Query: Classify Sentence from Multiple Perspectives with Category Expert Attention Network

Relevant passages:
A Convolutional Neural Network for Modelling Sentences <=SEP=> The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test the DCNN in four experiments: small scale binary and multi-class sentiment prediction, six-way question classification and Twitter sentiment prediction by distant supervision. The network achieves excellent performance in the first three tasks and a greater than 25% error reduction in the last task with respect to the strongest baseline.

Glove: Global Vectors for Word Representation <=SEP=> Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition.

Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales <=SEP=> We address therating-inference problem, wherein rather than simply decide whether a review is “thumbs up” or “thumbs down”, as in previous sentiment analysis work, one must determine an author’s evaluation with respect to a multi-point scale (e.g., one to five “stars”). This task represents an interesting twist on standard multi-class text categorization because there are several different degrees of similarity between class labels; for example, “three stars” is intuitively closer to “four stars” than to “one star”. We first evaluate human performance at the task. Then, we apply a metaalgorithm, based on a metric labelingformulation of the problem, that alters a given n-ary classifier’s output in an explicit attempt to ensure that similar items receive similar labels. We show that the meta-algorithm can provide significant improvements over both multi-class and regression versions of SVMs when we employ a novel similarity measure appropriate to the problem. Publication info: Proceedings of the ACL, 2005.

Convolutional Neural Networks for Sentence Classification <=SEP=> We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. We show that a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks. Learning task-specific vectors through fine-tuning offers further gains in performance. We additionally propose a simple modification to the architecture to allow for the use of both task-specific and static vectors. The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification.

Learning Distributed Representations of Sentences from Unlabelled Data <=SEP=> Unsupervised methods for learning distributed representations of words are ubiquitous in today’s NLP research, but far less is known about the best ways to learn distributed phrase or sentence representations from unlabelled data. This paper is a systematic comparison of models that learn such representations. We find that the optimal approach depends critically on the intended application. Deeper, more complex models are preferable for representations to be used in supervised systems, but shallow log-bilinear models work best for building representation spaces that can be decoded with simple spatial distance metrics. We also propose two new unsupervised representation-learning objectives designed to optimise the trade-off between training time, domain portability and performance.

Number : AUTCON-D16-00058 R 1 Title : BIM for Infrastructure : An Overall Review and Constructor Perspective <=SEP=> 6 The subject of Building Information Modelling (BIM) has become a central topic to the improvement of the AECOO 7 (Architecture, Engineering, Construction, Owner and Operator) industry around the world, to the point where the 8 concept is being expanded into domains it was not originally conceived to address. Transitioning BIM into the 9 domain of infrastructure projects has provided challenges and emphasized the constructor perspective of BIM. 10 Therefore, this study aims to collect the relevant literature regarding BIM within the Infrastructure domain and its 11 use from the constructor perspective to review and analyse the current industry positioning and research state of 12 the art, with regards to the set criteria. The review highlighted a developing base of BIM for infrastructure. From 13 the analysis, the related research gaps were identified regarding information integration, alignment of BIM 14 processes to constructor business processes & the effective governance and value of information. From this a 15 unique research strategy utilising a framework for information governance coupled with a graph based distributed 16 data environment is outlined to further progress the integration and efficiency of AECOO Infrastructure projects. 17

Wristband-Type Driver Vigilance Monitoring System Using Smartwatch <=SEP=> Studies have presented that the driver vigilance level has serious implication in the causation of road accidents. This paper focuses on integrating both the vehicle-based control behavior and physiological state to predict the driver vigilance index which is evaluated by using a smartwatch. The vehicle control behavior can be observed from the steering wheel movement. Our study utilized the smartwatch motion sensors to study the steering wheel behavior. Meanwhile, physiological state of driver reflects the driver capability of safety alert driving which is estimated by photoplethysmogram (PPG) and respiration signals in this paper. The PPG sensor is integrated in a sport wristband with a Bluetooth low energy module, transmitted the PPG signals to smartwatch in real time. The steering angle is derived by the reading from smartwatch built-in accelerometer and gyroscope sensors. On the other hand, the respiration is derived using the PPG peak baseline method. In order to utterly investigate the sleepiness-induced factors, the time, spectral, and phase space domain features are calculated. Considering the smartwatch processing capability, mutual-information technique is applied to designate the ten most descriptive features. Then, the extracted descriptive features are serve as parameters to a classifier to determine the driver aptitude status. The features are analyzed for their correlation with the subjective Koralinska sleepiness scale and through recorded video observations. The experimental results reveal that our system is capable of estimating driver hypervigilance at average of 96.5% accuracy rate by evaluating on both driving behavior and driver physiological state, provided a novel and low-cost implementation.

A Framework for Detecting MAC and IP Spoofing Attacks with Network Characteristics <=SEP=> This paper presents a spoofing attack detection framework based on physical network characteristics (e.g., received signal strength indicator round trip time and link quality indicator) that cannot easily be mimicked by artificial means. Unlike most previous studies that are sensitive to changes in network conditions, we propose a spoofing attack detection method, which is highly robust to the changes of network conditions over time. The proposed framework can monitor devices' physical network characteristics in real time and check if any significant changes have been made in the monitored measurements to effectively detect device spoofing attacks. To demonstrate the feasibility of the proposed framework, we analyzed how the RSSI values of packets were changed with varying physical distances in a real ZigBee (IEEE 802.15.4) network environment.

Summarization from medical documents: a survey <=SEP=> OBJECTIVE
The aim of this paper is to survey the recent work in medical documents summarization.


BACKGROUND
During the last decade, documents summarization got increasing attention by the AI research community. More recently it also attracted the interest of the medical research community as well, due to the enormous growth of information that is available to the physicians and researchers in medicine, through the large and growing number of published journals, conference proceedings, medical sites and portals on the World Wide Web, electronic medical records, etc.


METHODOLOGY
This survey gives first a general background on documents summarization, presenting the factors that summarization depends upon, discussing evaluation issues and describing briefly the various types of summarization techniques. It then examines the characteristics of the medical domain through the different types of medical documents. Finally, it presents and discusses the summarization techniques used so far in the medical domain, referring to the corresponding systems and their characteristics.


DISCUSSION AND CONCLUSIONS
The paper discusses thoroughly the promising paths for future research in medical documents summarization. It mainly focuses on the issue of scaling to large collections of documents in various languages and from different media, on personalization issues, on portability to new sub-domains, and on the integration of summarization technology in practical applications.

TrustOTP: Transforming Smartphones into Secure One-Time Password Tokens <=SEP=> Two-factor authentication has been widely used due to the vulnerabilities associated with traditional text-based password. One-time password (OTP) plays an indispensable role on authenticating mobile users to critical web services that demand a high level of security. As the smartphones are increasingly gaining popularity nowadays, software-based OTP generators have been developed and installed into smartphones as software apps, which bring great convenience to the users without introducing extra burden. However, software-based OTP solutions cannot guarantee the confidentiality of the generated passwords or even the seeds when the mobile OS is compromised. Moreover, they also suffer from denial-of-service attacks when the mobile OS crashes. Hardware-based OTP tokens can solve these security problems in the software-based OTP solutions; however, it is inconvenient for the users to carry physical tokens with them, particularly, when there are more than one token to be carried. In this paper, we present TrustOTP, a secure one-time password solution that can achieve both the flexibility of software tokens and the security of hardware tokens by using ARM TrustZone technology. TrustOTP can not only protect the confidentiality of the OTPs against a malicious mobile OS, but also guarantee reliable OTP generation and trusted OTP display when the mobile OS is compromised or even crashes. It is flexible to integrate multiple OTP algorithms and instances for different application scenarios on the same smartphone platform without modifying the mobile OS. We develop a prototype of TrustOTP on Freescale i.MX53 QSB. The experimental results show that TrustOTP has small impacts on the mobile OS and its power consumption is low.

Keyword Search in Spatial Databases: Towards Searching by Document <=SEP=> This work addresses a novel spatial keyword query called the m-closest keywords (mCK) query. Given a database of spatial objects, each tuple is associated with some descriptive information represented in the form of keywords. The mCK query aims to find the spatially closest tuples which match m user-specified keywords. Given a set of keywords from a document, mCK query can be very useful in geotagging the document by comparing the keywords to other geotagged documents in a database. To answer mCK queries efficiently, we introduce a new index called the bR*-tree, which is an extension of the R*-tree. Based on bR*-tree, we exploit a priori-based search strategies to effectively reduce the search space. We also propose two monotone constraints, namely the distance mutex and keyword mutex, as our a priori properties to facilitate effective pruning. Our performance study demonstrates that our search strategy is indeed efficient in reducing query response time and demonstrates remarkable scalability in terms of the number of query keywords which is essential for our main application of searching by document.

Free-form shape design using triangulated surfaces <=SEP=> We present an approach to modeling with truly mutable yet completely controllable free-form surfaces of arbitrary topology. Surfaces may be pinned down at points and along curves, cut up and smoothly welded back together, and faired and reshaped in the large. This style of control is formulated as a constrained shape optimization, with minimization of squared principal curvatures yielding graceful shapes that are free of the parameterization worries accompanying many patch-based approaches. Triangulated point sets are used to approximate these smooth variational surfaces, bridging the gap between patch-based and particle-based representations. Automatic refinement, mesh smoothing, and re-triangulation maintain a good computational mesh as the surface shape evolves, and give sample points and surface features much of the freedom to slide around in the surface that oriented particles enjoy. The resulting surface triangulations are constructed and maintained in real time.

Imitation and Mechanisms of Joint Attention : A Developmental Structure for Building Social Skills on a Humanoid Robot <=SEP=> Adults are extremely adept at recognizing social cues, such as eye direction or pointing gestures, that establish the basis of joint attention. These skills serve as the developmental basis for more complex forms of metaphor and analogy by allowing an infant to ground shared experiences and by assisting in the development of more complex communication skills. In this chapter, we review some of the evidence for the developmental course of these joint attention skills from developmental psychology, from disorders of social development such as autism, and from the evolutionary development of these social skills. We also describe an on-going research program aimed at testing existing models of joint attention development by building a human-like robot which communicates naturally with humans using joint attention. Our group has constructed an upper-torso humanoid robot, called Cog, in part to investigate how to build intelligent robotic systems by following a developmental progression of skills similar to that observed in human development. Just as a child learns social skills and conventions through interactions with its parents, our robot will learn to interact with people using natural social communication. We further consider the critical role that imitation plays in bootstrapping a system from simple visual behaviors to more complex social skills. We will present data from a face and eye finding system that serves as the basis of this developmental chain, and an example of how this system can imitate the head movements of an individual.

Suicide prevention strategies: a systematic review. <=SEP=> CONTEXT
In 2002, an estimated 877,000 lives were lost worldwide through suicide. Some developed nations have implemented national suicide prevention plans. Although these plans generally propose multiple interventions, their effectiveness is rarely evaluated.


OBJECTIVES
To examine evidence for the effectiveness of specific suicide-preventive interventions and to make recommendations for future prevention programs and research.


DATA SOURCES AND STUDY SELECTION
Relevant publications were identified via electronic searches of MEDLINE, the Cochrane Library, and PsychINFO databases using multiple search terms related to suicide prevention. Studies, published between 1966 and June 2005, included those that evaluated preventative interventions in major domains; education and awareness for the general public and for professionals; screening tools for at-risk individuals; treatment of psychiatric disorders; restricting access to lethal means; and responsible media reporting of suicide.


DATA EXTRACTION
Data were extracted on primary outcomes of interest: suicidal behavior (completion, attempt, ideation), intermediary or secondary outcomes (treatment seeking, identification of at-risk individuals, antidepressant prescription/use rates, referrals), or both. Experts from 15 countries reviewed all studies. Included articles were those that reported on completed and attempted suicide and suicidal ideation; or, where applicable, intermediate outcomes, including help-seeking behavior, identification of at-risk individuals, entry into treatment, and antidepressant prescription rates. We included 3 major types of studies for which the research question was clearly defined: systematic reviews and meta-analyses (n = 10); quantitative studies, either randomized controlled trials (n = 18) or cohort studies (n = 24); and ecological, or population- based studies (n = 41). Heterogeneity of study populations and methodology did not permit formal meta-analysis; thus, a narrative synthesis is presented.


DATA SYNTHESIS
Education of physicians and restricting access to lethal means were found to prevent suicide. Other methods including public education, screening programs, and media education need more testing.


CONCLUSIONS
Physician education in depression recognition and treatment and restricting access to lethal methods reduce suicide rates. Other interventions need more evidence of efficacy. Ascertaining which components of suicide prevention programs are effective in reducing rates of suicide and suicide attempt is essential in order to optimize use of limited resources.

A Solution to Separation and Multicollinearity in Multiple Logistic Regression. <=SEP=> In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27-38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth's penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study.

Feature selection via sensitivity analysis of SVM probabilistic outputs <=SEP=> Feature selection is an important aspect of solving data-mining and machine-learning problems. This paper proposes a feature-selection method for the Support Vector Machine (SVM) learning. Like most feature-selection methods, the proposed method ranks all features in decreasing order of importance so that more relevant features can be identified. It uses a novel criterion based on the probabilistic outputs of SVM. This criterion, termed Feature-based Sensitivity of Posterior Probabilities (FSPP), evaluates the importance of a specific feature by computing the aggregate value, over the feature space, of the absolute difference of the probabilistic outputs of SVM with and without the feature. The exact form of this criterion is not easily computable and approximation is needed. Four approximations, FSPP1-FSPP4, are proposed for this purpose. The first two approximations evaluate the criterion by randomly permuting the values of the feature among samples of the training data. They differ in their choices of the mapping function from standard SVM output to its probabilistic output: FSPP1 uses a simple threshold function while FSPP2 uses a sigmoid function. The second two directly approximate the criterion but differ in the smoothness assumptions of criterion with respect to the features. The performance of these approximations, used in an overall feature-selection scheme, is then evaluated on various artificial problems and real-world problems, including datasets from the recent Neural Information Processing Systems (NIPS) feature selection competition. FSPP1-3 show good performance consistently with FSPP2 being the best overall by a slight margin. The performance of FSPP2 is competitive with some of the best performing feature-selection methods in the literature on the datasets that we have tested. Its associated computations are modest and hence it is suitable as a feature-selection method for SVM applications.

The Traveling Salesman Problem: A Survey <=SEP=> Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Improved algorithm for mining maximum frequent patterns based on FP-Tree <=SEP=> Mining association rule is an important matter in data mining, in which mining maximum frequent patterns is a key problem. Many of the previous algorithms mine maximum frequent patterns by producing candidate patterns firstly, then pruning. But the cost of producing candidate patterns is very high, especially when there exists long patterns. In this paper, the structure of a FP-tree is improved, we propose a fast algorithm based on FP-Tree for mining maximum frequent patterns, the algorithm does not produce maximum frequent candidate patterns and is more effectively than other improved algorithms. The new FP-Tree is a one-way tree and only retains pointers to point its father in each node, so at least one third of memory is saved. Experiment results show that the algorithm is efficient and saves memory space. Keywordsdata mining;association rule;maximum frequent pattern;FP-Tree

Comparison Research on Text Pre-processing Methods on Twitter Sentiment Analysis <=SEP=> Twitter sentiment analysis offers organizations ability to monitor public feeling towards the products and events related to them in real time. The first step of the sentiment analysis is the text pre-processing of Twitter data. Most existing researches about Twitter sentiment analysis are focused on the extraction of new sentiment features. However, to select the pre-processing method is ignored. This paper discussed the effects of text pre-processing method on sentiment classification performance in two types of classification tasks, and summed up the classification performances of six pre-processing methods using two feature models and four classifiers on five Twitter datasets. The experiments show that the accuracy and F1-measure of Twitter sentiment classification classifier are improved when using the pre-processing methods of expanding acronyms and replacing negation, but barely changes when removing URLs, removing numbers or stop words. The Naive Bayes and Random Forest classifiers are more sensitive than Logistic Regression and support vector machine classifiers when various pre-processing methods were applied.

Alzheimer's Disease and the Impact of Music Therapy: A Systematic Literature Review <=SEP=> Currently ranked as the sixth leading cause of death in the United States, Alzheimer’s disease (AD) has become the most prevalent form of dementia, a term commonly associated with memory loss and other progressive cognitive deficits that compromise patients’ lives. What may begin as a mindlessly misplaced object or momentary inability to recall newly learned information will eventually advance to a loss of personal identity, forgotten loved ones, and utter misperception of reality. Although a cure for AD has yet to be discovered, there are several nonpharmacological treatments that can improve patients’ quality of life and provide temporary relief from the disabling manifestations, one of which is music therapy, the topic of this literature review. Music can be deeply connected with emotional processing and memory recall, and, when utilized as an interventional therapy for AD patients, can yield numerous cognitive and behavioral symptomatic benefits. The purpose of this project is to conduct a systematic literature review that evaluates the therapeutic relationship between AD and music therapy with a narrowed focus on familiar music therapy, the potential mechanisms of action that explain the efficacy of this intervention, and the resulting nursing implications that may be utilized in practice. MUSIC THERAPY AND ALZHEIMER’S DISEASE 6 Methods An exhaustive literature review was conducted using the database CINAHL (EBSCO). The search terms included: “Alzheimer’s disease AND music therapy”, “Alzheimer’s AND music therapy”, “Alzheimer’s disease symptoms AND music therapy”, “Alzheimer’s disease AND music therapy AND familiar music”, and “Alzheimer’s disease AND active music therapy.” The literature search included full text articles from academic journals in the English language, including international articles. Only articles published between 2007 through 2017 were reviewed, with the exception of one relevant article from 2005. From the initial search results, 35 articles matched the preliminary criteria, and were further reviewed if they addressed: a) Alzheimer’s disease, b) elements and types of music therapy, c) outcomes of music therapy, d) scientific explanation of music therapy efficacy and, e) nursing implications. A total of 13 articles met the inclusion criteria and were included in the literature review. Six of these articles focused on familiar music therapy, and are summarized in table 1. MUSIC THERAPY AND ALZHEIMER’S DISEASE 7 Alzheimer’s Disease Alzheimer’s Disease (AD), an irreversibly progressive brain disorder, has an unrelenting course, adversely impacting every aspect of a patient’s reality, often beginning with the inability to perform the most mundane tasks and resulting in utter dependency on others for the totality of self care. However, what may be even more staggering than the relentless nature of this disease are the associated statistics, for the prevalence of new cases is growing at an alarming rate – every 66 seconds to be exact. Currently, AD is the sixth leading cause of death in the United States, affecting more than 5 million individuals; however, this number is estimated to more than triple by 2050 (Alzheimer’s Association, 2017). While this disease often progresses gradually through the three stages – mild, moderate, and severe – every patient endures the associated manifestations differently, which can make it difficult to discern which stage a person is experiencing. An individual displaying the early symptoms of AD may show signs of forgetfulness, possibly misplacing objects or failing to recall a friend’s name, but will likely remain independent through the first stage of the disease. As the patient’s condition progresses into the middle stage, personality and behavior changes will be noted, such as frustration, anger, refusing to shower, etc., as well as a greater need for assistance with daily activities. The most disabling manifestations of the disease appear in the final stage, including a loss of environmental awareness, severe communication impairments, crippling physical limitations, and the need for constant supervision and care (Alzheimer’s Association, 2017). Perhaps one of the most troubling aspects of AD lies in the inevitable imminence of death once the disease begins to unfold, despite the pharmacological options that exist. Treatment efforts for AD patient currently focus on symptom management utilizing a multidisciplinary MUSIC THERAPY AND ALZHEIMER’S DISEASE 8 approach, combining both pharmacological and non-pharmacological interventions to achieve the highest possible quality of life. While somewhat beneficial, most pharmacological treatments yield limited improvement and may result in further patient deterioration due to the adverse effects associated with antipsychotic and/or psychotropic drugs (Guetin, 2013). However, various non-pharmacological therapies for symptom management have been identified and implemented to improve patients’ quality of life, and universal research efforts vigorously continue in the pursuit of answers to better understand the disease, determine effective treatment options, and prevent its development entirely. MUSIC THERAPY AND ALZHEIMER’S DISEASE 9 Music Therapy The use of non-pharmacological strategies as a potential treatment method for cognitive and behavioral disorders has become a more prevalent occurrence in patient care plans, with a specific emphasis on music therapy as an exceptionally effective intervention for AD, as acknowledged by the French Agency for Health Accreditation and Evaluation (Narme et al., 2013). Music therapy, as defined by the World Federation of Music Therapy, is “the use of music and/or its musical elements (sound, rhythm, melody and harmony) by a qualified music therapist, with a client or group, in a process designed to facilitate and promote communication, relationships, learning, mobilization, expression, organization and other relevant therapeutic objectives in order to meet physical, emotional, mental, social and cognitive needs” (Guetin et al., 2013, p. 621). While music therapy has gained popularity in recent years as an effective tool to manage a variety of health care needs, such as procedural pain control, behavioral disorder manifestations, and physical rehabilitation, this non-pharmacological intervention has ancient roots dating as far back as the sixth century BC (Cox, Nowak, & Buettner, 2011). There is hieroglyphic evidence suggesting the Egyptians treated a variety of ailments, such as pain, depression, and sleep disorders, with melodic hymns and incantations (Guetin et al., 2013). Plato, during ancient Grecian times, dubbed musical training a form of “mental hygiene,” as well as “medicine for the soul” (Guetin et al., 2013, p. 622). The 20 century brought about the scientific discovery of the physiological effects music can produce on blood pressure and heart rate, which helps explain the earlier findings detected by dental surgeons, who noticed their patients seemed to experience decreased pain and anxiety during procedures if a phonograph was playing simultaneously (Guetin et al., 2013). MUSIC THERAPY AND ALZHEIMER’S DISEASE 10 Although primitive, these historical efforts laid the foundation to legitimize music therapy professionally. The National Association for Music Therapy, created in the U.S. during the mid-20 century, became the original representative professional organization and was shortly followed by the emergence of the American Association for Music Therapy in 1971. Since then, these two associations have been consolidated into the present-day American Music Therapy Association (AMTA), which is responsible for setting the educational requirements for aspiring music therapists. In order to become Music Therapist-Board Certified (MT-BC) in the United States, these individuals must receive a bachelor’s, master’s, or doctoral degree from an accredited program, complete a clinical internship, and partake in 1200 clinical training hours before sitting for the board certification examination (Ahn & Ashida, 2012). In order to maintain the quality of services provided by the music therapy profession, the AMTA also established professional competencies, standards of clinical practice, and a code of ethics. The standards of clinical practice outline the general order in which music therapy services are delivered: 1) referral and acceptance, 2) assessment, 3) treatment planning, 4) implementation, 5) documentation, and 6) termination (American Music Therapy Association, 2015). MUSIC THERAPY AND ALZHEIMER’S DISEASE 11 Active vs. Receptive Music Therapy Although the exact physiological mechanisms responsible for the efficacy of music therapy on Alzheimer’s disease are not yet fully understood, the behavioral, cognitive, and physical improvements that have been noted during both therapy sessions and research studies are undeniably significant. The degree of improvement following musical intervention is primarily influenced by music therapists’ understanding of how best to utilize the distinct elements within music, which is further based on their knowledge of how each aspect will impact a patient’s cognitive and behavioral status (Guetin et al., 2009). The broader concept of music therapy encompasses two fundamental methods, active and receptive. Active music therapy requires the patient to be physically engaged, such as through the use of sound-producing objects, singing, dance-like movement, or playing instruments, for example. (Guetin et al., 2013). Receptive music therapy, considered a “controlled method for listening to music,” is comprised of specifically selected songs or live music played for the patient, with song choice based on individually meaningful elements, such as generation, culture, or personal history (Guetin et al., 2009). Both active and receptive music therapy can be used to target specific AD symptoms, such as memory loss, language deficits, depression, anx

Automatic recognition of unified parkinson's disease rating from speech with acoustic, i-vector and phonotactic features <=SEP=> Parkinson’s Disease is a neurodegenerative disease affecting millions of people globally, most of whom present difficulties producing speech sounds. In this paper, we describe a system to identify the degree to which a person suffers from the disease. We use a number of automatic phone recognition-based features and we augment these with i-vector features and utterancelevel acoustic aggregations. On the Interspeech 2015 ComParE challenge corpus, we find that these features allow for prediction well above the challenge baseline, particularly under crossvalidation evaluation.

High-dimensional signature compression for large-scale image classification <=SEP=> We address image classification on a large-scale, i.e. when a large number of images and classes are involved. First, we study classification accuracy as a function of the image signature dimensionality and the training set size. We show experimentally that the larger the training set, the higher the impact of the dimensionality on the accuracy. In other words, high-dimensional signatures are important to obtain state-of-the-art results on large datasets. Second, we tackle the problem of data compression on very large signatures (on the order of 105 dimensions) using two lossy compression strategies: a dimensionality reduction technique known as the hash kernel and an encoding technique based on product quantizers. We explain how the gain in storage can be traded against a loss in accuracy and/or an increase in CPU cost. We report results on two large databases — ImageNet and a dataset of lM Flickr images — showing that we can reduce the storage of our signatures by a factor 64 to 128 with little loss in accuracy. Integrating the decompression in the classifier learning yields an efficient and scalable training algorithm. On ILSVRC2010 we report a 74.3% accuracy at top-5, which corresponds to a 2.5% absolute improvement with respect to the state-of-the-art. On a subset of 10K classes of ImageNet we report a top-1 accuracy of 16.7%, a relative improvement of 160% with respect to the state-of-the-art.

Using Smart Sensor Strings for Continuous Monitoring of Temperature Stratification in Large Water Bodies <=SEP=> A "smart" thermistor string for continuous long-term temperature profiling in large water bodies is described allowing highly matched yet low-cost spatial and temporal temperature measurements. The sensor uses the three-wire SDI-12 communications standard to enable a low-powered radio or data logger on supporting buoys to command measurements and retrieve high-resolution temperature data in digital form. Each "smart" temperature sensor integrates a thermistor element, measurement circuitry, power control, calibration coefficient storage, temperature computation, and data communications. Multiple addressable sensors at discrete vertical depths are deployed along a three-wire cable that provides power and allows data transfer at regular intervals. Circuit, manufacturing, and automated calibration techniques allow temperature measurements with a resolution of plusmn0.003degC, and with intersensor matching of plusmn0.006degC. The low cost of each sensor is achieved by using poor tolerance thermistor and circuit components in conjunction with a 15-bit charge-balance analog-to-digital converter. Sensor inaccuracies and temperature coefficients are corrected by a two-point calibration procedure made possible by a standard-curve generator within the sensor, based upon the method of finite differences. This two-point calibration process allows in-field sensor string calibration in stratified water bodies and provides a means to correct for long-term calibration drift without having to return the string to a laboratory

Animating fluid sediment mixture in particle-laden flows <=SEP=> In this paper, we present a mixed explicit and semi-implicit Material Point Method for simulating particle-laden flows. We develop a Multigrid Preconditioned fluid solver for the Locally Averaged Navier Stokes equation. This is discretized purely on a semi-staggered standard MPM grid. Sedimentation is modeled with the Drucker-Prager elastoplasticity flow rule, enhanced by a novel particle density estimation method for converting particles between representations of either continuum or discrete points. Fluid and sediment are two-way coupled through a momentum exchange force that can be easily resolved with two MPM background grids. We present various results to demonstrate the efficacy of our method.

Lightweight Memory Tracing <=SEP=> Dynamic binary instrumentation is a technique that consists of translating the binary code of executables on-the-fly, including all the loaded libraries. The original binary code is instrumented in the process of translation. Applications of this technique range from sandboxes to debugging and profiling tools. In this thesis we describe lMem, a lightweight binary translation framework for the IA-32/x64 architectures. One of the challenges when writing instrumentation tools is that resources such as registers and memory have to be shared between the instrumented program and the instrumentation logic. A possible way to approach this problem is to translate a given machine code snippet in an intermediate representation (IR), add instrumentation code written in this IR and then recompile the snippet to machine code and run it. Tools such as Valgrind successfully make use of this technique. These IR-based methods however are heavyweight in the sense that a significant amount of work done by the compiler has to be replicated at runtime. IR-based recompilation increases the runtime overhead and the complexity of the binary translation framework itself. A second approach consists of saving and restoring some of the state before and after the inserted instrumentation code. This approach, which is used for example by the selDebug debugger, is simple but it suffers from overhead due to frequent saving and restoring of state. A third approach is to let the instrumentation code make use of hardware resources that are not used by the instrumented program. The minemu taint checking tool achieves good performance by exploiting the fact that some 32 bit machines lack SSE registers. This enables it to fool the application into thinking that it is running on such a machine while in reality these registers are used by the instrumentation code. lMem translates IA-32 programs to x64 and uses additional registers and memory for instrumentation. To gauge the performance of this approach we implement a simple application on top of lMem which enables to insert an arbitrary number of watchpoints. We observe that this lMem-based application has overheads of only 50% to 200% on SPEC CPU 2006 benchmarks.

An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization <=SEP=> Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a “base” learning algorithm. Breiman has pointed out that they rely for their effectiveness on the instability of the base learning algorithm. An alternative approach to generating an ensemble is to randomize the internal decisions made by the base algorithm. This general approach has been studied previously by Ali and Pazzani and by Dietterich and Kong. This paper compares the effectiveness of randomization, bagging, and boosting for improving the performance of the decision-tree algorithm C4.5. The experiments show that in situations with little or no classification noise, randomization is competitive with (and perhaps slightly superior to) bagging but not as accurate as boosting. In situations with substantial classification noise, bagging is much better than boosting, and sometimes better than randomization.

Kinematics analysis of a novel 4-dof stabilized platform <=SEP=> This paper presents a novel stabilized platform with three UPS-type and one RPU-type active limbs based on the shipboard helicopter's landing process and its kinematics are studied systematically. First, a prototype of this stabilized platform is constructed and its dof is analyzed, Second, analytic formulas for solving the inverse displacement kinematics are derived Third, analytic formulas for solving the forward displacement kinematics are derived and verified. Theoretical formulas and results provide a basis for the further optimization, control, and manufacturing.

AP16-OL7: A multilingual database for oriental languages and a language recognition baseline <=SEP=> We present the AP16-OL7 database which was released as the training and test data for the oriental language recognition (OLR) challenge on APSIPA 2016. Based on the database, a baseline system was constructed on the basis of the i-vector model. We report the baseline results evaluated in various metrics defined by the AP16-OLR evaluation plan and demonstrate that AP16-OL7 is a reasonable data resource for multilingual research.

Accurate odometry and error modelling for a mobile robot <=SEP=> This paper presents a low cost novel odometry design capable of achieving high accuracy deadreckoning. It also develops a statistical error model for estimating position and orientation errors of a mobile robot using odometry. Previous work on propagating odometry error covariance relies on incrementally updating the covariance matrix in small time steps. The approach taken here sums the noise theoretically over the entire path length to produce simple closed form expressions, allowing efficient covariance matrix updating after the completion of path segments. Closed form error covariance matrix is developed for a general circular arc and two special cases : (I) straight line and (II) turning about the centre of axle of the robot. Other paths can be composed of short segments of constant curvature arcs without great loss of accuracy. The model assumes that wheel distance measurement errors are exclusively random zero mean white noise. Systematic errors due to wheel radius and wheel base measurement were first calibrated with UMBmark [BorFen94]. Experimental results show that, despite its low cost, our system’s performance, with regard to dead-reckoning accuracy, is comparable to some of the best, award-winning vehicles around. The statistical error model, on the other hand, needs to be improved in light of new insights.

An Approximate Analysis of the LRU and FIFO Buffer Replacement Schemes <=SEP=> In this paper, we develop approximate analytical models for predicting the buffer hit probability under the Least Recently Used (LRU) and First In First Out (FIFO) buffer replacement policies under the independent reference model. In the case of the analysis of the LRU policy, the computational complexity for estimating the buffer hit probability is O(KB) where B is the size of the buffer and K denotes the number of items having distinct access probabilities. In the case of the FIFO policy, the solution algorithm is iterative and the computational complexity of each iteration is O(K). Results from these models are compared to exact results for models originally developed by King [KING71] for small values of the buffer size, B, and the total number of items sharing the buffer, D. Results are also compared with results from a simulation for large values of B and D. In most cases, the error is extremely small (less than 0.1%) for both LRU and FIFO, and a maximum error of 3% is observed for very small buffer size (less than 5) when the access probabilities are extremely skewed. To demonstrate the usefulness of the model, we consider two applications. In our first application, we compare the LRU and FIFO policies to an optimal static buffer allocation policy for a database consisting of two classes of data items. We observe that the performance of LRU is close to that of the optimal allocation. As the optimal allocation requires knowledge of the access probabilities, the LRU policy is preferred when this information is unavailable. We also observe that the LRU policy always performs better than the FIFO policy in our experiments. In our second application, we show that if multiple independent reference streams on mutually disjoint sets of data compete for the same buffer, it is better to partition the buffer using an optimal allocation policy than to share a common buffer.