Query: Health management and pattern analysis of daily living activities of people with dementia using in-home sensors and machine learning techniques

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A Survey on Ambient-Assisted Living Tools for Older Adults <=SEP=> In recent years, we have witnessed a rapid surge in assisted living technologies due to a rapidly aging society. The aging population, the increasing cost of formal health care, the caregiver burden, and the importance that the individuals place on living independently, all motivate development of innovative-assisted living technologies for safe and independent aging. In this survey, we will summarize the emergence of `ambient-assisted living” (AAL) tools for older adults based on ambient intelligence paradigm. We will summarize the state-of-the-art AAL technologies, tools, and techniques, and we will look at current and future challenges.

Behavioral Patterns of Older Adults in Assisted Living <=SEP=> In this paper, we examine at-home activity rhythms and present a dozen of behavioral patterns obtained from an activity monitoring pilot study of 22 residents in an assisted living setting with four case studies. Established behavioral patterns have been captured using custom software based on a statistical predictive algorithm that models circadian activity rhythms (CARs) and their deviations. The CAR was statistically estimated based on the average amount of time a resident spent in each room within their assisted living apartment, and also on the activity level given by the average n.umber of motion events per room. A validated in-home monitoring system (IMS) recorded the monitored resident's movement data and established the occupancy period and activity level for each room. Using these data, residents' circadian behaviors were extracted, deviations indicating anomalies were detected, and the latter were correlated to activity reports generated by the IMS as well as notes of the facility's professional caregivers on the monitored residents. The system could be used to detect deviations in activity patterns and to warn caregivers of such deviations, which could reflect changes in health status, thus providing caregivers with the opportunity to apply standard of care diagnostics and to intervene in a timely manner.

Clustering of time series data - a survey <=SEP=> Time series clustering has been shown effective in providing useful information in various domains. There seems to be an increased interest in time series clustering as part of the effort in temporal data mining research. To provide an overview, this paper surveys and summarizes previous works that investigated the clustering of time series data in various application domains. The basics of time series clustering are presented, including general-purpose clustering algorithms commonly used in time series clustering studies, the criteria for evaluating the performance of the clustering results, and the measures to determine the similarity/dissimilarity between two time series being compared, either in the forms of raw data, extracted features, or some model parameters. The past researchs are organized into three groups depending upon whether they work directly with the raw data either in the time or frequency domain, indirectly with features extracted from the raw data, or indirectly with models built from the raw data. The uniqueness and limitation of previous research are discussed and several possible topics for future research are identified. Moreover, the areas that time series clustering have been applied to are also summarized, including the sources of data used. It is hoped that this review will serve as the steppingstone for those interested in advancing this area of research. 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Activity Recognition in the Home Using Simple and Ubiquitous Sensors <=SEP=> In this work, a system for recognizing activities in the home setting using a set of small and simple state-change sensors is introduced. The sensors are designed to be “tape on and forget” devices that can be quickly and ubiquitously installed in home environments. The proposed sensing system presents an alternative to sensors that are sometimes perceived as invasive, such as cameras and microphones. Unlike prior work, the system has been deployed in multiple residential environments with non-researcher occupants. Preliminary results on a small dataset show that it is possible to recognize activities of interest to medical professionals such as toileting, bathing, and grooming with detection accuracies ranging from 25% to 89% depending on the evaluation criteria used .

Miniature Ultra-Wideband Power Divider Using Bridged T-Coils <=SEP=> In this work, a miniature ultra-wideband (UWB) power divider (PD) is proposed. By implementing the transmission lines of a two-stage Wilkinson PD using bridged T-coils, very compact size with no reduction in bandwidth can be achieved. Specifically, a proposed UWB two-way PD with a center frequency f<sub>0</sub>=5.5 GHz is implemented using the commercial GaAs pHEMT process. The circuit size without pads is only 1.45 mm × 0.84 mm, which is about 0.027λ<sub>0</sub> × 0.016λ<sub>0</sub> at f<sub>0</sub>. The fractional bandwidth for 15-dB input/output return loss and isolation is 110%, and the in-band insertion loss is within 1.3 ± 0.36 dB.

Using Syntactic Features for Phishing Detection <=SEP=> This paper reports on the comparison of the subject and object of verbs in their usage between phishing emails and legitimate emails. The purpose of this research is to explore whether the syntactic structures and subjects and objects of verbs can be distinguishable features for phishing detection. To achieve the objective, we have conducted two series of experiments: the syntactic similarity for sentences, and the subject and object of verb comparison. The results of the experiments indicated that both features can be used for some verbs, but more work has to be done for others. Keywords—phishing detection; syntactic similarity; parse tree path.

LTE-advanced in 3GPP Rel -13/14: an evolution toward 5G <=SEP=> As the fourth generation (4G) LTE-Advanced network becomes a commercial success, technologies for beyond 4G and 5G are being actively investigated from the research perspective as well as from the standardization perspective. While 5G will integrate the latest technology breakthroughs to achieve the best possible performance, it is expected that LTE-Advanced will continue to evolve, as a part of 5G technologies, in a backward compatible manner to maximize the benefit from the massive economies of scale established around the 3rd Generation Partnership Project (3GPP) LTE/LTE-Advanced ecosystem from Release 8 to Release 12. In this article we introduce a set of key technologies expected for 3GPP Release 13 and 14 with a focus on air interface aspects, as part of the continued evolution of LTE-Advanced and as a bridge from 4G to 5G.

Inferring the meaning of chord sequences via lyrics <=SEP=> This paper discusses how meanings associated with chord sequences can be inferred from word associations based on lyrics. The approach works by analyzing in-line chord annotations of lyrics to maintain co-occurrence statistics for chords and lyrics. This is analogous to the way parallel corpora are analyzed in order to infer translation lexicons. The result can benefit musical discovery systems by modeling how the chord structure complements the lyrics.

Search-Oriented Conversational AI (SCAI) <=SEP=> The aim of SCAI@ICTIR2017 is to bring together IR and AI communities to instigate future direction of search-oriented conversational systems. We identified the number of research areas related to conversational AI which is of actual interest to both communities and which have not been fully explored yet. We think it's beneficial to exchange our visions. We solicit the paper submissions and more importantly proposals for panel discussions where researchers can exchange opinions and experiences. We believe that the proposed workshop is relevant to ICTIR since we look for novel contributions to search-oriented conversational systems which are a new and promising area.

A Hybrid Word-Character Model for Abstractive Summarization <=SEP=> Automatic abstractive text summarization is an important and challenging research topic of natural language processing. Among many widely used languages, the Chinese language has a special property that a Chinese character contains rich information comparable to a word. Existing Chinese text summarization methods, either adopt totally character-based or word-based representations, fail to fully exploit the information carried by both representations. To accurately capture the essence of articles, we propose a hybrid word-character approach (HWC) which preserves the advantages of both wordbased and character-based representations. We evaluate the advantage of the proposed HWC approach by applying it to two existing methods, and discover that it generates state-of-the-art performance with a margin of 24 ROUGE points on a widely used dataset LCSTS. In addition, we find an issue contained in the LCSTS dataset and offer a script to remove overlapping pairs (a summary and a short text) to create a clean dataset for the community. The proposed HWC approach also generates the best performance on the new, clean LCSTS dataset.

Fabrication of extended-release patient-tailored prednisolone tablets via fused deposition modelling (FDM) 3D printing. <=SEP=> Rapid and reliable tailoring of the dose of controlled release tablets to suit an individual patient is a major challenge for personalized medicine. The aim of this work was to investigate the feasibility of using a fused deposition modelling (FDM) based 3D printer to fabricate extended release tablet using prednisolone loaded poly(vinyl alcohol) (PVA) filaments and to control its dose. Prednisolone was loaded into a PVA-based (1.75 mm) filament at approximately 1.9% w/w via incubation in a saturated methanolic solution of prednisolone. The physical form of the drug was assessed using differential scanning calorimetry (DSC) and X-ray powder diffraction (XRPD). Dose accuracy and in vitro drug release patterns were assessed using HPLC and pH change flow-through dissolution test. Prednisolone loaded PVA filament demonstrated an ability to be fabricated into regular ellipse-shaped solid tablets using the FDM-based 3D printer. It was possible to control the mass of printed tablet through manipulating the volume of the design (R(2) = 0.9983). On printing tablets with target drug contents of 2, 3, 4, 5, 7.5 and 10mg, a good correlation between target and achieved dose was obtained (R(2) = 0.9904) with a dose accuracy range of 88.7-107%. Thermal analysis and XRPD indicated that the majority of prednisolone existed in amorphous form within the tablets. In vitro drug release from 3D printed tablets was extended up to 24h. FDM based 3D printing is a promising method to produce and control the dose of extended release tablets, providing a highly adjustable, affordable, minimally sized, digitally controlled platform for producing patient-tailored medicines.

Leave-one-out error and stability of learning algorithms with applications <=SEP=> The leave-one-out error is an important statistical estimator of the performance of a learning algorithm. Unlike the empirical error, it is almost unbiased and is frequently used for model selection. We review attempts aiming at justifying the use of leave-one-out error in Machine Learning. We especially focus on the concept of stability of a learning algorithm and show how this can be used to formally link leaveone-out error to the generalization error. Stability has also motivated recent work on averaging techniques similar to bagging which we briefly summarize in the paper. The ideas we develop are illustrated in some details in the context of kernel-based learning algorithms.

Distortion correction formulas for pre-objective dual galvanometer laser scanning. <=SEP=> For many applications a laser beam needs to be scanned across a plane. Several methods exist to accomplish this.One method, dual galvanometer pre-objective scanning, is particularly well suited to precise laser marking applications. The laser beam impinges on a first mirror mounted on a galvanometer motor, then on a second mirror with its rota­ tion axis parallel to the laser axis, and passes then through the flat-field objective lens, which focuses the beam onto the image plane. Figure 1 shows a photograph of the SILAMATIK laser marker. This letter investigates what amount of distortion is intro­ duced and how it can be corrected electronically. We as­ sume that the lens is axial symmetric. The geometry is that of Fig. 2. Say the first mirror is rotated a mechanical angle a/2 from its rest position and the second mirror an angle 6/2. The direction vector lR of the beam that falls on the lens is

The CMU Pose, Illumination, and Expression Database <=SEP=> In the Fall of 2000 we collected a database of over 40,000 facial images of 68 people. Using the CMU 3D Room we imaged each person across 13 different poses, under 43 different illumination conditions, and with 4 different expressions. We call this the CMU Pose, Illumination, and Expression (PIE) database. We describe the imaging hardware, the collection procedure, the organization of the images, several possible uses, and how to obtain the database.

Scenario-Based Performance Analysis of Routing Protocols for Mobile ad-hoc Networks <=SEP=> This study is a comparison of three routing protocols proposed for wireless mobile ad-hoc networks. The protocols are: Destination Sequenced Distance Vector (DSDV), Ad-hoc On demand Distance Vector (AODV) and Dynamic Source Routing (DSR). Extensive simulations are made on a scenario where nodes moves randomly. Results are presented as a function of a novel mobility metric designed to reflect the relative speeds of the nodes in a scenario. Furthermore, three realistic scenarios are introduced to test the protocols in more specialized contexts. In most simulations the reactive protocols (AODV and DSR) performed significantly better than DSDV. At moderate traffic load DSR performed better than AODV for all tested mobility values, while AODV performed better than DSR at higher traffic loads. The latter is caused by the source routes in DSR data packets, which increase the load on the network. routers and hosts, thus a node may forward packets between other nodes as well as run user applications. Mobile ad-hoc networks have been the focus of many recent research and development efforts. Ad-hoc packet radio networks have so far mainly concerned military applications, where a decentralized network configuration is an operative advantage or even a necessity. Networks using ad-hoc configuration concepts can be used in many military applications, ranging from interconnected wireless access points to networks of wireless devices carried by individuals, e.g., digital maps, sensors attached to the body, voice communication, etc. Combinations of wide range and short range ad-hoc networks seek to provide robust, global coverage, even during adverse operating conditions.

From big smartphone data to worldwide research: The Mobile Data Challenge <=SEP=> This paper presents an overview of the Mobile Data Challenge (MDC), a large-scale research initiative aimed at generating innovations around smartphone-based research, as well as community-based evaluation of mobile data analysis methodologies. First, we review the Lausanne Data Collection Campaign (LDCC) – an initiative to collect unique, longitudinal smartphone data set for the MDC. Then, we introduce the Open and Dedicated Tracks of the MDC; describe the specific data sets used in each of them; discuss the key design and implementation aspects introduced in order to generate privacypreserving and scientifically relevant mobile data resources for wider use by the research community; and summarize the main research trends found among the 100+ challenge submissions. We finalize by discussing the main lessons learned from the participation of several hundred researchers worldwide in the MDC Tracks.

Improving recommendation lists through topic diversification <=SEP=> In this work we present topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests. Though being detrimental to average accuracy, we show that our method improves user satisfaction with recommendation lists, in particular for lists generated using the common item-based collaborative filtering algorithm.Our work builds upon prior research on recommender systems, looking at properties of recommendation lists as entities in their own right rather than specifically focusing on the accuracy of individual recommendations. We introduce the intra-list similarity metric to assess the topical diversity of recommendation lists and the topic diversification approach for decreasing the intra-list similarity. We evaluate our method using book recommendation data, including offline analysis on 361, !, 349 ratings and an online study involving more than 2, !, 100 subjects.

Healthy Menus Recommendation: Optimizing the Use of the Pantry <=SEP=> We are often unable to plan menus ahead, thus making poor and unhealthy choices of meals. Besides healthy, one may want menus in which ingredients harmonize and cover well the available ingredients in the pantry. In this paper, we propose a novel multi-objective-based recommender of menus that features an optimal balance between nutritional aspects, harmony and coverage of available ingredients. We conduct experiments on real-world and synthetic datasets and show that our approach achieves the desired levels of nutrients, harmonization and coverage of ingredients.

Learning a convolutional neural network for non-uniform motion blur removal <=SEP=> In this paper, we address the problem of estimating and removing non-uniform motion blur from a single blurry image. We propose a deep learning approach to predicting the probabilistic distribution of motion blur at the patch level using a convolutional neural network (CNN). We further extend the candidate set of motion kernels predicted by the CNN using carefully designed image rotations. A Markov random field model is then used to infer a dense non-uniform motion blur field enforcing motion smoothness. Finally, motion blur is removed by a non-uniform deblurring model using patch-level image prior. Experimental evaluations show that our approach can effectively estimate and remove complex non-uniform motion blur that is not handled well by previous approaches.

A Simple Compact Reconfigurable Slot Antenna With a Very Wide Tuning Range <=SEP=> A simple and compact slot antenna with a very wide tuning range is proposed. A 25 mm (roughly equal to λH/8, where λH corresponds to the highest frequency of the tuning range) open slot is etched at the edge of the ground. To achieve the tunability, only two lumped elements, namely, a PIN diode and a varactor diode are used in the structure. By switching the PIN diode placed at the open end of the slot, the slot antenna can resonate as a standard slot (when the switch is on) or a half slot (when the switch is off). Continuous tuning over a wide frequency range in those two modes can be achieved by adjusting the reverse bias (giving different capacitances) of the varactor diode loaded in the slot. Through optimal design, the tuning bands of the two modes are stitched together to form a very wide tuning range. The fabricated prototype has a tuning frequency range from 0.42 GHz to 1.48 GHz with Sll better than -10 dB, giving a frequency ratio (fR = fu/fL) of 3.52:1. The measured full spherical radiation patterns show consistent radiation characteristics of the proposed antenna within the whole tuning range.

Cellular and synaptic mechanisms of nicotine addiction. <=SEP=> The tragic health effects of nicotine addiction highlight the importance of investigating the cellular mechanisms of this complex behavioral phenomenon. The chain of cause and effect of nicotine addiction starts with the interaction of this tobacco alkaloid with nicotinic acetylcholine receptors (nAChRs). This interaction leads to activation of reward centers in the CNS, including the mesoaccumbens DA system, which ultimately leads to behavioral reinforcement and addiction. Recent findings from a number of laboratories have provided new insights into the biologic processes that contribute to nicotine self-administration. Examination of the nAChR subtypes expressed within the reward centers has identified potential roles for these receptors in normal physiology, as well as the effects of nicotine exposure. The high nicotine sensitivity of some nAChR subtypes leads to rapid activation followed in many cases by rapid desensitization. Assessing the relative importance of these molecular phenomena in the behavioral effects of nicotine presents an exciting challenge for future research efforts.

A survey of point-based POMDP solvers <=SEP=> The past decade has seen a significant breakthrough in research on solving partially observable Markov decision processes (POMDPs). Where past solvers could not scale beyond perhaps a dozen states, modern solvers can handle complex domains with many thousands of states. This breakthrough was mainly due to the idea of restricting value function computations to a finite subset of the belief space, permitting only local value updates for this subset. This approach, known as point-based value iteration, avoids the exponential growth of the value function, and is thus applicable for domains with longer horizons, even with relatively large state spaces. Many extensions were suggested to this basic idea, focusing on various aspects of the algorithm—mainly the selection of the belief space subset, and the order of value function updates. In this survey, we walk the reader through the fundamentals of point-based value iteration, explaining the main concepts and ideas. Then, we survey the major extensions to the basic algorithm, discussing their merits. Finally, we include an extensive empirical analysis using well known benchmarks, in order to shed light on the strengths and limitations of the various approaches.

Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps <=SEP=> 

Smartphone Addiction in University Students and Its Implication for Learning <=SEP=> 

Design and Implementation of a Wearable Gas Sensor Network for Oil and Gas Industry Workers <=SEP=> Industrial environment usually involves some types of hazardous substances including toxic and/or flammable gases. Accidental gas leakage can cause potential dangers to a plant, its employees and surrounding neighborhoods. Around 64% of accidents that happen in the oil fields are due to combustibles and/or toxic gases. The safety plan of most industries includes measures to reduce risk to humans and plants by incorporating early-warning devices, such as gas detectors. Most existing tools for monitoring gases are stationary and incapable of accurately measuring individual exposures that depend on personal lifestyles and environment. This paper provides a design and implementation of a wearable gas sensor network by building sensor nodes with wireless communication modules which communicate their data along the network. The system is designed to be flexible, low cost, low maintenance and with accurate performance to detect toxic gases in a timely fashion to warn employees before an existence of a disaster.

Estimating relatedness via data compression <=SEP=> We show that it is possible to use data compression on independently obtained hypotheses from various tasks to algorithmically provide guarantees that the tasks are sufficiently related to benefit from multitask learning. We give uniform bounds in terms of the empirical average error for the true average error of the n hypotheses provided by deterministic learning algorithms drawing independent samples from a set of n unknown computable task distributions over finite sets.

Sharing Personal Content Online: Exploring Channel Choice and Multi-Channel Behaviors <=SEP=> People share personal content online with varied audiences, as part of tasks ranging from conversational-style content sharing to collaborative activities. We use an interview- and diary-based study to explore: 1) what factors impact channel choice for sharing with particular audiences; and 2) what behavioral patterns emerge from the ability to combine or switch between channels. We find that in the context of different tasks, participants match channel features to selective-sharing and other task-based needs, shaped by recipient attributes and communication dynamics. Participants also combine multiple channels to create composite sharing features or reach broader audiences when one channel is insufficient. We discuss design implications of these channel dynamics.

Comparing Task Models for User Interface Design <=SEP=> Many task models, task analysis methods, and supporting tools have been introduced in the literature and are widely used in practice. With this comes need to understand their scopes and their differences. This chapter provides a thorough review of selected, significant task models along with their method and supporting tools. For this purpose, a meta-model of each task model is expressed as an Entity-Relationship-Attribute schema (ERA) and discussed. This leads to a comparative analysis of task models according to aims and goals, discipline, concepts and relationships, expressiveness of static and dynamic structures. Following is discussion of the model with respect to developing life cycle steps, tool support, advantages, and shortcomings. This comparative analysis provides a reference framework against which task models can be understood with respect to each other. The appreciation of the similarities and the differences allows practioners to identify a task model that fits a situation’s given requirements. It shows how a similar concept or relationship translates in different task usage models.

Regression from patch-kernel <=SEP=> In this paper, we present a patch-based regression framework for addressing the human age and head pose estimation problems. Firstly, each image is encoded as an ensemble of orderless coordinate patches, the global distribution of which is described by Gaussian mixture models (GMM), and then each image is further expressed as a specific distribution model by Maximum a Posteriori adaptation from the global GMM. Then the patch-kernel is designed for characterizing the Kullback-Leibler divergence between the derived models for any two images, and its discriminating power is further enhanced by a weak learning process, called inter-modality similarity synchronization. Finally, kernel regression is employed for ultimate human age or head pose estimation. These three stages are complementary to each other, and jointly minimize the regression error. The effectiveness of this regression framework is validated by three experiments: 1) on the YAMAHA aging database, our solution brings a more than 50% reduction in age estimation error compared with the best reported results; 2) on the FG-NET aging database, our solution based on raw image features performs even better than the state-of-the-art algorithms which require fine face alignment for extracting warped appearance features; and 3) on the CHIL head pose database, our solution significantly outperforms the best one reported in the CLEAR07 evaluation.