Query: DeepTransport: Prediction and Simulation of Human Mobility and Transportation Mode at a Citywide Level

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Sparse deep belief net model for visual area V2 <=SEP=> Motivated in part by the hierarchical organization of the cortex, a number of algorithms have recently been proposed that try to learn hierarchical, or “deep,” structure from unlabeled data. While several authors have formally or informally compared their algorithms to computations performed in visual area V1 (and the cochlea), little attempt has been made thus far to evaluate these algorithms in terms of their fidelity for mimicking computations at deeper levels in the cortical hierarchy. This paper presents an unsupervised learning model that faithfully mimics certain properties of visual area V2. Specifically, we develop a sparse variant of the deep belief networks of Hinton et al. (2006). We learn two layers of nodes in the network, and demonstrate that the first layer, similar to prior work on sparse coding and ICA, results in localized, oriented, edge filters, similar to the Gabor functions known to model V1 cell receptive fields. Further, the second layer in our model encodes correlations of the first layer responses in the data. Specifically, it picks up both colinear (“contour”) features as well as corners and junctions. More interestingly, in a quantitative comparison, the encoding of these more complex “corner” features matches well with the results from the Ito & Komatsu’s study of biological V2 responses. This suggests that our sparse variant of deep belief networks holds promise for modeling more higher-order features.

ImageNet Classification with Deep Convolutional Neural Networks <=SEP=> We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.

Multimodal Deep Learning <=SEP=> Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio). In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and show how to train a deep network that learns features to address these tasks. In particular, we demonstrate cross modality feature learning, where better features for one modality (e.g., video) can be learned if multiple modalities (e.g., audio and video) are present at feature learning time. Furthermore, we show how to learn a shared representation between modalities and evaluate it on a unique task, where the classifier is trained with audio-only data but tested with video-only data and vice-versa. We validate our methods on the CUAVE and AVLetters datasets with an audio-visual speech classification task, demonstrating superior visual speech classification on AVLetters and effective multimodal fusion.

Friendship and mobility: user movement in location-based social networks <=SEP=> Even though human movement and mobility patterns have a high degree of freedom and variation, they also exhibit structural patterns due to geographic and social constraints. Using cell phone location data, as well as data from two online location-based social networks, we aim to understand what basic laws govern human motion and dynamics. We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks. Short-ranged travel is periodic both spatially and temporally and not effected by the social network structure, while long-distance travel is more influenced by social network ties. We show that social relationships can explain about 10% to 30% of all human movement, while periodic behavior explains 50% to 70%. Based on our findings, we develop a model of human mobility that combines periodic short range movements with travel due to the social network structure. We show that our model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance than present models of human mobility.

Transportation mode detection using mobile phones and GIS information <=SEP=> The transportation mode such as walking, cycling or on a train denotes an important characteristic of the mobile user's context. In this paper, we propose an approach to inferring a user's mode of transportation based on the GPS sensor on her mobile device and knowledge of the underlying transportation network. The transportation network information considered includes real time bus locations, spatial rail and spatial bus stop information. We identify and derive the relevant features related to transportation network information to improve classification effectiveness. This approach can achieve over 93.5% accuracy for inferring various transportation modes including: car, bus, aboveground train, walking, bike, and stationary. Our approach improves the accuracy of detection by 17% in comparison with the GPS only approach, and 9% in comparison with GPS with GIS models. The proposed approach is the first to distinguish between motorized transportation modes such as bus, car and aboveground train with such high accuracy. Additionally, if a user is travelling by bus, we provide further information about which particular bus the user is riding. Five different inference models including Bayesian Net, Decision Tree, Random Forest, Naïve Bayesian and Multilayer Perceptron, are tested in the experiments. The final classification system is deployed and available to the public.

Proving Theorems about LISP Functions <=SEP=> We d e s c r i b e some s i m p l e h e u r i s t i c s comb in ing e v a l u a t i o n and ma themat i ca l i n d u c t i o n wh ich we have implemented in a program t h a t a u t o m a t i c a l l y p roves a wide v a r i e t y o f theorems about r e c u r s i v e LISP f u n c t i o n s . The method the program uses t o gene ra te i n d u c t i o n f o r m u l a s i s d e s c r i b e d a t l e n g t h . The theorems proved by t h e p r o ­ gram i n c l u d e t h a t REVERSE i s i t s own i n v e r s e and t h a t a p a r t i c u l a r SORT program is c o r r e c t . Append ix B c o n t a i n s a l i s t o f the theorems p roved by t h e p r o g r a m .

Power Consumption Modeling of Skid-Steer Tracked Mobile Robots on Rigid Terrain <=SEP=> Power consumption is a key element in outdoor mobile robot autonomy. This issue is very relevant in skid-steer tracked vehicles on account of their large ground contact area. In this paper, the power losses due to dynamic friction have been modeled from two different perspectives: 1) the power drawn by the rigid terrain and 2) the power supplied by the motors. Comparison of both approaches has provided new insight on skid steering on hard flat terrains at walking speeds. Experimental power models, which also include traction resistance and other power losses, have been obtained for two different track widths over marble flooring and asphalt with Auriga- beta, which is a full-size mobile robot. To this end, various internal probes have been set at different points of the power stream. Furthermore, new energy implications for navigation of these kinds of vehicles have been deduced and tested.

Collision avoidance of unmanned ships based on artificial potential field <=SEP=> The collision avoidance problem of autonomous ships is analyzed in this paper. Firstly, the background and the difficulties are reviewed. Then an improved artificial potential field method is designed to solve the collision avoidance problem of unmanned ships, and the system characteristics are investigated. Finally, simulation results demonstrate the ship's control effectiveness in the obstacle environment.

Putting value co-creation into practice: a case for advisory support <=SEP=> The concept of value co-creation and its notion of the customer as co-creator of value have gained much academic interest, notably in marketing and operations research. While several competing perspectives have been conceptually discussed in literature, research on the practical implications of value co-creation is scarce. Using the example of sales-oriented advisory, we show gaps between existing co-creation concepts and current practice in five problem areas. We develop four general solution perspectives on the advisor-client encounter as guidelines to overcome these gaps and discuss design requirements of their technological instantiations in advisory support systems. We present exemplary implementations of such systems in two domains: travel counseling and financial advisory. Revealing the practical implications of value co-creation on advisory encounters, these examples also demonstrate that the solution perspectives have to be implemented quite differently for individual domains.

The evaluation of children in the primary care setting when sexual abuse is suspected. <=SEP=> This clinical report updates a 2005 report from the American Academy of Pediatrics on the evaluation of sexual abuse in children. The medical assessment of suspected child sexual abuse should include obtaining a history, performing a physical examination, and obtaining appropriate laboratory tests. The role of the physician includes determining the need to report suspected sexual abuse; assessing the physical, emotional, and behavioral consequences of sexual abuse; providing information to parents about how to support their child; and coordinating with other professionals to provide comprehensive treatment and follow-up of children exposed to child sexual abuse.

Intrusion detection system for high-speed network <=SEP=> The increasing network throughput challenges the current Network Intrusion Detection Systems (NIDS) to have compatible highperformance data processing. In this paper, we describe an in-depth research on the related techniques of high-performance network intrusion detection and an implementation of a Rule-based High-performance Network Intrusion Detection System (RHPNIDS) for high-speed networks. By integrating several performance optimizing methods, the performance of RHPNIDS is very impressive compared with the popular open source NIDS Snort. q 2004 Elsevier B.V. All rights reserved.

Health data integration with Secured Record Linkage: A practical solution for Bangladesh and other developing countries <=SEP=> Knowledge discovery from various health data repositories requires the incorporation of healthcare data from diversified sources. Maintaining record linkage during the integration of medical data is an important research issue. Researchers have given different solutions to this problem that are applicable for developed countries where electronic health record of patients are maintained with identifiers like social security number (SSN), universal patient identifier (UPI), health insurance number, etc. These solutions cannot be used correctly for record linkage of health data of developing countries because of missing data, ambiguity in patient identification, and high amount of noise in patient information. Also, identifiable health data in electronic health repositories may produce a significant risk to patient privacy and also make the health information systems security vulnerable to hackers. In this paper, we have analyzed the practical problems of collecting and integrating healthcare data in Bangladesh for developing national health data warehouse. We have proposed a privacy preserved secured record linkage architecture that can support constrained health data of developing countries such as Bangladesh. Our technique can anonymize identifiable private data of the patients while maintaining record linkage in integrated health repositories to facilitate knowledge discovery process. Experimental results show that our proposed method successfully linked records with acceptable accuracy for noisy data in the absence of any standard ID like SSN.

A Survey on Multicarrier Communications: Prototype Filters, Lattice Structures, and Implementation Aspects <=SEP=> Due to their numerous advantages, communications over multicarrier schemes constitute an appealing approach for broadband wireless systems. Especially, the strong penetration of orthogonal frequency division multiplexing (OFDM) into the communications standards has triggered heavy investigation on multicarrier systems, leading to re-consideration of different approaches as an alternative to OFDM. The goal of the present survey is not only to provide a unified review of waveform design options for multicarrier schemes, but also to pave the way for the evolution of the multicarrier schemes from the current state of the art to future technologies. In particular, a generalized framework on multicarrier schemes is presented, based on what to transmit, i.e., symbols, how to transmit, i.e., filters, and where/when to transmit, i.e., lattice. Capitalizing on this framework, different variations of orthogonal, bi-orthogonal, and non-orthogonal multicarrier schemes are discussed. In addition, filter designs for various multicarrier systems are reviewed considering four different design perspectives: energy concentration, rapid decay, spectrum nulling, and channel/hardware characteristics. Subsequently, evaluation tools which may be used to compare different filters in multicarrier schemes are studied. Finally, multicarrier schemes are evaluated from the perspective of practical implementation aspects, such as lattice adaptation, equalization, synchronization, multiple antennas, and hardware impairments.

PEIR, the personal environmental impact report, as a platform for participatory sensing systems research <=SEP=> PEIR, the Personal Environmental Impact Report, is a participatory sensing application that uses location data sampled from everyday mobile phones to calculate personalized estimates of environmental impact and exposure. It is an example of an important class of emerging mobile systems that combine the distributed processing capacity of the web with the personal reach of mobile technology. This paper documents and evaluates the running PEIR system, which includes mobile handset based GPS location data collection, and server-side processing stages such as HMM-based activity classification (to determine transportation mode); automatic location data segmentation into "trips''; lookup of traffic, weather, and other context data needed by the models; and environmental impact and exposure calculation using efficient implementations of established models. Additionally, we describe the user interface components of PEIR and present usage statistics from a two month snapshot of system use. The paper also outlines new algorithmic components developed based on experience with the system and undergoing testing for integration into PEIR, including: new map-matching and GSM-augmented activity classification techniques, and a selective hiding mechanism that generates believable proxy traces for times a user does not want their real location revealed.

Toolkit-Based High-Performance Data Mining of Large Data on MapReduce Clusters <=SEP=> The enormous growth of data in a variety of applications has increased the need for high performance data mining based on distributed environments. However, standard data mining toolkits per se do not allow the usage of computing clusters. The success of MapReduce for analyzing large data has raised a general interest in applying this model to other, data intensive applications. Unfortunately current research has not lead to an integration of GUI based data mining toolkits with distributed file system based MapReduce systems. This paper defines novel principles for modeling and design of the user interface, the storage model and the computational model necessary for the integration of such systems. Additionally, it introduces a novel system architecture for interactive GUI based data mining of large data on clusters based on MapReduce that overcomes the limitations of data mining toolkits. As an empirical demonstration we show an implementation based on Weka and Hadoop.

Robust Distributed Spectrum Sensing in Cognitive Radio Networks <=SEP=> Distributed spectrum sensing (DSS) enables a Cognitive Radio (CR) network to reliably detect licensed users and avoid causing interference to licensed communications. The data fusion technique is a key component of DSS. We discuss the Byzantine failure problem in the context of data fusion, which may be caused by either malfunctioning sensing terminals or Spectrum Sensing Data Falsification (SSDF) attacks. In either case, incorrect spectrum sensing data will be reported to a data collector which can lead to the distortion of data fusion outputs. We investigate various data fusion techniques, focusing on their robustness against Byzantine failures. In contrast to existing data fusion techniques that use a fixed number of samples, we propose a new technique that uses a variable number of samples. The proposed technique, which we call Weighted Sequential Probability Ratio Test (WSPRT), introduces a reputation-based mechanism to the Sequential Probability Ratio Test (SPRT). We evaluate WSPRT by comparing it with a variety of data fusion techniques under various network operating conditions. Our simulation results indicate that WSPRT is the most robust against the Byzantine failure problem among the data fusion techniques that were considered.

Twitter as an instrument for crisis response: The Typhoon Haiyan case study <=SEP=> The research presented in this paper attempts an initial evaluation of Twitter as an instrument for emergency response in the context of a recent crisis event. The case of the 2013 disaster, when typhoon Haiyan hit Philippines is examined by analyzing nine consecutive days of Twitter messages and comparing them to the actual events. The results indicate that during disasters, Twitter users tend to post messages to enhance situation awareness and to motivate people to act. Furthermore, tweets were found reliable and provided valuable information content, supporting the argument that Twitter presents a very good potential to become a useful tool in situations where rapid emergency response is essential.

A Local Detection Approach for Named Entity Recognition and Mention Detection <=SEP=> In this paper, we study a novel approach for named entity recognition (NER) and mention detection (MD) in natural language processing. Instead of treating NER as a sequence labeling problem, we propose a new local detection approach, which relies on the recent fixed-size ordinally forgetting encoding (FOFE) method to fully encode each sentence fragment and its left/right contexts into a fixedsize representation. Subsequently, a simple feedforward neural network (FFNN) is learned to either reject or predict entity label for each individual text fragment. The proposed method has been evaluated in several popular NER and MD tasks, including CoNLL 2003 NER task and TAC-KBP2015 and TAC-KBP2016 Tri-lingual Entity Discovery and Linking (EDL) tasks. Our method has yielded pretty strong performance in all of these examined tasks. This local detection approach has shown many advantages over the traditional sequence labeling methods.

Variational Inference for Beta-Bernoulli Dirichlet Process Mixture Models <=SEP=> A commonly used paradigm in diverse application areas is to assume that an observed set of individual binary features is generated from a Bernoulli distribution with probabilities varying according to a Beta distribution. In this paper, we present our nonparametric variational inference algorithm for the Beta-Bernoulli observation model. Our primary focus is clustering discrete binary data using the Dirichlet process (DP) mixture model.

Inverted 'V--Y' advancement medial epicanthoplasty. <=SEP=> The presence of an epicanthal fold is a distinctive anatomic feature of the Asian population. Epicanthoplasty can be very helpful in resolving the above problematic anatomy and may produce a more aesthetic appearance. The goal of this report is to describe a modification in the traditional operative methods related to medial epicanthoplasty, to minimise scar appearance and maximise normal appearance. An inverse triangular flap of skin is based on the semilunar skin, which is elevated and retracted within the line of the normal eyelid. The design of four key points is crucial to this epicanthoplasty. A total of 62 patients were performed with this method; 42 of this group underwent simultaneous double-eyelid plasty. Most of the patients obtained satisfactory results aesthetically followed from 6 months to 4 years postoperation. There was no recurrence of the epicanthal fold. As many as 38 patients were followed by interview and photographs were taken at the same time. The rest of the patients were followed up through telephone. No patients complained about visible scarring in the epicanthal area. As many as 60 patients obtained satisfactory results aesthetically, two patients complained about the median canthal asymmetry. The result indicates the reliability and feasibility of epicanthoplasty.

Operator Splitting Methods in Compressive Sensing and Sparse Approximation <=SEP=> Compressive sensing and sparse approximation have many emerging applications, and are a relatively new driving force for development of splitting methods in optimization. Many sparse coding problems are well described by variational models with `1-norm penalties and constraints that are designed to promote sparsity. Successful algorithms need to take advantage of the separable structure of potentially complicated objectives by “splitting” them into simpler pieces and iteratively solving a sequence of simpler convex minimization problems. In particular, isolating `1 terms from the rest of the objective leads to simple soft thresholding updates or `1 ball projections. A few basic splitting techniques can be used to design a huge variety of relevant algorithms. This chapter will focus on operator splitting strategies that are based on proximal operators, duality, and alternating direction methods. These will be explained in the context of basis pursuit variants and through compressive sensing applications.

On the bipolarity in argumentation frameworks <=SEP=> In this paper, we propose a survey of the use of bipolarity in argumentation frameworks, i.e. the presence of two kinds of entities (a positive entity and a negative entity). An argumentation process follows three steps: building the arguments and the interactions between them, valuating the arguments using or not the interactions and finally defining the acceptability of the arguments. This paper shows on various applications and with some formal definitions that bipolarity appears (in some cases since always) and can be used in each step of this process under different forms.

Multi-type attributes driven multi-camera person re-identification <=SEP=> One of the major challenges in person Re-Identification (ReID) is the inconsistent visual appearance of a person. Current works on visual feature and distance metric learning have achieved significant achievements, but still suffer from the limited robustness to pose variations, viewpoint changes, etc., and the high computational complexity. This makes person ReID among multiple cameras still challenging. This work is motivated to learn mid-level human attributes which are robust to visual appearance variations and could be used as efficient features for person matching. We propose a weakly supervised multi-type attribute learning framework which considers the contextual cues among attributes and progressively boosts the accuracy of attributes only using a limited number of labeled data. Specifically, this framework involves a threestage training. A deep Convolutional Neural Network (dCNN) is first trained on an independent dataset labeled with attributes. Then it is fine-tuned on another dataset only labeled with person IDs using our defined triplet loss. Finally, the updated dCNN predicts attribute labels for the target dataset, which is combined with the independent dataset for the final round of fine-tuning. The predicted attributes, namely deep attributes exhibit promising generalization ability across different datasets. By directly using the deep attributes with simple Cosine distance, we have obtained competitive accuracy on four person ReID datasets. Experiments also show that a simple distance metric learning modular further boosts our method, making it outperform many recent ∗Corresponding author Email address: slzhang.jdl@pku.edu.cn (Shiliang Zhang) Preprint submitted to Pattern Recognition September 11, 2017

MULTI-SKILLED MOTION CONTROL <=SEP=> Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems, including agents that can move with skill and agility through their environment. An open problem in this setting is that of developing good strategies for integrating or merging policies for multiple skills, where each individual skill is a specialist in a specific skill and its associated state distribution. We extend policy distillation methods to the continuous action setting and leverage this technique to combine expert policies, as evaluated in the domain of simulated bipedal locomotion across different classes of terrain. We also introduce an input injection method for augmenting an existing policy network to exploit new input features. Lastly, our method uses transfer learning to assist in the efficient acquisition of new skills. The combination of these methods allows a policy to be incrementally augmented with new skills. We compare our progressive learning and integration via distillation (PLAID) method against three alternative baselines.

A gesture-driven computer interface using Kinect <=SEP=> Automatic recognition of human actions from video has been studied for many years. Although still very difficult in uncontrolled scenarios, it has been successful in more restricted settings (e.g., fixed viewpoint, no occlusions) with recognition rates approaching 100%. However, the best-performing methods are complex and computationally-demanding and thus not well-suited for real-time deployments. This paper proposes to leverage the Kinect camera for close-range gesture recognition using two methods. Both methods use feature vectors that are derived from the skeleton model provided by the Kinect SDK in real-time. Although both methods perform nearest-neighbor classification, one method does this in the space of features using the Euclidean distance metric, while the other method does this in the space of feature covariances using a log-Euclidean metric. Both methods recognize 8 hand gestures in real time achieving correct-classification rates of over 99% on a dataset of 20 subjects but the method based on Euclidean distance requires feature-vector collections to be of the same size, is sensitive to temporal misalignment, and has higher computation and storage requirements.

UTILIZING A GAME THEORETICAL APPROACH TO PREVENT COLLUSION AND INCENTIVIZE COOPERATION IN <=SEP=> Author: Arash Golchubian Title: Utilizing a Game Theoretical Approach to Prevent Collusion and Incentivize Cooperation in Cybersecurity Contexts Institution: Florida Atlantic University Thesis Advisor: Dr. Mehrdad Nojoumian Degree: Master of Science Year: 2017 In this research, a new reputation-based model is utilized to disincentivize collusion of defenders and attackers in Software Defined Networks (SDN), and also, to disincentivize dishonest mining strategies in Blockchain. In the context of SDN, the model uses the reputation values assigned to each entity to disincentivize collusion with an attacker. Our analysis shows that not-colluding actions become Nash Equilibrium using the reputationbased model within a repeated game setting. In the context of Blockchain and mining, we illustrate that by using the same socio-rational model, miners not only are incentivized to conduct honest mining but also disincentivized to commit to any malicious activities against other mining pools. We therefore show that honest mining strategies become Nash Equilibrium in our setting. This thesis is laid out in the following manner. In chapter 2 an introduction to game theory is provided followed by a survey of previous works in game theoretic network security, in chapter 3 a new reputation-based model is introduced to be used within the context of a Software Defined Network (SDN), in chapter 4 a reputation-based solution concept is introduced to force cooperation by each mining entity in Blockchain, and finally, in chapter 5, the concluding remarks and future works are presented. vii To: My loving wife, Sanaz. You inspire me to better myself. and My mother, Mina. You showed me that nothing is impossible. UTILIZING A GAME THEORETICAL APPROACH TO PREVENT COLLUSION AND INCENTIVIZE COOPERATION IN CYBERSECURITY CONTEXTS List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii

Orchestrating a mixed reality performance <=SEP=> A study of a professional touring mixed reality performance called Desert Rain yields insights into how performers orchestrate players' engagement in an interactive experience. Six players at a time journey through an extended physical and virtual set. Each sees a virtual world projected onto a screen made from a fine water spray. This acts as a traversable interface, supporting the illusion that performers physically pass between real and virtual worlds. Live and video-based observations of Desert Rain, coupled with interviews with players and the production team, have revealed how the performers create conditions for the willing suspension of disbelief, and how they monitor and intervene in the players experience without breaking their engagement. This involves carefully timed performances and “off-face” and “virtual” interventions. In turn, these are supported by the ability to monitor players' physical and virtual activity through asymmetric interfaces.

History of Computers, Electronic Commerce, and Agile Methods <=SEP=> The purpose of this document is to present a literature review relevant to a study of using agile methods to manage the development of Internet websites and their subsequent quality. This study places website quality within the context of the $2 trillion U.S. ecommerce industry. Thus, this section provides a history of electronic computers, electronic commerce, software methods, software quality metrics, agile methods, and studies of agile methods. None of these histories are without controversy. For instance, some scholars begin the study of the electronic computer by mentioning the emergence of the Sumerian text, Hammurabi code, or the Abacus. We, however, will align our history with the emergence of the modern electronic computer at the beginning of World War II. The history of electronic commerce also has poorly defined beginnings. Some studies of electronic commerce begin with the widespread use of the Internet in the early 1990s. However, electronic commerce cannot be appreciated without establishing a deeper context. The origins of the software industry are also somewhat poorly documented. It started sometime in the 1960s, though it didn't really take off until the 1980s. Software methods, however, are often associated with the rise of structured design in 1969, though they can firmly be traced back to the late 1950s. The first uses of agile methods are also not well defined. Many associate agile methods with the appearance of extreme programming in 1999, though agile methods began taking hold in the early 1990s, and most of its principles appeared before 1975. While research on agile methods began appearing in 1998, therein lies the issue. Few scholarly studies, if any, have been performed on agile methods, which is the basic purpose of this literature review. That is, to establish the context to conduct scholarly research within the fields of agile methods and electronic commerce. Since agile methods may be linked to outcomes such as software quality, an in-depth analysis of literature on software quality metrics is also included.History of Computers and Software Figure 1. Timeline and history of computers and software Page-3-Electronic computers. Electronic computers are simply machines that perform useful functions such as mathematical calculations or inputting, processing, and outputting data and information in meaningful forms (Rosen, 1969). Modern electronic computers are characterized by four major generations: first generation vacuum tube

Secure file storage in cloud computing using hybrid cryptography algorithm <=SEP=> Now a day's cloud computing is used in many areas like industry, military colleges etc to storing huge amount of data. We can retrieve data from cloud on request of user. To store data on cloud we have to face many issues. To provide the solution to these issues there are n number of ways. Cryptography and steganography techniques are more popular now a day's for data security. Use of a single algorithm is not effective for high level security to data in cloud computing. In this paper we have introduced new security mechanism using symmetric key cryptography algorithm and steganography. In this proposed system AES, blowfish, RC6 and BRA algorithms are used to provide block wise security to data. All algorithm key size is 128 bit. LSB steganography technique is introduced for key information security. Key information contains which part of file is encrypted using by which algorithm and key. File is splited into eight parts. Each and every part of file is encrypted using different algorithm. All parts of file are encrypted simultaneously with the help of multithreading technique. Data encryption Keys are inserted into cover image using LSB technique. Stego image is send to valid receiver using email. For file decryption purpose reverse process of encryption is applied.

A 4K-Input High-Speed Winner-Take-All (WTA) Circuit with Single-Winner Selection for Change-Driven Vision Sensors <=SEP=> Winner-Take-All (WTA) circuits play an important role in applications where a single element must be selected according to its relevance. They have been successfully applied in neural networks and vision sensors. These applications usually require a large number of inputs for the WTA circuit, especially for vision applications where thousands to millions of pixels may compete to be selected. WTA circuits usually exhibit poor response-time scaling with the number of competitors, and most of the current WTA implementations are designed to work with less than 100 inputs. Another problem related to the large number of inputs is the difficulty to select just one winner, since many competitors may have differences below the WTA resolution. In this paper, a WTA circuit is presented that handles more than four thousand inputs, to our best knowledge the hitherto largest WTA, with response times below the microsecond, and with a guaranty of just a single winner selection. This performance is obtained by the combination of a standard analog WTA circuit and a fast digital single-winner selector with almost no size penalty. This WTA circuit has been successfully employed in the fabrication of a Selective Change-Driven Vision Sensor based on 180 nm CMOS technology. Both simulated and experimental results are presented in the paper, showing that a single pixel event can be selected in just 560 ns, and a multipixel pixel event can be processed in 100 us. Similar results with a conventional approach would require a camera working at more than 1 Mfps for the single-pixel event detection, and 10 kfps for the whole multipixel event to be processed.