title: 5G security architecture and light weight security authentication

abstract: The concept of future 5G (the 5th generation of mobile networks) widely spreads in industry, but the related standards are still unclear. The definition of 5G is to provide adequate RF coverage, more bits/Hz and to interconnect all wireless heterogeneous networks to provide seamless, consistent telecom experience to the user. In this paper we have surveyed 5G network architecture and proposed a multi-tier security architecture and a lightweight authentication measure for 5G. The proposed scheme aims at achieving fast authentication and minimizing the packet transmission overhead without compromising the security requirements.


title: i-learning IoT: An intelligent self learning system for home automation using IoT

abstract: Internet of Things (IoT) is extension of current internet to provide communication, connection, and inter-networking between various devices or physical objects also known as “Things.” In this paper we have reported an effective use of IoT for Environmental Condition Monitoring and Controlling in Homes. We also provide fault detection and correction in any devices connected to this system automatically. Home Automation is nothing but automation of regular activities inside the home. Now a day's due to huge advancement in wireless sensor network and other computation technologies, it is possible to provide flexible and low cost home automation system. However there is no any system available in market which provide home automation as well as error detection in the devices efficiently. In this system we use prediction to find out the required solution if any problem occurs in any device connected to the system. To achieve that we are applying Data Mining concept. For efficient data mining we use Naive Bayes Classifier algorithm to find out the best possible solution. This gives a huge upper hand on other available home automation system, and we actually manage to provide a real intelligent system.


title: Understanding the GPU Microarchitecture to Achieve Bare-Metal Performance Tuning

abstract: In this paper, we present a methodology to understand GPU microarchitectural features and improve performance for compute-intensive kernels. The methodology relies on a reverse engineering approach to crack the GPU ISA encodings in order to build a GPU assembler. An assembly microbenchmark suite correlates microarchitectural features with their performance factors to uncover instruction-level and memory hierarchy preferences. We use SGEMM as a running example to show the ways to achieve bare-metal performance tuning. The performance boost is achieved by tuning FFMA throughput by activating dual-issue, eliminating register bank conflicts, adding non-FFMA instructions with little penalty, and choosing proper width of global/shared load instructions. On NVIDIA Kepler K20m, we develop a faster SGEMM with 3.1Tflop/s performance and 88% efficiency; the performance is 15% higher than cuBLAS7.0. Applying these optimizations to convolution, the implementation gains 39%-62% performance improvement compared with cuDNN4.0. The toolchain is an attempt to automatically crack different GPU ISA encodings and build an assembler adaptively for the purpose of performance enhancements to applications on GPUs.


title: Self-regulation and academic achievement in the transition to school.

abstract: Understanding of children's cognitive and social development has advanced significantly over the past 3 decades. Particularly with regard to those skills critical for academic success, research has revealed that multiple interacting factors in the child, home, school, and larger sociocultural context all contribute to children's literacy, numeracy, language, and social skills, starting early in life and continuing throughout the school years (Morrison, Bachman, & Connor, 2005). One factor, self-regulation, has surfaced recently as a crucial skill that uniquely predicts children's early school success. Poor self-regulation has been linked to high rates of expulsion, most dramatically in preschool classrooms (Gilliam & Shahar, 2006). Self-regulation refers to a complex of acquired, intentional skills involved in controlling, directing, and planning one's cognitions, emotions, and behaviot (Schunk & Zimmerman, 1997). In investigations beyond the class-


title: The FinCEN Artificial Intelligence System: Identifying Potential Money Laundering from Reports

abstract: The FinCEN* Artificial Intelligence System (FAR) links and evaluates reports of large cash transactions to identify potential money laundering. The objective of FAIS is to discover previously unknown, potential high value leads for possible investigation. FAIS integrates intelligent human and software agents in a cooperative discovery task on a very large data space. It is a complex system incorporating several aspects of AI technology, including rule-based reasoning and a blackboard. FAIS consists of an underlying database (which functions as a blackboard), a graphical user interface, and several pre-processing and analysis modules. FAIS has been in operational use at FinCEN since March 1993 by a dedicated group of analysts, processing approximately 200,000 transactions per week, and during which time over 400 investigative support reports corresponding to over $1 billion in potential laundered funds have been developed. FANS unique analytical power arises primarily from a transformation of view of the underlying data from a transaction oriented perspective to a subject (i.e., person or organization) oriented perspective. according to terms of the Bank Secrecy Act (BSA)'. FinCEN has developed a system, called the FinCEN Artificial Intelligence System (FAIS), which links and evaluates all reported transactions for indications of suspicious activity characteristic of money laundering, with the objective of identifying previously unknown, potential high value leads for follow-up investigation and, if warranted, prosecution (The Wall Street Journal 1993).


Generating Clinically Relevant Texts: A Case Study on Life-Changing Events


title: Forecasting Seasonal Time Series with Neural Networks: A Sensitivity Analysis of Architecture Parameters

abstract: Neural networks are widely applied in time series forecasting. However, no consensus exists on their capability of forecasting seasonal time series. As seasonal patterns frequently occur in empirical time series, it is imperative to establish their efficacy in forecasting seasonality. This paper seeks to evaluate the usefulness of multilayer perceptrons in forecasting time series with different forms of seasonal and trend components. Using eight synthetic time series, we systematically evaluate the impact of different combinations of hidden nodes, input nodes and activation functions on the distribution of the forecasting errors. We aim to a) establish the sensitivity of different architectural choices for neural networks in forecasting and b) analyze the relative accuracy of one or multiple neural network architectures as forecasting methods for seasonal time series. Results are presented in order to guide future selection of network parameters. We find that neural networks show sensitivity to selected architecture decisions but generally provide a robust and competitive forecasting performance on seasonal data.


title: Research on Improve Topic Representation over Short Text

abstract: According to the characteristics of sparseness, poor focus and lack of semantic information in short text, the existing studies mainly improved the topic representation from two aspects: Adjusting the structure of the topic model to increase word co-occurrence and incorporating word embedding to enrich the semantic information. In this paper, we review the existing topic representation methods for short text from these two aspects, select DMM, BTM and LF-DMM as targets and compare the quality of topic representations from them. Considering the traditional topic model does not incorporate word embedding, and single short text often lacks context information due to length limitation, we try to combine it with word embedding, we use two real-world datasets to evaluate the quality of topic representation in the classification task: Although the LF-DMM model incorporates word embedding, it performs poorly on short text, and the performance of DMM and BTM integrated with word embedding improve greatly.


title: Process & design impact on BVDSS stability of a shielded gate trench power MOSFET

abstract: This paper discusses the breakdown voltage (BV DSS ) characteristics of an n-channel charge balanced shielded gate trench power MOSFET. The study emphasizes on elements that affect the BV DSS stability of such devices to gain good control on design and process/device parameters in order to produce a robust product. Breakdown voltage (BV DSS ) walk-in or walk-out can be observed when certain process (e.g., epi doping concentration) and design layout (e.g., termination) conditions are not in coherence.


Peeking Beneath the Hood of Uber Le


title: A Health Decision Support System for Disease Diagnosis Based on Wearable Medical Sensors and Machine Learning Ensembles

abstract: Even with an annual expenditure of more than $3 trillion, the U.S. healthcare system is far from optimal. For example, the third leading cause of death in the U.S. is preventable medical error, immediately after heart disease and cancer. Computer-based clinical decision support systems (CDSSs) have been proposed to address such deficiencies and have significantly improved clinical practice over the past decade. However, they remain limited to clinics and hospitals, and do not take advantage of patient data that are obtained on a daily basis using wearable medical sensors (WMSs) that have the ability to bridge this information gap. WMSs can collect physiological signals from anyone anywhere anytime. Thus, they have the potential to usher in an era of pervasive healthcare. However, most prior work on WMSs only focuses on hardware and protocol design, and not on an information system that can fully utilize the collected signals for efficient disease diagnosis. In this paper, for the first time, we introduce a hierarchical health decision support system for disease diagnosis that integrates health data from WMSs into CDSSs. The proposed system has a multi-tier structure, starting with a WMS tier, backed by robust machine learning, that enables diseases to be tracked individually by a disease diagnosis module. We demonstrate the feasibility of such a system through six disease diagnosis modules aimed at four ICD-10-CM disease categories. We show that the system is scalable using five more disease categories. Just the WMS tier offers impressive diagnostic accuracies for various diseases: arrhythmia (86 percent), type-2 diabetes (78 percent), urinary bladder disorder (99 percent), renal pelvis nephritis (94 percent), and hypothyroid (95 percent). We estimate that the disease diagnosis modules of all known 69,000 human diseases would require just 62 GB of storage space in the WMS tier. This is practical even in today’s cloud or base station oriented WMS systems.


title: A survey of technical trend of ADAS and autonomous driving

abstract: For past 10 years Advanced Driving Assistance System (ADAS) has rapidly grown. Recently not only luxury cars but some entry level cars are equipped with ADAS applications, such as Automated Emergency Braking System (AEBS). The European New Car Assessment Programme (EuroNCAP) announced its introduction of AEBS test from 2014, which will accelerate the penetration of ADAS in Europe. Also DARPA challenge started from 2004 accelerated the research for autonomous driving. Several OEMs and universities have demonstrated autonomous driving cars. This paper gives a brief survey of technical trend of ADAS and autonomous driving focusing on algorithms actually used for autonomous driving prototype cars.


title: Randomized Near Neighbor Graphs, Giant Components, and Applications in Data Science

abstract: If we pick $n$ random points uniformly in $[0,1]^d$ and connect each point to its $k-$nearest neighbors, then it is well known that there exists a giant connected component with high probability. We prove that in $[0,1]^d$ it suffices to connect every point to $ c_{d,1} \log{\log{n}}$ points chosen randomly among its $ c_{d,2} \log{n}-$nearest neighbors to ensure a giant component of size $n - o(n)$ with high probability. This construction yields a much sparser random graph with $\sim n \log\log{n}$ instead of $\sim n \log{n}$ edges that has comparable connectivity properties. This result has nontrivial implications for problems in data science where an affinity matrix is constructed: instead of picking the $k-$nearest neighbors, one can often pick $k' \ll k$ random points out of the $k-$nearest neighbors without sacrificing efficiency. This can massively simplify and accelerate computation, we illustrate this with several numerical examples.


title: RECTENNAS DESIGN, DEVELOPMENT AND APPLICATIONS

abstract: The present paper describes the development of rectenna in terms of its applications in Microwave Power Transmission, Harmonic Rejection, CP radiation and ISM band. These rectennas consist of several antennas such as dipole, antenna arrays, slot meander line and rhombic loop antennas along with the rectifying diodes. In some cases more than one rectifying devices have also been used and antenna is found to be act at dual bands. It has also found that rectenna reject the harmonics upto 3 rd order and enhanced the performance characteristics. The maximum efficiency about 91% with 1.2 W of input power has been observed if the rectenna is used for microwave transmission.


Embracing Integrative Multiomics Approaches


title: Morphological classification of brains via high-dimensional shape transformations and machine learning methods

abstract: Abstract A high-dimensional shape transformation posed in a mass-preserving framework is used as a morphological signature of a brain image. Population differences with complex spatial patterns are then determined by applying a nonlinear support vector machine (SVM) pattern classification method to the morphological signatures. Significant reduction of the dimensionality of the morphological signatures is achieved via wavelet decomposition and feature reduction methods. Applying the method to MR images with simulated atrophy shows that the method can correctly detect subtle and spatially complex atrophy, even when the simulated atrophy represents only a 5% variation from the original image. Applying this method to actual MR images shows that brains can be correctly determined to be male or female with a successful classification rate of 97%, using the leave-one-out method. This proposed method also shows a high classification rate for old adults' age classification, even under difficult test scenarios. The main characteristic of the proposed methodology is that, by applying multivariate pattern classification methods, it can detect subtle and spatially complex patterns of morphological group differences which are often not detectable by voxel-based morphometric methods, because these methods analyze morphological measurements voxel-by-voxel and do not consider the entirety of the data simultaneously.


title: Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)

abstract: Employing part-level features offers fine-grained information for pedestrian image description. A prerequisite of part discovery is that each part should be well located. Instead of using external resources like pose estimator, we consider content consistency within each part for precise part location. Specifically, we target at learning discriminative part-informed features for person retrieval and make two contributions. (i) A network named Part-based Convolutional Baseline (PCB). Given an image input, it outputs a convolutional descriptor consisting of several part-level features. With a uniform partition strategy, PCB achieves competitive results with the state-of-the-art methods, proving itself as a strong convolutional baseline for person retrieval. (ii) A refined part pooling (RPP) method. Uniform partition inevitably incurs outliers in each part, which are in fact more similar to other parts. RPP re-assigns these outliers to the parts they are closest to, resulting in refined parts with enhanced within-part consistency. Experiment confirms that RPP allows PCB to gain another round of performance boost. For instance, on the Market-1501 dataset, we achieve (77.4+4.2)% mAP and (92.3+1.5)% rank-1 accuracy, surpassing the state of the art by a large margin. Code is available at: https://github.com/syfafterzy/PCB_RPP


title: Population growth of human Y chromosomes : a study of Y chromosome microsatellites

abstract: We use variation at a set of eight human Y chromosome microsatellite loci to investigate the demographic history of the Y chromosome. Instead of assuming a population of constant size, as in most of the previous work on the Y chromosome, we consider a model which permits a period of recent population growth. We show that for most of the populations in our sample this model fits the data far better than a model with no growth. We estimate the demographic parameters of this model for each population and also the time to the most recent common ancestor. Since there is some uncertainty about the details of the microsatellite mutation process, we consider several plausible mutation schemes and estimate the variance in mutation size simultaneously with the demographic parameters of interest. Our finding of a recent common ancestor (probably in the last 120,000 years), coupled with a strong signal of demographic expansion in all populations, suggests either a recent human expansion from a small ancestral population, or natural selection acting on the Y chromosome.


title: Electrically switchable Fresnel lens using a polymer-separated composite film

abstract: A Fresnel lens with electrically-tunable diffraction efficiency while possessing high image quality is demonstrated using a phase-separated composite film (PSCOF). The light scattering-free PSCOF is obtained by anisotropic phase separation between liquid crystal and polymer. Such a lens can be operated below 12 volts and its switching time is reasonably fast (~10 ms). The maximum diffraction efficiency reaches ~35% for a linearly polarized light, which is close to the theoretical limit of 41%.


Latent Semantic Aware Multi-view Multi-label Classification


title: The Past, Present, and Future of Virtual and Augmented Reality Research: A Network and Cluster Analysis of the Literature

abstract: The recent appearance of low cost Virtual Reality (VR) technologies – like the Oculus Rift, the HTC Vive and the Sony PlayStation VR – and Mixed Reality Interfaces (MRITF) – like the Hololens – is attracting the attention of users and researchers suggesting it may be the next largest stepping stone in technological innovation. However, the history of VR technology is longer than it may seem: the concept of VR was formulated in the 1960s and the first commercial VR tools appeared in the late 1980s. For this reason, during the last twentyyears, hundreds of researchers explored the processes, effects and applications of this technology producing thousands of scientific papers. What is the outcome of this significant research work? This paper wants to provide an answer to this question by exploring, using advanced scientometric techniques, the existing research corpus in the field. We collected all the existent articles about VR in the Web of Science Core Collection scientific database, and the resultant dataset contained 21,667 records for VR and 9,944 for AR. The bibliographic record contained various fields, such as author, title, abstract, country, and all the references (needed for the citation analysis). The network and cluster analysis of the literature showed a composite panorama characterized by evolutions over the time. Indeed, whether until five years ago, the main publication media on VR concerned both conference proceeding and journals, more recently journals constitute the main medium. Similarly, if at first computer science was the leading research field, nowadays clinical areas increased, as well as the number of countries involved in virtual reality research. The present work discusses the evolution of the use of virtual reality in the main areas of application with an emphasis on the future expected virtual reality’s capacities, increases and challenges. We conclude considering the disruptive contribution that VR/AR/MRITF will be able to get in scientific fields, as well in human communication and interaction, as already happened with the advent of mobile phones by increasing the use and the development of scientific applications (e.g. in clinical areas) and by modifying the social communication and interaction among people.


title: A-RSA: Augmented RSA

abstract: Today, RSA algorithm is the most widely used public-key cryptosystem around the world. It is used for security in everything from online shopping to cell phones. However, the basic RSA is not semantically secure, i.e., encrypting the same message more than once always gives the same ciphertext. For this reason, the basic RSA is vulnerable to set of indirect attacks, such as known plaintext, chosen plaintext, timing, common modulus, and frequency of blocks (FOB) attacks. Moreover, RSA is known to be much slower than the standards symmetric key encryption and it does not used for encrypting large data. In this paper, we design and implement a swift and secure variant of RSA based on Rabin and Huffman coding called Augmented RSA (A-RSA) to solve aforementioned limitations of the basic RSA. A new additional randomization component r is added in A-RSA. This component is encrypted by Rabin algorithm to improve the security level of RSA against the indirect attacks and make RSA semantically secure. Moreover, A-RSA makes the factorization problem harder, since the attackers need to break the factorization of large numbers for both RSA and Rabin. Besides, employing Huffman Coding compression in A-RSA prevents FOB attack and speeds up the execution time for the A-RSA. Our testing results over set of file sizes of 1MB, 2MB, 3MB, to 10 MB show that A-RSA's average execution time is equal to 0.55 of the average execution time of the basic RSA in encryption process and 0.01 in decryption process. Also, we found that RSA system increases the size of ciphertext by 1% compared to the original file size, while the average size of A-RSA files is equal 0.46 of its original sizes.


title: Robust Adversarial Reinforcement Learning

abstract: Deep neural networks coupled with fast simulation and improved computation have led to recent successes in the field of reinforcement learning (RL). However, most current RL-based approaches fail to generalize since: (a) the gap between simulation and real world is so large that policy-learning approaches fail to transfer; (b) even if policy learning is done in real world, the data scarcity leads to failed generalization from training to test scenarios (e.g., due to different friction or object masses). Inspired from H-infinity control methods, we note that both modeling errors and differences in training and test scenarios can be viewed as extra forces/disturbances in the system. This paper proposes the idea of robust adversarial reinforcement learning (RARL), where we train an agent to operate in the presence of a destabilizing adversary that applies disturbance forces to the system. The jointly trained adversary is reinforced -- that is, it learns an optimal destabilization policy. We formulate the policy learning as a zero-sum, minimax objective function. Extensive experiments in multiple environments (InvertedPendulum, HalfCheetah, Swimmer, Hopper and Walker2d) conclusively demonstrate that our method (a) improves training stability; (b) is robust to differences in training/test conditions; and c) outperform the baseline even in the absence of the adversary.


title: Modeling crowd evacuation of a building based on seven methodological approaches

abstract: Crowd evacuation of a building has been studied over the last decades. In this paper, seven methodological approaches for crowd evacuation have been identified. These approaches include cellular automata models, lattice gas models, social force models, fluid-dynamic models, agent-based models, game theoretic models, and approaches based on experiments with animals. According to available literatures, we discuss the advantages and disadvantages of these approaches, and conclude that a variety of different kinds of approaches should be combined to study crowd evacuation. Psychological and physiological elements affecting individual and collective behaviors should be also incorporated into the evacuation models.


title: Isolated pulmonary metastases define a favorable subgroup in metastatic pancreatic cancer.

abstract: Abstract Purpose Liver metastasis represents the first site of dissemination in >80% of metastatic pancreatic cancer (PC) patients. Pulmonary metastasis as first site of dissemination in PC is a rare event and might define a biologically distinct subgroup in metastatic PC. Methods Consecutive PC patients who were diagnosed or treated with isolated pulmonary metastases at our high-volume comprehensive cancer center were included in a prospectively maintained database between 2002 and 2015. Medical records and correlating computed tomography findings (CT) were retrospectively analyzed. Results A total of 40 PC patients with isolated pulmonary metastases were identified. Pulmonary metastases represented disease recurrence after initial resection of PC in 22 patients and disease progression of locally advanced pancreatic cancer in 5 patients. 14 out of 27 PC patients (56%) had received chemoradiotherapy for localized disease prior to pulmonary metastasis. Data on 1st-line treatment for pulmonary metastases was available for 38 patients: most patients (71%) received a gemcitabine-based chemotherapy regimen, 5 patients (13%) received best supportive care. After a median follow-up of 37.3 months, median survival after diagnosis of pulmonary metastasis was estimated with 25.5 months (95% CI 19.1–31.8); a significantly improved survival after diagnosis of pulmonary metastasis was observed for patients with less than 10 lung metastases (31.3 vs 18.7 months, p = 0.003) and for an unilateral localization of lung involvement (31.3 vs 21.8 months, p = 0.03). Conclusions Our results suggest a favorable outcome of PC patients with isolated pulmonary metastases. Further research is warranted to elucidate the specific molecular characteristics of this rare subgroup.


title: Classification of wild tomatoes: a review

abstract: Wild tomatoes are native to western South America. The generic status of wild tomatoes within the Solanaceae has been controversial since the eighteen century. Linnaeus in 1753 placed tomatoes in Solanum while Miller, a contemporary ofLinnaeus, classified tomatoes in a new genus Lycopersicon. The majority of later botanists have followed Miller. Differing numbers of species and conflicting supraspecific classifications have been proposed, based on morphology or crossing studies. Two major crossability groups have been identified, one that includes mainly self-compatible species that easily cross with the cultivated tomato, and another that comprises self-incompatible species not easily cross with the cultivated tomato. Recent molecular investigations using appropriate outgroups have shown that tomatoes and potatoes are close related phylogenetically, and support the inclusion of tomatoes within Solanum, the classification advocated here. We discuss the conflicting goals of classifications based on predictivity versus stability, a continuing controversy in systematics.


title: Open Database of Epileptic EEG with MRI and Postoperational Assessment of Foci—a Real World Verification for the EEG Inverse Solutions

abstract: This paper introduces a freely accessible database http://eeg.pl/epi, containing 23 datasets from patients diagnosed with and operated on for drug-resistant epilepsy. This was collected as part of the clinical routine at the Warsaw Memorial Child Hospital. Each record contains (1) pre-surgical electroencephalography (EEG) recording (10–20 system) with inter-ictal discharges marked separately by an expert, (2) a full set of magnetic resonance imaging (MRI) scans for calculations of the realistic forward models, (3) structural placement of the epileptogenic zone, recognized by electrocorticography (ECoG) and post-surgical results, plotted on pre-surgical MRI scans in transverse, sagittal and coronal projections, (4) brief clinical description of each case. The main goal of this project is evaluation of possible improvements of localization of epileptic foci from the surface EEG recordings. These datasets offer a unique possibility for evaluating different EEG inverse solutions. We present preliminary results from a subset of these cases, including comparison of different schemes for the EEG inverse solution and preprocessing. We report also a finding which relates to the selective parametrization of single waveforms by multivariate matching pursuit, which is used in the preprocessing for the inverse solutions. It seems to offer a possibility of tracing the spatial evolution of seizures in time.


title: The BCL-2 protein family: opposing activities that mediate cell death

abstract: BCL-2 family proteins, which have either pro- or anti-apoptotic activities, have been studied intensively for the past decade owing to their importance in the regulation of apoptosis, tumorigenesis and cellular responses to anti-cancer therapy. They control the point of no return for clonogenic cell survival and thereby affect tumorigenesis and host- pathogen interactions and regulate animal development. Recent structural, phylogenetic and biological analyses, however, suggest the need for some reconsideration of the accepted organizational principles of the family and how the family members interact with one another during programmed cell death. Although these insights into interactions among BCL-2 family proteins reveal how these proteins are regulated, a unifying hypothesis for the mechanisms they use to activate caspases remains elusive.


title: Negative Results in Computer Vision: A Perspective

abstract: A negative result is when the outcome of an experiment or a model is not what is expected or when a hypothesis does not hold. Despite being often overlooked in the scientific community, negative results are results and they carry value. While this topic has been extensively discussed in other fields such as social sciences and biosciences, less attention has been paid to it in the computer vision community. The unique characteristics of computer vision, particularly its experimental aspect, call for a special treatment of this matter. In this paper, I will address what makes negative results important, how they should be disseminated and incentivized, and what lessons can be learned from cognitive vision research in this regard. Further, I will discuss issues such as computer vision and human vision interaction, experimental design and statistical hypothesis testing, explanatory versus predictive modeling, performance evaluation, model comparison, as well as computer vision research culture.


title: Multimodal Affect Analysis for Product Feedback Assessment

abstract: Consumers often react expressively to products such as food samples, perfume, jewelry, sunglasses, and clothing accessories. This research discusses a multimodal affect recognition system developed to classify whether a consumer likes or dislikes a product tested at a counter or kiosk, by analyzing the consumer's facial expression, body posture, hand gestures, and voice after testing the product. A depth-capable camera and microphone system - Kinect for Windows - is utilized. An emotion identification engine has been developed to analyze the images and voice to determine affective state of the customer. The image is segmented using skin color and adaptive threshold. Face, body and hands are detected using the Haar cascade classifier. Canny edges are identified and the lip, body and hand contours are extracted using spatial filtering. Edge count and orientation around the mouth, cheeks, eyes, shoulders, fingers and the location of the edges are used as features. Classification is done by an emotion template mapping algorithm and training a classifier using support vector machines. The real-time performance, accuracy and feasibility for multimodal affect recognition in feedback assessment are evaluated.


BeaverDam : Video Annotation Tool for Computer Vision Training Labels


title: Pairwise geometric matching for large-scale object retrieval

abstract: Spatial verification is a key step in boosting the performance of object-based image retrieval. It serves to eliminate unreliable correspondences between salient points in a given pair of images, and is typically performed by analyzing the consistency of spatial transformations between the image regions involved in individual correspondences. In this paper, we consider the pairwise geometric relations between correspondences and propose a strategy to incorporate these relations at significantly reduced computational cost, which makes it suitable for large-scale object retrieval. In addition, we combine the information on geometric relations from both the individual correspondences and pairs of correspondences to further improve the verification accuracy. Experimental results on three reference datasets show that the proposed approach results in a substantial performance improvement compared to the existing methods, without making concessions regarding computational efficiency.


title: Downlink NOMA with SIC using unified user grouping for non-orthogonal user multiplexing and decoding order

abstract: In this paper, we propose a practical method for user grouping and decoding-order setting in a successive interference canceller (SIC) for downlink non-orthogonal multiple access (NOMA). While the optimal user grouping and decoding order, which depend on the instantaneous channel conditions among users within a cell, are assumed in previous work, the proposed method uses user grouping and a decoding order that are unified among all frequency blocks. The proposed decoding order in the SIC enables the application of NOMA with a SIC to a system where all the elements within a codeword for a user are distributed among multiple frequency blocks (resource blocks). The unified user grouping eases the complexity in the SIC process at the user terminal. The unified user grouping also reduces the complexity of the efficient downlink control signaling in NOMA with a SIC. The unified user grouping and decoding order among frequency blocks in principle reduce the achievable throughput compared to the optimal one. However, based on numerical results, we show that the proposed method does not significantly degrade the system-level throughput in downlink cellular networks.


title: Detecting Spammer Groups From Product Reviews: A Partially Supervised Learning Model

abstract: Nowadays, online product reviews play a crucial role in the purchase decision of consumers. A high proportion of positive reviews will bring substantial sales growth, while negative reviews will cause sales loss. Driven by the immense financial profits, many spammers try to promote their products or demote their competitors’ products by posting fake and biased online reviews. By registering a number of accounts or releasing tasks in crowdsourcing platforms, many individual spammers could be organized as spammer groups to manipulate the product reviews together and can be more damaging. Existing works on spammer group detection extract spammer group candidates from review data and identify the real spammer groups using unsupervised spamicity ranking methods. Actually, according to the previous research, labeling a small number of spammer groups is easier than one assumes, however, few methods try to make good use of these important labeled data. In this paper, we propose a partially supervised learning model (PSGD) to detect spammer groups. By labeling some spammer groups as positive instances, PSGD applies positive unlabeled learning (PU-Learning) to study a classifier as spammer group detector from positive instances (labeled spammer groups) and unlabeled instances (unlabeled groups). Specifically, we extract reliable negative set in terms of the positive instances and the distinctive features. By combining the positive instances, extracted negative instances and unlabeled instances, we convert the PU-Learning problem into the well-known semi-supervised learning problem, and then use a Naive Bayesian model and an EM algorithm to train a classifier for spammer group detection. Experiments on real-life Amazon.cn data set show that the proposed PSGD is effective and outperforms the state-of-the-art spammer group detection methods.


title: Capacitively-tuned split microstrip resonators for RFID barcodes

abstract: A new method of realising RF barcodes is presented using arrays of identical microstrip dipoles capacitively tuned to be resonant at different frequencies within the desired licensed-free ISM bands. When interrogated, the reader detects each dipole's resonance frequency and with n resonant dipoles, potentially 2/sup n/-1 items in the field can be tagged and identified. Results for RF barcode elements in the 5.8 GHz band are presented. It is shown that with accurate centre frequency prediction and by operating over multiple ISM and other license-exempt bands, a useful number of information bits can be realised. Further increase may be possible using ultra-wideband (UWB) technology. Low cost lithographic printing techniques based on using metal ink on low cost substrates could lead to an economical alternative to current RFID systems in many applications.


Adaptive Scaffolding in an Intelligent Game-Based Learning Environment for Computer Science


title: Fascia redefined: anatomical features and technical relevance in fascial flap surgery

abstract: Fascia has traditionally been thought of as a passive structure that envelops muscles, and the term “fascia” was misused and confusing. However, it is now evident that fascia is a dynamic tissue with complex vasculature and innervation. A definition of fascia as an integral tissue has been provided here, highlighting the main features of the superficial and deep fasciae. Wide anatomic variations and site-specific differences in fascial structure are described, coupled with results of our extensive investigations of fascial anatomy. This will enable surgeons to make better decisions on selecting the appropriate fascia in the construction of fascial flaps. The use of the superficial or deep fasciae in the creation of a fascial flap cannot be selected at random, but must be guided by the anatomical features of the different types of fasciae. In particular, we suggest the use of the superficial fascia, such as the parascapular fascio-cutaneous free flap or any cutaneous flap, when a well-vascularized elastic flap, with the capacity to adhere to underlying tissues, is required, and a fascio-cutaneous flap formed by aponeurotic fascia to resurface any tendon or joints exposures. Moreover, the aponeurotic fascia, such as the fascia lata, can be used as a surgical patch if the plastic surgeon requires strong resistance to stress and/or the capacity to glide freely. Finally, the epimysial fascia, such as in the latissimus dorsi flap, can be used with success when used together with the underlying muscles. Clearly, extensive clinical experience and judgment are necessary for assessment of their potential use.


title: Nested Arrays: A Novel Approach to Array Processing With Enhanced Degrees of Freedom

abstract: A new array geometry, which is capable of significantly increasing the degrees of freedom of linear arrays, is proposed. This structure is obtained by systematically nesting two or more uniform linear arrays and can provide O(N2) degrees of freedom using only N physical sensors when the second-order statistics of the received data is used. The concept of nesting is shown to be easily extensible to multiple stages and the structure of the optimally nested array is found analytically. It is possible to provide closed form expressions for the sensor locations and the exact degrees of freedom obtainable from the proposed array as a function of the total number of sensors. This cannot be done for existing classes of arrays like minimum redundancy arrays which have been used earlier for detecting more sources than the number of physical sensors. In minimum-input-minimum-output (MIMO) radar, the degrees of freedom are increased by constructing a longer virtual array through active sensing. The method proposed here, however, does not require active sensing and is capable of providing increased degrees of freedom in a completely passive setting. To utilize the degrees of freedom of the nested co-array, a novel spatial smoothing based approach to DOA estimation is also proposed, which does not require the inherent assumptions of the traditional techniques based on fourth-order cumulants or quasi stationary signals. As another potential application of the nested array, a new approach to beamforming based on a nonlinear preprocessing is also introduced, which can effectively utilize the degrees of freedom offered by the nested arrays. The usefulness of all the proposed methods is verified through extensive computer simulations.


title: Towards a Learning Analytics Support for Intelligent Tutoring Systems on MOOC Platforms

abstract: Nowadays, many MOOC platforms have arisen to provide free knowledge. These platforms have a large catalog of courses for different specializations that progressively demand more specific learning resources and assessment methods to evaluate the progression of students. Current MOOC platforms are gradually giving support to these new requirements but with a limited assistance. This paper presents the state of art of the analytical system for three relevant MOOC platforms, one of the main pillars for analyzing the progression of courses. Other initiatives are also reviewed to show that current MOOC analytical systems are not ready to support custom MOOC-aware intelligent tutoring systems (ITSs). Thus, the design of a learning analytics system to assist these tools for MOOC platforms is presented.


title: Bringing relational databases into the semantic web: a survey

abstract: Relational databases are considered one of the most popular storage solutions for various kinds of data and they have been recognized as a key factor in generating huge amounts of data for Semantic Web applications. Ontologies, on the other hand, are one of the key concepts and main vehicle of knowledge in the Semantic Web research area. The problem of bridging the gap between relational databases and ontologies has attracted the interest of the Semantic Web community, even from the early years of its existence and is commonly referred to as the database-to-ontology mapping problem. However, this term has been used interchangeably for referring to two distinct problems: namely, the creation of an ontology from an existing database instance and the discovery of mappings between an existing database instance and an existing ontology. In this paper, we clearly define these two problems and present the motivation, benefits, challenges and solutions for each one of them. We attempt to gather the most notable approaches proposed so far in the literature, present them concisely in tabular format and group them under a classification scheme. We finally explore the perspectives and future research steps for a seamless and meaningful integration of databases into the Semantic Web.


title: Routing and spectrum assignment with delayed allocation in elastic optical networks

abstract: In response to bandwidth-hungry applications, the field of elastic optical networks (EONs) has emerged. In this paper, we investigate the provisioning and allocation problem in EONs, which presents a number of new and exciting challenges not otherwise present in optical networking solutions.We propose a novel technique for solving the routing and spectrum allocation problem, which improves resource allocation efficiency for scheduled demands. This approach, called delayed spectrum allocation (DSA), promises network resources to each demand upon request for scheduling, but delays the selection and allocation of those specific resources until immediately before the transmission begins. Through extensive simulations, we are able to conclude that the DSA improves resource consumption efficiency and lowers call blocking. We also evaluate demands with flexible reservation windows such that a range of acceptable start times can be specified by the user. This flexibility is found to significantly increase the performance improvement of the DSA with little additional delay.


title: MORPH: a longitudinal image database of normal adult age-progression

abstract: This paper details MORPH a longitudinal face database developed for researchers investigating all facets of adult age-progression, e.g. face modeling, photo-realistic animation, face recognition, etc. This database contributes to several active research areas, most notably face recognition, by providing: the largest set of publicly available longitudinal images; longitudinal spans from a few months to over twenty years; and, the inclusion of key physical parameters that affect aging appearance. The direct contribution of this data corpus for face recognition is highlighted in the evaluation of a standard face recognition algorithm, which illustrates the impact that age-progression, has on recognition rates. Assessment of the efficacy of this algorithm is evaluated against the variables of gender and racial origin. This work further concludes that the problem of age-progression on face recognition (FR) is not unique to the algorithm used in this work.


title: Comparison of questionnaire screening and urine toxicology for detection of pregnancy complicated by substance use.

abstract: OBJECTIVES ::: To compare the efficacy of structured questionnaire screening and prenatal urine toxicology for the detection of substance use by pregnant women, and to describe substance use patterns in a group of women presenting to a university-based obstetric clinic. ::: ::: ::: METHODS ::: All patients presenting to our obstetric clinic for their first prenatal visit were evaluated for evidence of current use of alcohol or any illicit substances. Nursing personnel administered an extensive questionnaire, which included detailed questions about past and current substance use patterns. Urine samples were examined by a commercial laboratory for alcohol and a number of illicit substances. Current users were defined as those who admitted use within the past 30 days or who had positive urine toxicology. ::: ::: ::: RESULTS ::: Fifty-eight of 302 patients (19.2%) were identified as current users of alcohol or illicit substances. Only 17 of 41 women (41.5%) with toxicologic evidence of recent use admitted to current use. Only 17 of 34 (50.0%) admitting to current use had toxicology positive for any substance. Patients with a history of multiple past substance use were significantly more likely to have positive urine toxicology than those without such a history (26.1 versus 7.4%; P < .005). Among current users, multiple substance use (34.5%) and cigarette smoking (52%) were common. ::: ::: ::: CONCLUSION ::: A screening combination of structured questionnaire and universal urine toxicology identifies more current users than either technique alone, and neither is clearly superior to the other. A history of multiple substance use may be an important indicator of current use.


title: N-terminal flanking residues of a diabetes-associated GAD65 determinant are necessary for activation of antigen-specific T cells in diabetes-resistant mice.

abstract: A diabetes-associated peptide in the glutamic acid decarboxylase 65 (GAD65) molecule, p524-543, activates two distinct populations of T cells, which apparently play opposite roles in the development of diabetes in NOD mice. By comparing the fine specificity of these two T cell repertoires using a nested set of truncated peptides that cover the p524-543 region, we found, surprisingly, that all clones recognized the same core within this peptide, p530-539. The core itself was non-immunogenic, but the residues flanking this shared sequence played the crucial role in selecting T cells to activate. A peptide missing N-terminal flanking residues at position 528 and 529 was stimulatory in NOD but not in MHC-matched, NOD-resistant (NOR) mice, suggesting that a protective response in the resistant mice may require T cell recognition of one or more of the N-terminal flanking residues. T cell repertoire studies demonstrated selective clonal expansions within the BV4 TCR family that dominates the p524-543 response in NOD but not in NOR mice. These data suggest that processing or trimming events affecting T cell recognition of very few flanking residues of diabetes-associated determinants might be involved in the protective response in NOR mice.


title: Managing medical and psychiatric comorbidity in individuals with major depressive disorder and bipolar disorder

abstract: BACKGROUND ::: Most individuals with mood disorders experience psychiatric and/or medical comorbidity. Available treatment guidelines for major depressive disorder (MDD) and bipolar disorder (BD) have focused on treating mood disorders in the absence of comorbidity. Treating comorbid conditions in patients with mood disorders requires sufficient decision support to inform appropriate treatment. ::: ::: ::: METHODS ::: The Canadian Network for Mood and Anxiety Treatments (CANMAT) task force sought to prepare evidence- and consensus-based recommendations on treating comorbid conditions in patients with MDD and BD by conducting a systematic and qualitative review of extant data. The relative paucity of studies in this area often required a consensus-based approach to selecting and sequencing treatments. ::: ::: ::: RESULTS ::: Several principles emerge when managing comorbidity. They include, but are not limited to: establishing the diagnosis, risk assessment, establishing the appropriate setting for treatment, chronic disease management, concurrent or sequential treatment, and measurement-based care. ::: ::: ::: CONCLUSIONS ::: Efficacy, effectiveness, and comparative effectiveness research should emphasize treatment and management of conditions comorbid with mood disorders. Clinicians are encouraged to screen and systematically monitor for comorbid conditions in all individuals with mood disorders. The common comorbidity in mood disorders raises fundamental questions about overlapping and discrete pathoetiology.


title: Mining Mid-level Features for Image Classification

abstract: Mid-level or semi-local features learnt using class-level information are potentially more distinctive than the traditional low-level local features constructed in a purely bottom-up fashion. At the same time they preserve some of the robustness properties with respect to occlusions and image clutter. In this paper we propose a new and effective scheme for extracting mid-level features for image classification, based on relevant pattern mining. In particular, we mine relevant patterns of local compositions of densely sampled low-level features. We refer to the new set of obtained patterns as Frequent Local Histograms or FLHs. During this process, we pay special attention to keeping all the local histogram information and to selecting the most relevant reduced set of FLH patterns for classification. The careful choice of the visual primitives and an extension to exploit both local and global spatial information allow us to build powerful bag-of-FLH-based image representations. We show that these bag-of-FLHs are more discriminative than traditional bag-of-words and yield state-of-the-art results on various image classification benchmarks, including Pascal VOC.


title: Hardware security: threat models and metrics

abstract: The globalized semiconductor supply chain is vulnerable to hardware attacks including: Trojans, piracy of intellectual properties (IPs) and/or overbuilding of integrated circuits (ICs), reverse engineering, side-channels, and counterfeiting. In this paper, we explain the threat models, the state-of-the-art defenses, and the metrics used to evaluate the defenses. The threat models outlined in this paper enables one to understand the attacks. Defenses and metrics can help defenders to build stronger countermeasures and evaluate them against other protection techniques using the metrics.


title: A Compact Wearable Sensor Package for Clinical Gait

abstract: We have developed a compact, wireless, wearable, shoe-mounted sensor package that is designed to provide continuous and real-time monitoring of gait for clinical biomotion analysis. This paper discusses the initial design of our hardware, consisting of a sensor-laden insole and a removable instrumented shoe attachment. Information about the three-dimensional motion, relative position, and pressure distribution of the foot is provided with real-time and wireless transmission of the data from the instrumented shoe attachment to a receiver connected to a nearby laptop or wearable PDA platform. Initial data collection demonstrates the capability of the sensor system to detect different types of gait. Currently, clinical gait analysis [1] is primarily done in either a motion laboratory, with full analysis of the motion of all body segments using highly accurate optical systems and a precision force-measuring plate mounted on a rigid floor, or in a doctor's office with the physician making visual observations. Analysis in a motion laboratory produces well-quantified and accurate results for gait over a short distance, while the results of visual observations are qualitative and difficult to compare across multiple visits or between differing physicians. However, a


title: Momentum-Mapped Inverted Pendulum Models for Controlling Dynamic Human Motions

abstract: Designing a unified framework for simulating a broad variety of human behaviors has proven to be challenging. In this article, we present an approach for control system design that can generate animations of a diverse set of behaviors including walking, running, and a variety of gymnastic behaviors. We achieve this generalization with a balancing strategy that relies on a new form of inverted pendulum model (IPM), which we call the momentum-mapped IPM (MMIPM). We analyze reference motion capture data in a pre-processing step to extract the motion of the MMIPM. To compute a new motion, the controller plans a desired motion, frame by frame, based on the current pendulum state and a predicted pendulum trajectory. By tracking this time-varying trajectory, the controller creates a character that dynamically balances, changes speed, makes turns, jumps, and performs gymnastic maneuvers.


title: A novel driving and protection circuit for reverse blocking IGBT used in matrix converter

abstract: A novel drive and protection circuit for reverse-blocking insulated gate bipolar transistor (RB-IGBT) is proposed in this paper. For the drive circuit, a dynamic current source is introduced to reduce the turn-on and turn-off transients. Meanwhile, the di/dt of the collector current and the dv/dt of the collector-emitter voltage are strictly restricted, so do the respective stresses. The drive circuit consists of a conventional push-pull driver and two controllable current sources-a current generator and a current sink. These two current sources work in switching transitions. For the protection circuit, a novel collector current detecting circuit suitable for RB-IGBT is proposed. This method detects the collector current by sensing collector-emitter voltage of the device. Further study shows that this method can be used to acquire the current signs in commutation transitions of matrix converter. A series of experiments has been carried out concerning the proposed drive and protection circuit and the experimental setup; results as well as detailed analysis are presented


title: Desugaring Haskell's do-notation into applicative operations

abstract: Monads have taken the world by storm, and are supported by do-notation (at least in Haskell). Programmers are increasingly waking up to the usefulness and ubiquity of Applicatives, but they have so far been hampered by the absence of supporting notation. In this paper we show how to re-use the very same do-notation to work for Applicatives as well, providing efficiency benefits for some types that are both Monad and Applicative, and syntactic convenience for those that are merely Applicative. The result is fully implemented as an optional extension in GHC, and is in use at Facebook to make it easy to write highly-parallel queries in a distributed system.


title: GSR and Blink Features for Cognitive Load Classification

abstract: A system capable of monitoring its user’s mental workload can evaluate the suitability of its interface and interactions for user’s current cognitive status and properly change them when necessary. Galvanic skin response (GSR) and eye blinks are cognitive load measures which can be captured conveniently and at low cost. The present study has assessed multiple features of these two signals in classification of cognitive workload level. The experiment included arithmetic tasks with four difficulty levels and two types of machine learning algorithms have been applied for classification. Obtained results show that the studied features of blink and GSR can reasonably discriminate workload levels and combining features of the two modalities improves the accuracy of cognitive load classification. We have achieved around 75% for binary classification and more than 50% for four-class classification.


title: JT gravity as a matrix integral

abstract: We present exact results for partition functions of Jackiw-Teitelboim (JT) gravity on two-dimensional surfaces of arbitrary genus with an arbitrary number of boundaries. The boundaries are of the type relevant in the NAdS${}_2$/NCFT${}_1$ correspondence. We show that the partition functions correspond to the genus expansion of a certain matrix integral. A key fact is that Mirzakhani's recursion relation for Weil-Petersson volumes maps directly onto the Eynard-Orantin "topological recursion" formulation of the loop equations for this matrix integral. The matrix integral provides a (non-unique) nonperturbative completion of the genus expansion, sensitive to the underlying discreteness of the matrix eigenvalues. In matrix integral descriptions of noncritical strings, such effects are due to an infinite number of disconnected worldsheets connected to D-branes. In JT gravity, these effects can be reproduced by a sum over an infinite number of disconnected geometries -- a type of D-brane logic applied to spacetime.


Fixed Point Theorems for a Family of Self-Map on Rings


title: A three translational DoFs parallel cube-manipulator

abstract: This paper concerns the presentation and analysis of a type of three translational degrees of freedom (DoFs) parallel cube-manipulator. The parallel manipulators are the topology architectures of the DELTA robot and Tsai's manipulator, respectively, which have three translational DoFs. In the design, the three actuators are arranged according to the Cartesian coordinate system, which means that the actuating directions are normal to each other, and the joints connecting to the moving platform are located on three sides of a cube, for such reason we call this type of manipulator the parallel cube-manipulator. The kinematics problems, singularity, workspace, compliance characteristic of the manipulator are investigated in the paper. The analysis results show that the manipulators have the advantages of no singularities in the workspace, relatively more simple forward kinematics, and existence of a compliance center. The parallel cube-manipulator can be applied to the fields of micro-motion manipulators, remote center compliance (RCC) devices, assembly, and so on.


title: A Novel Power Efficient XOR Gate Based on Single Inverted Input

abstract: In this paper a novel area optimized XOR gate is proposed. The proposed design is based on the concept of single inverted inputs. The function is also implemented using CMOS and PTL logic. The comparison between the logics is made using power and delay. The novel design using only one inverted input is found to be 80.67% optimized for power in comparison to CMOS logic and 59.59% power optimized in comparison with PTL logic. The tool for implementation is ngspice on 16 nm technology.


title: What is a biobank? Differing definitions among biobank stakeholders

abstract: While there is widespread agreement on the broad aspects of what constitutes a biobank, there is much disagreement regarding the precise definition. This research aimed to describe and analyze the definitions of the term biobank offered by various stakeholders in biobanking. Interviews were conducted with 36 biobanking stakeholders with international experience currently working in Switzerland. The results show that, in addition to the core concepts of biological samples and linked data, the planned use of samples (including sharing) is held to be a key criterion. It also emerges that some researchers avoid the term in order to circumvent certain regulatory guidelines, including informed consent requirements. Developments in the field of biobanking will be complicated if researchers are unaware, or deny that their collection is a biobank. A clear definition of the term is therefore an important step towards fostering collaboration amongst researchers, enabling them to more easily identify potential sources of samples.


title: Clinical Trial Regulations in India: Progress and Challenges Arising from Recent Amendments to Schedule Y of the Drugs and Cosmetics (D&C) Act 1940 (D&C Rules 1945)

abstract: The Central Drugs Standard Control Organization, which is the drug regulatory authority in India, has recently amended the regulatory framework surrounding the conduct of clinical trials in the country. In 2005–2009, India witnessed mammoth growth in the field of clinical research; however, recent years have seen a series of controversies and issues, publicized by various non-governmental organizations wishing to shine a light on the deficiencies in the clinical trial regulatory milieu in India. As a result, the regulatory environment has undergone dramatic changes, with the passage of new laws to recognize and fill these lacunae, thereby bringing transparency and accountability to the clinical trial process. Important amendments have been made to serious adverse event reporting, remittance to trial participants, audiovisual recording of informed consent, Ethics Committee registration and the accreditation process of clinical sites throughout the country. Although the spirit of these new laws is a welcome move, some changes are insufficient to fill the lacunae. This article summarizes these amendments and highlights the significant threats and challenges to various stakeholders involved in clinical research in complying with them. The laws should be framed for regulating clinical trials, not for constraining human research in the country.


LOG-PERIODIC DIPOLE ARRAY FOR ULTRA-WIDEBAND APPLICATIONS


title: Fast Two-Sample Testing with Analytic Representations of Probability Measures

abstract: We propose a class of nonparametric two-sample tests with a cost linear in the sample size. Two tests are given, both based on an ensemble of distances between analytic functions representing each of the distributions. The first test uses smoothed empirical characteristic functions to represent the distributions, the second uses distribution embeddings in a reproducing kernel Hilbert space. Analyticity implies that differences in the distributions may be detected almost surely at a finite number of randomly chosen locations/frequencies. The new tests are consistent against a larger class of alternatives than the previous linear-time tests based on the (non-smoothed) empirical characteristic functions, while being much faster than the current state-of-the-art quadratic-time kernel-based or energy distance-based tests. Experiments on artificial benchmarks and on challenging real-world testing problems demonstrate that our tests give a better power/time tradeoff than competing approaches, and in some cases, better outright power than even the most expensive quadratic-time tests. This performance advantage is retained even in high dimensions, and in cases where the difference in distributions is not observable with low order statistics.


title: Epigenetics and the embodiment of race: Developmental origins of US racial disparities in cardiovascular health

abstract: The relative contribution of genetic and environmental influences to the US black-white disparity in cardiovascular disease (CVD) is hotly debated within the public health, anthropology, and medical communities. In this article, we review evidence for developmental and epigenetic pathways linking early life environments with CVD, and critically evaluate their possible role in the origins of these racial health disparities. African Americans not only suffer from a disproportionate burden of CVD relative to whites, but also have higher rates of the perinatal health disparities now known to be the antecedents of these conditions. There is extensive evidence for a social origin to prematurity and low birth weight in African Americans, reflecting pathways such as the effects of discrimination on maternal stress physiology. In light of the inverse relationship between birth weight and adult CVD, there is now a strong rationale to consider developmental and epigenetic mechanisms as links between early life environmental factors like maternal stress during pregnancy and adult race-based health disparities in diseases like hypertension, diabetes, stroke, and coronary heart disease. The model outlined here builds upon social constructivist perspectives to highlight an important set of mechanisms by which social influences can become embodied, having durable and even transgenerational influences on the most pressing US health disparities. We conclude that environmentally responsive phenotypic plasticity, in combination with the better-studied acute and chronic effects of social-environmental exposures, provides a more parsimonious explanation than genetics for the persistence of CVD disparities between members of socially imposed racial categories.


title: 360-degree fog projection interactive display

abstract: The aim of this research is to develop a fog display which enables observers to recognize a 3D shape of virtual objects. A fog display is one of immaterial display systems. Foregoing systems (e.g. [Rakkolainen et al. 2005]) realized to project objects and images floating in mid-air. However, these systems provided only 2D images on a flat screen. We propose a novel fog display system consists of one cylindrical fog screen and multiple projectors, which brings motion parallax of the virtual object to observers. The concept of the proposed display is similar to 360-degrees viewable 3D displays which utilize projection of multiple images, such as Hitachi's Transpost [Otsuka et al. 2006] and Sony's RayModeler [Ito et al. 2010]. The advantage of the proposed fog display is that the proposed display enables direct touching operation to the virtual objects by observers' hands.


title: A left ventricular segmentation method on 3D echocardiography using deep learning and snake

abstract: Segmentation of left ventricular (LV) endocardium from 3D echocardiography is important for clinical diagnosis because it not only can provide some clinical indices (e.g. ventricular volume and ejection fraction) but also can be used for the analysis of anatomic structure of ventricle. In this work, we proposed a new full-automatic method, combining the deep learning and deformable model, for the segmentation of LV endocardium. We trained convolutional neural networks to generate a binary cuboid to locate the region of interest (ROI). And then, using ROI as the input, we trained stacked autoencoder to infer the LV initial shape. At last, we adopted snake model initiated by inferred shape to segment the LV endocardium. In the experiments, we used 3DE data, from CETUS challenge 2014 for training and testing by segmentation accuracy and clinical indices. The results demonstrated the proposed method is accuracy and efficiency respect to expert's measurements.


title: (www.interscience.wiley.com). DOI: 10.1002/bdm.501 The Heat of the Moment: The Effect of Sexual Arousal on Sexual Decision Making

abstract: Despite the social importance of decisions taken in the “heat of the moment,” very little research has examined the effect of sexual arousal on judgment and decision making. Here we examine the effect of sexual arousal, induced by self-stimulation, on judgments and hypothetical decisions made by male college students. Students were assigned to be in either a state of sexual arousal or a neutral state and were asked to: (1) indicate how appealing they find a wide range of sexual stimuli and activities, (2) report their willingness to engage in morally questionable behavior in order to obtain sexual gratification, and (3) describe their willingness to engage in unsafe sex when sexually aroused. The results show that sexual arousal had a strong impact on all three areas of judgment and decision making, demonstrating the importance of situational forces on preferences, as well as subjects' inability to predict these influences on their own behavior. Copyright © 2005 John Wiley & Sons, Ltd.


title: Ultra-Compact 100 × 100 μm2 Footprint Hybrid Device with Spin-Valve Nanosensors

abstract: Magnetic field mapping with micrometric spatial resolution and high sensitivity is a challenging application, and the technological solutions are usually based on large area devices integrating discrete magnetic flux guide elements. In this work we demonstrate a high performance hybrid device with improved field sensitivity levels and small footprint, consisting of a ultra-compact 2D design where nanometric spin valve sensors are inserted within the gap of thin-film magnetic flux concentrators. Pole-sensor distances down to 400 nm are demonstrated using nanofabrication techniques combined with an optimized liftoff process. These 100 × 100 μm2 pixel sensors can be integrated in modular devices for surface mapping without moving parts.


title: Classification of Things in DBpedia using Deep Neural Networks

abstract: The Semantic Web aims at representing knowledge about the real world at web scale - things, their attributes and relationships among them can be represented as nodes and edges in an inter-linked semantic graph. In the presence of noisy data, as is typical of data on the Semantic Web, a software Agent needs to be able to robustly infer one or more associated actionable classes for the individuals in order to act automatically on it. We model this problem as a multi-label classification task where we want to robustly identify types of the individuals in a semantic graph such as DBpedia, which we use as an exemplary dataset on the Semantic Web. Our approach first extracts multiple features for the individuals using random walks and then performs multi-label classification using fully-connected Neural Networks. Through systematic exploration and experimentation, we identify the effect of hyper-parameters of the feature extraction and the fully-connected Neural Network structure on the classification performance. Our final results show that our method performs better than state-of-the-art inferencing systems like SDtype and SLCN, from which we can conclude that random-walk-based feature extraction of individuals and their multi-label classification using Deep Neural Networks is a promising alternative to these systems for type classification of individuals on the Semantic Web. The main contribution of our work is to introduce a novel approach that allows us to use Deep Neural Networks to identify types of individuals in a noisy semantic graph by extracting features using random walks


The impact of financial variables on firms’ real decisions: evidence from Spanish firm-level data


title: OpenSoT: A whole-body control library for the compliant humanoid robot COMAN

abstract: A fundamental aspect of controlling humanoid robots lies in the capability to exploit the whole body to perform tasks. This work introduces a novel whole body control library called OpenSoT. OpenSoT is combined with joint impedance control to create a framework that can effectively generate complex whole body motion behaviors for humanoids according to the needs of the interaction level of the tasks. OpenSoT gives an easy way to implement tasks, constraints, bounds and solvers by providing common interfaces. We present the mathematical foundation of the library and validate it on the compliant humanoid robot COMAN to execute multiple motion tasks under a number of constraints. The framework is able to solve hierarchies of tasks of arbitrary complexity in a robust and reliable way.


title: The personalization privacy paradox: An exploratory study of decision making process for location-aware marketing

abstract: Despite the vast opportunities offered by location-aware marketing (LAM), mobile customers' privacy concerns appear to be a major inhibiting factor in their acceptance of LAM. This study extends the privacy calculus model to explore the personalization-privacy paradox in LAM, with considerations of personal characteristics and two personalization approaches (covert and overt). Through an experimental study, we empirically validated the proposed model. Results suggest that the influences of personalization on the privacy risk/benefit beliefs vary upon the type of personalization systems (covert and overt), and that personal characteristics moderate the parameters and path structure of the privacy calculus model.


title: Tracking the Trackers: Towards Understanding the Mobile Advertising and Tracking Ecosystem

abstract: Third-party services form an integral part of the mobile ecosystem: they allow app developers to add features such as performance analytics and social network integration, and to monetize their apps by enabling user tracking and targeted ad delivery. At present users, researchers, and regulators all have at best limited understanding of this third-party ecosystem. In this paper we seek to shrink this gap. Using data from users of our ICSI Haystack app we gain a rich view of the mobile ecosystem: we identify and characterize domains associated with mobile advertising and user tracking, thereby taking an important step towards greater transparency. We furthermore outline our steps towards a public catalog and census of analytics services, their behavior, their personal data collection processes, and their use across mobile apps.


title: Animating explosions

abstract: In this paper, we introduce techniques for animating explosions and their effects. The primary effect of an explosion is a disturbance that causes a shock wave to propagate through the surrounding medium. The disturbance determines the behavior of nearly all other secondary effects seen in explosion. We simulate the propagation of an explosion through the surrounding air using a computational fluid dynamics model based on the equations for compressible, viscous flow. To model the numerically stable formation of shocks along blast wave fronts, we employ an integration method that can handle steep pressure gradients without introducing inappropriate damping. The system includes two-way coupling between solid objects and surrounding fluid. Using this technique, we can generate a variety of effects including shaped explosive charges, a projectile propelled from a chamber by an explosion, and objects damaged by a blast. With appropriate rendering techniques, our explosion model can be used to create such visual effects as fireballs, dust clouds, and the refraction of light caused by a blast wave.


title: An action agenda for HIV and sex workers

abstract: The women, men, and transgender people who sell sex globally have disproportionate risks and burdens of HIV in countries of low, middle, and high income, and in concentrated and generalised epidemic contexts. The greatest HIV burdens continue to be in African female sex workers. Worldwide, sex workers still face reduced access to needed HIV prevention, treatment, and care services. Legal environments, policies, police practices, absence of funding for research and HIV programmes, human rights violations, and stigma and discrimination continue to challenge sex workers' abilities to protect themselves, their families, and their sexual partners from HIV. These realities must change to realise the benefits of advances in HIV prevention and treatment and to achieve global control of the HIV pandemic. Effective combination prevention and treatment approaches are feasible, can be tailored for cultural competence, can be cost-saving, and can help to address the unmet needs of sex workers and their communities in ways that uphold their human rights. To address HIV in sex workers will need sustained community engagement and empowerment, continued research, political will, structural and policy reform, and innovative programmes. But such actions can and must be achieved for sex worker communities everywhere.


Body vectoring technique with Radiesse® for tightening of the abdomen, thighs, and brachial zone


title: Wireless Networks 3 (1997) 421--433 421 Cyberguide: A mobile context-aware tour guide

abstract: Future computing environments will free the user from the constraints of the desktop. Applications for a mobile environment should take advantage of contextual information, such as position, to offer greater services to the user. In this paper, we present the Cyberguide project, in which we are building prototypes of a mobile context-aware tour guide. Knowledge of the user's current location, as well as a history of past locations, are used to provide more of the kind of services that we come to expect from a real tour guide. We describe the architecture and features of a variety of Cyberguide prototypes developed for indoor and outdoor use on a number of different hand-held platforms. We also discuss the general research issues that have emerged in our context-aware applications development in a mobile environment.


DOI:10.1068/b32047 Mobile Landscapes: using location data from cell phones for urban analysis


title: Patient care information systems and health care work: a sociotechnical approach

abstract: Abstract Those who face the difficulties of developing useful patient care information systems (PCISs) often stress the importance of ‘organizational issues’. Building upon recent sociological insights in the construction and use of information technologies for (health care) work, this paper underscores the importance of these insights for the development and evaluation of these systems. A sociotechnical approach to PCISs in health care is outlined, and two implications of this empirically grounded approach for the practices of developing and evaluating IT applications in health care practices are discussed. First, getting such technologies to work in concrete health care practices appears to be a politically textured process of organizational change, in which users have to be put at center-stage. This requires an iterative approach, in which the distinctions between ‘analysis’, ‘design’, ‘implementation’ and ‘evaluation’ blur. Second, a sociotechnical approach sheds new light on the potential roles of IT applications in health care practices. It is critical of approaches that denounce the ‘messy’ and ‘ad hoc’ nature of health care work, and that attempt to structure this work through the formal, standardized and ‘rational’ nature of IT systems. Optimal utilization of IT applications, it is argued, is dependent on the meticulous interrelation of the system’s functioning with the skilled and pragmatically oriented work of health care professionals.


title: Citizen’s Participation: Between Continuity and Change in Nigeria

abstract: Despite Nigeria’s uninterrupted democratic governance and transition of political power from one democratic government to another in the last fifteen years, there is a growing crisis of detachment of government and the people, lack of transparency, lack of accountability, lack of responsiveness to the needs of the masses and corruption in the country. In order to tackle these issues; citizens participation is needed at all levels of governance. Five different events that took place between 2010 and 2014 indicate that increase citizen’s participation in governance is crucial in enhancing the process that help government set the appropriate priorities, transparency, and accountability. DOI: 10.5901/mjss.2015.v6n6s1p328


title: Towards the Science of Security and Privacy in Machine Learning

abstract: Advances in machine learning (ML) in recent years have enabled a dizzying array of applications such as data analytics, autonomous systems, and security diagnostics. ML is now pervasive---new systems and models are being deployed in every domain imaginable, leading to rapid and widespread deployment of software based inference and decision making. There is growing recognition that ML exposes new vulnerabilities in software systems, yet the technical community's understanding of the nature and extent of these vulnerabilities remains limited. We systematize recent findings on ML security and privacy, focusing on attacks identified on these systems and defenses crafted to date. We articulate a comprehensive threat model for ML, and categorize attacks and defenses within an adversarial framework. Key insights resulting from works both in the ML and security communities are identified and the effectiveness of approaches are related to structural elements of ML algorithms and the data used to train them. We conclude by formally exploring the opposing relationship between model accuracy and resilience to adversarial manipulation. Through these explorations, we show that there are (possibly unavoidable) tensions between model complexity, accuracy, and resilience that must be calibrated for the environments in which they will be used.


title: Generating regular expression signatures for network traffic classification in trusted network management

abstract: Network traffic classification is a critical foundation for trusted network management and security systems. Matching application signatures in traffic payload is widely considered to be the most reliable classifying method. However, deriving accurate and efficient signatures for various applications is not a trivial task, for which current practice is mostly manual thus error-prone and of low efficiency. In this paper, we tackle the problem of automatic signature generation. In particular, we focus on generating regular expression signatures with a certain subset of standard syntax rules, which are of sufficient expressive power and compatible with most practical systems. We propose a novel approach that takes as input a labeled training data set and produces a set of signatures for matching the application classes presented in the data. The approach involves four procedures: pre-processing to extract application session payload, tokenization to find common substrings and incorporate position constraints, multiple sequence alignment to find common subsequences, and signature construction to transform the results into regular expressions. A real life full payload traffic trace is used to evaluate the proposed system, and signatures for a range of applications are automatically derived. The results indicate that the signatures are of high quality, and exhibit low false negatives and false positives.


title: A Comparative Study for Various Methods of Classification

abstract: This paper discusses data mining techniques to process a medical dataset and identify the relevance of liver disorder and drinking alcohol drink by classification of blood test data. We have used four different classification methods including decision tree, Bayesian algorithms (Naive Bayes and Bayesian Networks), Neural Network classification and Rough Sets methods. To evaluate the methods, we have used the Waikato Environment for Knowledge Analysis (WEKA) open source tool. WEKA is a collection of machine learning algorithms that can be used for different processing tasks such as classification, and clustering. Bayesian algorithms and Neural Network classification methods are implemented with WEKA. However, as WEKA does not support methods based on Rough Sets we have used Rosetta. We have provided an evaluation based on applying these classification methods to our dataset and measuring the accuracy of test results. The evaluation results show that using Neural Networks obtains the best result among the other methods.


title: Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering

abstract: Several unsupervised learning algorithms based on an eigendecomposition provide either an embedding or a clustering only for given training points, with no straightforward extension for out-of-sample examples short of recomputing eigenvectors. This paper provides a unified framework for extending Local Linear Embedding (LLE), Isomap, Laplacian Eigenmaps, Multi-Dimensional Scaling (for dimensionality reduction) as well as for Spectral Clustering. This framework is based on seeing these algorithms as learning eigenfunctions of a data-dependent kernel. Numerical experiments show that the generalizations performed have a level of error comparable to the variability of the embedding algorithms due to the choice of training data.


Acid phosphatase test for identification of seminal stains.


title: Small Cell Cluster-Based Resource Allocation for Wireless Backhaul in Two-Tier Heterogeneous Networks With Massive MIMO

abstract: In this paper, we investigate the resource allocation problem in two-tier heterogenous networks (HetNets) with massive MIMO, where two frequency bands, cellular frequency band and millimeter wave frequency band, are used for wireless backhaul links. To coordinate the interference among small cells (SCs), SCs are grouped into multiple SC clusters (SCCs), where SC users are jointly served by SC base stations (BSs) (SBSs) belonging to the same SCC. To eliminate the multi-user and inter-tier interference, we propose a block diagonalization based precoding scheme at macro-BS and each SCC, where the transmit precoding matrix for users is projected onto the null space of interference channels. Following this, we formulate a power allocation optimization problem to maximize the downlink sum rate of the system subject to per-SCC power and users’ quality of service requirements. Since the formulated problem is nonconvex, we transform it into a convex one by appropriate approximation, and a constrained concave convex procedure based iterative algorithm is proposed to obtain the solution. Furthermore, we prove that the obtained solution converges to the local optimum. Meanwhile, according to the structure of the original formulated problem, we divide it into multiple subproblems and propose an optimal iterative water-filling algorithm. Next, we extend the original problem with per-SBS power constraint, which results in that the precoding of each SCC has to be redesigned. On this basis, we propose an iterative water-filling algorithm to obtain the optimal power allocation and precoding. Numerical results are presented to verify the performance of our proposed algorithms.


title: Multi-Scale multi-band densenets for audio source separation

abstract: This paper deals with the problem of audio source separation. To handle the complex and ill-posed nature of the problems of audio source separation, the current state-of-the-art approaches employ deep neural networks to obtain instrumental spectra from a mixture. In this study, we propose a novel network architecture that extends the recently developed densely connected convolutional network (DenseNet), which has shown excellent results on image classification tasks. To deal with the specific problem of audio source separation, an up-sampling layer, block skip connection and band-dedicated dense blocks are incorporated on top of DenseNet. The proposed approach takes advantage of long contextual information and outperforms state-of-the-art results on SiSEC 2016 competition by a large margin in terms of signal-to-distortion ratio. Moreover, the proposed architecture requires significantly fewer parameters and considerably less training time compared with other methods.


title: On Speculative Enactments

abstract: Speculative Enactments are a novel approach to speculative design research with participants. They invite the empirical analysis of participants acting amidst speculative but consequential circumstances. HCI as a broadly pragmatic, experience-centered, and participant-focused field is well placed to innovate methods that invite first-hand interaction and experience with speculative design projects. We discuss three case studies of this approach in practice, based on our own work: Runner Spotters, Metadating and a Quantified Wedding. In distinguishing Speculative Enactments we offer not just practical guidelines, but a set of conceptual resources for researchers and practitioners to critique the different contributions that speculative approaches make to HCI discourse.


title: Kinematics analysis and simulation of a five-bar Cobot

abstract: Cobot (collaborative robot) is a new kind of robot, and it can cooperate with an operator to accomplish a task. The operator provides the force to move the Cobot end. At the same time the Cobot controls the trajectory of its end,making it move along the desired trajectory. Here the complex number vector method is used to analyze the direct and inverse kinematics of a kind of five-bar Cobot. The kinematics function was deduced. The simulation results of working space, displacement, speed and constraint characteristics were given, showing that the kinematics function is correct and the trajectory constraint control is feasible.


title: Securing handheld devices and fingerprint readers with ECG biometrics

abstract: This paper investigates the feasibility of collecting viable biometric electrocardiogram (ECG) signals from fingertips. A system for biometric recognition on handheld electronic devices is proposed and analyzed from a flexibility and a permanence point of view. The recognition algorithm is based on the previously proposed Autocorrelation/ Linear Discriminant Analysis (AC/LDA) [3]. In order to assess the flexibility of the acquisition procedure from the fingertips, various electrode configurations are examined along with signal permanence over different signal collection periods. The experimental results indicate very promising error rates (EER of 8.7% over 22 subjects), for ECG data obtained when touching a sensing surface with the fingertips. The envisioned applications include security on handheld electronic devices, such as smartphones and smart cards, as well as multimodal systems which combine ECG and fingerprint biometric readers.


title: Supervised Morphological Segmentation in a Low-Resource Learning Setting using Conditional Random Fields

abstract: We discuss data-driven morphological segmentation, in which word forms are segmented into morphs, the surface forms of morphemes. Our focus is on a lowresource learning setting, in which only a small amount of annotated word forms are available for model training, while unannotated word forms are available in abundance. The current state-of-art methods 1) exploit both the annotated and unannotated data in a semi-supervised manner, and 2) learn morph lexicons and subsequently uncover segmentations by generating the most likely morph sequences. In contrast, we discuss 1) employing only the annotated data in a supervised manner, while entirely ignoring the unannotated data, and 2) directly learning to predict morph boundaries given their local sub-string contexts instead of learning the morph lexicons. Specifically, we employ conditional random fields, a popular discriminative log-linear model for segmentation. We present experiments on two data sets comprising five diverse languages. We show that the fully supervised boundary prediction approach outperforms the state-of-art semi-supervised morph lexicon approaches on all languages when using the same annotated data sets.


title: Dependency Injection for Programming by Optimization

abstract: Programming by Optimization tools perform automatic software configuration according to the specification supplied by a software developer. Developers specify design spaces for program components, and the onerous task of determining which configuration best suits a given use case is determined using automated analysis tools and optimization heuristics. However, in current approaches to Programming by Optimization, design space specification and exploration relies on external configuration algorithms, executable wrappers and fragile, preprocessed programming language extensions. ::: Here we show that the architectural pattern of Dependency Injection provides a superior alternative to the traditional Programming by Optimization pipeline. We demonstrate that configuration tools based on Dependency Injection fit naturally into the software development process, while requiring less overhead than current wrapper-based mechanisms. Furthermore, the structural correspondence between Dependency Injection and context-free grammars yields a new class of evolutionary metaheuristics for automated algorithm configuration. We found that the new heuristics significantly outperform existing configuration algorithms on many problems of interest (in one case by two orders of magnitude). We anticipate that these developments will make Programming by Optimization immediately applicable to a large number of enterprise software projects.


title: Design and Implementation of a WiFi Based Home Automation System

abstract: This paper presents a design and prototype implementation of new home automation system that uses WiFi technology as a network infrastructure connecting its parts. The proposed system consists of two main components; the first part is the server (web server), which presents system core that manages, controls, and monitors users' home. Users and system administrator can locally (LAN) or remotely (internet) manage and control system code. Second part is hardware interface module, which provides appropriate interface to sensors and actuator of home automation system. Unlike most of available home automation system in the market the proposed system is scalable that one server can manage many hardware interface modules as long as it exists on WiFi network coverage. System supports a wide range of home automation devices like power management components, and security components. The proposed system is better from the scalability and flexibility point of view than the commercially available home automation systems. Keywords—Home automation, Wireless LAN, WiFi, MicroControllers


Models and theories of prescribing decisions: A review and suggested a new model


Reduced graphene oxide–silver nanoparticle nanocomposite: a potential anticancer nanotherapy


title: The evolutionary origins of modularity

abstract: A central biological question is how natural organisms are so evolvable (capable of quickly adapting to new environments). A key driver of evolvability is the widespread modularity of biological networks—their organization as functional, sparsely connected subunits—but there is no consensus regarding why modularity itself evolved. Although most hypotheses assume indirect selection for evolvability, here we demonstrate that the ubiquitous, direct selection pressure to reduce the cost of connections between network nodes causes the emergence of modular networks. Computational evolution experiments with selection pressures to maximize network performance and minimize connection costs yield networks that are significantly more modular and more evolvable than control experiments that only select for performance. These results will catalyse research in numerous disciplines, such as neuroscience and genetics, and enhance our ability to harness evolution for engineering purposes.


title: Automatically Generating Personalized User Interfaces with Supple

abstract: Today's computer-human interfaces are typically designed with the assumption that they are going to be used by an able-bodied person, who is using a typical set of input and output devices, who has typical perceptual and cognitive abilities, and who is sitting in a stable, warm environment. Any deviation from these assumptions may drastically hamper the person's effectiveness-not because of any inherent barrier to interaction, but because of a mismatch between the person's effective abilities and the assumptions underlying the interface design. We argue that automatic personalized interface generation is a feasible and scalable solution to this challenge. We present our Supple system, which can automatically generate interfaces adapted to a person's devices, tasks, preferences, and abilities. In this paper we formally define interface generation as an optimization problem and demonstrate that, despite a large solution space (of up to 10^1^7 possible interfaces), the problem is computationally feasible. In fact, for a particular class of cost functions, Supple produces exact solutions in under a second for most cases, and in a little over a minute in the worst case encountered, thus enabling run-time generation of user interfaces. We further show how several different design criteria can be expressed in the cost function, enabling different kinds of personalization. We also demonstrate how this approach enables extensive user- and system-initiated run-time adaptations to the interfaces after they have been generated. Supple is not intended to replace human user interface designers-instead, it offers alternative user interfaces for those people whose devices, tasks, preferences, and abilities are not sufficiently addressed by the hand-crafted designs. Indeed, the results of our study show that, compared to manufacturers' defaults, interfaces automatically generated by Supple significantly improve speed, accuracy and satisfaction of people with motor impairments.