title: The ‘Conjunction Fallacy’ Revisited: How Intelligent Inferences Look Like Reasoning Errors

abstract: Findings in recent research on the ‘conjunction fallacy’ have been taken as evidence that our minds are not designed to work by the rules of probability. This conclusion springs from the idea that norms should be content-blind—in the present case, the assumption that sound reasoning requires following the conjunction rule of probability theory. But content-blind norms overlook some of the intelligent ways in which humans deal with uncertainty, for instance, when drawing semantic and pragmatic inferences. In a series of studies, we first show that people infer nonmathematical meanings of the polysemous term ‘probability’ in the classic Linda conjunction problem. We then demonstrate that one can design contexts in which people infer mathematical meanings of the term and are therefore more likely to conform to the conjunction rule. Finally, we report evidence that the term ‘frequency’ narrows the spectrum of possible interpretations of ‘probability’ down to its mathematical meanings, and that this fact—rather than the presence or absence of ‘extensional cues’—accounts for the low proportion of violations of the conjunction rule when people are asked for frequency judgments. We conclude that a failure to recognize the human capacity for semantic and pragmatic inference can lead rational responses to be misclassified as fallacies. Copyright © 1999 John Wiley & Sons, Ltd.


title: Financial Series Prediction: Comparison Between Precision of Time Series Models and Machine Learning Methods

abstract: Precise financial series predicting has long been a difficult problem because of unstableness and many noises within the series. Although Traditional time series models like ARIMA and GARCH have been researched and proved to be effective in predicting, their performances are still far from satisfying. Machine Learning, as an emerging research field in recent years, has brought about many incredible improvements in tasks such as regressing and classifying, and it's also promising to exploit the methodology in financial time series predicting. In this paper, the predicting precision of financial time series between traditional time series models and mainstream machine learning models including some state-of-the-art ones of deep learning are compared through experiment using real stock index data from history. The result shows that machine learning as a modern method far surpasses traditional models in precision.


title: Learning the Long-Term Structure of the Blues

abstract: In general music composed by recurrent neural networks (RNNs) suffers from a lack of global structure. Though networks can learn note-by-note transition probabilities and even reproduce phrases, they have been unable to learn an entire musical form and use that knowledge to guide composition. In this study, we describe model details and present experimental results showing that LSTM successfully learns a form of blues music and is able to compose novel (and some listeners believe pleasing) melodies in that style. Remarkably, once the network has found the relevant structure it does not drift from it: LSTM is able to play the blues with good timing and proper structure as long as one is willing to listen.


title: Planar Compact Square-Ring Tag Antenna With Circular Polarization for UHF RFID Applications

abstract: This paper describes a novel compact design of a planar circularly polarized (CP) tag antenna for use in a ultrahigh frequency (UHF) radio frequency identification (RFID) system. Introducing the meander strip into the right-arm of the square-ring structure enables the measured half-power bandwidth of the proposed CP tag antenna to exceed 100 MHz (860-960 MHz), which includes the entire operating bandwidth of the global UHF RFID system. A 3-dB axial-ratio bandwidth of approximately 36 MHz (902-938 MHz) can be obtained, which is suitable for American (902-928 MHz), European (918-926 MHz), and Taiwanese UHF RFID (922-928 MHz) applications. Since the overall antenna dimensions are only 54 × 54 mm ::: 2 ::: , the proposed tag antenna can be operated with a size that is 64% smaller than that of the tag antennas attached on the safety glass. With a bidirectional reading pattern, the measured reading distance is about 8.3 m. Favorable tag sensitivity is obtained across the desired frequency band.


title: Adaptive Performance Assessment For Drivers Through Behavioral Advantage

abstract: The potential positive impact of autonomous driving and driver assistance technolo- gies have been a major impetus over the last decade. On the flip side, it has been a challenging problem to analyze the performance of human drivers or autonomous driving agents quantitatively. In this work, we propose a generic method that compares the performance of drivers or autonomous driving agents even if the environmental conditions are different, by using the driver behavioral advantage instead of absolute metrics, which efficiently removes the environmental factors. A concrete application of the method is also presented, where the performance of more than 100 truck drivers was evaluated and ranked in terms of fuel efficiency, covering more than 90,000 trips spanning an average of 300 miles in a variety of driving conditions and environments.


title: SSMS - A Secure SMS Messaging Protocol for the M-payment Systems

abstract: The GSM network with the greatest worldwide number of users, succumbs to several security vulnerabilities. The short message service (SMS) is one of its superior and well-tried services with a global availability in the GSM networks. The main contribution of this paper is to introduce a new secure application layer protocol, called SSMS, to efficiently embed the desired security attributes in the SMS messages to be used as a secure bearer in the m-payment systems. SSMS efficiently embeds the confidentiality, integrity, authentication, and non-repudiation in the SMS messages. It provides an elliptic curve-based public key solution that uses public keys for the secret key establishment of a symmetric encryption. It also provides the attributes of public verification and forward secrecy. It efficiently makes the SMS messaging suitable for the m-payment applications where the security is the great concern.


Migration Path to Fully Integrated IP-SAN


title: Detection and Modeling of Cyber Attacks with Petri Nets

abstract: The aim of this article is to present an approach to develop and verify a method of formal modeling of cyber threats directed at computer systems. Moreover, the goal is to prove that the method enables one to create models resembling the behavior of malware that support the detection process of selected cyber attacks and facilitate the application of countermeasures. The most common cyber threats targeting end users and terminals are caused by malicious software, called malware. The malware detection process can be performed either by matching their digital signatures or analyzing their behavioral models. As the obfuscation techniques make the malware almost undetectable, the classic signature-based anti-virus tools must be supported with behavioral analysis. The proposed approach to modeling of malware behavior is based on colored Petri nets. This article is addressed to cyber defense researchers, security architects and developers solving up-to-date problems regarding the detection and prevention of advanced persistent threats.


title: Simulation of PMSM Vector Control System Based on Matlab/Simulink

abstract: At the basis of analysis of the mathematical model of the permanent-magnet synchronous motor (PMSM) and the principle of field-orientated vector control, a novel method for modeling and simulink of PMSM system based on SVPWM is proposed. In Matlab/simulink, the simulation model of the whole system is built, two control loops are used, which are inner current-loop and the outer speed-loop. By simulating the specific motor, the waveforms are presented, which achieves the expected effect, and the reasonability and validity has been proved.


title: Approaches to Mobile Robot Localization in Indoor Environments

abstract: This thesis deals with all aspects of mobile robot localization for indoor applications. The problems span from tracking the position given an initial estimate, over finding it without any prior position knowledge, to automatically building a representation of the environment while performing localization. The theme is the use of minimalistic models which capture the large scale structures of the environment, such as the dominant walls, to provide scalable and low-complexity solutions. In many cases it is enough to only maintain an estimate of the robot position. For such situation an extensively tested low-complexity, robust and accurate pose tracking method is presented which utilizes the minimalistic model in combination with a laser sensor. When the initial position is unknown the robot must perform global localization. Two different methods are investigated. The first one is a novel localization scheme, based on the ideas of Multiple Hypothesis Tracking. The second is an, experimentally verified, significant improvement of the standard Monte Carlo Localization technique. To automatically generate an environmental representation an hierarchical approach to simultaneous localization and mapping (SLAM) is presented. The map scaling issue is here addressed by dividing the environment into submaps, each representing a small area.


title: Layouts From Panoramic Images With Geometry and Deep Learning

abstract: In this paper, we propose a novel procedure for 3D layout recovery of indoor scenes from single 360 degrees panoramic images. With such images, all scene is seen at once, allowing to recover closed geometries. Our method combines strategically the accuracy provided by geometric reasoning (lines and vanishing points) with the higher level of data abstraction and pattern recognition achieved by deep learning techniques (edge and normal maps). Thus, we extract structural corners from which we generate layout hypotheses of the room assuming Manhattan world. The best layout model is selected, achieving good performance on both simple rooms (box-type) and complex shaped rooms (with more than four walls). Experiments of the proposed approach are conducted within two public datasets, SUN360 and Stanford (2D-3D-S) demonstrating the advantages of estimating layouts by combining geometry and deep learning and the effectiveness of our proposal with respect to the state of the art.


title: Learning Word Embeddings: Unsupervised Methods for Fixed-size Representations of Variable-length Speech Segments

abstract: Fixed-length embeddings of words are very useful for a variety of tasks in speech and language processing. Here we systematically explore two methods of computing fixed-length embeddings for variable-length sequences. We evaluate their susceptibility to phonetic and speaker-specific variability on English, a high resource language and Xitsonga, a low resource language, using two evaluation metrics: ABX word discrimination and ROC-AUC on same-different phoneme n-grams. We show that a simple downsampling method supplemented with length information can outperform the variable-length input feature representation on both evaluations. Recurrent autoencoders, trained without supervision, can yield even better results at the expense of increased computational complexity.


Image Region Forgery Detection: A Deep Learning Approach


title: Surviving threats: neural circuit and computational implications of a new taxonomy of defensive behaviour

abstract: Research on defensive behaviour in mammals has in recent years focused on elicited reactions; however, organisms also make active choices when responding to danger. We propose a hierarchical taxonomy of defensive behaviour on the basis of known psychological processes. Included are three categories of reactions (reflexes, fixed reactions and habits) and three categories of goal-directed actions (direct action-outcome behaviours and actions based on implicit or explicit forecasting of outcomes). We then use this taxonomy to guide a summary of findings regarding the underlying neural circuits.


title: Mobile Big Data: The Fuel for Data-Driven Wireless

abstract: In the past decade, the smart phone evolution has accelerated the proliferation of the mobile Internet and spurred a new wave of mobile applications, leading to an unprecedented mobile data volume generated from the mobile devices, content servers, and network operators, which are mainly nonstructured. In this big data era, such nonstructured data fragments are pieced together such that, drastically differing from the traditional practice where services determine and define the data, data is becoming a proactive entity that may drive and even create new services. Compared with the so-termed 5V characteristics of generic big data, namely volume, variety, velocity, veracity, and value, mobile big data is distinct in its unique multidimensional, personalized, multisensory, and real-time features. In this survey, we provide in-depth and comprehensive coverage on the features, sources and applications of mobile big data, as well as the current state-of-the-art, challenges and opportunities for research and development in this field, with an emphasis on the user modeling, infrastructure supporting, data management, and knowledge discovery aspects.


title: Nasal base surgery.

abstract: The nasal base is an important aspect of the nose with a complex anatomic architecture comprising a combination of cartilages, skin, connective tissues, and ligaments. Recent studies show that all nasal base deformities cannot be corrected by simple excision and suturing techniques. Alar release and medialization would be effective in some of these deformities. This article presents an overview of conventional concepts of alar base surgeries, which have remained unchanged over many years. Indications and limitations of each technique are discussed, followed by a more detailed description of alar release and medialization.


title: Using Commutative Assessments to Compare Conceptual Understanding in Blocks-based and Text-based Programs

abstract: Blocks-based programming environments are becoming increasingly common in introductory programming courses, but to date, little comparative work has been done to understand if and how this approach affects students' emerging understanding of fundamental programming concepts. In an effort to understand how tools like Scratch and Blockly differ from more conventional text-based introductory programming languages with respect to conceptual understanding, we developed a set of "commutative" assessments. Each multiple-choice question on the assessment includes a short program that can be displayed in either a blocks- based or text-based form. The set of potential answers for each question includes the correct answer along with choices informed by prior research on novice programming misconceptions. In this paper we introduce the Commutative Assessment, discuss the theoretical and practical motivations for the assessment, and present findings from a study that used the assessment. The study had 90 high school students take the assessment at three points over the course of the first ten weeks of an introduction to programming course, alternating the modality (blocks vs. text) for each question over the course of the three administrations of the assessment. Our analysis reveals differences on performance between blocks-based and text-based questions as well as differences in the frequency of misconceptions based on the modality. Future work, potential implications, and limitations of these findings are also discussed.


title: A Fuzzy Logic based Defense Mechanism against Distributed Denial of Service Attack in Cloud Computing Environment

abstract: Cloud defines a new age of computing solution that provides services to customers with its unique feat ures of agility and multi-tenancy. As the critical resources are ho sted at cloud provider's end, security is a big challenge in clou d computing. If the cloud environment is compromised and attackers get the access of core data centers, the availability of the critical resources becomes a big concern for the service consumers. Denial of Se rvice and Distributed Denial of Service kind of attacks are l aunched towards cloud environment to make the resources unavailable for legitimate users. In this paper we propose a fuzzy logic based defense mechanism that can be set with predefined rules by which it can detect the malicious packets and takes proper count er measures to mitigate the DDoS attack. Also a detailed study of different kind of DDoS attack and existing defense strategies has bee n carried out.


title: A High Throughput Pilot Allocation for M2M Communication in Crowded Massive MIMO Systems

abstract: A new scheme to resolve the intra-cell pilot collision for machine-to-machine (M2M) communication in crowded massive multiple-input multiple-output (MIMO) systems is proposed. The proposed scheme permits those failed user equipments (UEs), judged by a strongest-user collision resolution (SUCR) protocol, to contend for the idle pilots, i.e., the pilots that are not selected by any UE in the initial step. This scheme is called as SUCR combined idle pilots access (SUCR-IPA). To analyze the performance of the SUCR-IPA scheme, we develop a simple method to compute the access success probability of the UEs in each random access slot. The simulation results coincide well with the analysis. It is also shown that, compared with the SUCR protocol, the proposed SUCR-IPA scheme increases the throughput of the system significantly, and thus decreases the number of access attempts dramatically.


title: Light-weight pixel context encoders for image inpainting

abstract: In this work we propose Pixel Content Encoders (PCE), a light-weight image inpainting model, capable of generating novel con-tent for large missing regions in images. Unlike previously presented convolutional neural network based models, our PCE model has an order of magnitude fewer trainable parameters. Moreover, by incorporating dilated convolutions we are able to preserve fine grained spatial information, achieving state-of-the-art performance on benchmark datasets of natural images and paintings. Besides image inpainting, we show that without changing the architecture, PCE can be used for image extrapolation, generating novel content beyond existing image boundaries.


title: A Novel Pruning Algorithm for Optimizing Feedforward Neural Network of Classification Problems

abstract: Optimizing the structure of neural networks is an essential step for the discovery of knowledge from data. This paper deals with a new approach which determines the insignificant input and hidden neurons to detect the optimum structure of a feedforward neural network. The proposed pruning algorithm, called as neural network pruning by significance (N2PS), is based on a new significant measure which is calculated by the Sigmoidal activation value of the node and all the weights of its outgoing connections. It considers all the nodes with significance value below the threshold as insignificant and eliminates them. The advantages of this approach are illustrated by implementing it on six different real datasets namely iris, breast-cancer, hepatitis, diabetes, ionosphere and wave. The results show that the proposed algorithm is quite efficient in pruning the significant number of neurons on the neural network models without sacrificing the networks performance.


title: On Generalization and Regularization in Deep Learning

abstract: Why do large neural network generalize so well on complex tasks such as image classification or speech recognition? What exactly is the role regularization for them? These are arguably among the most important open questions in machine learning today. In a recent and thought provoking paper [C. Zhang et al.] several authors performed a number of numerical experiments that hint at the need for novel theoretical concepts to account for this phenomenon. The paper stirred quit a lot of excitement among the machine learning community but at the same time it created some confusion as discussions on OpenReview.net testifies. The aim of this pedagogical paper is to make this debate accessible to a wider audience of data scientists without advanced theoretical knowledge in statistical learning. The focus here is on explicit mathematical definitions and on a discussion of relevant concepts, not on proofs for which we provide references.


title: The Indoor Mobile Coverage Problem Using UAVs

abstract: Unmanned aerial vehicles (UAVs) can be used as aerial wireless base stations when cellular networks are not operational due to natural disasters. They can also be used to supplement the ground base station in order to provide better coverage and higher data rates for the users. Prior studies on UAV-based wireless coverage typically consider an air-to-ground path loss model, which assumes that the users are outdoor and located on a 2-D plane. In this paper, we propose using UAVs to provide wireless coverage for indoor users inside a high-rise building. First, we present realistic outdoor–indoor path loss models and describe the tradeoff introduced by these models. Then, we study the problem of efficient placement of a single UAV, where the objective is to minimize the total transmit power required to cover the entire high-rise building. The formulated problem is generally difficult to solve. To that end, we consider three cases of practical interest and provide efficient solutions to the formulated problem under these cases. Then, we study the problem of minimizing the number of UAVs required to provide wireless coverage to the high-rise building and prove that this problem is NP-complete. Due to the intractability of the problem, we use clustering to minimize the number of UAVs required to cover the indoor users. In our proposed algorithm, we check if the maximum transmit power of a UAV is sufficient to cover each cluster. If not, the number of clusters is incremented by one, and the problem is solved again. In the uniform split method, we split the building into $k$ regular structures and utilize $k$ UAVs to provide wireless coverage for indoor users regardless of user distribution. We demonstrate through simulations that the method that splits the building into regular structures requires 80% more number of UAVs relative to our proposed algorithm.


Small Sample Statistics for Classification Error Rates II: Confidence Intervals and Significance Tests


title: Leaf features based approach for automated identification of medicinal plants

abstract: Use of the correct plant extracts is crucial in Ayurvedic treatment. This paper focuses on the automatic identification of medicinal plants that are commonly used in Ayurveda. Previous work indicated a Gaussian distribution for various image related features of the medical plant leaves. This work strengthens this conclusion by extending the results through a better classifier design. In this work, we have considered additional plant species to prove that obtained image features are again Gaussian distributed, with the accuracy of the classifier computed.


title: ESPRIT-estimation of signal parameters via rotational invariance techniques

abstract: An approach to the general problem of signal parameter estimation is described. The algorithm differs from its predecessor in that a total least-squares rather than a standard least-squares criterion is used. Although discussed in the context of direction-of-arrival estimation, ESPRIT can be applied to a wide variety of problems including accurate detection and estimation of sinusoids in noise. It exploits an underlying rotational invariance among signal subspaces induced by an array of sensors with a translational invariance structure. The technique, when applicable, manifests significant performance and computational advantages over previous algorithms such as MEM, Capon's MLM, and MUSIC. >


title: Scheduling slack time in fixed priority pre-emptive systems

abstract: This paper addresses the problem of jointly scheduling tasks with both hard and soft time constraints. We present a new analysis which builds upon previous research into slack stealing algorithms. Our analysis determines the maximum processing time which may be stolen from hard deadline periodic or sporadic tasks, without jeopardising their timing constraints. It extends to tasks with characteristics such as synchronization, release jitter and stochastic execution times, as well as forming the basis for a family of optimal and approximate slack stealing algorithms. >


title: Smart Homes and Ambient Assisted Living Applications: From Data to Knowledge - Empowering or Overwhelming Older Adults?

abstract: Objectives : As health care systems face limited resources and workforce shortages to address the complex needs of older adult populations, innovative approaches utilizing information technology can support aging. Smart Home and Ambient Assisted Living (SHAAL) systems utilize advanced and ubiquitous technologies including sensors and other devices that are integrated in the residential infrastructure or wearable, to capture data describing activities of daily living and health related events. This paper highlights how data from SHAAL systems can lead to information and knowledge that ultimately improves clinical outcomes and quality of life for older adults as well as quality of health care services. Methods : We conducted a review of personal health record applications specifically for older adults and approaches to using information to improve elder care. We present a framework that showcases how data captured from SHAAL systems can be processed to provide meaningful information that becomes part of a personal health record. Results: Synthesis and visualization of information resulting from SHAAL systems can lead to knowledge and support education, delivery of tailored interventions and if needed, transitions in care. Such actions can involve multiple stakeholders as part of shared decision making. Conclusion: SHAAL systems have the potential to support aging and improve quality of life and decision making for older adults and their families. The framework presented in this paper demonstrates how emphasis needs to be placed into extracting meaningful information from new innovative systems that will support decision making. The challenge for informatics designers and researchers is to facilitate an evolution of SHAAL systems expanding beyond demonstration projects to actual interventions that will improve health care for older adults.


title: Annotating Real-World Objects Using Augmented Reality

abstract: Publisher Summary This chapter describes a system for annotating real-world objects using augmented reality. It also studies the fundamental features of augmented reality system, as well as those capabilities developed specifically for the application. It also provides an overview of the software and hardware architecture of our augmented reality system. The chapter also describes the general calibration procedures required for this and most other augmented reality applications. The issues of tracking and user interaction and describes the details of generating and displaying the annotations are also discussed. The augmented reality system uses a standard video camera and combines the video signal with computer-generated graphics, the result of which is presented on a normal video display. The core of the augmented reality system is an interactive 3D computer graphics system, providing methods for representing and viewing 3D geometric models. The mixing of the computer graphics and video input is done by a Folsom Research Otto 9500 Scan-Converter. Calibration is an essential component of augmented reality that provides the information that allows for the registration and overlaying of geometric and viewing models onto objects and cameras in the real world.


title: Gene identification in polytene chromosomes: some Balbiani ring 2 gene sequences are located in an interband-like region of Chironomus tentans

abstract: Injection of Chironomus tentans larvae with the nucleoside analogue DRB (5,6-dichloro-1-β-d-ribofuranosylbenzimidazole) led to a drastic reduction of RNA synthesis in the polytene salivary gland chromosomes, accompanied by a dissociation of RNA polymerase B (or II) from the chromosomal template. The DRB injection also led to a recondensation of the Balbiani ring 2 (BR2) puff and the formation of a well-defined banding pattern at the site of previous puffing. In the recondensed condition in situ hybridization of cloned BR2 DNA demonstrated that BR2 gene sequences are located in a region that light microscopically appears to be an interband between bands IV-3B9 and IV-3B10. No hybridization exists over bands IV-3B8 and IV-3B10. The in situ hybridization over interband IV-3B9/10 differs from the result of previous hybridization of BR2 RNA to polytene chromosomes from Malpighian tubules of C. tentans that showed that the BR2 gene is located in band IV-3B10 (Derksen et al. 1980). Due to the different hybridization probes used by the investigators both results are compatible with the location of the BR2 gene in a band-interband structure. These data seem to indicate: (1) that the repressed BR2 gene consists of two segments with different chromatin conformation (one with relatively extended interband chromatin and one with condensed band chromatin) and (2) that the transcriptional and regulatory unit of the BR2 gene may extend from the interband into the band or vice versa.


title: Multi-View Intact Space Learning

abstract: It is practical to assume that an individual view is unlikely to be sufficient for effective multi-view learning. Therefore, integration of multi-view information is both valuable and necessary. In this paper, we propose the Multi-view Intact Space Learning (MISL) algorithm, which integrates the encoded complementary information in multiple views to discover a latent intact representation of the data. Even though each view on its own is insufficient, we show theoretically that by combing multiple views we can obtain abundant information for latent intact space learning. Employing the Cauchy loss (a technique used in statistical learning) as the error measurement strengthens robustness to outliers. We propose a new definition of multi-view stability and then derive the generalization error bound based on multi-view stability and Rademacher complexity, and show that the complementarity between multiple views is beneficial for the stability and generalization. MISL is efficiently optimized using a novel Iteratively Reweight Residuals (IRR) technique, whose convergence is theoretically analyzed. Experiments on synthetic data and real-world datasets demonstrate that MISL is an effective and promising algorithm for practical applications.


title: Identification of Influencers- Measuring Influence in Customer Networks

abstract: Viral marketing refers to marketing techniques that use social networks to produce increases in brand awareness through self-replicating viral diffusion of messages, analogous to the spread of pathological and computer viruses. The idea has successfully been used by marketers to reach a large number of customers rapidly. If data about the customer network is available, centrality measures provide a structural measure that can be used in decision support systems to select influencers and spread viral marketing campaigns in a customer network. Usage stimulation and churn management are examples of DSS applications, where centrality of customers does play a role. The literature on network theory describes a large number of such centrality measures. A critical question is which of these measures is best to select an initial set of customers for a marketing campaign, in order to achieve a maximum dissemination of messages. In this paper, we present the results of computational experiments based on call data from a telecom company to compare different centrality measures for the diffusion of marketing messages. We found a significant lift when using central customers in message diffusion, but also found differences in the various centrality measures depending on the underlying network topology and diffusion process.


title: Motor Cortical Representation of Speed and Direction During Reaching

abstract: The motor cortical substrate associated with reaching was studied as monkeys moved their hands from a central position to one of eight targets spaced around a circle. Single-cell activity patterns ...


title: A Closer Look at Weak Label Learning for Audio Events

abstract: Audio content analysis in terms of sound events is an important research problem for a variety of applications. Recently, the development of weak labeling approaches for audio or sound event detection (AED) and availability of large scale weakly labeled dataset have finally opened up the possibility of large scale AED. However, a deeper understanding of how weak labels affect the learning for sound events is still missing from literature. In this work, we first describe a CNN based approach for weakly supervised training of audio events. The approach follows some basic design principle desirable in a learning method relying on weakly labeled audio. We then describe important characteristics, which naturally arise in weakly supervised learning of sound events. We show how these aspects of weak labels affect the generalization of models. More specifically, we study how characteristics such as label density and corruption of labels affects weakly supervised training for audio events. We also study the feasibility of directly obtaining weak labeled data from the web without any manual label and compare it with a dataset which has been manually labeled. The analysis and understanding of these factors should be taken into picture in the development of future weak label learning methods. Audioset, a large scale weakly labeled dataset for sound events is used in our experiments.


Assessing Test Suite E ↵ ectiveness Using Static Metrics


title: PANDA: Facilitating Usable AI Development

abstract: Recent advances in artificial intelligence (AI) and machine learning have created a general perception that AI could be used to solve complex problems, and in some situations over-hyped as a tool that can be so easily used. Unfortunately, the barrier to realization of mass adoption of AI on various business domains is too high because most domain experts have no background in AI. Developing AI applications involves multiple phases, namely data preparation, application modeling, and product deployment. The effort of AI research has been spent mostly on new AI models (in the model training stage) to improve the performance of benchmark tasks such as image recognition. Many other factors such as usability, efficiency and security of AI have not been well addressed, and therefore form a barrier to democratizing AI. Further, for many real world applications such as healthcare and autonomous driving, learning via huge amounts of possibility exploration is not feasible since humans are involved. In many complex applications such as healthcare, subject matter experts (e.g. Clinicians) are the ones who appreciate the importance of features that affect health, and their knowledge together with existing knowledge bases are critical to the end results. In this paper, we take a new perspective on developing AI solutions, and present a solution for making AI usable. We hope that this resolution will enable all subject matter experts (eg. Clinicians) to exploit AI like data scientists.


Learning Semantic Relatedness in Community Question Answering Using Neural Models


Running head : Consequences of bilingualism Consequences of Bilingualism for Cognitive Development


title: Online Learning of Nonparametric Mixture Models via Sequential Variational Approximation

abstract: Reliance on computationally expensive algorithms for inference has been limiting the use of Bayesian nonparametric models in large scale applications. To tackle this problem, we propose a Bayesian learning algorithm for DP mixture models. Instead of following the conventional paradigm - random initialization plus iterative update, we take an progressive approach. Starting with a given prior, our method recursively transforms it into an approximate posterior through sequential variational approximation. In this process, new components will be incorporated on the fly when needed. The algorithm can reliably estimate a DP mixture model in one pass, making it particularly suited for applications with massive data. Experiments on both synthetic data and real datasets demonstrate remarkable improvement on efficiency - orders of magnitude speed-up compared to the state-of-the-art.


title: Cloud computing for the architecture, engineering & construction sector: requirements, prototype & experience

abstract: The Architecture, Engineering & Construction (AEC) sector is a highly fragmented, data intensive, project based industry, involving a number of very different professions and organisations. Projects carried out within this sector involve collaboration between various people, using a variety of different systems. This, along with the industry’s strong data sharing and processing requirements, means that the management of building data is complex and challenging. This paper presents a solution to data sharing requirements of the AEC sector by utilising Cloud Computing. Our solution presents two key contributions, first a governance model for building data, based on extensive research and industry consultation. Second, a prototype implementation of this governance model, utilising the CometCloud autonomic Cloud Computing engine based on the Master/Worker paradigm. We have integrated our prototype with the 3D modelling software Google Sketchup. The approach and prototype presented has applicability in a number of other eScience related applications involving multi-disciplinary, collaborative working using Cloud Computing infrastructure.


title: Mobile IP-Based Architecture for Smart Homes

abstract: Smart Home technology is the integration of technology and services through home networking for a better quality of living. This paper provides a review of the recent developments in technologies for Smart Home Automation and Mobile IP and its potential application and integration to Smart Home systems. The principles of MIPv6 are adopted for providing mobility on the design of the architecture for MIP-Based Smart Home.


title: Multi-Photon Nanosurgery in Live Brain

abstract: In the last few years two-photon microscopy has been used to perform in vivo high spatial resolution imaging of neurons, glial cells and vascular structures in the intact neocortex. Recently, in parallel to its applications in imaging, multi-photon absorption has been used as a tool for the selective disruption of neural processes and blood vessels in living animals. In this review we present some basic features of multi-photon nanosurgery and we illustrate the advantages offered by this novel methodology in neuroscience research. We show how the spatial localization of multiphoton excitation can be exploited to perform selective lesions on cortical neurons in living mice expressing fluorescent proteins. This methodology is applied to disrupt a single neuron without causing any visible collateral damage to the surrounding structures. The spatial precision of this method allows to dissect single processes as well as individual dendritic spines, preserving the structural integrity of the main neuronal arbor. The same approach can be used to breach the blood-brain barrier through a targeted photo-disruption of blood vessels walls. We show how the vascular system can be perturbed through laser ablation leading towards two different models of stroke: intravascular clot and extravasation. Following the temporal evolution of the injured system (either a neuron or a blood vessel) through time lapse in vivo imaging, the physiological response of the target structure and the rearrangement of the surrounding area can be characterized. Multi-photon nanosurgery in live brain represents a useful tool to produce different models of neurodegenerative disease.


title: Gram-negative water bacteria in hemodialysis systems.

abstract: Gram-negative bacteria can multiply relatively fast in a variety of hospital associated fluids ranging from distilled, deionized, reverse osmosis, and softened water, which are normally considered devoid of nutrients, to intravenous solutions and fluids associated with hemodialysis. Excessive levels of these bacteria in the dialysate of artificial kidney machines can be responsible for pyrogenic reactions or sepsis or both.


title: Relations in biomedical ontologies

abstract: To enhance the treatment of relations in biomedical ontologies we advance a methodology for providing consistent and unambiguous formal definitions of the relational expressions used in such ontologies in a way designed to assist developers and users in avoiding errors in coding and annotation. The resulting Relation Ontology can promote interoperability of ontologies and support new types of automated reasoning about the spatial and temporal dimensions of biological and medical phenomena.


title: Shoulder mechanism design of an exoskeleton robot for stroke patient rehabilitation

abstract: Shoulder girdle movement is critical for stabilizing and orientating the arm during daily activities. During robotic arm rehabilitation with stroke patients, the robot must assist movements of the shoulder girdle. Shoulder girdle movement is characterized by a highly nonlinear function of the humeral orientation, which is different for each person. Hence it is improper to use pre-calculated shoulder girdle movement. If an exoskeleton robot cannot mimic the patient's shoulder girdle movement well, the robot axes will not coincide with the patient's, which brings reduced range of motion (ROM) and discomfort to the patients. A number of exoskeleton robots have been developed to assist shoulder girdle movement. The shoulder mechanism of these robots, along with the advantages and disadvantages, are introduced. In this paper, a novel shoulder mechanism design of exoskeleton robot is proposed, which can fully mimic the patient's shoulder girdle movement in real time.


title: Provable surface reconstruction from noisy samples

abstract: We present an algorithm for surface reconstruction in presence of noise. We show that, under a reasonable noise model, the algorithm has theoretical guarantees. Actual performance of the algorithm is illustrated by our experimental results.


title: Motivations and Methodologies for Automation of the compositional process

abstract: Our aim in this paper is to clarify the range of motivations that have inspired the development of computer programs for the composition of music. We consider this to be important since different methodologies are appropriate for different motivations and goals. We argue that a widespread failure to specify the motivations and goals involved has lead to a methodological malaise in music related research. A brief consideration of some of the earliest attempts to produce computational systems for the composition of music leads us to identify four activities involving the development of computer programs which compose music each of which is inspired by different practical or theoretical motivations. These activities are algorithmic composition, the design of compositional tools, the computational modelling of musical styles and the computational modelling of music cognition. We consider these four motivations in turn, illustrating the problems that have arisen from failing to distinguish between them. We pro...


title: Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex

abstract: Rapid variations in cortical state during wakefulness have a strong influence on neural and behavioural responses and are tightly coupled to changes in pupil size across species. However, the physiological processes linking cortical state and pupil variations are largely unknown. Here we demonstrate that these rapid variations, during both quiet waking and locomotion, are highly correlated with fluctuations in the activity of corticopetal noradrenergic and cholinergic projections. Rapid dilations of the pupil are tightly associated with phasic activity in noradrenergic axons, whereas longer-lasting dilations of the pupil, such as during locomotion, are accompanied by sustained activity in cholinergic axons. Thus, the pupil can be used to sensitively track the activity in multiple neuromodulatory transmitter systems as they control the state of the waking brain. In addition to light intensity, changes in pupil diameter are correlated with mental effort, attention and levels of arousal. Reimer et al. report that across behavioural states, fluctuations in pupil diameter are highly correlated with activity of noradrenergic and cholinergic projection neurons.


title: Bed-type robotic system for the bedridden

abstract: An intelligent bed robot system (IBRS) is proposed to help the elderly and the disabled for their independent life in bed. The IBRS is a special bed equipped with two robot arms and an array of pressure sensors attached onto the mattress. The pressure distribution on the mattress is used to estimate the pose of the patient, and an appropriate assistance is provided by the robot arms


title: Object Permanence in Young Infants: Further Evidence

abstract: Recent evidence suggests that 4.5- and even 3.5-month-old infants realize that objects continue to exist when hidden. The goal of the present experiments was to obtain converging evidence of object permanence in young infants. Experiments were conducted using paradigms previously used to demonstrate object permanence in 5.5-month-old infants and 6.5-month-old infants. In one experiment, 3.5-month-old infants watched a short or a tall carrot slide along a track. The track's center was hidden by a screen with a large window in its upper half. The short carrot was shorter than the window's lower edge and so did not appear in the window when passing behind the screen; the tall carrot was taller than the window's lower edge and hence should have appeared in the window but did not. The infants looked reliably longer at the tall than at the short carrot event, suggesting that they (a) represented the existence, height, and trajectory of each carrot behind the screen and (b) expected the tall carrot to appear in the screen window and were surprised that it did not. Control trials supported this interpretation. In another experiment, 4.0-month-old infants saw a toy car roll along a track that was partly hidden by a screen. A large toy mouse was placed behind the screen, either on top or in back of the track. The female infants looked reliably longer when the mouse stood on top as opposed to in back of the track, suggesting that they (a) represented the existence and trajectory of the car behind the screen, (b) represented the existence and location of the mouse behind the screen, and (c) were surprised to see the car reappear from behind the screen when the mouse stood in its path. A second experiment supported this interpretation. The results of these experiments provide further evidence that infants aged 3.5 months and older are able to represent and to reason about hidden objects.


Author ' s personal copy Can we teach digital natives digital literacy ?


title: Evaluating Facebook pages for small hotels: a systematic approach

abstract: Social networking websites play an increasingly important role in hotel promotion and marketing. However, designing effective social networks profiles still presents quite a challenge. To support the development of quality hotels’ profiles, we introduce a systematic approach based on an evaluation model that includes a number of heuristic factors and user ratings. The model was developed for Facebook, the most successful social networking website. The evaluation scheme was developed via (1) an online brainstorming with influential bloggers and experienced web designers and (2) the classification and integration of the evaluation characteristics and criteria. To test the applicability of the model, we performed an exploratory study on selected Facebook pages of small hotels in Italy and submitted them to a user evaluation. The results validated our model and highlighted that hotels can implement the model to optimize their pages in a number of ways to better exploit their Facebook presence.


title: Misdiagnosis of spinal cord injuries—The physiatrist's point of view

abstract: Thirteen different patients involving spinal cord pathology with delayed or false diagnosis are presented and these diagnostic pitfalls are discussed. Many seemingly simple explanations for para or quadriplegia are suddenly quite complex upon scrutiny. The problem of proper diagnosis is capital in instituting proper therapy, advising the patient and his family, and unfortunately in today's medical climate, for medical legal reasons.


title: Compact diplexer with dual-mode SIW resonators

abstract: A compact diplexer fabricated on substrate-integrated waveguide (SIW) technology is presented. The diplexer is a sixth order coupled-resonator network with no additional elements such as power dividers, composed exclusively of three dual-mode square SIW cavities. The coupling between the two orthogonal resonant modes of each cavity is generated by means of additional vias perturbing the cavity symmetry, while inductive irises implement the inter-cavity couplings. The coupling topology of the whole diplexer is restricted by the orientation of the resonant modes. A fabricated prototype with passbands around 10 GHz proves that this approach is feasible.


title: Joint Compression of Near-Duplicate Videos

abstract: The expanding social network and multimedia technologies encourage more and more people to store and transmit information in visual format, such as image and video. However, the cost of this convenience brings about a shock to traditional video severs and exposes them under the risk of overloading. In the huge volume of online videos, there are a large amount of near-duplicate videos (NDVs). Although quite a number of research work have been proposed to detect NDVs, little research effort is made to compress these NDVs in a more effective manner than independent video compression. In this study, we make an in-depth exploration of the data redundancy of NDVs and propose a video analysis and coding framework to jointly compress NDVs. In order to employ the proposed NDV analysis and coding framework, a graph-based similar video grouping method and a number of preprocessing functions are designed to explore the correlation of visual information among NDVs and thus suit the requirement of joint video coding. Experimental results verify that the proposed NDV analysis and coding framework is able to effectively compress NDVs and thus save video data storage.


title: Hierarchical Attribute-Based Access Control with Authentication for Outsourced Data in Cloud Computing

abstract: Access control is one of the most important security mechanisms in cloud computing. Attributed based encryption provides an approach that allows data owners to integrate data access policies within the encrypted data. However, little work has been done to explore flexible authorization in specifying the data user's privileges and enforcing the data owner's policy in cloud based environments. In this paper, we propose a hierarchical attribute based access control scheme by extending ciphertext-policy attribute-based encryption (CP-ABE) with a hierarchical structure of multiauthorities and exploiting attribute-based signature (ABS). The proposed scheme not only achieves scalability due to its hierarchical structure, but also inherits fine-grained access control with authentication in supporting write privilege on outsourced data in cloud computing. In addition, we decouple the task of policy management from security enforcement by using the extensible access control markup language (XACML) framework. Extensive analysis shows that our scheme is both efficient and scalable in dealing with access control for outsourced data in cloud computing.


title: The Proposition Bank: An Annotated Corpus of Semantic Roles

abstract: The Proposition Bank project takes a practical approach to semantic representation, adding a layer of predicate-argument information, or semantic role labels, to the syntactic structures of the Penn Treebank. The resulting resource can be thought of as shallow, in that it does not represent coreference, quantification, and many other higher-order phenomena, but also broad, in that it covers every instance of every verb in the corpus and allows representative statistics to be calculated.We discuss the criteria used to define the sets of semantic roles used in the annotation process and to analyze the frequency of syntactic/semantic alternations in the corpus. ::: ::: We describe an automatic system for semantic role tagging trained on the corpus and discuss the effect on its performance of various types of information, including a comparison of full syntactic parsing with a flat representation and the contribution of the empty ''trace'' categories of the treebank.


title: Parental Perception of Changes in Basic Life Needs of Children with Disabilities after Six Months of Therapeutic Horseback Riding: A Qualitative Study

abstract: Therapeutic horseback riding (THR) has a positive effect on the physical, cognitive, and psychosocial functioning of children with disabilities. Parents’ reports of the effects of THR on their children support professionals in individualizing the THR program. With this qualitative study, we aimed to explore parents’ perceptions of changes in the basic life needs of their children with disabilities after six months of THR lessons and to survey parents’ explanations for the causes of these changes. The study involved parents of 13 children with disabilities who were enrolled in a six-month THR program. Parents continuously monitored their children and wrote a report on possible changes in their child’s needs according to Virginia Henderson’s need theory. Qualitative content analysis of parents’ reports indicated only positive changes in 11 children. Most codes were identified in categories “relationships and communication with other people” and “movement and posturing”. Other categories identified codes such as easier breathing, a better quality of sleep, better appetite, better elimination of stool and urine, more independence in clothing and maintaining personal hygiene, and greater interest in play and learning. Parents’ reports are further supported by the assessments of professionals. Most parents think THR is responsible for the noticeable improvements in their children’s quality of life.


title: PPV: Pixel-Point-Volume Segmentation for Object Referencing in Collaborative Augmented Reality

abstract: We present a method for collaborative augmented reality (AR) that enables users from different viewpoints to interpret object references specified via 2D on-screen circling gestures. Based on a user's 2D drawing annotation, the method segments out the userselected object using an incomplete or imperfect scene model and the color image from the drawing viewpoint. Specifically, we propose a novel segmentation algorithm that utilizes both 2D and 3D scene cues, structured into a three-layer graph of pixels, 3D points, and volumes (supervoxels), solved via standard graph cut algorithms. This segmentation enables an appropriate rendering of the user's 2D annotation from other viewpoints in 3D augmented reality. Results demonstrate the superiority of the proposed method over existing methods.


title: Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents

abstract: In this paper, a new Mixture model of Dynamic pedestrian-Agents (MDA) is proposed to learn the collective behavior patterns of pedestrians in crowded scenes. Collective behaviors characterize the intrinsic dynamics of the crowd. From the agent-based modeling, each pedestrian in the crowd is driven by a dynamic pedestrian-agent, which is a linear dynamic system with its initial and termination states reflecting a pedestrian's belief of the starting point and the destination. Then the whole crowd is modeled as a mixture of dynamic pedestrian-agents. Once the model is unsupervisedly learned from real data, MDA can simulate the crowd behaviors. Furthermore, MDA can well infer the past behaviors and predict the future behaviors of pedestrians given their trajectories only partially observed, and classify different pedestrian behaviors in the scene. The effectiveness of MDA and its applications are demonstrated by qualitative and quantitative experiments on the video surveillance dataset collected from the New York Grand Central Station.


title: A New Three Object Triangulation Algorithm Based on the Power Center of Three Circles

abstract: Positioning is a fundamental issue in mobile robot applications that can be achieved in multiple ways. Among these methods, triangulation is a proven technique. As it exists for a long time, many variants of triangulation have been proposed. Which variant is most appropriate depends on the application because some methods ignore the beacon ordering while other have blind spots. Some methods are reliable but at a price of increasing complexity or special cases study. In this paper, we present a simple and new three object triangulation algorithm. Our algorithm works in the whole plane (except when the beacons and the robot are concyclic or colinear), and for any beacon ordering. Moreover, it does not need special cases study and has a strong geometrical meaning. Benchmarks show that our algorithm is faster than existing and comparable algorithms. Finally, a quality measure is intrinsically derived for the triangulation result in the whole plane, which can be used to identify the pathological cases, or as a validation gate in Kalman filters.


Feature Selection using One Class SVM : A New Perspective


title: Mining Twitter for Fine-Grained Political Opinion Polarity Classification, Ideology Detection and Sarcasm Detection

abstract: In this paper, we propose three models for socio-political opinion polarity classification of microblog posts. Firstly, a novel probabilistic model, Joint-Entity-Sentiment-Topic (JEST) model, which captures opinions as a combination of the target entity, sentiment and topic, will be proposed. Secondly, a model for ideology detection called JEST-Ideology will be proposed to identify an individual»s orientation towards topics/issues and target entities by extending the proposed opinion polarity classification framework. Finally, we propose a novel method to accurately detect sarcastic opinions by utilizing detected fine-grained opinion and ideology.


title: Optimal Beam Search for Machine Translation

abstract: Beam search is a fast and empirically effective method for translation decoding, but it lacks formal guarantees about search error. We develop a new decoding algorithm that combines the speed of beam search with the optimal certificate property of Lagrangian relaxation, and apply it to phrase- and syntax-based translation decoding. The new method is efficient, utilizes standard MT algorithms, and returns an exact solution on the majority of translation examples in our test data. The algorithm is 3.5 times faster than an optimized incremental constraint-based decoder for phrase-based translation and 4 times faster for syntax-based translation.


title: A new powered ankle-foot prosthesis with compact parallel spring mechanism

abstract: This paper presents an innovative powered ankle-foot prosthesis with a compact parallel spring mechanism. The parallel spring mechanism consisting a cam and two linear springs are used to reduce the power and torque requirement of actuators, while improving the system energy efficiency. Herein, the cam profile is designed to generate desired spring torque rather than the stroke versus angle curve. In that case, the spring can reproduce human ankle controlled dorsiflexion stiffness. Finally, for evaluating the proposed prosthesis, clinical trials on a below knee amputee are conducted. The experimental results show that the performances of prosthesis are similar to the normal gait.


Climate controls on C 3 vs . C 4 productivity in North American grasslands from carbon isotope composition of soil organic matter


title: Guided Data Repair

abstract: In this paper we present GDR, a Guided Data Repair framework that incorporates user feedback in the cleaning process to enhance and accelerate existing automatic repair techniques while minimizing user involvement. GDR consults the user on the updates that are most likely to be beneficial in improving data quality. GDR also uses machine learning methods to identify and apply the correct updates directly to the database without the actual involvement of the user on these specific updates. To rank potential updates for consultation by the user, we first group these repairs and quantify the utility of each group using the decision-theory concept of value of information (VOI). We then apply active learning to order updates within a group based on their ability to improve the learned model. User feedback is used to repair the database and to adaptively refine the training set for the model. We empirically evaluate GDR on a real-world dataset and show significant improvement in data quality using our user guided repairing process. We also, assess the trade-off between the user efforts and the resulting data quality.


title: NJAS: Sandboxing Unmodified Applications in non-rooted Devices Running stock Android

abstract: Malware poses a serious threat to the Android ecosystem. Moreover, even benign applications can sometimes constitute security and privacy risks to their users, as they might contain vulnerabilities, or they might perform unwanted actions. Previous research has shown that the current Android security model is not sufficient to protect against these threats, and several solutions have been proposed to enable the specification and enforcing of finer-grained security policies. Unfortunately, many existing solutions suffer from several limitations: they require modifications to the Android framework, root access to the device, to create a modified version of an existing app that cannot be installed without enabling unsafe options, or they cannot completely sandbox native code components. In this work, we propose a novel approach that aims to sandbox arbitrary Android applications. Our solution, called NJAS, works by executing an Android application within the context of another one, and it achieves sandboxing by means of system call interposition. In this paper, we show that our solution overcomes major limitations that affect existing solutions. In fact, it does not require any modification to the framework, does not require root access to the device, and does not require the user to enable unsafe options. Moreover, the core sandboxing mechanism cannot be evaded by using native code components.


title: Susceptibility profile and metabolic mechanisms involved in Aedes aegypti and Aedes albopictus resistant to DDT and deltamethrin in the Central African Republic

abstract: BackgroundAedes aegypti and Ae. albopictus are the main epidemic vectors of dengue, chikungunya and Zika viruses worldwide. Their control during epidemics relies mainly on control of larvae and adults with insecticides. Unfortunately, loss of susceptibility of both species to several insecticide classes limits the efficacy of interventions. In Africa, where Aedes-borne viruses are of growing concern, few data are available on resistance to insecticides. To fill this gap, we assessed the susceptibility to insecticides of Ae. aegypti and Ae. albopictus populations in the Central African Republic (CAR) and studied the mechanisms of resistance.MethodsImmature stages were sampled between June and September 2014 in six locations in Bangui (the capital of CAR) for larval and adult bioassays according to WHO standard procedures. We also characterized DDT- and pyrethroid-resistant mosquitoes molecularly and biochemically, including tests for the activities of nonspecific esterases (α and β), mixed-function oxidases, insensitive acetylcholinesterase and glutathione S-transferases.ResultsLarval bioassays, carried out to determine the lethal concentrations (LC50 and LC95) and resistance ratios (RR50 and RR95), suggested that both vector species were susceptible to Bacillus thuringiensis var. israeliensis and to temephos. Bioassays of adults showed susceptibility to propoxur and fenitrothion, except for one Ae. albopictus population that was suspected to be resistant to fenithrothion. None of the Ae. aegypti populations was fully susceptible to DDT. Ae. albopictus presented a similar profile to Ae. aegypti but with a lower mortality rate (41%). Possible resistance to deltamethrin was observed among Ae. aegypti and Ae. albopictus, although some were susceptible. No kdr mutations were detected in either species; however, the activity of detoxifying enzymes was higher in most populations than in the susceptible Ae. aegypti strain, confirming decreased susceptibility to DDT and deltamethrin.ConclusionThese findings suggested that regular, continuous monitoring of resistance is necessary in order to select the most effective adulticides for arbovirus control in Bangui.


title: Hidden factors and hidden topics: understanding rating dimensions with review text

abstract: In order to recommend products to users we must ultimately predict how a user will respond to a new product. To do so we must uncover the implicit tastes of each user as well as the properties of each product. For example, in order to predict whether a user will enjoy Harry Potter, it helps to identify that the book is about wizards, as well as the user's level of interest in wizardry. User feedback is required to discover these latent product and user dimensions. Such feedback often comes in the form of a numeric rating accompanied by review text. However, traditional methods often discard review text, which makes user and product latent dimensions difficult to interpret, since they ignore the very text that justifies a user's rating. In this paper, we aim to combine latent rating dimensions (such as those of latent-factor recommender systems) with latent review topics (such as those learned by topic models like LDA). Our approach has several advantages. Firstly, we obtain highly interpretable textual labels for latent rating dimensions, which helps us to `justify' ratings with text. Secondly, our approach more accurately predicts product ratings by harnessing the information present in review text; this is especially true for new products and users, who may have too few ratings to model their latent factors, yet may still provide substantial information from the text of even a single review. Thirdly, our discovered topics can be used to facilitate other tasks such as automated genre discovery, and to identify useful and representative reviews.


title: Inverse Procedural Modeling by Automatic Generation of L-systems

abstract: We present an important step towards the solution of the problem of inverse procedural modeling by generating parametric context-free L-systems that represent an input 2D model. The L-systemrules efficiently code the regular structures and the parameters represent the properties of the structure transformations. The algorithm takes as input a 2D vector image that is composed of atomic elements, such as curves and poly-lines. Similar elements are recognized and assigned terminal symbols ofan L-systemalphabet. Theterminal symbols’ position and orientation are pair-wise compared and the transformations are stored as points in multiple 4D transformation spaces. By careful analysis of the clusters in the transformation spaces, we detect sequences of elements and code them as L-system rules. The coded elements are then removed from the clusters, the clusters are updated, and then the analysis attempts to code groups of elements in (hierarchies) the same way. The analysis ends with a single group of elements that is coded as an L-system axiom. We recognize and code branching sequences of linearly translated, scaled, and rotated elements and their hierarchies. The L-system not only represents the input image, but it can also be used for various editing operations. By changing the L-system parameters, the image can be randomized, symmetrized, and groups of elements and regular structures can be edited. By changing the terminal and non-terminal symbols, elements or groups of elements can be replaced.


title: Block Tensor Unfoldings

abstract: Within the field of numerical multilinear algebra, block tensors are increasingly important. Accordingly, it is appropriate to develop an infrastructure that supports reasoning about block tensor computation. In this paper we establish concise notation that is suitable for the analysis and development of block tensor algorithms, prove several useful block tensor identities, and make precise the notion of a block tensor unfolding.


title: Multi-Layer and Recursive Neural Networks for Metagenomic Classification

abstract: Recent advances in machine learning, specifically in deep learning with neural networks, has made a profound impact on fields such as natural language processing, image classification, and language modeling; however, feasibility and potential benefits of the approaches to metagenomic data analysis has been largely under-explored. Deep learning exploits many layers of learning nonlinear feature representations, typically in an unsupervised fashion, and recent results have shown outstanding generalization performance on previously unseen data. Furthermore, some deep learning methods can also represent the structure in a data set. Consequently, deep learning and neural networks may prove to be an appropriate approach for metagenomic data. To determine whether such approaches are indeed appropriate for metagenomics, we experiment with two deep learning methods: i) a deep belief network, and ii) a recursive neural network, the latter of which provides a tree representing the structure of the data. We compare these approaches to the standard multi-layer perceptron, which has been well-established in the machine learning community as a powerful prediction algorithm, though its presence is largely missing in metagenomics literature. We find that traditional neural networks can be quite powerful classifiers on metagenomic data compared to baseline methods, such as random forests. On the other hand, while the deep learning approaches did not result in improvements to the classification accuracy, they do provide the ability to learn hierarchical representations of a data set that standard classification methods do not allow. Our goal in this effort is not to determine the best algorithm in terms accuracy—as that depends on the specific application—but rather to highlight the benefits and drawbacks of each of the approach we discuss and provide insight on how they can be improved for predictive metagenomic analysis.


How Authoritarian Leadership and Renqing Orientation Improve Tacit Knowledge Sharing


title: A Confidence Agent: Toward More Effective Intelligent Distance Learning Environments

abstract: In this paper, we propose a multi-agent approach to building more Cooperative Intelligent Distance Learning Environments (CIDLE). We define a Confidence Agent in Intelligent Tutoring System (ITS) in such a way that an ITS would improve the quality and efficiency of its teaching. To achieve this goal, we propose a Confidence Intelligent Tutoring System (CITS) to manage negotiations within a community of on-line learners to improve CIDLE interactions among the participants. The proposed system can extract knowledge about domain knowledge and about learners behavior during a learning discussion. Therefore, it infers the behavior of learners, and adapts presentation of subject mater in order to improve their success rate in answering questions and boost their self-confidence during learning session. In addition, we discuss architectural problems of the CITS and their solutions..


title: Enhancing well-being and alleviating depressive symptoms with positive psychology interventions: a practice-friendly meta-analysis

abstract: Do positive psychology interventions—that is, treatment methods or intentional activities aimed at cultivating positive feelings, positive behaviors, or positive cognitions—enhance well-being and ameliorate depressive symptoms? A meta-analysis of 51 such interventions with 4,266 individuals was conducted to address this question and to provide practical guidance to clinicians. The results revealed that positive psychology interventions do indeed significantly enhance well-being (mean r=.29) and decrease depressive symptoms (mean r=.31). In addition, several factors were found to impact the effectiveness of positive psychology interventions, including the depression status, self-selection, and age of participants, as well as the format and duration of the interventions. Accordingly, clinicians should be encouraged to incorporate positive psychology techniques into their clinical work, particularly for treating clients who are depressed, relatively older, or highly motivated to improve. Our findings also suggest that clinicians would do well to deliver positive psychology interventions as individual (versus group) therapy and for relatively longer periods of time. © 2009 Wiley Periodicals, Inc. J Clin Psychol: In Session 65: 1–21, 2009.


title: Why machine learning algorithms fail in misuse detection on KDD intrusion detection data set

abstract: A large set of machine learning and pattern classification algorithms trained and tested on KDD intrusion detection data set failed to identify most of the user-to-root and remote-to-local attacks, as reported by many researchers in the literature. In light of this observation, this paper aims to expose the deficiencies and limitations of the KDD data set to argue that this data set should not be used to train pattern recognition or machine learning algorithms for misuse detection for these two attack categories. Multiple analysis techniques are employed to demonstrate, both objectively and subjectively, that the KDD training and testing data subsets represent dissimilar target hypotheses for user-to-root and remote-to-local attack categories. These techniques consisted of switching the roles of original training and testing data subsets to develop a decision tree classifier, cross-validation on merged training and testing data subsets, and qualitative and comparative analysis of rules generated independently on training and testing data subsets through the C4.5 decision tree algorithm. Analysis results clearly suggest that no pattern classification or machine learning algorithm can be trained successfully with the KDD data set to perform misuse detection for user-to-root or remote-to-local attack categories. It is further noted that the analysis techniques employed to assess the similarity between the two target hypotheses represented by the training and the testing data subsets can readily be generalized to data set pairs in other problem domains.


title: Multi-clip video editing from a single viewpoint

abstract: We propose a framework for automatically generating multiple clips suitable for video editing by simulating pan-tilt-zoom camera movements within the frame of a single static camera. Assuming important actors and objects can be localized using computer vision techniques, our method requires only minimal user input to define the subject matter of each sub-clip. The composition of each sub-clip is automatically computed in a novel L1-norm optimization framework. Our approach encodes several common cinematographic practices into a single convex cost function minimization problem, resulting in aesthetically pleasing sub-clips which can easily be edited together using off-the-shelf multi-clip video editing software. We demonstrate our approach on five video sequences of a live theatre performance by generating multiple synchronized subclips for each sequence.


title: Neural Cross-Lingual Named Entity Recognition with Minimal Resources

abstract: For languages with no annotated resources, unsupervised transfer of natural language processing models such as named-entity recognition (NER) from resource-rich languages would be an appealing capability. However, differences in words and word order across languages make it a challenging problem. To improve mapping of lexical items across languages, we propose a method that finds translations based on bilingual word embeddings. To improve robustness to word order differences, we propose to use self-attention, which allows for a degree of flexibility with respect to word order. We demonstrate that these methods achieve state-of-the-art or competitive NER performance on commonly tested languages under a cross-lingual setting, with much lower resource requirements than past approaches. We also evaluate the challenges of applying these methods to Uyghur, a low-resource language.


title: MILEPOST GCC: machine learning based research compiler

abstract: Tuning hardwired compiler optimizations for rapidly evolving hardware makes porting an optimizing compiler for each new platform extremely challenging. Our radical approach is to develop a modular, extensible, self-optimizing compiler that automatically learns the best optimization heuristics based on the behavior of the platform. In this paper we describe MILEPOST 1 GCC, a machine-learning-based compiler that automatically adjusts its optimization heuristics to improve the execution time, code size, or compilation time of specific programs on different architectures. Our preliminary experimental results show that it is possible to considerably reduce execution time of the MiBench benchmark suite on a range of platforms entirely automatically.


title: Porting an Open Information Extraction System from English to German

abstract: Many downstream NLP tasks can benefit from Open Information Extraction (Open IE) as a semantic representation. While Open IE systems are available for English, many other languages lack such tools. In this paper, we present a straightforward approach for adapting PropS, a rule-based predicate-argument analysis for English, to a new language, German. With this approach, we quickly obtain an Open IE system for German covering 89% of the English rule set. It yields 1.6 extractions per sentence with 60% precision, making it readily usable in downstream applications.


title: Domain Adaptation with Adversarial Training and Graph Embeddings

abstract: The success of deep neural networks (DNNs) is heavily dependent on the availability of labeled data. However, obtaining labeled data is a big challenge in many real-world problems. In such scenarios, a DNN model can leverage labeled and unlabeled data from a related domain, but it has to deal with the shift in data distributions between the source and the target domains. In this paper, we study the problem of classifying social media posts during a crisis event (e.g., Earthquake). For that, we use labeled and unlabeled data from past similar events (e.g., Flood) and unlabeled data for the current event. We propose a novel model that performs adversarial learning based domain adaptation to deal with distribution drifts and graph based semi-supervised learning to leverage unlabeled data within a single unified deep learning framework. Our experiments with two real-world crisis datasets collected from Twitter demonstrate significant improvements over several baselines.


An Exhaustive DPLL Algorithm for Model Counting


title: Regression testing in an industrial environment

abstract: his article discusses these problemsand presents both management andtechnical techniques to address them.Some of these solutions are alreadybeing used by software developers,even though they may not be widelyknown. This article proposes a multilevel regressiontesting framework that can be easily adapted into asoftware development and maintenance process inwhich regression testing can play a key role inimproving and ensuring consistent software quality.Our observations are based on our accumulatedexperience in software development and mainte-nance in the U.S. and Japan, including Hitachi Soft-ware Engineering (HSK).Regression testing has been extensively studiedby researchers [1, 3, 7, 10, 11] from a theoreticalpoint of view. However, there has been little discus-sion on applying regression testing in industrialenvironments. While researchers are mostly con-cerned with reducing the number of test cases forretesting, there are other important issues in usingregression testing in an industrial environment.Some of our observations include:• Regression testing is used extensively. In fact,


title: Secure communications with D2D cooperation

abstract: Device to Device (D2D) communications provides a promising technique for 5G wireless networks, supporting higher data rates. Security of data transmission over wireless clouds could put constraints on devices; whether to cooperate or not. Therefore, our aim is to provide analytical framework for the security at the physical layer and to define the constraints embodied with cooperation in wireless clouds1. In this paper, two legitimate transmitters Alice and John cooperate to increase the reliable transmission rate received by their common legitimate receiver Bob, where one eavesdropper, Eve exists. We propose a distributed algorithm that allows the devices to select whether to cooperate or not and to adapt their optimal power allocation based on the cooperation framework selected. Moreover, we define distance constraints to enforce the benefits of cooperation between devices in a wireless cloud.


title: A 94GHz 4TX-4RX phased-array for FMCW radar with integrated LO and flip-chip antenna package

abstract: A prototype phased-array IC with four transmitters, four receivers, and integrated LO generation was designed and fabricated in a 130nm SiGe BiCMOS technology. Including LO phase shifter power consumption, the transmit array consumes 71mW per element with a per-element output power of +6.4dBm at 94GHz. The receiver array consumes 56mW per element, and achieves an RX element noise figure of 12.5dB at 94GHz. Integrated LO generation includes a 47GHz VCO, 2× frequency multiplier, 94GHz LO buffers, and a 32× CML divider chain. The transceiver has been integrated into a flip chip antenna module with four transmit and four receive antennas, and achieves TX and RX beam steering over a scan angle range of ±20°. Including LO and bias overhead power, the array has improved per-element power consumption compared with state-of-the-art 94GHz arrays, consuming only 106mW per TX channel and 91mW per RX channel, while achieving comparable performance and levels of integration.


title: Exploring capturable everyday memory for autobiographical authentication

abstract: We explore how well the intersection between our own everyday memories and those captured by our smartphones can be used for what we call autobiographical authentication-a challenge-response authentication system that queries users about day-to-day experiences. Through three studies-two on MTurk and one field study-we found that users are good, but make systematic errors at answering autobiographical questions. Using Bayesian modeling to account for these systematic response errors, we derived a formula for computing a confidence rating that the attempting authenticator is the user from a sequence of question-answer responses. We tested our formula against five simulated adversaries based on plausible real-life counterparts. Our simulations indicate that our model of autobiographical authentication generally performs well in assigning high confidence estimates to the user and low confidence estimates to impersonating adversaries.


KroahHartman: ”Linux Security Module Framework


title: A Soft Jellyfish Robot Driven by a Dielectric Elastomer Actuator

abstract: Although dielectric elastomers have been extensively studied recently, to date there has been little research into application of dielectric elastomer actuators to undersea robots. This letter focuses on development of a jellyfish robot using a dielectric elastomer actuator, which exhibits muscle-like properties including large deformation and high energy density. We carry out experiments to test the actuator’s deformation and force. Theoretical simulations are conducted to analyze the performance of the actuator, which are qualitatively consistent with the experiments. The preliminary studies show that this jellyfish robot based on dielectric elastomer technology can move effectively in water. The robot also exhibits fast response and high capacity of payload (compared to its self-weight).


title: A style-free and high speed algorithm for License Plate Detection

abstract: Automatic License Plate Recognition (ALPR) is one of the most prominent tools in intelligent transportation system applications. In ALPR algorithm implementation, License Plate Detection (LPD) is a critical stage. Despite many state-of-the-art researches, some parameters such as low/high illumination, type of camera, or a different style of License Plate (LP) causes LPD step is still a challenging problem. In this paper, we propose a new style-free method based on the cross power spectrum. Our method has three steps; designing adaptive binarized filter, filtering using cross power spectrum and verification. Experimental results show that the recognition accuracy of the proposed approach is 98% among 2241 Iranian cars images including two styles of the LP. In addition, the process of the plate detection takes 44 milliseconds, which is suitable for real-time processing.


title: The neural components of empathy: Predicting daily prosocial behavior

abstract: Previous neuroimaging studies on empathy have not clearly identified neural systems that support the three components of empathy: affective congruence, perspective-taking, and prosocial motivation. These limitations stem from a focus on a single emotion per study, minimal variation in amount of social context provided, and lack of prosocial motivation assessment. In the current investigation, 32 participants completed a functional magnetic resonance imaging session assessing empathic responses to individuals experiencing painful, anxious, and happy events that varied in valence and amount of social context provided. They also completed a 14-day experience sampling survey that assessed real-world helping behaviors. The results demonstrate that empathy for positive and negative emotions selectively activates regions associated with positive and negative affect, respectively. In addition, the mirror system was more active during empathy for context-independent events (pain), whereas the mentalizing system was more active during empathy for context-dependent events (anxiety, happiness). Finally, the septal area, previously linked to prosocial motivation, was the only region that was commonly activated across empathy for pain, anxiety, and happiness. Septal activity during each of these empathic experiences was predictive of daily helping. These findings suggest that empathy has multiple input pathways, produces affect-congruent activations, and results in septally mediated prosocial motivation.


title: Perturbation of differentiated functions in Vivo during persistent viral infection III. Decreased growth hormone mRNA

abstract: Abstract Lymphocytic choriomeningitis virus (LCMV) persistent infection that results from the inoculation of C3H/St newborn mice causes growth hormone (GH) deficiency and associated disease characterized by both reduced weight and serum glucose levels. Molecular analysis of pituitary nucleic acids shows that GH deficient mice have, on average, fivefold reduced levels of GH mRNA although the histopathology of such GH producing cells is normal. Northern blots indicate that the length of GH mRNA is comparable in the GH deficient, virus infected mice and the GH normal, uninfected age-matched controls. Hence, truncated GH mRNA cannot account for hormonal defect. Mice infected congenitally through mating of persistently infected parents have normal growth and blood glucose levels. GH mRNA levels in pituitaries of these mice are equivalent to those of uninfected age-matched controls but significantly greater than those seen in neonatally infected GH deficient mice. Although infectious virus titers in the sera are equivalent in congenitally and neonatally infected age- and sex-matched mice, virus titers are significantly lower in pituitaries and brains of the congenitally infected mice when compared to neonatally inoculated mice. Additionally, the number of GH-producing pituitary cells expressing viral proteins is less in congenitally infected mice relative to those in neonatally inoculated mice. Hence there is a direct association between viral replication in GH-producing cells, lowered GH mRNA, and GH deficiency.


title: In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning

abstract: We present experiments demonstrating that some other form of capacity control, different from network size, plays a central role in learning multilayer feed-forward networks. We argue, partially through analogy to matrix factorization, that this is an inductive bias that can help shed light on deep learning.


title: AirNet: a machine learning dataset for air quality forecasting

abstract: In the past decade, many urban areas in China have suffered from serious air pollution problems, making air quality forecast a hot spot. Conventional approaches rely on numerical methods to estimate the pollutant concentration and require lots of computing power. To solve this problem, we applied the widely used deep learning methods. Deep learning requires large-scale datasets to train an effective model. In this paper, we introduced a new dataset, entitled as AirNet, containing the 0.25 degree resolution grid map of mainland China, with more than two years of continued air quality measurement and meteorological data. We published this dataset as an open resource for machine learning researches and set up a baseline of a 5-day air pollution forecast. The results of experiments demonstrated that this dataset could facilitate the development of new algorithms on the air quality forecast.


A Memristor - Based Hyperchaotic System with Hidden Attractors : Dynamics , Synchronization and Circuital Emulating


title: A High-Definition LIDAR System Based on Two-Mirror Deflection Scanners

abstract: This paper addresses the problem of adopting a state-of-art laser marking system with a two-mirror deflection scanner to make a high-definition light detection and ranging (LIDAR) system. To this end, a galvanometer scanner is modeled with parameterization and then the well-known raster scanning strategy is analyzed considering the physical scanning movement and the minimum spanning tree. As a result of this analysis, the relationship between the field of view (FOV) of the captured image and the scanning speed is clearly described. Furthermore, sufficient conditions are derived for an acquired image to fully cover the FOV and also for captured objects to be well aligned for a target frame rate. Finally, a prototype LIDAR system is developed to verify the proposed concepts and to prove that it successfully generates images at various resolutions depending on the target frame rates. Experimental results show that the scanner achieves the frame rates of 17.6, 9.0, and 4.6 frames per second fps for image sizes of $240\times 16$ , $240\times 32$ , and $240\times 64$ resolutions, respectively.


title: Mesh arrangements for solid geometry

abstract: Many high-level geometry processing tasks rely on low-level constructive solid geometry operations. Though trivial for implicit representations, boolean operations are notoriously difficult to execute robustly for explicit boundary representations. Existing methods for 3D triangle meshes fall short in one way or another. Some methods are fast but fail to produce closed, self-intersection free output. Other methods are robust but place prohibitively strict assumptions on the input, e.g., no hollow cavities, non-manifold edges or self-intersections. We propose a systematic recipe for conducting a family of exact constructive solid geometry operations. The two-stage method makes no general position assumptions and does not resort to numerical perturbation. The method is variadic, operating on any number of input meshes. This generalizes unary mesh-repair operations, classic binary boolean differencing, and n-ary operations such as finding all regions inside at least k out of n inputs. We demonstrate the superior effectiveness and robustness of our method on a dataset of 10,000 "real-world" meshes from a popular online repository. To encourage development, validation, and comparison, we release both our code and dataset to the public.


title: A compact-size microstrip spiral resonator and its application to microwave oscillator

abstract: This letter presents a compact size microstrip spiral resonator and its application to a low phase noise oscillator. This resonator has stopband characteristics to be used in the series feedback oscillator topology. The whole circuit area of the proposed resonator is within 1/10 wavelength, which results in the reduction of the circuit area and cost. A 10-GHz oscillator incorporated with this resonator was designed, fabricated and measured. It shows low phase noise performance of -95.4-dBc/Hz at 100 kHz offset.


title: CloudArmor: Supporting Reputation-Based Trust Management for Cloud Services

abstract: Trust management is one of the most challenging issues for the adoption and growth of cloud computing. The highly dynamic, distributed, and non-transparent nature of cloud services introduces several challenging issues such as privacy, security, and availability. Preserving consumers’ privacy is not an easy task due to the sensitive information involved in the interactions between consumers and the trust management service. Protecting cloud services against their malicious users (e.g., such users might give misleading feedback to disadvantage a particular cloud service) is a difficult problem. Guaranteeing the availability of the trust management service is another significant challenge because of the dynamic nature of cloud environments. In this article, we describe the design and implementation of CloudArmor, a reputation-based trust management framework that provides a set of functionalities to deliver trust as a service (TaaS), which includes i) a novel protocol to prove the credibility of trust feedbacks and preserve users’ privacy, ii) an adaptive and robust credibility model for measuring the credibility of trust feedbacks to protect cloud services from malicious users and to compare the trustworthiness of cloud services, and iii) an availability model to manage the availability of the decentralized implementation of the trust management service. The feasibility and benefits of our approach have been validated by a prototype and experimental studies using a collection of real-world trust feedbacks on cloud services.


title: Making sense of number sense: Implications for children with mathematical disabilities

abstract: Drawing on various approaches to the study of mathematics learning, Gersten, Jordan, and Flojo (in this issue) explore the implications of this research for identifying children at risk for developing mathematical disabilities. One of the key topics Gersten et al. consider in their review is that of “number sense.” I expand on their preliminary effort by examining in detail the diverse set of components purported to be encompassed by this construct. My analysis reveals some major differences between the ways in which number sense is defined in the mathematical cognition literature and its definition in the literature in mathematics education. I also present recent empirical evidence and theoretical perspectives bearing on the importance of measuring the speed of making magnitude comparisons. Finally, I discuss how differing conceptions of number sense inform the issue of whether and to what extent it may be teachable.


title: Reducing the Dimensionality of Data with Neural Networks

abstract: High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. Gradient descent can be used for fine-tuning the weights in such “autoencoder” networks, but this works well only if the initial weights are close to a good solution. We describe an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data.


title: Knowledge-Rich Word Sense Disambiguation Rivaling Supervised Systems

abstract: One of the main obstacles to high-performance Word Sense Disambiguation (WSD) is the knowledge acquisition bottleneck. In this paper, we present a methodology to automatically extend WordNet with large amounts of semantic relations from an encyclopedic resource, namely Wikipedia. We show that, when provided with a vast amount of high-quality semantic relations, simple knowledge-lean disambiguation algorithms compete with state-of-the-art supervised WSD systems in a coarse-grained all-words setting and outperform them on gold-standard domain-specific datasets.