title: Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition

abstract: This paper presents a new approach to hierarchical reinforcement learning based on decomposing the target Markov decision process (MDP) into a hierarchy of smaller MDPs and decomposing the value function of the target MDP into an additive combination of the value functions of the smaller MDPs. The decomposition, known as the MAXQ decomposition, has both a procedural semantics--as a subroutine hierarchy--and a declarative semantics--as a representation of the value function of a hierarchical policy. MAXQ unifies and extends previous work on hierarchical reinforcement learning by Singh, Kaelbling, and Dayan and Hinton. It is based on the assumption that the programmer can identify useful subgoals and define subtasks that achieve these subgoals. By defining such subgoals, the programmer constrains the set of policies that need to be considered during reinforcement learning. The MAXQ value function decomposition can represent the value function of any policy that is consistent with the given hierarchy. The decomposition also creates opportunities to exploit state abstractions, so that individual MDPs within the hierarchy can ignore large parts of the state space. This is important for the practical application of the method. This paper defines the MAXQ hierarchy, proves formal results on its representational power, and establishes five conditions for the safe use of state abstractions. The paper presents an online model-free learning algorithm, MAXQ-Q, and proves that it converges with probability 1 to a kind of locally-optimal policy known as a recursively optimal policy, even in the presence of the five kinds of state abstraction. The paper evaluates the MAXQ representation and MAXQ-Q through a series of experiments in three domains and shows experimentally that MAXQ-Q (with state abstractions) converges to a recursively optimal policy much faster than flat Q learning. The fact that MAXQ learns a representation of the value function has an important benefit: it makes it possible to compute and execute an improved, non-hierarchical policy via a procedure similar to the policy improvement step of policy iteration. The paper demonstrates the effectiveness of this nonhierarchical execution experimentally. Finally, the paper concludes with a comparison to related work and a discussion of the design tradeoffs in hierarchical reinforcement learning.


title: Safe autonomous stair climbing for a tracked mobile robot using a kinematics based controller

abstract: Planning and implementation of a safe autonomous stair climbing algorithm for a tracked mobile robot is presented in this paper. Two major factors are defined to make the climb as safe as possible. Based on these factors a desired trajectory is planned. Data of two laser range finders are used to locate the robot over the stairs. The error signal is generated by comparing the located and desired postures. To compensate this error, a kinematics based controller is utilized. A combination of a nominal feedforward command with a feedback action on error at each time step is used in this tracking control scheme. The proposed algorithm is implemented on ResQuake. This is a tele-operative rescue mobile robot with great capabilities in climbing obstacles in destructed areas, and its performance was demonstrated in Rescue robot league of RoboCup 2005 in Osaka (Japan), achieving the 2nd Best Design Award, and RoboCup 2008 in China (Suzhou) achieving the 2nd Best in Class Mobility Award. Implementation of algorithm on different staircases resulted in successful climb in all experiments. Results obtained on a sample staircase are demonstrated at the end of this paper.


title: Instructing people for training gestural interactive systems

abstract: Entertainment and gaming systems such as the Wii and XBox Kinect have brought touchless, body-movement based interfaces to the masses. Systems like these enable the estimation of movements of various body parts from raw inertial motion or depth sensor data. However, the interface developer is still left with the challenging task of creating a system that recognizes these movements as embodying meaning. The machine learning approach for tackling this problem requires the collection of data sets that contain the relevant body movements and their associated semantic labels. These data sets directly impact the accuracy and performance of the gesture recognition system and should ideally contain all natural variations of the movements associated with a gesture. This paper addresses the problem of collecting such gesture datasets. In particular, we investigate the question of what is the most appropriate semiotic modality of instructions for conveying to human subjects the movements the system developer needs them to perform. The results of our qualitative and quantitative analysis indicate that the choice of modality has a significant impact on the performance of the learnt gesture recognition system; particularly in terms of correctness and coverage.


A Comparative Study on Hybrid IT Project Managment Using Traditional Project Management and Agile Approach


title: Query Centric Partitioning and Allocation for Partially Replicated Database Systems

abstract: A key feature of database systems is to provide transparent access to stored data. In distributed database systems, this includes data allocation and fragmentation. Transparent access introduces data dependencies and increases system complexity and inter-process communication. Therefore, many developers are exchanging transparency for better scalability using sharding and similar techniques. However, explicitly managing data distribution and data flow requires a deep understanding of the distributed system and the data access, and it reduces the possibilities for optimizations. To address this problem, we present an approach for efficient data allocation that features good scalability while keeping the data distribution transparent. We propose a workload-aware, query-centric, heterogeneity-aware analytical model. We formalize our approach and present an efficient allocation algorithm. The algorithm optimizes the partitioning and data layout for local query execution and balances the workload on homogeneous and heterogeneous systems according to the query history. In the evaluation, we demonstrate that our approach scales well in performance for OLTP- and OLAP-style workloads and reduces storage requirements significantly over replicated systems while guaranteeing configurable availability.


title: Methods for Developing Trust Models for Intelligent Systems

abstract: Our research goals are to understand and model the factors that affect trust in intelligent systems across a variety of application domains. In this chapter, we present two methods that can be used to build models of trust for such systems. The first method is the use of surveys, in which large numbers of people are asked to identify and rank factors that would influence their trust of a particular intelligent system. Results from multiple surveys, each exploring different application domains, can be used to build a core model of trust and to identify domain specific factors that are needed to modify the core model to improve its accuracy and usefulness. The second method involves conducting experiments where human subjects use the intelligent system, where a variety of factors can be controlled in the studies to explore different factors. Based upon the results of these human subjects experiments, a trust model can be built. These trust models can be used to create design guidelines, to predict initial trust levels before the start of a system’s use, and to measure the evolution of trust over the use of a system. With increased understanding of how to model trust, we can build systems that will be more accepted and used appropriately by target populations.


title: The TaSSt: Tactile sleeve for social touch

abstract: In this paper we outline the design process of the TaSST (Tactile Sleeve for Social Touch), a touch-sensitive vibrotactile arm sleeve. The TaSST was designed to enable two people to communicate different types of touch over a distance. The touch-sensitive surface of the sleeve consists of a grid of 4×3 sensor compartments filled with conductive wool. Each compartment controls the vibration intensity of a vibration motor, located in a grid of 4×3 motors beneath the touch-sensitive layer. An initial evaluation of the TaSST revealed that it was mainly suitable for communicating protracted (e.g. pressing), and simple (e.g. poking) touches.


title: Machine Intelligence on Resource-Constrained IoT Devices: The Case of Thread Granularity Optimization for CNN Inference

abstract: Despite their remarkable performance in various machine intelligence tasks, the computational intensity of Convolutional Neural Networks (CNNs) has hindered their widespread utilization in resource-constrained embedded and IoT systems. To address this problem, we present a framework for synthesis of efficient CNN inference software targeting mobile SoC platforms. We argue that thread granularity can substantially impact the performance and energy dissipation of the synthesized inference software, and demonstrate that launching the maximum number of logical threads, often promoted as a guiding principle by GPGPU practitioners, does not result in an efficient implementation for mobile SoCs. We hypothesize that the runtime of a CNN layer on a particular SoC platform can be accurately estimated as a linear function of its computational complexity, which may seem counter-intuitive, as modern mobile SoCs utilize a plethora of heterogeneous architectural features and dynamic resource management policies. Consequently, we develop a principled approach and a data-driven analytical model to optimize granularity of threads during CNN software synthesis. Experimental results with several modern CNNs mapped to a commodity Android smartphone with a Snapdragon SoC show up to 2.37X speedup in application runtime, and up to 1.9X improvement in its energy dissipation compared to existing approaches.


title: Framework to Detect Malicious Transactions in Database System

abstract: Every organization is associated with huge amount of information which is more valuable. Data is important and so it should be consistent, accurate and correct. Today many approaches are used to protect the data as well as networks from attackers (attacks like SQLIA, Brute-force attack). One way to make data more secure is using Intrusion Detection System (IDS). Many researches are done in this intrusion detection field but it mainly concentrated on networks and operating system. This approach is for database so that it will prevent the data loss, maintain consistency and accuracy. Database security research is concerned about the protection of database from unauthorized access. The unauthorized access may be in the form of execution of malicious transaction and this may lead to break the integrity of the system. Banking is one of the sectors which are suffering from million dollars losses only because of this unauthorized activities and malicious transactions. So, it is today’s demand to detect malicious transactions and also to provide some recommendation. In this paper, we provided the detection system for the real-world problem of intrusion detection in the banking system and we are going to give some preventive measures to avoid or reduce future attacks. In order to detect malicious transactions, we used data mining algorithm for framing a data dependency miner for our banking database IDS. Our approach extracts the read-write dependency rules and then these rules are used to check whether the coming transaction is malicious or not. Our system not only finds the malicious transactions that corrupt data items but also identify the transactions that write data without permission and read data without permission.


title: Proportionality in Asian and North American Caucasian Faces Using Neoclassical Facial Canons as Criteria

abstract: Nine projective linear measurements were taken to determine morphometric differences of the face among healthy young adult Chinese, Vietnamese, and Thais (60 in each group) and to assess the validity of six neoclassical facial canons in these populations. In addition, the findings in the Asian ethnic groups were compared to the data of 60 North American Caucasians. The canons served as criteria for determining the differences between the Asians and Caucasians. In neither Asian nor Caucasian subjects were the three sections of the facial profile equal. The validity of the five other facial canons was more frequent in Caucasians (range: 16.7-36.7%) than in Asians (range: 1.7-26.7%). Horizontal measurement results were significantly greater in the faces of the Asians (en-en, al-al, zy-zy) than in their white counterparts; as a result, the variation between the classical proportions and the actual measurements was significantly higher among Asians (range: 90-100%) than Caucasians (range: 13.3-48%). The dominant characteristics of the Asian face were a wider intercanthal distance in relation to a shorter palpebral fissure, a much wider soft nose within wide facial contours, a smaller mouth width, and a lower face smaller than the forehead height. In the absence of valid anthropometric norms of craniofacial measurements and proportion indices, our results, based on quantitative analysis of the main vertical and horizontal measurements of the face, offers surgeons guidance in judging the faces of Asian patients in preparation for corrective surgery.


title: Robust speaker identification in noisy and reverberant conditions

abstract: Robustness of speaker recognition systems is crucial for real-world applications, which typically contain both additive noise and room reverberation. However, the combined effects of additive noise and convolutive reverberation have been rarely studied in speaker identification (SID). This paper addresses this issue in two phases. We first remove background noise through binary masking using a deep neural network classifier. Then we perform robust SID with speaker models trained in selected reverberant conditions, on the basis of bounded marginalization and direct masking. Evaluation results show that the proposed system substantially improves SID performance over related systems in a wide range of reverberation time and signal-to-noise ratios.


title: On-the-Fly QoE-Aware Transcoding in the Mobile Edge

abstract: To enhance video streaming experience for mobile users, we propose an approach towards Quality-of-Experience (QoE) aware on-the-fly transcoding. The proposed approach relies on the concept of Mobile Edge Computing (MEC) as a key enabler in enhancing service quality. Our scheme involves an autonomic creation of a transcoding service as a Virtual Network Function (VNF) and ensures dynamic rate switching of the streamed video to maintain the desirable quality. This edge-assistive transcoding and adaptive streaming results in reduced computational loads and reduced core network traffic. The proposed solution represents a complete miniature content delivery network infrastructure on the edge, ensuring reduced latency and better quality of experience


title: Development and Validation of an Instrument to Measure Maturity of IT Business Strategic Alignment Mechanisms

abstract: Achieving IT-business alignment has been a long-standing, critical, information management issue. A theoretical framework of the maturity levels of management practices and strategic IT choices that facilitate alignment was empirically tested and validated. Confirmatory factor analysis CFA validated 6 factors and identified 22 indices to measure strategic alignment maturity. A mixed model repeated measure analysis of variance ANOVA obtained significant results for both the main effect and interaction effect of differences for the 6 maturity factors across the 11 business units. Regression analysis found a positive association between overall strategic alignment maturity and respondents' self-rated maturity. These exploratory findings show promise for the assessment instrument to be used as a diagnostic tool for organizations to improve their IT-business alignment maturity levels.


title: Detection of weak bus through Fast Voltage Stability index and inherent structural characteristics of power system

abstract: Modern power system is large, complex and characterized by continuous increase in electrical load demand. As a result, the system is stressed to operate closer to stability limit and this may result in voltage collapse. Therefore, it becomes imperative to implement techniques for detecting collapse in bus bar or lines before its occurrence in the power system. This paper presents the concept of inherent structural characteristics of power networks and the power flow based technique of the Fast Voltage Stability index (FVSI). To detect the weakest line with respect to a bus using the conventional based approach of the Fast Voltage Stability Index (FVSI), a repetitive power flow solution is performed while varying the reactive power load at a particular load bus. The line or bus that has its FVSI closest or equal to 1 is identified as the weakest line or bus. The concept of inherent structural characteristics of the power network is formulated based on the fundamental circuit theory laws which employs the use of eigenvalue decomposition method in predicting the bus liable to instability. The two approaches are then compared. The result obtained shows that, weak bus is better detected through the concept of inherent structural characteristics of power network as it saves time and does not involve performing the time consuming traditional load flow before the weak bus is known.


title: Voltage dependent load models of charging electric vehicles

abstract: Plug-in Electric Vehicle (PEV) integration on distribution grids is an important topic of research due to the potential problem involved. In order to accurately assess the impact of EVs on system voltages and power flows, accurate voltage dependent models must be used. In this work, the voltage response of four different EV types is measured and ZIP models are developed for each which can be used in EV integration studies. The measurements show that the behavior varies significantly between the different EVs and thus an accurate generic model cannot be made.


title: Design limitations of 6-slot 2-pole high-speed permanent Magnet Synchronous machines with Tooth-Coil windings

abstract: Mechanical and electro-magnetic limitations of high-speed tooth-coil electrical machines having 6 slots and 2 poles are investigated. The paper analyses the performance of a designed high-speed Permanent Magnet Synchronous Generator with Tooth-Coil windings (TC-PMSM) with the topology mentioned. The generator nominal power is designed for 11 kW and nominal rotation speed is set to 31 200 rpm.


title: B-HoD: A lightweight and fast binary descriptor for 3D object recognition and registration

abstract: 3D object recognition and registration in computer vision applications has lately drawn much attention as it is capable of superior performance compared to its 2D counterpart. Although a number of high performing solutions do exist, it is still challenging to further reduce processing time and memory requirements to meet the needs of time critical applications. In this paper we propose an extension of the 3D descriptor Histogram of Distances (HoD) into the binary domain named the Binary-HoD (B-HoD). Our binary quantization procedure along with the proposed preprocessing step reduce an order of magnitude both processing time and memory requirements compared to current state of the art 3D descriptors. Evaluation on two popular low quality datasets shows its promising performance.


title: Assessing symptoms of excessive SNS usage based on user behavior and emotion

abstract: The worldwide use of social networking sites (SNSs) continues to dramatically increase. People are spending unexpected and unprecedented amounts of time online. However, many studies have issued warnings about the negative consequences of excessive SNS usage, including the risk of addictive behavior. This research is conducted to detect the symptoms of excessive SNS use by studying user behaviors and emotions in SNSs. We employed questionnaires, SNS APIs, and biological signals as methods. The data obtained from the study will characterize SNS usage to detect excessive use. Finally, the analytic results will be applied for developing prevention strategies to increase the awareness of the risks of excessive SNS usage.


title: Brain-Machine Interfaces beyond Neuroprosthetics

abstract: The field of invasive brain-machine interfaces (BMIs) is typically associated with neuroprosthetic applications aiming to recover loss of motor function. However, BMIs also represent a powerful tool to address fundamental questions in neuroscience. The observed subjects of BMI experiments can also be considered as indirect observers of their own neurophysiological activity, and the relationship between observed neurons and (artificial) behavior can be genuinely causal rather than indirectly correlative. These two characteristics defy the classical object-observer duality, making BMIs particularly appealing for investigating how information is encoded and decoded by neural circuits in real time, how this coding changes with physiological learning and plasticity, and how it is altered in pathological conditions. Within neuroengineering, BMI is like a tree that opens its branches into many traditional engineering fields, but also extends deep roots into basic neuroscience beyond neuroprosthetics.


title: ZigBee-GSM based Automatic Meter Reading system

abstract: Tenaga Nasional Berhad (TNB) as the power utility company have to deliver good services to their customers. Metering method is one of the problems that TNB have to overcome. Automatic Meter Reading (AMR) technology has proved effective in reading meter readings with TNB's large customers (LPC, Large Power Customers). Global System for Mobile communication-based (GSM) AMR technology is used. Here GSM module is attached to each LPC's meter, and data reading can be done automatically without visiting the customer's sites. The same way can be done with their domestic customers (OPC, Ordinary Power Customers), but the cost would be quite high since the numbers of OPC quite big. In this paper we described the AMR technology with least cost. The system is a combination of ZigBee and GSM technology. In this propose system, ZigBee module will be attached to the meter by using interface board and the data collector will be connected to the central computer by using GSM. The system is suitable with Malaysian environment which was already implemented GSM-based AMR in LPC. With this system TNB can save cost in doing meter reading and provide better services to their customers.


title: Characterization and evolution of dermal filaments from patients with Morgellons disease

abstract: Morgellons disease is an emerging skin disease characterized by formation of dermal filaments associated with multisystemic symptoms and tick-borne illness. Some clinicians hypothesize that these often colorful dermal filaments are textile fibers, either self-implanted by patients or accidentally adhering to lesions, and conclude that patients with this disease have delusions of infestation. We present histological observations and electron microscopic imaging from representative Morgellons disease samples revealing that dermal filaments in these cases are keratin and collagen in composition and result from proliferation and activation of keratinocytes and fibroblasts in the epidermis. Spirochetes were detected in the dermatological specimens from our study patients, providing evidence that Morgellons disease is associated with an infectious process.


Introduction to This Special Issue on the Lived Experience of Personal Informatics


title: An Integration-Oriented Ontology to Govern Evolution in Big Data Ecosystems

abstract: Big Data architectures allow to flexibly store and process heterogeneous data, from multiple sources, in its original format. The structure of those data, commonly supplied by means of REST APIs, is continuously evolving, forcing data analysts using it need to adapt their analytical processes after each release. This gets more challenging when aiming to perform an integrated or historical analysis of multiple sources. To cope with such complexity, in this paper we present the Big Data Integration ontology, the core construct for a data governance protocol that systematically annotates and integrates data from multiple sources in its original format. To cope with syntactic evolution in the sources, we present an algorithm that semi-automatically adapts the ontology upon new releases. A functional evaluation on real world APIs is performed in order to validate our approach.


title: Randomization offers new benefits for large-scale linear algebra computations . BY

abstract: Randomization offers new benefits for large-scale linear algebra computations.


title: Game Playing: The Next Moves

abstract: Computer programs now play many board games as well or better than the most expert humans. Human players, however, learn, plan, allocate resources, and integrate multiple streams of knowledge. This paper highlights recent achievements in game playing, describes some cognitively-oriented work, and poses three related challenge problems for the AI community.


title: Moving beyond Stereotypes of Empathy

abstract: People tend to think that happiness is good and stress is bad, but neither of these propositions is always true. Bloom [1] counters another emotional stereotype: that empathy is a universally reliable moral compass that inspires kind and healthy action. In an earlier piece, he highlighted this point through the case of Baby Jessica, a child stranded in a well who drew immense empathy, and a disproportionate outpouring of aid, while others suffered in silence. Here, he lists other compelling examples of empathy's limits.


title: Open Information Extraction via Contextual Sentence Decomposition

abstract: We show how contextual sentence decomposition (CSD), a technique originally developed for high-precision semantic search, can be used for open information extraction (OIE). Intuitively, CSD decomposes a sentence into the parts that semantically "belong together". By identifying the (implicit or explicit) verb in each such part, we obtain facts like in OIE. We compare our system, called CSD-IE, to three state-of-the-art OIE systems: ReVerb, OLLIE, and ClausIE. We consider the following aspects: accuracy (does the extracted triple express a meaningful fact, which is also expressed in the original sentence), minimality (can the extracted triple be further decomposed into smaller meaningful triples), coverage (percentage of text contained in at least one extracted triple), and number of facts extracted. We show how CSD-IE clearly outperforms ReVerb and OLLIE in terms of coverage and recall, but at comparable accuracy and minimality, and how CSD-IE achieves precision and recall comparable to ClausIE, but at significantly better minimality.


title: Bug or Not? Bug Report Classification Using N-Gram IDF

abstract: Previous studies have found that a significant number of bug reports are misclassified between bugs and nonbugs, and that manually classifying bug reports is a timeconsuming task. To address this problem, we propose a bug reports classification model with N-gram IDF, a theoretical extension of Inverse Document Frequency (IDF) for handling words and phrases of any length. N-gram IDF enables us to extract key terms of any length from texts, these key terms can be used as the features to classify bug reports. We build classification models with logistic regression and random forest using features from N-gram IDF and topic modeling, which is widely used in various software engineering tasks.With a publicly available dataset, our results show that our N-gram IDF-based models have a superior performance than the topic-based models on all of the evaluated cases. Our models show promising results and have a potential to be extended to other software engineering tasks.


title: First steps towards handheld augmented reality

abstract: In this paper we describe the first stand-alone AugmentedReality (AR) system with self-tracking running on anunmodified personal digital assistant (PDA) with acommercial camera. The project exploits the readyavailability of consumer devices with a minimal need forinfrastructure. The application provides the user with athree-dimensional augmented view of the environment.Our system achieves good overlay registration accuracyby using a popular marker-based tracking toolkit(ARToolKit), which runs directly on the PDA. Weintroduce an optional client/server architecture that isbased on wireless networking and is able to dynamicallyand transparently offload the tracking task in order toprovide better performance in select areas. Thehardware/software framework is modular and can beeasily combined with many elements of an existing ARframework. As a demonstration of the effectiveness, wepresent a 3D navigation application that guides a userthrough an unknown building to a chosen location.


title: Quantifying the Challenges in Parsing Patent Claims

abstract: the 1st International Workshop on Advances in Patent Information Retrieval (AsPIRe-2010), 28 maart 2010


title: All-at-once Optimization for Coupled Matrix and Tensor Factorizations

abstract: Joint analysis of data from multiple sources has the potential to improve our understanding of the underlying structures in complex data sets. For instance, in restaurant recommendation systems, recommendations can be based on rating histories of customers. In addition to rating histories, customers' social networks (e.g., Facebook friendships) and restaurant categories information (e.g., Thai or Italian) can also be used to make better recommendations. The task of fusing data, however, is challenging since data sets can be incomplete and heterogeneous, i.e., data consist of both matrices, e.g., the person by person social network matrix or the restaurant by category matrix, and higher-order tensors, e.g., the "ratings" tensor of the form restaurant by meal by person. ::: In this paper, we are particularly interested in fusing data sets with the goal of capturing their underlying latent structures. We formulate this problem as a coupled matrix and tensor factorization (CMTF) problem where heterogeneous data sets are modeled by fitting outer-product models to higher-order tensors and matrices in a coupled manner. Unlike traditional approaches solving this problem using alternating algorithms, we propose an all-at-once optimization approach called CMTF-OPT (CMTF-OPTimization), which is a gradient-based optimization approach for joint analysis of matrices and higher-order tensors. We also extend the algorithm to handle coupled incomplete data sets. Using numerical experiments, we demonstrate that the proposed all-at-once approach is more accurate than the alternating least squares approach.


title: An Empirical Test of a Clinical Metacognitive Model of Rumination and Depression

abstract: Rumination has attracted increasing theoretical and empirical interest in the past 15 years. Previous research has demonstrated significant relationships between rumination, depression, and metacognition. Two studies were conducted to further investigate these relationships and test the fit of a clinical metacognitive model of rumination and depression in samples of both depressed and nondepressed participants. In these studies, we collected cross-sectional data of rumination, depression, and metacognition. The relationships among variables were examined by testing the fit of structural equation models. In the study on depressed participants, a good model fit was obtained consistent with predictions. There were similarities and differences between the depressed and nondepressed samples in terms of relationships among metacognition, rumination, and depression. In each case, theoretically consistent paths between positive metacognitive beliefs, rumination, negative metacognitive beliefs, and depression were evident. The conceptual and clinical implications of these data are discussed.


title: Preference Semantics For Message Understanding

abstract: The design of effective natural language processing systems requires a combination of the theoretical and the practical. We want to have a theoretically well-founded design so that we can take advantage of gradual improvements in our knowledge of syntax, semantics, discourse structures, and the subject domain. At the same time we need to adopt a practical approach which recognizes the inevitable shortcomings of our knowledge in these areas. We need to create robust systems which are able to deal appropriately with these shortcomings. We are interested in particular in systems for extracting specified information from a text. Such systems are robust if they are able to extract at least partial information despite the presence of ill-formed or unexpected syntactic, semantic, or discourse structures.


title: Brain-Computer Interfacing and Games

abstract: Recently research into Brain-Computer Interfacing (BCI) applications for healthy users, such as games, has been initiated. But why would a healthy person use a still-unproven technology such as BCI for game interaction? BCI provides a combination of information and features that no other input modality can offer. But for general acceptance of this technology, usability and user experience will need to be taken into account when designing such systems. Therefore, this chapter gives an overview of the state of the art of BCI in games and discusses the consequences of applying knowledge from Human-Computer Interaction (HCI) to the design of BCI for games. The integration of HCI with BCI is illustrated by research examples and showcases, intended to take this promising technology out of the lab. Future research needs to move beyond feasibility tests, to prove that BCI is also applicable in realistic, real-world settings.


title: Stable biped locomotion using improved Proportional Derivative Controller

abstract: In this paper, we propose a modified version of the standard proportional-derivative (PD) controller for biped locomotion. Our improvements stabilize the biped for high gain PD controllers. The main idea of our approach involves applying corrective component to the existing framework, so that it prevents overshooting at high gains to stabilize the biped. We use pose control graphs to represent various gaits for the biped. We demonstrate with our improvements that the biped controller is stable while walking on irregular terrains. We also demonstrate that our formulation provides additional stability to the biped under minor impediments while in motion.


title: Automatic fruit and vegetable classification from images

abstract: Contemporary Vision and Pattern Recognition problems such as face recognition, fingerprinting identification, image categorization, and DNA sequencing often have an arbitrarily large number of classes and properties to consider. To deal with such complex problems using just one feature descriptor is a difficult task and feature fusion may become mandatory. Although normal feature fusion is quite effective for some problems, it can yield unexpected classification results when the different features are not properly normalized and preprocessed. Besides it has the drawback of increasing the dimensionality which might require more training data. To cope with these problems, this paper introduces a unified approach that can combine many features and classifiers that requires less training and is more adequate to some problems than a naive method, where all features are simply concatenated and fed independently to each classification algorithm. Besides that, the presented technique is amenable to continuous learning, both when refining a learned model and also when adding new classes to be discriminated. The introduced fusion approach is validated using a multi-class fruit-and-vegetable categorization task in a semi-controlled environment, such as a distribution center or the supermarket cashier. The results show that the solution is able to reduce the classification error in up to 15 percentage points with respect to the baseline.


title: Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds

abstract: In recent years, unfolding iterative algorithms as neural networks has become an empirical success in solving sparse recovery problems. However, its theoretical understanding is still immature, which prevents us from fully utilizing the power of neural networks. In this work, we study unfolded ISTA (Iterative Shrinkage Thresholding Algorithm) for sparse signal recovery. We introduce a weight structure that is necessary for asymptotic convergence to the true sparse signal. With this structure, unfolded ISTA can attain a linear convergence, which is better than the sublinear convergence of ISTA/FISTA in general cases. Furthermore, we propose to incorporate thresholding in the network to perform support selection, which is easy to implement and able to boost the convergence rate both theoretically and empirically. Extensive simulations, including sparse vector recovery and a compressive sensing experiment on real image data, corroborate our theoretical results and demonstrate their practical usefulness. We have made our codes publicly available: this https URL.


Checkers Reinforcement learning project: AI Checkers


The Problem Is Epistemology, Not Statistics: Replace Significance Tests by Confidence Intervals and Quantify Accuracy of Risky Numerical Predictions


title: Noncontact Monitoring of Blood Oxygen Saturation Using Camera and Dual-Wavelength Imaging System

abstract: We present a noncontact method to monitor blood oxygen saturation (SpO2). The method uses a CMOS camera with a trigger control to allow recording of photoplethysmography (PPG) signals alternatively at two particular wavelengths, and determines the SpO2 from the measured ratios of the pulsatile to the nonpulsatile components of the PPG signals at these wavelengths. The signal-to-noise ratio (SNR) of the SpO2 value depends on the choice of the wavelengths. We found that the combination of orange $(\lambda = 611\, {\rm nm})$ and near infrared $(\lambda = 880\, {\rm nm})$ provides the best SNR for the noncontact video-based detection method. This combination is different from that used in traditional contact-based SpO 2 measurement since the PPG signal strengths and camera quantum efficiencies at these wavelengths are more amenable to SpO2 measurement using a noncontact method. We also conducted a small pilot study to validate the noncontact method over an SpO2 range of 83%–98%. This study results are consistent with those measured using a reference contact SpO2 device ( $r = 0.936$ , $p ). The presented method is particularly suitable for tracking one's health and wellness at home under free-living conditions, and for those who cannot use traditional contact-based PPG devices.


title: TrustOTP: Transforming Smartphones into Secure One-Time Password Tokens

abstract: Two-factor authentication has been widely used due to the vulnerabilities associated with traditional text-based password. One-time password (OTP) plays an indispensable role on authenticating mobile users to critical web services that demand a high level of security. As the smartphones are increasingly gaining popularity nowadays, software-based OTP generators have been developed and installed into smartphones as software apps, which bring great convenience to the users without introducing extra burden. However, software-based OTP solutions cannot guarantee the confidentiality of the generated passwords or even the seeds when the mobile OS is compromised. Moreover, they also suffer from denial-of-service attacks when the mobile OS crashes. Hardware-based OTP tokens can solve these security problems in the software-based OTP solutions; however, it is inconvenient for the users to carry physical tokens with them, particularly, when there are more than one token to be carried. In this paper, we present TrustOTP, a secure one-time password solution that can achieve both the flexibility of software tokens and the security of hardware tokens by using ARM TrustZone technology. TrustOTP can not only protect the confidentiality of the OTPs against a malicious mobile OS, but also guarantee reliable OTP generation and trusted OTP display when the mobile OS is compromised or even crashes. It is flexible to integrate multiple OTP algorithms and instances for different application scenarios on the same smartphone platform without modifying the mobile OS. We develop a prototype of TrustOTP on Freescale i.MX53 QSB. The experimental results show that TrustOTP has small impacts on the mobile OS and its power consumption is low.


title: Peer Feedback on Second Language Writing through Blogs: The Case of a Vietnamese EFL Classroom

abstract: This study explores how Vietnamese EFL students view blogs as tools with which to practise writing, examines whether student comments assist in peer revision, and evaluates whether peer comments result in substantive revisions of written drafts. Participants in this study included 11 students in an English-as-a-Foreign-Language EFL classroom in Vietnam. Data collected included students' first and final drafts for two writing topics, comments posted online, and student responses to a questionnaire. Student responses to the questionnaire were analysed and their comments were coded as revision-oriented or non-revision-oriented Liu & Sadler, 2003. This study's findings indicate that most students expressed positive attitudes toward using blogs to practise second language L2 writing and that most students made revision-oriented comments on their peers' drafts. There is also evidence that students used their peers' comments to revise their own final drafts. The implications of this study for language teaching practice are discussed.


title: Legolization: optimizing LEGO designs

abstract: Building LEGO sculptures requires accounting for the target object's shape, colors, and stability. In particular, finding a good layout of LEGO bricks that prevents the sculpture from collapsing (due to its own weight) is usually challenging, and it becomes increasingly difficult as the target object becomes larger or more complex. We devise a force-based analysis for estimating physical stability of a given sculpture. Unlike previous techniques for Legolization, which typically use heuristic-based metrics for stability estimation, our force-based metric gives 1) an ordering in the strength so that we know which structure is more stable, and 2) a threshold for stability so that we know which one is stable enough. In addition, our stability analysis tells us the weak portion of the sculpture. Building atop our stability analysis, we present a layout refinement algorithm that iteratively improves the structure around the weak portion, allowing for automatic generation of a LEGO brick layout from a given 3D model, accounting for color information, required workload (in terms of the number of bricks) and physical stability. We demonstrate the success of our method with real LEGO sculptures built up from a wide variety of 3D models, and compare against previous methods.


title: Behavioural and neural characterization of optimistic reinforcement learning

abstract: When forming and updating beliefs about future life outcomes, people tend to consider good news and to disregard bad news. This tendency is assumed to support the optimism bias. Whether this learning bias is specific to ‘high-level’ abstract belief update or a particular expression of a more general ‘low-level’ reinforcement learning process is unknown. Here we report evidence in favour of the second hypothesis. In a simple instrumental learning task, participants incorporated better-than-expected outcomes at a higher rate than worse-than-expected ones. In addition, functional imaging indicated that inter-individual difference in the expression of optimistic update corresponds to enhanced prediction error signalling in the reward circuitry. Our results constitute a step towards the understanding of the genesis of optimism bias at the neurocomputational level. Lefebvre et al. present behavioural and neural evidence showing that the ‘optimism bias’ is a manifestation of a general cognitive tendency for preferential learning from positive, compared with negative, outcomes.


title: TCP Offload Is a Dumb Idea Whose Time Has Come

abstract: Network interface implementors have repeatedly attempted to offload TCP processing from the host CPU. These efforts met with little success, because they were based on faulty premises. TCP offload per se is neither of much overall benefit nor free from significant costs and risks. But TCP offload in the service of very specific goals might actually be useful. In the context of the replacement of storage-specific interconnect via commoditized network hardware, TCP offload (and more generally, offloading the transport protocol) appropriately solves an important problem.


title: Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks.

abstract: Several different factors contribute to injury severity in traffic accidents, such as driver characteristics, highway characteristics, vehicle characteristics, accidents characteristics, and atmospheric factors. This paper shows the possibility of using Bayesian Networks (BNs) to classify traffic accidents according to their injury severity. BNs are capable of making predictions without the need for pre assumptions and are used to make graphic representations of complex systems with interrelated components. This paper presents an analysis of 1536 accidents on rural highways in Spain, where 18 variables representing the aforementioned contributing factors were used to build 3 different BNs that classified the severity of accidents into slightly injured and killed or severely injured. The variables that best identify the factors that are associated with a killed or seriously injured accident (accident type, driver age, lighting and number of injuries) were identified by inference.


title: Multibody Structure-from-Motion in Practice

abstract: Multibody structure from motion (SfM) is the extension of classical SfM to dynamic scenes with multiple rigidly moving objects. Recent research has unveiled some of the mathematical foundations of the problem, but a practical algorithm which can handle realistic sequences is still missing. In this paper, we discuss the requirements for such an algorithm, highlight theoretical issues and practical problems, and describe how a static structure-from-motion framework needs to be extended to handle real dynamic scenes. Theoretical issues include different situations in which the number of independently moving scene objects changes: Moving objects can enter or leave the field of view, merge into the static background (e.g., when a car is parked), or split off from the background and start moving independently. Practical issues arise due to small freely moving foreground objects with few and short feature tracks. We argue that all of these difficulties need to be handled online as structure-from-motion estimation progresses, and present an exemplary solution using the framework of probabilistic model-scoring.


title: Techniques for Learning Binary Stochastic Feedforward Neural Networks

abstract: Stochastic binary hidden units in a multi-layer perceptron (MLP) network give at least three potential benefits when compared to deterministic MLP networks. (1) They allow to learn one-to-many type of mappings. (2) They can be used in structured prediction problems, where modeling the internal structure of the output is important. (3) Stochasticity has been shown to be an excellent regularizer, which makes generalization performance potentially better in general. However, training stochastic networks is considerably more difficult. We study training using M samples of hidden activations per input. We show that the case M=1 leads to a fundamentally different behavior where the network tries to avoid stochasticity. We propose two new estimators for the training gradient and propose benchmark tests for comparing training algorithms. Our experiments confirm that training stochastic networks is difficult and show that the proposed two estimators perform favorably among all the five known estimators.


title: Ride On! Mobility Business Models for the Sharing Economy

abstract: The public perception of shared goods has changed substantially in the past few years. While co-owning properties has been widely accepted for a while (e.g., timeshares), the notion of sharing bikes, cars, or even rides on an on-demand basis is just now starting to gain widespread popularity. The emerging “sharing economy” is particularly interesting in the context of cities that struggle with population growth and increasing density. While sharing vehicles promises to reduce inner-city traffic, congestion, and pollution problems, the associated business models are not without problems themselves. Using agency theory, in this article we discuss existing shared mobility business models in an effort to unveil the optimal relationship between service providers (agents) and the local governments (principals) to achieve the common objective of sustainable mobility. Our findings show private or public models are fraught with conflicts, and point to a merit model as the most promising alignment of the strengths of agents and principals.


title: A framework for post-event timeline reconstruction using neural networks

abstract: Post-event timeline reconstruction plays a critical role in forensic investigation and serves as a means of identifying evidence of the digital crime. We present an artificial neural networks based approach for post-event timeline reconstruction using the file system activities. A variety of digital forensic tools have been developed during the past two decades to assist computer forensic investigators undertaking digital timeline analysis, but most of the tools cannot handle large volumes of data efficiently. This paper looks at the effectiveness of employing neural network methodology for computer forensic analysis by preparing a timeline of relevant events occurring on a computing machine by tracing the previous file system activities. Our approach consists of monitoring the file system manipulations, capturing file system snapshots at discrete intervals of time to characterise the use of different software applications, and then using this captured data to train a neural network to recognise execution patterns of the application programs. The trained version of the network may then be used to generate a post-event timeline of a seized hard disk to verify the execution of different applications at different time intervals to assist in the identification of available evidence.


title: User Perception of Adapting Video Quality

abstract: In general, video quality adaptation and video quality evaluation are distinct activities. Most adaptive delivery mechanisms for streaming multimedia content do not explicitly consider user-perceived quality when making adaptation decisions. Equally, video quality evaluation techniques are not designed to evaluate instantaneous quality where the quality is changing over time. We propose that an Optimal Adaptation Trajectory (OAT) through the set of possible encoding exists, and that it indicates how to adapt encoding quality in response to changes in network conditions in order to maximize user-perceived quality. The subjective and objective tests carried out to find such trajectories for a number of different MPEG-4 video clips are described. Experimental subjective testing results are presented that demonstrate the dynamic nature of user perception with adapting multimedia. The results demonstrate that adaptation using the OAT out-performs conventional adaptation strategies in which only a single aspect of the video quality is adapted. In contrast, the OAT provides a mechanism to adapt multiple aspects of the video quality thereby giving better user-perceived quality in both the short and long term.


title: Patient turnover and the relationship between nurse staffing and patient outcomes.

abstract: High patient turnover (patient throughput generated by admissions, discharges, and transfers) contributes to increased demands and resources for care. We examined how the relationship between registered nurse (RN) staffing and failure-to-rescue (FTR) varied with patient turnover levels by analyzing quarterly data from the University HealthSystem Consortium. The data included 42 hospitals, representing 759 nursing units and about 1 million inpatients. Higher RN staffing was associated with lower FTR. When patient turnover increased from 48.6% to 60.7% on nonintensive units (non-ICUs), the beneficial effect of non-ICU RN staffing on FTR was reduced by 11.5%. RN staffing should be adjusted according to patient turnover because turnover increases patient care demand beyond that presented by patient count, and outcomes may be adversely affected.


title: Dynamic Pricing in Spatial Crowdsourcing: A Matching-Based Approach

abstract: In spatial crowdsourcing, requesters submit their task-related locations and increase the demand of a local area. The platform prices these tasks and assigns spatial workers to serve if the prices are accepted by requesters. There exist mature pricing strategies which specialize in tackling the imbalance between supply and demand in a local market. However, in global optimization, the platform should consider the mobility of workers; that is, any single worker can be the potential supply for several areas, while it can only be the true supply of one area when assigned by the platform. The hardness lies in the uncertainty of the true supply of each area, hence the existing pricing strategies do not work. In the paper, we formally define this G lobal D ynamic P ricing(GDP) problem in spatial crowdsourcing. And since the objective is concerned with how the platform matches the supply to areas, we let the matching algorithm guide us how to price. We propose a MA tching-based P ricing S trategy (MAPS) with guaranteed bound. Extensive experiments conducted on the synthetic and real datasets demonstrate the effectiveness of MAPS.


title: Design and Implementation of a Prototype Smart PARKing (SPARK) System Using Wireless Sensor Networks

abstract: With the rapid proliferation of vehicle availability and usage in recent years, finding a vacant car parking space is becoming more and more difficult, resulting in a number of practical conflicts. Parking problems are becoming ubiquitous and ever growing at an alarming rate in every major city. Lot of research and development is being done all over the world to implement better and smarter parking management mechanisms. Widespread use of wireless technologies paired with the recent advances in wireless applications for parking, manifests that digital data dissemination could be the key to solve emerging parking problems. Wireless Sensor Network (WSN) technology has attracted increased attention and is rapidly emerging due to their enormous application potential in diverse fields. This buoyant field is expected to provide an efficient and cost-effective solution to the effluent car parking problems. This paper proposes a Smart Parking (SPARK) Management System based on wireless sensor network technology which provides advanced features like remote parking monitoring, automated guidance, and parking reservation mechanism. The paper describes the overall system architecture of SPARK from hardware to software implementation in the view point of sensor networks. We implemented a full-fledged prototype system for parking management to realize the design functionalities and features mentioned. Our preliminary test results show that the performance of this WSN based system can effectively satisfy the needs and requirements of existing parking hassles thereby minimizing the time consumed to find vacant parking lot, real-time information rendering, and smart reservation mechanisms.


title: A Region-Based Algorithm for Discovering Petri Nets from Event Logs

abstract: The paper presents a new method for the synthesis of Petri nets from event logs in the area of Process Mining. The method derives a bounded Petri net that over-approximates the behavior of an event log. The most important property is that it produces a net with the smallest behavior that still contains the behavior of the event log. The methods described in this paper have been implemented in a tool and tested on a set of examples.


title: Design considerations of coplanar-to-coaxial transitions for wideband HTS delay lines

abstract: Transitions from narrow coplanar waveguide to three types of wider transmission lines have been designed for 2-18 GHz, to facilitate the connection with coaxial connectors. They are conventional coplanar waveguide (CPW) taper transition, conductor-backed coplanar waveguide (CPWG)-to-microstrip transition and CPWG-to-CPW transition. The match between the coplanar and the coaxial is optimized. The interfacial configurations between coplanar line and coaxial connector are modeled using 3D full-wave simulations. The CPW taper transition and the CPWG-to-CPW transition are implemented in the 25 ns CPW and CPWG superconducting delay lines. The reflection coefficients of both transitions, connected to the coaxial connectors, are less than 0.04, measured in time domain.


title: Network attached storage architecture

abstract: SAN with Fibre Channel network hardware that has a greater effect on a user’s purchasing decisions. This article is about how emerging technology may blur the network-centric distinction between NAS and SAN. For example, the decreasing specialization of SAN protocols promises SAN-like devices on Ethernet network hardware. Alternatively, the increasing specialization of NAS systems may embed much of the file system into storage devices. For users, it is increasingly worthwhile to investigate networked storage core and emerging technologies. Today, bits stored online on magnetic disks are so inexpensive that users are finding new, previously unaffordable, uses for storage. At Dataquest’s Storage2000 conference last June in Orlando, Fla., IBM reported that online disk storage is now significantly cheaper than paper or film, the dominant traditional information storage media. Not surprisingly, users are adding storage capacity at about 100% per year. Moreover, the rapid growth of e-commerce, with its huge global customer base and easy-to-use, online transactions, has introduced new market requirements, including bursty, unpredictable spurts in capacity, that demand vendors minimize the time from a user’s order to installation of new storage. In our increasingly Internet-dependent business and computing environment, network storage is the computer. NETWORK ATTACHED STORAGE ARCHITECTURE


title: Cinema 3D: large scale automultiscopic display

abstract: While 3D movies are gaining popularity, viewers in a 3D cinema still need to wear cumbersome glasses in order to enjoy them. Automultiscopic displays provide a better alternative to the display of 3D content, as they present multiple angular images of the same scene without the need for special eyewear. However, automultiscopic displays cannot be directly implemented in a wide cinema setting due to variants of two main problems: (i) The range of angles at which the screen is observed in a large cinema is usually very wide, and there is an unavoidable tradeoff between the range of angular images supported by the display and its spatial or angular resolutions. (ii) Parallax is usually observed only when a viewer is positioned at a limited range of distances from the screen. This work proposes a new display concept, which supports automultiscopic content in a wide cinema setting. It builds on the typical structure of cinemas, such as the fixed seat positions and the fact that different rows are located on a slope at different heights. Rather than attempting to display many angular images spanning the full range of viewing angles in a wide cinema, our design only displays the narrow angular range observed within the limited width of a single seat. The same narrow range content is then replicated to all rows and seats in the cinema. To achieve this, it uses an optical construction based on two sets of parallax barriers, or lenslets, placed in front of a standard screen. This paper derives the geometry of such a display, analyzes its limitations, and demonstrates a proof-of-concept prototype.


title: Infinite resolution textures

abstract: We propose a new texture sampling approach that preserves crisp silhouette edges when magnifying during close-up viewing, and benefits from image pre-filtering when minifying for viewing at farther distances. During a pre-processing step, we extract curved silhouette edges from the underlying images. These edges are used to adjust the texture coordinates of the requested samples during magnification. The original image is then sampled --- only once! --- with the modified coordinates. The new technique provides a resolution-independent image representation capable of billions of texels per second on a mid-range graphics card.


title: Flourishing Across Europe: Application of a New Conceptual Framework for Defining Well-Being

abstract: Governments around the world are recognising the importance of measuring subjective well-being as an indicator of progress. But how should well-being be measured? A conceptual framework is offered which equates high well-being with positive mental health. Well-being is seen as lying at the opposite end of a spectrum to the common mental disorders (depression, anxiety). By examining internationally agreed criteria for depression and anxiety (DSM and ICD classifications), and defining the opposite of each symptom, we identify ten features of positive well-being. These combine feeling and functioning, i.e. hedonic and eudaimonic aspects of well-being: competence, emotional stability, engagement, meaning, optimism, positive emotion, positive relationships, resilience, self esteem, and vitality. An operational definition of flourishing is developed, based on psychometric analysis of indicators of these ten features, using data from a representative sample of 43,000 Europeans. Application of this definition to respondents from the 23 countries which participated in the European Social Survey (Round 3) reveals a four-fold difference in flourishing rate, from 41% in Denmark to less than 10% in Slovakia, Russia and Portugal. There are also striking differences in country profiles across the 10 features. These profiles offer fresh insight into cultural differences in well-being, and indicate which features may provide the most promising targets for policies to improve well-being. Comparison with a life satisfaction measure shows that valuable information would be lost if well-being was measured by life satisfaction. Taken together, our findings reinforce the need to measure subjective well-being as a multi-dimensional construct in future surveys.


title: The Business Case for Corporate Sustainability: Literature Review and Research Options

abstract: In the last ten years, the notion of a 'business case' for corporate sustainability has increasingly been used by the corporate sector, environmental organizations, consultancies and so on, to seek justification for sustainability strategies within organizations. In this paper, we aim to systemize and assess existing research and tools related to this increasingly popular concept. We present a review of (1) theoretical frameworks, (2) instrumental studies aiming to either prove or disprove a hypothesized causal sequence between corporate social or environmental performance and financial performance, (3) descriptive studies examining manager's actual perceptions and practices, and finally (4) tools. We identify a clearly insufficient understanding of manager's key arguments or business logic for adopting corporate sustainability strategies (how 'business cases' are built, how effective they are and what barriers they face). We attribute this primarily to lack of descriptive research in these areas.


title: SNE: Signed Network Embedding

abstract: Several network embedding models have been developed for unsigned networks. However, these models based on skip-gram cannot be applied to signed networks because they can only deal with one type of link. In this paper, we present our signed network embedding model called SNE. Our SNE adopts the log-bilinear model, uses node representations of all nodes along a given path, and further incorporates two signed-type vectors to capture the positive or negative relationship of each edge along the path. We conduct two experiments, node classification and link prediction, on both directed and undirected signed networks and compare with four baselines including a matrix factorization method and three state-of-the-art unsigned network embedding models. The experimental results demonstrate the effectiveness of our signed network embedding.


The SIMON and SPECK Families of Lightweight Block Ciphers.


title: Normal Image Manipulation for Bas-relief Generation with Hybrid Styles

abstract: We introduce a normal-based bas-relief generation and stylization method which is motivated by the recent advancement in this topic. Creating bas-relief from normal images has successfully facilitated bas-relief modeling in image space. However, the use of normal images in previous work is often restricted to certain type of operations only. This paper is intended to extend normal-based methods and construct bas-reliefs from normal images in a versatile way. Our method can not only generate a new normal image by combining various frequencies of existing normal images and details transferring, but also build bas-reliefs from a single RGB image and its edge-based sketch image. In addition, we introduce an auxiliary function to represent a smooth base surface and generate a layered global shape. To integrate above considerations into our framework, we formulate the bas- relief generation as a variational problem which can be solved by a screened Poisson equation. Some advantages of our method are that it expands the bas-relief shape space and generates diversified styles of results, and that it is capable of transferring details from one region to other regions. Our method is easy to implement, and produces good-quality bas-relief models. We experiment our method on a range of normal images and it compares favorably to other popular classic and state-of-the-art methods.


title: Tamper detection and recovery of medical image watermarking using modified LSB and Huffman compression

abstract: Medical image is an object which can help doctors to diagnose the patient. However, due to the rapid development of technology nowadays, mostly there are several parties that did some manipulation towards the medical image by using this technology. It can be harm for the patient. One of the concrete solutions to protect the authenticity of an image from the manipulation is by applying a watermarking. By inserting the watermark, the process to detecting the location of a manipulated medical image can be done by this system. Hence, the manipulated medical image can also be recovered until it similar with the original image by inserting a feature extraction such as the average intensity of the image blocks. This paper will discuss a scheme of reversible watermark using a modified LSB and Huffman compression to detecting and recovering the manipulated medical image. The testing results shows that this system is capable to detect the attack with an accuracy of up to 100% and can do the recovery with an accuracy of recovery rate up to 98% for some attacks.


title: Learning empathy: a data-driven framework for modeling empathetic companion agents

abstract: Affective reasoning plays an increasingly important role in cognitive accounts of social interaction. Humans continuously assess one another's situational context, modify their own affective state accordingly, and then respond to these outcomes by expressing empathetic behaviors. Synthetic agents serving as companions should respond similarly. However, empathetic reasoning is riddled with the complexities stemming from the myriad factors bearing upon situational assessment. A key challenge posed by affective reasoning in synthetic agents is devising empirically informed models of empathy that accurately respond in social situations. This paper presents Care, a data-driven affective architecture and methodology for learning models of empathy by observing human-human social interactions. First, in CARE training sessions, one trainer directs synthetic agents to perform a sequence of tasks while another trainer manipulates companion agents' affective states to produce empathetic behaviors (spoken language, gesture, posture). Care tracks situational data including locational, intentional, and temporal information to induce a model of empathy. At runtime, CARE uses the model of empathy to drive situation-appropriate empathetic behaviors. CARE has been used in a virtual environment testbed, and an evaluation suggests that the CARE paradigm can provide the basis for effective empathetic behavior control for companion agents.


title: Facial Landmark Localization - A Literature Survey

abstract: Automatic facial point detection plays arguably the most important role as an intermediary step for many subsequent face processing operations that ranges from biometric recognition to the understanding of mental states. Distinguishable characteristic of points on face is known as facial landmarks eg. eye corner, mouth corner etc. Though it is conceptually very simple to locate landmarks, the computer vision problem has proven extremely challenging due to compound factors such as scale, pose, expression, occlusion, illumination and inherent face variability. The purpose of this survey is to give an overview of facial landmarks localization techniques and their progress over last 7-8 years.


title: Hardware-assisted rootkits: abusing performance counters on the ARM and x86 architectures

abstract: In this paper, a novel hardware-assisted rootkit is introduced, which leverages the performance monitoring unit (PMU) of a CPU. By configuring hardware performance counters to count specific architectural events, this research effort proves it is possible to transparently trap system calls and other interrupts driven entirely by the PMU. This offers an attacker the opportunity to redirect control flow to malicious code without requiring modifications to a kernel image. ::: ::: The approach is demonstrated as a kernel-mode rootkit on both the ARM and Intel x86-64 architectures that is capable of intercepting system calls while evading current kernel patch protection implementations such as PatchGuard. A proof-of-concept Android rootkit is developed targeting ARM (Krait) chipsets found in millions of smartphones worldwide, and a similar Windows rootkit is developed for the Intel x86-64 architecture. The prototype PMU-assisted rootkit adds minimal overhead to Android, and less than 10% overhead to Windows OS. Further analysis into performance counters also reveals that the PMU can be used to trap returns from secure world on ARM as well as returns from System Management Mode on x86-64.


title: Automated trading with performance weighted random forests and seasonality

abstract: Seasonality effects and empirical regularities in financial data have been well documented in the financial economics literature for over seven decades. This paper proposes an expert system that uses novel machine learning techniques to predict the price return over these seasonal events, and then uses these predictions to develop a profitable trading strategy. While simple approaches to trading these regularities can prove profitable, such trading leads to potential large drawdowns (peak-to-trough decline of an investment measured as a percentage between the peak and the trough) in profit. In this paper, we introduce an automated trading system based on performance weighted ensembles of random forests that improves the profitability and stability of trading seasonality events. An analysis of various regression techniques is performed as well as an exploration of the merits of various techniques for expert weighting. The performance of the models is analysed using a large sample of stocks from the DAX. The results show that recency-weighted ensembles of random forests produce superior results in terms of both profitability and prediction accuracy compared with other ensemble techniques. It is also found that using seasonality effects produces superior results than not having them modelled explicitly.


title: Crowding is unlike ordinary masking: Distinguishing feature integration from detection

abstract: A letter in the peripheral visual field is much harder to identify in the presence of nearby letters. This is “crowding.” Both crowding and ordinary masking are special cases of “masking,” which, in general, refers to any effect of a “mask” pattern on the discriminability of a signal. Here we characterize crowding, and propose a diagnostic test to distinguish it from ordinary masking. In ordinary masking, the signal disappears. In crowding, it remains visible, but is ambiguous, jumbled with its neighbors. Masks are usually effective only if they overlap the signal, but the crowding effect extends over a large region. The width of that region is proportional to signal eccentricity from the fovea and independent of signal size, mask size, mask contrast, signal and mask font, and number of masks. At 4 deg eccentricity, the threshold contrast for identification of a 0.32 deg signal letter is elevated (up to six-fold) by mask letters anywhere in a 2.3 deg region, 7 times wider than the signal. In ordinary masking, threshold contrast rises as a power function of mask contrast, with a shallow log-log slope of 0.5 to 1, whereas, in crowding, threshold is a sigmoidal function of mask contrast, with a steep log-log slope of 2 at close spacing. Most remarkably, although the threshold elevation decreases exponentially with spacing, the threshold and saturation contrasts of crowding are independent of spacing. Finally, ordinary masking is similar for detection and identification, but crowding occurs only for identification, not detection. More precisely, crowding occurs only in tasks that cannot be done based on a single detection by coarsely coded feature detectors. These results (and observers’ introspections) suggest that ordinary masking blocks feature detection, so the signal disappears, while crowding (like “illusory conjunction”) is excessive feature integration — detected features are integrated over an inappropriately large area because there are no smaller integration fields — so the integrated signal is ambiguous, jumbled with the mask. In illusory conjunction, observers see an object that is not there made up of features that are. A survey of the illusory conjunction literature finds that most of the illusory conjunction results are consistent with the spatial crowding described here, which depends on spatial proximity, independent of time pressure. The rest seem to arise through a distinct phenomenon that one might call “temporal crowding,” which depends on time pressure (“overloading attention”), independent of spatial proximity.


title: Going Deeper into Action Recognition: A Survey

abstract: Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation. Over the last decade, human action analysis evolved from earlier schemes that are often limited to controlled environments to nowadays advanced solutions that can learn from millions of videos and apply to almost all daily activities. Given the broad range of applications from video surveillance to human-computer interaction, scientific milestones in action recognition are achieved more rapidly, eventually leading to the demise of what used to be good in a short time. This motivated us to provide a comprehensive review of the notable steps taken towards recognizing human actions. To this end, we start our discussion with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches. We aim to remain objective throughout this survey, touching upon encouraging improvements as well as inevitable fallbacks, in the hope of raising fresh questions and motivating new research directions for the reader.


title: AUTOMATIC DOMAIN ONTOLOGY EXTRACTION FOR CONTEXT -SENSITIVE OPINION MINING

abstract: Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform contextsensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline.


Small Satellite Cloud Detection Based On Deep Learning and Image Compression


title: A Cyber Science Based Ontology for Artificial General Intelligence Containment

abstract: The development of artificial general intelligence is considered by many to be inevitable. What such intelligence does after becoming aware is not so certain. To that end, research suggests that the likelihood of artificial general intelligence becoming hostile to humans is significant enough to warrant inquiry into methods to limit such potential. Thus, containment of artificial general intelligence is a timely and meaningful research topic. While there is limited research exploring possible containment strategies, such work is bounded by the underlying field the strategies draw upon. Accordingly, we set out to construct an ontology to describe necessary elements in any future containment technology. Using existing academic literature, we developed a single domain ontology containing five levels, 32 codes, and 32 associated descriptors. Further, we constructed ontology diagrams to demonstrate intended relationships. We then identified humans, AGI, and the cyber world as novel agent objects necessary for future containment activities. Collectively, the work addresses three critical gaps: (a) identifying and arranging fundamental constructs; (b) situating AGI containment within cyber science; and (c) developing scientific rigor within the field.


title: Situation Awareness in an Augmented Reality Cockpit: Design, Viewpoints and Cognitive Glue

abstract: The Taxiway Navigation and Situation Awareness (T-NASA) system is a prototype augmented reality commercial airline cockpit display suite developed to increase efficiency and enhance situation awareness (SA) during airport surface taxi operations. The T-NASA system consists of a head-up display (HUD) and an electronic moving map (EMM), which allow for the display of navigation information using augmented reality techniques. The T-NASA HUD, an egocentric augmented reality display, uses "scene-linked symbology" (Foyle et al., 1996), derived from a set of theoretically-derived and experimentally-validated principles to enhance local guidance and route awareness. The EMM, is an exocentric perspective display that enhances route and global awareness. The principles for enhancing situation awareness in augmented reality systems are reviewed.


title: Manipulation of Voting Schemes: A General Result

abstract: It has been conjectured that no system of voting can preclude strategic voting-the securing by a voter of an outcome he prefers through misrepresentation of his preferences. In this paper, for all significant systems of voting in which chance plays no role, the conjecture is verified. To prove the conjecture, a more general theorem in game theory is proved: a gameform is a game without utilities attached to outcomes; only a trivial game form, it is shown, can guarantee that whatever the utilities of the players may be, each player will have a dominant pure strategy. I SHALL PROVE in this paper that any non-dictatorial voting scheme with at least three possible outcomes is subject to individual manipulation. By a "voting scheme," I mean any scheme which makes a community's choice depend entirely on individuals' professed preferences among the alternatives. An individual "manipulates" the voting scheme if, by misrepresenting his preferences, he secures an outcome he prefers to the "honest" outcome-the choice the community would make if he expressed his true preferences. The result on voting schemes follows from a theorem I shall prove which covers schemes of a more general kind. Let a gameform be any scheme which makes an outcome depend on individual actions of some specified sort, which I shall call strategies. A voting scheme, then, is a game form in which a strategy is a profession of preferences, but many game forms are not voting schemes. Call a strategy dominant for someone if, whatever anyone else does, it achieves his goals at least as well as would any alternative strategy. Only trivial game forms, I shall show, ensure that each individual, no matter what his preferences are, will have available a dominant strategy. Hence in particular, no non-trivial voting scheme guarantees that honest expression of preferences is a dominant strategy. These results are spelled out and proved in Section 3. The theorems in this paper should come as no surprise. It is well-known that many voting schemes in common use are subject to individual manipulation. Consider a "rank-order" voting scheme: each voter reports his preferences among the alternatives by ranking them on a ballot; first place on a ballot gives an alternative four votes, second place three, third place two, and fourth place one. The alternative with the greatest total number of votes wins. Here is a case in which an individual can manipulate the scheme. There are three voters and four alternatives; voter a ranks the alternatives in order xyzw on his ballot; voter b in order wxyz; and voter c's true preference ordering is wxyz. If c votes honestly, then, the winner is his second choice, x, with ten points. If c pretends that x is his last choice by giving his preference ordering as wyzx, then x gets only eight points, and c's first choice, w, wins with nine points. Thus c does best to misrepresent his


title: Efficiently indexing shortest paths by exploiting symmetry in graphs

abstract: Shortest path queries (SPQ) are essential in many graph analysis and mining tasks. However, answering shortest path queries on-the-fly on large graphs is costly. To online answer shortest path queries, we may materialize and index shortest paths. However, a straightforward index of all shortest paths in a graph of N vertices takes O(N2) space. In this paper, we tackle the problem of indexing shortest paths and online answering shortest path queries. As many large real graphs are shown richly symmetric, the central idea of our approach is to use graph symmetry to reduce the index size while retaining the correctness and the efficiency of shortest path query answering. Technically, we develop a framework to index a large graph at the orbit level instead of the vertex level so that the number of breadth-first search trees materialized is reduced from O(N) to O(|Δ|), where |Δ| ≤ N is the number of orbits in the graph. We explore orbit adjacency and local symmetry to obtain compact breadth-first-search trees (compact BFS-trees). An extensive empirical study using both synthetic data and real data shows that compact BFS-trees can be built efficiently and the space cost can be reduced substantially. Moreover, online shortest path query answering can be achieved using compact BFS-trees.


title: GENETIC CONTENT AND BUOYANT DENSITY OF RABBIT SPERMATOZOA

abstract: The genetic content and buoyant density of rabbit (albino and partial dominant light chinchilla) spermatozoa were discussed. Spermatozoa of whole single ejaculates were fractionated into different buoyant density classes by centrifugation to near-equilibrium after layering upon 4 ml dextran-based ensity gradients. Offspring were weighed at birth sexed by dissection and their phenotypes were scored. No evidence of any gross difference in buoyant density between X and Y spermatozoa or genotypes was seen. 6 observed classes of buoyant density variation in birth weight due to the estimated effects of the basic classes of buoyant density were significant (p less than .05). Mean birth weights (antilogs of log means) in the 4 basic classes were estimated as Class less than 1.11=45.7 gm Class 1.11-1.12=51.5 gm Class 1.12-1.13= 56.7 gm and Class greater than 1.13=57 gm.


title: A novel model predictive control for single-phase grid-connected photovoltaic inverters

abstract: Single-phase grid-connected inverters with LCL filter are widely used to connect photovoltaic systems to the utility grid. Among the existing control schemes, predictive control methods are faster and more accurate but also more complicated to implement. Recently, the Model Predictive Control (MPC) algorithm for single-phase inverter has been presented, where the algorithm implementation is straightforward. In the MPC approach, all switching states are considered in each switching period to achieve the control objectives. However, since the number of switching states in single-phase inverters is small, the inverter output current has a high Total Harmonic Distortions (THD). In order to reduce this, this paper presents an improved MPC for single-phase grid-connected inverters. In the proposed approach, the switching algorithm is changed and the number of the switching states is increased by means of virtual vectors. Simulation results show that the proposed approach lead to a lower THD in the injected current combined with fast dynamics. The proposed predictive control has been simulated and implemented on a 1 kW single-phase HERIC (highly efficient and reliable inverter concept) inverter with an LCL filter at the output.


Modeling Password Guessing with Neural Networks


title: Multi-layered Gesture Recognition with Kinect.

abstract: This paper proposes a novel multi-layered gesture recognition method with Kinect. We explore the essential linguistic characters of gestures: the components concurrent character and the sequential organization character, in a multi-layered framework, which extracts features from both the segmented semantic units and the whole gesture sequence and then sequentially classifies the motion, location and shape components. In the first layer, an improved principle motion is applied to model the motion component. In the second layer, a particle-based descriptor and a weighted dynamic time warping are proposed for the location component classification. In the last layer, the spatial path warping is further proposed to classify the shape component represented by unclosed shape context. The proposed method can obtain relatively high performance for one-shot learning gesture recognition on the ChaLearn Gesture Dataset comprising more than 50, 000 gesture sequences recorded with Kinect.


title: Mining Databases and Data Streams with Query Languages and Rules

abstract: Among data-intensive applications that are beyond the reach of traditional Data Base Management Systems (DBMS), data mining stands out because of practical importance and the complexity of the research problems that must be solved before the vision of Inductive DBMS can become a reality. In this paper, we first discuss technical developments that have occurred since the very notion of Inductive DBMS emerged as a result of the seminal papers authored by Imielinski and Mannila a decade ago. The research progress achieved since then can be subdivided into three main problem subareas as follows: (i) language (ii) optimization, and (iii) representation. We discuss the problems in these three areas and the different approaches to Inductive DBMS that are made possible by recent technical advances. Then, we pursue a language-centric solution, and introduce simple SQL extensions that have proven very effective at supporting data mining. Finally, we turn our attention to the related problem of supporting data stream mining using Data Stream Management Systems (DSMS) and introduce the notion of Inductive DSMS. In addition to continuous query languages, DSMS provide support for synopses, sampling, load shedding, and other built-in functions that are needed for data stream mining. Moreover, we show that Inductive DSMS can be achieved by generalizing DSMS to assure that their continuous query languages support efficiently data stream mining applications. Thus, DSMS extended with inductive capabilities will provide a uniquely supportive environment for data stream mining applications.


title: An optimal time algorithm for minimum linear arrangement of chord graphs

abstract: A linear arrangement @f of an undirected graph G=(V,E) with |V|=n nodes is a bijective function @f:V->{0,...,n-1}. The cost function is cost(G,@f)[email protected]?"u"v"@?"E|(@f(u)[email protected](v))| and opt(G)=min"@?"@fcost(G,@f). The problem of finding opt(G) is called minimum linear arrangement (MINLA). The Minimum Linear Arrangement is an NP-hard problem in general. But there are some classes of graphs optimally solvable in polynomial time. In this paper, we show that the label of each node equals to the reverse of binary representation of its id in the optimal arrangement. Then, we design an O(n) algorithm to solve the minimum linear arrangement problem of Chord graphs.


title: Simulation of a Smart Grid City with Software Agents

abstract: In the future smart city, new information and communication technologies will enable a better management of the available resources. The future smart grid infrastructure is emerging as a complex system where fine-grained monitoring and control of energy generating and/or consuming entities within the electricity network is possible. This will result to better approaches that will boost energy efficiency. A simulation of a dynamic ecosystem such as the smart city, will enable us to test new concepts and resource-optimization approaches. Therefore we have analyzed, designed, and build a simulator based on software agents that attempts to create the dynamic behavior of a smart city. It simulates discrete heterogeneous devices that consume and/or produce energy, that are able to act autonomously and collaborate. The behavior of these devices and their groupings e.g. smart houses, has been modeled in order to map as near as possible the real behavior patterns of the respective physical objects.


title: Perpetual Assurances for Self-Adaptive Systems

abstract: Providing assurances for self-adaptive systems is challenging. A primary underlying problem is uncertainty that may stem from a variety of different sources, ranging from incomplete knowledge to sensor noise and uncertain behavior of humans in the loop. Providing assurances that the self-adaptive system complies with its requirements calls for an enduring process spanning the whole lifetime of the system. In this process, humans and the system jointly derive and integrate new evidence and arguments, which we coined perpetual assurances for self-adaptive systems. In this paper, we provide a background framework and the foundation for perpetual assurances for self-adaptive systems. We elaborate on the concrete challenges of offering perpetual assurances, requirements for solutions, realization techniques and mechanisms to make solutions suitable. We also present benchmark criteria to compare solutions. We then present a concrete exemplar that researchers can use to assess and compare approaches for perpetual assurances for self-adaptation.


title: Effect of toluene diisocyanate index on morphology and physical properties of flexible slabstock polyurethane foams

abstract: The effect of toluene diisocyanate (TDI) index on the physical properties, structure, and morphology of flexible slabstock polyurethane foams was investigated. Foams based on a 2700 molecular weight triol, 6 pph water, and varying amounts of an 80/20 mixture of 2,4- and 2,6-TDIs were characterized using a number of physical property and morphological measurements. Extraction experiments using dimethyl formamide (DMF) showed that increasing the index increased the level of covalent crosslinking with perhaps a maximum being reached at an index ca. 100. Viscoelastic measurements also supported the claim of increased crosslinking with TDI index. The initial load in load relaxation experiments at 65% strain systematically increased with increasing TDI while the percent decay in a 3-h period decreased. Temperature and/or humidity “plasticized” the load relaxation behavior in all the foams studied, indicating that the hard segment domain physical “crosslinks” play a significant role in the properties of these materials. Interestingly, compression set measurements appeared to be independent of the index, likely due to some level of hard segment continuity, but the induced recovery of the compression set at elevated temperatures was indeed sensitive to the index. The amount of recovery systematically increased with increasing TDI index due to the more enhanced “recoverable” covalent network. Scanning electron microscopy (SEM) studies of the foams showed that the cellular structure was not significantly affected by the index. However, SEM also showed that the structure of the high index foam was not greatly altered by the extraction process while the lowest index foam's cellular structure was severely disfigured. The fine structure of the foams was found to be influenced by the TDI index. Small angle X-ray scattering, differential scanning calorimetry, and dynamic mechanical analysis all provided evidence that an increase in the TDI index promoted some phase mixing of the soft and hard segments. FTIR showed that the short-range ordering within the hard segment domains displayed a maximum at an index of 100. This was attributed to the concentration of hard segment domains being lower at a lower index and their ordering being disrupted at higher indexes due to more extensive covalent crosslinking prior to completion of phase separation. Wide angle X-ray scattering results also confirmed that for the highest index level, the short-range ordering of the TDI moieties was decreased. © 1997 John Wiley & Sons, Inc. J Appl Polym Sci 66: 2395–2408, 1997


title: PCA vs. ICA: A Comparison on the FERET Data Set

abstract: Over the last ten years, face recognition has become a specialized applications area within the field of computer vision. Sophisticated commercial systems have been developed that achieve high recognition rates. Although elaborate, many of these systems include a subspace projection step and a nearest neighbor classifier. The goal of this paper is to rigorously compare two subspace projection techniques within the context of a baseline system on the face recognition task. The first technique is principal component analysis (PCA), a well-known “baseline” for projection techniques. The second technique is independent component analysis (ICA), a newer method that produces spatially localized and statistically independent basis vectors. Testing on the FERET data set (and using standard partitions), we find that, when a proper distance metric is used, PCA significantly outperforms ICA on a human face recognition task. This is contrary to previously published results.


title: Event-Triggered H $\infty$ Fuzzy Filtering for Networked Control Systems With Quantization and Delays

abstract: This paper investigates the event-triggered $H_{\infty } $ filtering for networked control systems (NCSs) with quantization and network-induced delays. With consideration of the limited capacity of the communication channels in NCSs, a quantizer is proposed to quantize control signals before being transmitted into the next node. First, an event-triggered scheme is addressed between the quantizer and the fuzzy filter, which aims to mitigate transmission rate and improve the usage of network resource. Based on the event-triggered scheme, the fuzzy filtering error system is established with quantization and delays. Second, a novel Lyapunov-Krasovskii functional is constructed, and the Writinger inequality is used to deal with the integral item of the derivative of the Lyapunov-Krasovskii functional, which can obtain more useful items. Then, a new stability criterion is addressed to ensure that the filtering error system is asymptotically stable with a prescribe $H_{\infty } $ performance level. By introducing matrix decoupling technique, the fuzzy filter is designed without the coupling matrices. Finally, numerical simulations are given to show the effectiveness of the proposed method.