title: Weakly supervised user intent detection for multi-domain dialogues

abstract: Users interact with mobile apps with certain intents such as finding a restaurant. Some intents and their corresponding activities are complex and may involve multiple apps; for example, a restaurant app, a messenger app and a calendar app may be needed to plan a dinner with friends. However, activities may be quite personal and third-party developers would not be building apps to specifically handle complex intents (e.g., a DinnerPlanner). Instead we want our intelligent agent to actively learn to understand these intents and provide assistance when needed. This paper proposes a framework to enable the agent to learn an inventory of intents from a small set of task-oriented user utterances. The experiments show that on previously unseen user activities, the agent is able to reliably recognize user intents using graph-based semi-supervised learning methods. The dataset, models, and the system outputs are available to research community.


Wavelength Analysis Using Equivalent Circuits in a Fast and Slow Wave Waffle-Iron Ridge Guide


title: Algorithms for nonnegative matrix and tensor factorizations : a unified view based on block coordinate descent framework

abstract: We review algorithms developed for nonnegative matrix factorization (NMF) and nonnegative tensor factorization (NTF) from a unified view based on the block coordinate descent (BCD) framework. NMF and NTF are low-rank approximation methods for matrices and tensors in which the low-rank factors are constrained to have only nonnegative elements. The nonnegativity constraints have been shown to enable natural interpretations and allow better solutions in numerous applications including text analysis, computer vision, and bioinformatics. However, the computation of NMF and NTF remains challenging and expensive due the constraints. Numerous algorithmic approaches have been proposed to efficiently compute NMF and NTF. The BCD framework in constrained non-linear optimization readily explains the theoretical convergence properties of several efficient NMF and NTF algorithms, which are consistent with experimental observations reported in literature. In addition, we discuss algorithms that do not fit in the BCD framework contrasting them from those based on the BCD framework. With insights acquired from the unified perspective, we also propose efficient algorithms for updating NMF when there is a small change in the reduced dimension or in the data. The effectiveness of the proposed updating algorithms are validated experimentally with synthetic and real-world data sets.


title: Adaptive traffic signal control system using camera sensor and embedded system

abstract: Adaptive traffic signal control system is needed to avoid traffic congestion that has many disadvantages. This paper presents an adaptive traffic signal control system using camera as an input sensor that providing real-time traffic data. Principal Component Analysis (PCA) is used to analyze and to classify object on video frame for detecting vehicles. Distributed Constraint Satisfaction Problem (DCSP) method determine the duration of each traffic signal, based on counted number of vehicles at each lane. The system is implemented in embedded systems using BeagleBoard™.


title: Battery Cell Equalization via Megahertz Multiple-Receiver Wireless Power Transfer

abstract: This paper proposes a new battery cell voltage equalization approach using multiple-receiver wireless power transfer (WPT) working at megahertz (MHz). Compared with existing multiwinding transformer, the MHz multiple-receiver WPT system is advantageous in terms of saved weight and space, ease of implementation, and improved safety. In this paper, the unique operating principle of the WPT-based equalization is first explained, through which equalization currents are naturally determined by the battery cell voltage distribution. The currents are also analytically derived. This facilitates the discussion on power amplifier (PA) design. Performance analysis is then provided to investigate the influences of system parameters on the efficiency and equalization ability of the proposed WPT-based equalization system, and thus guide following design and implementation. Considering the uncertainty in PA load due to the random cell voltage distribution, a current-model Class $E$ PA is designed that enables approximately constant PA output current. Experimental results show that the proposed multiple-receiver WPT-based battery cell equalization system can achieve high overall system efficiency (above 71%) when equalizing six lithium-ion battery cells under loosely coupling ( $k$ = 0.065). A good match between the experimental and calculation results also validates the correctness of the theoretical discussion.


title: More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch

abstract: For humans, the process of grasping an object relies heavily on rich tactile feedback. Most recent robotic grasping work, however, has been based only on visual input, and thus cannot easily benefit from feedback after initiating contact. In this paper, we investigate how a robot can learn to use tactile information to iteratively and efficiently adjust its grasp. To this end, we propose an end-to-end action-conditional model that learns regrasping policies from raw visuo-tactile data. This model -- a deep, multimodal convolutional network -- predicts the outcome of a candidate grasp adjustment, and then executes a grasp by iteratively selecting the most promising actions. Our approach requires neither calibration of the tactile sensors, nor any analytical modeling of contact forces, thus reducing the engineering effort required to obtain efficient grasping policies. We train our model with data from about 6,450 grasping trials on a two-finger gripper equipped with GelSight high-resolution tactile sensors on each finger. Across extensive experiments, our approach outperforms a variety of baselines at (i) estimating grasp adjustment outcomes, (ii) selecting efficient grasp adjustments for quick grasping, and (iii) reducing the amount of force applied at the fingers, while maintaining competitive performance. Finally, we study the choices made by our model and show that it has successfully acquired useful and interpretable grasping behaviors.


title: Towards gameplay analysis via gameplay metrics

abstract: User-oriented research in the game industry is undergoing a change from relying on informal user-testing methods adapted directly from productivity software development to integrating modern approaches to usability- and user experience testing. Gameplay metrics analysis form one of these techniques, being based on instrumentation methods in HCI. Gameplay metrics are instrumentation data about the user behavior and user-game interaction, and can be collected during testing, production and the live period of the lifetime of a digital game. The use of instrumentation data is relatively new to commercial game development, and remains a relatively unexplored method of user research. In this paper, the focus is on utilizing game metrics for informing the analysis of gameplay during commercial game production as well as in research contexts. A series of case studies are presented, focusing on the major commercial game titles Kane & Lynch and Fragile Alliance.


title: An Investigation of Deep-Learning Frameworks for Speaker Verification Antispoofing

abstract: In this study, we explore the use of deep-learning approaches for spoofing detection in speaker verification. Most spoofing detection systems that have achieved recent success employ hand-craft features with specific spoofing prior knowledge, which may limit the feasibility to unseen spoofing attacks. We aim to investigate the genuine-spoofing discriminative ability from the back-end stage, utilizing recent advancements in deep-learning research. In this paper, alternative network architectures are exploited to target spoofed speech. Based on this analysis, a novel spoofing detection system, which simultaneously employs convolutional neural networks (CNNs) and recurrent neural networks (RNNs) is proposed. In this framework, CNN is treated as a convolutional feature extractor applied on the speech input. On top of the CNN processed output, recurrent networks are employed to capture long-term dependencies across the time domain. Novel features including Teager energy operator critical band autocorrelation envelope, perceptual minimum variance distortionless response, and a more general spectrogram are also investigated as inputs to our proposed deep-learning frameworks. Experiments using the ASVspoof 2015 Corpus show that the integrated CNN–RNN framework achieves state-of-the-art single-system performance. The addition of score-level fusion further improves system robustness. A detailed analysis shows that our proposed approach can potentially compensate for the issue due to short duration test utterances, which is also an issue in the evaluation corpus.


title: DBToaster: A SQL Compiler for High-Performance Delta Processing in Main-Memory Databases

abstract: We present DBToaster, a novel query compilation framework for producing high performance compiled query executors that incrementally and continuously answer standing aggregate queries using in-memory views. DBToaster targets applications that require efficient main-memory processing of standing queries (views) fed by high-volume data streams, recursively compiling view maintenance (VM) queries into simple C++ functions for evaluating database updates (deltas). While today's VM algorithms consider the impact of single deltas on view queries to produce maintenance queries, we recursively consider deltas of maintenance queries and compile to thoroughly transform queries into code. Recursive compilation successively elides certain scans and joins, and eliminates significant query plan interpreter overheads. ::: ::: In this demonstration, we walk through our compilation algorithm, and show the significant performance advantages of our compiled executors over other query processors. We are able to demonstrate 1--3 orders of magnitude improvements in processing times for a financial application and a data warehouse loading application, both implemented across a wide range of database systems, including PostgreSQL, HSQLDB, a commercial DBMS 'A', the Stanford STREAM engine, and a commercial stream processor 'B'.


Impacts of Environmental Temperature and Dietary Energy on Core Body Temperature and Efficiency in Broiler Breeder Females


title: Gaussian Process Regression for Predictive But Interpretable Machine Learning Models: An Example of Predicting Mental Workload across Tasks

abstract: There is increasing interest in real-time brain-computer interfaces (BCIs) for passive monitoring of human cognitive state, including cognitive workload. Too often, however, effective BCIs based on machine learning may function as “black boxes” that are difficult to analyze or interpret. In an effort toward more interpretable BCIs, we studied a family of N-back working memory tasks using a machine learning model, Gaussian Process Regression (GPR), that was both powerful and amenable to analysis. Participants performed the N-back task with three stimulus variants, auditory-verbal, visual-spatial, and visual-numeric, each at three working memory loads. GPR models were trained and tested on data from all three task variants combined, in an effort to identify a model that could be predictive of mental workload demand regardless of stimulus modality. To provide a comparison for GPR performance, a model was additionally trained using multiple linear regression (MLR).The model was effective when trained on individual human participant data, resulting in an average standardized mean squared error (SMSE) between true and predicted n-back levels of 0.44. In comparison, the MLR model using the same data resulted in an average SMSE of 0.55. We demonstrate how GPR can be used to identify which EEG features are highly influential in predictions of cognitive workload in an individual participant. A fraction of EEG features account for the predictive power, and only the top 25% of features were sufficient for near for maximum accuracy. Subsets of features identified by linear models (ANOVA) were not as efficient as subsets identified by GPR. Similarly, multiple linear regression was less effective than GPR for accurately predicting task load. This raises the possibility of real-time BCIs that are simpler (requiring fewer model features) but that still capture all the neurophysiological information needed to achieve high predictive accuracy. This study sought to establish effectiveness and interpretability for a rapidly trainable, real-time, passive BCI for cognitive monitoring in individuals, without regard for specific theories of neural function. Potential valuable applications beyond the scope of the present study include characterizing the neural basis of workloading or other cognitive states, in individual participants and across groups of human participants.


title: A Secure Interactive Architecture for Vehicular Cloud Environment

abstract: Cloud computing is gaining more and more popularity due to the wide use of the Internet. It can now be integrated into vehicle applications to enhance mobility, preventing accidents, improve safety, and improve driving comfort. In this paper, we propose a secure architecture of inter-vehicle communication which is obtained from a cloud services, including architecture level, communication level and associated security challenges. With the help of these cloud services, real time vehicle simulations are conducted to find the optimal route from a given source to the destination based on some factors such as distance, road width, traffic count, junction delay and velocity – all of which play an essential role in minimizing the travel time. Instead of deploying sensors and devices on each vehicle, authorized drivers will subscribe to the cloud-provided platform and applications as services on-demand with payment based on usage. On the other hand, this study tackles one of the most important and conical attacks in cloud vehicle offering services, which is the Shrew attack. A number of steps should be taken first in order to explore these attacks like activating a loss-based probe to identify the locations and capabilities of the current bottlenecks, making use the data centers low-tenancy in order to synchronize the attacks flow and analyzing the necessary and the sufficient traffic for an effective attack.


title: On the Depth of Deep Neural Networks: A Theoretical View

abstract: People believe that depth plays an important role in success of deep neural networks (DNN). However, this belief lacks solid theoretical justifications as far as we know. We investigate role of depth from perspective of margin bound. In margin bound, expected error is upper bounded by empirical margin error plus Rademacher Average (RA) based capacity term. First, we derive an upper bound for RA of DNN, and show that it increases with increasing depth. This indicates negative impact of depth on test performance. Second, we show that deeper networks tend to have larger representation power (measured by Betti numbers based complexity) than shallower networks in multi-class setting, and thus can lead to smaller empirical margin error. This implies positive impact of depth. The combination of these two results shows that for DNN with restricted number of hidden units, increasing depth is not always good since there is a tradeoff between positive and negative impacts. These results inspire us to seek alternative ways to achieve positive impact of depth, e.g., imposing margin-based penalty terms to cross entropy loss so as to reduce empirical margin error without increasing depth. Our experiments show that in this way, we achieve significantly better test performance.


title: Convolutional Neural Network for Pixel-Wise Skyline Detection

abstract: Outdoor augmented reality applications are an emerging class of software systems that demand the fast identification of natural objects, such as plant species or mountain peaks, in low power mobile devices. Convolutional Neural Networks (CNN) have exhibited superior performance in a variety of computer vision tasks, but their training is a labor intensive task and their execution requires non negligible memory and CPU resources. This paper presents the results of training a CNN for the fast extraction of mountain skylines, which exhibits a good balance between accuracy (94,45% in best conditions and 86,87% in worst conditions), memory consumption (9,36 MB on average) and runtime execution overhead (273 ms on a Nexus 6 mobile phone), and thus has been exploited for implementing a real-world augmented reality applications for mountain peak recognition running on low to mid-end mobile phones.


title: Pickup and delivery with time windows: algorithms and test case generation

abstract: In the pickup and delivery problem with time windows (PDPTW), vehicles have to transport loads from origins to destinations respecting capacity and time constraints. In this paper, we present a two-phase method to solve the PDPTW. In the first phase, we apply a novel construction heuristics to generate an initial solution. In the second phase, a tabu search method is proposed to improve the solution. Another contribution of this paper is a strategy to generate good problem instances and benchmarking solutions for PDPTW, based on Solomon's benchmark test cases for VRPTW. Experimental results show that our approach yields very good solutions when compared with the benchmarking solutions.


title: Enhancement and noise reduction of very low light level images

abstract: A general method for image contrast enhancement and noise reduction is proposed in this paper. The method is developed especially for enhancing images acquired under very low light conditions where the features of images are nearly invisible and the noise is serious. By applying an improved and effective image de-haze algorithm to the inverted input image, the intensity can be amplified so that the dark areas become bright and the contrast get enhanced. Then, the joint-bilateral filter with the original green component as the edge image is introduced to suppress the noise. Experimental results validate the performance of the proposed approach.


title: Highly stretchable and tough hydrogels

abstract: Hydrogels with improved mechanical properties, made by combining polymer networks with ionic and covalent crosslinks, should expand the scope of applications, and may serve as model systems to explore mechanisms of deformation and energy dissipation.


title: Collision Detection for Interactive Graphics Applications

abstract: Collision detection and response are important for interactive graphics applications such as vehicle simulators and virtual reality. Unfortunately, previous collision detection algorithms are too slow for interactive use. The paper presents a new algorithm for rigid or articulated objects that meets performance goals through a form of time critical computing. The algorithm supports progressive refinement, detecting collisions between successively tighter approximations to object surfaces as the application allows it more processing time. The algorithm uses simple four dimensional geometry to approximate motion, and hierarchies of spheres to approximate three dimensional surfaces at multiple resolutions. In a sample application, the algorithm allows interactive performance that is not possible with a good previous algorithm. In particular, the new algorithm provides acceptable accuracy while maintaining a steady and high frame rate, which in some cases improves on the previous algorithm's rate by more than two orders of magnitude. >


title: The Perception-Behavior Expressway : Automatic Effects of Social Perception on Social

abstract: Publisher Summary This chapter discusses the functional relation between perception and behavior. It presents a general perspective on perception and action along with elaborating the direct relation between perception and behavior and specifically on one consequence of this relation—namely, the imitation. The chapter describes the core concepts of social perception. Furthermore, the chapter examines all three forms of social perception that lead directly to corresponding overt behavioral tendencies. The cognitive approach that has dominated psychology for over 30 years has changed psychology's perspective on perception. Certainly, perception is essential for us to comprehend our environment but that does not mean that this understanding is an end in itself. The chapter concludes with a discussion on the perception-behavior link from a functional perspective. In specific, perception provides an understanding of the world. Social perception refers to the activation of a perceptual representation, which generally has a direct effect on social behavior. Perceptual inputs are translated automatically into corresponding behavioral outputs.


title: Uniquely human social cognition

abstract: Recent data identify distinct components of social cognition associated with five brain regions. In posterior temporal cortex, the extrastriate body area is associated with perceiving the form of other human bodies. A nearby region in the posterior superior temporal sulcus is involved in interpreting the motions of a human body in terms of goals. A distinct region at the temporo-parietal junction supports the uniquely human ability to reason about the contents of mental states. Medial prefrontal cortex is divided into at least two subregions. Ventral medial prefrontal cortex is implicated in emotional empathy, whereas dorsal medial prefrontal cortex is implicated in the uniquely human representation of triadic relations between two minds and an object, supporting shared attention and collaborative goals.


title: PARN: Pyramidal Affine Regression Networks for Dense Semantic Correspondence

abstract: This paper presents a deep architecture for dense semantic correspondence, called pyramidal affine regression networks (PARN), that estimates locally-varying affine transformation fields across images. To deal with intra-class appearance and shape variations that commonly exist among different instances within the same object category, we leverage a pyramidal model where affine transformation fields are progressively estimated in a coarse-to-fine manner so that the smoothness constraint is naturally imposed within deep networks. PARN estimates residual affine transformations at each level and composes them to estimate final affine transformations. Furthermore, to overcome the limitations of insufficient training data for semantic correspondence, we propose a novel weakly-supervised training scheme that generates progressive supervisions by leveraging a correspondence consistency across image pairs. Our method is fully learnable in an end-to-end manner and does not require quantizing infinite continuous affine transformation fields. To the best of our knowledge, it is the first work that attempts to estimate dense affine transformation fields in a coarse-to-fine manner within deep networks. Experimental results demonstrate that PARN outperforms the state-of-the-art methods for dense semantic correspondence on various benchmarks.


Preparation of Lawyers In England and the United States: A Comparative Glimpse


title: An Initial Examination of Ease of Use for 2D and 3D Information Visualizations of Web Content Kirsten Risden

abstract: We present a discussion and initial empirical investigation of user-interface designs for a set of three Web browsers. The target end-user population we identified were experienced software engineers who maintained large Web sites or portals. The user study demonstrated the strengths and weaknesses of two conventional 2D browsers for this target user, as well as that of XML3D, a novel browser that integrates an interactive 3D hyperbolic graph view with a more traditional 2D list view of the data. A standard collapse/expand tree browser and a Web-based hierarchical categorization similar to Yahoo!, were competitively evaluated against XML3D. No reliable difference between the two 2D browsers was observed. However, the results showed clear differences between XML3D and the 2D user interfaces combined. With XML3D, participants performed search tasks within existing categories reliably faster with no decline in the quality of their responses. It was informally observed that integrating the ability to view the overall structure of the information space with the ability to easily assess local and global relationships was key to successful search performance. XML3D was the only tool of the three that efficiently showed the overall structure within one visualization. The XML3D browser accomplished this by combining a 3D graph layout view as well as an accompanying 2D list view. Users did opt to use the 2D user-interface components of XML3D during new category search tasks, and the XML3D performance advantage was no longer obtained in those conditions. In addition, there were no reliable differences in overall user satisfaction across the three user-interface designs. Since we observed subjects using the XML3D features differently depending on the kind of search task, future studies should explore optimal ways of integrating the use of novel focus+context visualizations and 2D lists for effective information retrieval. The contribution of this paper is that it includes empirical data to demonstrate where novel focus+context views might benefit experienced users over and above more conventional user-interface techniques, in addition to where design improvements are warranted.


title: A wearable ear-EEG recording system based on dry-contact active electrodes

abstract: This work reports an ear-EEG acquisition system with dry-contact active electrodes for future wearable applications. Employing dedicated chopper buffer in the active electrodes, a prototype fabricated in a 0.18-µm CMOS demonstrates input impedance as large as 18GΩ@DC and 6.7GΩ@50Hz, and 3.03fA/vHz input current noise, and a total input-referred noise (IRN) of 0.67µV rms in 0.5–100Hz bandwidth. System's CMRR in combination with active electrodes is higher than 100dB@DC. Under large source impedance imbalance of 1MΩ, a 78-dB@50Hz CMRR is still obtained. To validate the system's ability to record EEG, an auditory stead-state response was measured, showing same SNR with wet electrodes and a commercial EEG amplifier.


title: URLNet: Learning a URL Representation with Deep Learning for Malicious URL Detection

abstract: Malicious URLs host unsolicited content and are used to perpetrate cybercrimes. It is imperative to detect them in a timely manner. Traditionally, this is done through the usage of blacklists, which cannot be exhaustive, and cannot detect newly generated malicious URLs. To address this, recent years have witnessed several efforts to perform Malicious URL Detection using Machine Learning. The most popular and scalable approaches use lexical properties of the URL string by extracting Bag-of-words like features, followed by applying machine learning models such as SVMs. There are also other features designed by experts to improve the prediction performance of the model. These approaches suffer from several limitations: (i) Inability to effectively capture semantic meaning and sequential patterns in URL strings; (ii) Requiring substantial manual feature engineering; and (iii) Inability to handle unseen features and generalize to test data. To address these challenges, we propose URLNet, an end-to-end deep learning framework to learn a nonlinear URL embedding for Malicious URL Detection directly from the URL. Specifically, we apply Convolutional Neural Networks to both characters and words of the URL String to learn the URL embedding in a jointly optimized framework. This approach allows the model to capture several types of semantic information, which was not possible by the existing models. We also propose advanced word-embeddings to solve the problem of too many rare words observed in this task. We conduct extensive experiments on a large-scale dataset and show a significant performance gain over existing methods. We also conduct ablation studies to evaluate the performance of various components of URLNet.


title: Self-compassion in recovery following potentially traumatic stress: longitudinal study of at-risk youth

abstract: Despite promising theory, empirical study of the putative protective properties of self-compassion (SC) with respect to resilience to and recovery from traumatic stress is limited. The present study tested the theorized protective role(s) of SC with respect to trauma-related psychopathology over time among an at-risk sample of adolescents (N = 64, 26 % females, M(SD) age = 17.5(1.07) years-old, range age = 15–19; grades 9–12) directly exposed to a potentially traumatic stressful event – the Mount Carmel Forest Fire Disaster. The longitudinal design involved three assessment time-points – within 30-days of the potentially traumatic event (T1) and then at 3- (T2) and 6-months (T3) follow-up intervals. Consistent with prediction, multi-level modeling of mediation documented the prospective protective function(s) of SC, above and beyond dispositional mindfulness, with respect to posttraumatic stress and panic symptoms, depressive symptoms, and suicidality symptoms, but not well-being. The findings are discussed, theoretically, with respect to SC as a malleable protective factor for trauma-related psychopathology outcomes; and, clinically, with respect to SC as a target for future trauma-related selective-prevention and -early intervention research.


title: RECOGNITION AND RETRIEVAL PROCESSES IN FREE RECALL

abstract: A model of free recall is described which identifies two processes in free recall: a retrieval process by which the subject accesses the words, and a recognition process by which the subject decides whether an implicitly retrieved word is a to-be-recalled word. Submodels for the recognition process and the retrieval process are described. The recognition model assumes that during the study phase, the subject associates "list markers" to the to-be-recalled words. The establishment of such associates is postulated to be an all-or-none stochastic process. In the test phase, the subject recognizes to-be-recalled words by deciding which words have relevant list markers as associates. A signal detectability model is developed for this decision process. The retrieval model is introduced as a computer program that tags associative paths between list words. In several experiments, subjects studied and were tested on a sequence of overlapping sublists sampled from a master set of common nouns. The twoprocess model predicts that the subject's ability to retrieve the words should increase as more overlapping sublists are studied, but his ability to differentiate the words on the most recent list should deteriorate. Experiments confirmed this predicted dissociation of recognition and retrieval. Further predictions derived from the free recall model were also supported. This paper has several aims: First, we offer a critique of two popular "strength" theories, one which relates recall and recognition to the strength of one and the same memory trace, and another which relates only recognition memory to a similar strength measure; second, we develop a particular conceptualization about recognition memory which we believe satisfies the criticisms of the traditional strength theory; third, we illustrate how that recognition mechanism could be interfaced with a retrieval mechanism so as to yield a viable theory about multilist free recall. Along with reviewing published data relevant to these points, we also shall present new 1 This research was supported by Grant MH


title: Automatic solar photovoltaic panel detection in satellite imagery

abstract: The quantity of rooftop solar photovoltaic (PV) installations has grown rapidly in the US in recent years. There is a strong interest among decision makers in obtaining high quality information about rooftop PV, such as the locations, power capacity, and energy production of existing rooftop PV installations. Solar PV installations are typically connected directly to local power distribution grids, and therefore it is important for the reliable integration of solar energy to have information at high geospatial resolutions: by county, zip code, or even by neighborhood. Unfortunately, traditional means of obtaining this information, such as surveys and utility interconnection filings, are limited in availability and geospatial resolution. In this work a new approach is investigated where a computer vision algorithm is used to detect rooftop PV installations in high resolution color satellite imagery and aerial photography. It may then be possible to use the identified PV images to estimate power capacity and energy production for each array of panels, yielding a fast, scalable, and inexpensive method to obtain rooftop PV estimates for regions of any size. The aim of this work is to investigate the feasibility of the first step of the proposed approach: detecting rooftop PV in satellite imagery. Towards this goal, a collection of satellite rooftop images is used to develop and evaluate a detection algorithm. The results show excellent detection performance on the testing dataset and that, with further development, the proposed approach may be an effective solution for fast and scalable rooftop PV information collection.


Inventory of Transcribed and Translated Talks


title: A memristive astable multivibrator based on 555 timer

abstract: The main purpose of this paper is to explore the oscillating characteristics of an astable multivibrator based on memristor and 555 timer. An analog circuit is utilized to simulate magnetic flux controlled memristor and then applied to implement the astable multivibrator. The voltage across the memristor emulator is analytically calculated. Simulation is carried out to confirm the theoretical analysis. The agreement between simulation and analytical calculation validates the feasibility of using memritor to achieve astable multivibrator.


title: ECC Parity: A Technique for Efficient Memory Error Resilience for Multi-Channel Memory Systems

abstract: Servers and HPC systems often use a strong memory error correction code, or ECC, to meet their reliability and availability requirements. However, these ECCs often require significant capacity and/or power overheads. We observe that since memory channels are independent from one another, error correction typically needs to be performed for one channel at a time. Based on this observation, we show that instead of always storing in memory the actual ECC correction bits as do existing systems, it is sufficient to store the bitwise parity of the ECC correction bits of different channels for fault-free memory regions, and store the actual ECC correction bits only for faulty memory regions. By trading off the resultant ECC capacity overhead reduction for improved memory energy efficiency, the proposed technique reduces memory energy per instruction by 54.4% and 20.6%, respectively, compared to a commercial chip kill correct ECC and a DIMM-kill correct ECC, while incurring similar or lower capacity overheads.


title: Assessing the Quality of Activities in a Smart Environment

abstract: OBJECTIVES ::: Pervasive computing technology can provide valuable health monitoring and assistance technology to help individuals live independent lives in their own homes. As a critical part of this technology, our objective is to design software algorithms that recognize and assess the consistency of activities of daily living that individuals perform in their own homes. ::: ::: ::: METHODS ::: We have designed algorithms that automatically learn Markov models for each class of activity. These models are used to recognize activities that are performed in a smart home and to identify errors and inconsistencies in the performed activity. ::: ::: ::: RESULTS ::: We validate our approach using data collected from 60 volunteers who performed a series of activities in our smart apartment testbed. The results indicate that the algorithms correctly label the activities and successfully assess the completeness and consistency of the performed task. ::: ::: ::: CONCLUSIONS ::: Our results indicate that activity recognition and assessment can be automated using machine learning algorithms and smart home technology. These algorithms will be useful for automating remote health monitoring and interventions.


title: Convolutional Sequence to Sequence Model for Human Dynamics

abstract: Human motion modeling is a classic problem in computer vision and graphics. Challenges in modeling human motion include high dimensional prediction as well as extremely complicated dynamics.We present a novel approach to human motion modeling based on convolutional neural networks (CNN). The hierarchical structure of CNN makes it capable of capturing both spatial and temporal correlations effectively. In our proposed approach, a convolutional long-term encoder is used to encode the whole given motion sequence into a long-term hidden variable, which is used with a decoder to predict the remainder of the sequence. The decoder itself also has an encoder-decoder structure, in which the short-term encoder encodes a shorter sequence to a short-term hidden variable, and the spatial decoder maps the long and short-term hidden variable to motion predictions. By using such a model, we are able to capture both invariant and dynamic information of human motion, which results in more accurate predictions. Experiments show that our algorithm outperforms the state-of-the-art methods on the Human3.6M and CMU Motion Capture datasets. Our code is available at the project website1.


title: AutoStyle: Toward Coding Style Feedback at Scale

abstract: While large-scale automatic grading of student programs for correctness is widespread, less effort has focused on automating feedback for good programming style:} the tasteful use of language features and idioms to produce code that is not only correct, but also concise, elegant, and revealing of design intent. We hypothesize that with a large enough (MOOC-sized) corpus of submissions to a given programming problem, we can observe a range of stylistic mastery from naive to expert, and many points in between, and that we can exploit this continuum to automatically provide hints to learners for improving their code style based on the key stylistic differences between a given learner's submission and a submission that is stylistically slightly better. We are developing a methodology for analyzing and doing feature engineering on differences between submissions, and for learning from instructor-provided feedback as to which hints are most relevant. We describe the techniques used to do this in our prototype, which will be deployed in a residential software engineering course as an alpha test prior to deploying in a MOOC later this year.


title: Collective Tweet Wikification based on Semi-supervised Graph Regularization

abstract: Wikification for tweets aims to automatically identify each concept mention in a tweet and link it to a concept referent in a knowledge base (e.g., Wikipedia). Due to the shortness of a tweet, a collective inference model incorporating global evidence from multiple mentions and concepts is more appropriate than a noncollecitve approach which links each mention at a time. In addition, it is challenging to generate sufficient high quality labeled data for supervised models with low cost. To tackle these challenges, we propose a novel semi-supervised graph regularization model to incorporate both local and global evidence from multiple tweets through three fine-grained relations. In order to identify semanticallyrelated mentions for collective inference, we detect meta path-based semantic relations through social networks. Compared to the state-of-the-art supervised model trained from 100% labeled data, our proposed approach achieves comparable performance with 31% labeled data and obtains 5% absolute F1 gain with 50% labeled data.


title: Phishing website detection using URL-assisted brand name weighting system

abstract: In this paper, we propose an anti-phishing technique to safeguard users against phishing attacks in the internet. The scope of our study focuses primarily on the detection of phishing websites with English content. In order to convince users on whom the website claims to be, phishers normally place brand names in different parts of the URL. We exploit this phishing pattern by assigning weights to words extracted from the HTML content, based on their co-appearance at hostname, path and filenames of URLs. These weights are then added to their corresponding TF-IDF weights. The most probable words are selected and submitted to Yahoo Search to retrieve the highest frequency domain name among the top 30 search results. A WHOIS lookup is conducted to reveal the owner behind the selected domain name. A phishing website can be easily distinguished if the owner of query domain name differs from the owner of domain name returned by the search engine. Experiments conducted over a dataset of phishing and legitimate websites achieves a true positive rate of 98.2% while maintaining a false positive rate of 5.9%. Our findings prove that brand names in HTML content are very effective in detecting phishing websites.


title: An e-business chatbot using AIML and LSA

abstract: The e-business has completely changed the way of selling products. E-commerce is one of the e-business models which mostly do business over the internet. The major drawback of this field is quality of customer service they provide. In every e-business model, customers have to wait for a long time to get response from the customer service representative. Especially in case of live chat, they talk to multiple customers at a time. The responses may not be relevant as they copy paste pre-written answers. Also, the slow response and the long time wait for the service agent is the biggest headache in this field of online services. As a solution to this problem, we propose a chatbot which automatically gives immediate responses to the users based on the data set of Frequently Answered Questions(FAQs), using Artificial Intelligence Markup Language (AIML) and Latent Semantic Analysis (LSA). Template based questions like greetings and general questions will be answered using AIML and other service related questions use LSA to give responses.


title: REGULARIZATION TOOLS: A Matlab package for analysis and solution of discrete ill-posed problems

abstract: The package REGULARIZATION TOOLS consists of 54 Matlab routines for analysis and solution of discrete ill-posed problems, i.e., systems of linear equations whose coefficient matrix has the properties that its condition number is very large, and its singular values decay gradually to zero. Such problems typically arise in connection with discretization of Fredholm integral equations of the first kind, and similar ill-posed problems. Some form of regularization is always required in order to compute a stabilized solution to discrete ill-posed problems. The purpose of REGULARIZATION TOOLS is to provide the user with easy-to-use routines, based on numerical robust and efficient algorithms, for doing experiments with regularization of discrete ill-posed problems. By means of this package, the user can experiment with different regularization strategies, compare them, and draw conclusions from these experiments that would otherwise require a major programming effert. For discrete ill-posed problems, which are indeed difficult to treat numerically, such an approach is certainly superior to a single black-box routine. This paper describes the underlying theory gives an overview of the package; a complete manual is also available.


title: Harvest, yield, and scalable tolerant systems

abstract: The cost of reconciling consistency and state management with high availability is highly magnified by the unprecedented scale and robustness requirements of today's Internet applications. We propose two strategies for improving overall availability using simple mechanisms that scale over large applications whose output behavior tolerates graceful degradation. We characterize this degradation in terms of harvest and yield, and map it directly onto engineering mechanisms that enhance availability by improving fault isolation, and in some cases also simplify programming. By collecting examples of related techniques in the literature and illustrating the surprising range of applications that can benefit from these approaches, we hope to motivate a broader research program in this area.


title: UNSUPERVISED LABELING OF DATA FOR SUPERVISED LEARNING AND ITS APPLICATION TO MEDICAL CLAIMS PREDICTION

abstract: The task identifying changes and irregularities in medical insurance claim pay-ments is a diﬃcult process of which the traditional practice involves queryinghistorical claims databases and ﬂagging potential claims as normal or abnor-mal. Because what is considered as normal payment is usually unknown andmay change over time, abnormal payments often pass undetected; only to bediscovered when the payment period has passed.This paper presents the problem of on-line unsupervised learning from datastreams when the distribution that generates the data changes or drifts overtime. Automated algorithms for detecting drifting concepts in a probabilitydistribution of the data are presented. The idea behind the presented driftdetection methods is to transform the distribution of the data within a slidingwindow into a more convenient distribution. Then, a test statistics p-value ata given signiﬁcance level can be used to infer the drift rate, adjust the windowsize and decide on the status of the drift. The detected concepts drifts areused to label the data, for subsequent learning of classiﬁcation models by asupervised learner. The algorithms were tested on several synthetic and realmedical claims data sets.


title: Review of Face Detection Systems Based Artificial Neural Networks Algorithms

abstract: Face detection is one of the most relevant applications of image processing and biometric systems. Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition. There is lack of literature surveys which give overview about the studies and researches related to the using of ANN in face detection. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included also.


title: Management of Knowledge, Innovation and Performance in SMEs

abstract: Introduction The era of knowledge plays an essential role in the economic growth and development of all enterprises (Foray, 2004; Mosconi & Roy, 2013). With the arrival of globalization, knowledge has become an intangible resource generator of permanent competitive advantage (Ikujiro & Hiroshi, 2013; Tunc Bozbura, 2007) and contributes to the generation of intellectual capital and to the economic activities of organizations (Kristandl & Bontis, 2007). In these times of constant motion, small and medium-sized enterprises (SMEs) require the extraction of knowledge from both domestic sources and foreign sources to achieve greater participation in the markets, foster innovation, and improve performance (Gold, Malhotra, & Segars, 2001; Morgan & Berthon, 2008). Knowledge management (KM) is a comprehensive approach that includes capture, receipt, and transfer of information in a company that considers the policies, procedures, knowledge, and experience of employees (Duhon, 1998). In addition, technology systems influence the behavior of employees and strengthen a culture based on the use and transfer of information (Davenport, 1994). KM is a business practice that integrates essential strategies, policies, techniques, and procedures (Davenport, 2013; Lavergne & Earl, 2006). The alignment of corporate strategy with KM is vital for adding value and achieving results (Chuang, 2004; M. H. Zack & Singh, 2010). KM influences business systems by increasing profitability, creating a harmonious atmosphere among employees, and ensuring businesses' sustainability and competitiveness (Darvish, Mohammadi, & Afsharpour, 2012; Darvish & Nazari, 2013). However, KM is not sufficiently widespread in SMEs, mainly due to the lack of strategic planning, lack of financial resources, distaste to change cultural, uncertainty regarding benefits, and technological immaturity, which are typical in these organizations (Edvardsson & Durst, 2013; Yew Wong, 2005). A reasonable number of empirical studies have analyzed the impact of KM and innovation in SMEs (Constantinescu, 2009; C. Yu, Yu-Fang, & Yu-Cheh, 2013). However, the relationship between innovation and performance is still a wide field for exploration (Price, Stoica, & Boncella, 2013; Vaccaro, Parente, & Veloso, 2010). Works on KM have placed minimal emphasis on the benefits generated in SMEs; the majority of research focuses on large organizations (Darroch, 2005; Roxas, Battisti, & Deakins, 2014). In addition, minimal understanding of how companies create, transfer, and use knowledge has led to difficulty in transforming knowledge into a competitive advantage (C. Lin, Wu, & Yen, 2012; Perrin, Vidal, & McGill, 2006). The difficulty of measuring KM in SMEs provides a reasonable explanation for the scarcity of empirical studies in this developing discipline (Becerra-Fernandez & Sabherwal, 2014; Choi, Poon, & Davis, 2008). By the great influence of the KM in the progress and development of SMEs, it is important to develop this type of study more regularly (Imran, 2014; Vaccaro et al., 2010). The objective of this work is to empirically analyze the relationships between KM, innovation, and performance in SMEs in the region of Murcia, Spain. In the current competitive global environment, it is important to analyze the key factors that affect the development and growth of these companies. The research questions that we attempt to answer are as follows: 1. Does knowledge management influence innovation activities in SMEs? 2. Does business innovation exert any influence on SME performance? Literature reveals that KM can help SMEs to professionally develop employees, improve innovation processes, grow sales, satisfy customers and thus achieve organizational success (Edvardsson & Durst, 2013; Lopez-Nicolas & Merono-Cerdan, 2011; Madrid-Guijarro, Garcia, & Van Auken, 2009). …


Adoption of E-Learning in Higher Education : Expansion of UTAUT Model


title: Ambulatory motor assessment in Parkinson's disease

abstract: We developed an algorithm that distinguishes between on and off states in patients with Parkinson's disease during daily life activities. Twenty-three patients were monitored continuously in a home-like situation for approximately 3 hours while they carried out normal daily-life activities. Behavior and comments of patients during the experiment were used to determine the on and off periods by a trained observer. Behavior of the patients was measured using triaxial accelerometers, which were placed at six different positions on the body. Parameters related to hypokinesia (percentage movement), bradykinesia (mean velocity), and tremor (percentage peak frequencies above 4 Hz) were used to distinguish between on and off states. The on-off detection was evaluated using sensitivity and specificity. The performance for each patient was defined as the average of the sensitivity and specificity. The best performance to classify on and off states was obtained by analysis of movements in the frequency domain with a sensitivity of 0.97 and a specificity of 0.97. We conclude that our algorithm can distinguish between on and off states with a sensitivity and specificity near 0.97. This method, together with our previously published method to detect levodopa-induced dyskinesia, can automatically assess the motor state of Parkinson's disease patients and can operate successfully in unsupervised ambulatory conditions.


title: Efficient Parallel RSA Decryption Algorithm for Many-core GPUs with CUDA

abstract: Cryptography is an important technique among various applications. In the telecommunication, cryptography is necessary when an untrusted medium is communicated in the network. RSA is a public-key cryptography algorithm to use a pair (N, E) as the public key and D as the private key. The N is the product of two large prime numbers p and q that are kept secret. It is very hard and no known polynomial time algorithms can be used to extract p and q from a large number N. There are many methods of factoring large numbers have been proposed. The advantages of computing power and memory bandwidth for modern GPUs have made porting applications on it become a very important issue. In this paper, we proposed an efficient parallel RSA decryption algorithm for many-core GPUs with CUDA. The experimental results showed that the proposed GPU-based algorithm can achieve 1197.5x average speedup compared with the CPU-based algorithm, and within a reasonable time to find out the result of factoring large numbers.


Large-aperture high-power third harmonic conversion using a type I/type II phase matching scheme


title: A data mining framework for detecting subscription fraud in telecommunication

abstract: Service providing companies including telecommunication companies often receive substantial damage from customers' fraudulent behaviors. One of the common types of fraud is subscription fraud in which usage type is in contradiction with subscription type. This study aimed at identifying customers' subscription fraud by employing data mining techniques and adopting knowledge discovery process. To this end, a hybrid approach consisting of preprocessing, clustering, and classification phases was applied, and appropriate tools were employed commensurate to each phase. Specifically, in the clustering phase SOM and K-means were combined, and in the classification phase decision tree (C4.5), neural networks, and support vector machines as single classifiers and bagging, boosting, stacking, majority and consensus voting as ensembles were examined. In addition to using clustering to identify outlier cases, it was also possible - by defining new features - to maintain the results of clustering phase for the classification phase. This, in turn, contributed to better classification results. A real dataset provided by Telecommunication Company of Tehran was applied to demonstrate the effectiveness of the proposed method. The efficient use of synergy among these techniques significantly increased prediction accuracy. The performance of all single and ensemble classifiers is evaluated based on various metrics and compared by statistical tests. The results showed that support vector machines among single classifiers and boosted trees among all classifiers have the best performance in terms of various metrics. The research findings show that the proposed model has a high accuracy, and the resulting outcomes are significant both theoretically and practically.


CSE 255 Assignment 1 : Movie Rating Prediction using the MovieLens dataset


title: RF Energy Harvesting Wireless Powered Sensor Networks for Smart Cities

abstract: This paper investigates the optimal energy beamforming and time assignment in radio frequency (RF) energy harvesting (EH) wireless powered sensor networks for smart cities, where sensor nodes (SNs) first harvest energy from a sink node, and then transmit their collected data to the sink node via time-division-multiple-access (TDMA) manner by using the harvested energy. In order to achieve green system design, we formulate a problem to minimize the energy requirement of the sink node to support transmission between the sink node and the SNs under data amount constraint and EH constraint. For practical design, the energy consumed by circuit and information processing is also considered. Since the problem is non-convex, we use semidefinite relaxation (SDR) method to relax it into a convex optimization problem and then solve it efficiently. We theoretically prove that when the number of SNs are not greater than two, the relaxed problem guarantees rank-one constraint and when the number of SNs exceeds two, our obtained results are very close to the optimal ones. Simulation results show that when the data amount is relatively small, the energy consumed by circuit and information processing affects the system performance greatly, but for a relatively large data amount, the energy requirement of the sink node on its own signal processing is affected very limited and the system energy requirement is dominated by the transmit power consumption at the SNs. Furthermore, we also discuss the effects of the other parameters on the system performance, which provide some useful insights in future smart city planning.


title: Research Series Eliminating Bias in Randomized Controlled Trials: Importance of Allocation Concealment and Masking

abstract: Randomization in randomized controlled trials involves more than generation of a random sequence by which to assign subjects. For randomization to be successfully implemented, the randomization sequence must be adequately protected (concealed) so that investigators, involved health care providers, and subjects are not aware of the upcoming assignment. The absence of adequate allocation concealment can lead to selection bias, one of the very problems that randomization was supposed to eliminate. Authors of reports of randomized trials should provide enough details on how allocation concealment was achieved so the reader can determine the likelihood of success. Fortunately, a plan of allocation concealment can always be incorporated into the design of a randomized trial. Certain methods minimize the risk of concealment failing more than others. Keeping knowledge of subjects' assignment after allocation from subjects, investigators/health care providers, or those assessing outcomes is referred to as masking (also known as blinding). The goal of masking is to prevent ascertainment bias. In contrast to allocation concealment, masking cannot always be incorporated into a randomized controlled trial. Both allocation concealment and masking add to the elimination of bias in randomized controlled trials.


title: The effects of out-of-school time on changes in youth risk of obesity across the adolescent years.

abstract: Abstract This study examined the longitudinal effects of out-of-school time (OST) activities on youth weight-status through mid-to-late adolescence. First, using pattern-centered methods, we identified the prominent ways in which youth allocate their OST across 12 common active and sedentary activities available to them. Second, through multi-level modeling procedures we examined the relation of OST activity patterns to: 1) BMI-status during the 11th grade, and; 2) within-person change in BMI-status across the adolescent years. After accounting for race, gender, SES, pubertal-status, and gaming, youth who participated in a sports-dominant activity pattern for 2 or more years had significantly lower 11th grade odds of being at-risk for overweight/obesity compared to youth in all other activity patterns. Youth of all other activity patterns had similar odds of being at-risk as Low-Activity youth and each other. Understanding the relations of OST to youth healthy weight is a critical first step in developing healthy OST settings.


title: An approch for monitoring and smart planning of urban solid waste management using smart-M3 platform

abstract: Solid waste management is one of the most important challenges in urban areas throughout the world and it is becoming a critical issue in developing countries where a rapid increase in population has been observed. Waste collection is a complex process that requires the use of large amount of money and an elaborate management of logistics. In this paper an approch to smart waste collection is proposed able to improve and optimize the handling of solid urban waste. Context of smart waste management requires interconnection among heterogeneous devices and data sharing involving a large amount of people. Smart-M3 platform solves these problems offering a high degree of decoupling and scalability.Waste collection is made by real-time monitoring the level of bin's fullness through sensors placed inside the containers. This method enables to exempt from collecting semi-empty bins. Furthermore, incoming data can be provided to decisional algorithms in order to determine the optimal number of waste vehicles or bins to distribute in the territory. The presented solution gives important advantages for both service providers and consumers. The formers could obtain a sensible cost reduction. On the other hand, users may benefit from a higher level of service quality. In order to make users feel closer to their community, they can interact with the system to be aware about the fulness state of the nearest bins. Finally, a mechanism for collecting “green points” was introduced for encouraging citizens to recycle.


title: The Accuracy Rate of Holt-Winters Model with Particle Swarm Optimization in Forecasting Exchange Rates

abstract: Exchange rates forecasting is a crucial and challenging task. Accurate forecasting of the imminent movements of exchange rates is very important in investments, trade and economics. In this paper, an exponential smoothing using the Holt-Winters Model is used for forecasting exchange rates. Parameter search for the smoothing constants is done through computer simulation using Particle Swarm Optimization (PSO). Experiment results show that PSO is able to compute good values for the smoothing constants, producing forecasts with accuracy in determining the direction (rise or fall) of exchange rates. Furthermore, the computed Mean Absolute Deviation (MAD) and the Residual Standard Error (RSE) of the exchange rate forecasts from the actual observed data indicate that PSO can also be used to improve forecasting precision.


title: A simple gate drive for SiC MOSFET with switching transient improvement

abstract: This paper presents a new active gate drive for SiC MOSFETs switching. The proposed driver is based on feedforward control method. The switch is benefited from a simple and effective analog gate driver (GD). The main achievement of this GD is the transient enhancement with minimum undesirable effect on the switching efficiency. Also, the electromagnetic interference (EMI) as the main threat to the operation of SiC MOSFET is eliminated by this method. The proposed GD has been validated through the simulation and experimental tests. All the evaluation have been carried out in a hard switching condition with high-frequency switching.


title: Count Models Based on Weibull Interarrival Times

abstract: In this paper, we introduce a generalized model for count data based upon an assumed Weibull interarrival process that nests the Poisson and negative binomial models as special cases. In addition, we demonstrate that this new Weibull count model can model both over and underdispersed count data, allow covariates to be introduced in a straightforward manner through the hazard function, and be computed in standard software.


Augmented Reality, Architecture and Ubiquity: Technologies, Theories and Frontiers


title: Porcine Single Nucleotide Polymorphism (SNP) Development and Population Structure of Pigs Assessed by Validated SNPs

abstract: In this study, we identified porcine single nucleotide polymorphisms (SNPs) by aligning eight sequences generated with two approaches: amplification of 665 intronic regions using one sample from each of eight breeds, including three East Asian pigs, and amplification of 289 3'-UTR regions using two samples from each of four major commercial breeds. The 1,760 and 599 SNPs were validated using two 384-sample DNA panels by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. The phylogenetic tree and Structure analyses classified the pigs into two large clusters: Euro-American and East Asian populations. The membership proportions, however, differed between inferred clusters for K = 2 generated by the two approaches. With intronic SNPs, Euro-American breeds constituted about 100% of the Euro-American cluster, but with 3'-UTR SNPs, about 17% of the East Asian cluster comprised five Euro-American breeds. The differences in the SNP discovery panels may affect population structure found in study panels of large samples.


title: Effects of Music Listening on Pre-treatment Anxiety and Stress Levels in a Dental Hygiene Recall Population

abstract: Background ::: Waiting for a medical procedure can exert significant feelings of state anxiety in patients. Music listening has been shown to be effective in decreasing anxiety levels. No study so far examined the potential anxiety and stress-reducing effect of a music intervention on pre-treatment anxiety and stress in patients waiting for dental hygiene treatment. Knowing whether the anxiety-reducing effect of music would also be detectible in the context of preventive routine medical procedures in healthy individuals would widen the area of application of music from the hospital or clinical environment to medical offices in general.


Children with Lyme Arthritis in Slovenia : Demographic , Clinical and Laboratory Characteristics


title: DolphinAtack: Inaudible Voice Commands

abstract: Speech recognition (SR) systems such as Siri or Google Now have become an increasingly popular human-computer interaction method, and have turned various systems into voice controllable systems (VCS). Prior work on attacking VCS shows that the hidden voice commands that are incomprehensible to people can control the systems. Hidden voice commands, though "hidden", are nonetheless audible. In this work, we design a totally inaudible attack, DolphinAttack, that modulates voice commands on ultrasonic carriers (e.g., f > 20 kHz) to achieve inaudibility. By leveraging the nonlinearity of the microphone circuits, the modulated low-frequency audio commands can be successfully demodulated, recovered, and more importantly interpreted by the speech recognition systems. We validated DolphinAttack on popular speech recognition systems, including Siri, Google Now, Samsung S Voice, Huawei HiVoice, Cortana and Alexa. By injecting a sequence of inaudible voice commands, we show a few proof-of-concept attacks, which include activating Siri to initiate a FaceTime call on iPhone, activating Google Now to switch the phone to the airplane mode, and even manipulating the navigation system in an Audi automobile. We propose hardware and software defense solutions, and suggest to re-design voice controllable systems to be resilient to inaudible voice command attacks.


title: Convolutional Learning of Spatio-temporal Features

abstract: We address the problem of learning good features for understanding video data. We introduce a model that learns latent representations of image sequences from pairs of successive images. The convolutional architecture of our model allows it to scale to realistic image sizes whilst using a compact parametrization. In experiments on the NORB dataset, we show our model extracts latent "flow fields" which correspond to the transformation between the pair of input frames. We also use our model to extract low-level motion features in a multi-stage architecture for action recognition, demonstrating competitive performance on both the KTH and Hollywood2 datasets.


title: Integrating photonics with silicon nanoelectronics for the next generation of systems on a chip

abstract: Electronic and photonic technologies have transformed our lives—from computing and mobile devices, to information technology and the internet. Our future demands in these fields require innovation in each technology separately, but also depend on our ability to harness their complementary physics through integrated solutions1,2. This goal is hindered by the fact that most silicon nanotechnologies—which enable our processors, computer memory, communications chips and image sensors—rely on bulk silicon substrates, a cost-effective solution with an abundant supply chain, but with substantial limitations for the integration of photonic functions. Here we introduce photonics into bulk silicon complementary metal–oxide–semiconductor (CMOS) chips using a layer of polycrystalline silicon deposited on silicon oxide (glass) islands fabricated alongside transistors. We use this single deposited layer to realize optical waveguides and resonators, high-speed optical modulators and sensitive avalanche photodetectors. We integrated this photonic platform with a 65-nanometre-transistor bulk CMOS process technology inside a 300-millimetre-diameter-wafer microelectronics foundry. We then implemented integrated high-speed optical transceivers in this platform that operate at ten gigabits per second, composed of millions of transistors, and arrayed on a single optical bus for wavelength division multiplexing, to address the demand for high-bandwidth optical interconnects in data centres and high-performance computing3,4. By decoupling the formation of photonic devices from that of transistors, this integration approach can achieve many of the goals of multi-chip solutions5, but with the performance, complexity and scalability of ‘systems on a chip’1,6–8. As transistors smaller than ten nanometres across become commercially available9, and as new nanotechnologies emerge10,11, this approach could provide a way to integrate photonics with state-of-the-art nanoelectronics.A way of integrating photonics with silicon nanoelectronics is described, using polycrystalline silicon on glass islands alongside transistors on bulk silicon complementary metal–oxide–semiconductor chips.


Google search by voice: A case study


title: Early Vocabulary Acquisition by Children with Down Syndrome.

abstract: Early acquisition of object names by 6 children with Down syndrome relative to the acquisition pattern of 6 nonretarded children was investigated. Each child was followed for 14 to 21 months. At the start of the study the children with Down syndrome were 17 to 19 months old. The nonretarded children were 9 months old. Results indicated that the children with Down syndrome were at the same level of cognitive development as the nonretarded children at the onset of both comprehension and production of object names. Soon after language acquisition began, however, the language development of the Down syndrome group began to lag behind their cognitive development: Level of vocabulary development was lower than would have been expected based on level of cognitive development.


title: Decimal multiplication with efficient partial product generation

abstract: Decimal multiplication is important in many commercial applications including financial analysis, banking, tax calculation, currency conversion, insurance, and accounting. This paper presents a novel design for fixed-point decimal multiplication that utilizes a simple recoding scheme to produce signed-magnitude representations of the operands thereby greatly simplifying the process of generating partial products for each multiplier digit. The partial products are generated using a digit-by-digit multiplier on a word-by-digit basis, first in a signed-digit form with two digits per position, and then combined via a combinational circuit. As the signed-digit partial products are developed one at a time while traversing the recoded multiplier operand from the least significant digit to the most significant digit, each partial product is added along with the accumulated sum of previous partial products via a signed-digit adder. This work is significantly different from other work employing digit-by-digit multipliers due to the efficiency gained by restricting the range of digits throughout the multiplication process.


title: Robust Monte Carlo localization for mobile robots

abstract: Mobile robot localization is the problem of determining a robot’s pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot’s belief by a set of weighted hypotheses (samples), which approximate the posterior under a common Bayesian formulation of the localization problem. Building on the basic MCL algorithm, this article develops a more robust algorithm called MixtureMCL, which integrates two complimentary ways of generating samples in the estimation. To apply this algorithm to mobile robots equipped with range finders, a kernel density tree is learned that permits fast sampling. Systematic empirical results illustrate the robustness and computational efficiency of the approach.  2001 Published by Elsevier Science B.V.


title: Zero-knowledge against quantum attacks

abstract: This paper proves that several interactive proof systems are zero-knowledge against general quantum attacks. This includes the well-known Goldreich-Micali-Wigderson classical zero-knowledge protocols for Graph Isomorphism and Graph 3-Coloring (assuming the existence of quantum computationally concealing commitment schemes in the second case). Also included is a quantum interactive protocol for a complete problem for the complexity class of problems having "honest verifier" quantum statistical zero-knowledge proofs, which therefore establishes that honest verifier and general quantum statistical zero-knowledge are equal: QSZK = QSZKHV. Previously no non-trivial proof systems were known to be zero-knowledge against quantum attacks, except in restricted settings such as the honest-verifier and common reference string models. This paper therefore establishes for the first time that true zero-knowledge is indeed possible in the presence of quantum information and computation.


title: Modeling crowdsourcing systems: design and analysis of incentive mechanism and rating system

abstract: Over the past few years, we have seen an increasing popularity of crowdsourcing services [5]. Many companies are now providing such services, e.g., Amazon Mechanical Turk [1], Google Helpouts [3], and Yahoo! Answers [8], etc. Briefly speaking, crowdsourcing is an online, distributed problem solving paradigm and business production platform. It uses the power of today’s Internet to solicit the collective intelligence of large number of users. Relying on the wisdom of the crowd to solve posted tasks (or problems), crowdsourcing has become a promising paradigm to obtain “solutions” which can have higher quality or lower costs than the conventional method of solving problems via specialized employees or contractors in a company. Typically, a crowdsourcing system operates with three basic components: Users, tasks and rewards. Users are classified into requesters and workers. A user can be a requester or a worker, and in some cases, a user can be a requester/worker at the same time. Requesters outsource tasks to workers and associate each task with certain rewards, which will be granted to the workers who solve the task. Workers, on the other hand, solve the assigned tasks and reply to requesters with solutions, and then take the reward, which can be in form of money [1], entertainment [7] or altruism [8], etc. To have a successful crowdsourcing website, it is pertinent to attract high volume of participation of users (requesters and workers), and at the same time, solutions by workers have to be of high quality. In this paper we design a rating system and a mechanism to encourage users to participate, and incentivize workers to high quality solutions. First, we develop a game-theoretic model to characterize workers’ strategic behavior. We then design a class effective incentive mechanisms which consist of a task bundling scheme and a rating system, and pay workers according to solution ratings from requesters. We develop a model to characterize the design space of a class of commonly users rating systems– threshold based rating system. We quantify the impact of such rating systems, and the bundling scheme on reducing requesters’ reward payment in guaranteeing high quality solutions. We find out that a simplest rating system, e.g., two rating points, is an effective system in which requesters only need to provide binary feedbacks to indicate whether they are satisfied or not with a solution.


title: Are measures of self-esteem, neuroticism, locus of control, and generalized self-efficacy indicators of a common core construct?

abstract: The authors present results of 4 studies that seek to determine the discriminant and incremental validity of the 3 most widely studied traits in psychology—self-esteem, neuroticism, and locus of control—along with a 4th, closely related trait— generalized self-efficacy. Meta-analytic results indicated that measures of the 4 traits were strongly related. Results also demonstrated that a single factor explained the relationships among measures of the 4 traits. The 4 trait measures display relatively poor discriminant validity, and each accounted for little incremental variance in predicting external criteria relative to the higher order construct. In light of these results, the authors suggest that measures purporting to assess self-esteem, locus of control, neuroticism, and generalized self-efficacy may be markers of the same higher order concept.


title: Deep Coevolutionary Network: Embedding User and Item Features for Recommendation

abstract: Recommender systems often use latent features to explain the behaviors of users and capture the properties of items. As users interact with different items over time, user and item features can influence each other, evolve and co-evolve over time. The compatibility of user and item's feature further influence the future interaction between users and items. Recently, point process based models have been proposed in the literature aiming to capture the temporally evolving nature of these latent features. However, these models often make strong parametric assumptions about the evolution process of the user and item latent features, which may not reflect the reality, and has limited power in expressing the complex and nonlinear dynamics underlying these processes. To address these limitations, we propose a novel deep coevolutionary network model (DeepCoevolve), for learning user and item features based on their interaction graph. DeepCoevolve use recurrent neural network (RNN) over evolving networks to define the intensity function in point processes, which allows the model to capture complex mutual influence between users and items, and the feature evolution over time. We also develop an efficient procedure for training the model parameters, and show that the learned models lead to significant improvements in recommendation and activity prediction compared to previous state-of-the-arts parametric models.


title: ItemRank: A Random-Walk Based Scoring Algorithm for Recommender Engines

abstract: Recommender systems are an emerging technology that helps consumers to find interesting products. A recommender system makes personalized product suggestions by extracting knowledge from the previous users interactions. In this paper, we present "ItemRank", a random-walk based scoring algorithm, which can be used to rank products according to expected user preferences, in order to recommend top-rank items to potentially interested users. We tested our algorithm on a standard database, the MovieLens data set, which contains data collected from a popular recommender system on movies, that has been widely exploited as a benchmark for evaluating recently proposed approaches to recommender system (e.g. [Fouss et al., 2005; Sarwar et al., 2002]). We compared ItemRank with other state-of-the-art ranking techniques (in particular the algorithms described in [Fouss et al., 2005]). Our experiments show that ItemRank performs better than the other algorithms we compared to and, at the same time, it is less complex than other proposed algorithms with respect to memory usage and computational cost too.


title: Improving software text retrieval using conceptual knowledge in source code

abstract: A large software project usually has lots of various textual learning resources about its API, such as tutorials, mailing lists, user forums, etc. Text retrieval technology allows developers to search these API learning resources for related documents using free-text queries, but it suffers from the lexical gap between search queries and documents. In this paper, we propose a novel re-ranking approach for improving the retrieval of API learning resources through leveraging software-specific conceptual knowledge in software source code. The basic idea behind this approach is that the semantic relatedness between queries and documents could be measured according to software-specific concepts involved in them, and software source code contains a large amount of software-specific conceptual knowledge. In detail, firstly we extract an API graph from software source code and use it as software-specific conceptual knowledge. Then we discover API entities involved in queries and documents, and infer semantic document relatedness through analyzing structural relationships between these API entities. We evaluate our approach in three popular open source software projects. Comparing to the state-of-the-art text retrieval approaches, our approach lead to at least 13.77% improvement with respect to mean average precision (MAP).


title: A study of link farm distribution and evolution using a time series of web snapshots

abstract: In this paper, we study the overall link-based spam structure and its evolution which would be helpful for the development of robust analysis tools and research for Web spamming as a social activity in the cyber space. First, we use strongly connected component (SCC) decomposition to separate many link farms from the largest SCC, so called the core. We show that denser link farms in the core can be extracted by node filtering and recursive application of SCC decomposition to the core. Surprisingly, we can find new large link farms during each iteration and this trend continues until at least 10 iterations. In addition, we measure the spamicity of such link farms. Next, the evolution of link farms is examined over two years. Results show that almost all large link farms do not grow anymore while some of them shrink, and many large link farms are created in one year.


title: Generating Neural Networks with Neural Networks

abstract: Hypernetworks are neural networks that transform a random input vector into weights for a specified target neural network. We formulate the hypernetwork training objective as a compromise between accuracy and diversity, where the diversity takes into account trivial symmetry transformations of the target network. We show that this formulation naturally arises as a relaxation of an optimistic probability distribution objective for the generated networks, and we explain how it is related to variational inference. We use multi-layered perceptrons to form the mapping from the low dimensional input random vector to the high dimensional weight space, and demonstrate how to reduce the number of parameters in this mapping by weight sharing. We perform experiments on a four layer convolutional target network which classifies MNIST images, and show that the generated weights are diverse and have interesting distributions.


title: A 39 GHz MIMO Transceiver Based on Dynamic Multi-Beam Architecture for 5G Communication with 150 Meter Coverage

abstract: This paper presents a dynamic multi-beam (DMB) architecture for 5G communication. Compared to the phased array with phase shifters, the proposed DMB architecture does not require calibration and has advantages in the searching time between the base station (BS) and the user equipment (UE). Based on the DMB architecture, a 39 GHz MIMO transceiver is designed. A CMOS transceiver chipset is designed by in a standard 65 nm CMOS process and mounted in a LTCC package for the 39 GHz DMB MIMO transceiver. The indoor communication link shows the transceiver supports 256 QAM modulation of EVM 4.0% with 4 m distance. A very good performance through an online outdoor communication link for one stream and two stream transmission is also achieved over 5 m to 150 m distance.


title: Use fast R-CNN and cascade structure for face detection

abstract: Face detection is challenging in real-world due to various poses, occlusions, lighting conditions and so on. Recently, deep learning achieves excellent performance on this task. In this paper, we propose a novel deep cascade convolutional network that uses Fast Region-based Convolutional Network (Fast R-CNN) [3] at the end of the structure. Our method achieves outstanding performance on the FDDB [1] and AFW [2] datasets. Especially, it achieves a high recall of 91.87% on the challenging FDDB benchmark, outperforming the state-of-the-art methods. We adopt a cascaded structure to quickly reject the background regions first. At the last stage, we choose Fast R-CNN to deal with the challenging candidates and it performs well.


title: Understanding and controlling the filter bubble through interactive visualization: a user study

abstract: The "filter bubble" is a term which refers to people getting encapsulated in streams of data such as news or social network updates that are personalized to their interests. While people need protection from information overload and maybe prefer to see content they feel familiar or agree with, there is the danger that important issues that should be of concern for everyone will get filtered away and people will lack exposure to different views, living in "echo-chambers", blissfully unaware of the reality. We have proposed a design of an interactive visualization, which provides the user of a social networking site with awareness of the personalization mechanism (the semantics and the source of the content that is filtered away), and with means to control the filtering mechanism. The visualization has been implemented in a peer-to-peer social network, called MADMICA, and we present here the results of a large scale lab study with 163 crowd-sourced participants. The results demonstrate that the visualization leads to increased users' awareness of the filter bubble, understandability of the filtering mechanism and to a feeling of control over their data stream.


title: Replication of the BANK1 genetic association with systemic lupus erythematosus in a European-derived population

abstract: Systemic lupus erythematosus (SLE) is an autoimmune disease with highly variable clinical presentation. Patients suffer from immunological abnormalities that target T-cell, B-cell and accessory cell functions. B cells are hyperactive in SLE patients. An adapter protein expressed in B cells called BANK1 (B-cell scaffold protein with ankyrin repeats) was reported in a previous study to be associated with SLE in a European population. The objective of this study was to assess the BANK1 genotype-phenotype association in an independent replication sample. We genotyped 38 single nucleotide polymorphisms (SNPs) in BANK1 on 1892 European-derived SLE patients and 2652 European-derived controls. The strongest associations with SLE and BANK1 were at rs17266594 (corrected P-value=1.97 x 10(-5), odds ratio (OR)=1.22, 95% CI 1.12-1.34) and rs10516487 (corrected P-value=2.59 x 10(-5), OR=1.22, 95% CI 1.11-1.34). Our findings suggest that the association is explained by these two SNPs, confirming previous reports that these polymorphisms contribute to the risk of developing lupus. Analysis of patient subsets enriched for hematological, immunological and renal ACR criteria or the levels of autoantibodies, such as anti-RNP A and anti-SmRNP, uncovers additional BANK1 associations. Our results suggest that BANK1 polymorphisms alter immune system development and function to increase the risk for developing lupus.


title: Opinion The free-energy principle: a rough guide to the brain?

abstract: This article reviews a free-energy formulation that advances Helmholtz's agenda to find principles of brain function based on conservation laws and neuronal energy. It rests on advances in statistical physics, theoretical biology and machine learning to explain a remarkable range of facts about brain structure and function. We could have just scratched the surface of what this formulation offers; for example, it is becoming clear that the Bayesian brain is just one facet of the free-energy principle and that perception is an inevitable consequence of active exchange with the environment. Furthermore, one can see easily how constructs like memory, attention, value, reinforcement and salience might disclose their simple relationships within this framework.


title: Learning Activation Functions to Improve Deep Neural Networks

abstract: Artificial neural networks typically have a fixed, non-linear activation function at each neuron. We have designed a novel form of piecewise linear activation function that is learned independently for each neuron using gradient descent. With this adaptive activation function, we are able to improve upon deep neural network architectures composed of static rectified linear units, achieving state-of-the-art performance on CIFAR-10 (7.51%), CIFAR-100 (30.83%), and a benchmark from high-energy physics involving Higgs boson decay modes.


title: Institutional Disability: The Saga of Transportation Policy for the Disabled

abstract: This case study of transportation policy for disabled people illustrates the flaws in policymaking that lead many Americans to believe government is not working as it should. Robert A. Katzmann examines the workings of the legislative, administrative, and judicial processes, both separately and in interaction, as he relates the erratic path of transportation policy for the disabled over two decades. An estimated 13.4 million people in this country have difficulty using public transportation, but the federal response to their problems of mobility is of fairly recent vintage, beginning with legislation in the early 1970s. Since then, there have been many twists and turns in policy, involving a wide array of governmental institutions. These constant shifts have confused state and local governments, the transit industry, and the disabled community. Assessing why policy was so erratic, Katzmann concludes that in part the confusion resulted from the inability to choose between conflicting approaches to the problem--one oriented toward the rights of equal access for the disabled, and the other favoring effective mobility by any practical means. In addition, the conflict between these two policy approaches was compounded by increasing fragmentation within and among national institutions.


title: Predicting personality with social behavior: a comparative study

abstract: In this paper, we study the problem of predicting personality with features based on social behavior. While network position and text analysis are often used in personality prediction, the use of social behavior is fairly new. Often studies of social behavior either concentrate on a single behavior or trait, or simply use behavior to predict ties that are then used in analysis of network position. To study this problem, we introduce novel features based on a person’s social actions in general, towards specific individuals in particular. We also compute the variation of these actions among all the social contacts of a person as well as the actions of friends. We show that social behavior alone, without the help of any textual or network position information, provides a good basis for personality prediction. We then provide a unique comparative study that finds the most significant features based on social behavior in predicting personality for three different communication mediums: Twitter, SMS and phone calls. These mediums offer us with social behavior from public and private contexts, containing messaging and voice call type exchanges. We find behaviors that are distinctive and normative among the ones we study. We also illustrate how behavioral features relate to different personality traits. We also show the various similarities and differences between different mediums in terms of social behavior. Note that all behavioral features are based on statistical properties of the number and the time of social actions and do not consider the textual content. As a result, they can be applied in many different settings. Furthermore, our findings show us how behavioral features can be customized to a specific medium and personality trait.


title: On dynamic memory allocation in sliding-window parallel patterns for streaming analytics

abstract: This work studies the issues related to dynamic memory management in Data Stream Processing, an emerging paradigm enabling the real-time processing of live data streams. In this paper, we consider two streaming parallel patterns and we discuss different implementation variants related to how dynamic memory is managed. The results show that the standard mechanisms provided by modern C++ are not entirely adequate for maximizing the performance. Instead, the combined use of an efficient general purpose memory allocator, a custom allocator optimized for the pattern considered and a custom variant of the C++ shared pointer mechanism, provides a performance improvement up to 16% on the best case.


title: Backscatter Communications for Internet-of-Things: Theory and Applications.

abstract: The Internet-of-Things (IoT) is an emerging concept of network connectivity at anytime and anywhere for billions of everyday objects, which has recently attracted tremendous attentions from both the industry and academia. The rapid growth of IoT has been driven by recent advancements in consumer electronics, wireless network densification, 5G communication technologies [e.g., millimeter wave and massive multiple-input and multiple-output (MIMO)], and cloud-computing enabled big-data analytics. One of the remaining key challenges for IoT is the limited network lifetime due to massive IoT devices being powered by batteries with finite capacities. The low-power and low-complexity backscatter communications (BackCom) has emerged to be a promising technology for tackling the challenge. In this article, we present an overview of the active area by discussing basic principles, system and network architectures and relevant techniques. Last, we describe the IoT applications for BackCom and how the technology can solve the energy challenge for IoT.


title: A new metric on the manifold of kernel matrices with application to matrix geometric means

abstract: Symmetric positive definite (spd) matrices pervade numerous scientific disciplines, including machine learning and optimization. We consider the key task of measuring distances between two spd matrices; a task that is often nontrivial whenever the distance function must respect the non-Euclidean geometry of spd matrices. Typical non-Euclidean distance measures such as the Riemannian metric δR(X, Y) = ||log(Y-1/2XY-1/2)||F, are computationally demanding and also complicated to use. To allay some of these difficulties, we introduce a new metric on spd matrices, which not only respects non-Euclidean geometry but also offers faster computation than δR while being less complicated to use. We support our claims theoretically by listing a set of theorems that relate our metric to δR(X, Y), and experimentally by studying the nonconvex problem of computing matrix geometric means based on squared distances.


title: Gesture Recognition Technology: A Review

abstract: Gesture Recognition Technology has evolved greatly over the years. The past has seen the contemporary Human – Computer Interface techniques and their drawbacks, which limit the speed and naturalness of the human brain and body. As a result gesture recognition technology has developed since the early 1900s with a view to achieving ease and lessening the dependence on devices like keyboards, mice and touchscreens. Attempts have been made to combine natural gestures to operate with the technology around us to enable us to make optimum use of our body gestures making our work faster and more human friendly. The present has seen huge development in this field ranging from devices like virtual keyboards, video game controllers to advanced security systems which work on face, hand and body recognition techniques. The goal is to make full use of the movements of the body and every angle made by the parts of the body in order to supplement technology to become human friendly and understand natural human behavior and gestures. The future of this technology is very bright with prototypes of amazing devices in research and development to make the world equipped with digital information at hand whenever and wherever required.


title: CANONICAL CORRELATION ANALYSIS

abstract: We discuss algorithms for performing canonical correlation analysis. In canonical correlation analysis we try to find correlations between two data sets. The canonical correlation coe cients can be calculated directly from the two data sets or from (reduced) representations such as the covariance matrices. The algorithms for both representations are based on singular value decomposition. The methods described here have been implemented in the speech analysis program PRAAT (Boersma & Weenink, 1996), and some examples will be demonstated for formant frequency and formant level data from 50 male Dutch speakers as were reported by Pols et al. (1973).


Conflict in Adult Close Relationships : An Attachment Perspective


title: Clone detection algorithm based on the Abstract Syntax Tree approach

abstract: In this paper we present useful methodologies in analyzing program code written in C programming language in order to detect source code clones between two or multiple files. For detecting student plagiarism in C code, we implemented a three phase clone detection algorithm based on the AST (Abstract Syntax Tree) approach. Starting from the state of the art in this domain, we make a short description and comparison between the proposed methods and finally we present a case study upon student assignments for the Programming Techniques classes. In the end, we conclude pointing the future directions of development and improvement in order to obtain a better clone detection tool.