title: CHARM: An Efficient Algorithm for Closed Association Rule Mining

abstract: The task of mining association rules consists of two main steps. The first involves finding the set of all frequent itemsets. The second step involves testing and generating all high confidence rules among itemsets. In this paper we show that it is not necessary to mine all frequent itemsets in the first step, instead it is sufficient to mine the set of closed frequent itemsets, which is much smaller than the set of all frequent itemsets. It is also not necessary to mine the set of all possible rules. We show that any rule between itemsets is equivalent to some rule between closed itemsets. Thus many redundant rules can be eliminated. Furthermore, we present CHARM, an efficient algorithm for mining all closed frequent itemsets. An extensive experimental evaluation on a number of real and synthetic databases shows that CHARM outperforms previous methods by an order of magnitude or more. It is also linearly scalable in the number of transactions and the number of closed itemsets found.


title: Dynamic Frame skip Deep Q Network

abstract: Deep Reinforcement Learning methods have achieved state of the art performance in learning control policies for the games in the Atari 2600 domain. One of the important parameters in the Arcade Learning Environment (ALE) is the frame skip rate. It decides the granularity at which agents can control game play. A frame skip value of $k$ allows the agent to repeat a selected action $k$ number of times. The current state of the art architectures like Deep Q-Network (DQN) and Dueling Network Architectures (DuDQN) consist of a framework with a static frame skip rate, where the action output from the network is repeated for a fixed number of frames regardless of the current state. In this paper, we propose a new architecture, Dynamic Frame skip Deep Q-Network (DFDQN) which makes the frame skip rate a dynamic learnable parameter. This allows us to choose the number of times an action is to be repeated based on the current state. We show empirically that such a setting improves the performance on relatively harder games like Seaquest.


title: Crafting Adversarial Examples For Speech Paralinguistics Applications

abstract: Computational paralinguistic analysis is increasingly being used in a wide range of applications, including security-sensitive applications such as speaker verification, deceptive speech detection, and medical diagnosis. While state-of-the-art machine learning techniques, such as deep neural networks, can provide robust and accurate speech analysis, they are susceptible to adversarial attacks. In this work, we propose a novel end-to-end scheme to generate adversarial examples by perturbing directly the raw waveform of an audio recording rather than specific acoustic features. Our experiments show that the proposed adversarial perturbation can lead to a significant performance drop of state-of-the-art deep neural networks, while only minimally impairing the audio quality.


title: Improving energy efficiency of location sensing on smartphones

abstract: Location-based applications have become increasingly popular on smartphones over the past years. The active use of these applications can however cause device battery drain owing to their power-intensive location-sensing operations. This paper presents an adaptive location sensing framework that significantly improves the energy efficiency of smartphones running location-based applications. The underlying design principles of the proposed framework involve substitution, suppression, piggybacking, and adaptation of applications' location-sensing requests to conserve energy. We implement these design principles on Android-based smartphones as a middleware. Our evaluation results show that the design principles reduce the usage of the power-intensive GPS (Global Positioning System) by up to 98% and improve battery life by up to 75%.


title: Hedge Fund Performance: Sources and Measures

abstract: The concept of the gamma of a financed return as the highest level of stress that a return distribution can withstand is introduced. Stress is measured by positive expectation under a concave distortion of the return distribution accessed. Four distortions introduced in Cherny and Madan (2008) are employed in studying the distribution of returns available in the hedge fund universe. It is shown that the skewness, peakedness and tailweightedness of the standardized investment return significantly affects the Sharpe ratios required to reach a target gamma level.


title: TG13 current terminology, etiology, and epidemiology of acute cholangitis and cholecystitis.

abstract: While referring to the evidence adopted in the Tokyo Guidelines 2007 (TG07) as well as subsequently obtained evidence, further discussion took place on terminology, etiology, and epidemiological data. In particular, new findings have accumulated on the occurrence of symptoms in patients with gallstones, frequency of severe cholecystitis and cholangitis, onset of cholecystitis and cholangitis after endoscopic retrograde cholangiopancreatography and medications, mortality rate, and recurrence rate. The primary etiology of acute cholangitis/cholecystitis is the presence of stones. Next to stones, the most significant etiology of acute cholangitis is benign/malignant stenosis of the biliary tract. On the other hand, there is another type of acute cholecystitis, acute acalculous cholecystitis, in which stones are not involved as causative factors. Risk factors for acute acalculous cholecystitis include surgery, trauma, burn, and parenteral nutrition. After 2000, the mortality rate of acute cholangitis has been about 10 %, while that of acute cholecystitis has generally been less than 1 %. After the publication of TG07, diagnostic criteria and severity assessment criteria were standardized, and the distribution of cases according to severity and comparison of clinical data among target populations have become more subjective. The concept of healthcare-associated infections is important in the current treatment of infection. The treatment of acute cholangitis and cholecystitis substantially differs from that of community-acquired infections. Cholangitis and cholecystitis as healthcare-associated infections are clearly described in the updated Tokyo Guidelines (TG13). Free full-text articles and a mobile application of TG13 are available via http://www.jshbps.jp/en/guideline/tg13.html.


title: Telerehabilitation robotics: bright lights, big future?

abstract: The potential for remote diagnosis and treatment over the Internet using robotics is now a reality. The state of the art is exemplified by several Internet applications, and we explore the current trends in developing new systems. We review the technical challenges that lie ahead, along with some potential solutions. Some promising results for a new bilateral system involving two InMotion2 robots are presented. Finally, we discuss the future direction and commercial outlook for rehabilitation robots over the next 15 years.


title: Joint learning of named entity recognition and relation extraction

abstract: Named entities are important to extract relations. Accurate relation classification helps recognize named entities. The paper presents a joint approach of named entity recognition and relation identification. The identified relation is utilized to improve named entity recognition. The method has been applied to identify the names of persons and organizations and five relations between them. The result shows that the joint approach has improved the recall and F-measure of named entities without scarifying the precision. Meanwhile, the recall and F-measure are also improved in the relation extraction.


title: Sum-Rate Analysis for High Altitude Platform (HAP) Drones With Tethered Balloon Relay

abstract: High altitude platform (HAP) drones can provide broadband wireless connectivity to ground users in rural areas by establishing line-of-sight (LoS) links and exploiting effective beamforming techniques. However, at high altitudes, acquiring the channel state information (CSI) for HAPs, which is a key component to perform beamforming, is challenging. In this paper, by exploiting an interference alignment (IA) technique, a novel method for achieving the maximum sum-rate in HAP-based communications without CSI is proposed. In particular, to realize IA, a multiple-antenna tethered balloon is used as a relay between multiple HAP drones and ground stations (GSs). Here, a multiple-input multiple-output X network system is considered. The capacity of the considered M*N X network with a tethered balloon relay is derived in closed-form. Simulation results corroborate the theoretical findings and show that the proposed approach yields the maximum sum-rate in multiple HAPs-GSs communications in absence of CSI. The results also show the existence of an optimal balloon's altitude for which the sum-rate is maximized.


title: Neural Network Dynamics for Path Planning and Obstacle Avoidance

abstract: Abstract A model of a topologically organized neural network of a Hopfield type with nonlinear analog neurons is shown to be very effective for path planning and obstacle avoidance. This deterministic system can rapidly provide a proper path, from any arbitrary start position to any target position, avoiding both static and moving obstacles of arbitrary shape. The model assumes that an (external) input activates a target neuron, corresponding to the target position, and specifies obstacles in the topologically ordered neural map. The path follows from the neural network dynamics and the neural activity gradient in the topologically ordered map. The analytical results are supported by computer simulations to illustrate the performance of the network.


title: Vulnerability Analysis of MMS User Agents

abstract: The Multimedia Messaging Service (MMS) is becoming more popular, as mobile phones integrate audio and video recording functionality. Multimedia messages are delivered to users through a multi-step process, whose end-points are the MMS User Agents that reside on the users? mobile phones. The security of these components is critical, be- cause they might have access to private information and, if compromised, could be leveraged to spread an MMS-based worm. Unfortunately, the vulnerability analysis of these components is made more difficult by the fact that they are mostly closed-source and the testing has to be performed through the mobile phone network, which makes the testing time-consuming and costly. This paper presents a novel approach to the security testing of MMS User Agents. Our approach takes into account the effects of the infrastructure on the delivery of MMS messages and then uses a virtual infrastructure to speed up the testing process. Our testing approach was able to identify a number of previously unknown vulnerabilities, which, in one case, allowed for the execution of arbitrary code.


title: Polynomial heuristics for query optimization

abstract: Research on query optimization has traditionally focused on exhaustive enumeration of an exponential number of candidate plans. Alternatively, heuristics for query optimization are restricted in several ways, such as by either focusing on join predicates only, ignoring the availability of indexes, or in general having high-degree polynomial complexity. In this paper we propose a heuristic approach to very efficiently obtain execution plans for complex queries, which takes into account the presence of indexes and goes beyond simple join reordering. We also introduce a realistic workload generator and validate our approach using both synthetic and real data.


title: Work Environment-Related Factors in Obtaining and Maintaining Work in a Competitive Employment Setting for Employees with Intellectual Disabilities: A Systematic Review

abstract: AbstractBackground People with an intellectual disability value work as a significant part of their lives, and many of them want to participate in regular paid employment. Current estimates show that the number of people with ID who have some form of paid employment are very low, ranging from 9 to 40 % across different countries, despite legislations. This review examines papers published in the past 20 years in an attempt to answer the following research question: ‘What work environment-related factors contribute to obtaining or maintaining work in competitive employment for people with an intellectual disability?’ Method The databases of PubMed, PsycINFO, CINAHL, Embase and Web of Science were searched for relevant papers published between 1993 and 2013. All papers were independently screened by two researchers. Methodological quality of the studies was evaluated, and data on work environment-related factors stimulating employment for people with intellectual disabilities were extracted and grouped into categories. Results A total of 1932 articles were retrieved. After extensive screening for relevance and quality, 26 articles were included in this review. Four themes/categories with work environment-related factors that could influence work participation were distinguished. Five studies were conducted on employers’ decisions and opinions. Eight focused on job content and performance, and eight on workplace interaction and culture. Five studies evaluated support by job coaches. Conclusion Despite ongoing legislation to promote participation of people with intellectual disabilities in the paid workforce, research in this area is still extremely scarce. In the past 20 years, very few studies have focused on work environment-related factors that can enhance competitive work for people with intellectual disabilities. This review shows that relevant work environment-related factors for obtaining and maintaining work in competitive employment include supporting the employers by paying specific attention to: employer’s decisions, job content, integration and work culture and job coaches.


title: A Survey on Behaviour Recognition Using WiFi Channel State Information

abstract: In this article, we present a survey of recent advances in passive human behaviour recognition in indoor areas using the channel state information (CSI) of commercial WiFi systems. Movement of human body causes a change in the wireless signal reflections, which results in variations in the CSI. By analyzing the data streams of CSIs for different activities and comparing them against stored models, human behaviour can be recognized. This is done by extracting features from CSI data streams and using machine learning techniques to build models and classifiers. The techniques from the literature that are presented herein have great performances, however, instead of the machine learning techniques employed in these works, we propose to use deep learning techniques such as long-short term memory (LSTM) recurrent neural network (RNN), and show the improved performance. We also discuss about different challenges such as environment change, frame rate selection, and multi-user scenario, and suggest possible directions for future work.


title: Adaptive Distance Metric Learning for Clustering

abstract: A good distance metric is crucial for unsupervised learning from high-dimensional data. To learn a metric without any constraint or class label information, most unsupervised metric learning algorithms appeal to projecting observed data onto a low-dimensional manifold, where geometric relationships such as local or global pairwise distances are preserved. However, the projection may not necessarily improve the separability of the data, which is the desirable outcome of clustering. In this paper, we propose a novel unsupervised adaptive metric learning algorithm, called AML, which performs clustering and distance metric learning simultaneously. AML projects the data onto a low-dimensional manifold, where the separability of the data is maximized. We show that the joint clustering and distance metric learning can be formulated as a trace maximization problem, which can be solved via an iterative procedure in the EM framework. Experimental results on a collection of benchmark data sets demonstrated the effectiveness of the proposed algorithm.


Algorithmic motion planning


title: Evaluating Fuzz Testing

abstract: Fuzz testing has enjoyed great success at discovering security critical bugs in real software. Recently, researchers have devoted significant effort to devising new fuzzing techniques, strategies, and algorithms. Such new ideas are primarily evaluated experimentally so an important question is: What experimental setup is needed to produce trustworthy results? We surveyed the recent research literature and assessed the experimental evaluations carried out by 32 fuzzing papers. We found problems in every evaluation we considered. We then performed our own extensive experimental evaluation using an existing fuzzer. Our results showed that the general problems we found in existing experimental evaluations can indeed translate to actual wrong or misleading assessments. We conclude with some guidelines that we hope will help improve experimental evaluations of fuzz testing algorithms, making reported results more robust.


title: Barcode Modulation Method for Data Transmission in Mobile Devices

abstract: The concept of 2-D barcodes is of great relevance for use in wireless data transmission between handheld electronic devices. In a typical setup, any file on a cell phone, for example, can be transferred to a second cell phone through a series of images on the LCD which are then captured and decoded through the camera of the second cell phone. In this study, a new approach for data modulation in 2-D barcodes is introduced, and its performance is evaluated in comparison to other standard methods of barcode modulation. In this new approach, orthogonal frequency-division multiplexing (OFDM) modulation is used together with differential phase shift keying (DPSK) over adjacent frequency domain elements. A specific aim of this study is to establish a system that is proven tolerant to camera movements, picture blur, and light leakage within neighboring pixels of an LCD.


title: The Shape Boltzmann Machine: A Strong Model of Object Shape

abstract: A good model of object shape is essential in applications such as segmentation, detection, inpainting and graphics. For example, when performing segmentation, local constraints on the shapes can help where object boundaries are noisy or unclear, and global constraints can resolve ambiguities where background clutter looks similar to parts of the objects. In general, the stronger the model of shape, the more performance is improved. In this paper, we use a type of deep Boltzmann machine (Salakhutdinov and Hinton, International Conference on Artificial Intelligence and Statistics, 2009) that we call a Shape Boltzmann Machine (SBM) for the task of modeling foreground/background (binary) and parts-based (categorical) shape images. We show that the SBM characterizes a strong model of shape, in that samples from the model look realistic and it can generalize to generate samples that differ from training examples. We find that the SBM learns distributions that are qualitatively and quantitatively better than existing models for this task.


title: Towards Robust Deep Neural Networks

abstract: We examine the relationship between the energy landscape of neural networks and their robustness to adversarial attacks. Combining energy landscape techniques developed in computational chemistry with tools drawn from formal methods, we produce empirical evidence that networks corresponding to lower-lying minima in the landscape tend to be more robust. The robustness measure used is the inverse of the sensitivity measure, which we define as the volume of an over-approximation of the reachable set of network outputs under all additive $l_{\infty}$ bounded perturbations on the input data. We present a novel loss function which contains a weighted sensitivity component in addition to the traditional task-oriented and regularization terms. In our experiments on standard machine learning and computer vision datasets (e.g., Iris and MNIST), we show that the proposed loss function leads to networks which reliably optimize the robustness measure as well as other related metrics of adversarial robustness without significant degradation in the classification error.


title: Design and implementation of a group-based RO PUF

abstract: The silicon physical unclonable functions (PUF) utilize the uncontrollable variations during integrated circuit (IC) fabrication process to facilitate security related applications such as IC authentication. In this paper, we describe a new framework to generate secure PUF secret from ring oscillator (RO) PUF with improved hardware efficiency. Our work is based on the recently proposed group-based RO PUF with the following novel concepts: an entropy distiller to filter the systematic variation; a simplified grouping algorithm to partition the ROs into groups; a new syndrome coding scheme to facilitate error correction; and an entropy packing method to enhance coding efficiency and security. Using RO PUF dataset available in the public domain, we demonstrate these concepts can create PUF secret that can pass the NIST randomness and stability tests. Compared to other state-of-the-art RO PUF design, our approach can generate an average of 72% more PUF secret with the same amount of hardware.


title: Design of Low-Power CMOS Cell Structures Using Subthreshold Conduction Region

abstract: Subthreshold (leakage or cut-off) currents are a necessary evil in traditional VLSI design methodologies. These currents increase exponentially as threshold voltage scales, creating a serious problem for traditional design approaches. This work is based on the exclusive use of subthreshold conduction currents to perform circuit operations, turning this problem into an opportunity. It yields a dramatic improvement in power consumption compared to traditional circuit design approaches. This improvement makes it feasible to design extreme low-power circuits with such an approach. The CMOS digital circuits for this work have been designed using standard TSMC 0.18 �� m Technology. Index Terms—Low-Power, Leakage Current, Subthreshold Conduction. —————————— a —————————— 1 I With the growing scale of integration, more and more sophisticated signal processing systems need to be im- plemented on a VLSI chip. For these signal processing applications, power consumption has become a critical concern in today's VLSI system design. The need for low- power VLSI systems arises from two main forces. First, with the steady growth of processing capacity per chip, large current has to be delivered and the heat due to large power consumption must be removed by proper cooling techniques. Second, battery life in portable electronic de- vices is limited. Low-power design directly leads to pro- longed operation time in these portable devices. Also, with shrinking technology sizes, energy effi- ciency has become a critical aspect of designing digital circuits. Traditionally, voltage scaling, a mechanism in which the supply voltage is varying and the threshold voltage is constant, has been an effective solution in meet- ing stringent energy requirements. However, voltage scal- ing does come at a cost of reduction in performance. The limits of voltage scaling, and therefore energy minimiza- tion, can be explored by operating a circuit at subthresh- old (1). In subthreshold circuits, the supply voltage is re- duced well below the threshold voltage of a transistor. Due to the quadratic reduction in power with respect to the supply voltage, subthreshold circuits are classified as ultra low-power circuits. Specifically in application areas where performance can be sacrificed for low-power, subthreshold circuits are an ideal fit. Some of the applications include devices such as digital wrist watches, radio frequency identification (RFID), sensor nodes, pacemakers and battery operated devices such as, cellular phones.


Three levels of socio-spatial polarisation in Brussels


title: Modelling and analysis of switching DC-to-DC converters in constant-frequency current-programmed mode

abstract: An analysis of dc-to-dc switching converters in constant-frequency current-programmed continous conduction mode is performed, and leads to two significant resuslts. The first is that a ramp function, used to eliminate a potential instability, can be chosen uniquely to assure both stability and the fastest possible transient response of the programmed current. The second is the development of an extension of the state-space averaging technique by means of which both the input and output small-signal properties of any such converter may be accurately represented by a linear small-signal equivalent-circuit model. The model is presented and experimentally verified for the cuk converter and for the conventional buck, boost, and buck-boost converters. All models exhibit basically a one-pole control-to-output transfer fuction response.


title: Security analysis of the ADS-B implementation in the next generation air transportation system

abstract: Abstract The US Federal Aviation Administration’s Next Generation (NextGen) upgrade proposes a fundamental transformation that is intended to increase the capacity and safety of the air transportation system. A key component of the upgrade is the Automatic Dependent Surveillance Broadcast (ADS-B) system. ADS-B provides continual broadcast of aircraft position, identity, velocity and other information over unencrypted data links to generate a precise air picture for air traffic management. The Federal Aviation Administration claims that operational requirements necessitate the use of unencrypted data links and maintains that there is a low likelihood of malicious exploitation. This paper analyzes the security vulnerabilities associated with the ADS-B implementation. It describes a taxonomy of attacks and examines the potential impact that the attacks may have on air transportation operations. The taxonomy helps provide a comprehensive understanding of the threats associated with the ADS-B implementation, thereby supporting risk analysis and risk management efforts. The paper also provides recommendations that could enhance security if integrated into the ADS-B implementation plan.


title: Commemorative plaques

abstract: Earlier this year English Heritage, the BDA Museum and The Lindsay Society for the History of Dentistry unveiled a plaque to Lilian Lindsay (1871-1960). This paper outlines the history and administration of the blue plaque scheme and celebrates the contributions made by dentists who have been commemorated with plaques.


title: Electronic Voting Service Using Block-Chain

abstract: Cryptocurrency, and its underlying technologies, has been gaining popularity for transaction management beyond financial transactions. Transaction information is maintained in the block-chain, which can be used to audit the integrity of the transaction. The focus on this paper is the potential availability of block-chain technology of other transactional uses. Block-chain is one of the most stable open ledgers that preserves transaction information, and is difficult to forge. Since the information stored in block-chain is not related to personally identify information, it has the characteristics of anonymity. Also, the block-chain allows for transparent transaction verification since all information in the block-chain is open to the public. These characteristics are the same as the requirements for a voting system. That is, strong robustness, anonymity, and transparency. In this paper, we propose an electronic voting system as an application of block-chain, and describe block-chain based voting at a national level through examples.


title: Parameter Estimation For Probabilistic Finite-State Transducers

abstract: Weighted finite-state transducers suffer from the lack of a training algorithm. Training is even harder for transducers that have been assembled via finite-state operations such as composition, minimization, union, concatenation, and closure, as this yields tricky parameter tying. We formulate a "parameterized FST" paradigm and give training algorithms for it, including a general bookkeeping trick ("expectation semirings") that cleanly and efficiently computes expectations and gradients.


title: Reading Car License Plates Using Deep Convolutional Neural Networks and LSTMs

abstract: In this work, we tackle the problem of car license plate detection and recognition in natural scene images. Inspired by the success of deep neural networks (DNNs) in various vision applications, here we leverage DNNs to learn high-level features in a cascade framework, which lead to improved performance on both detection and recognition. ::: Firstly, we train a $37$-class convolutional neural network (CNN) to detect all characters in an image, which results in a high recall, compared with conventional approaches such as training a binary text/non-text classifier. False positives are then eliminated by the second plate/non-plate CNN classifier. Bounding box refinement is then carried out based on the edge information of the license plates, in order to improve the intersection-over-union (IoU) ratio. The proposed cascade framework extracts license plates effectively with both high recall and precision. Last, we propose to recognize the license characters as a {sequence labelling} problem. A recurrent neural network (RNN) with long short-term memory (LSTM) is trained to recognize the sequential features extracted from the whole license plate via CNNs. The main advantage of this approach is that it is segmentation free. By exploring context information and avoiding errors caused by segmentation, the RNN method performs better than a baseline method of combining segmentation and deep CNN classification; and achieves state-of-the-art recognition accuracy.


IoT Security : Performance Evaluation of Grain , MICKEY , and Trivium-Lightweight Stream Ciphers


title: The origin and innervation of the adductor pollicis muscle

abstract: A gross dissection of twenty human adductor pollicis muscles indicated an extensive origin of both the transverse and oblique heads. The origins included the second, third, and fourth metacarpal bones and the capitate, trapezoid, and trapezium bones. Soft tissue origins included intermetacarpal ligaments and the anterior fascia of palmar interosseous muscles. An interfascicular dissection of the deep branch of the ulnar nerve within the substance of the adductor pollicis muscle revealed three distinct patterns of terminal branching to the transverse and oblique heads, and to the first dorsal interosseous muscle.


Malware Detection through Low-level Features and Stacked Denoising Autoencoders


title: MaaT: Effective and scalable coordination of distributed transactions in the cloud

abstract: The past decade has witnessed an increasing adoption of cloud database technology, which provides better scalability, availability, and fault-tolerance via transparent partitioning and replication, and automatic load balancing and fail-over. However, only a small number of cloud databases provide strong consistency guarantees for distributed transactions, despite decades of research on distributed transaction processing, due to practical challenges that arise in the cloud setting, where failures are the norm, and human administration is minimal. For example, dealing with locks left by transactions initiated by failed machines, and determining a multi-programming level that avoids thrashing without under-utilizing available resources, are some of the challenges that arise when using lock-based transaction processing mechanisms in the cloud context. Even in the case of optimistic concurrency control, most proposals in the literature deal with distributed validation but still require the database to acquire locks during two-phase commit when installing updates of a single transaction on multiple machines. Very little theoretical work has been done to entirely eliminate the need for locking in distributed transactions, including locks acquired during two-phase commit. In this paper, we re-design optimistic concurrency control to eliminate any need for locking even for atomic commitment, while handling the practical issues in earlier theoretical work related to this problem. We conduct an extensive experimental study to evaluate our approach against lock-based methods under various setups and workloads, and demonstrate that our approach provides many practical advantages in the cloud context.


Software Development Lifecycle Models The Basic Types


title: ReBudget: Trading Off Efficiency vs. Fairness in Market-Based Multicore Resource Allocation via Runtime Budget Reassignment

abstract: Efficiently allocating shared resources in computer systems is critical to optimizing execution. Recently, a number of market-based solutions have been proposed to attack this problem. Some of them provide provable theoretical bounds to efficiency and/or fairness losses under market equilibrium. However, they are limited to markets with potentially important constraints, such as enforcing equal budget for all players, or curve-fitting players' utility into a specific function type. Moreover, they do not generally provide an intuitive "knob" to control efficiency vs. fairness. In this paper, we introduce two new metrics, Market Utility Range (MUR) and Market Budget Range (MBR), through which we provide for the first time theoretical bounds on efficiency and fairness of market equilibria under arbitrary budget assignments. We leverage this result and propose ReBudget, an iterative budget re-assignment algorithm that can be used to control efficiency vs. fairness at run-time. We apply our algorithm to a multi-resource allocation problem in multicore chips. Our evaluation using detailed execution-driven simulations shows that our budget re-assignment technique is intuitive, effective, and efficient.


title: A ROBOTIC EQUIPMENT FOR THE GUIDANCE OF A VECTORED THRUSTOR AUV

abstract: 1 ABSTRACT The paper deals with developing a cheap autonomous underwater vehicle, SWAN, Submarine Wobble-free Autonomous Navigator, entrusted of extended manoeuvrability for surveying and docking missions with accurate control, both, of trajectory tracking and of attitude keeping. SWAN belongs to easily transported devices (weigh 30-50 kg, length 1-2 m), capable of travelling at low speed (0.8-1.2 m/s), at proper depth (10- 500 m deep), to carry out the chosen task with proper autonomy and without relevant impact on the surroundings. The currently available AUVs, generally, make use of several propellers, distributed around the body, to achieve the desired mobility with due attitude control. The solution requires the closure of multiple loops between sensors and actuators, highly dependent on dynamics non- linearity and marine surroundings disturbances (1). The prospected solution considers a properly shaped vehicle to grant minimal drag for lowering power consumption. A single rear propeller has in charge propulsion and manoeuvring. The propeller can suitably be oriented around two axes by, say, a pitch angle (for heaving) and a yaw angle (for veering); the hydrodynamic torque of the screw would generate a twist with roll, surge and sway effects, unless proper balance is provided. The joint path and attitude control is obtained by driving the propeller assembly through a three degrees of freedom parallel kinematics robotic wrist. The worked-out lay-out consists in a duct-restricted propeller. The duct, further to the pitch and yaw angles, can supply a continuous rotation to counter-act the screw twist or, as case arises, can modify the bent of externally carried fins, so that the AUV attitude is accurately preserved. The paper introduces the generalities of the lay-out and presents example details obtained, for explanatory purposes, by virtual checks on a digital mock-up.


title: Region-Based Particle Filter for Video Object Segmentation

abstract: We present a video object segmentation approach that extends the particle filter to a region-based image representation. Image partition is considered part of the particle filter measurement, which enriches the available information and leads to a re-formulation of the particle filter. The prediction step uses a co-clustering between the previous image object partition and a partition of the current one, which allows us to tackle the evolution of non-rigid structures. Particles are defined as unions of regions in the current image partition and their propagation is computed through a single co-clustering. The proposed technique is assessed on the SegTrack dataset, leading to satisfactory perceptual results and obtaining very competitive pixel error rates compared with the state-of-the-art methods.


title: Interactive development of cross-device user interfaces

abstract: Current GUI builders provide a design environment for user interfaces that target either a single type or fixed set of devices, and provide little support for scenarios in which the user interface, or parts of it, are distributed over multiple devices. Distributed user interfaces have received increasing attention over the past years. There are different, often model-based, approaches that focus on technical issues. This paper presents XDStudio--a new GUI builder designed to support interactive development of cross-device web interfaces. XDStudio implements two complementary authoring modes with a focus on the design process of distributed user interfaces. First, simulated authoring allows designing for a multi-device environment on a single device by simulating other target devices. Second, on-device authoring allows the design process itself to be distributed over multiple devices, as design and development take place on the target devices themselves. To support interactive development for multi-device environments, where not all devices may be present at design and run-time, XDStudio supports switching between the two authoring modes, as well as between design and use modes, as required. This paper focuses on the design of XDStudio, and evaluates its support for two distribution scenarios.


title: Effective attacks and provable defenses for website fingerprinting

abstract: Website fingerprinting attacks allow a local, passive eavesdropper to identify a user's web activity by leveraging packet sequence information. These attacks break the privacy expected by users of privacy technologies, including low-latency anonymity networks such as Tor. In this paper, we show a new attack that achieves significantly higher accuracy than previous attacks in the same field, further highlighting website fingerprinting as a genuine threat to web privacy. We test our attack under a large open-world experimental setting, where the client can visit pages that the attacker is not aware of. We found that our new attack is much more accurate than previous attempts, especially for an attacker monitoring a set of sites with low base incidence rate. We can correctly determine which of 100 monitored web pages a client is visiting (out of a significantly larger universe) at an 85% true positive rate with a false positive rate of 0.6%, compared to the best of 83% true positive rate with a false positive rate of 6% in previous work. ::: ::: To defend against such attacks, we need provably effective defenses. We show how simulatable, deterministic defenses can be provably private, and we show that bandwidth overhead optimality can be achieved for these defenses by using a supersequence over anonymity sets of packet sequences. We design a new defense by approximating this optimal strategy and demonstrate that this new defense is able to defeat any attack at a lower cost on bandwidth than the previous best.


title: EntiTables: Smart Assistance for Entity-Focused Tables

abstract: Tables are among the most powerful and practical tools for organizing and working with data. Our motivation is to equip spreadsheet programs with smart assistance capabilities. We concentrate on one particular family of tables, namely, tables with an entity focus. We introduce and focus on two specifc tasks: populating rows with additional instances (entities) and populating columns with new headings. We develop generative probabilistic models for both tasks. For estimating the components of these models, we consider a knowledge base as well as a large table corpus. Our experimental evaluation simulates the various stages of the user entering content into an actual table. A detailed analysis of the results shows that the models' components are complimentary and that our methods outperform existing approaches from the literature.


title: Human Detection Using Oriented Histograms of Flow and Appearance

abstract: Detecting humans in films and videos is a challenging problem owing to the motion of the subjects, the camera and the background and to variations in pose, appearance, clothing, illumination and background clutter. We develop a detector for standing and moving people in videos with possibly moving cameras and backgrounds, testing several different motion coding schemes and showing empirically that orientated histograms of differential optical flow give the best overall performance. These motion-based descriptors are combined with our Histogram of Oriented Gradient appearance descriptors. The resulting detector is tested on several databases including a challenging test set taken from feature films and containing wide ranges of pose, motion and background variations, including moving cameras and backgrounds. We validate our results on two challenging test sets containing more than 4400 human examples. The combined detector reduces the false alarm rate by a factor of 10 relative to the best appearance-based detector, for example giving false alarm rates of 1 per 20,000 windows tested at 8% miss rate on our Test Set 1.


title: A review of using eye-tracking technology in exploring learning from 2000 to 2012

abstract: This study aims to disclose how eye-tracking technology has been applied to studies of learning, and what eye movement measures have been used for investigations by reviewing studies that have employed the eye-tracking approach. A total of 81 papers including 113 studies were selected from the Social Sciences Citation Index database from 2000 to 2012. Content analysis showed that eye movements and learning were studied under the following seven themes: patterns of information processing, effects of instructional design, reexamination of existing theories, individual differences, effects of learning strategies, patterns of decision making, and conceptual development. As for eye-tracking measurements, the most often used indices were temporal measures, followed by count and spatial measures, although the choice of measures was often motivated by the specific research question. Research development trends show that the use of the eye-tracking method has proliferated recently. This study concludes that the eye-tracking method provides a promising channel for educational researchers to connect learning outcomes to cognitive processes.


title: The Ontological Foundation of REA Enterprise Information Systems

abstract: An inking apparatus for printing presses wherein inking rolls are mounted so that adjustment of one of the rolls does not disturb the mutual setting and adjustments of the remaining rolls; this is achieved by means of three form rolls contacting two distribution rolls at four contact points; various roll position adjustment means are disclosed for accomplishing the adjustments noted.


title: Beyond basic faceted search

abstract: This paper extends traditional faceted search to support richer information discovery tasks over more complex data models. Our first extension adds exible, dynamic business intelligence aggregations to the faceted application, enabling users to gain insight into their data that is far richer than just knowing the quantities of documents belonging to each facet. We see this capability as a step toward bringing OLAP capabilities, traditionally supported by databases over relational data, to the domain of free-text queries over metadata-rich content. Our second extension shows how one can efficiently extend a faceted search engine to support correlated facets - a more complex information model in which the values associated with a document across multiple facets are not independent. We show that by reducing the problem to a recently solved tree-indexing scenario, data with correlated facets can be efficiently indexed and retrieved


title: Questioning the Resistance/Aerobic Training Dichotomy: A Commentary on Physiological Adaptations Determined by Effort Rather than Exercise Modality

abstract: Abstract This paper discusses and challenges the current opinion that exercise adaptation is generally defined by modality; resistance exercise (RE), or aerobic exercise (AE). In presenting a strong body of recent research which demonstrably challenges these perceptions we suggest alternate hypotheses towards physiological adaptation which is hinged more upon the effort than the exercise modality. Practical implications of this interpretation of exercise adaptation might effect change in exercise adherence since existing barriers to exercise of time, costs, specialized equipment, etc. become nullified. In presenting the evidence herein we suggest that lay persons wishing to attain the health and fitness (including strength and muscle hypertrophy) benefits of exercise can choose from a wide range of potential exercise modalities so long as the effort is high. Future research should consider this hypothesis by directly comparing RE and AE for acute responses and chronic adaptations.


title: A real-time surveillance mini-rover based on OpenCV-Python-JAVA using Raspberry Pi 2

abstract: Real-time surveillance is a vital component in any situation or environment where there is a high need of security for both personal and commercial property and assets. Technology today is used in many different ways in order to provide us such surveillance. An ideal solution for surveillance involves not only the assortment of necessary sensors and devices using appropriate tools, but also should be provided in the most optimized way for obtaining feed and data while keeping the expenditure minimized. In our project, we aimed at developing a low-cost, real-time video surveillance mini-rover which will be capable of providing real-time video footage and roam around in the area which we want to observe. Our target was to use hardware which is very low of cost and easily available.


title: Impression Fraud in Online Advertising via Pay-Per-View Networks

abstract: Advertising is one of the primary means for revenue generation for millions of websites and mobile apps. While the majority of online advertising revenues are based on pay-per-click, alternative forms such as impression-based display and video advertising have been growing rapidly over the past several years. In this paper, we investigate the problem of invalid traffic generation that aims to inflate advertising impressions on websites. Our study begins with an analysis of purchased traffic for a set of honeypot websites. Data collected from these sites provides a window into the basic mechanisms used for impression fraud and in particular enables us to identify pay-per-view (PPV) networks. PPV networks are comprised of legitimate websites that use JavaScript provided by PPV network service providers to render unwanted web pages "underneath" requested content on a real user's browser so that additional advertising impressions are registered. We describe the characteristics of the PPV network ecosystem and the typical methods for delivering fraudulent impressions. We also provide a case study of scope of PPV networks in the Internet. Our results show that these networks deliver hundreds of millions of fraudulent impressions per day, resulting in hundreds of millions of lost advertising dollars annually. Characteristics unique to traffic delivered via PPV networks are also discussed. We conclude with recommendations for countermeasures that can reduce the scope and impact of PPV networks.


title: A temporal pattern mining approach for classifying electronic health record data

abstract: We study the problem of learning classification models from complex multivariate temporal data encountered in electronic health record systems. The challenge is to define a good set of features that are able to represent well the temporal aspect of the data. Our method relies on temporal abstractions and temporal pattern mining to extract the classification features. Temporal pattern mining usually returns a large number of temporal patterns, most of which may be irrelevant to the classification task. To address this problem, we present the Minimal Predictive Temporal Patterns framework to generate a small set of predictive and nonspurious patterns. We apply our approach to the real-world clinical task of predicting patients who are at risk of developing heparin-induced thrombocytopenia. The results demonstrate the benefit of our approach in efficiently learning accurate classifiers, which is a key step for developing intelligent clinical monitoring systems.


title: A novel AC-to-DC adaptor with ultra-high power density and efficiency

abstract: This paper proposes a novel ac-to-dc adaptor circuit with ultra-high power density and efficiency, which is based on a two-stage topology, consists of a bridgeless boost converter followed by a LLC converter. The bridgeless boost converter is in charge of regulating output voltage and minimizing bulk capacitor, while the LLC converter is operated as a dc transformer (DCX) and plays a role of providing isolated voltage step-down, maximizing circuit efficiency and simplifying the control circuit. Furthermore, in order to achieve ultra-high power density and high efficiency, the proposed circuit applies gallium nitride (GaN) devices and planar transformer, while operates the circuit to around 1MHz to shrink both the sizes of EMI filter and magnetic components. By applying the proposed circuit, both the features of high performance and compact volume could be achieved; up to 44.22W/in ::: 3 ::: power density and 94% efficiency are obtained from a 65W ac-to-dc adaptor prototype.


title: Attenuating Tumour Angiogenesis: A Preventive Role of Metformin against Breast Cancer

abstract: Metformin is one of the most widely prescribed antidiabetics for type 2 diabetes. A critical role of metformin against tumorigenesis has recently been implicated, although several studies also reported the lack of anticancer property of the antidiabetics. Given the controversies regarding the potential role of metformin against tumour progression, the effect of metformin against breast, cervical, and ovarian tumour cell lines was examined followed by in vivo assessment of metformin on tumour growth using xenograft breast cancer models. Significant inhibitory impact of metformin was observed in MCF-7, HeLa, and SKOV-3 cells, suggesting an antiproliferative property of metformin against breast, cervical, and ovarian tumour cells, respectively, with the breast tumour cells, MCF-7, being the most responsive. In vivo assessment was subsequently carried out, where mice with breast tumours were treated with metformin (20 mg/kg body weight) or sterile PBS solution for 15 consecutive days. No inhibition of breast tumour progression was detected. However, tumour necrosis was significantly increased in the metformin-treated group, accompanied by decreased capillary formation within the tumours. Thus, despite the lack of short-term benefit of metformin against tumour progression, a preventive role of metformin against breast cancer was implicated, which is at partially attributable to the attenuation of tumour angiogenesis.


title: On the feasibility and suitability of MR and ER based actuators in human friendly manipulators

abstract: For several decades now, the robotics industry has postulated the emergence of a new breed of manipulators capable of safely interacting with humans. The integration of robots into human environments has always possessed a unique set of challenges. The primary concern has always been for the safety of the human coworkers. Insuring their safety has led to the desire to design manipulators with intrinsic safety characteristics. Specifically, it is noted that modern ‘human-safe’ manipulators are successful if they have minimized both their weight and reflected actuator inertia. The leading research accomplishing this relies on complex actuation systems. Such systems commonly suffer from degraded performance and high cost of development. Magneto- and Electro-Rheological fluids are a class of smart-materials that can instantaneously, and reversibly alter their rheological properties under the influence of an applied field. Devices developed with such fluids are known to possess superior torque-to-inertia characteristics over conventional servo systems. Moreover, their simple mechanical construction suggests that manipulators using such devices could be developed at a lower cost. In this paper, we will discuss the potential benefits rheological fluids can bring to the field of human friendly manipulators.


title: Document Clustering: A Review

abstract: As the internet is exploding with huge volume of text documents, the need of grouping similar documents together for versatile applications have hold the attention of researchers in this area. Document clustering can facilitate the tasks of document organization and web browsing, search engine results, corpus summarization, documents classification, information retrieval and filtering. However several attempts have been made to develop efficient document clustering algorithms but most of the clustering methods suffer from challenges in dealing with problems of high dimensionality, scalability, accuracy and meaningful cluster labels. This paper intends to provide a brief summary over methods studied and current state of documents clustering research, including basic traditional methods as well as advanced fuzzy based, GA, PSO, HS oriented techniques etc. Also document representation model and its challenges, dimensionality reduction mechanisms, issues in document clustering, and cluster quality evaluation criteria are discussed.


title: Deep learning-based classification of massive electrocardiography data

abstract: Classification is the basis of electrocardiography (ECG) analysis. In the last decades, a large number of methods were proposed to deal with the classification of ECG beats. In this paper a kind of deep learning method is introduced into ECG beats classification. We create a classifier with stacked sparse autoencoder (SAE), and then combine the softmax regression with the SAE networks to consummate the classifier, from which we can get higher accuracy in the classification task. In the deep networks architecture, we use the stacked sparse autoencoder to get high-level features. Experimental results with the MIT-BIH ECG dataset confirmed that classifier build in this autoencoder based deep networks method perform better in the classical classification problem.


title: A Model to Determine Customer Lifetime Value in a Retail Banking Context

abstract: During the past decade, the retail banking industry started to face a set of radically new challenges that had an overall negative impact on industry margin and profitability. In response to these challenges, more and more retail banks have focused on increasing the scale of their operations, which has led to a rising importance of mergers and acquisitions (M&A). From a Marketing perspective, M&A transactions are nothing other than the acquisition of the customer base of one company by another one, usually based on the assumption that the acquiring bank can manage this customer base more profitably than the selling bank was able to. It is therefore not surprising that questions about the valuation of customers have become more important than ever in the retail banking industry. Our article provides a contribution in this area by presenting a customer valuation model that we developed in cooperation with a leading German retail bank, which takes account of the specific requirements of this industry. Our model is based on a combination of first-order Markov chain modeling and CART (classification and regression tree) and can deal equally well with discrete one-time transactions as with continuous revenue streams. Furthermore, it is based on the analysis of homogeneous groups instead of individual customers and is easy to understand and parsimonious in nature. In our article we provide proof of the practical value of our approach by validating our model using 6.2 million datasets. This validation shows how our model can be applied in day-to-day business life.


title: Security threats for Big Data

abstract: The world is advancing towards technological evolution day by day resulting in an exponential rise in Data. This massive volume of Data has introduced the idea of Big Data, which has captured the attention of business and IT scholars as a blessing and a source of immense opportunities for large organizations. Securing this huge amount of data has become a great concern in the field of information and communication technology. In this paper, we have conducted two studies to observe the security threats for big data: (a) first study was carried out on Enron email dataset (that contains about half a million of emails) to investigate the security challenges of big data in the field of email communication; and (b) second study was carried out on 35 undergraduate students to observe how phishing email generation based on users' intention or behavior may break the security system. The study results showed that big data analysis helps phishers to understand the behavior or interest of email users that can help them (phisher or hackers) to generate the phishing sites/emails to create big data security threats.


DeepSecure: Scalable Provably-Secure Deep Learning


Identifying Speakers and Listeners of Quoted Speech in Literary Works


title: Phase-Change Memory—Towards a Storage-Class Memory

abstract: Phase-change memory (PCM) has undergone significant academic and industrial research in the last 15 years. After much development, it is now poised to enter the market as a storage-class memory (SCM), with performance and cost between that of NAND flash and DRAM. In this paper, we review the history of phase-transforming chalcogenides leading up to our current understanding of PCM as either a storage-type SCM, with high-density and better than NAND flash endurance, write speeds, and retention, or a memory-type SCM, with fast read/write times to function as a nonvolatile DRAM. Several of the key findings from the community relating to device dimensional scaling, cell design, thermal engineering, material exploration, and storing multiple levels per cell will be discussed. These areas have dramatically impacted the course of development and understanding of PCM. We will highlight the performance gains attained and the future prospects, which will help drive PCM to be as ubiquitous as NAND flash in the upcoming decade.


title: Modeling 4D Human-Object Interactions for Joint Event Segmentation, Recognition, and Object Localization

abstract: In this paper, we present a 4D human-object interaction (4DHOI) model for solving three vision tasks jointly: i) event segmentation from a video sequence, ii) event recognition and parsing, and iii) contextual object localization. The 4DHOI model represents the geometric, temporal, and semantic relations in daily events involving human-object interactions. In 3D space, the interactions of human poses and contextual objects are modeled by semantic co-occurrence and geometric compatibility. On the time axis, the interactions are represented as a sequence of atomic event transitions with coherent objects. The 4DHOI model is a hierarchical spatial-temporal graph representation which can be used for inferring scene functionality and object affordance. The graph structures and parameters are learned using an ordered expectation maximization algorithm which mines the spatial-temporal structures of events from RGB-D video samples. Given an input RGB-D video, the inference is performed by a dynamic programming beam search algorithm which simultaneously carries out event segmentation, recognition, and object localization. We collected a large multiview RGB-D event dataset which contains 3,815 video sequences and 383,036 RGB-D frames captured by three RGB-D cameras. The experimental results on three challenging datasets demonstrate the strength of the proposed method.


Neural Nets Can Learn Function Type Signatures From Binaries


title: 1MHz LLC resonant DC-DC converter with PWM output regulation capability

abstract: This paper proposes a 1MHz resonant DC transformer (DCX) with output regulation capability based on the traditional LLC resonant converter. The proposed regulated DCX (RDCX) has a configuration with an auxiliary transformer in series with the main transformer at primary side but independent at the secondary side. And, the auxiliary transformer creates an output to feed the cascaded PWM dc-dc converter. Since the output of the PWM dc-dc converter is parallel with the main output, the tight regulation of output voltage can be realized by adjusting the duty cycle of the auxiliary dc-dc stage. The majority of power is delivered to load through the main transformer, and only small portion of power is converted by two-stage. Therefore, the total conversion efficiency is almost as high as conventional LLC-DCX. The steady state operating principle of the proposed converter is analyzed and a 1MHz prototype is built-up to verify the theoretical analysis in this paper.


title: Image Processing and Pattern Recognition: Fundamentals and Techniques

abstract: PART I: FUNDAMENTALS. 1 INTRODUCTION. 1.1 The World of Signals. 1.2 Digital Image Processing. 1.3 Elements of an Image Processing System. Appendix 1.A Selected List of Books on Image Processing and Computer Vision from Year 2000. References. 2 MATHEMATICAL PRELIMINARIES. 2.1 Laplace Transform. 2.2 Fourier Transform. 2.3 Z-Transform. 2.4 Cosine Transform. 2.5 Wavelet Transform. 3 IMAGE ENHANCEMENT. 3.1 Grayscale Transformation. 3.2 Piecewise Linear Transformation. 3.3 Bit Plane Slicing. 3.4 Histogram Equalization. 3.5 Histogram Specification. 3.6 Enhancement by Arithmetic Operations. 3.7 Smoothing Filter. 3.8 Sharpening Filter. 3.9 Image Blur Types and Quality Measures. 4 MATHEMATICAL MORPHOLOGY. 4.1 Binary Morphology. 4.2 Opening and Closing. 4.3 Hit-or-Miss Transform. 4.4 Grayscale Morphology. 4.5 Basic Morphological Algorithms. 4.6 Morphological Filters. 5 IMAGE SEGMENTATION. 5.1 Thresholding. 5.2 Object (Component) Labeling. 5.3 Locating Object Contours by the Snake Model. 5.4 Edge Operators. 5.5 Edge Linking by Adaptive Mathematical Morphology. 5.6 Automatic Seeded Region Growing. 5.7 A Top-Down Region Dividing Approach. 6 DISTANCE TRANSFORMATION AND SHORTEST PATH PLANNING. 6.1 General Concept. 6.2 Distance Transformation by Mathematical Morphology. 6.3 Approximation of Euclidean Distance. 6.4 Decomposition of Distance Structuring Element. 6.5 The 3D Euclidean Distance. 6.6 The Acquiring Approaches. 6.7 The Deriving Approaches. 6.8 The Shortest Path Planning. 6.9 Forward and Backward Chain Codes for Motion Planning. 6.10 A Few Examples. 7 IMAGE REPRESENTATION AND DESCRIPTION. 7.1 Run-Length Coding. 7.2 Binary Tree and Quadtree. 7.3 Contour Representation. 7.4 Skeletonization by Thinning. 7.5 Medial Axis Transformation. 7.6 Object Representation and Tolerance. 8 FEATURE EXTRACTION. 8.1 Fourier Descriptor and Moment Invariants. 8.2 Shape Number and Hierarchical Features. 8.3 Corner Detection. 8.4 Hough Transform. 8.5 Principal Component Analysis. 8.6 Linear Discriminate Analysis. 8.7 Feature Reduction in Input and Feature Spaces. 9 PATTERN RECOGNITION. 9.1 The Unsupervised Clustering Algorithm. 9.2 Bayes Classifier. 9.3 Support Vector Machine. 9.4 Neural Networks. 9.5 The Adaptive Resonance Theory Network. 9.6 Fuzzy Sets in Image Analysis. PART II: APPLICATIONS. 10 FACE IMAGE PROCESSING AND ANALYSIS. 10.1 Face and Facial Feature Extraction. 10.2 Extraction of Head and Face Boundaries and Facial Features. 10.3 Recognizing Facial Action Units. 10.4 Facial Expression Recognition in JAFFE Database. 11 DOCUMENT IMAGE PROCESSING AND CLASSIFICATION. 11.1 Block Segmentation and Classification. 11.2 Rule-Based Character Recognition System. 11.3 Logo Identification. 11.4 Fuzzy Typographical Analysis for Character Preclassification. 11.5 Fuzzy Model for Character Classification. 12 IMAGE WATERMARKING. 12.1 Watermarking Classification. 12.2 Spatial Domain Watermarking. 12.3 Frequency-Domain Watermarking. 12.4 Fragile Watermark. 12.5 Robust Watermark. 12.6 Combinational Domain Digital Watermarking. 13 IMAGE STEGANOGRAPHY. 13.1 Types of Steganography. 13.2 Applications of Steganography. 13.3 Embedding Security and Imperceptibility. 13.4 Examples of Steganography Software. 13.5 Genetic Algorithm-Based Steganography. 14 SOLAR IMAGE PROCESSING AND ANALYSIS. 14.1 Automatic Extraction of Filaments. 14.2 Solar Flare Detection. 14.3 Solar Corona Mass Ejection Detection. INDEX.


title: Non-local sparse and low-rank regularization for structure-preserving image smoothing

abstract: This paper presents a new image smoothing method that better preserves prominent structures. Our method is inspired by the recent non-local image processing techniques on the patch grouping and filtering. Overall, it has three major contributions over previous works. First, we employ the diffusion map as the guidance image to improve the accuracy of patch similarity estimation using the region covariance descriptor. Second, we model structure-preserving image smoothing as a low-rank matrix recovery problem, aiming at effectively filtering the texture information in similar patches. Lastly, we devise an objective function, namely the weighted robust principle component analysis (WRPCA), by regularizing the low rank with the weighted nuclear norm and sparsity pursuit with L1 norm, and solve this non-convex WRPCA optimization problem by adopting the alternative direction method of multipliers (ADMM) technique. We experiment our method with a wide variety of images and compare it against several state-of-the-art methods. The results show that our method achieves better structure preservation and texture suppression as compared to other methods. We also show the applicability of our method on several image processing tasks such as edge detection, texture enhancement and seam carving.


title: A Single-Stage Two-Switch PFC Rectifier With Wide Output Voltage Range and Automatic AC Ripple Power Decoupling

abstract: Conventional single-phase power-factor-correction (PFC) rectifiers with active power decoupling capability typically require more than three active switches in their circuits. By exploring the concept of power-buffer cell, a new single-stage PFC rectifier with two active switches, one inductor and one small power-buffering capacitor is reported in this paper. The proposed converter can achieve high-power factor, wide output voltage range, and power decoupling function without using electrolytic capacitor. Additionally, an automatic power decoupling control scheme that is simple and easy to implement is proposed in this paper. The operating principle, control method, and design considerations of the proposed rectifier are also provided. A 100-W prototype with ac input voltage of 110 Vrms and a regulated dc output voltage ranging from 30 to 100 V has been successfully designed and practically tested. The experimental results show that with only a 15 μ F power-buffering film capacitor, the proposed converter can achieve an input power factor of over 0.98, peak efficiency of 93.9%, and output voltage ripple of less than 3%, at 100-W output power.


title: Elastic textures for additive fabrication

abstract: We introduce elastic textures: a set of parametric, tileable, printable, cubic patterns achieving a broad range of isotropic elastic material properties: the softest pattern is over a thousand times softer than the stiffest, and the Poisson's ratios range from below zero to nearly 0.5. Using a combinatorial search over topologies followed by shape optimization, we explore a wide space of truss-like, symmetric 3D patterns to obtain a small family. This pattern family can be printed without internal support structure on a single-material 3D printer and can be used to fabricate objects with prescribed mechanical behavior. The family can be extended easily to create anisotropic patterns with target orthotropic properties. We demonstrate that our elastic textures are able to achieve a user-supplied varying material property distribution. We also present a material optimization algorithm to choose material properties at each point within an object to best fit a target deformation under a prescribed scenario. We show that, by fabricating these spatially varying materials with elastic textures, the desired behavior is achieved.


title: Design and Realization of a GA-Optimized VHF/UHF Antenna With "On-Body" Matching Network

abstract: This letter presents a VHF/UHF loaded wire antenna with an on-body matching network for broadband or multiband wireless applications. A genetic algorithm (GA) is used to optimize antenna structure and the on-body matching network. The designed 1.47-m antenna with a lossless LC parallel circuit load and a 0.32-m sleeve pedestal can work from 30 to 520 MHz. In the working band, the measured voltage standing-wave ratio (VSWR) and the gain of the loaded antenna with the on-body matching network are less than 2 and greater than 0 dBi, respectively. The efficiency of the on-body matching network is greater than 35% over the frequency range of 30 ˜ 520 MHz, greater than 38% over 60 ˜ 200 MHz, and greater than 42% over 210 ˜ 520 MHz.


title: End-to-End Interpretation of the French Street Name Signs Dataset

abstract: We introduce the French Street Name Signs (FSNS) Dataset consisting of more than a million images of street name signs cropped from Google Street View images of France. Each image contains several views of the same street name sign. Every image has normalized, title case folded ground-truth text as it would appear on a map. We believe that the FSNS dataset is large and complex enough to train a deep network of significant complexity to solve the street name extraction problem “end-to-end” or to explore the design trade-offs between a single complex engineered network and multiple sub-networks designed and trained to solve sub-problems. We present such an “end-to-end” network/graph for Tensor Flow and its results on the FSNS dataset.


title: Internet of Things (IoT) enabled water monitoring system

abstract: Water is always a crucial part of everyday life. Due to global environmental situation, water management and conservation is vital for human survival. In recent times, there were huge needs of consumer based humanitarian projects that could be rapidly developed using Internet of Things (IoT) technology. In this paper, we propose an IoT based water monitoring system that measures water level in real-time. Our prototype is based on idea that the level of the water can be very important parameter when it comes to the flood occurrences especially in disaster prone areas. A water level sensor is used to detect the desired parameter, and if the water level reaches the parameter, the signal will be feed in realtime to social network like Twitter. A cloud server was configured as data repository. The measurement of the water levels are displayed in remote dashboard.


Addressing Overgeneration Error: An Effective and Effcient Approach to Keyphrase Extraction from Scientific Papers


title: Sem-GAN: Semantically-Consistent Image-to-Image Translation

abstract: Unpaired image-to-image translation is the problem of mapping an image in the source domain to one in the target domain, without requiring corresponding image pairs. To ensure the translated images are realistically plausible, recent works, such as Cycle-GAN, demands this mapping to be invertible. While, this requirement demonstrates promising results when the domains are unimodal, its performance is unpredictable in a multi-modal scenario such as in an image segmentation task. This is because, invertibility does not necessarily enforce semantic correctness. To this end, we present a semantically-consistent GAN framework, dubbed Sem-GAN, in which the semantics are defined by the class identities of image segments in the source domain as produced by a semantic segmentation algorithm. Our proposed framework includes consistency constraints on the translation task that, together with the GAN loss and the cycle-constraints, enforces that the images when translated will inherit the appearances of the target domain, while (approximately) maintaining their identities from the source domain. We present experiments on several image-to-image translation tasks and demonstrate that Sem-GAN improves the quality of the translated images significantly, sometimes by more than 20% on the FCN score. Further, we show that semantic segmentation models, trained with synthetic images translated via Sem-GAN, leads to significantly better segmentation results than other variants.


title: High-Resolution Mammogram Synthesis using Progressive Generative Adversarial Networks

abstract: The ability to generate synthetic medical images is useful for data augmentation, domain transfer, and out-of-distribution detection. However, generating realistic, high-resolution medical images is challenging, particularly for Full Field Digital Mammograms (FFDM), due to the textural heterogeneity, fine structural details and specific tissue properties. In this paper, we explore the use of progressively trained generative adversarial networks (GANs) to synthesize mammograms, overcoming the underlying instabilities when training such adversarial models. This work is the first to show that generation of realistic synthetic medical images is feasible at up to 1280x1024 pixels, the highest resolution achieved for medical image synthesis, enabling visualizations within standard mammographic hanging protocols. We hope this work can serve as a useful guide and facilitate further research on GANs in the medical imaging domain.


title: Water Microbiology. Bacterial Pathogens and Water

abstract: Water is essential to life, but many people do not have access to clean and safe drinking water and many die of waterborne bacterial infections. In this review a general characterization of the most important bacterial diseases transmitted through water— cholera, typhoid fever and bacillary dysentery—is presented, focusing on the biology and ecology of the causal agents and on the diseases' characteristics and their life cycles in the environment. The importance of pathogenic Escherichia coli strains and emerging pathogens in drinking water-transmitted diseases is also briefly discussed. Microbiological water analysis is mainly based on the concept of fecal indicator bacteria. The main bacteria present in human and animal feces (focusing on their behavior in their hosts and in the environment) and the most important fecal indicator bacteria are presented and discussed (focusing on the advantages and limitations of their use as markers). Important sources of bacterial fecal pollution of environmental waters are also briefly indicated. In the last topic it is discussed which indicators of fecal pollution should be used in current drinking water microbiological analysis. It was concluded that safe drinking water for all is one of the major challenges of the 21st century and that microbiological control of drinking water should be the norm everywhere. Routine basic microbiological analysis of drinking water should be carried out by assaying the presence of Escherichia coli by culture methods. Whenever financial resources are available, fecal coliform determinations should be complemented with the quantification of enterococci. More studies are needed in order to check if ammonia is reliable for a preliminary screening for emergency fecal pollution outbreaks. Financial resources should be devoted to a better understanding of the ecology and behavior of human and animal fecal bacteria in environmental waters.


Nuclei Segmentation of Whole Slide Images in Digital Pathology


title: A new field programmable system-on-a-chip for mixed signal integration

abstract: A new RAM-based, mixed-signal, multicontext dynamically reconfigurable Field Programmable Device with on-chip microprocessor is described. A completely integrated mixed-signal CAD and microprocessor programming environment is used to design and simulate electronic systems composed by microprocessor code and digital and analog hardware. The very flexible communication between the microprocessor, the configurable digital cells and the programmable analog blocks makes possible powerful integration, real-time emulation (internal signals and configuration are available to the microprocessor) and advanced run-time reconfiguration.


title: Very Compact Fully Lumped Decoupling Network for a Coupled Two-Element Array

abstract: Without the need of any transmission line, a very compact decoupling network based on reactive lumped elements is presented for a two-element closely spaced array. The lumped network, consisting of two series and four shunt elements, can be analytically designed using the even-odd mode analysis. In the even mode, the half-circuit of the decoupling network is identical to an L-section matching network, while in the odd mode it is equivalent to a π-section one. The proposed decoupling network can deal with the matching conditions of the even and odd modes independently so as to simultaneously achieve good impedance matching and port isolation of the whole antenna array. The design principle, formulation, and experimental results including the radiation characteristics are introduced.


title: A heuristic algorithm for a single vehicle static bike sharing rebalancing problem

abstract: The static bike rebalancing problem (SBRP) concerns the task of repositioning bikes among stations in self-service bike-sharing systems. This problem can be seen as a variant of the one-commodity pickup and delivery vehicle routing problem, where multiple visits are allowed to be performed at each station, i.e., the demand of a station is allowed to be split. Moreover, a vehicle may temporarily drop its load at a station, leaving it in excess or, alternatively, collect more bikes from a station (even all of them), thus leaving it in default. Both cases require further visits in order to meet the actual demands of such station. This paper deals with a particular case of the SBRP, in which only a single vehicle is available and the objective is to find a least-cost route that meets the demand of all stations and does not violate the minimum (zero) and maximum (vehicle capacity) load limits along the tour. Therefore, the number of bikes to be collected or delivered at each station should be appropriately determined in order to respect such constraints. We propose an iterated local search (ILS) based heuristic to solve the problem. The ILS algorithm was tested on 980 benchmark instances from the literature and the results obtained are quite competitive when compared to other existing methods. Moreover, our heuristic was capable of finding most of the known optimal solutions and also of improving the results on a number of open instances.


title: Fast smoothing of manipulator trajectories using optimal bounded-acceleration shortcuts

abstract: This paper considers a shortcutting heuristic to smooth jerky trajectories for many-DOF robot manipulators subject to collision constraints, velocity bounds, and acceleration bounds. The heuristic repeatedly picks two points on the trajectory and attempts to replace the intermediate trajectory with a shorter, collision-free segment. Here, we construct segments that interpolate between endpoints with specified velocity in a time-optimal fashion, while respecting velocity and acceleration bounds. These trajectory segments consist of parabolic and straight-line curves, and can be computed in closed form. Experiments on reaching tasks in cluttered human environments demonstrate that the technique can generate smooth, collision-free, and natural-looking motion in seconds for a PUMA manipulator and the Honda ASIMO robot.


title: A Novel Myoelectric Pattern Recognition Strategy for Hand Function Restoration After Incomplete Cervical Spinal Cord Injury

abstract: This study presents a novel myoelectric pattern recognition strategy towards restoration of hand function after incomplete cervical spinal cord Injury (SCI). High density surface electromyogram (EMG) signals comprised of 57 channels were recorded from the forearm of nine subjects with incomplete cervical SCI while they tried to perform six different hand grasp patterns. A series of pattern recognition algorithms with different EMG feature sets and classifiers were implemented to identify the intended tasks of each SCI subject. High average overall accuracies (> 97%) were achieved in classification of seven different classes (six intended hand grasp patterns plus a hand rest pattern), indicating that substantial motor control information can be extracted from partially paralyzed muscles of SCI subjects. Such information can potentially enable volitional control of assistive devices, thereby facilitating restoration of hand function. Furthermore, it was possible to maintain high levels of classification accuracy with a very limited number of electrodes selected from the high density surface EMG recordings. This demonstrates clinical feasibility and robustness in the concept of using myoelectric pattern recognition techniques toward improved function restoration for individuals with spinal injury.


title: Internet of Things-enabled multiagent system for residential DC microgrids

abstract: This paper proposes an Internet of Things (IoT)-enabled multiagent system (MAS) for residential DC microgrids (RDCMG). The proposed MAS consisting of smart home agents (SHAs) aims to cooperate each other to alleviate the peak load of the RDCMG and to minimize the electricity costs for smart homes. These are achieved by agent utility functions and the best operating time algorithm (BOT) in the MAS. Moreover, IoT-based efficient and cost-effective agent communication method is proposed, which applies message queuing telemetry transport (MQTT) publish/subscribe protocol via MQTT brokers. The proposed IoT-enabled MAS and smart home models are implemented in five Raspberry pi 3 boards and validated by experimental studies for a RDCMG with five smart homes.


title: Cannabidiol increases survival and promotes rescue of cognitive function in a murine model of cerebral malaria

abstract: Cerebral malaria (CM) is a severe complication resulting from Plasmodium falciparum infection that might cause permanent neurological deficits. Cannabidiol (CBD) is a nonpsychotomimetic compound of Cannabis sativa with neuroprotective properties. In the present work, we evaluated the effects of CBD in a murine model of CM. Female mice were infected with Plasmodium berghei ANKA (PbA) and treated with CBD (30mg/kg/day - 3 or 7days i.p.) or vehicle. On 5th day-post-infection (dpi), at the peak of the disease), animals were treated with single or repeated doses of Artesunate, an antimalarial drug. All groups were tested for memory impairment (Novel Object Recognition or Morris Water Maze) and anxiety-like behaviors (Open field or elevated plus maze test) in different stages of the disease (at the peak or after the complete clearance of the disease). Th1/Th2 cytokines and neurotrophins (brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF)) were measured in the prefrontal cortex and hippocampus of experimental groups. PbA-infected mice displayed memory deficits and exhibited increase in anxiety-like behaviors on the 5dpi or after the clearance of the parasitemia, effects prevented by CBD treatment. On 5dpi, TNF-α and IL-6 increased in the hippocampus, while only IL-6 increased in the prefrontal cortex. CBD treatment resulted in an increase in BDNF expression in the hippocampus and decreased levels of proinflammatory cytokines in the hippocampus (TNF-α) and prefrontal cortex (IL-6). Our results indicate that CBD exhibits neuroprotective effects in CM model and might be useful as an adjunctive therapy to prevent neurological symptoms following this disease.


title: Suitability for Psychotherapy Scale (SPS) and its reliability, validity, and prediction.

abstract: Objectives. To present an interview-based 7-item Suitability for Psychotherapy Scale (SPS) created to evaluate suitability for short- and long-term therapy, and to assess its reliability, validity, and prediction. ::: ::: Design. Reliability of the SPS was evaluated by measuring both repeatability and agreement (cross-sectional design). Validity of the SPS was evaluated by measuring both criterion and discriminating validity (cross-sectional design). Prediction of the SPS was evaluated using a cohort study design. ::: ::: Methods. Suitability of 326 psychiatric outpatients from the Helsinki Psychotherapy Study was assessed at baseline with the SPS, and a summary score of the seven items was formed. Reliability of the SPS was evaluated using kappa coefficients and validity using linear models. The ability of the SPS to predict changes in symptoms (SCL-90-GSI) during a 1-year follow-up was measured. The analysis of validity and prediction of SPS were based on data of all 326 patients and analysis of reliability of SPS on a sample of 28 patients. ::: ::: Results. Both the repeatability of the interviewers’ assessments over 3 years and agreement between interviewers and reference were fair or good. An association of the SPS with personality functions but not with psychiatric symptoms supported criterion and discriminating validity of the SPS. The SPS also significantly predicted changes in symptoms during follow-up. ::: ::: Conclusions. The SPS appeared to be a valid and reliable method for assessing pre-treatment suitability, with good prediction of psychotherapy outcome.


title: Dynamic convolutional neural network for activity recognition

abstract: In this paper, a novel Dynamic Convolutional Neural Network (D-CNN) is proposed using sensor data for activity recognition. Sensor data collected for activity recognition is usually not well-aligned. It may also contains noises and variations from different persons. To overcome these challenges, Gaussian Mixture Models (GMM) is exploited to capture the distribution of each activity. Then, sensor data and the GMMs are screened into different segments. These segments form multiple paths in the Convolutional Neural Network. During testing, Gaussian Mixture Regression (GMR) is applied to dynamically fit segments of test signals into corresponding paths in the CNN. Experimental results demonstrate the superior performance of D-CNN to other learning methods.


Swarm Robotics


title: Application of TextRank Algorithm for Credibility Assessment

abstract: In this article we examine the use of Text Rank algorithm for identifying web content credibility. Text Rank has come to be a widely applied method for automated text summarization. In our research we apply it to see how well does it fare in recognizing credible statements from a given corpus. So far, research into use of NLP algorithms in credibility assessment was focused more on extracting the most informative statements, or dealing with recognizing the relation between claims within a document. In our paper, we use a collection of 100 websites reviewed by human subjects in regard to their credibility, therefore allowing us to check the algorithm's performance in this task. The data collected showed that the Text Rank algorithm can be used for recognizing credibility on the level of aggregated statement credibility.


title: Single Feed Stacked Patch Circular Polarized Antenna for Triple Band GPS Receivers

abstract: A novel design of a circular polarized antenna for multiband GPS receivers is presented. The design employs the concept of multistacked patches fed through a single coaxial probe. Three patches being stacked together with a slit and symmetry I-slot are used to achieve triple operating frequency bands for GPS including L1 (1.575 GHz), L2 (1.227 GHz) and L5 (1.176 GHz). The proposed antenna has achieved a bandwidth of 2.0%, 1.5%, and 1.7% at GPS L1, L2, and L5 bands, respectively. It exhibits a minimum axial ratio of 0.51 dB with broad beamwidth in the upper hemisphere required for the GPS applications. The design of the proposed antenna is verified in the experiment. In addition, a detailed analysis has been carried out to study the effects of different geometrical parameters on the performance of the antenna.


title: Ms. Pac-Man Versus Ghost Team CIG 2016 competition

abstract: This paper introduces the revival of the popular Ms. Pac-Man Versus Ghost Team competition. We present an updated game engine with Partial Observability constraints, a new Multi-Agent Systems approach to developing Ghost agents, and several sample controllers to ease the development of entries. A restricted communication protocol is provided for the Ghosts, providing a more challenging environment than before. The competition will debut at the IEEE Computational Intelligence and Games Conference 2016. Some preliminary results showing the effects of Partial Observability and the benefits of simple communication are also presented.


title: The anaphase-promoting complex/cyclosome (APC/C): cell-cycle-dependent and -independent functions

abstract: The APC/C (anaphase-promoting complex/cyclosome) is an E3 ubiquitin ligase that targets specific substrates for degradation by the 26S proteasome. APC/C activity depends on two cofactors, namely Cdc20 (cell division cycle 20) and Cdh1, which select the appropriate targets for ubiquitination. It is well established that APC/C is a target of the SAC (spindle assembly checkpoint) during mitosis and has critical roles in controlling the protein levels of major regulators of mitosis and DNA replication. In addition, recent studies have suggested new cell-cycle-independent functions of APC/C in non-mitotic cells and specifically in neuronal structure and function. Given the relevant functions of APC/C in cell proliferation and neuronal physiology, modulating APC/C activity may have beneficial effects in the clinic.


title: PsyBoG: A scalable botnet detection method for large-scale DNS traffic

abstract: Domain Name System (DNS) traffic has become a rich source of information from a security perspective. However, the volume of DNS traffic has been skyrocketing, such that security analyzers experience difficulties in collecting, retrieving, and analyzing the DNS traffic in response to modern Internet threats. More precisely, much of the research relating to DNS has been negatively affected by the dramatic increase in the number of queries and domains. This phenomenon has necessitated a scalable approach, which is not dependent on the volume of DNS traffic. In this paper, we introduce a fast and scalable approach, called PsyBoG, for detecting malicious behavior within large volumes of DNS traffic. PsyBoG leverages a signal processing technique, power spectral density (PSD) analysis, to discover the major frequencies resulting from the periodic DNS queries of botnets. The PSD analysis allows us to detect sophisticated botnets regardless of their evasive techniques, sporadic behavior, and even normal users’ traffic. Furthermore, our method allows us to deal with large-scale DNS data by only utilizing the timing information of query generation regardless of the number of queries and domains. Finally, PsyBoG discovers groups of hosts which show similar patterns of malicious behavior. PsyBoG was evaluated by conducting experiments with two different data sets, namely DNS traces generated by real malware in controlled environments and a large number of real-world DNS traces collected from a recursive DNS server, an authoritative DNS server, and Top-Level Domain (TLD) servers. We utilized the malware traces as the ground truth, and, as a result, PsyBoG performed with a detection accuracy of 95%. By using a large number of DNS traces, we were able to demonstrate the scalability and effectiveness of PsyBoG in terms of practical usage. Finally, PsyBoG detected 23 unknown and 26 known botnet groups with 0.1% false positives.


title: A novel soil measuring wireless sensor network

abstract: Emerging technologies have made low-power and low-cost wireless sensor networks feasible. This paper presents a hierarchical wireless sensor network for measuring soil parameters such as temperature and humidity. Specifically, we designed sensor nodes that are placed completely underground and are used to collect soil measurements. These nodes use their radios to deliver the collected measurements to one of multiple relay nodes located above ground. In turn, relay nodes that are capable of long-range communications forward the data collected from the network's sensor nodes to a base node, which is connected to a workstation. The proposed hierarchical wireless sensor network uses a probabilistic communication protocol to achieve a very low duty cycle and hence a long life-time for soil monitoring applications.


title: Exploring Hierarchical Structures for Recommender Systems

abstract: Items in real-world recommender systems exhibit certain hierarchical structures. Similarly, user preferences also present hierarchical structures. Recent studies show that incorporating the hierarchy of items or user preferences can improve the performance of recommender systems. However, hierarchical structures are often not explicitly available, especially those of user preferences. Thus, there's a gap between the importance of hierarchies and their availability. In this paper, we investigate the problem of exploring the implicit hierarchical structures for recommender systems when they are not explicitly available. We propose a novel recommendation framework to bridge the gap, which enables us to explore the implicit hierarchies of users and items simultaneously. We then extend the framework to integrate explicit hierarchies when they are available, which gives a unified framework for both explicit and implicit hierarchical structures. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework by incorporating implicit and explicit structures.


Process Algebra with Recursive Operations


title: Automated hand gesture recognition for educational applications

abstract: Introduction of new schemes for recognizing hand gestures is an active research field related with these of Machine Learning (ML) and Human Computer Interaction (HCI). Numerous applications can be integrated with such automated algorithms for facilitating either more advanced or less experienced users inside their working or educational environment. A simple method for hand gesture recognition tasks is presented here, using effective enough methods for extracting the final training data and Random Forest learner for prediction. The contribution of this work is the low-dimensionality of the final dataset, based on well-known feature metrics and the high level of accuracy that is achieved using cheap enough equipment, being affordable both for personal use and for more academic activities or online courses.


title: Towards face unlock: on the difficulty of reliably detecting faces on mobile phones

abstract: Currently, reliable face detection and recognition are becoming more important on mobile devices -- e.g. to unlock the screen. However, using only frontal face images for authentication purposes can no longer be considered secure under the assumption of easy availability of frontal snapshots of the respective device owners from social networks or other media. In most current implementations, a sufficiently high-resolution face image displayed on another mobile device will be enough to circumvent security measures. In this paper, we analyze current methods to face detection and recognition regarding their usability in the mobile domain, and then propose an approach to a Face Unlock system on a smart phone intended to be more secure than current approaches while still being convenient to use: we use both frontal and profile face information available during a pan shot around the user's head, by combining camera images and movement sensor data. Current results to face detection are promising, but reliable face recognition needs further research.


title: Untold Tales of Goats in Deadly Indian Monsoons: Adapt or Rain-Retreat under Global Warming?

abstract: This paper examines the unique characteristics of the monsoon climate system and whether Indian farmers can adapt to an even deadlier monsoon climate caused by global climatic shifts. This paper shows that the monsoon climate system can be best captured by the Monsoon Variability Index (MVI) constructed by the present author which is defined as the coefficient of variation in the ratio of monsoon rainfall over non-monsoon rainfall for the 40-year period from 1971 to 2010. Monthly precipitation data are based on the observations at 304 weather stations located across India. This paper shows that the traditional measures of the monsoon climate such as monsoon precipitation do not explain the Indian farmers’ behaviors in response to the climate system. Second, this paper shows that the number of goats owned by farm households increases as the MVI increases, that is, as the monsoon climate intensifies. This paper finds that 50% increase in the MVI leads to 23% increase in the number of goats owned by farms. T...