title: A Convolution-LSTM-Based Deep Neural Network for Cross-Domain MOOC Forum Post Classification

abstract: Learners in a massive open online course often express feelings, exchange ideas and seek help by posting questions in discussion forums. Due to the very high learner-to-instructor ratios, it is unrealistic to expect instructors to adequately track the forums, find all of the issues that need resolution and understand their urgency and sentiment. In this paper, considering the biases among different courses, we propose a transfer learning framework based on a convolutional neural network and a long short-term memory model, called ConvL, to automatically identify whether a post expresses confusion, determine the urgency and classify the polarity of the sentiment. First, we learn the feature representation for each word by considering the local contextual feature via the convolution operation. Second, we learn the post representation from the features extracted through the convolution operation via the LSTM model, which considers the long-term temporal semantic relationships of features. Third, we investigate the possibility of transferring parameters from a model trained on one course to another course and the subsequent fine-tuning. Experiments on three real-world MOOC courses confirm the effectiveness of our framework. This work suggests that our model can potentially significantly increase the effectiveness of monitoring MOOC forums in real time.


title: Analysis and Design of a Three-Level LLC Series Resonant Converter for High- and Wide-Input-Voltage Applications

abstract: In this paper, the analysis and design of a three-level LLC series resonant converter (TL LLC SRC) for high- and wide-input-voltage applications is presented. The TL LLC SRC discussed in this paper consists of two half-bridge LLC SRCs in series, sharing a resonant inductor and a transformer. Its main advantages are that the voltage across each switch is clamped at half of the input voltage and that voltage balance is achieved. Thus, it is suitable for high-input-voltage applications. Moreover, due to its simple driving signals, the additional circulating current of the conventional TL LLC SRCs does not appear in the converter, and a simpler driving circuitry is allowed to be designed. With this converter, the operation principles, the gain of the LLC resonant tank, and the zero-voltage-switching condition under wide input voltage variation are analyzed. Both the current and voltage stresses over different design factors of the resonant tank are discussed as well. Based on the results of these analyses, a design example is provided and its validity is confirmed by an experiment involving a prototype converter with an input of 400-600 V and an output of 48 V/20 A. In addition, a family of TL LLC SRCs with double-resonant tanks for high-input-voltage applications is introduced. While this paper deals with a TL LLC SRC, the analysis results can be applied to other TL LLC SRCs for wide-input-voltage applications.


title: Active Electrodes for Wearable EEG Acquisition: Review and Electronics Design Methodology

abstract: Active electrodes (AEs), i.e., electrodes with built-in readout circuitry, are increasingly being implemented in wearable healthcare and lifestyle applications due to AEs' robustness to environmental interference. An AE locally amplifies and buffers μV-level EEG signals before driving any cabling. The low output impedance of an AE mitigates cable motion artifacts, thus enabling the use of high-impedance dry electrodes for greater user comfort. However, developing a wearable EEG system, with medical grade signal quality on noise, electrode offset tolerance, common-mode rejection ratio, input impedance, and power dissipation, remains a challenging task. This paper reviews state-of-the-art bio-amplifier architectures and low-power analog circuits design techniques intended for wearable EEG acquisition, with a special focus on an AE system interfaced with dry electrodes.


3 Fundamentals of spatial data warehousing


title: Big Data and Urban Informatics: Innovations and Challenges to Urban Planning and Knowledge Discovery

abstract: Big Data is the term being used to describe a wide spectrum of observational or “naturally-occurring” data generated through transactional, operational, planning and social activities that are not specifically designed for research. Due to the structure and access conditions associated with such data, their use for research and analysis becomes significantly complicated. New sources of Big Data are rapidly emerging as a result of technological, institutional, social, and business innovations. The objective of this background paper is to describe emerging sources of Big Data, their use in urban research, and the challenges that arise with their use. To a certain extent, Big Data in the urban context has become narrowly associated with sensor (e.g., Internet of Things) or socially generated (e.g., social media or citizen science) data. However, there are many other sources of observational data that are meaningful to different groups of urban researchers and user communities. Examples include privately held transactions data, confidential administrative micro-data, data from arts and humanities collections, and hybrid data consisting of synthetic or linked data.


title: Nudges for Privacy and Security: Understanding and Assisting Users’ Choices Online

abstract: Advancements in information technology often task users with complex and consequential privacy and security decisions. A growing body of research has investigated individuals’ choices in the presence of privacy and information security tradeoffs, the decision-making hurdles affecting those choices, and ways to mitigate such hurdles. This article provides a multi-disciplinary assessment of the literature pertaining to privacy and security decision making. It focuses on research on assisting individuals’ privacy and security choices with soft paternalistic interventions that nudge users toward more beneficial choices. The article discusses potential benefits of those interventions, highlights their shortcomings, and identifies key ethical, design, and research challenges.


title: The Palliative Effects of System Justification on the Health and Happiness of Lesbian, Gay, Bisexual, and Transgender Individuals

abstract: Across three studies, we examine the correlates of subjective well-being and mental and physical health among members of a historically disadvantaged group, namely, lesbian, gay, bisexual, and tran...


Gate Count Capacity Metrics for FPGAs


title: Development of human brain

abstract: The human has the most complex brain of the primates group. Their development is prolonged beyond birth and it is not completed structurally nor neurochemically until age of 20 years. Decades later, degenerative phenomena begin to be evident, that little by little will drive us to death. At the end of the life, 113 grams aproximate of cerebral mass are lost. The endowment of furrows and convolutions are fixed before birth, while the cerebral surface ends up having values next to the adult at 2 years. However the cerebral weight reaches its maximum at the 20 year-old decade. A first phase of neuronal death appears during the prenatal life, which is continued by another postnatal phase, and it ends with the definitive number of neurons. However, along life, the number of neurons decreases little by little at the time of the neuroglial cells increase. Much about brain knowledge at the moment is difficult to apply to the cranial endocasts, although approaches from the MNR are hopeful.


title: Drain Work Function Engineered Doping-Less Charge Plasma TFET for Ambipolar Suppression and RF Performance Improvement: A Proposal, Design, and Investigation

abstract: A novel device configuration is presented for doping-less charge plasma tunnel FET (TFET) for suppression of ambipolar nature with improved high-frequency figures of merit. For this, the drain electrode, which is used to induce n+ drain region, is separated into two sections of high and low work functions. The work function of the drain electrode section near to channel is considered relatively higher than other part for restricting the tunneling of holes at drain/channel interface for negative gate bias. This concept creates asymmetrical charge carrier concentration in the drain region, which increases the tunneling width at the drain/channel interface. Therefore, the proposed device offers better performance in terms of ambipolar current, parasitic capacitance, and RF parameters. In this regard, a comparative study of the proposed device is performed with conventional and dual-metal gate doping-less TFETs. Furthermore, the optimization of the length and higher work function of the drain electrode near to channel is discussed in detail for the proposed device. Apart from above-mentioned advantages, the doping-less nature of the proposed device provides fabrication simplicity and immunity against random dopant fluctuations in comparison with the physically doped TFET.


title: Visual Graphs from Motion (VGfM): Scene understanding with object geometry reasoning

abstract: Recent approaches on visual scene understanding attempt to build a scene graph -- a computational representation of objects and their pairwise relationships. Such rich semantic representation is very appealing, yet difficult to obtain from a single image, especially when considering complex spatial arrangements in the scene. Differently, an image sequence conveys useful information using the multi-view geometric relations arising from camera motion. Indeed, in such cases, object relationships are naturally related to the 3D scene structure. To this end, this paper proposes a system that first computes the geometrical location of objects in a generic scene and then efficiently constructs scene graphs from video by embedding such geometrical reasoning. Such compelling representation is obtained using a new model where geometric and visual features are merged using an RNN framework. We report results on a dataset we created for the task of 3D scene graph generation in multiple views.


title: Contextual Memory Trees

abstract: We design and study a Contextual Memory Tree (CMT), a learning memory controller that inserts new memories into an experience store of unbounded size. It is designed to efficiently query for memories from that store, supporting logarithmic time insertion and retrieval operations. Hence CMT can be integrated into existing statistical learning algorithms as an augmented memory unit without substantially increasing training and inference computation. Furthermore CMT operates as a reduction to classification, allowing it to benefit from advances in representation or architecture. We demonstrate the efficacy of CMT by augmenting existing multi-class and multi-label classification algorithms with CMT and observe statistical improvement. We also test CMT learning on several image-captioning tasks to demonstrate that it performs computationally better than a simple nearest neighbors memory system while benefitting from reward learning.


title: Business Analytics in the Context of Big Data: A Roadmap for Research

abstract: This paper builds on academic and industry discussions from the 2012 and 2013 pre-ICIS events: BI Congress III and the Special Interest Group on Decision Support Systems (SIGDSS) workshop, respectively. Recognizing the potential of “big data” to offer new insights for decision making and innovation, panelists at the two events discussed how organizations can use and manage big data for competitive advantage. In addition, expert panelists helped to identify research gaps. While emerging research in the academic community identifies some of the issues in acquiring, analyzing, and using big data, many of the new developments are occurring in the practitioner community. We bridge the gap between academic and practitioner research by presenting a big data analytics framework that depicts a process view of the components needed for big data analytics in organizations. Using practitioner interviews and literature from both academia and practice, we identify the current state of big data research guided by the framework and propose potential areas for future research to increase the relevance of academic research to practice.


title: A hovering control strategy for a tail-sitter VTOL UAV that increases stability against large disturbance

abstract: The application range of UAVs (unmanned aerial vehicles) is expanding along with performance upgrades. Vertical take-off and landing (VTOL) aircraft has the merits of both fixed-wing and rotary-wing aircraft. Tail-sitting is the simplest way for the VTOL maneuver since it does not need extra actuators. However, conventional hovering control for a tail-sitter UAV is not robust enough against large disturbance such as a blast of wind, a bird strike, and so on. It is experimentally observed that the conventional quaternion feedback hovering control often fails to keep stability when the control compensates large attitude errors. This paper proposes a novel hovering control strategy for a tail-sitter VTOL UAV that increases stability against large disturbance. In order to verify the proposed hovering control strategy, simulations and experiments on hovering of the UAV are performed giving large attitude errors. The results show that the proposed control strategy successfully compensates initial large attitude errors keeping stability, while the conventional quaternion feedback controller fails.


title: ABAid: Navigation Aid for Blind People Using Acoustic Signal

abstract: Blind mobility aid is a primary part in the daily life of blind people. Although plenty of systems or devices are invented to make the navigation of blind people easier, those are generally expensive and hardly affordable for them. To solve these issues, we introduce ABAid, a novel system designed for blind or visually impaired people to navigate, with commercial off-the-shelf (COTS) mobile devices. Based on in-depth acoustic localization and gyroscope techniques, this system is not only the means of huge convenience to carry, but also is capable of detecting obstacles before reaching them. In our experiments designed to detect the distance of wall, the proposed system achieves 3.24% average error rate. It can further measure the direction of wall, and the average error in this case is 2.73°. With high accuracy and stable measurement, ABAid is able to help blind people move independently in fairly uncomplicated scenarios.


title: Collision-minimizing CSMA and its applications to wireless sensor networks

abstract: Recent research in sensor networks, wireless location systems, and power-saving in ad hoc networks suggests that some applications' wireless traffic be modeled as an event-driven workload: a workload where many nodes send traffic at the time of an event, not all reports of the event are needed by higher level protocols and applications, and events occur infrequently relative to the time needed to deliver all required event reports. We identify several applications that motivate the event-driven workload and propose a protocol that is optimal for this workload. Our proposed protocol, named CSMA/p/sup */, is nonpersistent carrier sense multiple access (CSMA) with a carefully chosen nonuniform probability distribution p/sup */ that nodes use to randomly select contention slots. We show that CSMA/p/sup */ is optimal in the sense that p/sup */ is the unique probability distribution that minimizes collisions between contending stations. CSMA/p/sup */ has knowledge of N. We conclude with an exploration of how p/sup */ could be used to build a more practical medium access control protocol via a probability distribution with no knowledge of N that approximates p/sup */.


title: Self-produced Guidance for Weakly-Supervised Object Localization

abstract: Weakly supervised methods usually generate localization results based on attention maps produced by classification networks. However, the attention maps exhibit the most discriminative parts of the object which are small and sparse. We propose to generate Self-produced Guidance (SPG) masks which separate the foreground, the object of interest, from the background to provide the classification networks with spatial correlation information of pixels. A stagewise approach is proposed to incorporate high confident object regions to learn the SPG masks. The high confident regions within attention maps are utilized to progressively learn the SPG masks. The masks are then used as an auxiliary pixel-level supervision to facilitate the training of classification networks. Extensive experiments on ILSVRC demonstrate that SPG is effective in producing high-quality object localizations maps. Particularly, the proposed SPG achieves the Top-1 localization error rate of 43.83% on the ILSVRC validation set, which is a new state-of-the-art error rate.


title: Which facial profile do humans expect after seeing a frontal view? a comparison with a linear face model

abstract: Manipulated versions of three-dimensional faces that have different profiles, but almost the same appearance in frontal views, provide a novel way to investigate if and how humans use class-specific knowledge to infer depth from images of faces. After seeing a frontal view, participants have to select the profile that matches that view. The profiles are original (ground truth), average, random other, and two solutions computed with a linear face model (3D Morphable Model). One solution is based on 2D vertex positions, the other on pixel colors in the frontal view. The human responses demonstrate that humans neither guess nor just choose the average profile. The results also indicate that humans actually use the information from the front view, and not just rely on the plausibility of the profiles per se. All our findings are perfectly consistent with a correlation-based inference in a linear face model. The results also verify that the 3D reconstructions from our computational algorithms (stimuli 4 and 5) are similar to what humans expect, because they are chosen to be the true profile equally often as the ground-truth profiles. Our experiments shed new light on the mechanisms of human face perception and present a new quality measure for 3D reconstruction algorithms.


title: Consumers' online repurchase intentions: Perspectives from Malaysia

abstract: This paper investigated the factors that influence consumers in Malaysia to repurchase online. The emergence of Internet technology has created avenues for all firms to stay competitive through a more proactive approach, which involves building a sustainable business practice that leads to better business opportunities in this digital era. It is becoming critical for online firms to understand their customers in order to retain them. The conceptual framework is based on extended Technology Acceptance Model with eight independent variables. Data analysis was based on 348 valid responses of young adults in Malaysia. Results indicate that perceived value, perceived ease of use, perceived usefulness, firm reputation, privacy, trust, reliability and functionality tend to influence consumers' intentions to repurchase online. The findings would be useful for literature development in the subject area, particularly in Malaysia. The findings would also be useful to theoretical development for online business research.


title: A Demand-Aware Predictive Dynamic Bandwidth Allocation Mechanism for Wireless Network-on-Chip

abstract: Long distance data communication over multi-hop wireline paths in conventional Networks-on-Chips (NoCs) cause high energy consumption and degradation in bandwidth. Wireless interconnects in the millimeter-wave band have emerged as an energy-efficient interconnection paradigm for multi-core chips interconnected with NoCs. However, spatial variations in traffic distribution and temporal variations in workloads can exert variable bandwidth demands on the NoC fabric. Wireless interconnects which do not require a physical layout of interconnects can be utilized to mitigate this issue. In order to dynamically allocate variable bandwidth to the wireless transceivers depending on the demand, the design of a dynamic and efficient Medium Access Control (MAC) mechanism to grant access to the on-chip wireless communication channel is needed. In this paper, a history based predictor, which can predict the bandwidth demand of the wireless nodes in the wireless NoC is designed. Based on these predicted demands we propose the design of two MAC mechanisms that are able to dynamically allocate bandwidth to the wireless transceivers. Through system level simulations, we show that the demand-aware MAC mechanisms are more energy efficient as well as capable of sustaining higher data bandwidth in wireless NoCs.


title: Pill-ID : Matching and retrieval of drug pill images q

abstract: Highlights? This is the first systematic study of drug pill matching and retrieval. ? We have used robust SIFT and MLBP descriptors. ? The Pill-ID system can handle pill images with missing or damaged features. Worldwide, law enforcement agencies are encountering a substantial increase in the number of illicit drug pills being circulated in our society. Identifying the source and manufacturer of these illicit drugs will help deter drug-related crimes. We have developed an automatic system, called Pill-ID to match drug pill images based on several features (i.e., imprint, color, and shape) of the tablet. The color and shape information is encoded as a three-dimensional histogram and invariant moments, respectively. The imprint on the pill is encoded as feature vectors derived from SIFT and MLBP descriptors. Experimental results using a database of drug pill images (1029 illicit drug pill images and 14,002 legal drug pill images) show 73.04% (84.47%) rank-1 (rank-20) retrieval accuracy.


Full H-band waveguide-to-coupled microstrip transition using dipole antenna with directors


title: Middleware for Internet of things

abstract: The Future Internet enables us to have an immediate access to information about the physical world and its objects. As such, Internet of Things (IoT) has been introduced to integrate the virtual world of information and the real world of devices. Internet of Things covers the infrastructure, which can be hardware, software and services, to support the networking of physical objects. IoT aims to provide a simple interaction between the physical world and the virtual world, by integrating a large numbers of real-world physical devices (or things) into the Internet. ::: IoT has increasingly gained attention in industry to interact with different types of devices. This popularity cause a demand to use IoT vision for different types of device. While each type of devices can support its own communication protocol and required data to provide data for each interaction. This heterogeneous device interaction cause difficulties to interact with the devices to gather information from the environment. The solution that has introduced in the literatures is defining a middleware layer between the devices and the user of the IoT- based system. ::: In this research, we investigate on developing a middleware for an IoT-based system like video Monitoring System (VMS), to facilitate configuration and deployment for non-expert end-users. A VMS is responsible to provide full video coverage to monitor an area for an end-user, such as a guard. The configuration and deployment can be facilitated by providing a homogeneous Graphical User Interface to interact with different types of camera in a uniform way. ::: A VMS must support the technical details of different types of cameras. However, these variations should be hidden from non-expert end-users. Thus, we extract a model from the required features to configure different types of cameras. In this project, we developed a VMS that consist of a Middleware for video Monitoring System (MVMS) and applications, which run on top of the middleware. Our VMS let non-experts end-users configure cameras through communicating with third-party camera service providers which is responsible to apply end- users configuration on cameras. ::: To evaluate our VMS to achieve the ease of configuration and deployment for non-expert end-users, we developed a prototype and interview with the practitioners in a company which has developed VMS.


Balancing latency and availability in geo-distributed cloud data stores


title: Fast DTT: a near linear algorithm for decomposing a tensor into factor tensors

abstract: As tensors provide a natural representation for the higher-order relations, tensor factorization techniques such as Tucker decomposition and CANDECOMP/PARAFAC decomposition have been applied to many fields. Tucker decomposition has strong capacity of expression, but the time complexity is unpractical for the large-scale real problems. On the other hand, CANDECOMP/PARAFAC decomposition is linear in the feature dimensionality, but the assumption is so strong that it abandons some important information. Besides, both of TD and CP decompose a tensor into several factor matrices. However, the factor matrices are not natural for the representation of the higher-order relations. To overcome these problems, we propose a near linear tensor factorization approach, which decompose a tensor into factor tensors in order to model the higher-order relations, without loss of important information. In addition, to reduce the time complexity and the number of the parameters, we decompose each slice of the factor tensors into two smaller matrices. We conduct experiments on both synthetic datasets and real datasets. The experimental results on the synthetic datasets validate that our model has strong capacity of expression. The results on the real datasets show that our approach outperforms the state-of-the-art tensor factorization methods.


title: Fuzzy Number Intuitionistic Fuzzy Arithmetic Aggregation Operators

abstract: A fuzzy number intuitionistic fuzzy set (FNIFS) is a generalization of intuitionistic fuzzy set. The fundamental characteristic of FNIFS is that the values of its membership function and non-membership function are trigonometric fuzzy numbers rather than exact numbers. In this paper, we define some operational laws of fuzzy number intuitionistic fuzzy numbers, and, based on these operational laws, develop some new arithmetic aggregation operators, such as the fuzzy number intuitionistic fuzzy weighted averaging (FIFWA) operator, the fuzzy number intuitionistic fuzzy ordered weighted averaging (FIFOWA) operator and the fuzzy number intuitionistic fuzzy hybrid aggregation (FIFHA) operator for aggregating fuzzy number intuitionistic fuzzy information. Furthermore, we give an application of the FIFHA operator to multiple attribute decision making based on fuzzy number intuitionistic fuzzy information. Finally, an illustrative example is given to verify the developed approach.


title: A critical review of techniques for classifying quality attributes in the Kano model

abstract: Purpose – The purpose of this paper is: to review the most commonly used approaches to the classification of quality attributes according to the Kano model; to identify the theoretical/practical strengths and weaknesses of these techniques; and to provide guidance for future research and managerial practice in this area.Design/methodology/approach – Based on an extensive review of the literature on the Kano model and the relevant marketing/management literature, five approaches (Kano's method; “penalty‐reward contrast analysis”; “importance grid”; qualitative data methods; and “direct classification”) are evaluated in terms of their validity and reliability for categorising attributes in the Kano model. Several illustrative examples provide empirical evidence for the theoretical arguments advanced in the study.Findings – The Kano questionnaire and the direct‐classification method are the only approaches that are capable of classifying Kano attributes in the design stage of a product/service. Penalty‐rewar...


title: Online impedance spectroscopy estimation of a battery

abstract: Electrochemical impedance spectroscopy (EIS) is a helpful tool to understand how a battery is behaving and how it degrades. One of the disadvantages is that it is typically an 'off-line' process. This paper investigates an alternative method of looking at impedance spectroscopy of a battery system while it is on-line and operational by manipulating the switching pattern of the dc-dc converter to generate low frequency harmonics in conjunction with the normal high frequency switching pattern to determine impedance in real time. However, this adds extra ripple on the inductor which needs to be included in the design calculations. The paper describes the methodology and presents some experimental results in conjunction with EIS results to illustrate the concept.


Probabilistic Logic and the Synthesis of Reliable Organisms from Unreliable Components


title: The price of anarchy in supply chains: Quantifying the efficiency of price-only contracts

abstract: In this paper, we quantify the efficiency of decentralized supply chains that use price-only contracts. With a price-only contract, a buyer and a seller agree only on a constant transaction price, without specifying the amount that will be transferred. It is well known that these contracts do not provide incentives to the parties to coordinate their inventory/capacity decisions. We measure efficiency with the price of anarchy (PoA), defined as the largest ratio of profits between the integrated supply chain (that is, fully coordinated) and the decentralized supply chain. We characterize the efficiency of various supply chain configurations: push or pull inventory positioning, two or more stages, serial or assembly systems, single or multiple competing suppliers, and single or multiple competing retailers.


title: NIR- and FRET-based sensing of Cu2+ and S2- in physiological conditions and in live cells.

abstract: We have synthesized a new indole functionalized rhodamine derivative L1 which specifically binds to Cu2+ in the presence of large excess of other competing ions with visually observable changes in their electronic and fluorescence spectral behavior. These spectral changes are significant enough in the NIR and visible region of the spectrum and thus enable naked eye detection. The receptor, L1, could be employed as a resonance energy transfer (RET) based sensor for detection of Cu2+ based on the process involving the donor indole and the acceptor Cu2+ bound xanthene fragment. Studies reveal that L1–Cu complex is selectively and fully reversible in presence of sulfide anions. Further, fluorescence microscopic studies confirmed that the reagent L1 could also be used as an imaging probe for detection of uptake of these ions in HeLa cells.


BUILDING INFORMATION MODELLING AND HERITAGE DOCUMENTATION


title: Social Network Structures in Open Source Software Development Teams

abstract: Drawing on social network theories and previous studies, this research examines the dynamics of social network structures in open source software (OSS) teams. Three projects were selected from SourceForge.net in terms of their similarities as well as their differences. Monthly data were extracted from the bug tracking systems in order to achieve a longitudinal view of the interaction pattern of each project. Social network analysis was used to generate the indices of social structure. The finding suggests that the interaction pattern of OSS projects evolves from a single hub at the beginning to a core/periphery model as the projects move forward.


title: Legal issues of honeynet's generations

abstract: Honeynets have now become a standard part of security measures within the organization. Their purpose is to protect critical information systems and information; this is complemented by acquisition of information about the network threats, attackers and attacks. It is very important to consider issues affecting the deployment and usage of the honeypots and honeynets. This paper discusses the legal issues of honeynets considering their generations. Paper focuses on legal issues of core elements of honeynets, especially data control, data capture and data collection. Paper also draws attention on the issues pertaining to privacy and liability. The analysis of legal issues is based on EU law and it is supplemented by a review of the research literature, related to legal aspects of honeypots and honeynets.


title: Acquisition Multiple Access ( FAMA ) inSingle-Channel Wireless Networks

abstract: The FAMA-NCS protocol is introduced for wireless LANs and ad-hoc networks that are based on a single channel and asynchronous transmissions (i.e., no time slotting). FAMA-NCS (for floor acquisition multiple access with non-persistent carrier sensing) guarantees that a single sender is able to send data packets free of collisions to a given receiver at any given time. FAMA-NCS is based on a three-way handshake between sender and receiver in which the sender uses non-persistent carrier sensing to transmit a request-to-send (RTS) and the receiver sends a clear-to-send (CTS) that lasts much longer than the RTS to serve as a “busy tone” that forces all hidden nodes to back off long enough to allow a collision-free data packet to arrive at the receiver. It is shown that carrier sensing is needed to support collision-free transmissions in the presence of hidden terminals when nodes transmit RTSs asynchronously. The throughput of FAMA-NCS is analyzed for single-channel networks with and without hidden terminals; the analysis shows that FAMA-NCS performs better than ALOHA, CSMA, and all prior proposals based on collision avoidance dialogues (e.g., MACA, MACAW, and IEEE 802.11 DFWMAC) in the presence of hidden terminals. Simulation experiments are used to confirm the analytical results.


Challenges in the implementation of Fourth Generation Wireless Systems


title: Managing the Pre- and Post-analytical Phases of the Total Testing Process

abstract: For many years, the clinical laboratory's focus on analytical quality has resulted in an error rate of 4-5 sigma, which surpasses most other areas in healthcare. However, greater appreciation of the prevalence of errors in the pre- and post-analytical phases and their potential for patient harm has led to increasing requirements for laboratories to take greater responsibility for activities outside their immediate control. Accreditation bodies such as the Joint Commission International (JCI) and the College of American Pathologists (CAP) now require clear and effective procedures for patient/sample identification and communication of critical results. There are a variety of free on-line resources available to aid in managing the extra-analytical phase and the recent publication of quality indicators and proposed performance levels by the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) working group on laboratory errors and patient safety provides particularly useful benchmarking data. Managing the extra-laboratory phase of the total testing cycle is the next challenge for laboratory medicine. By building on its existing quality management expertise, quantitative scientific background and familiarity with information technology, the clinical laboratory is well suited to play a greater role in reducing errors and improving patient safety outside the confines of the laboratory.


title: Malware Analysis using Multiple API Sequence Mining Control Flow Graph

abstract: Malwares are becoming persistent by creating full- edged variants of the same or different family. Malwares belonging to same family share same characteristics in their functionality of spreading infections into the victim computer. These similar characteristics among malware families can be taken as a measure for creating a solution that can help in the detection of the malware belonging to particular family. In our approach we have taken the advantage of detecting these malware families by creating the database of these characteristics in the form of n-grams of API sequences. We use various similarity score methods and also extract multiple API sequences to analyze malware effectively.


title: Retrieval practice does not safeguard memories from interference-based forgetting

abstract: Abstract Retrieval enhances long-term retention. However, reactivation of a memory also renders it susceptible to modifications as shown by studies on memory reconsolidation. The present study explored whether retrieval diminishes or enhances subsequent retroactive interference (RI) and intrusions. Participants learned a list of objects. Two days later, they were either asked to recall the objects, given a subtle reminder, or were not reminded of the first learning session. Then, participants learned a second list of objects or performed a distractor task. After another two days, retention of List 1 was tested. Although retrieval enhanced List 1 memory, learning a second list impaired memory in all conditions. This shows that testing did not protect memory from RI. While a subtle reminder before List 2 learning caused List 2 items to later intrude into List 1 recall, very few such intrusions were observed in the testing and the no reminder conditions. The findings are discussed in reference to the reconsolidation account and the testing effect literature, and implications for educational practice are outlined.


title: Moving target defense: state of the art and characteristics

abstract: Moving target defense (MTD) has emerged as one of the game-changing themes to alter the asymmetric situation between attacks and defenses in cyber-security. Numerous related works involving several facets of MTD have been published. However, comprehensive analyses and research on MTD are still absent. In this paper, we present a survey on MTD technologies to scientifically and systematically introduce, categorize, and summarize the existing research works in this field. First, a new security model is introduced to describe the changes in the traditional defense paradigm and security model caused by the introduction of MTD. A function-and-movement model is provided to give a panoramic overview on different perspectives for understanding the existing MTD research works. Then a systematic interpretation of published literature is presented to describe the state of the art of the three main areas in the MTD field, namely, MTD theory, MTD strategy, and MTD evaluation. Specifically, in the area of MTD strategy, the common characteristics shared by the MTD strategies to improve system security and effectiveness are identified and extrapolated. Thereafter, the methods to implement these characteristics are concluded. Moreover, the MTD strategies are classified into three types according to their specific goals, and the necessary and sufficient conditions of each type to create effective MTD strategies are then summarized, which are typically one or more of the aforementioned characteristics. Finally, we provide a number of observations for the future direction in this field, which can be helpful for subsequent researchers.


title: Matrix Metalloproteinase 12 Overexpression in Lung Epithelial Cells Plays a Key Role in Emphysema to Lung Bronchioalveolar Adenocarcinoma Transition

abstract: Chronic obstructive pulmonary disease (COPD) and lung cancer are two diseases that are related to smoking in humans. The molecular mechanism linking these two diseases is poorly understood. Matrix metalloproteinase 12 (MMP12) is a member of the MMP family, which can be induced by smoking. Because MMP12 overexpression in epithelial cells has been reported in inflammation-triggered lung remodeling, a murine CCSP-rtTA/(tetO)(7)-MMP12 bitransgenic model was created. In this model, MMP12-Flag fusion protein overexpression and its increased enzymatic activity were observed in the lung in an inducible manner, which led to inflammatory cell infiltration and increased epithelial growth. In sequential events, spontaneous emphysema and bronchioalveolar adenocarcinoma were developed as a result of MMP12 overexpression. During this process, the concentration of interleukin-6 was steadily increased in bronchioalveolar lavage fluid, which activated the oncogenic signal transducer and activator of transcription 3 (Stat3) in alveolar type II epithelial cells. Expression of Stat3 downstream genes that are known to stimulate inflammation and tumor formation was significantly increased in the lung. When tested in humans, MMP12 up-regulation was highly associated with COPD and lung cancer in patients. Together, these studies support that MMP12 is a potent proinflammatory and oncogenic molecule. MMP12 up-regulation plays a critical role in emphysema to lung cancer transition that is facilitated by inflammation.


title: Emerging trends in regenerative medicine : a scientometric analysis in CiteSpace

abstract: INTRODUCTION ::: Regenerative medicine involves research in a number of fields and disciplines such as stem cell research, tissue engineering and biological therapy in general. As research in these areas advances rapidly, it is critical to keep abreast of emerging trends and critical turns of the development of the collective knowledge. ::: ::: ::: AREAS COVERED ::: A progressively synthesized network is derived from 35,963 original research and review articles that cite 3875 articles obtained from an initial topic search on regenerative medicine between 2000 and 2011. CiteSpace is used to facilitate the analysis of the intellectual structure and emerging trends. ::: ::: ::: EXPERT OPINION ::: A major ongoing research trend is concerned with finding alternative reprogramming techniques as well as refining existing ones for induced pluripotent stem cells (iPSCs). A more recent emerging trend focuses on the structural and functional equivalence between iPSCs and human embryonic stem cells and potential clinical and therapeutic implications on regenerative medicine in a long run. The two trends overlap in terms of what they cite, but they are distinct and have different implications on future research. Visual analytics of the literature provides a valuable, timely, repeatable and flexible approach in addition to traditional systematic reviews so as to track the development of new emerging trends and identify critical evidence.


title: RFID in robot-assisted indoor navigation for the visually impaired

abstract: We describe how radio frequency identification (RFID) can be used in robot-assisted indoor navigation for the visually impaired. We present a robotic guide for the visually impaired that was deployed and tested both with and without visually unpaired participants in two indoor environments. We describe how we modified the standard potential fields algorithms to achieve navigation at moderate walking speeds and to avoid oscillation in narrow spaces. The experiments illustrate that passive RFID tags deployed in the environment can act as reliable stimuli that trigger local navigation behaviors to achieve global navigation objectives.


title: An empirical analysis of different sparse penalties for autoencoder in unsupervised feature learning

abstract: Machine learning algorithms depend heavily on the data representation, which dominates its success in experiment accuracy. Autoencoder model structure is proposed to learn from data a good representation with the least possible amount of distortion. Furthermore, it has been proven that boosting sparsity when learning representation can significantly improve performance on classification tasks and also make the feature vector easy to interpret. One straightforward approach for autoencoder to obtain sparse representation is to impose sparse penalty on its overall cost function. Nevertheless, few comparative analysis has been conducted to evaluate which sparse penalty term works better. In this paper, we adopt L1 norm, L2 norm, Student-t penalties, which are rarely deployed to penalise the hidden unit outputs, and commonly used penalty KL-divergence in the literature. Then, we present a detailed analysis to evaluate which penalty achieves better result in terms of reconstruction error, sparseness of representation and classification performance on test datasets. Experimental study on MNIST, CIFAR-10, SVHN, OPTDIGITS and NORB datasets reveals that all these penalties achieve sparse representation and outperforms representations learned by pure autoencoder on classification performance and sparseness of feature vectors. Moreover, we hope this topics and the practices would provide insights for future research.


N4ITK: Improved N3 bias correction with robust B-spline approximation


title: 60GHz low-loss compact phase shifters using a transformer-based hybrid in 65nm CMOS

abstract: Two compact, low-loss, passive reflective type 60GHz phase shifters are presented in a standard 65nm CMOS technology. The designs use lumped-element baluns to implement the hybrid with an insertion loss better than 0.7dB. The first architecture achieves 180 degrees phase shift with an average loss of 6.6dB and area of 0.031mm2. The second phase shifter demonstrates the best reported average loss of 4.5dB with an area of 0.048mm2 while having ∼150 degrees of phase shift. Both designs provide more than 10GHz of bandwidth. These are the smallest reported 60GHz phase shifters in silicon.


title: Poisson Vector Graphics (PVG) and Its Closed-Form Solver

abstract: This paper presents Poisson vector graphics, an extension of the popular first-order diffusion curves, for generating smooth-shaded images. Armed with two new types of primitives, namely Poisson curves and Poisson regions, PVG can easily produce photorealistic effects such as specular highlights, core shadows, translucency and halos. Within the PVG framework, users specify color as the Dirichlet boundary condition of diffusion curves and control tone by offsetting the Laplacian, where both controls are simply done by mouse click and slider dragging. The separation of color and tone not only follows the basic drawing principle that is widely adopted by professional artists, but also brings three unique features to PVG, i.e., local hue change, ease of extrema control, and permit of intersection among geometric primitives, making PVG an ideal authoring tool. ::: To render PVG, we develop an efficient method to solve 2D Poisson's equations with piecewise constant Laplacians. In contrast to the conventional finite element method that computes numerical solutions only, our method expresses the solution using harmonic B-spline, whose basis functions can be constructed locally and the control coefficients are obtained by solving a small sparse linear system. Our closed-form solver is numerically stable and it supports random access evaluation, zooming-in of arbitrary resolution and anti-aliasing. Although the harmonic B-spline based solutions are approximate, computational results show that the relative mean error is less than 0.3%, which cannot be distinguished by naked eyes.


title: Academic Stress and Self-Regulation among University Students in Malaysia: Mediator Role of Mindfulness

abstract: Academic stress is the most common emotional or mental state that students experience during their studies. Stress is a result of a wide range of issues, including test and exam burden, a demanding course, a different educational system, and thinking about future plans upon graduation. A sizeable body of literature in stress management research has found that self-regulation and being mindful will help students to cope up with the stress and dodge long-term negative consequences, such as substance abuse. The present study aims to investigate the influence of academic stress, self-regulation, and mindfulness among undergraduate students in Klang Valley, Malaysia, and to identify mindfulness as the mediator between academic stress and self-regulation. For this study, a total of 384 undergraduate students in Klang Valley, Malaysia were recruited. Using Correlational analysis, results revealed that there was a significant relationship between academic stress, self-regulation, and mindfulness. However, using SPSS mediational analysis, mindfulness did not prove the mediator role in the study.


title: Supporter of stability or agent of agitation? The effect of US foreign policy on coups in Latin America, 1960—99:

abstract: This article takes a two-step approach to improving our understanding of how US foreign policy signals affect the likelihood of coups in Latin America. First, a large body of qualitative literature has developed a â€˜conventional wisdomâ€™ on this subject, suggesting that pressure from the USA plays a key role in stabilizing favored leaders and destabilizing unfavored leaders. Meanwhile, quantitative scholarship analyzing coups focuses almost exclusively on intrastate factors. The first step brings these two bodies of work together by providing quantitative evidence that hostile US signals increase the likelihood of coups, while supportive signals have a stabilizing effect. The second step moves beyond the conventional wisdom by (1) reconsidering theoretical assumptions within the conventional argument and (2) identifying anomalies within the preliminary empirical analyses. These efforts reveal several factors that are likely to impact how coup plotters respond to US signals. Among these factors, empirical analyses indicate that US signals are particularly important when economic dependence on the USA increases, during the middle of a US presidentâ€™s term in office, when they have moderate levels of consistency, and when they specifically mention the military. Overall, the first stage of this article provides a robust confirmation of the conventional wisdom, while the second stage moves the literature down a path that is largely unexplored by previous work.


title: A TOA-based location algorithm reducing the errors due to non-line-of-sight (NLOS) propagation

abstract: An effective location algorithm, which considers non-line-of-sight (NLOS) propagation, is presented. By using a new variable to replace the square term and adding the loose variable, the problem becomes a mathematical programming problem, and then the NLOS propagation's effect can be evaluated. This method is simple and does not add much computation time. Compared with other methods, it has high accuracy.


title: The mean-square stabilization of networked feedback systems with channel delays

abstract: This work studies a stabilization problem via output feedback for networked linear time-invariant (LTI) discrete-time systems with quantization effects and network-induced delays. Logarithm quantization laws are adopted in the systems and the quantization errors are modeled as multiplicative noises. The network-induced delays under consideration are fixed delays. The necessary and sufficient condition of mean-square stabilization is studied for a SISO LTI networked control system. And then an analytic expression for the trade-off between quantization noise and channel delays in the mean-square stabilization problem is presented. The largest quantization noise variance with which the mean-square stability of the networked feedback system is guaranteed is obtained in terms of the fixed channel delay. While, the largest delay step which is allowed in the systems is obtained for a given quantization noise variance. Moreover, these results are extended to the case for minimum phase MIMO LTI systems.


title: Deep divergence-based clustering

abstract: A promising direction in deep learning research is to learn representations and simultaneously discover cluster structure in unlabeled data by optimizing a discriminative loss function. Contrary to supervised deep learning, this line of research is in its infancy and the design and optimization of a suitable loss function with the aim of training deep neural networks for clustering is still an open challenge. In this paper, we propose to leverage the discriminative power of information theoretic divergence measures, which have experienced success in traditional clustering, to develop a new deep clustering network. Our proposed loss function incorporates explicitly the geometry of the output space, and facilitates fully unsupervised training end-to-end. Experiments on real datasets show that the proposed algorithm achieves competitive performance with respect to other state-of-the-art methods.


title: High Levels of Process Capability in CMMI and ISO/IEC 15504

abstract: The recent release of CMMI V1.3 incorporates a number of changes to the model and framework; one of the more interesting is the decision to do away with Capability Levels 4 and 5 in the Continuous Representation, while retaining the high levels of Organizational Maturity. This paper examines some of the issues that may have driven this decision, and explores the opportunity provided for greater interaction between CMMI and ISO/IEC 15504.


title: Circularly Polarized Square Slot Antenna With a Largely Enhanced Axial-Ratio Bandwidth

abstract: A new easy-to-apply design procedure is proposed in this letter for constructing small wideband circularly polarized (CP) bidirectional and unidirectional square slot antennas. With an asymmetric T-shaped strip protruded from the signal line of the feeding coplanar waveguide (CPW) into the square slot, a reference antenna with a fractional 3-dB axial-ratio bandwidth (abbreviated as CPBW) of about 11% can be obtained. The proposed bidirectional CP slot antenna (BICPSA) can be modified from the reference antenna by stretching the left end of the T-shaped strip upward, by embedding a rectangular metal patch in the square slot, and by implanting a grounded metal strip from the right edge of the slot. The resulting BICPSA has a much larger CPBW of 39.1%, which is about 3.55 times that of the reference antenna. Finally, by placing a simple reflector underneath this BICPSA without changing its structural dimensions, one can obtain unidirectional radiation patterns with a greatly increased gain of 7.6 dBic, but with only a slightly decreased CPBW of 34.3%.


title: Investigating the IEEE 802.11ad Standard for Millimeter Wave Automotive Radar

abstract: Millimeter wave (mmWave) technology is widely used for automotive radar applications, like adaptive cruise control and obstacle detection. Unlike conventional radar waveforms which are usually propriety, this paper explores the use of a consumer wireless local area network (WLAN) waveform in the 60GHz unlicensed mmWave band for automotive radar applications. In particular, this paper develops a joint framework of long range automotive radar (LRR) and vehicle-to-vehicle communication (V2V) at 60 GHz by exploiting the special data-aided structure (repeated Golay complimentary sequences) of an IEEE 802.11ad single carrier physical layer (SCPHY) frame. This framework leverages the signal processing algorithms used in the typical WLAN receiver for time and frequency synchronization to perform radar parameter estimation. The initial simulation results show that it is possible to achieve the desired range accuracy of 0.1 m with a very high probability of detection (above 99%) using the preamble of a SCPHY frame. Furthermore, the velocity estimation algorithm achieves the desired accuracy of 0.1 m/s at high SNR using the preamble and pilot words of only a single frame.


title: Evaluation in Information Visualization: Heuristic Evaluation

abstract: This paper presents a review of heuristic evaluation and recommendations for how to apply the method for information visualization evaluation. Heuristic evaluation is a widely known and popular method within the area of human-computer interaction and the information visualization community now also recognizes its usefulness. However, in this area it is not applied to the same extent. In its original form the method has limitations that need to be considered in order for it to be optimal for information visualization. The aim with this paper is to provide the reader with knowledge about the method and awareness of what issues that call for refined or supplemental actions and resources in order for it to generate as valid and useful results as possible. The paper also discusses the research challenges for future work in how to further improve the method.


title: Ramus or chin grafts for maxillary sinus inlay and local onlay augmentation: comparison of donor site morbidity and complications.

abstract: BACKGROUND ::: The placement of endosseous implants in edentulous areas is frequently limited by inadequate bone volume of the residual ridge. Local bone grafts from the mandible are a convenient source of autogenous bone for alveolar reconstruction prior to implant placement. ::: ::: ::: PURPOSE ::: The aim of the present study was to document and compare the morbidity and the frequency of complications occurring at two intraoral donor sites: the mandibular symphysis and the mandibular ramus. ::: ::: ::: MATERIAL AND METHODS ::: This study reviewed 53 consecutively treated patients: 29 with autogenous bone grafts from the mandibular symphysis and 24 with mandibular ramus bone grafts. Each patient received a questionnaire 18 months after surgery regarding problems that may have occurred during the postoperative period. ::: ::: ::: RESULTS ::: In the patients in whom bone was harvested from the mandibular ramus, there were fewer postoperative symptoms immediately after the operation than with mandibular symphysis harvesting. Twenty-two of the 29 patients with symphysis grafts experienced decreased sensitivity in the skin innervated by the mental nerve 1 month after the operation. Five of the 24 patients with ramus grafts experienced decreased sensitivity in the vestibular mucosa corresponding to the innervation of the buccal nerve. Eighteen months after the surgery, 15 of the 29 patients in the symphysis group still had some decreased sensitivity and presented with permanent altered sensation. Only one of the patients grafted from the mandibular ramus presented with permanent altered sensation in the posterior vestibular area. No major complication occurred in the donor sites in any of the 53 patients. ::: ::: ::: CONCLUSION ::: The results of this study favored the use of the ascending mandibular ramus as an intraoral donor site for bone grafting.


title: How emotion is made and measured

abstract: How we design and evaluate for emotions depends crucially on what we take emotions to be. In affective computing, affect is often taken to be another kind of information-discrete units or states internal to an individual that can be transmitted in a loss-free manner from people to computational systems and back. While affective computing explicitly challenges the primacy of rationality in cognitivist accounts of human activity, at a deeper level it often relies on and reproduces the same information-processing model of cognition. Drawing on cultural, social, and interactional critiques of cognition which have arisen in human-computer interaction (HCI), as well as anthropological and historical accounts of emotion, we explore an alternative perspective on emotion as interaction: dynamic, culturally mediated, and socially constructed and experienced. We demonstrate how this model leads to new goals for affective systems-instead of sensing and transmitting emotion, systems should support human users in understanding, interpreting, and experiencing emotion in its full complexity and ambiguity. In developing from emotion as objective, externally measurable unit to emotion as experience, evaluation, too, alters focus from externally tracking the circulation of emotional information to co-interpreting emotions as they are made in interaction.


title: Newsinessence: summarizing online news topics

abstract: A news delivery and summarization system, acting as a user's agent, gathers and recaps news items based on specifications and interests.


title: 28.7 A 0.7V 12b 160MS/s 12.8fJ/conv-step pipelined-SAR ADC in 28nm CMOS with digital amplifier technique

abstract: Wireless standards, e.g. 802.11ac Wave 2 and 802.11ax draft, aim to boost user throughput to cope with growing data traffic. High-speed (f s >100MS/s) and high-resolution (ENOB>9.5b) ADCs are essential for leading-edge wireless SoCs, given the bandwidth and PAPR specifications. Also, low power dissipation (FoM<20fJ/conv) is crucial for mobile applications. A number of pipelined-SAR ADCs have been presented which satisfy these design targets [1–3]. However, in deep submicron CMOS, design of a high DC-gain opamp for the MDAC is a serious obstacle due to reduced intrinsic transistor gain and sub-1V supply voltage. Hence, all designs utilize digital calibration to counter gain error and tolerate the use of a low-gain amplifier. Calibration times of at least several tens of ms are required, resulting in lengthy start-up times and reduced SoC power efficiency. Moreover, such calibration cannot track sudden supply voltage variations and suppressing such fluctuations with bypass capacitors significantly impacts chip cost [1–2]. Furthermore, amplifier non-linearity remains unsolved; with lower supply voltages, the limited amplifier swing tightens SAR noise requirements.


Doppler Frequency Effect and BER Performance of FFT Based OFDM System


title: Grammar inference algorithms and applications in software engineering

abstract: Many problems exist whose solutions take the form of patterns that may be expressed using grammars (e.g., speech recognition, text processing, genetic sequencing). Construction of these grammars is usually carried out by computer scientists working with domain experts. In the case when there is a lack of domain experts, grammar inference can be applied. In this paper, two grammar inference algorithms are briefly described and their application to software engineering is presented.


title: Pedestrian Behaviour Monitoring: Methods and Experiences.

abstract: The investigation of pedestrian spatio-temporal behaviour is of particular interest in many different research fields. Disciplines like travel behaviour research and tourism research, social sciences, artificial intelligence, geoinformation and many others have approached this subject from different perspectives. Depending on the particular research questions, various methods of data collection and analysis have been developed and applied in order to gain insight into specific aspects of human motion behaviour and the determinants influencing spatial activities. In this contribution, we provide a general overview about most commonly used methods for monitoring and analysing human spatio-temporal behaviour. After discussing frequently used empirical methods of data collection and emphasising related advantages and limitations, we present seven case studies concerning the collection and analysis of human motion behaviour following different purposes.


title: Low speed automation: Technical feasibility of the driving sharing in urban areas

abstract: This article presents the technical feasibility of fully automated driving at speeds below 50 km/h in urban and suburban areas with an adequate infrastructure quality (no intersections, known road geometry and lane markings available) focusing on congested and heavy traffic. This requires implementation of several systems: lane keeping system, Adaptive Cruise Control (ACC), lane changing,... Feasibility has been demonstrated through a complex scenario during the final ABV project event.


title: Pipeline-based processing of the deep learning framework caffe

abstract: Many life-log analysis applications, which transfer data from cameras and sensors to a Cloud and analyze them in the Cloud, have been developed with the spread of various sensors and Cloud computing technologies. However, difficulties arise because of the limitation of the network bandwidth between the sensors and the Cloud. In addition, sending raw sensor data to a Cloud may introduce privacy issues. Therefore, we propose distributed deep learning processing between sensors and the Cloud in a pipeline manner to reduce the amount of data sent to the Cloud and protect the privacy of the users. In this paper, we have developed a pipeline-based distributed processing method for the Caffe deep learning framework and investigated the processing times of the classification by varying a division point and the parameters of the network models using data sets, CIFAR-10 and ImageNet. The experiments show that the accuracy of deep learning with coarse-grain data is comparable to that with the default parameter settings, and the proposed distributed processing method has performance advantages in cases of insufficient network bandwidth with actual sensors and a Cloud environment.


title: Security Limits for Compromising Emanations

abstract: Nearly half a century ago, military organizations introduced “Tempest” emission-security test standards to control information leakage from unintentional electromagnetic emanations of digital electronics. The nature of these emissions has changed with evolving technology; electromechanic devices have vanished and signal frequencies increased several orders of magnitude. Recently published eavesdropping attacks on modern flat-panel displays and cryptographic coprocessors demonstrate that the risk remains acute for applications with high protection requirements. The ultra-wideband signal processing technology needed for practical attacks finds already its way into consumer electronics. Current civilian RFI limits are entirely unsuited for emission security purposes. Only an openly available set of test standards based on published criteria will help civilian vendors and users to estimate and manage emission-security risks appropriately. This paper outlines a proposal and rationale for civilian electromagnetic emission-security limits. While the presented discussion aims specifically at far-field video eavesdropping in the VHF and UHF bands, the most easy to demonstrate risk, much of the presented approach for setting test limits could be adapted equally to address other RF emanation risks.


title: Scaling up machine learning: parallel and distributed approaches

abstract: This tutorial gives a broad view of modern approaches for scaling up machine learning and data mining methods on parallel/distributed platforms. Demand for scaling up machine learning is task-specific: for some tasks it is driven by the enormous dataset sizes, for others by model complexity or by the requirement for real-time prediction. Selecting a task-appropriate parallelization platform and algorithm requires understanding their benefits, trade-offs and constraints. This tutorial focuses on providing an integrated overview of state-of-the-art platforms and algorithm choices. These span a range of hardware options (from FPGAs and GPUs to multi-core systems and commodity clusters), programming frameworks (including CUDA, MPI, MapReduce, and DryadLINQ), and learning settings (e.g., semi-supervised and online learning). The tutorial is example-driven, covering a number of popular algorithms (e.g., boosted trees, spectral clustering, belief propagation) and diverse applications (e.g., recommender systems and object recognition in vision). The tutorial is based on (but not limited to) the material from our upcoming Cambridge U. Press edited book which is currently in production. Visit the tutorial website at http://hunch.net/~large_scale_survey/


title: Association Between Early Lactate Levels and 30-Day Mortality in Clinically Suspected Sepsis in Children

abstract: Importance ::: Improving emergency care of pediatric sepsis is a public health priority, but optimal early diagnostic approaches are unclear. Measurement of lactate levels is associated with improved outcomes in adult septic shock, but pediatric guidelines do not endorse its use, in part because the association between early lactate levels and mortality is unknown in pediatric sepsis. ::: ::: ::: Objective ::: To determine whether the initial serum lactate level is associated with 30-day mortality in children with suspected sepsis. ::: ::: ::: Design, Setting, and Participants ::: This observational cohort study of a clinical registry of pediatric patients with suspected sepsis in the emergency department of a tertiary children's hospital from April 1, 2012, to December 31, 2015, tested the hypothesis that a serum lactate level of greater than 36 mg/dL is associated with increased mortality compared with a serum lactate level of 36 mg/dL or less. Consecutive patients with sepsis were identified and included in the registry following consensus guidelines for clinical recognition (infection and decreased mental status or perfusion). Among 2520 registry visits, 1221 were excluded for transfer from another medical center, no measurement of lactate levels, and patients younger than 61 days or 18 years or older, leaving 1299 visits available for analysis. Lactate testing is prepopulated in the institutional sepsis order set but may be canceled at clinical discretion. ::: ::: ::: Exposures ::: Venous lactate level of greater than 36 mg/dL on the first measurement within the first 8 hours after arrival. ::: ::: ::: Main Outcomes and Measures ::: Thirty-day in-hospital mortality was the primary outcome. Odds ratios were calculated using logistic regression to account for potential confounders. ::: ::: ::: Results ::: Of the 1299 patients included in the analysis (753 boys [58.0%] and 546 girls [42.0%]; mean [SD] age, 7.3 [5.3] years), 899 (69.2%) had chronic medical conditions and 367 (28.3%) had acute organ dysfunction. Thirty-day mortality occurred in 5 of 103 patients (4.8%) with lactate levels greater than 36 mg/dL and 20 of 1196 patients (1.7%) with lactate levels of 36 mg/dL or less. Initial lactate levels of greater than 36 mg/dL were significantly associated with 30-day mortality in unadjusted (odds ratio, 3.00; 95% CI, 1.10-8.17) and adjusted (odds ratio, 3.26; 95% CI, 1.16- 9.16) analyses. The sensitivity of lactate levels greater than 36 mg/dL for 30-day mortality was 20.0% (95% CI, 8.9%-39.1%), and specificity was 92.3% (90.7%-93.7%). ::: ::: ::: Conclusions and Relevance ::: In children treated for sepsis in the emergency department, lactate levels greater than 36 mg/dL were associated with mortality but had a low sensitivity. Measurement of lactate levels may have utility in early risk stratification of pediatric sepsis.


title: Session-based Recommendations with Recurrent Neural Networks

abstract: We apply recurrent neural networks (RNN) on a new domain, namely recommender systems. Real-life recommender systems often face the problem of having to base recommendations only on short session-based data (e.g. a small sportsware website) instead of long user histories (as in the case of Netflix). In this situation the frequently praised matrix factorization approaches are not accurate. This problem is usually overcome in practice by resorting to item-to-item recommendations, i.e. recommending similar items. We argue that by modeling the whole session, more accurate recommendations can be provided. We therefore propose an RNN-based approach for session-based recommendations. Our approach also considers practical aspects of the task and introduces several modifications to classic RNNs such as a ranking loss function that make it more viable for this specific problem. Experimental results on two data-sets show marked improvements over widely used approaches.


title: Scalable work stealing

abstract: Irregular and dynamic parallel applications pose significant challenges to achieving scalable performance on large-scale multicore clusters. These applications often require ongoing, dynamic load balancing in order to maintain efficiency. Scalable dynamic load balancing on large clusters is a challenging problem which can be addressed with distributed dynamic load balancing systems. Work stealing is a popular approach to distributed dynamic load balancing; however its performance on large-scale clusters is not well understood. Prior work on work stealing has largely focused on shared memory machines. In this work we investigate the design and scalability of work stealing on modern distributed memory systems. We demonstrate high efficiency and low overhead when scaling to 8,192 processors for three benchmark codes: a producer-consumer benchmark, the unbalanced tree search benchmark, and a multiresolution analysis kernel.


title: An investigation of the Therac-25 accidents

abstract: Between June 1985 and January 1987, the Therac-25 medical electron accelerator was involved in six massive radiation overdoses. As a result, several people died and others were seriously injured. A detailed investigation of the factors involved in the software-related overdoses and attempts by users, manufacturers, and government agencies to deal with the accidents is presented. The authors demonstrate the complex nature of accidents and the need to investigate all aspects of system development and operation in order to prevent future accidents. The authors also present some lessons learned in terms of system engineering, software engineering, and government regulation of safety-critical systems containing software components. >


title: FudanNLP: A Toolkit for Chinese Natural Language Processing

abstract: The growing need for Chinese natural language processing (NLP) is largely in a range of research and commercial applications. However, most of the currently Chinese NLP tools or components still have a wide range of issues need to be further improved and developed. FudanNLP is an open source toolkit for Chinese natural language processing (NLP), which uses statistics-based and rule-based methods to deal with Chinese NLP tasks, such as word segmentation, part-ofspeech tagging, named entity recognition, dependency parsing, time phrase recognition, anaphora resolution and so on.


title: Building credit scoring models using genetic programming

abstract: Credit scoring models have been widely studied in the areas of statistics, machine learning, and artificial intelligence (AI). Many novel approaches such as artificial neural networks (ANNs), rough sets, or decision trees have been proposed to increase the accuracy of credit scoring models. Since an improvement in accuracy of a fraction of a percent might translate into significant savings, a more sophisticated model should be proposed to significantly improving the accuracy of the credit scoring mode. In this paper, genetic programming (GP) is used to build credit scoring models. Two numerical examples will be employed here to compare the error rate to other credit scoring models including the ANN, decision trees, rough sets, and logistic regression. On the basis of the results, we can conclude that GP can provide better performance than other models.


title: Region-Based Selective Flow-Sensitive Pointer Analysis

abstract: We introduce a new region-based SELective Flow-Sensitive (Selfs) approach to inter-procedural pointer analysis for C that operates on the regions partitioned from a program. Flow-sensitivity is maintained between the regions but not inside, making traditional flow-insensitive and flow-sensitive as well as recent sparse flow-sensitive analyses all special instances of our Selfs framework. By separating region partitioning as an independent concern from the rest of the pointer analysis, Selfs facilitates the development of flow-sensitive variations with desired efficiency and precision tradeoffs by reusing existing pointer resolution algorithms. We also introduce a new unification-based approach for region partitioning to demonstrate the generality and flexibility of our Selfs framework, as evaluated using SPEC2000/2006 benchmarks in LLVM.


title: Enhanced Face Recognition Algorithm using PCA with Artificial Neural Networks

abstract: Face detection from a long database of face images with different backgrounds is not an easy task. In this work, we demonstrate the face detection system of colored face images which is invariant to the background and acceptable illumination conditions. A threshold level is set to reject the non-human face images and the unknown human face images which are not present in the input database of face images. In this paper, the global features extraction is completed using PCA based eigenface computation method and the detection part is completed using multi-layered feed forward Artificial Neural Networks with back propagation process. This algorithm is implemented using MATLAB software. The learning process of neurons is used to train the input face images with 1000 iterations to minimize the error. In this system, face recognition task is completed with improved accuracy and success rate even for noisy face images. Face Recognition System is a computer based digital technology and is an active area of research. The Face Recognition System has various applications like various authentication systems, security systems and searching of persons etc. These applications are cost effective and save the time. Moreover the face database can be easily designed by using any image of the person. In past few years various face recognition techniques are purposed with varied and successful results. As the brain of human beings create the learning ability to recognize the persons by face even the feature characteristics of the face changes with time. The neurons of the human brain are trained by reading or learning the face of a person and they can identify that face quickly even after several years. This ability of training and identifying is converted into machine systems using the Artificial Neural Networks. The basic function for the face recognition system is to compare the face of a person which is to be recognized with the faces already trained in the Artificial Neural Networks and it recognized the best matching face as output even at different lightening conditions, viewing conditions and facial expressions. In this paper, the features of the face images are extracted by creating the feature vectors of maximum varied face points and computing s Covariance column matrix using PCA. These faces are projected onto the face space that spans the significant variations in the face images stored in the database (7). These feature vectors are the eigenvectors of covariance matrix and having the face like appearance so that we call them eigenfaces which are used as input to train the Artificial Neural Networks. The learning of the correlated patterns between the input face images is one of the useful properties of Artificial Neural Networks. After training the Artificial Neural Networks, we tested it with known and unknown face images for success and rejection rate analysis. Database used in this work contains 49 different face images of nine persons resized to 180×200 pixels including the non-human and unknown face images for improving the rejection rate.


title: A Mixed Methods Small Pilot Study to Describe the Effects of Upper Limb Training Using a Virtual Reality Gaming System in People with Chronic Stroke

abstract: Introduction. This small pilot study aimed to examine the feasibility of an upper limb rehabilitation system (the YouGrabber) in a community rehabilitation centre, qualitatively explore participant experiences, and describe changes after using it. Methods and Material. Chronic stroke participants attending a community rehabilitation centre in the UK were randomised to either a YouGrabber or a gym group and completed 18 training sessions over 12 weeks. The motor activity log, box and block, and fatigue severity score were administered by a blinded assessor before and after the intervention. Semistructured interviews were used to ascertain participants' views about using the YouGrabber. Results. Twelve participants (6 females) with chronic stroke were recruited. All adhered to the intervention. There were no adverse events, dropouts, or withdrawal. There were no significant differences between the YouGrabber and gym groups although there were significant within group improvements on the motor activity log (median change: 0.59, range: 0.2-1.25; p < 0.05) within the YouGrabber group. Participants reported that the YouGrabber was motivational but they expressed frustration with technical challenges. Conclusions. The YouGrabber appeared practical and may improve upper limb activities in people several months after stroke. Future work could examine cognition, cost effectiveness, and different training intensities.


title: GPU based Single-Pass Ray Casting of Large Heightfields Using Clipmaps

abstract: Heightelds have proved to be useful for rendering terrains or polygonal surfaces with ne- structured details. While GPU-based ray casting has become popular for the latter setting, terrains are com- monly rendered by using mesh-based techniques, be- cause the heightelds can be very large and hence ray


title: Characterizing Machines and Workloads on a Google Cluster

abstract: Cloud computing offers high scalability, flexibility and cost-effectiveness to meet emerging computing requirements. Understanding the characteristics of real workloads on a large production cloud cluster benefits not only cloud service providers but also researchers and daily users. This paper studies a large-scale Google cluster usage trace dataset and characterizes how the machines in the cluster are managed and the workloads submitted during a 29-day period behave. We focus on the frequency and pattern of machine maintenance events, job- and task-level workload behavior, and how the overall cluster resources are utilized.


title: Automatic prosody prediction and detection with Conditional Random Field (CRF) models

abstract: While the current TTS systems can deliver quite acceptable segmental quality of synthesized speech for voice user interface applications, its prosody is still perceived by users as “robotic” or not expressive. In this paper, we investigate how to improve TTS prosody prediction and detection. Conditional Random Field (CRF), a discriminative probabilistic model for the labeling the sequential data, is adopted. Rich syntactic and acoustic, contextual features are used in building the CRF models. Experiments performed on Boston University Radio Speech Corpus show that CRF models trained on our proposed rich contextual features can improve the accuracy of prosody prediction and detection in both speaker-dependent and speaker-independent cases. The performance is either comparable or better than the best reported results.


title: Image segmentation for large-scale subcategory flower recognition

abstract: We propose a segmentation algorithm for the purposes of large-scale flower species recognition. Our approach is based on identifying potential object regions at the time of detection. We then apply a Laplacian-based segmentation, which is guided by these initially detected regions. More specifically, we show that 1) recognizing parts of the potential object helps the segmentation and makes it more robust to variabilities in both the background and the object appearances, 2) segmenting the object of interest at test time is beneficial for the subsequent recognition. Here we consider a large-scale dataset containing 578 flower species and 250,000 images. This dataset is developed by our team for the purposes of providing a flower recognition application for general use and is the largest in its scale and scope. We tested the proposed segmentation algorithm on the well-known 102 Oxford flowers benchmark [11] and on the new challenging large-scale 578 flower dataset, that we have collected. We observed about 4% improvements in the recognition performance on both datasets compared to the baseline. The algorithm also improves all other known results on the Oxford 102 flower benchmark dataset. Furthermore, our method is both simpler and faster than other related approaches, e.g. [3, 14], and can be potentially applicable to other subcategory recognition datasets.


Framework for Automatic Examination Paper Generation System


title: Closed-Loop Brain–Machine–Body Interfaces for Noninvasive Rehabilitation of Movement Disorders

abstract: Traditional approaches for neurological rehabili- tation of patients affected with movement disorders, such as Parkinson's disease (PD), dystonia, and essential tremor (ET) consist mainly of oral medication, physical therapy, and botulinum toxin injections. Recently, the more invasive method of deep brain stimulation (DBS) showed significant improvement of the physical symptoms associated with these disorders. In the past several years, the adoption of feedback control theory helped DBS protocols to take into account the progressive and dynamic nature of these neurological move- ment disorders that had largely been ignored so far. As a result, a more efficient and effective management of PD cardinal symptoms has emerged. In this paper, we review closed-loop systems for rehabilitation of movement disor- ders, focusing on PD, for which several invasive and noninvasive methods have been developed during the last decade, reducing the complications and side effects associ- ated with traditional rehabilitation approaches and paving the way for tailored individual therapeutics. We then present a novel, transformative, noninvasive closed-loop framework based on force neurofeedback and discuss several future developments of closed-loop systems that might bring us closer to individualized solutions for neurological rehabilita- tion of movement disorders.


title: Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables

abstract: This study compared two alternative techniques for predicting forest cover types from cartographic variables. The study evaluated four wilderness areas in the Roosevelt National Forest, located in the Front Range of northern Colorado. Cover type data came from US Forest Service inventory information, while the cartographic variables used to predict cover type consisted of elevation, aspect, and other information derived from standard digital spatial data processed in a geographic information system (GIS). The results of the comparison indicated that a feedforward artificial neural network model more accurately predicted forest cover type than did a traditional statistical model based on Gaussian discriminant analysis.


title: A receiver - TDC chip set for accurate pulsed time-of-flight laser ranging

abstract: The pulsed time-of-flight (TOF) laser distance measurement method is based on measurement of the transit time of a short laser pulse (typically 3-4 ns) to an optically visible target and back to the receiver. This method is considered to be a potential solution for applications such as the measurement of levels and geometrical shapes in silos and containers, the positioning of tools and vehicles, velocity measurement, anticollision radars, proximity sensors and perception systems in traffic and in robot vision, for example. In this work we have developed an integrated chip set that realizes the receiver channel and time interval measurement functionalities of a pulsed time-offlight laser radar. The input to the receiver channel chip (0.35m BiCMOS) is the current signal from the APD (avalanche photo detector) and the output an accurate logic-level timing signal for the time-to-digital converter circuit (TDC). The main systematic error in the pulsed time-of-flight sensor is the dependence of the timing point on the amplitude of the laser echo (timing walk). This error is compensated for here by measuring not only the position of the received pulse with respect to the transmitted pulse but also its width with a multi-channel TDC IC and then by using the known relation (calibration) between the walk error and the timing pulse width. The receiver achieves an input noise current of about 100nArms with a bandwidth of 300MHz and a timing error of less than 50ps in a range of more than 1:10000. The multi-channel timeto-digital converter chip (0.35µm CMOS technology) uses a low-frequency crystal as a reference and measures the time intervals with counter and delay line interpolation techniques. The TDC circuit is used to determine the time intervals between the transmitted laser pulse and the resulting optical echo pulses (up to three) and also the widths or rise times of the echoes. The circuit offers a measurement precision of about 10ps and a measurement range of up to 74µs. In terms of laser distance measurement its performance is equivalent to millimetre-level precision within an 11 km range. These circuit techniques are being used in several R&D projects aiming at the development of new accurate and highly integrated generations of laser scanners and radars.


title: JointSem: Combining Query Entity Linking and Entity based Document Ranking

abstract: Entity-based ranking systems often employ entity linking systems to align entities to query and documents. Previously, entity linking systems were not designed specifically for search engines and were mostly used as a preprocessing step. This work presents JointSem, a joint semantic ranking system that combines query entity linking and entity-based document ranking. In JointSem, the spotting and linking signals are used to describe the importance of candidate entities in the query, and the linked entities are utilized to provide additional ranking features for the documents. The linking signals and the ranking signals are combined by a joint learning-to-rank model, and the whole system is fully optimized towards end-to-end ranking performance. Experiments on TREC Web Track datasets demonstrate the effectiveness of joint learning of entity linking and entity-based ranking.


title: Design of Efficient Adaptive Neuro-Fuzzy Controller Based on Supervisory Learning Capable for Speed and Torque Control of BLDC Motor

abstract: Brushless DC (BLDC) motors have been widely used in many field of drives for their high power/weight, high torque, high efficiency, long operating life, noiseless operation, high speed ranges and ease of control. In this paper, a Neuro-Fuzzy Controller (NFC) based on supervisory learning is presented for the speed and torque control of BLDC motors to enhance high control performance of the drive under transient and steady state conditions. This designed controller is combination of Neural Networks (NNs) and Fuzzy Logic (FL), therefore has parallel processing and learning abilities of NNs and inference capacity of FL. For improvement the performance of leaning algorithm and thereupon increase efficiency of drive, instead of usual Error Back Propagation (EBP) learning technique, a fuzzy based supervisory learning algorithm is employed. The proposed controller has simple structure and also due to its modest fuzzy rule in rule-base is relatively easy for implementation. This controller has high accuracy, suitable performance, high robustness and high tracking efficiency. In order to demonstrate the NFC ability to tracking reference speed and torque and also test of robustness and rejection ability of controller against undesired disturbances or suddenly changes in speed and torque, these designs are simulated with MATLAB/SIMULINK. In some cases, results are compared with that of a conventional PID controller and other designs. Streszczenie. W artykule zaprezentowane uklad sterowania bezszczotkowym silnikiem DC z wykorzystaniem sterownika Neuro-Fuzzy. Dla poprawienia efektywności uczenia sieci zamiast wstecznej propagacji bledu zaproponowano algorytm wykorzystujący logike rozmytą. Sterownik okazal sie byc dokladny, odporny i o duzej efektywności śledzenia zmian. Porownano mozliwości kontrolera z konwencjonalnym sterownikiem PID. (Projekt skutecznego sterownika silnika bezszczotkowego DC z wykorzystaniem sieci neuronowych i logiki rozmytej)


title: Cloudrise: Exploring Cloud Computing Adoption and Governance with the TOE Framework

abstract: Executives around the globe are investigating the benefits that cloud computing can deliver above and beyond cost savings. What is its contribution to competiveness - through improved agility, expanded business networks, and enhanced decision-making? At the same time, the organizational factors inhibiting or supporting cloud computing adoption are poorly understood. This paper builds on Tornatzky et al.'s Technology-Organization-Environment (TOE) frame-work to investigate the factors influencing cloud computing adoption. Another objective pursued is to conceptualize and understand how IT governance processes and structures moderate those factors. Our augmented TOE framework is developed in a set of hypotheses that are tested in a quantitative study of 24 global enterprises across various industries. Our results indeed indicate that the technology and organization context affect implementation decisions. A discussion and identification of next research steps round off the paper.


title: Towards the next generation of bug tracking systems

abstract: Developers typically rely on the information submitted by end-users to resolve bugs. We conducted a survey on information needs and commonly faced problems with bug reporting among several hundred developers and users of the APACHE, ECLIPSE and MOZILLA projects. In this paper, we present the results of a card sort on the 175 comments sent back to us by the responders of the survey. The card sort revealed several hurdles involved in reporting and resolving bugs, which we present in a collection of recommendations for the design of new bug tracking systems. Such systems could provide contextual assistance, reminders to add information, and most important, assistance to collect and report crucial information to developers.


title: Mobility modeling in wireless networks: categorization, smooth movement, and border effects

abstract: The movement pattern of mobile users plays an important role in performance analysis of wireless computer and communication networks. In this paper, we first give an overview and classification of mobility models used for simulation-based studies. Then, we present an enhanced random mobility model, which makes the movement trace of mobile stations more realistic than common approaches for random mobility. Our movement concept is based on random processes for speed and direction control in which the new values are correlated to previous ones. Upon a speed change event, a new target speed is chosen, and an acceleration is set to achieve this target speed. The principles for direction changes are similar. Finally, we discuss strategies for the stations' border behavior (i.e., what happens when nodes move out of the simulation area) and show the effects of certain border behaviors and mobility models on the spatial user distribution.


title: Flexible Spatio-Temporal Networks for Video Prediction

abstract: We describe a modular framework for video frame prediction. We refer to it as a Flexible Spatio-Temporal Network (FSTN) as it allows the extrapolation of a video sequence as well as the estimation of synthetic frames lying in between observed frames and thus the generation of slow-motion videos. By devising a customized objective function comprising decoding, encoding, and adversarial losses, we are able to mitigate the common problem of blurry predictions, managing to retain high frequency information even for relatively distant future predictions. We propose and analyse different training strategies to optimize our model. Extensive experiments on several challenging public datasets demonstrate both the versatility and validity of our model.


title: SkimpyStash: RAM space skimpy key-value store on flash-based storage

abstract: We present SkimpyStash, a RAM space skimpy key-value store on flash-based storage, designed for high throughput, low latency server applications. The distinguishing feature of SkimpyStash is the design goal of extremely low RAM footprint at about 1 (± 0.5) byte per key-value pair, which is more aggressive than earlier designs. SkimpyStash uses a hash table directory in RAM to index key-value pairs stored in a log-structured manner on flash. To break the barrier of a flash pointer (say, 4 bytes) worth of RAM overhead per key, it "moves" most of the pointers that locate each key-value pair from RAM to flash itself. This is realized by (i) resolving hash table collisions using linear chaining, where multiple keys that resolve (collide) to the same hash table bucket are chained in a linked list, and (ii) storing the linked lists on flash itself with a pointer in each hash table bucket in RAM pointing to the beginning record of the chain on flash, hence incurring multiple flash reads per lookup. Two further techniques are used to improve performance: (iii) two-choice based load balancing to reduce wide variation in bucket sizes (hence, chain lengths and associated lookup times), and a bloom filter in each hash table directory slot in RAM to disambiguate the choice during lookup, and (iv) compaction procedure to pack bucket chain records contiguously onto flash pages so as to reduce flash reads during lookup. The average bucket size is the critical design parameter that serves as a powerful knob for making a continuum of tradeoffs between low RAM usage and low lookup latencies. Our evaluations on commodity server platforms with real-world data center applications show that SkimpyStash provides throughputs from few 10,000s to upwards of 100,000 get-set operations/sec.


title: Unsupervised lexical clustering of speech segments using fixed-dimensional acoustic embeddings

abstract: Unsupervised speech processing methods are essential for applications ranging from zero-resource speech technology to modelling child language acquisition. One challenging problem is discovering the word inventory of the language: the lexicon. Lexical clustering is the task of grouping unlabelled acoustic word tokens according to type. We propose a novel lexical clustering model: variable-length word segments are embedded in a fixed-dimensional acoustic space in which clustering is then performed. We evaluate several clustering algorithms and find that the best methods produce clusters with wide variation in sizes, as observed in natural language. The best probabilistic approach is an infinite Gaussian mixture model (IGMM), which automatically chooses the number of clusters. Performance is comparable to that of non-probabilistic Chinese Whispers and average-linkage hierarchical clustering. We conclude that IGMM clustering of fixed-dimensional embeddings holds promise as the lexical clustering component in unsupervised speech processing systems.