title: Relation Extraction with Matrix Factorization and Universal Schemas

abstract: © 2013 Association for Computational Linguistics. Traditional relation extraction predicts relations within some fixed and finite target schema. Machine learning approaches to this task require either manual annotation or, in the case of distant supervision, existing structured sources of the same schema. The need for existing datasets can be avoided by using a universal schema: the union of all involved schemas (surface form predicates as in OpenIE, and relations in the schemas of preexisting databases). This schema has an almost unlimited set of relations (due to surface forms), and supports integration with existing structured data (through the relation types of existing databases). To populate a database of such schema we present matrix factorization models that learn latent feature vectors for entity tuples and relations. We show that such latent models achieve substantially higher accuracy than a traditional classification approach. More importantly, by operating simultaneously on relations observed in text and in pre-existing structured DBs such as Freebase, we are able to reason about unstructured and structured data in mutually-supporting ways. By doing so our approach outperforms stateof- the-Art distant supervision.


title: Development of folded dual-polarization dividers for broadband ortho-mode transducers

abstract: A waveguide divider with folded lateral arms is presented for separating dual orthogonal linear polarizations in broadband ortho-mode transducers. The structure is based on a well-known double symmetry junction, where the metallic pins have been eliminated and the lateral outputs have been folded to achieve a combined effect: matching for the vertical polarization and a very significant size reduction. In addition, since the path for the lateral branches has been reduced, the insertion losses for the different polarizations are balanced. The isolation between orthogonal polarizations is kept because of the double-symmetry of the junction. From the mechanical point of view, the proposed junction allows a more simple manufacture and assembly of the ortho-mode transducer parts, which has been shown with a Ku-band design, covering the full Ku-band from 12.6 to 18.25 GHz. The experimental prototype has demonstrated a measured return loss better than 28 dB in the design band and insertion loss smaller than 0.15 dB for both polarizations.


title: A novel delay-aware routing algorithm (DARA) for a hybrid wireless-optical broadband access network (WOBAN)

abstract: A hybrid wireless-optical broadband access network (WOBAN) is a promising architecture for future access networks. Recently, the WOBAN has been gaining increasing attention, and early versions are being deployed as municipal access solutions. This architecture saves on network deployment cost because fiber need not penetrate to each end user. However, a major research opportunity exists in developing an efficient routing algorithm for the wireless front-end of the WOBAN. We propose and investigate the characteristics of the delay-aware routing algorithm (DARA) that minimizes the average packet delay in the wireless front-end of a WOBAN. In DARA we model wireless routers as queues and predict wireless link states periodically. Our performance studies show that DARA achieves less delay and congestion, and improved load balancing compared to traditional approaches such as the minimum-hop routing algorithm, shortest-path routing algorithm, and predictive throughput routing algorithm.


title: Experimental investigation of full bridge series resonant inverters for induction-heating cooking appliances

abstract: In this paper, a full-bridge resonant-type IGBT inverter suitable for heating magnetic and nonmagnetic materials at high frequency is experimentally described. A series arrangement of capacitors is adopted and an optimum mode of operation is proposed. The actual performance was tested on an experimental prototype of full bridge series resonance inverter for induction-heating cooking appliances. The low-cost developed hybrid inverter is characterized by simplicity of design and operation. Some analysis and detailed experimental results are presented.


title: Joint 3D Estimation of Vehicles and Scene Flow

abstract: Abstract. driving. While much progress has been made in recent years, imaging conditions in natural outdoor environments are still very challenging for current reconstruction and recognition methods. In this paper, we propose a novel unified approach which reasons jointly about 3D scene flow as well as the pose, shape and motion of vehicles in the scene. Towards this goal, we incorporate a deformable CAD model into a slanted-plane conditional random field for scene flow estimation and enforce shape consistency between the rendered 3D models and the parameters of all superpixels in the image. The association of superpixels to objects is established by an index variable which implicitly enables model selection. We evaluate our approach on the challenging KITTI scene flow dataset in terms of object and scene flow estimation. Our results provide a prove of concept and demonstrate the usefulness of our method.


title: A flexible learning system for wrapping tables and lists in HTML documents

abstract: A program that makes an existing website look like a database is called a wrapper. Wrapper learning is the problem of learning website wrappers from examples. We present a wrapper-learning system called WL2 that can exploit several different representations of a document. Examples of such different representations include DOM-level and token-level representations, as well as two-dimensional geometric views of the rendered page (for tabular data) and representations of the visual appearance of text asm it will be rendered. Additionally, the learning system is modular, and can be easily adapted to new domains and tasks. The learning system described is part of an "industrial-strength" wrapper management system that is in active use at WhizBang Labs. Controlled experiments show that the learner has broader coverage and a faster learning rate than earlier wrapper-learning systems.


title: Dynamin-2 Stabilizes the HIV-1 Fusion Pore with a Low Oligomeric State

abstract: Summary One of the key research areas surrounding HIV-1 concerns the regulation of the fusion event that occurs between the virus particle and the host cell during entry. Even if it is universally accepted that the large GTPase dynamin-2 is important during HIV-1 entry, its exact role during the first steps of HIV-1 infection is not well characterized. Here, we have utilized a multidisciplinary approach to study the DNM2 role during fusion of HIV-1 in primary resting CD4 T and TZM-bl cells. We have combined advanced light microscopy and functional cell-based assays to experimentally assess the role of dynamin-2 during these processes. Overall, our data suggest that dynamin-2, as a tetramer, might help to establish hemi-fusion and stabilizes the pore during HIV-1 fusion.


title: A verifiable multi-secret sharing scheme based on cellular automata

abstract: In this paper, we propose a verifiable (t,n)-threshold multi-secret sharing scheme, based on one-dimensional cellular automata where the number of secrets is not restricted by n or t. We show that our scheme can be used to solve an open problem proposed recently in Alvarez et al. (2008) [G. Alvarez, L. Hernandez Encinas, A. Martin del Rey, A multisecret sharing scheme for color images based on cellular automata, Information Sciences 178 (2008) 4382-4395].


title: A galvanic skin response interface for people with severe motor disabilities

abstract: Biometric input devices can provide assistive technology access to people who have little or no motor control. We explore a biometric control interface based on the Galvanic Skin Response, to determine its effectiveness as a non-muscular channel of input. This paper presents data from several online studies of a locked-in subject using a Galvanic Skin Response system for communication and control. We present issues with GSR control, and approaches that may improve accuracy and information transfer rate.


title: Optimizing F-Measure with Support Vector Machines

abstract: Support vector machines (SVMs) are regularly used for classification of unbalanced data by weighting more heavily the error contribution from the rare class. This heuristic technique is often used to learn classifiers with high F-measure, although this particular application of SVMs has not been rigorously examined. We provide significant and new theoretical results that support this popular heuristic. Specifically, we demonstrate that with the right parameter settings SVMs approximately optimize F-measure in the same way that SVMs have already been known to approximately optimize accuracy. This finding has a number of theoretical and practical implications for using SVMs in F-measure optimization.


title: Mining the Situation: Spatiotemporal Traffic Prediction With Big Data

abstract: With the vast availability of traffic sensors from which traffic information can be derived, a lot of research effort has been devoted to developing traffic prediction techniques, which in turn improve route navigation, traffic regulation, urban area planning, etc. One key challenge in traffic prediction is how much to rely on prediction models that are constructed using historical data in real-time traffic situations, which may differ from that of the historical data and change over time. In this paper, we propose a novel online framework that could learn from the current traffic situation (or context) in real-time and predict the future traffic by matching the current situation to the most effective prediction model trained using historical data. As real-time traffic arrives, the traffic context space is adaptively partitioned in order to efficiently estimate the effectiveness of each base predictor in different situations. We obtain and prove both short-term and long-term performance guarantees (bounds) for our online algorithm. The proposed algorithm also works effectively in scenarios where the true labels (i.e., realized traffic) are missing or become available with delay. Using the proposed framework, the context dimension that is the most relevant to traffic prediction can also be revealed, which can further reduce the implementation complexity as well as inform traffic policy making. Our experiments with real-world data in real-life conditions show that the proposed approach significantly outperforms existing solutions.


title: Workload Matters: Why RDF Databases Need a New Design

abstract: The Resource Description Framework (RDF) is a standard for conceptually describing data on the Web, and SPARQL is the query language for RDF. As RDF is becoming widely utilized, RDF data management systems are being exposed to more diverse and dynamic workloads. Existing systems are workload-oblivious, and are therefore unable to provide consistently good performance. We propose a vision for a workload-aware and adaptive system. To realize this vision, we re-evaluate relevant existing physical design criteria for RDF and address the resulting set of new challenges.


AN AUTOMATIC LEAF RECOGNITION SYSTEM FOR PLANT IDENTIFICATION USING MACHINE VISION TECHNOLOGY


title: The influence of colour on memory performance: a review

abstract: Human cognition involves many mental processes that are highly interrelated, such as perception, attention, memory, and thinking. An important and core cognitive process is memory, which is commonly associated with the storing and remembering of environmental information. An interesting issue in memory research is on ways to enhance memory performance, and thus, remembering of information. Can colour result in improved memory abilities? The present paper highlights the relationship between colours, attention, and memory performance. The significance of colour in different settings is presented first, followed by a description on the nature of human memory. The role of attention and emotional arousal on memory performance is discussed next. The review of several studies on colours and memory are meant to explain some empirical works done in the area and related issues that arise from such studies.


Nutrient content and uptake by arrowroot (Maranta arundinacea) as influenced by agronomic practices when grown as intercrop in coconut (Cocos nucifera) garden.


title: Power factor metering system using Arduino

abstract: The power factor (PF) levels are established by each country's national standards. In Brazil the lower power factor level allowed is 0.92. In order to monitor this quantity, the present paper uses Arduino to develop a PF metering system. It presents all the necessary theories concerning the calculus of the power factor itself, the programming logic applied to the data acquisition, and the details of the hardware components used in this system. In the end, two tests are conducted so that the meter accuracy can be evaluated. The results are compared with regulated measuring instruments.


Contextual and Feature-based Models by PolyU Team at the NTCIR-13 STC-2 Task †


title: A cylindrical surface model to rectify the bound document image

abstract: We propose a novel approach on how to rectify the photo image of the bound document. The surface of the document is modeled by a cylindrical surface. By the geometry of camera image formation, the equations using the cue of directrixes to map the points on the surface in the 3D scene to the points on the image plane are achieved. Baselines of the horizontal text line are extracted as projections of directrixes to estimate the bending extent of the surface, and then the images are rectified. The proposed method needs no auxiliary device. Experimental results are presented to demonstrate the feasibility and the application of the method.


title: Optical wireless data transmission with enhanced substitution Caesar Cipher WHEEL encryption

abstract: The paper describes a microcontroller based secured optical wireless communication system using laser and phototransistor. These days the usage of Wi-Fi has reached to every nook and corner of the world. There are some downsides belong to the usage of Wi-Fi such as those concerning to the speed, limited bandwidth, security and range of its usage. In order to overcome these hitches we can use the advanced version of Li-Fi which is efficient, high speed, and fully networked wireless communication. If light contains encrypted message then both privacy and prevention from unwanted access along with high data rate can be achievable from Li-Fi. A new encryption technique based on substitution of ASCII value of characters implemented and reliable data transmission carried out. The proposed encryption algorithm primarily follows Caesar Cipher WHEEL substitution, acts similarly with Caesar wheel device by rotating circularly and changes the ASCII value of original message according to predefined values and length of repetition. Encryption of data, transmission, reception and conversion to original message are implemented successfully using laser, phototransistor, microcontroller and associated devices. Microcontroller performs as overall controlling and processing unit.


title: Description and classification for facilitating interoperability of heterogeneous data/events/services in the Internet of Things

abstract: Abstract The Internet of Things (IoT) refers to an infrastructure that integrates things over standard wired/wireless networks and allows them to exchange information with each other. The IoT is a very complex heterogeneous network, enabling seamless integration of these things is a huge challenge. A publish/subscribe method of integration can be formulated to solve the problems of interconnecting billions of heterogeneous things. In our work, an IoT framework that uses an abstraction layer that decouples an application from the service calls and network interfaces is required to send and receive messages on a particular thing. This paper provides definitions and classifications for heterogeneous data/events/services according to the properties of the things in order to integrate them into a framework for description. Based on these definitions and classifications, heterogeneous data/events/services in the IoT were integrated via topic description through the Data Distribution Service (DDS) middleware standard for real-time publish/subscribe. This paper also concludes with general remarks and a discussion of future work.


title: Supporting parent-child conversations in a history museum.

abstract: BACKGROUND: Museums can serve as rich resources for families to learn about the social world through engagement with exhibits and parent-child conversation about exhibits. ::: ::: AIMS: This study examined ways of engaging parents and child about two related exhibits at a cultural and history museum. Sample participants consisted of families visiting the Animal Antics and the Gone Potty exhibits at the British Museum. ::: ::: METHODS: Whilst visiting two exhibits at the British Museum, 30 families were assigned to use a backpack of activities, 13 were assigned to a booklet of activities, and 15 were assigned to visit the exhibits without props (control condition). ::: ::: RESULTS: Compared to the families in the control condition, the interventions increased the amount of time parents and children engaged together with the exhibit. Additionally, recordings of the conversations revealed that adults asked more questions related to the exhibits when assigned to the two intervention conditions compared to the control group. Children engaged in more historical talk when using the booklets than in the other two conditions. ::: ::: CONCLUSIONS: The findings suggest that providing support with either booklets or activities for children at exhibits may prove beneficial to parent-child conversations and engagement with museum exhibits.


title: Convex and Semi-Nonnegative Matrix Factorizations

abstract: We present several new variations on the theme of nonnegative matrix factorization (NMF). Considering factorizations of the form X = FGT, we focus on algorithms in which G is restricted to containing nonnegative entries, but allowing the data matrix X to have mixed signs, thus extending the applicable range of NMF methods. We also consider algorithms in which the basis vectors of F are constrained to be convex combinations of the data points. This is used for a kernel extension of NMF. We provide algorithms for computing these new factorizations and we provide supporting theoretical analysis. We also analyze the relationships between our algorithms and clustering algorithms, and consider the implications for sparseness of solutions. Finally, we present experimental results that explore the properties of these new methods.


title: A history-based test prioritization technique for regression testing in resource constrained environments

abstract: Regression testing is an expensive and frequently executed maintenance process used to revalidate modified software. To improve it, regression test selection (RTS) techniques strive to lower costs without overly reducing effectiveness by carefully selecting a subset of the test suite. Under certain conditions, some can even guarantee that the selected test cases perform no worse than the original test suite.But this ignores certain software development realities such as resource and time constraints that may prevent using RTS techniques as intended (e.g., regression testing must be done overnight, but RTS selection returns two days worth of tests). In practice, testers work around this by prioritizing the test cases and running only those that fit within existing constraints. Unfortunately this generally violates key RTS assumptions, voiding RTS technique guarantees and making regression testing performance unpredictable.Despite this, existing prioritization techniques are memoryless, implicitly assuming that local choices can ensure adequate long run performance. Instead, we proposed a new technique that bases prioritization on historical execution data. We conducted an experiment to assess its effects on the long run performance of resource constrained regression testing. Our results expose essential tradeoffs that should be considered when using these techniques over a series of software releases.


title: ELECTRONIC WORD-OF-MOUTH VIA CONSUMER-OPINION PLATFORMS: WHAT MOTIVATES CONSUMERS TO ARTICULATE THEMSELVES ON THE INTERNET?

abstract: Through Web-based consumer opinion platforms (e.g., epinions.com), the Internet enables customers to share their opinions on, and experiences with, goods and services with a multitude of other consumers; that is, to engage in electronic wordof-mouth (eWOM) communication. Drawing on findings from research on virtual communities and traditional word-of-mouth literature, a typology for motives of consumer online articulation is


title: Context-Enhanced Citation Sentiment Detection

abstract: Sentiment analysis of citations in scientific papers and articles is a new and interesting problem which can open up many exciting new applications in bibliographic search and bibliometrics. Current work on citation sentiment detection focuses on only the citation sentence. In this paper, we address the problem of context-enhanced citation sentiment detection. We present a new citation sentiment corpus which has been annotated to take the dominant sentiment in the entire citation context into account. We believe that this gold standard is closer to the truth than annotation that looks only at the citation sentence itself. We then explore the effect of context windows of different lengths on the performance of a state-of-the-art citation sentiment detection system when using this context-enhanced gold standard definition.


title: Neuroanatomy and Histology of the Central Nervous System in Short Mackerel, Rastrelliger brachysoma (Bleeker, 1851)

abstract: A detailed anatomy and histological structure of the brain and spinal cord, referring to the central nervous system and special sensory organ, in the short mackerel, Rastrelliger brachysoma , were observed in this study. All specimens revealed that the brain nomenclature in R. brachysoma anatomically consisted of the telencephalon, mesencephalon, diencephalon, metencephalon, and myelencephalon, continued by the spinal cord. Different structures and cell components were observed due to histology and histochemistry studies. The telencephalon of R. brachysoma was less developed, mainly consisting of olfactory lobes and cerebral hemispheres. The largest part, the optic lobe, in the mesencephalon, was observed to have 6 histologically distinct layers; the stratum marginale, stratum opticum, stratum fibroetgricialem, stratum album central, stratum griseum central, and stratum periventriculae, respectively. The diencephalon was situated beneath mesencephalon. The epithalamus, thalamus and hypothalamus were easily observable in this region. The cerebellum in the metencephalon was composed of the corpus and the valvula cerebelli. The last region of the brain was the myelencephalon, which held the medulla oblongata and vagal lobes, before joining the spinal cord. The eye structure, composed of the inner, middle, and external layers, was investigated.


title: Fusion for Free!

abstract: Algebraic effect handlers are a recently popular approach for modelling side-effects that separates the syntax and semantics of effectful operations. The shape of syntax is captured by functors, and free monads over these functors denote syntax trees. The semantics is captured by algebras, and effect handlers pass these over the syntax trees to interpret them into a semantic domain.


Vector Space Models of Lexical Meaning


title: Cloud Computing: Theory and Practice

abstract: Cloud Computing: Theory and Practice provides students and IT professionals with an in-depth analysis of the cloud from the ground up. Beginning with a discussion of parallel computing and architectures and distributed systems, the book turns to contemporary cloud infrastructures, how they are being deployed at leading companies such as Amazon, Google and Apple, and how they can be applied in fields such as healthcare, banking and science. The volume also examines how to successfully deploy a cloud application across the enterprise using virtualization, resource management and the right amount of networking support, including content delivery networks and storage area networks. Developers will find a complete introduction to application development provided on a variety of platforms. Learn about recent trends in cloud computing in critical areas such as: resource management, security, energy consumption, ethics, and complex systems Get a detailed hands-on set of practical recipes that help simplify the deployment of a cloud based system for practical use of computing clouds along with an in-depth discussion of several projects Understand the evolution of cloud computing and why the cloud computing paradigm has a better chance to succeed than previous efforts in large-scale distributed computing


title: Classifying Computer Generated Charts

abstract: We present an approach for classifying images of charts based on the shape and spatial relationships of their primitives. Five categories are considered: bar-charts, curve-plots, pie-charts, scatter-plots and surface-plots. We introduce two novel features to represent the structural information based on (a) region segmentation and (b) curve saliency. The local shape is characterized using the Histograms of Oriented Gradients (HOG) and the Scale Invariant Feature Transform (SIFT) descriptors. Each image is represented by sets of feature vectors of each modality. The similarity between two images is measured by the overlap in the distribution of the features -measured using the Pyramid Match algorithm. A test image is classified based on its similarity with training images from the categories. The approach is tested with a database of images collected from the Internet.


title: Hierarchical 3D fully convolutional networks for multi-organ segmentation

abstract: Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of full volumetric images. In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of seven abdominal structures (artery, vein, liver, spleen, stomach, gallbladder, and pancreas) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training organ-specific models. To this end, we propose a two-stage, coarse-to-fine approach that trains an FCN model to roughly delineate the organs of interest in the first stage (seeing $\sim$40% of the voxels within a simple, automatically generated binary mask of the patient's body). We then use these predictions of the first-stage FCN to define a candidate region that will be used to train a second FCN. This step reduces the number of voxels the FCN has to classify to $\sim$10% while maintaining a recall high of $>$99%. This second-stage FCN can now focus on more detailed segmentation of the organs. We respectively utilize training and validation sets consisting of 281 and 50 clinical CT images. Our hierarchical approach provides an improved Dice score of 7.5 percentage points per organ on average in our validation set. We furthermore test our models on a completely unseen data collection acquired at a different hospital that includes 150 CT scans with three anatomical labels (liver, spleen, and pancreas). In such challenging organs as the pancreas, our hierarchical approach improves the mean Dice score from 68.5 to 82.2%, achieving the highest reported average score on this dataset.


title: An Improved Discrete Bat Algorithm for Symmetric and Asymmetric Traveling Salesman Problems

abstract: Bat algorithm is a population metaheuristic proposed in 2010 which is based on the echolocation or bio-sonar characteristics of microbats. Since its first implementation, the bat algorithm has been used in a wide range of fields. In this paper, we present a discrete version of the bat algorithm to solve the well-known symmetric and asymmetric Traveling Salesman Problems. In addition, we propose an improvement in the basic structure of the classic bat algorithm. To prove that our proposal is a promising approximation method, we have compared its performance in 37 instances with the results obtained by five different techniques: evolutionary simulated annealing, genetic algorithm, an island based distributed genetic algorithm, a discrete firefly algorithm and an imperialist competitive algorithm. In order to obtain fair and rigorous comparisons, we have conducted three different statistical tests along the paper: the Student's t-test, the Holm's test, and the Friedman test. We have also compared the convergence behavior shown by our proposal with the ones shown by the evolutionary simulated annealing, and the discrete firefly algorithm. The experimentation carried out in this study has shown that the presented improved bat algorithm outperforms significantly all the other alternatives in most of the cases.


title: Crowdsourcing a Word-Emotion Association Lexicon

abstract: Even though considerable attention has been given to the polarity of words (positive and negative) and the creation of large polarity lexicons, research in emotion analysis has had to rely on limited and small emotion lexicons. In this paper, we show how the combined strength and wisdom of the crowds can be used to generate a large, high-quality, word–emotion and word–polarity association lexicon quickly and inexpensively. We enumerate the challenges in emotion annotation in a crowdsourcing scenario and propose solutions to address them. Most notably, in addition to questions about emotions associated with terms, we show how the inclusion of a word choice question can discourage malicious data entry, help to identify instances where the annotator may not be familiar with the target term (allowing us to reject such annotations), and help to obtain annotations at sense level (rather than at word level). We conducted experiments on how to formulate the emotion-annotation questions, and show that asking if a term is associated with an emotion leads to markedly higher interannotator agreement than that obtained by asking if a term evokes an emotion.


title: A randomized, placebo-controlled trial of 5% and 2% topical minoxidil solutions in the treatment of female pattern hair loss.

abstract: BACKGROUND ::: To pical minoxidil solution 2% stimulates new hair growth and helps stop the loss of hair in men with androgenetic alopecia and women with female pattern hair loss. Results can be variable, and historic experience suggests that higher concentrations of topical minoxidil may enhance efficacy. ::: ::: ::: OBJECTIVE ::: The purpose of this 48-week, double-blind, placebo-controlled, randomized, multicenter trial was to compare the efficacy and safety of 5% topical minoxidil with 2% topical minoxidil and placebo in the treatment of female pattern hair loss. ::: ::: ::: METHODS ::: A total of 381 women (18-49 years old) with female pattern hair loss applied 5% topical minoxidil solution (n = 153), 2% topical minoxidil solution (n = 154), or placebo (vehicle for 5% solution; n = 74) twice daily. Primary efficacy variables were change in nonvellus hair count at week 48, and patient and investigator assessments of change in hair growth/scalp coverage at week 48. ::: ::: ::: RESULTS ::: After 48 weeks of therapy, 5% topical minoxidil was superior to placebo for each of the 3 primary efficacy measures. The 2% topical minoxidil group demonstrated superiority over placebo for hair count and investigator assessment of hair growth/scalp coverage at week 48; differences in patient assessment of hair growth at week 48 were not significantly different from placebo. The 5% topical minoxidil group demonstrated statistical superiority over the 2% topical minoxidil group in the patient assessment of treatment benefit at week 48. Both 5% and 2% topical minoxidil helped improve psychosocial perceptions of hair loss in women with female pattern hair loss. An increased occurrence of pruritus, local irritation, and hypertrichosis was observed with 5% topical minoxidil versus 2% topical minoxidil and placebo. ::: ::: ::: CONCLUSION ::: In this 48-week study of 381 women with female pattern hair loss, 5% topical minoxidil was superior to placebo on each of the 3 primary efficacy end points: promoting hair growth as measured by change in nonvellus hair count and patient/investigator assessments of hair growth and scalp coverage. Application of 2% topical minoxidil was superior to placebo for assessments of nonvellus hair counts and investigator assessment of hair growth/scalp coverage at week 48; differences in patient assessment of hair growth at week 48 were not significantly different from placebo. At week 48, the 5% topical minoxidil group demonstrated statistical superiority over the 2% topical minoxidil group in the patient assessment of treatment benefit. Both concentrations of topical minoxidil were well tolerated by the women in this trial without evidence of systemic adverse effects. With the introduction of numerous herbal remedies for hair loss, of which most have not been tested in randomized, double-blind, placebo-controlled trials, it is important to describe well-controlled trials that demonstrate the efficacy and safety of topical drugs.


title: Intelligent Robotic IoT System (IRIS)Testbed

abstract: We present the Intelligent Robotic IoT System (IRIS), a modular, portable, scalable, and open-source testbed for robotic wireless network research. There are two key features that separate IRIS from most of the state-of-the-art multi-robot testbeds. (1)Portability: IRIS does not require a costly static global positioning system such as a VICON system nor time-intensive vision-based SLAM for its operation. Designed with an inexpensive Time Difference of Arrival (TDoA)localization system with centimeter level accuracy, the IRIS testbed can be deployed in an arbitrary uncontrolled environment in a matter of minutes. (2)Programmable Wireless Communication Stack: IRIS comes with a modular programmable low-power IEEE 802.15.4 radio and IPv6 network stack on each node. For the ease of administrative control and communication, we also developed a lightweight publish-subscribe overlay protocol called ROMANO that is used for bootstrapping the robots (also referred to as the IRISbots), collecting statistics, and direct control of individual robots, if needed. We detail the modular architecture of the IRIS testbed design along with the system implementation details and localization performance statistics.


title: Quantifying Driver Stress: Developing a System for Collecting and Processing Bio-Metric Signals in Natural Situations

abstract: A system for quantifying the physiological features of emotional stress is being developed for use during a driving task. Two prototypes, using sensors that measure the driver's skin conductance, respiration, muscle activity, and heart activity are presented. The first system allows sampling rates of 200 Hz on two fast channels and 20 Hz on six additional channels. It uses a wearable computer to do real-time processing on the signals and has an attached digital camera which was used to capture images of the driver's facial expression once every minute. The second system uses a car-based computer that allows a sampling rate of 1984 samples per second on eight channels. This system uses multiple video cameras to continuously capture the driver's facial expression and road conditions. The data is then synchronized with the physiological signals using a video quad-splitter. The methods for extracting physiological features in the driving environment are discussed, including measurement of the skin conductance orienting response, muscle activity, pulse, and respiration patterns. Preliminary studies show how using multiple modalities of sensors can help discriminate reactions to driving events and how individual's response to similar driving conditions can vary from day to day.


title: Catalyst: GPU-assisted rapid memory deduplication in virtualization environments

abstract: Content based page sharing techniques improve memory efficiency in virtualized systems by identifying and merging identical pages. Kernel Same-page Merging (KSM), a Linux kernel utility for page sharing, sequentially scans memory pages of virtual machines to deduplicate pages. Sequential scanning of pages has several undesirable side effects---wasted CPU cycles when no sharing opportunities exist, and rate of discovery of sharing being dependent on the scanning rate and corresponding CPU availability. In this work, we exploit presence of GPUs on modern systems to enable rapid memory sharing through targeted scanning of pages. Our solution, Catalyst, works in two phases, the first where pages of virtual machines are processed by the GPU to identify likely pages for sharing and a second phase that performs page-level similarity checks on a targeted set of shareable pages. Opportunistic usage of the GPU to produce sharing hints enables rapid and low-overhead duplicate detection, and sharing of memory pages in virtualization environments. We evaluate Catalyst against various benchmarks and workloads to demonstrate that Catalyst can achieve higher memory sharing in lesser time compared to different scan rate configurations of KSM, at lower or comparable compute costs.


title: Urban growth trend analysis using Shannon Entropy approach – A case study in North-East India

abstract: Urbanization is considered as the most influential drivers of land use and land cover change associated with growth of populations and economy. The integration of remote sensing (RS) and Geographical Information System (GIS) technique acts as an effective tool for detecting urban growth and modelling in the recent years. In order to maintain a systematic urban growth pattern at regional or global level, effective planning has to be first prepared at local level. The present study aims to quantifying the urban growth in Jorhat district of Assam using Shannon's Entropy Approach. The entropy values obtained in the study were 2.32 in 1996 and 2.49 in the year 2010 which are closer to the upper limit of log (n), i.e. 2.63, indicating a dispersed development. The study also emphasizes the potentiality of Landsat TM data in the urban studies at local level. The present finding will have high utility in understanding the present and projecting the future growth scenario. This will further help in preparation of effective planning and management strategies for controlled and systematic urban growth both at regional as well as local level.


title: Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization

abstract: Nonlinear dimensionality reduction methods are often used to visualize high-dimensional data, although the existing methods have been designed for other related tasks such as manifold learning. It has been difficult to assess the quality of visualizations since the task has not been well-defined. We give a rigorous definition for a specific visualization task, resulting in quantifiable goodness measures and new visualization methods. The task is information retrieval given the visualization: to find similar data based on the similarities shown on the display. The fundamental tradeoff between precision and recall of information retrieval can then be quantified in visualizations as well. The user needs to give the relative cost of missing similar points vs. retrieving dissimilar points, after which the total cost can be measured. We then introduce a new method NeRV (neighbor retrieval visualizer) which produces an optimal visualization by minimizing the cost. We further derive a variant for supervised visualization; class information is taken rigorously into account when computing the similarity relationships. We show empirically that the unsupervised version outperforms existing unsupervised dimensionality reduction methods in the visualization task, and the supervised version outperforms existing supervised methods.


title: Motion artifact reduction in EEG recordings using multi-channel contact impedance measurements

abstract: Dry-contact electrodes have paved the way for easy-to-use electroencephalography (EEG) systems with minimal setup time, which are of particular interest in ambulatory as well as real-life environments. However, the presence of motion artifacts forms a major obstacle for such systems. In previous studies, it has been shown that continuous electrode-tissue impedance monitoring can be used to handle motion artifacts. In this paper, we demonstrate that the in-phase and quadrature components of the contact impedance provide complementary information that can be used to improve the prediction of motion artifacts. Furthermore, we demonstrate that the prediction of motion artifacts at one electrode can be further improved by also incorporating the impedance measurements at other electrodes. With this, we propose a motion artifact reduction algorithm based on a multi-channel linear prediction (MLP) filter. Although the MLP filter is not able to completely remove motion artifacts, a substantial reduction can indeed be achieved.


title: BLSTM supported GEV beamformer front-end for the 3RD CHiME challenge

abstract: We present a new beamformer front-end for Automatic Speech Recognition and apply it to the 3rd-CHiME Speech Separation and Recognition Challenge. Without any further modification of the back-end, we achieve a 53% relative reduction of the word error rate over the best baseline enhancement system for the relevant test data set. Our approach leverages the power of a bi-directional Long Short-Term Memory network to robustly estimate soft masks for a subsequent beamforming step. The utilized Generalized Eigenvalue beamforming operation with an optional Blind Analytic Normalization does not rely on a Direction-of-Arrival estimate and can cope with multi-path sound propagation, while at the same time only introducing very limited speech distortions. Our quite simple setup exploits the possibilities provided by simulated training data while still being able to generalize well to the fairly different real data. Finally, combining our front-end with data augmentation and another language model nearly yields a 64 % reduction of the word error rate on the real data test set.


title: Online Peer-to-Peer Lending: A Lenders' Perspective

abstract: Online Peer-to-Peer lending platforms claim to be beneficial for both borrowers and lenders by eliminating expensive intermediaries and reducing transaction costs. However, are the often inexperienced lenders who operate in a pseudonymous online environment with potentially significant information asymmetries really able to obtain an attractive return on their investment? This paper discusses the question by presenting profitability data from the US platform Prosper. Although the overall investment performance has not been satisfactory for most rating categories, it is shown that following some simple investment rules improves profitability of a portfolio and leads to acceptable returns for all credit rating categories with exception of the high-risk one.


Cyber Security Zone Modeling in Practice


title: Classification in networked data -- A Toolkit and a . . .

abstract: This paper is about classifying entities that are interlinked with entities for which the class is known. After surveying prior work, we present NetKit, a modular toolkit for classification in networked data, and a case-study of its application to networked data used in prior machine learning research. NetKit is based on a node-centric framework in which classifiers comprise a local classifier, a relational classifier, and a collective inference procedure. Various existing node-centric relational learning algorithms can be instantiated with appropriate choices for these components, and new combinations of components realize new algorithms. The case study focuses on univariate network classification, for which the only information used is the structure of class linkage in the network (i.e., only links and some class labels). To our knowledge, no work previously has evaluated systematically the power of class-linkage alone for classification in machine learning benchmark data sets. The results demonstrate that very simple network-classification models perform quite well---well enough that they should be used regularly as baseline classifiers for studies of learning with networked data. The simplest method (which performs remarkably well) highlights the close correspondence between several existing methods introduced for different purposes---that is, Gaussian-field classifiers, Hopfield networks, and relational-neighbor classifiers. The case study also shows that there are two sets of techniques that are preferable in different situations, namely when few versus many labels are known initially. We also demonstrate that link selection plays an important role similar to traditional feature selection.


title: A low-power 10-bit 50-MS/s SAR ADC using a parasitic-compensated split-capacitor DAC

abstract: This paper presents a low-power 10-bit 50-MS/s successive approximation register (SAR) analog-to-digital converter (ADC). To reduce power and area, the monotonic switching procedure is combined with a parasitic-compensated split-capacitor DAC that also has an improved capacitor matching. The nonlinearity of the conventional split-capacitor DAC due to parasitic capacitance and capacitor mismatch is improved by modifying the capacitor bank so that the bridge capacitor is an integer multiple of the unit capacitor (as opposed to fractional multiple in the conventional circuit) and by including two dummy unit capacitors connected to ground. The proposed 10-bit ADC is designed and simulated using a 90-nm CMOS technology. Post-layout simulation results show that at 1.0-V supply and 50 MS/s, the ADC achieves a signal-to-noise-and-distortion ratio (SNDR) of 60.10 dB and consumes 0.32 mW with an input capacitance of 0.48 pF, resulting in a figure of merit (FoM) of 8.44 fJ/conversion-step. The ADC core occupies an active area of 215 × 215 µm2


title: A reconfigurable slot antenna with switchable polarization

abstract: A novel reconfigurable slot antenna architecture allowing polarization switching is presented. The antenna shape consists of a slot-ring with perturbations which are switched on and off using pin-diodes. Two antennas allowing switching either between linear and circular polarization or between two circular polarizations are demonstrated. The antenna architecture is interesting for commercial wireless applications because it is compatible with modern fabrication processes and can be realized on low cost dielectric materials.


title: Real-time facial feature detection using conditional regression forests

abstract: Although facial feature detection from 2D images is a well-studied field, there is a lack of real-time methods that estimate feature points even on low quality images. Here we propose conditional regression forest for this task. While regression forest learn the relations between facial image patches and the location of feature points from the entire set of faces, conditional regression forest learn the relations conditional to global face properties. In our experiments, we use the head pose as a global property and demonstrate that conditional regression forests outperform regression forests for facial feature detection. We have evaluated the method on the challenging Labeled Faces in the Wild [20] database where close-to-human accuracy is achieved while processing images in real-time.


title: The impact of emotionality and self-disclosure on online dating versus traditional dating

abstract: Online dating is unique in the pursuit of romance. The bond created between potential partners takes a different path than normal dating relationships. Online dating usually begins with a flurry of e-mail messages, each more intimate than the last. Traditional dating relationships that might take months to develop in the real world, take weeks or even days online. Much has been written about cyber-dating, but little research has been done. This series of four studies examines the online dating process, similarities and differences between online and traditional dating, and the impact of emotionality and self-disclosure on first (e-mail) impressions of a potential partner. Results indicate that the amount of emotionality and self-disclosure affected a person's perception of a potential partner. An e-mail with strong emotional words (e.g., excited, wonderful) led to more positive impressions than an e-mail with fewer strong emotional words (e.g., happy, fine) and resulted in nearly three out of four subjects selecting the e-mailer with strong emotional words for the fictitious dater of the opposite sex. Results for self-disclosure e-mails were complex, but indicate that levels of self-disclosure led to different impressions. Low levels of self-disclosure were generally preferred in choosing for the fictitious dater, although these preferences differed by gender, education, and ethnic background. Results were discussed in terms of theories of computer-mediated communication.


title: Perturbation theory for Markov chains via Wasserstein distance

abstract: Perturbation theory for Markov chains addresses the question how small differences in the transitions of Markov chains are reflected in differences between their distributions. We prove powerful and flexible bounds on the distance of the $n$th step distributions of two Markov chains when one of them satisfies a Wasserstein ergodicity condition. Our work is motivated by the recent interest in approximate Markov chain Monte Carlo (MCMC) methods in the analysis of big data sets. By using an approach based on Lyapunov functions, we provide estimates for geometrically ergodic Markov chains under weak assumptions. In an autoregressive model, our bounds cannot be improved in general. We illustrate our theory by showing quantitative estimates for approximate versions of two prominent MCMC algorithms, the Metropolis-Hastings and stochastic Langevin algorithms.


title: Random projection-based multiplicative data perturbation for privacy preserving distributed data mining

abstract: This paper explores the possibility of using multiplicative random projection matrices for privacy preserving distributed data mining. It specifically considers the problem of computing statistical aggregates like the inner product matrix, correlation coefficient matrix, and Euclidean distance matrix from distributed privacy sensitive data possibly owned by multiple parties. This class of problems is directly related to many other data-mining problems such as clustering, principal component analysis, and classification. This paper makes primary contributions on two different grounds. First, it explores independent component analysis as a possible tool for breaching privacy in deterministic multiplicative perturbation-based models such as random orthogonal transformation and random rotation. Then, it proposes an approximate random projection-based technique to improve the level of privacy protection while still preserving certain statistical characteristics of the data. The paper presents extensive theoretical analysis and experimental results. Experiments demonstrate that the proposed technique is effective and can be successfully used for different types of privacy-preserving data mining applications.


title: Machine learning in cardiovascular medicine: are we there yet?

abstract: Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine.


Measuring Policy Success: Case Studies from Canada and Australia


title: Information Uncertainty and Stock Returns

abstract: There is substantial evidence of short-term stock price continuation, which the prior literature often attributes to investor behavioral biases such as underreaction to new information. This paper investigates the role of information uncertainty in price continuation anomalies and cross-sectional variations in stock returns. If short-term price continuation is due to investor behavioral biases, we should observe greater price drift when there is greater information uncertainty. As a result, greater information uncertainty should produce relatively higher expected returns following good news and relatively lower expected returns following bad news. My evidence supports this hypothesis.


title: Motion planning for urban driving using RRT

abstract: This paper provides a detailed analysis of the motion planning subsystem for the MIT DARPA Urban Challenge vehicle. The approach is based on the Rapidly-exploring Random Trees (RRT) algorithm. The purpose of this paper is to present the numerous extensions made to the standard RRT algorithm that enable the on-line use of RRT on robotic vehicles with complex, unstable dynamics and significant drift, while preserving safety in the face of uncertainty and limited sensing. The paper includes numerous simulation and race results that clearly demonstrate the effectiveness of the planning system.


title: A deterministic single exponential time algorithm for most lattice problems based on voronoi cell computations

abstract: We give deterministic ~O(22n+o(n))-time algorithms to solve all the most important computational problems on point lattices in NP, including the Shortest Vector Problem (SVP), Closest Vector Problem (CVP), and Shortest Independent Vectors Problem (SIVP). This improves the nO(n) running time of the best previously known algorithms for CVP (Kannan, Math. Operation Research 12(3):415--440, 1987) and SIVP (Micciancio, Proc. of SODA, 2008), and gives a deterministic and asymptotically faster alternative to the 2O(n)-time (and space) randomized algorithm for SVP of (Ajtai, Kumar and Sivakumar, STOC 2001). The core of our algorithm is a new method to solve the closest vector problem with preprocessing (CVPP) that uses the Voronoi cell of the lattice (described as intersection of half-spaces) as the result of the preprocessing function. In the process, we also give algorithms for several other lattice problems, including computing the kissing number of a lattice, and computing the set of all Voronoi relevant vectors. All our algorithms are deterministic, and have 2O(n) time and space complexity.


THU_NGN at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases with Deep LSTM


title: A feature reduced intrusion detection system using ANN classifier

abstract: A Feature reduced Intrusion Detection System has been proposed.Pre-processing is done to compensate less occurring and frequent occurring attacks.Feature reduction has been done on basis of information gain and correlation.A classifier based on artificial neural network has been used.Comparison with state of art methods has been done. Rapid increase in internet and network technologies has led to considerable increase in number of attacks and intrusions. Detection and prevention of these attacks has become an important part of security. Intrusion detection system is one of the important ways to achieve high security in computer networks and used to thwart different attacks. Intrusion detection systems have curse of dimensionality which tends to increase time complexity and decrease resource utilization. As a result, it is desirable that important features of data must be analyzed by intrusion detection system to reduce dimensionality. This work proposes an intelligent system which first performs feature ranking on the basis of information gain and correlation. Feature reduction is then done by combining ranks obtained from both information gain and correlation using a novel approach to identify useful and useless features. These reduced features are then fed to a feed forward neural network for training and testing on KDD99 dataset. Pre-processing of KDD-99 dataset has been done to normalize number of instances of each class before training. The system then behaves intelligently to classify test data into attack and non-attack classes. The aim of the feature reduced system is to achieve same degree of performance as a normal system. The system is tested on five different test datasets and both individual and average results of all datasets are reported. Comparison of proposed method with and without feature reduction is done in terms of various performance metrics. Comparisons with recent and relevant approaches are also tabled. Results obtained for proposed method are really encouraging.


title: A System for Transforming the Emotion in Speech: Combining Data-Driven Conversion Techniques for Prosody and Voice Quality

abstract: This paper describes a system that combines independent transformation techniques to endow a neutral utterance with some required target emotion. The system consists of three modules that are each trained on a limited amount of speech data and act on differing temporal layers. F0 contours are modelled and generated using context-sensitive syllable HMMs, while durations are transformed using phone-based relative decision trees. For spectral conversion which is applied at the segmental level, two methods were investigated: a GMM-based voice conversion approach and a codebook selection approach. Converted test data were evaluated for three emotions using an independent emotion classifier as well as perceptual listening tests. The listening test results show that perception of sadness output by our system was comparable with the perception of human sad speech while the perception of surprise and anger was around 5% worse than that of a human speaker.


title: Optimal routing, link scheduling and power control in multihop wireless networks

abstract: In this paper, we study the problem of joint routing, link scheduling and power control to support high data rates for broadband wireless multihop networks. We first address the problem of finding an optimal link scheduling and power control policy that minimizes the total average transmission power in the wireless multihop network, subject to given constraints regarding the minimum average data rate per link, as well as peak transmission power constraints per node. Multiaccess signal interference is explicitly modeled. We use a duality approach whereby, as a byproduct of finding the optimal policy, we find the sensitivity of the minimal total average power with respect to the average data rate for each link. Since the minimal total average power is a convex function of the required minimum average data rates, shortest path algorithms with the link weights set to the link sensitivities can be used to guide the search for a globally optimum routing. We present a few simple examples that show our algorithm can find policies that support data rates that are not possible with conventional approaches. Moreover, we find that optimum allocations do not necessarily route traffic over minimum energy paths.


title: Multi‐Scale Geometric‐Semantic Modeling of Shield Tunnels for GIS and BIM Applications

abstract: The planning of large infrastructure facilities such as inner-city subway tracks requires the consideration of widely differing scales, ranging from the kilometer scale for the general routing of the track down to the centimeter scale for detailed design of connection points. On the one hand this implies the utilization of both, Geographic Information Systems (GIS) as well as Building Information Modeling (BIM) tools, for performing the required analysis, modeling, and visualization tasks. On the other hand, a sound foundation of handling multi-scale representations is required. Although multi-scale modeling is already well established in the GIS field, there are no corresponding approaches in Infrastructure BIM so far. However, multi-scale concepts are also much needed in the BIM context, as the planning process typically provides only rough information in the early stages and increasingly detailed and fine-grained information in later stages. To meet this demand, this article presents a comprehensive concept for incorporating multi-scale representations with building information models, with a particular focus on the geometric-semantic modeling of shield tunnels. Based on a detailed analysis of the data modeling methods used in CityGML and the requirements present in the context of infrastructure planning projects, we discuss potential extensions to the BIM data model Industry Foundation Classes (IFC) for incorporating multi-scale representations of shield tunnels. Particular emphasis is put on providing means for preserving the consistency of the representation across the different Levels-of-Detail (LoDs), while taking into account both semantics and geometry. For realizing consistency preservation mechanisms, we propose to apply a procedural geometry description which makes it possible to define explicit dependencies between geometric entities on different LoDs. The modification of an object on a coarse level consequently results in an automated update of all dependent objects on the finer levels. Finally, we discuss the transformation of the IFC-based multi-scale tunnel model into a CityGML compliant tunnel representation.


title: Android Malware Detection & Protection: A Survey

abstract: Android has become the most popular smartphone operating system. This rapidly increasing adoption of Android has resulted in significant increase in the number of malwares when compared with previous years. There exist lots of antimalware programs which are designed to effectively protect the users’ sensitive data in mobile systems from such attacks. In this paper, our contribution is twofold. Firstly, we have analyzed the Android malwares and their penetration techniques used for attacking the systems and antivirus programs that act against malwares to protect Android systems. We categorize many of the most recent antimalware techniques on the basis of their detection methods. We aim to provide an easy and concise view of the malware detection and protection mechanisms and deduce their benefits and limitations. Secondly, we have forecast Android market trends for the year up to 2018 and provide a unique hybrid security solution and take into account both the static and dynamic analysis an android application.


title: Bidirectional Joint Representation Learning with Symmetrical Deep Neural Networks for Multimodal and Crossmodal Applications

abstract: Common approaches to problems involving multiple modalities (classification, retrieval, hyperlinking, etc.) are early fusion of the initial modalities and crossmodal translation from one modality to the other. Recently, deep neural networks, especially deep autoencoders, have proven promising both for crossmodal translation and for early fusion via multimodal embedding. In this work, we propose a flexible crossmodal deep neural network architecture for multimodal and crossmodal representation. By tying the weights of two deep neural networks, symmetry is enforced in central hidden layers thus yielding a multimodal representation space common to the two original representation spaces. The proposed architecture is evaluated in multimodal query expansion and multimodal retrieval tasks within the context of video hyperlinking. Our method demonstrates improved crossmodal translation capabilities and produces a multimodal embedding that significantly outperforms multimodal embeddings obtained by deep autoencoders, resulting in an absolute increase of 14.14 in precision at 10 on a video hyperlinking task.


title: Secure nearest neighbor revisited

abstract: In this paper, we investigate the secure nearest neighbor (SNN) problem, in which a client issues an encrypted query point E(q) to a cloud service provider and asks for an encrypted data point in E(D) (the encrypted database) that is closest to the query point, without allowing the server to learn the plaintexts of the data or the query (and its result). We show that efficient attacks exist for existing SNN methods [21], [15], even though they were claimed to be secure in standard security models (such as indistinguishability under chosen plaintext or ciphertext attacks). We also establish a relationship between the SNN problem and the order-preserving encryption (OPE) problem from the cryptography field [6], [5], and we show that SNN is at least as hard as OPE. Since it is impossible to construct secure OPE schemes in standard security models [6], [5], our results imply that one cannot expect to find the exact (encrypted) nearest neighbor based on only E(q) and E(D). Given this hardness result, we design new SNN methods by asking the server, given only E(q) and E(D), to return a relevant (encrypted) partition E(G) from E(D) (i.e., G ⊆ D), such that that E(G) is guaranteed to contain the answer for the SNN query. Our methods provide customizable tradeoff between efficiency and communication cost, and they are as secure as the encryption scheme E used to encrypt the query and the database, where E can be any well-established encryption schemes.


title: Automatic Detection and Classification of Colorectal Polyps by Transferring Low-Level CNN Features From Nonmedical Domain

abstract: Colorectal cancer (CRC) is a leading cause of cancer deaths worldwide. Although polypectomy at early stage reduces CRC incidence, 90% of the polyps are small and diminutive, where removal of them poses risks to patients that may outweigh the benefits. Correctly detecting and predicting polyp type during colonoscopy allows endoscopists to resect and discard the tissue without submitting it for histology, saving time, and costs. Nevertheless, human visual observation of early stage polyps varies. Therefore, this paper aims at developing a fully automatic algorithm to detect and classify hyperplastic and adenomatous colorectal polyps. Adenomatous polyps should be removed, whereas distal diminutive hyperplastic polyps are considered clinically insignificant and may be left in situ . A novel transfer learning application is proposed utilizing features learned from big nonmedical datasets with 1.4-2.5 million images using deep convolutional neural network. The endoscopic images we collected for experiment were taken under random lighting conditions, zooming and optical magnification, including 1104 endoscopic nonpolyp images taken under both white-light and narrowband imaging (NBI) endoscopy and 826 NBI endoscopic polyp images, of which 263 images were hyperplasia and 563 were adenoma as confirmed by histology. The proposed method identified polyp images from nonpolyp images in the beginning followed by predicting the polyp histology. When compared with visual inspection by endoscopists, the results of this study show that the proposed method has similar precision (87.3% versus 86.4%) but a higher recall rate (87.6% versus 77.0%) and a higher accuracy (85.9% versus 74.3%). In conclusion, automatic algorithms can assist endoscopists in identifying polyps that are adenomatous but have been incorrectly judged as hyperplasia and, therefore, enable timely resection of these polyps at an early stage before they develop into invasive cancer.


title: Geometry-Based Symbolic Approximation for Fast Sequence Matching on Manifolds

abstract: In this paper, we consider the problem of fast and efficient indexing techniques for sequences evolving in non-Euclidean spaces. This problem has several applications in the areas of human activity analysis, where there is a need to perform fast search, and recognition in very high dimensional spaces. The problem is made more challenging when representations such as landmarks, contours, and human skeletons etc. are naturally studied in a non-Euclidean setting where even simple operations are much more computationally intensive than their Euclidean counterparts. We propose a geometry and data adaptive symbolic framework that is shown to enable the deployment of fast and accurate algorithms for activity recognition, dynamic texture recognition, motif discovery. Toward this end, we present generalizations of key concepts of piece-wise aggregation and symbolic approximation for the case of non-Euclidean manifolds. We show that one can replace expensive geodesic computations with much faster symbolic computations with little loss of accuracy in activity recognition and discovery applications. The framework is general enough to work across both Euclidean and non-Euclidean spaces, depending on appropriate feature representations without compromising on the ultra-low bandwidth, high speed and high accuracy. The proposed methods are ideally suited for real-time systems and low complexity scenarios.


title: One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL

abstract: Humans are experts at high-fidelity imitation -- closely mimicking a demonstration, often in one attempt. Humans use this ability to quickly solve a task instance, and to bootstrap learning of new tasks. Achieving these abilities in autonomous agents is an open problem. In this paper, we introduce an off-policy RL algorithm (MetaMimic) to narrow this gap. MetaMimic can learn both (i) policies for high-fidelity one-shot imitation of diverse novel skills, and (ii) policies that enable the agent to solve tasks more efficiently than the demonstrators. MetaMimic relies on the principle of storing all experiences in a memory and replaying these to learn massive deep neural network policies by off-policy RL. This paper introduces, to the best of our knowledge, the largest existing neural networks for deep RL and shows that larger networks with normalization are needed to achieve one-shot high-fidelity imitation on a challenging manipulation task. The results also show that both types of policy can be learned from vision, in spite of the task rewards being sparse, and without access to demonstrator actions.


title: Protecting Voice Controlled Systems Using Sound Source Identification Based on Acoustic Cues

abstract: Over the last few years, a rapidly increasing number of Internet-of-Things (IoT) systems that adopt voice as the primary user input have emerged. These systems have been shown to be vulnerable to various types of voice spoofing attacks. Existing defense techniques can usually only protect from a specific type of attack or require an additional authentication step that involves another device. Such defense strategies are either not strong enough or lower the usability of the system. Based on the fact that legitimate voice commands should only come from humans rather than a playback device, we propose a novel defense strategy that is able to detect the sound source of a voice command based on its acoustic features. The proposed defense strategy does not require any information other than the voice command itself and can protect a system from multiple types of spoofing attacks. Our proof-of-concept experiments verify the feasibility and effectiveness of this defense strategy.


title: A Blended Deep Learning Approach for Predicting User Intended Actions

abstract: User intended actions are widely seen in many areas. Forecasting these actions and taking proactive measures to optimize business outcome is a crucial step towards sustaining the steady business growth. In this work, we focus on predicting attrition, which is one of typical user intended actions. Conventional attrition predictive modeling strategies suffer a few inherent drawbacks. To overcome these limitations, we propose a novel end-to-end learning scheme to keep track of the evolution of attrition patterns for the predictive modeling. It integrates user activity logs, dynamic and static user profiles based on multi-path learning. It exploits historical user records by establishing a decaying multi-snapshot technique. And finally it employs the precedent user intentions via guiding them to the subsequent learning procedure. As a result, it addresses all disadvantages of conventional methods. We evaluate our methodology on two public data repositories and one private user usage dataset provided by Adobe Creative Cloud. The extensive experiments demonstrate that it can offer the appealing performance in comparison with several existing approaches as rated by different popular metrics. Furthermore, we introduce an advanced interpretation and visualization strategy to effectively characterize the periodicity of user activity logs. It can help to pinpoint important factors that are critical to user attrition and retention and thus suggests actionable improvement targets for business practice. Our work will provide useful insights into the prediction and elucidation of other user intended actions as well.


title: The Runtime System Problem Identification Method Based on Log Analysis

abstract: Currently system logs are an important source of information for system administrators to monitor system behaviors and to identify system problems. The manual examining is infeasible for the complex system and the existing automated methods for identifying system problems have different disadvantages such as the extreme dependency on the source code of the system, the low accuracy of predicting or identifying the system problems, or the requirement of the balanced and labeled training data set. This paper proposes a one- class Support Vector Machine (OCSVM) based method to identify the runtime system problems. Firstly, log sequences are generated for describing the running trajectories of the monitored system by parsing log messages; Secondly, variable length n-gram features are extracted, and moreover, the log sequences are represented as feature vectors based on these variable length n-gram features and Vector Space Model (VSM). Finally, all the feature vectors of the training log sequence set, which only includes the labeled normal log sequences, are input into OCSVM. Experimental results show that it performs better to use linear kernel to train OCSVM on our feature vectors than Gaussian kernel and the size of the sliding window hardly affects the performance of our method. Moreover, the proposed method achieves better performance on unbalanced training dataset than the method based on Random Indexing (RI) and weighted SVM.


A Sokoban Solver Using Multiple Search Algorithms and Q-learning


title: Deep Learning for Sensor-based Activity Recognition: A Survey

abstract: Sensor-based activity recognition seeks the profound high-level knowledge about human activity from multitudes of low-level sensor readings. Conventional pattern recognition approaches have made tremendous progress in the past years. However, most of those approaches heavily rely on heuristic hand-crafted feature extraction methods, which dramatically hinder their generalization performance. Additionally, those methods often produce unsatisfactory results for unsupervised and incremental learning tasks. Meanwhile, the recent advancement of deep learning makes it possible to perform automatic high-level feature extraction thus achieves promising performance in many areas. Since then, deep learning based methods have been widely adopted for the sensor-based activity recognition tasks. In this paper, we survey and highlight the recent advancement of deep learning approaches for sensor-based activity recognition. Specifically, we summarize existing literatures from three aspects: sensor modality, deep model and application. We also present a detailed discussion and propose grand challenges for future direction.


title: Ecosystem services in decision making: time to deliver

abstract: Over the past decade, efforts to value and protect ecosystem services have been promoted by many as the last, best hope for making conservation mainstream – attractive and commonplace worldwide. In theory, if we can help individuals and institutions to recognize the value of nature, then this should greatly increase investments in conservation, while at the same time fostering human well-being. In practice, however, we have not yet developed the scientific basis, nor the policy and finance mechanisms, for incorporating natural capital into resource- and land-use decisions on a large scale. Here, we propose a conceptual framework and sketch out a strategic plan for delivering on the promise of ecosystem services, drawing on emerging examples from Hawai‘i. We describe key advances in the science and practice of accounting for natural capital in the decisions of individuals, communities, corporations, and governments.


title: Internet Addiction Disorder: An Italian Study

abstract: The Italian version of the Young's Internet Addiction Test (IAT) was administered online to a sample of Italian chatters (n = 236) who were different in terms of gender, age, and occupation. Results revealed that young users are more at-risk subjects for Internet addiction than adults, perceiving a compromised social and individual quality of their life that led them to make a compensatory usage of the Internet. Similarly, employed users perceive their social and individual quality of life as more compromised by the Internet than students. Moreover, subjects who declared spending much time online obtained IAT scores higher than others in all the IAT subscales. Finally, nightly users are more at-risk subjects for developing an Internet addiction disorder, diminishing their individual quality of life and disabling their time control.


title: Intercalation and Push‐Out Process with Spinel‐to‐Rocksalt Transition on Mg Insertion into Spinel Oxides in Magnesium Batteries

abstract: On the basis of the similarity between spinel and rocksalt structures, it is shown that some spinel oxides (e.g., MgCo2O4, etc) can be cathode materials for Mg rechargeable batteries around 150 °C. The Mg insertion into spinel lattices occurs via “intercalation and push-out” process to form a rocksalt phase in the spinel mother phase. For example, by utilizing the valence change from Co(III) to Co(II) in MgCo2O4, Mg insertion occurs at a considerably high potential of about 2.9 V vs. Mg2+/Mg, and similarly it occurs around 2.3 V vs. Mg2+/Mg with the valence change from Mn(III) to Mn(II) in MgMn2O4, being comparable to the ab initio calculation. The feasibility of Mg insertion would depend on the phase stability of the counterpart rocksalt XO of MgO in Mg2X2O4 or MgX3O4 (X = Co, Fe, Mn, and Cr). In addition, the normal spinel MgMn2O4 and MgCr2O4 can be demagnesiated to some extent owing to the robust host structure of Mg1−xX2O4, where the Mg extraction/insertion potentials for MgMn2O4 and MgCr2O4 are both about 3.4 V vs. Mg2+/Mg. Especially, the former “intercalation and push-out” process would provide a safe and stable design of cathode materials for polyvalent cations.


title: A design of phased array antenna with metamaterial circular SRR for 5G applications

abstract: In this paper, a design of Phased array antenna based on Metamaterial Circular Split Ring Resonator CSRR for 5G application is presented. This antenna is printed on a epoxy FR4 substrate with a thickness of 0.8 mm and a relative permittivity of 4.3. The proposed antenna size is 13∗52.87mm and it is designed and simulated using CST Software. The return loss of the propsed antenna is indeed below −10 dB in the frequency 28 GHz. The use of CSRR onto the antenna will improve the directivity and the gain.


title: Voltage doubler application in isolated resonant converters

abstract: Two basic conditions of voltage-doubler adopted in isolated DC/DC converter are analyzed. According to the basic conditions, that voltage-doubler can be both adopted in LC series resonant converter and LLC multi-resonant converter. Operation characteristic of voltage-doubler adopted in isolated DC/DC converter is described in detail based on the analysis of voltage-doubler rectifying LLC multi-resonant converter. Advantages of voltage-doubler rectifying LLC multi-resonant converter are described in detail as follows, structure of transformer, in which one winding is involved on its secondary side, is simple, voltage stress of output capacitor is half of output and extra voltage balancing circuit is excluded. Two rectifier diodes are included on output end, voltage and current stress of diodes respectively equal output voltage and current. Therefore, the topology is a preferable candidate for such application as low-medium power level DC/DC converter with high output voltage. In addition, principle of automatic voltage balancing for output capacitor is presented, moreover design of key parameter is specified as well. Presented characteristics of the very topology are verified by a prototype, whose output is 500 V and efficiency of full load reaches 92.3%.


title: Generalized vulnerability extrapolation using abstract syntax trees

abstract: The discovery of vulnerabilities in source code is a key for securing computer systems. While specific types of security flaws can be identified automatically, in the general case the process of finding vulnerabilities cannot be automated and vulnerabilities are mainly discovered by manual analysis. In this paper, we propose a method for assisting a security analyst during auditing of source code. Our method proceeds by extracting abstract syntax trees from the code and determining structural patterns in these trees, such that each function in the code can be described as a mixture of these patterns. This representation enables us to decompose a known vulnerability and extrapolate it to a code base, such that functions potentially suffering from the same flaw can be suggested to the analyst. We evaluate our method on the source code of four popular open-source projects: LibTIFF, FFmpeg, Pidgin and Asterisk. For three of these projects, we are able to identify zero-day vulnerabilities by inspecting only a small fraction of the code bases.


title: An efficient multi-rate LDPC-CC decoder with layered decoding algorithm

abstract: An efficient multi-rate Low-Density Parity-Check Convolutional Code decoder will be present in this paper. We will introduce layered decoding algorithm into LDPC-CC decoding. Simulation results shows that our method can achieve better performance than the original brief propagation algorithm with less processors. Besides a new ASIC architecture which adopt proposed algorithm and can support all code rate (1/2, 2/3, 3/4, 4/5) of the LDPC-CC code in IEEE 1901 is proposed. Based on SMIC 130 nm CMOS process, our decoder attaints a maximum throughput of 333.3 Mb/s at 200 MHz. The core area is 3.55 mm2 with 10 processors. The average power consumption is 262 mW at code rate 4/5 and 200 MHz. The VLSI result shows that our decoder is both memory efficient and area efficient.


Interactive Phone Call: Exploring Interactions during Phone Calls using Projector Phones.


title: Nearest prototype classifier designs: an experimental study

abstract: We compare eleven methods for finding prototypes upon which to base the nearest prototype classifier. Four methods for prototype selection are discussed: Wilson+Hart (a condensation+error-editing method), and three types of combinatorial search—random search, genetic algorithm, and tabu search. Seven methods for prototype extraction are discussed: unsupervised vector quantization, supervised learning vector quantization (with and without training counters), decision surface mapping, a fuzzy version of vector quantization, c-means clustering, and bootstrap editing. These eleven methods can be usefully divided two other ways: by whether they employ pre- or postsupervision; and by whether the number of prototypes found is user-defined or “automatic.” Generalization error rates of the 11 methods are estimated on two synthetic and two real data sets. Offering the usual disclaimer that these are just a limited set of experiments, we feel confident in asserting that presupervised, extraction methods offer a better chance for success to the casual user than postsupervised, selection schemes. Finally, our calculations do not suggest that methods which find the “best” number of prototypes “automatically” are superior to methods for which the user simply specifies the number of prototypes. © 2001 John Wiley & Sons, Inc.


title: Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression

abstract: Electric load forecasting is an important issue for power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR), this paper presents a SVR model hybridized with the differential empirical mode decomposition (DEMD) method and auto regression (AR) for electric load forecasting. The differential EMD method is used to decompose the electric load into several detail parts associated with high frequencies (intrinsic mode function (IMF)) and an approximate part associated with low frequencies. The electric load data from the New South Wales (NSW, Australia) market and the New York Independent System Operator (NYISO, USA) are employed for comparing the forecasting performances of different alternative models. The results illustrate the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.


title: Review: survey of directly mapping sql databases to the semantic web

abstract: The Semantic Web anticipates integrated access to a large number of information sources on the Internet represented as Resource Description Framework (RDF). Given the large number of websites that are backed by SQL databases, methods that automate the translation of those databases to RDF are crucial. One approach, taken by a number of researchers, is to directly map the SQL schema to an equivalent Web Ontology Language (OWL) or RDF Schema representation, which in turn, implies an RDF representation for the relational data. This paper reviews this research, and derives a consolidated, overarching set of translation rules expressible as a stratified Datalog program. We present all the possible key combinations in an SQL schema and consider their implied semantic properties. We review the approaches and characterize them with respect to the scope of their coverage of SQL constructs.


title: Adaptive Robust Online Constructive Fuzzy Control of a Complex Surface Vehicle System

abstract: In this paper, a novel adaptive robust online constructive fuzzy control (AR-OCFC) scheme, employing an online constructive fuzzy approximator (OCFA), to deal with tracking surface vehicles with uncertainties and unknown disturbances is proposed. Significant contributions of this paper are as follows: 1) unlike previous self-organizing fuzzy neural networks, the OCFA employs decoupled distance measure to dynamically allocate discriminable and sparse fuzzy sets in each dimension and is able to parsimoniously self-construct high interpretable T-S fuzzy rules; 2) an OCFA-based dominant adaptive controller (DAC) is designed by employing the improved projection-based adaptive laws derived from the Lyapunov synthesis which can guarantee reasonable fuzzy partitions; 3) closed-loop system stability and robustness are ensured by stable cancelation and decoupled adaptive compensation, respectively, thereby contributing to an auxiliary robust controller (ARC); and 4) global asymptotic closed-loop system can be guaranteed by AR-OCFC consisting of DAC and ARC and all signals are bounded. Simulation studies and comprehensive comparisons with state-of-the-arts fixed- and dynamic-structure adaptive control schemes demonstrate superior performance of the AR-OCFC in terms of tracking and approximation accuracy.


title: Combining local appearance and holistic view: Dual-Source Deep Neural Networks for human pose estimation

abstract: We propose a new learning-based method for estimating 2D human pose from a single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN). Recently, many methods have been developed to estimate human pose by using pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective. In this paper, we propose to integrate both the local (body) part appearance and the holistic view of each local part for more accurate human pose estimation. Specifically, the proposed DS-CNN takes a set of image patches (category-independent object proposals for training and multi-scale sliding windows for testing) as the input and then learns the appearance of each local part by considering their holistic views in the full body. Using DS-CNN, we achieve both joint detection, which determines whether an image patch contains a body joint, and joint localization, which finds the exact location of the joint in the image patch. Finally, we develop an algorithm to combine these joint detection/localization results from all the image patches for estimating the human pose. The experimental results show the effectiveness of the proposed method by comparing to the state-of-the-art human-pose estimation methods based on pose priors that are estimated from physiologically inspired graphical models or learned from a holistic perspective.


title: Product Characterisation towards Personalisation: Learning Attributes from Unstructured Data to Recommend Fashion Products

abstract: We describe a solution to tackle a common set of challenges in e-commerce, which arise from the fact that new products are continually being added to the catalogue. The challenges involve properly personalising the customer experience, forecasting demand and planning the product range. We argue that the foundational piece to solve all of these problems is having consistent and detailed information about each product, which is rarely available or consistent given the multitude of suppliers and types of products. We describe in detail the architecture and methodology implemented at ASOS, one of the world's largest fashion e-commerce retailers, to tackle this problem. We then show how this quantitative understanding of the products can be leveraged to improve recommendations in a hybrid recommender system approach.


title: Generating Conceptual Metaphors from Proposition Stores

abstract: Contemporary research on computational processing of linguistic metaphors is divided into two main branches: metaphor recognition and metaphor interpretation. We take a different line of research and present an automated method for generating conceptual metaphors from linguistic data. Given the generated conceptual metaphors, we find corresponding linguistic metaphors in corpora. In this paper, we describe our approach and its evaluation using English and Russian data.


title: Automatically Generating Government Linked Data from Tables

abstract: Most open government data is encoded and published in structured tables found in reports, on the Web, and in spreadsheets or databases. Current approaches to generating Semantic Web representations from such data requires human input to create schemas and often results in graphs that do not follow best practices for linked data. Evidence for a table’s meaning can be found in its column headers, cell values, implicit relations between columns, caption and surrounding text but also requires general and domain-specific background knowledge. We describe techniques grounded in graphical models and probabilistic reasoning to infer meaning (semantics) associated with a table using background knowledge from the Linked Open Data cloud. We represent a table’s meaning by mapping columns to classes in an appropriate ontology, linking cell values to literal constants, implied measurements, or entities in the linked data cloud (existing or new) and discovering or and identifying relations between columns.


title: D.: Category independent object proposals

abstract: We propose a category-independent method to produce a bag of regions and rank them, such that top-ranked regions are likely to be good segmentations of different objects. Our key objectives are completeness and diversity: every object should have at least one good proposed region, and a diverse set should be top-ranked. Our approach is to generate a set of segmentations by performing graph cuts based on a seed region and a learned affinity function. Then, the regions are ranked using structured learning based on various cues. Our experiments on BSDS and PASCAL VOC 2008 demonstrate our ability to find most objects within a small bag of proposed regions.


title: Revisiting the VLAD image representation

abstract: Recent works on image retrieval have proposed to index images by compact representations encoding powerful local descriptors, such as the closely related VLAD and Fisher vector. By combining such a representation with a suitable coding technique, it is possible to encode an image in a few dozen bytes while achieving excellent retrieval results. This paper revisits some assumptions proposed in this context regarding the handling of "visual burstiness", and shows that ad-hoc choices are implicitly done which are not desirable. Focusing on VLAD without loss of generality, we propose to modify several steps of the original design. Albeit simple, these modifications significantly improve VLAD and make it compare favorably against the state of the art.


title: Model construction and verification of a BLDC motor using MATLAB/SIMULINK and FPGA control

abstract: This paper presents the model construction of a brushless DC motor via MATLAB/SIMULINK and FPGA control, so that one can evaluate the performance of the BLDC motor control with various control schemes using MATLAB/SIMULINK. The constructed model consists of a BLDC motor dynamics block, a Hall-sensor signals generation block, a back-emf block, and a PWM-generation logic block. In order to evaluate the model, various cases of simulation studies are carried out. The control and PWM-generation logic block then can be transferred to digital hardware circuit in VHDL hardware description language for co-simulation verification in the MATLAB/SIMULINK and ModelSim environment. Experiment results by using FPGA control are also obtained to verify the correctness of the constructed BLDC model.


title: Partial Occlusion Handling for Visual Tracking via Robust Part Matching

abstract: Part-based visual tracking is advantageous due to its robustness against partial occlusion. However, how to effectively exploit the confidence scores of individual parts to construct a robust tracker is still a challenging problem. In this paper, we address this problem by simultaneously matching parts in each of multiple frames, which is realized by a locality-constrained low-rank sparse learning method that establishes multi-frame part correspondences through optimization of partial permutation matrices. The proposed part matching tracker (PMT) has a number of attractive properties. (1) It exploits the spatial-temporal localityconstrained property for robust part matching. (2) It matches local parts from multiple frames jointly by considering their low-rank and sparse structure information, which can effectively handle part appearance variations due to occlusion or noise. (3) The proposed PMT model has the inbuilt mechanism of leveraging multi-mode target templates, so that the dilemma of template updating when encountering occlusion in tracking can be better handled. This contrasts with existing methods that only do part matching between a pair of frames. We evaluate PMT and compare with 10 popular state-of-the-art methods on challenging benchmarks. Experimental results show that PMT consistently outperform these existing trackers.


title: Volunteerism Tendency Prediction via Harvesting Multiple Social Networks

abstract: Volunteers have always been extremely crucial and in urgent need for nonprofit organizations (NPOs) to sustain their continuing operations. However, it is expensive and time-consuming to recruit volunteers using traditional approaches. In the Web 2.0 era, abundant and ubiquitous social media data opens a door to the possibility of automatic volunteer identification. In this article, we aim to fully explore this possibility by proposing a scheme that is able to predict users’ volunteerism tendency from user-generated contents collected from multiple social networks based on a conceptual volunteering decision model. We conducted comprehensive experiments to investigate the effectiveness of our proposed scheme and further discussed its generalizibility and extendability. This novel interdisciplinary research will potentially inspire more promising and important human-centered applications.