title: Measuring user satisfaction from e-Government services: Lessons from Jordan

abstract: Abstract User satisfaction is a crucial factor for continual usage of e-Government services and for the success or failure of e-Government projects. The main challenge for Jordanians is what are the key determinants of their satisfaction? This paper aims to identify the key factors that determine Jordanians' e-Satisfaction with Jordan's e-Government services portal. Based on an extensive review of relevant literature, five hypotheses are formulated and five factors are identified (i.e., security and privacy, trust, accessibility, awareness of public services, and quality of public services) that may affect the Jordanians' level of satisfaction towards using the Jordan e-Government portal ( www.jordan.gov.jo ). Survey data from 400 employees in four universities in the northern region of Jordan were collected and used to test the proposed hypotheses. Based on multiple linear regression and factor analyses, our empirical analysis demonstrates several key findings. These findings indicate the usefulness and importance to unveil the key drivers of e-Satisfaction so as to provide feedback in a set of recommendations that will enable creating e-Government portals which are compatible with citizens' needs, desires, and expectations. They also provide insights for both practitioners and governmental policy-makers to enhance e-Government portals via accounting diverse factors of technical, behavioral, managerial, and motivational aspects. Finally, implications and recommendations of these findings were discussed.


title: GDPRtEXT - GDPR as a Linked Data Resource

abstract: The General Data Protection Regulation (GDPR) is the new European data protection law whose compliance affects organisations in several aspects related to the use of consent and personal data. With emerging research and innovation in data management solutions claiming assistance with various provisions of the GDPR, the task of comparing the degree and scope of such solutions is a challenge without a way to consolidate them. With GDPR as a linked data resource, it is possible to link together information and approaches addressing specific articles and thereby compare them. Organisations can take advantage of this by linking queries and results directly to the relevant text, thereby making it possible to record and measure their solutions for compliance towards specific obligations. GDPR text extensions (GDPRtEXT) uses the European Legislation Identifier (ELI) ontology published by the European Publications Office for exposing the GDPR as linked data. The dataset is published using DCAT and includes an online webpage with HTML id attributes for each article and its subpoints. A SKOS vocabulary is provided that links concepts with the relevant text in GDPR. To demonstrate how related legislations can be linked to highlight changes between them for reusing existing approaches, we provide a mapping from Data Protection Directive (DPD), which was the previous data protection law, to GDPR showing the nature of changes between the two legislations. We also discuss in brief the existing corpora of research that can benefit from the adoption of this resource.


title: Fuzzing: a survey

abstract: Security vulnerability is one of the root causes of cyber-security threats. To discover vulnerabilities and fix them in advance, researchers have proposed several techniques, among which fuzzing is the most widely used one. In recent years, fuzzing solutions, like AFL, have made great improvements in vulnerability discovery. This paper presents a summary of the recent advances, analyzes how they improve the fuzzing process, and sheds light on future work in fuzzing. Firstly, we discuss the reason why fuzzing is popular, by comparing different commonly used vulnerability discovery techniques. Then we present an overview of fuzzing solutions, and discuss in detail one of the most popular type of fuzzing, i.e., coverage-based fuzzing. Then we present other techniques that could make fuzzing process smarter and more efficient. Finally, we show some applications of fuzzing, and discuss new trends of fuzzing and potential future directions.


title: Towards Robust Training of Neural Networks by Regularizing Adversarial Gradients

abstract: In recent years, neural networks have demonstrated outstanding effectiveness in a large amount of applications.However, recent works have shown that neural networks are susceptible to adversarial examples, indicating possible flaws intrinsic to the network structures. To address this problem and improve the robustness of neural networks, we investigate the fundamental mechanisms behind adversarial examples and propose a novel robust training method via regulating adversarial gradients. The regulation effectively squeezes the adversarial gradients of neural networks and significantly increases the difficulty of adversarial example generation.Without any adversarial example involved, the robust training method could generate naturally robust networks, which are near-immune to various types of adversarial examples. Experiments show the naturally robust networks can achieve optimal accuracy against Fast Gradient Sign Method (FGSM) and C\&W attacks on MNIST, Cifar10, and Google Speech Command dataset. Moreover, our proposed method also provides neural networks with consistent robustness against transferable attacks.


title: Submodularity-inspired Data Selection for Goal-oriented Chatbot Training based on Sentence Embeddings

abstract: Goal-oriented (GO) dialogue systems rely on an initial natural language understanding (NLU) module to determine the user's intention and parameters thereof - also known as slots. Since the systems, also known as bots, help the users with solving problems in relatively narrow domains, they require training data within those domains. This leads to significant data availability issues that inhibit the development of successful bots. To alleviate this problem, we propose a technique of data selection in the low-data regime that allows training with significantly fewer labeled sentences, thus smaller labelling costs. ::: We create a submodularity-inspired data ranking function, the ratio penalty marginal gain, to select data points to label based solely on the information extracted from the textual embedding space. We show that the distances in the embedding space are a viable source of information for data selection. This method outperforms several known active learning techniques, without using the label information. This allows for cost-efficient training of NLU units for goal-oriented bots. Moreover, our proposed selection technique does not need the retraining of the model in between the selection steps, making it time-efficient as well.


title: for a family of smooth spline spaces over Powell-Sabin triangulations

abstract: We construct a suitable B-spline representation for a family of bivariate spline functions with smoothness r≥1 and polynomial degree 3r−1. They are defined on a triangulation with Powell–Sabin refinement. The basis functions have a local support, they are nonnegative, and they form a partition of unity. The construction involves the determination of triangles that must contain a specific set of points. We further consider a number of CAGD applications. We show how to define control points and control polynomials (of degree 2r−1), and we provide an efficient and stable computation of the Bernstein–Bezier form of such splines.


title: A novel fusion framework for robust human tracking by a service robot

abstract: Abstract This work investigates the problem of robust vision-based human tracking by a human following robot using point-based features like SURF. The problem is challenging owing to failures arising because of variation in illumination, change in pose, size or scale, camera motion and partial or full occlusion. While point-based features provide robust detection against photometric and geometric distortions, the tracking of these features over subsequent frames becomes difficult as the number of matching points between a pair of images drops quickly with slight variation in target attribute owing to above mentioned variations. The problem of robust human tracking by the robot is solved by proposing a multi-tracker fusion framework that allows one to combine multiple tracker to ensure long term tracking of the target. This fusion framework also allows for creating a dynamic template pool of target features which gets updated over time. The interaction between the first two trackers is used to update the template pool of the target attribute while the last tracker is used to estimate the location of the target in case of full occlusion. The working of the framework is demonstrated by combining a SURF-based mean-shift tracker, an optical-flow tracker and a Kalman filter to provide robust tracking over a long time. The efficacy of the resulting tracker is demonstrated through rigorous testing on a variety of video datasets.


title: A vision for supporting autonomous navigation in urban environments

abstract: The autonomous navigation systems (ANS), such as autonomous ground vehicles (AGVs), unmanned aerial vehicles (UAVs), and unmanned submersible vehicles (USVs), and modern vehicles with actuators, sensors, and computer control perform three basic functions: context gathering using sensors, processing, and action. Most researchers have put all three functions into the ANS or the robot itself to overcome occlusions and handle the environment's dynamics. However, this causes the ANS and robotic systems to be bulky and expensive. It also impedes the introduction of vehicles with ANS in urban environments, where they must coexist with existing cars and highways. The approach presented distributes the context-gathering and processing functions using sensor networks and wireless communications technologies to reduce costs and make ANS widespread. The system uses sensors mounted on moving vehicles and stationary objects such as lampposts, traffic lights, toll plazas, and buildings to gather information at different levels.


title: Shift-Reduce CCG Parsing using Neural Network Models

abstract: We present a neural network based shift- reduce CCG parser, the first neural-network based parser for CCG. We also study the im- pact of neural network based tagging mod- els, and greedy versus beam-search parsing, by using a structured neural network model. Our greedy parser obtains a labeled F-score of 83.27%, the best reported result for greedy CCG parsing in the literature (an improve- ment of 2.5% over a perceptron based greedy parser) and is more than three times faster. With a beam, our structured neural network model gives a labeled F-score of 85.57% which is 0.6% better than the perceptron based counterpart.


title: Subspace Clustering with Priors via Sparse Quadratically Constrained Quadratic Programming

abstract: This paper considers the problem of recovering a subspace arrangement from noisy samples, potentially corrupted with outliers. Our main result shows that this problem can be formulated as a convex semi-definite optimization problem subject to an additional rank constrain that involves only a very small number of variables. This is established by first reducing the problem to a quadratically constrained quadratic problem and then using its special structure to find conditions guaranteeing that a suitably built convex relaxation is indeed exact. When combined with the standard nuclear norm relaxation for rank, the results above lead to computationally efficient algorithms with optimality guarantees. A salient feature of the proposed approach is its ability to incorporate existing a-priori information about the noise, co-ocurrences, and percentage of outliers. These results are illustrated with several examples.


title: Pondering on the Key Functionality of Model Driven Development Tools: the Case of Mendix

abstract: Model Driven Architectures and Model Driven Development (MDD) have been used in information system (IS) development projects for almost a decade. While the methodological support for the MDD process is important, the success of a project taking the model driven approach to development also heavily depends on the tool. The tool simply needs to support a set of key functionalities, such as an appropriate level of model abstraction, the refinement of models and finally the execution of models. In this paper we analyze a new MDD tool, namely Mendix, with respect to a number of functionality areas needed to achieve success in a project and capitalize on the benefits of MDD. Our findings are that Mendix use a well selected set of models and that these models are well integrated and suitable for the construction of small systems. Based on the key functionality areas we also point out the weaknesses of the tool.


title: On the Convergence of Alternating Direction Lagrangian Methods for Nonconvex Structured Optimization Problems

abstract: Nonconvex and structured optimization problems arise in many engineering applications that demand scalable and distributed solution methods. The study of the convergence properties of these methods is in general difficult due to the nonconvexity of the problem. In this paper, two distributed solution methods that combine the fast convergence properties of augmented Lagrangian-based methods with the separability properties of alternating optimization are investigated. The first method is adapted from the classic quadratic penalty function method and is called the Alternating Direction Penalty Method (ADPM). Unlike the original quadratic penalty function method, in which single-step optimizations are adopted, ADPM uses an alternating optimization, which in turn makes it scalable. The second method is the well-known Alternating Direction Method of Multipliers (ADMM). It is shown that ADPM for nonconvex problems asymptotically converges to a primal feasible point under mild conditions and an additional condition ensuring that it asymptotically reaches the standard first order necessary conditions for local optimality are introduced. In the case of the ADMM, novel sufficient conditions under which the algorithm asymptotically reaches the standard first order necessary conditions are established. Based on this, complete convergence of ADMM for a class of low dimensional problems are characterized. Finally, the results are illustrated by applying ADPM and ADMM to a nonconvex localization problem in wireless sensor networks.


title: Passive in-home measurement of stride-to-stride gait variability comparing vision and Kinect sensing

abstract: We present an analysis of measuring stride-to-stride gait variability passively, in a home setting using two vision based monitoring techniques: anonymized video data from a system of two web-cameras, and depth imagery from a single Microsoft Kinect. Millions of older adults fall every year. The ability to assess the fall risk of elderly individuals is essential to allowing them to continue living safely in independent settings as they age. Studies have shown that measures of stride-to-stride gait variability are predictive of falls in older adults. For this analysis, a set of participants were asked to perform a number of short walks while being monitored by the two vision based systems, along with a marker based Vicon motion capture system for ground truth. Measures of stride-to-stride gait variability were computed using each of the systems and compared against those obtained from the Vicon.


title: SegFlow: Joint Learning for Video Object Segmentation and Optical Flow

abstract: This paper proposes an end-to-end trainable network, SegFlow, for simultaneously predicting pixel-wise object segmentation and optical flow in videos. The proposed SegFlow has two branches where useful information of object segmentation and optical flow is propagated bidirectionally in a unified framework. The segmentation branch is based on a fully convolutional network, which has been proved effective in image segmentation task, and the optical flow branch takes advantage of the FlowNet model. The unified framework is trained iteratively offline to learn a generic notion, and fine-tuned online for specific objects. Extensive experiments on both the video object segmentation and optical flow datasets demonstrate that introducing optical flow improves the performance of segmentation and vice versa, against the state-of-the-art algorithms.


title: I/O efficient Core Graph Decomposition at web scale

abstract: Core decomposition is a fundamental graph problem with a large number of applications. Most existing approaches for core decomposition assume that the graph is kept in memory of a machine. Nevertheless, many real-world graphs are big and may not reside in memory. In the literature, there is only one work for I/O efficient core decomposition that avoids loading the whole graph in memory. However, this approach is not scalable to handle big graphs because it cannot bound the memory size and may load most parts of the graph in memory. In addition, this approach can hardly handle graph updates. In this paper, we study I/O efficient core decomposition following a semi-external model, which only allows node information to be loaded in memory. This model works well in many web-scale graphs. We propose a semi-external algorithm and two optimized algorithms for I/O efficient core decomposition using very simple structures and data access model. To handle dynamic graph updates, we show that our algorithm can be naturally extended to handle edge deletion. We also propose an I/O efficient core maintenance algorithm to handle edge insertion, and an improved algorithm to further reduce I/O and CPU cost by investigating some new graph properties. We conduct extensive experiments on 12 real large graphs. Our optimal algorithm significantly outperform the existing I/O efficient algorithm in terms of both processing time and memory consumption. In many memory-resident graphs, our algorithms for both core decomposition and maintenance can even outperform the in-memory algorithm due to the simple structures and data access model used. Our algorithms are very scalable to handle web-scale graphs. As an example, we are the first to handle a web graph with 978.5 million nodes and 42.6 billion edges using less than 4.2 GB memory.


title: [Regulative mechanism of renal inflammatory-related p38MAPK signaling pathway in diabetic nephropathy and interventional effects of Chinese herbal medicine].

abstract: It is reported, in the process of diabetic nephropathy (DN), inflammatory-related p38 mitogen-activated protein kinase (MAPK) signaling pathway has a close relationship with renal injury. On the one hand,many factors in the upstream including hyperglycemia, abnormal hemodynamics, oxidative stress, and pro-inflammatory cytokines could activate p38MAPK signaling pathway. On the other hand,the activated p38MAPK signaling pathway could lead to renal damage via activating inflammatory cells, inducing the expression of inflammatory mediators, and intervening cytokines production. CHM could intervene p38MAPK signaling pathway through multi-ways, including inhibiting inflammatory cytokines expression, regulating phosphorylated p38MAPK (p-p38MAPK) expression, and reducing fibrogenic factors expression.


title: Design, simulation, and fabrication of broadband inkjet-printed log-periodic antennas

abstract: We present design and computational analysis of log-periodic antennas that are fabricated by using low-cost inkjet printing technology. The designed antennas operate in the 1.5–2.5 GHz range and are suitable for diverse applications, including energy harvesting at WiFi and GSM bands. Suitable designs are fabricated by using silver-based inks in standard commercial printers. Despite the challenges in both design and fabrication processes, we demonstrate log-periodic antennas with desired operating properties at the target frequencies. Details of the design procedures, parametric analysis, as well as simulation and measurement results are presented.


title: A driver fatigue detection method based on multi-sensor signals

abstract: Fatigue during long-time driving threatens the safety of drivers and transportation. In this paper, we provide an effective method based on multi-sensor signals collected from Kinect2.0 camera and PPG pulse sensor to build a driver fatigue detection system. Unlike most traditional works, we define the transitional process of fatigue and elaborate its effect on training classifiers. The simulation experiments are then designed and 15 groups of data are collected. Our method works in the following steps: 1) feature extraction and fusion, 2) sample labelling and 3) SVM classifier designing. The 10-fold cross-validation accuracy of the classifier is 90.10% and the test accuracy is 83.82%. Experimental results verify that our method to deal with samples in transitional process is universal and more accurate than traditional methods. Moreover, our method based on multi-sensor works better than those dealing with single-sensor.


title: Classifying Smart Objects using capabilities

abstract: The Internet Of Things has emerged, providing an umbrella for the increasing number of heterogeneous Smart Objects that are becoming part of our daily activities. In this scenario, classification approaches are useful to understand differences and identify opportunities of generalization and common solutions, especially when different disciplines are coming together and bringing their individual terms and concepts. We propose a novel model for classifying Smart Objects using capabilities. This five-level model, inspired in the Capability Maturity Models, aims to be simple and inclusive, separating objects with basic capabilities from those with complex ones. In addition, examples of objects for each level are provided as validation of the proposal. The model is useful to identify requirements that Smart Objects have to cover externally as they cannot themselves support them and thus it allows for clear understanding of the external support system (or Middleware) into which the smart object is embedded.


title: Development of a high-voltage closed-loop power supply for ozone generation

abstract: This paper deals with the design of an AC-220-volt-mains-fed power supply for ozone generation. A power stage consisting of a buck converter to regulate the output power plus a current-fed parallel-resonant push-pull inverter to supply an ozone generator (OG) is proposed and analysed. A closed-loop operation is presented as a method to compensate variations in the AC source voltage. Inverter's step-up transformer issues and their effect on the performance of the overall circuit are also studied. The use of a UC3872 integrated circuit is proposed to control both the push-pull inverter and the buck converter, as well as to provide the possibility to protect the power supply in case a short circuit, an open-lamp operation or any other circumstance might occur. Implementation of a 100 W prototype and experimental results are shown and discussed.


title: Factors influencing acceptance of e-learning by teachers in the Czech Republic

abstract: Effects of various factors on the acceptance of e-learning (AEL) were studied.Prior experience with e-learning has no effect on AEL.Positive expectations before an e-learning course have significant effect on AEL.Teachers working individually through the courses exhibit higher level of AEL.Positive subjective evaluation of various aspects of the course lead to high AEL. The goal of this study was to explore the factors which impact the acceptance of an e-learning course by teachers of elementary and secondary schools in the Czech Republic. The survey involved 228 teachers from 68 different primary and secondary schools. These teachers took part in some of 16 different asynchronous on-line courses aimed at the use of modern technologies in education. It was found that the factors with major impact on the e-learning course acceptance include the voluntary participation of teachers in the course and the positive expectations held before the course start (p<0.001). Working through the course on one's own and the subjective evaluation of various aspects of the course plays an important role as well (p<0.001). The factors which had no statistically significant impact (p0.05) included the teacher's age, gender, type of school, prior experience with e-learning, individual habits in terms of completing voluntary tasks and taking notes, the involvement in discussions during the course and the time schedule of the course. We conclude that considerable attention should be paid to the psychological aspects of further education of Czech teachers using e-learning.


title: CHIPPER: A low-complexity bufferless deflection router

abstract: As Chip Multiprocessors (CMPs) scale to tens or hundreds of nodes, the interconnect becomes a significant factor in cost, energy consumption and performance. Recent work has explored many design tradeoffs for networks-on-chip (NoCs) with novel router architectures to reduce hardware cost. In particular, recent work proposes bufferless deflection routing to eliminate router buffers. The high cost of buffers makes this choice potentially appealing, especially for low-to-medium network loads. However, current bufferless designs usually add complexity to control logic. Deflection routing introduces a sequential dependence in port allocation, yielding a slow critical path. Explicit mechanisms are required for livelock freedom due to the non-minimal nature of deflection. Finally, deflection routing can fragment packets, and the reassembly buffers require large worst-case sizing to avoid deadlock, due to the lack of network backpressure. The complexity that arises out of these three problems has discouraged practical adoption of bufferless routing. To counter this, we propose CHIPPER (Cheap-Interconnect Partially Permuting Router), a simplified router microarchitecture that eliminates in-router buffers and the crossbar. We introduce three key insights: first, that deflection routing port allocation maps naturally to a permutation network within the router; second, that livelock freedom requires only an implicit token-passing scheme, eliminating expensive age-based priorities; and finally, that flow control can provide correctness in the absence of network backpressure, avoiding deadlock and allowing cache miss buffers (MSHRs) to be used as reassembly buffers. Using multiprogrammed SPEC CPU2006, server, and desktop application workloads and SPLASH-2 multithreaded workloads, we achieve an average 54.9% network power reduction for 13.6% average performance degradation (multipro-grammed) and 73.4% power reduction for 1.9% slowdown (multithreaded), with minimal degradation and large power savings at low-to-medium load. Finally, we show 36.2% router area reduction relative to buffered routing, with comparable timing.


title: Fast and Reliable Restoration Method of Virtual Resources on OpenStack

abstract: We propose a fast and reliable restoration method of virtual resources on OpenStack when physical servers or virtual machines are down. Many providers have recently started cloud services, and the use of OpenStack, which is open source IaaS software, is increasing. When physical servers are down, there is a fail-over method using the high-availability cluster software such as Pacemaker to restore virtual resources. However, it takes a long time to restore all virtual resources. There is also a method for monitoring each virtual machine by using Ping or other methods and restoring a virtual machine when it is down. However, data may be destroyed due to the double mounts of virtual machines depending on the timing of failures because restoration methods of failed physical servers and virtual machines are independent. Therefore, we propose a fast and reliable restoration method with a uniform way for plural types virtual resources. In our method, Pacemaker only detects a physical server failure and notifies a failure to a virtual resource arrangement scheduler, then a virtual resource arrangement scheduler determines multiple physical servers to restore virtual resources and calls OpenStack APIs to rebuild. The virtual resource arrangement scheduler also detects virtual machine failures by using a Libvirt monitoring module and restores virtual machines without data loss by handling Pacemaker and Libvirt notifications uniformly. We implemented the proposed method and showed its effectiveness regarding fast restoration through performance measurements.


title: Interest mining from user tweets

abstract: We build a system to extract user interests from Twitter messages. Specifically, we extract interest candidates using linguistic patterns and rank them using four different keyphrase ranking techniques: TFIDF, TextRank, LDA-TextRank, and Relevance-Interestingness-Rank (RI-Rank). We also explore the complementary relation between TFIDF and TextRank in ranking interest candidates. Top ranked interests are evaluated with user feedback gathered from an online survey. The results show that TFIDF and TextRank are both suitable for extracting user interests from tweets. Moreover, the combination of TFIDF and TextRank consistently yields the highest user positive feedback.


title: A multivariate random forest based framework for drug sensitivity prediction

abstract: Drug sensitivity prediction based on genomic characterization remains a significant challenge in the area of systems medicine. Multiple approaches have been proposed for mapping genomic characterization to drug sensitivity and among them ensemble based learning techniques like Random Forests (RF) have been a top performer [1, 2]. The majority of the current approaches infer a predictive model for each drug individually but correlation between different drug sensitivities suggests that multiple response prediction incorporating the co-variance of the different drug responses can possibly improve prediction accuracy. In this abstract, we report a prediction and analysis framework based on Multivariate Random Forests (MRF) that incorporates the correlation between different drug sensitivities.


title: Weakly Supervised PatchNets: Describing and Aggregating Local Patches for Scene Recognition

abstract: Traditional feature encoding scheme (e.g., Fisher vector) with local descriptors (e.g., SIFT) and recent convolutional neural networks (CNNs) are two classes of successful methods for image recognition. In this paper, we propose a hybrid representation, which leverages the discriminative capacity of CNNs and the simplicity of descriptor encoding schema for image recognition, with a focus on scene recognition. To this end, we make three main contributions from the following aspects. First, we propose a patch-level and end-to-end architecture to model the appearance of local patches, called PatchNet . PatchNet is essentially a customized network trained in a weakly supervised manner, which uses the image-level supervision to guide the patch-level feature extraction. Second, we present a hybrid visual representation, called VSAD , by utilizing the robust feature representations of PatchNet to describe local patches and exploiting the semantic probabilities of PatchNet to aggregate these local patches into a global representation. Third, based on the proposed VSAD representation, we propose a new state-of-the-art scene recognition approach, which achieves an excellent performance on two standard benchmarks: MIT Indoor67 (86.2%) and SUN397 (73.0%).


title: Set similarity search beyond MinHash

abstract: We present an improved data structure for approximate similarity (or"nearest neighbor") search under Braun-Blanquet similarity. For $\mathbf{x}, \mathbf{y} \subseteq \{1, \dots, d\}$, Braun-Blanquet similarity is defined as $B(\mathbf{x}, \mathbf{y}) = |\mathbf{x} \cap \mathbf{y}| / \max(|\mathbf{x}|, |\mathbf{y}|)$. Given a collection $P$ of sets, $(b_1,b_2)$-approximate Braun-Blanquet similarity search asks to preprocess $P$ such that given a query set $\mathbf{q}$ for which there exists $\mathbf{x} \in P$ with $B(\mathbf{q},\mathbf{x}) \geq b_1$, we can efficiently return $\mathbf{x}' \in P$ with $B(\mathbf{q},\mathbf{x}')>b_2$. Historically a different measure, the Jaccard similarity $J(\mathbf{x},\mathbf{y}) = |\mathbf{x} \cap \mathbf{y}|/|\mathbf{x} \cup \mathbf{y}|$, has been more widely studied. We argue that it suffices to study regular data structures in which the size of the query set $\mathbf{q}$ and the sizes of sets in $P$ are fixed. In this setting there is a 1-1 mapping between Jaccard similarity and Braun-Blanquet similarity, so the problems are equivalent. It follows from the seminal paper of Indyk and Motwani (STOC 1998) that the so-called MinHash locality-sensitive hash function can be used to construct a data structure for $(j_1,j_2)$-approximate Jaccard similarity with space $O(n^{1+\rho} + dn)$ and query time $O(dn^\rho)$, where $n = |P|$ and $\rho = \log(1/j_1)/ \log(1/j_2)<1$. In the regular setting our data structure improves $\rho$ in the exponent to $\rho' = \log\left(\tfrac{1 + j_1}{2j_1}\right)/\log\left(\tfrac{1+j_2}{2j_2}\right)$ which is always strictly smaller than $\rho$. The exponent in terms of Braun-Blanquet similarity is $\rho'=\log(1/b_1)/ \log(1/b_2)$. Our main technical idea (continued...)


title: Active Vision via Extremum Seeking for Robots in Unstructured Environments: Applications in Object Recognition and Manipulation

abstract: In this paper, a novel active vision strategy is proposed for optimizing the viewpoint of a robot’s vision sensor for a given success criterion. The strategy is based on extremum seeking control (ESC), which introduces two main advantages: 1) Our approach is model free: It does not require an explicit objective function or any other task model to calculate the gradient direction for viewpoint optimization. This brings new possibilities for the use of active vision in unstructured environments, since a priori knowledge of the surroundings and the target objects is not required. 2) ESC conducts continuous optimization backed up with mechanisms to escape from local maxima. This enables an efficient execution of an active vision task. We demonstrate our approach with two applications in the object recognition and manipulation fields, where the model-free approach brings various benefits: for object recognition, our framework removes the dependence on offline training data for viewpoint optimization, and provides robustness of the system to occlusions and changing lighting conditions. In object manipulation, the model-free approach allows us to increase the success rate of a grasp synthesis algorithm without the need of an object model; the algorithm only uses continuous measurements of the objective value, i.e., the grasp quality. Our experiments show that continuous viewpoint optimization can efficiently increase the data quality for the underlying algorithm, while maintaining the robustness. Note to Practitioners —Vision sensors provide robots flexibility and robustness both in industrial and domestic settings by supplying required data to analyze the surroundings and the state of the task. However, the quality of these data can be very high or poor depending on the viewing angle of the vision sensor. For example, if the robot aims to recognize an object, images taken from certain angles (e.g., feature rich surfaces) can be more descriptive than the others, or if the robot’s goal is to manipulate an object, observing it from a viewpoint that reveals easy-to-grasp “handles” makes the task simpler to execute. The algorithm presented in this paper aims to provide the robot high quality visual data relative to the task at hand by changing vision sensors’ viewpoint. Different from other methods in the literature, our method does not require any task models (therefore, it is model free), and only utilizes a quality value that can be measured from the current viewpoint (e.g., object recognition success rate for the current image). The viewpoint of the sensor is changed continuously for increasing the quality value until the robot is confident enough about the success of the execution. We demonstrate the application of the algorithm in the object recognition and manipulation domains. Nevertheless, it can be applied to many other robotics tasks, where viewing angle of the scene affects the robot’s performance.


title: Immersive Journalism: Immersive Virtual Reality for the First-Person Experience of News

abstract: This paper introduces the concept and discusses the implications of immersive journalism, which is the production of news in a form in which people can gain first-person experiences of the events or situation described in news stories. The fundamental idea of immersive journalism is to allow the participant, typically represented as a digital avatar, to actually enter a virtually recreated scenario representing the news story. The sense of presence obtained through an immersive system (whether a Cave or head-tracked head-mounted displays [HMD] and online virtual worlds, such as video games and online virtual worlds) affords the participant unprecedented access to the sights and sounds, and possibly feelings and emotions, that accompany the news. This paper surveys current approaches to immersive journalism and the theoretical background supporting claims regarding avatar experience in immersive systems. We also provide a specific demonstration: giving participants the experience of being in an interrogation room in an offshore prison. By both describing current approaches and demonstrating an immersive journalism experience, we open a new avenue for research into how presence can be utilized in the field of news and nonfiction.


title: TEEN: a routing protocol for enhanced efficiency in wireless sensor networks

abstract: Wireless sensor networks are expected to find wide applicability and increasing deployment in the near future. In this paper, we propose a formal classification of sensor networks, based on their mode of functioning, as proactive and reactive networks. Reactive networks, as opposed to passive data collecting proactive networks, respond immediately to changes in the relevant parameters of interest. We also introduce a new energy efficient protocol, TEEN (Threshold sensitive Energy Efficient sensor Network protocol) for reactive networks. We evaluate the performance of our protocol for a simple temperature sensing application. In terms of energy efficiency, our protocol has been observed to outperform existing conventional sensor network protocols.


title: Theory and Experiments on Vector Quantized Autoencoders

abstract: Deep neural networks with discrete latent variables offer the promise of better symbolic reasoning, and learning abstractions that are more useful to new tasks. There has been a surge in interest in discrete latent variable models, however, despite several recent improvements, the training of discrete latent variable models has remained challenging and their performance has mostly failed to match their continuous counterparts. Recent work on vector quantized autoencoders (VQ-VAE) has made substantial progress in this direction, with its perplexity almost matching that of a VAE on datasets such as CIFAR-10. In this work, we investigate an alternate training technique for VQ-VAE, inspired by its connection to the Expectation Maximization (EM) algorithm. Training the discrete bottleneck with EM helps us achieve better image generation results on CIFAR-10, and together with knowledge distillation, allows us to develop a non-autoregressive machine translation model whose accuracy almost matches a strong greedy autoregressive baseline Transformer, while being 3.3 times faster at inference.


title: Simple and effective defense against evil twin access points

abstract: Wireless networking is widespread in public places such as cafes. Unsuspecting users may become victims of attacks based on "evil twin" access points. These rogue access points are operated by criminals in an attempt to launch man-in-the-middle attacks. We present a simple protection mechanism against binding to an evil twin. The mechanism leverages short authentication string protocols for the exchange of cryptographic keys. The short string verification is performed by encoding the short strings as a sequence of colors, rendered sequentially by the user's device and by the designated access point of the cafe. The access point must have a light capable of showing two colors and must be mounted prominently in a position where users can have confidence in its authenticity. We conducted a usability study with patrons in several cafes and participants found our mechanism very usable.


title: Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics

abstract: We consider the task of estimating, from observed data, a probabilistic model that is parameterized by a finite number of parameters. In particular, we are considering the situation where the model probability density function is unnormalized. That is, the model is only specified up to the partition function. The partition function normalizes a model so that it integrates to one for any choice of the parameters. However, it is often impossible to obtain it in closed form. Gibbs distributions, Markov and multi-layer networks are examples of models where analytical normalization is often impossible. Maximum likelihood estimation can then not be used without resorting to numerical approximations which are often computationally expensive. We propose here a new objective function for the estimation of both normalized and unnormalized models. The basic idea is to perform nonlinear logistic regression to discriminate between the observed data and some artificially generated noise. With this approach, the normalizing partition function can be estimated like any other parameter. We prove that the new estimation method leads to a consistent (convergent) estimator of the parameters. For large noise sample sizes, the new estimator is furthermore shown to behave like the maximum likelihood estimator. In the estimation of unnormalized models, there is a trade-off between statistical and computational performance. We show that the new method strikes a competitive trade-off in comparison to other estimation methods for unnormalized models. As an application to real data, we estimate novel two-layer models of natural image statistics with spline nonlinearities.


INFORMATION TECHNOLOGY AND BUSINESS-LEVEL STRATEGY : TOWARD AN INTEGRATED THEORETICAL PERSPECTIVE 1


title: Forensic mycology: the use of fungi in criminal investigations

abstract: This is the first overview to be published of the whole field of forensic mycology. It is based on all available information located in the literature, together with 13 examples from recent casework. Background information on fungi is given, and this is followed by an outline of the value, and potentially wide application, of mycology in criminal investigation. Applications include roles in: providing trace evidence; estimating time since death (post-mortem interval); ascertaining time of deposition; investigating cause of death, hallucinations, or poisonings; locating buried corpses; and biological warfare. Previous work has been critically evaluated, with particular attention to its evidential value, and suitability for presentation in a court of law. The situations where mycology might assist an investigation are summarised, and issues relating to the further development of the subject are presented. A comprehensive bibliography with 120 citations is provided.


title: Information Set Monte Carlo Tree Search

abstract: Monte Carlo tree search (MCTS) is an AI technique that has been successfully applied to many deterministic games of perfect information. This paper investigates the application of MCTS methods to games with hidden information and uncertainty. In particular, three new information set MCTS (ISMCTS) algorithms are presented which handle different sources of hidden information and uncertainty in games. Instead of searching minimax trees of game states, the ISMCTS algorithms search trees of information sets, more directly analyzing the true structure of the game. These algorithms are tested in three domains with different characteristics, and it is demonstrated that our new algorithms outperform existing approaches to handling hidden information and uncertainty in games.


title: Using Data Analytics and Data Mining Methods to Determine a High Net Worth IndividualÃ¢ÂÂs Electronic Banking Behavior

abstract: Electronic banking is becoming more popular every day. Financial institutions have accepted the transformation to provide electronic banking facilities to their customers in order to remain relevant and thrive in an environment that is competitive. A contributing factor to the customer retention rate is the frequent use of multiple online functionality however despite all the benefits of electronic banking, some are still hesitant to use it because of security concerns. The perception is that gender, age, education level, salary, culture and profession all have an impact on electronic banking usage. This study reports on how the Knowledge Discovery and Data Mining (KDDM) process was used to determine characteristics and electronic banking behavior of high net worth individuals at a South African bank. Findings indicate that product range and age had the biggest impact on electronic banking behavior. The value of user segmentation is that the financial institution can provide a more accurate service to their users based on their preferences and online banking behavior.


title: Institutional Efficiency in Selected Universities in Uganda.

abstract: Universities are accountable This study looked into Institutional Efficiency in selected Universities in Central Uganda. The study was guided by the following objectives; Determine the level of institutional efficiency of the universities in terms of ;educational efficiency; research efficiency and community service efficiency. The study employed ex-post facto, descriptive, comparative and correlation survey research design. Two hundred and twenty (220) administrators from the two selected institutions of Makerere University and Kampala International University were selected through purposive and systematic random sampling. The study adopted a standardized questionnaire. Data analysis was done using frequencies and percentages, means, t-test, PLCC and regression analysis. The study discovered that there are more male than female administrators in the two selected institutions. Majority were; youths (61.0%), Ugandans (67.3%), heads of departments (46.4%) and had masters degrees (54.1%) The study also discovered that the extent of Administrative Behaviors is very good and that the level of Institutional Efficiency in the selected universities is high. The study finally found out that there was significance difference in the extent of administrative behavior between males and females although the extent of administrative behaviors among females was slightly higher than in males. It was therefore concluded that research is crucial for Africa and African universities should be in the forefront in undertaking research. African governments should allocate funds to their universities specifically for research and also set up a research council to encourage, coordinate and fund research nationally. It was further recommended that university managers should take leadership courses to boost their managerial skills as a significant step towards improving institutional efficiency. The skills acquired should be sufficient to respond to the challenges of quality education bedevilling the universities. There is need to educate the society to stop stereotyping roles according to gender, the great need of university administrators to together to effectively deal with enormous challenges facing higher education today in Uganda and the world over, the need for strong research policy for the university faculty and students if the community and the world at large is to benefit from the university values.


title: Error and attack tolerance of complex networks

abstract: Many complex systems display a surprising degree of tolerance against errors. For example, relatively simple organisms grow, persist and reproduce despite drastic pharmaceutical or environmental interventions, an error tolerance attributed to the robustness of the underlying metabolic network1. Complex communication networks2 display a surprising degree of robustness: although key components regularly malfunction, local failures rarely lead to the loss of the global information-carrying ability of the network. The stability of these and other complex systems is often attributed to the redundant wiring of the functional web defined by the systems' components. Here we demonstrate that error tolerance is not shared by all redundant systems: it is displayed only by a class of inhomogeneously wired networks, called scale-free networks, which include the World-Wide Web3,4,5, the Internet6, social networks7 and cells8. We find that such networks display an unexpected degree of robustness, the ability of their nodes to communicate being unaffected even by unrealistically high failure rates. However, error tolerance comes at a high price in that these networks are extremely vulnerable to attacks (that is, to the selection and removal of a few nodes that play a vital role in maintaining the network's connectivity). Such error tolerance and attack vulnerability are generic properties of communication networks.


Immediately Loading Dental Implants. Doing It Right for Long-Term Success.


title: DACs: Bringing Direct Access to Variable-Length Codes

abstract: We present a new variable-length encoding scheme for sequences of integers, Directly Addressable Codes (DACs), which enables direct access to any element of the encoded sequence without the need of any sampling method. Our proposal is a kind of implicit data structure that introduces synchronism in the encoded sequence without using asymptotically any extra space. We show some experiments demonstrating that the technique is not only simple, but also competitive in time and space with existing solutions in several applications, such as the representation of LCP arrays or high-order entropy-compressed sequences.


title: An introduction to the construction and verification of Alphard programs

abstract: The programming language Alphard is designed to provide support for both the methodologies of “well-structured” programming and the techniques of formal program verification. Language constructs allow a programmer to isolate an abstraction , specifying its behavior publicly while localizing knowledge about its implementation. The verification of such an abstraction consists of showing that its implementation behaves in accordance with its public specifications; the abstraction can then be used with confidence in constructing other programs, and the verification of that use employs only the public specifications. This paper introduces Alphard by developing and verifying a data structure definition and a program that uses it. It shows how each language construct contributes to the development of the abstraction and discusses the way the language design and the verification methodology were tailored to each other. It serves not only as an introduction to Alphard, but also as an example of the symbiosis between verification and methodology in language design. The strategy of program structuring, illustrated for Alphard, is also applicable to most of the “data abstraction” mechanisms now appearing.


title: Towards Practical Active Learning for Classification

abstract: In recent decades, the availability of a large amount of data has propelled the field of machine learning enormously. Machine learning, however, relies heavily on the availability of annotated data, typically labels indicating to which class a data instance belongs. With the huge amounts of data, this raises the question of how to efficiently annotate data, certainly when having limited resources. This thesis addresses the particular challenge of using as few annotations as possible, while at the same time, maintaining a good learning performance. For that we utilize active learning, which iteratively chooses the most valuable instances as to obtain the labels froman oracle (e.g. a human expert). Though many studies have demonstrated that active learning can reduce the annotation cost, there are still several issues that limit its practical use. This thesis makes a further step forwards making active learning more practical for real-world applications. We first provide a benchmark and comparison of six different categories of active learning algorithms built on logistic regression. This work provides a better understanding of the underlying characteristics of various active learners and illustrates the potential benefits of using such techniques, but it also provides many cases for which active learning fails to outperform passive learning (i.e. randomly selecting instances for labeling). Those failed cases motivate us to propose two novel active learning methods that show a clear advantage over passive learning. The first one proposes to weight the so-called retraining-based criteria with an uncertainty score that is measured by the estimated posterior probability. The second one measures the usefulness of unlabeled instances according to the variance of the predictive probability. This method takes an additional step towards practical active learning, clearly outperforming current state of the art on binary andmulti-class classification tasks. We further consider two realistic issues when applying active learning to real-world problems. One is how to find an initial set that contains at least one instance per class to start the active labeling cycle. The other one is dealing with the absence of human annotators in the interactive labeling loop. We propose new approaches to tackle the above problems and observe good performance compared to existing methods. This thesis concludes with an analysis of the contributions and limitations of our work, as well as research directions that deserve further studies. We hope that this thesis also inspires others to make active learning more suitable for real-world applications.


title: An optimal round-robin arbiter design for NoC

abstract: A new optimal arbiter is designed. We proposed a set of optimal Boolean functions and the corresponding circuit for it, and showed that the arbitration Boolean functions derived are optimal (simplest). This new arbiter is fair for any input combinations and faster than all previous arbiters we knew. Using Synopsys design tools with TSMC 0.18μm technology, the design results have shown that our arbiter has 22.8% improvement of execution time and 39.1% of cost (area) reduction compared with the existing fastest arbiter, SA [7]. Because of this small arbiter's the high-performance, it is extremely useful for the realizations of NoC routers, MPSoC arbitration, and ultra-high- speed switches. This new arbiter is being applied for a patent of the R.O.C. (application No.: 0972020612-0).


title: Preview on structures and algorithms of deep learning

abstract: Deep learning proposed by Hinton et al is a new learning algorithm of multi-layer neural network, and it is also a new study field in machine learning. This paper describes the structures and advantages to shallow learning of deep learning, and analyzes current popular learning algorithm in detail. Finally, this paper analyzes research directions and future prospects of deep learning.


title: Real-time traffic cone detection for autonomous vehicle

abstract: Traffic signs recognition is a basic task for autonomous vehicle. Among the numerous traffic signs, traffic cone is a very important mark used to guide cars where to go. A method based on vision and radar sensors information fusion is proposed to detect traffic cone in this paper. The algorithm mainly includes two parts: finding where the obstacle is in the image and recognizing whether it is a cone. We use homography to calibrate camera and radar, from which the radar data can be mapped on the image and a small corresponding image patch can be easily cutout. Then, a method based on contour feature called chamfer matching is used to determine whether the obstacle in the image patch is a cone. The approach has been tested on our autonomous vehicle, which shows it can guarantee both effectiveness and instantaneity.


title: Design implications of memristor-based RRAM cross-point structures

abstract: Emerging non-volatile memory (NVM) technologies are getting mature in recent years. These emerging NVM technologies have demonstrated great potentials for the universal memory hierarchy design. Among all the technology candidates, resistive random-access memory (RRAM) is considered to be the most promising as it operates faster than phase-change memory (PCRAM), and it has simpler and smaller cell structure than magnetic memory (MRAM or STT-RAM). In contrast to a conventional MOS-accessed memory cell, memristor-based RRAM has the potential of forming a cross-point structure without using access devices, achieving ultra high density. The cross-point structure, however, brings extra challenges to the peripheral circuitry design. In this work, we study the memristor-based RRAM array design and focus on the choices of different peripherals to achieve the best trade-off among performance, energy, and area. In addition, a system-level model is built to estimate the performance, energy, and area values1.


title: Transformed Schatten-1 Iterative Thresholding Algorithms for Low Rank Matrix Completion

abstract: c 2017 International Press C OMMUN. M ATH. S CI. Vol. 15, No. 3, pp. 839–862 TRANSFORMED SCHATTEN-1 ITERATIVE THRESHOLDING ALGORITHMS FOR LOW RANK MATRIX COMPLETION ∗ SHUAI ZHANG † , PENGHANG YIN ‡ , AND JACK XIN § Abstract. We study a non-convex low-rank promoting penalty function, the transformed Schatten- 1 (TS1), and its applications in matrix completion. The TS1 penalty, as a matrix quasi-norm deﬁned on its singular values, interpolates the rank and the nuclear norm through a nonnegative parameter a ∈ (0,+∞). We consider the unconstrained TS1 regularized low-rank matrix recovery problem and develop a ﬁxed point representation for its global minimizer. The TS1 thresholding functions are in closed analytical form for all parameter values. The TS1 threshold values diﬀer in subcritical (supercritical) parameter regime where the TS1 threshold functions are continuous (discontinuous). We propose TS1 iterative thresholding algorithms and compare them with some state-of-the-art algorithms on matrix completion test problems. For problems with known rank, a fully adaptive TS1 iterative thresholding algorithm consistently performs the best under diﬀerent conditions, where ground truth matrices are generated by multivariate Gaussian, (0,1) uniform and Chi-square distributions. For problems with unknown rank, TS1 algorithms with an additional rank estimation procedure approach the level of IRucL-q which is an iterative reweighted algorithm, non-convex in nature and best in performance. Keywords. transformed Schatten-1 penalty; ﬁxed point representation; closed form thresholding function; iterative thresholding algorithms; matrix completion AMS subject classiﬁcations. 90C26; 90C46 1. Introduction Low rank matrix completion problems arise in many applications such as collabora- tive ﬁltering in recommender systems [4,17], minimum order system and low-dimensional Euclidean embedding in control theory [14,15], network localization [18], and others [26]. The mathematical problem is: min rank(X) X∈ m×n s.t. X ∈ L where L is a convex set. In this paper, we are interested in methods for solving the aﬃne rank minimization problem (ARMP) min rank(X) X∈ m×n s.t. A (X) = b in p , where the linear transformation A : m×n → p and vector b ∈ p are given. The matrix completion problem min rank(X) X∈ m×n s.t. X i,j = M i,j , (i,j) ∈ Ω is a special case of (1.2), where X and M are both m × n matrices and Ω is a subset of index pairs {(i,j)}. ∗ Received: September 25, 2016; accepted (in revised form): October 28, 2016. Communicated by Wotao Yin. The work was partially supported by NSF grant DMS-1222507 and DMS-1522383. † Department of Mathematics, University of California, Irvine, CA, 92697, USA (szhang3@uci.edu). Phone: (949)-824-5309. Fax: (949)-824-7993. ‡ Department of Mathematics, University of California, Irvine, CA, 92697, USA (penghany@uci.edu). § Department of Mathematics, University of California, Irvine, CA, 92697, USA (jxin@math.uci.edu).


title: GeoWeight: internet host geolocation based on a probability model for latency measurements

abstract: Knowing the geographical location of an Internet host is of importance to many of today's Internet services. In this paper we focus on geolocating Internet hosts based purely on latency measurements. Existing latency measurement-based geolocation techniques use the observed latencies from multiple landmarks to the target host to determine maximum bound or both the maximum and minimum bounds of the geographical region where the target host is located. Due to the large variance of Internet latency measurements, the region constrained based on such maximum-minimum bounds tends to be relatively large resulting in large estimation errors. We propose a geolocation algorithm, GeoWeight, which improves the geolocation accuracy by further limiting the possible target region by dividing the constrained region to sub-regions of different weights. The weight assigned to a subregion indicates the probability of the target being in that sub-region; a higher weight indicating a more probable region. By considering latency measurements from multiple landmarks and computing the resultant weights of overlapping regions a better constrained target region can be obtained. This paper presents the GeoWeight algorithm and evaluates its performance using both synthetic and real data by geolocating target hosts in North America. We compare GeoWeight with two popular geolocation techniques, Octant and CBG, by geolocating the same set of targets. The results show that the GeoWeight algorithm outperforms existing techniques.


title: Research How do People Solve the “Weather Prediction” Task?: Individual Variability in Strategies for Probabilistic Category Learning

abstract: Probabilistic category learning is often assumed to be an incrementally learned cognitive skill, dependent on nondeclarative memory systems. One paradigm in particular, the weather prediction task, has been used in over half a dozen neuropsychological and neuroimaging studies to date. Because of the growing interest in using this task and others like it as behavioral tools for studying the cognitive neuroscience of cognitive skill learning, it becomes especially important to understand how subjects solve this kind of task and whether all subjects learn it in the same way. We present here new experimental and theoretical analyses of the weather prediction task that indicate that there are at least three different strategies that describe how subjects learn this task. (1) An optimal multi-cue strategy, in which they respond to each pattern on the basis of associations of all four cues with each outcome; (2) a one-cue strategy, in which they respond on the basis of presence or absence of a single cue, disregarding all other cues; or (3) a singleton strategy, in which they learn only about the four patterns that have only one cue present and all others absent. This variability in how subjects approach this task may have important implications for interpreting how different brain regions are involved in probabilistic category learning.


title: Non-negative tensor factorization with applications to statistics and computer vision

abstract: We derive algorithms for finding a non-negative n-dimensional tensor factorization (n-NTF) which includes the non-negative matrix factorization (NMF) as a particular case when n = 2. We motivate the use of n-NTF in three areas of data analysis: (i) connection to latent class models in statistics, (ii) sparse image coding in computer vision, and (iii) model selection problems. We derive a "direct" positive-preserving gradient descent algorithm and an alternating scheme based on repeated multiple rank-1 problems.


Advanced OR and AI Methods in Transportation ROAD AND TRAFFIC SIGN DETECTION AND RECOGNITION


title: On Dimension Bounds for Auxiliary Quantum Systems

abstract: Expressions of several capacity regions in quantum information theory involve an optimization over auxiliary quantum registers. Evaluating such expressions requires bounds on the dimension of the Hilbert space of these auxiliary registers, for which no nontrivial technique is known; we lack a quantum analog of the Caratheodory theorem. In this paper, we develop a new non-Caratheodory-type tool for evaluating expressions involving a single quantum auxiliary register and several classical random variables. As we show, such expressions appear in problems of entanglement-assisted Gray-Wyner and entanglement-assisted channel simulation, where the question of whether entanglement helps in these settings is related to that of evaluating expressions with a single quantum auxiliary register. To evaluate such expressions, we argue that developing a quantum analog of the Caratheodory theorem requires a better understanding of a notion which we call “quantum conditioning.” We then proceed by proving a few results about quantum conditioning, one of which is that quantum conditioning is strictly richer than the usual classical conditioning.


title: A Surface-Growing Approach to Multi-View Stereo Reconstruction

abstract: We present a new approach to reconstruct the shape of a 3D object or scene from a set of calibrated images. The central idea of our method is to combine the topological flexibility of a point-based geometry representation with the robust reconstruction properties of scene-aligned planar primitives. This can be achieved by approximating the shape with a set of surface elements (surfels) in the form of planar disks which are independently fitted such that their footprint in the input images matches. Instead of using an artificial energy functional to promote the smoothness of the recovered surface during fitting, we use the smoothness assumption only to initialize planar primitives and to check the feasibility of the fitting result. After an initial disk has been found, the recovered region is iteratively expanded by growing further disks in tangent direction. The expansion stops when a disk rotates by more than a given threshold during the fitting step. A global sampling strategy guarantees that eventually the whole surface is covered. Our technique does not depend on a shape prior or silhouette information for the initialization and it can automatically and simultaneously recover the geometry, topology, and visibility information which makes it superior to other state-of-the-art techniques. We demonstrate with several high-quality reconstruction examples that our algorithm performs highly robustly and is tolerant to a wide range of image capture modalities.


title: Australian sign language recognition

abstract: This paper presents an automatic Australian sign language (Auslan) recognition system, which tracks multiple target objects (the face and hands) throughout an image sequence and extracts features for the recognition of sign phrases. Tracking is performed using correspondences of simple geometrical features between the target objects within the current and the previous frames. In signing, the face and a hand of a signer often overlap, thus the system needs to segment these for the purpose of feature extraction. Our system deals with the occlusion of the face and a hand by detecting the contour of the foreground moving object using a combination of motion cues and the snake algorithm. To represent signs, features that are invariant to scaling, 2D rotations and signing speed are used for recognition. The features represent the relative geometrical positioning and shapes of the target objects, as well as their directions of motion. These are used to recognise Auslan phrases using Hidden Markov Models. Experiments were conducted using 163 test sign phrases with varying grammatical formations. Using a known grammar, the system achieved over 97% recognition rate on a sentence level and 99% success rate at a word level.


An Energy Consumption Framework for Distributed Javabased Systems


title: Automatic Indexing of Audio 1 Fast Caption Alignment for Automatic Indexing of Audio

abstract: For large archives of audio media, just as with text archives, indexing is important for allowing quick and accurate searches. Similar to text archives, audio archives can use text for indexing. Generating this text requires using transcripts of the spoken portions of the audio. From them, an alignment can be made that allows users to search for specific content and immediately view the content at the position where the search terms were spoken. Although previous research has addressed this issue, the solutions align the transcripts only in real-time or greater. In this paper, the authors propose AutoCap. It is capable of producing accurate audio indexes in faster than real-time for archived audio and in real-time for live audio. In most cases it takes less than one quarter the original duration for archived audio. This paper discusses the architecture and evaluation of the AutoCap project as well as two of its applications.


title: Accuracy study of frequency converter estimates used in the sensorless diagnostics of induction-motor-driven systems

abstract: Modern frequency converters allow energy efficient operation of induction-motor-driven systems such as pumps and fans. In addition, they can be used as a sensorless monitoring unit for the pump and fan systems, as shown by previous studies. Sensorless diagnostics of induction-motor-driven systems relies on the accurate estimation of the rotational speed, shaft torque and shaft power of the motor, which is why all frequency converters are not suitable for this use. In this paper, accuracy of the rotational speed, shaft torque and shaft power estimates is studied by laboratory tests for two different frequency converters. Using the test results, estimation accuracy of a vector- and a direct-torque-controlled frequency converter are compared with each other. The effect of erroneous motor estimates on the pump flow rate estimation is also demonstrated in the paper.


title: Click Trajectories: End-to-End Analysis of the Spam Value Chain

abstract: Spam-based advertising is a business. While it has engendered both widespread antipathy and a multi-billion dollar anti-spam industry, it continues to exist because it fuels a profitable enterprise. We lack, however, a solid understanding of this enterprise's full structure, and thus most anti-Spam interventions focus on only one facet of the overall spam value chain (e.g., spam filtering, URL blacklisting, site takedown).In this paper we present a holistic analysis that quantifies the full set of resources employed to monetize spam email -- including naming, hosting, payment and fulfillment -- usingextensive measurements of three months of diverse spam data, broad crawling of naming and hosting infrastructures, and over 100 purchases from spam-advertised sites. We relate these resources to the organizations who administer them and then use this data to characterize the relative prospects for defensive interventions at each link in the spam value chain. In particular, we provide the first strong evidence of payment bottlenecks in the spam value chain, 95% of spam-advertised pharmaceutical, replica and software products are monetized using merchant services from just a handful of banks.


title: Convolutional Neural Networks for Document Image Classification

abstract: This paper presents a Convolutional Neural Network (CNN) for document image classification. In particular, document image classes are defined by the structural similarity. Previous approaches rely on hand-crafted features for capturing structural information. In contrast, we propose to learn features from raw image pixels using CNN. The use of CNN is motivated by the the hierarchical nature of document layout. Equipped with rectified linear units and trained with dropout, our CNN performs well even when document layouts present large inner-class variations. Experiments on public challenging datasets demonstrate the effectiveness of the proposed approach.


Techniques for the Automated Analysis of Musical Audio


title: Reviewer Recommender of Pull-Requests in GitHub

abstract: Pull-Request (PR) is the primary method for code contributions from thousands of developers in GitHub. To maintain the quality of software projects, PR review is an essential part of distributed software development. Assigning new PRs to appropriate reviewers will make the review process more effective which can reduce the time between the submission of a PR and the actual review of it. However, reviewer assignment is now organized manually in GitHub. To reduce this cost, we propose a reviewer recommender to predict highly relevant reviewers of incoming PRs. Combining information retrieval with social network analyzing, our approach takes full advantage of the textual semantic of PRs and the social relations of developers. We implement an online system to show how the reviewer recommender helps project managers to find potential reviewers from crowds. Our approach can reach a precision of 74% for top-1 recommendation, and a recall of 71% for top-10 recommendation.


title: Setting lower bounds on truthfulness: extended abstract

abstract: We present and discuss general techniques for proving inapproximability results for truthful mechanisms. We make use of these techniques to prove lower bounds on the approximability of several non-utilitarian multi-parameter problems. In particular, we demonstrate the strength of our techniques by exhibiting a lower bound of $2-\frac{1}{m}$ for the scheduling problem with unrelated machines (formulated as a mechanism design problem in the seminal paper of Nisan and Ronen on Algorithmic Mechanism Design). Our lower bound applies to truthful randomized mechanisms (disregarding any computational assumptions on the running time of these mechanisms). Moreover, it holds even for the weaker notion of truthfulness for randomized mechanisms -- i.e., truthfulness in expectation. This lower bound nearly matches the known $\frac{7}{4}$ (randomized) truthful upper bound for the case of two machines (a non-truthful FPTAS exists). No lower bound for truthful randomized mechanisms in multi-parameter settings was previously known. We show an application of our techniques to the workload-minimization problem in networks. We prove our lower bounds for this problem in the inter-domain routing setting presented by Feigenbaum, Papadimitriou, Sami, and Shenker. Finally, we discuss several notions of non-utilitarian"fairness"(Max-Min fairness, Min-Max fairness, and envy minimization). We show how our techniques can be used to prove lower bounds for these notions.


title: Citation Classification for Behavioral Analysis of a Scientific Field

abstract: Citations are an important indicator of the state of a scientific field, reflecting how authors frame their work, and influencing uptake by future scholars. However, our understanding of citation behavior has been limited to small-scale manual citation analysis. We perform the largest behavioral study of citations to date, analyzing how citations are both framed and taken up by scholars in one entire field: natural language processing. We introduce a new dataset of nearly 2,000 citations annotated for function and centrality, and use it to develop a state-of-the-art classifier and label the entire ACL Reference Corpus. We then study how citations are framed by authors and use both papers and online traces to track how citations are followed by readers. We demonstrate that authors are sensitive to discourse structure and publication venue when citing, that online readers follow temporal links to previous and future work rather than methodological links, and that how a paper cites related work is predictive of its citation count. Finally, we use changes in citation roles to show that the field of NLP is undergoing a significant increase in consensus.


title: Classification of Short Text Comments by Sentiment and Actionability for VoiceYourView

abstract: Much has been documented in the literature on sentiment analysis and document summarisation. Much of this applies to long structured text in the form of documents and blog posts. With a shift in social media towards short commentary (see Facebook status updates and twitter tweets), the difference in comment structure may affect the accuracy of sentiment analysis techniques. From our VoiceYourView trial, we collected over 2000 individual short comments on the topic of library refurbishment, many of which are transcribed spoken comments. We have shown success in determining the theme of comments by looking for the first noun and using a semantic tag set to categorise this noun and hence the comment for short comments. Sentiment is a measure of how positive or negative a comment is, and the actionability metric is a measure of how actionable the comment is, i.e. how useful it is. This paper looks towards applying methods from the literature to our dataset with the aim of evaluating methods of automatic sentiment and actionability analysis for our VoiceyourView application data and has relevance to data from other applications, e.g. those from the social media. With many social media commentary applications moving to add speech platforms, VoiceYourView data may be representative of the type of free-form spoken text input to be expected in such platforms.


title: SHAREK: A Scalable Dynamic Ride Sharing System

abstract: Due to its significant economic and environmental impact, sharing the ride among a number of drivers (i.e., Car pooling) has recently gained significant interest from industry and academia. Hence, a number of ride sharing services have appeared along with various algorithms on how to match a rider request to a driver who can provide the ride sharing service. However, existing techniques have several limitations that affect the quality of the ride sharing service, and hence hinder its wide applicability. This paper proposes SHAREK, a scalable and efficient ride sharing service that overcomes the limitations of existing approaches. SHAREK allows riders requesting the ride sharing service to indicate the maximum price they are willing to pay for the service and the maximum waiting time before being picked up. In the mean time, SHAREK computes the price of the service based on the distance of the rider trip and the detour that the driver will make to offer the service. Then, SHAREK returns a set of drivers that can make it to the rider within its price and temporal constraints. Since there could be many of such drivers, SHAREK internally prunes those drivers that are dominated by others, i.e., They provide higher price and higher waiting time than other drivers. To realize its efficiency and scalability, SHAREK employs a set of early pruning techniques that minimize the need for any actual shortest path computations.


title: A Taxonomy of Cyber Attacks on SCADA Systems

abstract: Supervisory Control and Data Acquisition(SCADA) systems are deeply ingrained in the fabric of critical infrastructure sectors. These computerized real-time process control systems, over geographically dispersed continuous distribution operations, are increasingly subject to serious damage and disruption by cyber means due to their standardization and connectivity to other networks. However, SCADA systems generally have little protection from the escalating cyber threats. In order to understand the potential danger and to protect SCADA systems, in this paper, we highlight their difference from standard IT systems and present a set of security property goals. Furthermore, we focus on systematically identifying and classifying likely cyber attacks including cyber-induced cyber-physical attack son SCADA systems. Determined by the impact on control performance of SCADA systems, the attack categorization criteria highlights commonalities and important features of such attacks that define unique challenges posed to securing SCADA systems versus traditional Information Technology(IT) systems.


title: Child-based personas: need, ability and experience

abstract: Interactive technologies are becoming ubiquitous in many children’s lives. From school to home, technologies are changing the way children live. However, current methods of designing these technologies do not adequately consider children’s needs and developmental abilities. This paper describes and illustrates a new approach for creating user abstractions of children called the child-persona technique. Child-personas integrate theoretical concepts, empirically generated data and experiential goals. An analysis of the utility of this technique provides insights about how this technique can benefit designers by generating realistic child-user abstractions through a process which supports designers in child-centric design.


title: Animating Lip-Sync Characters

abstract: Speech animation is traditionally considered as important but tedious work for most applications, especially when taking lip synchronization (lip-sync) into consideration, because the muscles on the face are complex and interact dynamically. Although there are several methods proposed to ease the burden on artists to create facial and speech animation, almost none are fast and efficient. In this paper, we introduce a framework for synthesizing lip-sync character speech animation from a given speech sequence and its corresponding text. Starting from training the dominated animeme models for each kind of phoneme by learning the animation control signals of the character through an EM-style optimization approach, and further decomposing the dominated animeme models to the polynomial-fitted animeme models and corresponding dominance functions while taking coarticulation into account. Finally, given a novel speech sequence and its corresponding text, a lip-sync character speech animation can be synthesized in a very short time with the dominated animeme models. The synthesized lipsync animation can even preserve exaggerated characteristics of the character’s facial geometry. Moreover, since our method can synthesize an acceptable and robust lip-sync animation in almost realtime, it can be used for many applications, such as lip-sync animation prototyping, multilingual animation reproduction, avatar speech, mass animation production, etc.


title: SeDaSC: Secure Data Sharing in Clouds

abstract: Cloud storage is an application of clouds that liberates organizations from establishing in-house data storage systems. However, cloud storage gives rise to security concerns. In case of group-shared data, the data face both cloud-specific and conventional insider threats. Secure data sharing among a group that counters insider threats of legitimate yet malicious users is an important research issue. In this paper, we propose the Secure Data Sharing in Clouds (SeDaSC) methodology that provides: 1) data confidentiality and integrity; 2) access control; 3) data sharing (forwarding) without using compute-intensive reencryption; 4) insider threat security; and 5) forward and backward access control. The SeDaSC methodology encrypts a file with a single encryption key. Two different key shares for each of the users are generated, with the user only getting one share. The possession of a single share of a key allows the SeDaSC methodology to counter the insider threats. The other key share is stored by a trusted third party, which is called the cryptographic server. The SeDaSC methodology is applicable to conventional and mobile cloud computing environments. We implement a working prototype of the SeDaSC methodology and evaluate its performance based on the time consumed during various operations. We formally verify the working of SeDaSC by using high-level Petri nets, the Satisfiability Modulo Theories Library, and a Z3 solver. The results proved to be encouraging and show that SeDaSC has the potential to be effectively used for secure data sharing in the cloud.


title: CrossNorm: Normalization for Off-Policy TD Reinforcement Learning

abstract: Off-policy Temporal Difference (TD) learning methods, when combined with function approximators, suffer from the risk of divergence, a phenomenon known as the deadly triad. It has long been noted that some feature representations work better than others. In this paper we investigate how feature normalization can prevent divergence and improve training. Our method, which we call CrossNorm, can be regarded as a new variant of batch normalization that re-centers data for multi-modal distributions, which occur in the off-policy TD updates. We show empirically that CrossNorm improves the stability of the learning process. We apply CrossNorm to DDPG and TD3 and achieve stable training and improved performance across a range of MuJoCo benchmark tasks. Moreover, for the first time, we are able to train DDPG stably without the use of target networks.


title: Improving generalization ability in a puzzle game using reinforcement learning

abstract: Nowadays machine learning has attracted much attention. In order to apply it to various problems without relearning, its generalization ability is needed. Geometry Friends is a puzzle game where a player has to collect all targets in a two-dimensional world, and it is used in some artificial intelligence competitions. Although sufficient generalization ability is needed to apply the machine learning to this game, such machine learning methods are not proposed yet. In this paper, we propose a method based on reinforcement learning in which the generalization ability is improved for Geometry Friends.


Adaptive companions in FPS games


title: Multi-objective particle swarm optimization on computer grids

abstract: In recent years, a number of authors have successfully extended particle swarmoptimization to problem domains with multiple objec\-tives. This paper addresses theissue of parallelizing multi-objec\-tive particle swarms. We propose and empirically comparetwo parallel versions which differ in the way they divide the swarminto subswarms that can be processed independently on differentprocessors. One of the variants works asynchronouslyand is thus particularly suitable for heterogeneous computer clusters asoccurring e.g.\ in moderngrid computing platforms.


title: Exploiting intra-packet dependency for fine-grained protocol format inference

abstract: Given the increasing volume and complexity of network traffic nowadays, network operators often leverage application-layer protocols to differentiate network traffic, so as to improve quality-of-service control, security protection, and resource profiling. We present ProGraph, a tool that accurately infers protocol message formats at both byte-level and bit-level granularities. Unlike existing approaches that mainly exploit statistical features across packets, ProGraph exploits intra-packet dependency among the values of different portions of a packet payload. It systematically constructs a graphical model that captures intra-packet dependency, using various techniques in graph theory and information theory. It also achieves several important design properties for real deployment, including fine-grained inference, protocol independence, simple parameterization, robustness to noisy training sets, and fast execution. We show via trace-driven evaluations that ProGraph achieves more accurate inference than existing approaches. We further show how ProGraph can be used for classifying traffic.


title: Automatic brain tissue segmentation in MR images using Random Forests and Conditional Random Fields

abstract: Abstract Background The segmentation of brain tissue into cerebrospinal fluid, gray matter, and white matter in magnetic resonance imaging scans is an important procedure to extract regions of interest for quantitative analysis and disease assessment. Manual segmentation requires skilled experts, being a laborious and time-consuming task; therefore, reliable and robust automatic segmentation methods are necessary. New method We propose a segmentation framework based on a Conditional Random Field for brain tissue segmentation, with a Random Forest encoding the likelihood function. The features include intensities, gradients, probability maps, and locations. Additionally, skull stripping is critical for achieving an accurate segmentation; thus, after extracting the brain we propose to refine its boundary during segmentation. Results The proposed framework was evaluated on the MR Brain Image Segmentation Challenge and the Internet Brain Segmentation Repository databases. The segmentations of brain tissues obtained with the proposed algorithm were competitive both in normal and diseased subjects. The skull stripping refinement significantly improved the results, when comparing against no refinement. Comparison with existing methods In the MR Brain Image Segmentation Challenge database, the results were competitive when comparing with top methods. In the Internet Brain Segmentation Repository database, the proposed approach outperformed other well-established algorithms. Conclusions The combination of a Random Forest and Conditional Random Field for brain tissue segmentation performed well for normal and diseased subjects. Additionally, refinement of the skull stripping at segmentation time is feasible in learning-based methods and significantly improves the segmentation of cerebrospinal fluid and intracranial volume.


title: Mediators and Mechanisms of Change in Psychotherapy Research

abstract: There has been enormous progress in psychotherapy research. This has culminated in recognition of several treatments that have strong evidence in their behalf. Even so, after decades of psychotherapy research, we cannot provide an evidence-based explanation for how or why even our most well studied interventions produce change, that is, the mechanism(s) through which treatments operate. This chapter presents central requirements for demonstrating mediators and mechanisms of change and reviews current data-analytic and designs approaches and why they fall short of meeting these requirements. The role of the therapeutic alliance in psychotherapy and cognitive changes in cognitive therapy for depression are highlighted to illustrate key issues. Promising lines of work to identify mediators and mechanisms, ways of bringing to bear multiple types of evidence, recommendations to make progress in understanding how therapy works, and conceptual and research challenges in evaluating mediators and mechanisms are also presented.


title: Targeted reinnervation for enhanced prosthetic arm function in a woman with a proximal amputation: a case study

abstract: Summary Background The function of current artificial arms is limited by inadequate control methods. We developed a technique that used nerve transfers to muscle to develop new electromyogram control signals and nerve transfers to skin, to provide a pathway for cutaneous sensory feedback to the missing hand. Methods We did targeted reinnervation surgery on a woman with a left arm amputation at the humeral neck. The ulnar, median, musculocutaneous, and distal radial nerves were transferred to separate segments of her pectoral and serratus muscles. Two sensory nerves were cut and the distal ends were anastomosed to the ulnar and median nerves. After full recovery the patient was fit with a new prosthesis using the additional targeted muscle reinnervation sites. Functional testing was done and sensation in the reinnervated skin was quantified. Findings The patient described the control as intuitive; she thought about using her hand or elbow and the prosthesis responded appropriately. Functional testing showed substantial improvement: mean scores in the blocks and box test increased from 4·0 (SD 1·0) with the conventional prosthesis to 15·6 (1·5) with the new prosthesis. Assessment of Motor and Process Skills test scores increased from 0·30 to 1·98 for motor skills and from 0·90 to 1·98 for process skills. The denervated anterior chest skin was reinnervated by both the ulnar and median nerves; the patient felt that her hand was being touched when this chest skin was touched, with near-normal thresholds in all sensory modalities. Interpretation Targeted reinnervation improved prosthetic function and ease of use in this patient. Targeted sensory reinnervation provides a potential pathway for meaningful sensory feedback.


title: IEEE TRANSACTIONS ON COMMUNICATIONS, VOL. COM-33, NO. 7, JULY-1985 Analysis and Simulation of a Digital Mobile Channel Using Orthogonal Frequency Division Multiplexing

abstract: This paper discusses the analysis and simulation of a technique for combating the effects of multipath propagation and cochannel interference on a narrow-band digital mobile channel. This system uses the discrete Fourier transform to orthogonally frequency multiplex many narrow subchannels, each signaling at a very low rate, into one high-rate channel. When this technique is used with pilot-based correction, the effects of flat Rayleigh fading can be reduced significantly. An improvement in signal-to-interference ratio of 6 dB can be obtained over the bursty Rayleigh channel. In addition, with each subchannel signaling at a low rate, this technique can provide added protection against delay spread. To enhance the behavior of the technique in a heavily frequency-selective environment, interpolated pilots are used. A frequency offset reference scheme is employed for the pilots to improve protection against cochannel interference.


title: Wideband Planar Microwave Lenses Using Sub-Wavelength Spatial Phase Shifters

abstract: We present a new technique for designing low-profile planar microwave lenses. The proposed lenses consist of numerous miniature spatial phase shifters distributed over a planar surface. The topology of each spatial phase shifter (SPS) is based on the design of a class of bandpass frequency selective surfaces composed entirely of sub-wavelength, non-resonant periodic structures. A procedure for designing the proposed lenses and their constituting spatial phase shifters is also presented in the paper. This design procedure is applied to two different planar lenses operating at X-band. Each lens is a low-profile structure with an overall thickness of 0.08λ0 and uses sub-wavelength SPSs with dimensions of 0.2λ0 × 0.2λ0, where λ0 is the free-space wavelength at 10 GHz. These prototypes are fabricated and experimentally characterized using a free-space measurement system and the results are reported in the paper. The fabricated prototypes demonstrate relatively wide bandwidths of approximately 20%. Furthermore, the lenses demonstrate stable responses when illuminated under oblique angles of incidence. This feature is of practical importance if these lenses are to be used in beam-scanning antenna applications.


title: The Hippocampus, Memory, and Place Cells Is It Spatial Memory or a Memory Space?

abstract: The authors thank Eric Kandel, Richard Morris, Peter Rapp, and Larry Squire for their thoughtful comments and criticisms on versions of this manuscript. This research is supported by grants from NIMH and NIA.


title: Niacin, an Old Drug, has New Effects on Central Nervous System Disease

abstract: Increased serum cholesterol and decreased high-density lipoprotein (HDL) cholesterol level in serum and cerebro-spinal fluid is a risk factor for the development of Alzheimer disease, and also a predictor of cardiovascular events and stroke in epidemiologic studies. Niacin (vitamin B 3 or nicotinic acid) is the most effective medication in current clinical use for increasing HDL cholesterol and it substantially lowers triglycerides and LDL cholesterol. This review provides an update on the role of the increasing HDL cholesterol agent, niacin, as a neuroprotective and neurorestorative agent which promotes angiogenesis and arteriogenesis after stroke and improves neurobehavioral recovery following central nervous system diseases such as stroke, Alzheimer's disease and multiple sclerosis. The mechanisms underlying the niacin induced neuroprotective and neurorestorative effects after stroke are discussed. The primary focus of this review is on stroke, with shorter discussion on Alzheimer disease and multiple sclerosis.


title: Mispronunciation Detection and Diagnosis in L2 English Speech Using Multidistribution Deep Neural Networks

abstract: This paper investigates the use of multidistribution deep neural networks DNNs for mispronunciation detection and diagnosis MDD, to circumvent the difficulties encountered in an existing approach based on extended recognition networks ERNs. The ERNs leverage existing automatic speech recognition technology by constraining the search space via including the likely phonetic error patterns of the target words in addition to the canonical transcriptions. MDDs are achieved by comparing the recognized transcriptions with the canonical ones. Although this approach performs reasonably well, it has the following issues: 1 Learning the error patterns of the target words to generate the ERNs remains a challenging task. Phones or phone errors missing from the ERNs cannot be recognized even if we have well-trained acoustic models; and 2 acoustic models and phonological rules are trained independently, and hence, contextual information is lost. To address these issues, we propose an acoustic-graphemic-phonemic model AGPM using a multidistribution DNN, whose input features include acoustic features, as well as corresponding graphemes and canonical transcriptions encoded as binary vectors. The AGPM can implicitly model both grapheme-to-likely-pronunciation and phoneme-to-likely-pronunciation conversions, which are integrated into acoustic modeling. With the AGPM, we develop a unified MDD framework, which works much like free-phone recognition. Experiments show that our method achieves a phone error rate PER of 11.1%. The false rejection rate FRR, false acceptance rate FAR, and diagnostic error rate DER for MDD are 4.6%, 30.5%, and 13.5%, respectively. It outperforms the ERN approach using DNNs as acoustic models, whose PER, FRR, FAR, and DER are 16.8%, 11.0%, 43.6%, and 32.3%, respectively.