the 3D volumetric recognition of rigid objects within this complex volumetric environment. A detailed overview of the approach and results when applied to a set of exemplar CT volumetric imagery is presented.

Pulsating torque minimization techniques for permanent magnet AC motor drives-a review <=SEP=> AbstructPermanent magnet ac (PMAC) motor drives are finding expanded use in high-performance applications where torque smoothness is essential. This paper reviews a wide range of motorand controller-based design techniques that have been described in the literature for minimizing the generation of cogging and ripple torques in both sinusoidal and trapezoidal PMAC motor drives. Sinusoidal PMAC drives generally show the greatest potential for pulsating torque minimization using wellknown motor design techniques such as skewing and fractional slot pitch windings. In contrast, trapezoidal PMAC drives pose more difficult tradeoffs in both the motor and controller design which may require compromises in drive simplicity and cost to improve torque smoothness. Controller-based techniques for minimizing pulsating torque typically involve the use of active cancellation algorithms which depend on either accurate tuning or adaptive control schemes for effectiveness. In the end, successful suppression of pulsating torque ultimately relies on an orchestrated systems approach to all aspects of the PMAC machine and controller design which often requires a carefully selected combination of minimization techniques.

Commutation torque ripple reduction in brushless DC motor drives using a single DC current sensor <=SEP=> This paper presents a comprehensive study on reducing commutation torque ripples generated in brushless DC motor drives with only a single DC-link current sensor provided. In such drives, commutation torque ripple suppression techniques that are practically effective in low speed as well as high speed regions are scarcely found. The commutation compensation technique proposed here is based on a strategy that the current slopes of the incoming and the outgoing phases during the commutation interval can be equalized by a proper duty-ratio control. Being directly linked with deadbeat current control scheme, the proposed control method accomplishes suppression of the spikes and dips superimposed on the current and torque responses during the commutation intervals of the inverter. Effectiveness of the proposed control method is verified through simulations and experiments.

Targeted Restoration of the Intestinal Microbiota with a Simple, Defined Bacteriotherapy Resolves Relapsing Clostridium difficile Disease in Mice <=SEP=> Relapsing C. difficile disease in humans is linked to a pathological imbalance within the intestinal microbiota, termed dysbiosis, which remains poorly understood. We show that mice infected with epidemic C. difficile (genotype 027/BI) develop highly contagious, chronic intestinal disease and persistent dysbiosis characterized by a distinct, simplified microbiota containing opportunistic pathogens and altered metabolite production. Chronic C. difficile 027/BI infection was refractory to vancomycin treatment leading to relapsing disease. In contrast, treatment of C. difficile 027/BI infected mice with feces from healthy mice rapidly restored a diverse, healthy microbiota and resolved C. difficile disease and contagiousness. We used this model to identify a simple mixture of six phylogenetically diverse intestinal bacteria, including novel species, which can re-establish a health-associated microbiota and clear C. difficile 027/BI infection from mice. Thus, targeting a dysbiotic microbiota with a defined mixture of phylogenetically diverse bacteria can trigger major shifts in the microbial community structure that displaces C. difficile and, as a result, resolves disease and contagiousness. Further, we demonstrate a rational approach to harness the therapeutic potential of health-associated microbial communities to treat C. difficile disease and potentially other forms of intestinal dysbiosis.

Partitioning and Segment Organization Strategies for Real-Time Selective Search on Document Streams <=SEP=> The basic idea behind selective search is to partition a collection into topical clusters, and for each query, consider only a subset of the clusters that are likely to contain relevant documents. Previous work on web collections has shown that it is possible to retain high-quality results while considering only a small fraction of the collection. These studies, however, assume static collections where it is feasible to run batch clustering algorithms for partitioning. In this work, we consider the novel formulation of selective search on document streams (specifically, tweets), where partitioning must be performed incrementally. In our approach, documents are partitioned into temporal segments and selective search is performed within each segment: these segments can either be clustered using batch or online algorithms, and at different temporal granularities. For efficiency, we take advantage of word embeddings to reduce the dimensionality of the document vectors. Experiments with test collections from the TREC Microblog Tracks show that we are able to achieve precision indistinguishable from exhaustive search while considering only around 5% of the collection. Interestingly, we observe no significant effectiveness differences between batch vs. online clustering and between hourly vs. daily temporal segments, despite them being very different index organizations. This suggests that architectural choices should be primarily guided by efficiency considerations.

A novel three-phase DC/DC converter for high-power applications <=SEP=> This paper proposes a new three-phase series resonant converter. The principle working of the converter is described analytically in detail for switching frequencies equal to the resonant frequency and for switching frequencies greater than the resonant frequency. Additionally, detailed simulations are performed to analyse the converter. Based on the analysis, design criteria for a 5 kW breadboard are derived. The 5 kW breadboard version of the converter has been built to validate the analytical investigations and simulations experimentally. Moreover, the breadboard is used to investigate the ZVS and ZCS possibilities of the topology and the influence of the deadtime and the switching frequency on the overall efficiency.

Facebook® and academic performance <=SEP=> There is much talk of a change in modern youth – often referred to as digital natives or Homo Zappiens – with respect to their ability to simultaneously process multiple channels of information. In other words, kids today can multitask. Unfortunately for proponents of this position, there is much empirical documentation concerning the negative effects of attempting to simultaneously process different streams of information showing that such behavior leads to both increased study time to achieve learning parity and an increase in mistakes while processing information than those who are sequentially or serially processing that same information. This article presents the preliminary results of a descriptive and exploratory survey study involving Facebook use, often carried out simultaneously with other study activities, and its relation to academic performance as measured by self-reported Grade Point Average (GPA) and hours spent studying per week. Results show that Facebook users reported having lower GPAs and spend fewer hours per week studying than nonusers. 2010 Elsevier Ltd. All rights reserved.

Silk - Generating RDF Links while Publishing or Consuming Linked Data <=SEP=> The central idea of the Web of Data is to interlink data items using RDF links. However, in practice most data sources are not sufficiently interlinked with related data sources. The Silk Link Discovery Framework addresses this problem by providing tools to generate links between data items based on user-provided link specifications. It can be used by data publishers to generate links between data sets as well as by Linked Data consumers to augment Web data with additional RDF links. In this poster we present the Silk Link Discovery Framework and report on two usage examples in which we employed Silk to generate links between two data sets about movies as well as to find duplicate persons in a stream of data items that is crawled from the Web.

Towards End-to-End Lane Detection: an Instance Segmentation Approach <=SEP=> Modern cars are incorporating an increasing number of driver assist features, among which automatic lane keeping. The latter allows the car to properly position itself within the road lanes, which is also crucial for any subsequent lane departure or trajectory planning decision in fully autonomous cars. Traditional lane detection methods rely on a combination of highly-specialized, hand-crafted features and heuristics, usually followed by post-processing techniques, that are computationally expensive and prone to scalability due to road scene variations. More recent approaches leverage deep learning models, trained for pixel-wise lane segmentation, even when no markings are present in the image due to their big receptive field. Despite their advantages, these methods are limited to detecting a pre-defined, fixed number of lanes, e.g. ego-lanes, and can not cope with lane changes. In this paper, we go beyond the aforementioned limitations and propose to cast the lane detection problem as an instance segmentation problem - in which each lane forms its own instance - that can be trained end-to-end. To parametrize the segmented lane instances before fitting the lane, we further propose to apply a learned perspective transformation, conditioned on the image, in contrast to a fixed ”bird’s-eye view” transformation. By doing so, we ensure a lane fitting which is robust against road plane changes, unlike existing approaches that rely on a fixed, predefined transformation. In summary, we propose a fast lane detection algorithm, running at 50 fps, which can handle a variable number of lanes and cope with lane changes. We verify our method on the tuSimple dataset and achieve competitive results.

Information Extraction Using Web Usage Mining, Web Scrapping and Semantic Annotation <=SEP=> Extracting useful information from the web is the most significant issue of concern for the realization of semantic web. This may be achieved by several ways among which Web Usage Mining, Web Scrapping and Semantic Annotation plays an important role. Web mining enables to find out the relevant results from the web and is used to extract meaningful information from the discovery patterns kept back in the servers. Web usage mining is a type of web mining which mines the information of access routes/manners of users visiting the web sites. Web scraping, another technique, is a process of extracting useful information from HTML pages which may be implemented using a scripting language known as Prolog Server Pages(PSP) based on Prolog. Third, Semantic annotation is a technique which makes it possible to add semantics and a formal structure to unstructured textual documents, an important aspect in semantic information extraction which may be performed by a tool known as KIM(Knowledge Information Management). In this paper, we revisit, explore and discuss some information extraction techniques on web like web usage mining, web scrapping and semantic annotation for a better or efficient information extraction on the web illustrated with examples.

Making Arabic PDF books accessible using gamification <=SEP=> Most of online Arabic books are not accessible to Arab people with visual impairments. They cannot read online books because they are usually scanned images of the original ones. There is also a problem in PDF encoding of some of the textual books. One of the solutions is to use an Arabic OCR to convert scanned books into text; however Arabic OCR is still in its early stages and suffers from many limitations. In this paper we propose the use of human recognition skills to replace OCR limitations by incorporating the concepts of crowdsourcing and gamification. Our proposed system is in the form of a mobile recall game that presents players with word images segmented from the books to be converted into text. The players' answers are checked using techniques similar to what is used in word spotting. We initially implemented two components of the system; the segmentation, and the feature extraction and matching components. For the feature extraction and matching component, which is used to verify the player's answers, we performed four tests to choose a similarity measure threshold for accepting an entered word as a correct answer. Future work will consider other means of input correctness assurance.

Drug delivery and nanoparticles: Applications and hazards <=SEP=> The use of nanotechnology in medicine and more specifically drug delivery is set to spread rapidly. Currently many substances are under investigation for drug delivery and more specifically for cancer therapy. Interestingly pharmaceutical sciences are using nanoparticles to reduce toxicity and side effects of drugs and up to recently did not realize that carrier systems themselves may impose risks to the patient. The kind of hazards that are introduced by using nanoparticles for drug delivery are beyond that posed by conventional hazards imposed by chemicals in classical delivery matrices. For nanoparticles the knowledge on particle toxicity as obtained in inhalation toxicity shows the way how to investigate the potential hazards of nanoparticles. The toxicology of particulate matter differs from toxicology of substances as the composing chemical(s) may or may not be soluble in biological matrices, thus influencing greatly the potential exposure of various internal organs. This may vary from a rather high local exposure in the lungs and a low or neglectable exposure for other organ systems after inhalation. However, absorbed species may also influence the potential toxicity of the inhaled particles. For nanoparticles the situation is different as their size opens the potential for crossing the various biological barriers within the body. From a positive viewpoint, especially the potential to cross the blood brain barrier may open new ways for drug delivery into the brain. In addition, the nanosize also allows for access into the cell and various cellular compartments including the nucleus. A multitude of substances are currently under investigation for the preparation of nanoparticles for drug delivery, varying from biological substances like albumin, gelatine and phospholipids for liposomes, and more substances of a chemical nature like various polymers and solid metal containing nanoparticles. It is obvious that the potential interaction with tissues and cells, and the potential toxicity, greatly depends on the actual composition of the nanoparticle formulation. This paper provides an overview on some of the currently used systems for drug delivery. Besides the potential beneficial use also attention is drawn to the questions how we should proceed with the safety evaluation of the nanoparticle formulations for drug delivery. For such testing the lessons learned from particle toxicity as applied in inhalation toxicology may be of use. Although for pharmaceutical use the current requirements seem to be adequate to detect most of the adverse effects of nanoparticle formulations, it can not be expected that all aspects of nanoparticle toxicology will be detected. So, probably additional more specific testing would be needed.

A Lightweight Component Caching Scheme for Satisfiability Solvers <=SEP=> We introduce in this paper a lightweight technique for reducing work repetition caused by non–chronological backtracking commonly practiced by DPLL–based SAT solvers. The presented technique can be viewed as a partial component caching scheme. Empirical evaluation of the technique reveals significant improvements on a broad range of in-

Efficient mining of weighted association rules (WAR) <=SEP=> ABSTRACT In this paper, we extend the tradition association rule problem by allowing a weight to be associated with each item in a transaction, to re ect interest/intensity of the item within the transaction. This provides us in turn with an opportunity to associate a weight parameter with each item in the resulting association rule. We call it weighted association rule (WAR). WAR not only improves the con dence of the rules, but also provides a mechanism to do more effective target marketing by identifying or segmenting customers based on their potential degree of loyalty or volume of purchases. Our approach mines WARs by rst ignoring the weight and nding the frequent itemsets (via a traditional frequent itemset discovery algorithm), and is followed by introducing the weight during the rule generation. It is shown by experimental results that our approach not only results in shorter average execution times, but also produces higher quality results than the generalization of previous known methods on quantitative association rules.

Fuzzy Neural Network-Based Adaptive Control for a Class of Uncertain Nonlinear Stochastic Systems <=SEP=> This paper studies an adaptive tracking control for a class of nonlinear stochastic systems with unknown functions. The considered systems are in the nonaffine pure-feedback form, and it is the first to control this class of systems with stochastic disturbances. The fuzzy-neural networks are used to approximate unknown functions. Based on the backstepping design technique, the controllers and the adaptation laws are obtained. Compared to most of the existing stochastic systems, the proposed control algorithm has fewer adjustable parameters and thus, it can reduce online computation load. By using Lyapunov analysis, it is proven that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded in probability and the system output tracks the reference signal to a bounded compact set. The simulation example is given to illustrate the effectiveness of the proposed control algorithm.

Detecting Avocados to Zucchinis: What Have We Done, and Where Are We Going? <=SEP=> The growth of detection datasets and the multiple directions of object detection research provide both an unprecedented need and a great opportunity for a thorough evaluation of the current state of the field of categorical object detection. In this paper we strive to answer two key questions. First, where are we currently as a field: what have we done right, what still needs to be improved? Second, where should we be going in designing the next generation of object detectors? Inspired by the recent work of Hoiem et al. on the standard PASCAL VOC detection dataset, we perform a large-scale study on the Image Net Large Scale Visual Recognition Challenge (ILSVRC) data. First, we quantitatively demonstrate that this dataset provides many of the same detection challenges as the PASCAL VOC. Due to its scale of 1000 object categories, ILSVRC also provides an excellent test bed for understanding the performance of detectors as a function of several key properties of the object classes. We conduct a series of analyses looking at how different detection methods perform on a number of image-level and object-class-level properties such as texture, color, deformation, and clutter. We learn important lessons of the current object detection methods and propose a number of insights for designing the next generation object detectors.