title: Socioeconomic differences in attitudes and beliefs about healthy lifestyles

abstract: STUDY OBJECTIVE ::: s: The factors underlying socioeconomic status differences in smoking, leisure time physical activity, and dietary choice are poorly understood. This study investigated attitudes and beliefs that might underlie behavioural choices, including health locus of control, future salience, subjective life expectancy, and health consciousness, in a nationally representative sample. ::: ::: ::: DESIGN ::: Data were collected as part of the monthly Omnibus survey of the Office of National Statistics in Britain. ::: ::: ::: PARTICIPANTS ::: A stratified, probability sample of 2728 households was selected by random sampling of addresses. One adult from each household was interviewed. ::: ::: ::: MAIN RESULTS ::: Higher SES respondents were less likely to smoke and more likely to exercise and eat fruit and vegetables daily. Lower SES was associated with less health consciousness (thinking about things to do to keep healthy), stronger beliefs in the influence of chance on health, less thinking about the future, and lower life expectancies. These attitudinal factors were in turn associated with unhealthy behavioural choices, independently of age, sex, and self rated health. ::: ::: ::: CONCLUSIONS ::: Socioeconomic differences in healthy lifestyles are associated with differences in attitudes to health that may themselves arise through variations in life opportunities and exposure to material hardship and ill health over the life course.


title: Map-Reduce for Machine Learning on Multicore

abstract: We are at the beginning of the multicore era. Computers will have increasingly many cores (processors), but there is still no good programming framework for these architectures, and thus no simple and unified way for machine learning to take advantage of the potential speed up. In this paper, we develop a broadly applicable parallel programming method, one that is easily applied to many different learning algorithms. Our work is in distinct contrast to the tradition in machine learning of designing (often ingenious) ways to speed up a single algorithm at a time. Specifically, we show that algorithms that fit the Statistical Query model [15] can be written in a certain "summation form," which allows them to be easily parallelized on multicore computers. We adapt Google's map-reduce [7] paradigm to demonstrate this parallel speed up technique on a variety of learning algorithms including locally weighted linear regression (LWLR), k-means, logistic regression (LR), naive Bayes (NB), SVM, ICA, PCA, gaussian discriminant analysis (GDA), EM, and backpropagation (NN). Our experimental results show basically linear speedup with an increasing number of processors.


title: Cloud E-health Systems: A Survay on Security Challenges and Solutions

abstract: Cloud progression have led it to be adapted in many aspects and application that serve human demands. One of the recent trends in cloud computing is the e-health services, as many healthcare institutes have begun to interchange toward the digitalized version of health records. E-health provide easy sharing of personal health care records between multiple organizations and offered real-time monitoring for the patient's state via storing this data over the cloud servers. However, having the data in such a distributed setting rises the need for having a secured data sharing with fine- grained access control, as the records are stored in a third-party service provider that is considered a semi trusted. Also, many different aspects of security should be taken into count with e-health, for instance the data is critical and should be only available to its owners. Numerous works have been done to secure the medical data in cloud environment. This work focuses on the security challenges and solutions for on Cloud-based E-health systems. Specifically, a state of the art is presented to preserve and protect E-health data, which consists of two tiers: cryptographic and non-cryptographic approach. Also, some of main issues and open problems that can be used in future studies are pointed out in this work.


title: A deep learning model generation method for code reuse and automatic machine learning

abstract: In recent years, deep neural networks are active in numerous applications such as predictive, advertising and healthcare applications using of image, voice and text recognitions. However, deep neural networks are useful methods but usually require a proper modeling to construct a deep neural network method in any application. Designing a model is a tedious task to be realized in the network, which opens an issue to design an effective software tool for modeling deep neural networks. To get an excellent model for deep neural networks, the developers should have sufficient understanding and experience for deep neural network methods. The developers also require coding skills with the deep learning frameworks and knowledge for the computing resources. This paper presents a software tool based on a Graphical User Interface (GUI) to develop deep neural network models, which train the models with external computing resources and automate the hyper-parameter tuning.


title: A Comparative Study of Breast Cancer Diagnosis Using Supervised Machine Learning Techniques

abstract: Cancer is a class of diseases, which is driven by change in cells of the body and increase beyond normal growth and control. Breast cancer is one of the frequent types of cancer. Prognosis of breast cancer recurrence is highly required to raise the survival rate of patient suffering from breast cancer. With the advancement of technology and machine learning techniques, the cancer diagnosis and detection accuracy has improved. Machine learning (ML) techniques offer various probabilistic and statistical methods that allow intelligent systems to learn from reoccurring past experiences to detect and identify patterns from a dataset. The research work presented an overview of evolve the machine learning techniques in cancer disease by applying learning algorithms on breast cancer Wisconsin data –Linear regression, Random Forest, Multi-layer Perceptron and Decision Trees (DT). The result outcome shows that Multilayer perceptron performs better than other techniques.


title: REAL-TIME FULLY AUTOMATED RESTAURANT MANAGEMENT AND COMMUNICATION SYSTEM “RESTO”

abstract: Existing restaurant systems are partially automated. We have developed a fully automated restaurant management and communication system. This paper covers the partial automation that exists and focuses the details regarding how we have implemented full automation in management and communication for restaurants. It also covers technical aspects of web based application and android application and how they are clubbed together with their advantages and disadvantages. It also lights the future work that can be added on regarding android application to make it more user friendly.


Construction Business Management


title: SENSOR-BASED OCCUPANCY BEHAVIORAL PATTERN RECOGNITION FOR ENERGY AND COMFORT MANAGEMENT IN INTELLIGENT BUILDINGS

abstract: There has been extensive research focusing on developing smart environments by integrating data mining techniques into environments that are equipped with sensors and actuators. The ultimate goal is to reduce the energy consumption in buildings while maintaining a maximum comfort level for occupants. However, there are few studies successfully demonstrating energy savings from occupancy behavioural patterns that have been learned in a smart environment because of a lack of a formal connection to building energy management systems. In this study, the objective is to develop and implement algorithms for sensor-based modelling and prediction of user behaviour in intelligent buildings and connect the behavioural patterns to building energy and comfort management systems through simulation tools. The results are tested on data from a room equipped with a distributed set of sensors, and building simulations through EnergyPlus suggest potential energy savings of 30% while maintaining an indoor comfort level when compared with other basic energy savings HVAC control strategies.


title: Acupuncture needling sensation: The neural correlates of deqi using fMRI

abstract: Abstract The needling sensation of deqi is considered by most acupuncturists to be an important component of acupuncture, yet neuroimaging research that investigates this needle sensation has been limited. In this study we have investigated the effect of deqi and acute pain needling sensations upon brain fMRI blood oxygen level-dependent (BOLD) signals . Seventeen right-handed participants who received acupuncture at the right LI-4 ( Hegu ) acupoint were imaged in a 3T MRI scanner. fMRI datasets were classified, on the basis of psychophysical participants' reports of needling scores, into those that were associated with predominantly deqi sensations versus those with predominantly acute pain sensations. Brain areas showing changes in BOLD signal increases (activations) and decreases (deactivations) were identified. Differences were demonstrated in the pattern of activations and deactivations between groupings of scans associated with deqi versus pain sensations. For the deqi grouping, significant deactivations occurred, whereas significant activations did not. In contrast, the predominantly acute pain grouping was associated with a mixture of activations and deactivations. For the comparison between the predominately deqi sensation grouping and the acute pain sensation grouping (deqi > pain contrast), only negative Z value voxels resulted (mainly from deactivations in the deqi grouping and activations in the pain grouping) in the limbic/sub-cortical structures and the cerebellum regions of interest. Our results show the importance of collecting and accounting for needle sensation data in neuroimaging studies of acupuncture.


title: Stochastic Methods for Transistor Size Optimization of CMOS VLSI Circuits

abstract: The performance of a CMOS circuit depends heavily on its transistor sizes. We have tested a standard optimizer, a Monte Carlo scheme and a method based on Genetic Algorithms combined with very accurate SPICE simulations to automatically optimize transistor sizes of three different digital CMOS circuits. While the standard optimizer and the Monte Carlo scheme are advantageous for small circuits, the method based on Genetic Algorithms was found to be more stable for larger circuits.


title: Open Source Social Media Analytics for Intelligence and Security Informatics Applications

abstract: Open-Source Intelligence OSINT is intelligence collected and inferred from publicly available and overt sources of information. Open-Source social media intelligence is a sub-field within OSINT with a focus on extracting insights from publicly available data in Web 2.0 platforms like Twitter micro-blogging website, YouTube video-sharing website and Facebook social-networking website. In this paper, we present an overview of Intelligence and Security Informatics ISI applications in the domain of open-source social media intelligence. We present technical challenges and introduce basic Machine Learning based framework, tools and techniques within the context of open-source social media intelligence using two case-studies. The focus of the paper is on mining free-form textual content present in social media websites. In particular we describe two important application: online radicalization and civil unrest. In addition to covering basic concepts and applications, we discuss open research problem, important papers, publication venues, research results and future directions.


Resilient Datacenter Load Balancing in the Wild SIGCOMM ’


title: Simulation optimization: A review of algorithms and applications

abstract: Simulation optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation. To address specific features of a particular simulation—discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise—various algorithms have been proposed in the literature. As one can imagine, there exist several competing algorithms for each of these classes of problems. This document emphasizes the difficulties in SO as compared to algebraic model-based mathematical programming, makes reference to state-of-the-art algorithms in the field, examines and contrasts the different approaches used, reviews some of the diverse applications that have been tackled by these methods, and speculates on future directions in the field.


title: ORGM: Occlusion Relational Graphical Model for Human Pose Estimation

abstract: Articulated human pose estimation from monocular image is a challenging problem in computer vision. Occlusion is a main challenge for human pose estimation, which is largely ignored in popular tree structured models. The tree structured model is simple and convenient for exact inference, but short in modeling the occlusion coherence especially in the case of self-occlusion. We propose an occlusion relational graphical model, which is able to model both self-occlusion and occlusion by the other objects simultaneously. The proposed model can encode the interactions between human body parts and objects, and enables it to learn occlusion coherence from data discriminatively. We evaluate our model on several public benchmarks for human pose estimation, including challenging subsets featuring significant occlusion. The experimental results show that our method is superior to the previous state-of-the-arts, and is robust to occlusion for 2D human pose estimation.


title: Absent Subjects and Empty Centers: Eichendorff's Romantic Phantasmagoria and Schumann's Liederkreis, Op. 39

abstract: A recurring theme in the reception of Schumann9s Eichendorff Liederkreis is the question mark over its sense of narrative continuity and the presence (or otherwise) of a central protagonist. Up until now, however, scarcely any attempt has been made to view these features in the context of Eichendorff9s wider literary production. This article proposes applying an Eichendorffian aesthetic to Schumann9s op. 39, viewing its phantasmagoric interconnections, absence of clear narrative order, sense of temporal dislocation and persistent theme of the loss of self as profoundly reflecting the concerns of Eichendorff9s prose fiction. Neither the view that Schumann9s cycle does possess a unified narrative and central protagonist, nor the converse, that it should be seen as a disparate group of songs, is adequate. Instead, it is the tension between the two views that emerges as crucial in coming to an aesthetic understanding of the cycle. Schumann9s procedure, in juxtaposing a number of poems drawn from disparate works, presents an extreme case whereby narrative and subjective identity are put to the test, and the listener is invited to fill the vacant space left by the withdrawal of a unifying subject with his or her own sense of subjectivity.


title: Gino - a guided input natural language ontology editor

abstract: The casual user is typically overwhelmed by the formal logic of the Semantic Web. The gap between the end user and the logic-based scaffolding has to be bridged if the Semantic Web’s capabilities are to be utilized by the general public. This paper proposes that controlled natural languages offer one way to bridge the gap. We introduce GINO, a guided input natural language ontology editor that allows users to edit and query ontologies in a language akin to English. It uses a small static grammar, which it dynamically extends with elements from the loaded ontologies. The usability evaluation shows that GINO is well-suited for novice users when editing ontologies. We believe that the use of guided entry overcomes thehabitability problem, which adversely affects most natural language systems. Additionally, the approach’s dynamic grammar generation allows for easy adaptation to new ontologies.


title: When Reverse-Engineering Meets Side-Channel Analysis – Digital Lockpicking in Practice

abstract: In the past years, various electronic access control systems have been found to be insecure. In consequence, attacks have emerged that permit unauthorized access to secured objects. One of the few remaining, allegedly secure digital locking systems--the system 3060 manufactured and marketed by SimonsVoss--is employed in numerous objects worldwide. Following the trend to analyze the susceptibility of real-world products towards implementation attacks, we illustrate our approach to understand the unknown embedded system and its components. Detailed investigations are performed in a step-by-step process, including the analysis of the communication between transponder and lock, reverse-engineering of the hardware, bypassing the read-out protection of a microcontroller, and reverse-engineering the extracted program code. Piecing all parts together, the security mechanisms of the system can be completely circumvented by means of implementation attacks. We present an EM side-channel attack for extracting the secret system key from a door lock. This ultimately gives access to all doors of an entire installation. Our technique targets a proprietary function used in combination with a DES for key derivation, probably originally implemented as an obscurity-based countermeasure to prevent attacks.


title: Identification of Failure Mechanisms to Enhance Prognostic Outcomes

abstract: Predicting the reliability of a system in its actual life cycle conditions and estimating its time to failure is helpful in decision making to mitigate system risks. There are three approaches to prognostics: the physics-of-failure approach, the data-driven approach, and the fusion approach. A key requirement in all these approaches is the identification of the appropriate parameter(s) to monitor the collection of the data that can be employed to assess impending failure. This article presents the physics-of-failure approach, which uses failure modes, mechanisms, and effects analysis (FMMEA) to enhance prognostics planning and implementation. This article also presents the fusion approach to prognostics and the applicability of FMMEA to this approach. As an example, a case of generating FMMEA information, and using that to identify appropriate parameters to monitor, is presented.


title: Speech Emotion Recognition Considering Local Dynamic Features

abstract: Recently, increasing attention has been directed to the study of the speech emotion recognition, in which global acoustic features of an utterance are mostly used to eliminate the content differences. However, the expression of speech emotion is a dynamic process, which is reflected through dynamic durations, energies, and some other prosodic information when one speaks. In this paper, a novel local dynamic pitch probability distribution feature, which is obtained by drawing the histogram, is proposed to improve the accuracy of speech emotion recognition. Compared with most of the previous works using global features, the proposed method takes advantage of the local dynamic information conveyed by the emotional speech. Several experiments on Berlin Database of Emotional Speech are conducted to verify the effectiveness of the proposed method. The experimental results demonstrate that the local dynamic information obtained with the proposed method is more effective for speech emotion recognition than the traditional global features.


title: Exploring Data Augmentation for Improved Singing Voice Detection with Neural Networks

abstract: In computer vision, state-of-the-art object recognition systems rely on label-preserving image transformations such as scaling and rotation to augment the training datasets. The additional training examples help the system to learn invariances that are difficult to build into the model, and improve generalization to unseen data. To the best of our knowledge, this approach has not been systematically explored for music signals. Using the problem of singing voice detection with neural networks as an example, we apply a range of label-preserving audio transformations to assess their utility for music data augmentation. In line with recent research in speech recognition, we find pitch shifting to be the most helpful augmentation method. Combined with time stretching and random frequency filtering, we achieve a reduction in classification error between 10 and 30%, reaching the state of the art on two public datasets. We expect that audio data augmentation would yield significant gains for several other sequence labelling and event detection tasks in music information retrieval.


title: Hadoop Based Scalable Cluster Deduplication for Big Data

abstract: The exponential growth of data has brought a tremendous challenge on the storage system in data center. Data deduplication technology which detects and eliminates redundant data in the dataset can greatly reduce the quantity of data and optimize the utilization of storage space. This paper presented a scalable and reliable cluster deduplication system Halodedu over the Hadoop-based cloud computing platform. Halodedu used MapReduce and HDFS to realize parallel deduplication processing and manage data storage, respectively. Intra-node local database was used to build up a fast and distributed chunk fingerprint index management. In order to maintain the availability and reliability of metadata, HBase was utilized to store the metadata of backup files. We further used virtual machine images as input dataset to evaluate Halodedu. The comparative experiments demonstrated that Halodedu has improvements on deduplication speed and system scalability.


title: A Provably Secure True Random Number Generator with Built-In Tolerance to Active Attacks

abstract: This paper is a contribution to the theory of true random number generators based on sampling phase jitter in oscillator rings. After discussing several misconceptions and apparently insurmountable obstacles, we propose a general model which, under mild assumptions, will generate provably random bits with some tolerance to adversarial manipulation and running in the megabit-per-second range. A key idea throughout the paper is the fill rate, which measures the fraction of the time domain in which the analog output signal is arguably random. Our study shows that an exponential increase in the number of oscillators is required to obtain a constant factor improvement in the fill rate. Yet, we overcome this problem by introducing a postprocessing step which consists of an application of an appropriate resilient function. These allow the designer to extract random samples only from a signal with only moderate fill rate and, therefore, many fewer oscillators than in other designs. Last, we develop fault-attack models and we employ the properties of resilient functions to withstand such attacks. All of our analysis is based on rigorous methods, enabling us to develop a framework in which we accurately quantify the performance and the degree of resilience of the design


title: Computational Differential Privacy

abstract: The definition of differential privacy has recently emerged as a leading standard of privacy guarantees for algorithms on statistical databases. We offer several relaxations of the definition which require privacy guarantees to hold only against efficient--i.e., computationally-bounded--adversaries. We establish various relationships among these notions, and in doing so, we observe their close connection with the theory of pseudodense sets by Reingold et al.[1]. We extend the dense model theorem of Reingold et al. to demonstrate equivalence between two definitions (indistinguishability- and simulatability-based) of computational differential privacy. ::: ::: Our computational analogues of differential privacy seem to allow for more accurate constructions than the standard information-theoretic analogues. In particular, in the context of private approximation of the distance between two vectors, we present a differentially-private protocol for computing the approximation, and contrast it with a substantially more accurate protocol that is only computationally differentially private.


Graphical Models for Discovering Knowledge


Gland Segmentation and Computerized Gleason Grading of Prostate Histology by Integrating Low-, High-level and Domain Specific Information


title: Adversarial Examples that Fool Detectors

abstract: An adversarial example is an example that has been adjusted to produce a wrong label when presented to a system at test time. To date, adversarial example constructions have been demonstrated for classifiers, but not for detectors. If adversarial examples that could fool a detector exist, they could be used to (for example) maliciously create security hazards on roads populated with smart vehicles. In this paper, we demonstrate a construction that successfully fools two standard detectors, Faster RCNN and YOLO. The existence of such examples is surprising, as attacking a classifier is very different from attacking a detector, and that the structure of detectors - which must search for their own bounding box, and which cannot estimate that box very accurately - makes it quite likely that adversarial patterns are strongly disrupted. We show that our construction produces adversarial examples that generalize well across sequences digitally, even though large perturbations are needed. We also show that our construction yields physical objects that are adversarial.


On User Choice for Android Unlock Patterns


title: Aminophylline for the prevention of apnea during prostaglandin E1 infusion.

abstract: BACKGROUND ::: Apnea is associated with prostaglandin E1 infusion (PGE1) used in the palliation of ductal-dependent congenital heart lesions. ::: ::: ::: HYPOTHESIS ::: Aminophylline is a central respiratory stimulant and will decrease the incidence of PGE1-associated apnea and the need for intubation for apnea in infants with ductal-dependent congenital heart disease. ::: ::: ::: METHODS ::: Informed consent was obtained for all patients. In a prospective, double-blinded, placebo-controlled study, newborn infants with ductal-dependent congenital heart disease were randomized to receive either aminophylline or placebo during initiation and maintenance of PGE1, which was started at 0.01 microg/kg/min and increased to 0.03 microg/kg/min. Aminophylline was given as a bolus dose of 6 mg/kg before or during initiation of PGE1, and continued at 2 mg/kg dose every 8 hours for 72 hours. Serum aminophylline levels were checked at 18 and 36 hours. The primary study endpoint was intubation for apnea, with a secondary endpoint of apnea, as defined as acute cessation of breathing with associated hypoxia and bradycardia. ::: ::: ::: RESULTS ::: The study evaluated 42 infants. The 2 groups were similar for gestational age, weight, hematocrit, and use of sedation. In the aminophylline group, serum levels were 7.6 +/- 1.2 microg/mL. No significant side effects of aminophylline were seen. Infants receiving aminophylline (n = 21) were less likely to have apnea (2 vs 11) or be intubated for apnea (0 vs 6). Length of postoperative stay and survival to discharge were similar between the 2 groups. ::: ::: ::: CONCLUSIONS ::: Aminophylline was effective for the prevention of apnea and intubation for apnea associated with PGE1 in infants with ductal-dependent congenital heart disease.


title: Lazy Random Walks for Superpixel Segmentation

abstract: We present a novel image superpixel segmentation approach using the proposed lazy random walk (LRW) algorithm in this paper. Our method begins with initializing the seed positions and runs the LRW algorithm on the input image to obtain the probabilities of each pixel. Then, the boundaries of initial superpixels are obtained according to the probabilities and the commute time. The initial superpixels are iteratively optimized by the new energy function, which is defined on the commute time and the texture measurement. Our LRW algorithm with self-loops has the merits of segmenting the weak boundaries and complicated texture regions very well by the new global probability maps and the commute time strategy. The performance of superpixel is improved by relocating the center positions of superpixels and dividing the large superpixels into small ones with the proposed optimization algorithm. The experimental results have demonstrated that our method achieves better performance than previous superpixel approaches.


title: Use of barbed threads in facial rejuvenation

abstract: Use of barbed threads, available with uni- and bi-directional cogs or barbs, is a semi-invasive method of lifting sagging skin of the face. Areas treated with this method include the eyebrows, the cheeks, the jowls and the neck. Ease of use and a shorter down-time have made their use popular. Specific indications, operative procedures, risks and complications are described and some clinical results of the author shown.


title: Low-Profile Two-Degree-of-Freedom Wrist Exoskeleton Device Using Multiple Spring Blades

abstract: Robotically assisted rehabilitation therapy, which is therapy performed using a robotic device that guides or assists motion and in some cases triggered by a biological signal (such as a myoelectric signal or brain signal) is effective in recovering motor function following impairment. In the field of rehabilitation robots and human support robot, exoskeleton robots have been widely studied both for upper and lower limbs. Applying the exoskeleton to a wrist joint remains challenging because the wrist has two degrees of freedom in a structure smaller than the structures of other upper and lower limb joints and the center of rotation moves according to motion owing to the complex anatomical structure. To tackle this problem, we developed a wrist exoskeleton mechanism that consists of two elastic elements that largely deform during motion in transmitting forces from two linear actuators implemented on the user's forearm and in transforming the forces to wrist flexion/extension and adduction/abduction. The advantages of the presented device are the device's compactness, low weight, and inherent flexibility due to the elastic structure. The present letter presents an overview of the newly developed wrist exoskeleton mechanism, prototype implementation, and preliminary mechanical evaluations. The technical verification of the exoskeleton revealed the feasibility of generating forces that can generate wrist flexion/extension and adduction/abduction movements.


Context Based Approach for Second Language Acquisition


title: A 6.4-Gb/s CMOS SerDes core with feed-forward and decision-feedback equalization

abstract: A 4.9-6.4-Gb/s two-level SerDes ASIC I/O core employing a four-tap feed-forward equalizer (FFE) in the transmitter and a five-tap decision-feedback equalizer (DFE) in the receiver has been designed in 0.13-/spl mu/m CMOS. The transmitter features a total jitter (TJ) of 35 ps p-p at 10/sup -12/ bit error rate (BER) and can output up to 1200 mVppd into a 100-/spl Omega/ differential load. Low jitter is achieved through the use of an LC-tank-based VCO/PLL system that achieves a typical random jitter of 0.6 ps over a phase noise integration range from 6 MHz to 3.2 GHz. The receiver features a variable-gain amplifier (VGA) with gain ranging from -6to +10dB in /spl sim/1dB steps, an analog peaking amplifier, and a continuously adapted DFE-based data slicer that uses a hybrid speculative/dynamic feedback architecture optimized for high-speed operation. The receiver system is designed to operate with a signal level ranging from 50 to 1200 mVppd. Error-free operation of the system has been demonstrated on lossy transmission line channels with over 32-dB loss at the Nyquist (1/2 Bd rate) frequency. The Tx/Rx pair with amortized PLL power consumes 290 mW of power from a 1.2-V supply while driving 600 mVppd and uses a die area of 0.79 mm/sup 2/.


title: Primary care supports for children with chronic health conditions: identifying and predicting unmet family needs.

abstract: OBJECTIVE ::: To examine unmet needs among families of children with chronic health conditions treated in primary care settings and to identify predictors of these needs. ::: ::: ::: METHOD ::: Primary care physicians referred 83 caregivers of children with chronic health conditions. Mothers completed the Family Needs Survey, as well as other measures of child and family functioning. ::: ::: ::: RESULTS ::: Mothers reported a high prevalence and broad range of unmet family needs. The most frequent area of need was for information about services and ways to promote child health and development. Predictors of total number of family needs included demographic characteristics, ratings of social support, and appraisals of family burden. Predictors of specific types of family needs varied according to category of need. ::: ::: ::: CONCLUSIONS ::: Innovative psychosocial intervention programs are needed in primary care settings to reduce family needs and promote child health. More intensive family supports may be indicated for those with minority-group or low socioeconomic status, limited social support, or high perceived burden.


title: A Comparative Analysis of Front-End and Back-End Compatible Silicon Photonic On-Chip Interconnects

abstract: Photonic devices fabricated with back-end compatible silicon pho-tonic (BCSP) materials can provide independence from the complex CMOS front-end compatible silicon photonic (FCSP) process, to sig-nificantly enhance photonic network-on-chip (PNoC) architecture performance. In this paper, we present a detailed comparative analy-sis of a number of design tradeoffs for CMOS front-end and back-end compatible devices for silicon photonic interconnects. A cross-layer optimization of multiple device-level and link-level design pa-rameters is performed to enable the design of energy-efficient on-chip photonic interconnects using BCSP devices. The optimized design of BCSP on-chip links renders more energy-efficiency and aggregate bandwidth than FCSP on-chip links, in spite of the inferior opto-elec-tronic properties of BCSP devices. Our experimental analysis com-pares the use of BCSP and FCSP links at the architecture level, and shows that the optimized design of the BCSP-based Firefly PNoC achieves 1.15x greater throughput and 12.4% less energy-per-bit on average than the optimized design of FCSP-based Firefly PNoC. Similarly, the optimized design of the BCSP-based Corona PNoC achieves 3.5x greater throughput and 39.5% less energy-per-bit on average than the optimized design of FCSP-based Corona PNoC.


title: Car Segmentation and Pose Estimation using 3D Object Models

abstract: Image segmentation and 3D pose estimation are two key cogs in any algorithm for scene understanding. However, state-of-the-art CRF-based models for image segmentation rely mostly on 2D object models to construct top-down high-order potentials. In this paper, we propose new top-down potentials for image segmentation and pose estimation based on the shape and volume of a 3D object model. We show that these complex top-down potentials can be easily decomposed into standard forms for efficient inference in both the segmentation and pose estimation tasks. Experiments on a car dataset show that knowledge of segmentation helps perform pose estimation better and vice versa.


title: Genomic Evidence Establishes Anatolia as the Source of the European Neolithic Gene Pool

abstract: Anatolia and the Near East have long been recognized as the epicenter of the Neolithic expansion through archaeological evidence. Recent archaeogenetic studies on Neolithic European human remains have shown that the Neolithic expansion in Europe was driven westward and northward by migration from a supposed Near Eastern origin [1-5]. However, this expansion and the establishment of numerous culture complexes in the Aegean and Balkans did not occur until 8,500 before present (BP), over 2,000 years after the initial settlements in the Neolithic core area [6-9]. We present ancient genome-wide sequence data from 6,700-year-old human remains excavated from a Neolithic context in Kumtepe, located in northwestern Anatolia near the well-known (and younger) site Troy [10]. Kumtepe is one of the settlements that emerged around 7,000 BP, after the initial expansion wave brought Neolithic practices to Europe. We show that this individual displays genetic similarities to the early European Neolithic gene pool and modern-day Sardinians, as well as a genetic affinity to modern-day populations from the Near East and the Caucasus. Furthermore, modern-day Anatolians carry signatures of several admixture events from different populations that have diluted this early Neolithic farmer component, explaining why modern-day Sardinian populations, instead of modern-day Anatolian populations, are genetically more similar to the people that drove the Neolithic expansion into Europe. Anatolia's central geographic location appears to have served as a connecting point, allowing a complex contact network with other areas of the Near East and Europe throughout, and after, the Neolithic.


title: Evaluation of discovered clinical pathways using process mining and joint agent-based discrete-event simulation

abstract: The analysis of clinical pathways from event logs provides new insights about care processes. In this paper, we propose a new methodology to automatically perform simulation analysis of patients' clinical pathways based on a national hospital database. Process mining is used to build highly representative causal nets, which are then converted to state charts in order to be executed. A joint multi-agent discrete-event simulation approach is used to implement models. A practical case study on patients having cardiovascular diseases and eligible to receive an implantable defibrillator is provided. A design of experiments has been proposed to study the impact of medical decisions, such as implanting or not a defibrillator, on the relapse rate, the death rate and the cost. This approach has proven to be an innovative way to extract knowledge from an existing hospital database through simulation, allowing the design and test of new scenarios.


title: A low noise amplifier employing noise canceling technique for ultrasound system applications

abstract: In this paper, the noise canceling technique is proposed to enhance the performance of ultrasound low noise amplifier (LNA). This not only diminishes effectively noise figure (NF) but also increases voltage gain. Besides, by utilizing this methodology, the proposed LNA can have lower power consumption than the conventional one does. An ultrasound LNA example designed in TSMC 0.18-μm CMOS process shows 4.9 dB NF min , 20 dB voltage gain and close to 100 MHz — 3-dB bandwidth with 20.3 mW power consumption.


title: Analysis of road traffic fatal accidents using data mining techniques

abstract: Roadway traffic safety is a major concern for transportation governing agencies as well as ordinary citizens. In order to give safe driving suggestions, careful analysis of roadway traffic data is critical to find out variables that are closely related to fatal accidents. In this paper we apply statistics analysis and data mining algorithms on the FARS Fatal Accident dataset as an attempt to address this problem. The relationship between fatal rate and other attributes including collision manner, weather, surface condition, light condition, and drunk driver were investigated. Association rules were discovered by Apriori algorithm, classification model was built by Naive Bayes classifier, and clusters were formed by simple K-means clustering algorithm. Certain safety driving suggestions were made based on statistics, association rules, classification model, and clusters obtained.


title: Detecting Phishing Websites using Data Mining

abstract: Phishing is one of the major cyber threats now, where the victims' credentials are obtained by an illegitimate website. This paper proposes a system which will detect old as well as newly generated phishing URLs that have completely no past behaviours to judge upon, using Data Mining. A cloud-based classification model will be created for the same wherein various extracted attributes through the URL will be used as input data. The model will be trained with an exhaustive dataset so as to ensure maximum accuracy.


title: A Probabilistic Approach to Cross-Region Matching-Based Image Retrieval

abstract: With deep convolutional features, cross-region matching (CRM) has recently shown superior performance on image retrieval. It evaluates image similarity by comparing image regions at different locations and scales, and is, therefore, more robust to geometric variance of objects. This paper first scrutinizes CRM-based image retrieval to provide a rigorous probabilistic interpretation by following the probability ranking principle. In addition to manifesting the assumptions implicitly taken by CRM, our interpretation highlights a fundamental issue hindering the performance of CRM—when comparing two image regions, CRM ignores modeling the distribution of the visual concept class associated with an image region, making the similarity comparison less precise. Taking advantage of the unprecedented representation capability of deep convolutional features, this paper proposes one approach to tackle that issue. It treats locally clustered image regions as a pseudo-labeled class sharing the same visual concept and utilizes them to model the distribution of the visual concept class associated with an image region. Both non-parametric and parametric methods are developed for this purpose, with careful probabilistic justification. Extensive experimental study on multiple benchmark data sets demonstrates the superior performance of the proposed pseudo-label approach to CRM and other comparable methods, with the maximum improvement of more than 10 percentage points over CRM.


Obstacles In The True Integration Of Cad And Cam


Usable Simulink Embedded Coder Target for Linux


title: Joint Tracking and Ground Plane Estimation

abstract: We propose a novel framework that jointly estimates the ground plane and a target's motion trajectory. This results in improvements for both. Estimating their joint posterior is based on Particle Markov Chain Monte Carlo (Particle MCMC). In Particle MCMC, the best target state is inferred by a particle filter and the best ground plane is obtained by MCMC. Compared with conventional sampling methods that iteratively infer the best target states and ground plane parameters, our method infers them jointly. This reduces sampling errors drastically. Experimental results demonstrate that our method outperforms several state-of-the-art tracking methods, while the ground plane accuracy is also improved.


title: Enhanced hydrophobicity of rough polymer surfaces.

abstract: Molecular dynamics simulations were used to study the effect of periodic roughness of PE and PVC polymer surfaces on the hydrophobicity. Pillars of different lateral dimensions and heights were derived from flat crystalline surfaces, and the results of nanoscale simulations on the structured surfaces were compared with theoretical predictions of the Wenzel and Cassie equations. Hydrophobicity increased on all rough surfaces, but the increase was greater on the structured PE surfaces because of the larger water contact angle on the flat PE surface than the corresponding PVC surface. Equally sized pillar structures on the two polymers resulted in different equilibrium wetting geometries. Composite contacts were observed on rough PE surfaces, and the contact angle increased with decreasing contact area between the solid and the liquid. Opposite results were obtained for rough PVC surfaces; the contact angle increased with the solid-liquid contact area, in agreement with Wenzel's equation. However, the composite contact was observed if the energies of the wetted and composite contacts were almost equal. Good agreement was obtained between the simulated contact angles and equilibrium droplet shapes and the theories but there were also some limitations of the nanoscale simulations.


Improving YANGsaf F0 Estimator with Adaptive Kalman Filter.


title: Delivering Deep Learning to Mobile Devices via Offloading

abstract: Deep learning has the potential to make Augmented Reality (AR) devices smarter, but few AR apps use such technology today because it is compute-intensive, and front-end devices cannot deliver sufficient compute power. We propose a distributed framework that ties together front-end devices with more powerful back-end "helpers" that allow deep learning to be executed locally or to be offloaded. This framework should be able to intelligently use current estimates of network conditions and back-end server loads, in conjunction with the application's requirements, to determine an optimal strategy. This work reports our preliminary investigation in implementing such a framework, in which the front-end is assumed to be smartphones. Our specific contributions include: (1) development of an Android application that performs real-time object detection, either locally on the smartphone or remotely on a server; and (2) characterization of the tradeoffs between object detection accuracy, latency, and battery drain, based on the system parameters of video resolution, deep learning model size, and offloading decision.


title: A High Efficiency Flyback Converter With New Active Clamp Technique

abstract: This paper proposes a flyback converter with a new noncomplementary active clamp control method. With the proposed control method, the energy in the leakage inductance can be fully recycled. The soft switching can be achieved for the main switch and the absorbed leakage energy is transferred to the output and input side. Compared to the conventional active clamp technique, the proposed methods can achieve high efficiency both for heavy-load and light-load condition, and the efficiency is almost not affected by the leakage inductance. The detailed operation principle and design considerations are presented. Performance of the proposed circuit is validated by the experimental results from a 16 V/4 A prototype.


title: Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: A hybrid feature extraction approach

abstract: Human eye is one of the most sophisticated organ, with retina, pupil, iris cornea, lens and optic nerve. Automatic retinal image analysis is emerging as an important screening tool for early detection of eye diseases. Uncontrolled diabetes retinopathy (DR) and glaucoma may lead to blindness. DR is caused by damage to the small blood vessels of the retina in the posterior part of the eye of the diabetic patient. The main stages of DR are non-proliferate diabetes retinopathy (NPDR) and proliferate diabetes retinopathy (PDR). The retinal fundus photographs are widely used in the diagnosis and treatment of various eye diseases in clinics. It is also one of the main resources used for mass screening of DR. We present an automatic screening system for the detection of normal and DR stages (NPDR and PDR). The proposed systems involves processing of fundus images for extraction of abnormal signs, such as area of hard exudates, area of blood vessels, bifurcation points, texture and entropies. Our protocol uses total of 156 subjects consisting of two stages of DR and normal. In this work, we have fed thirteen statistically significant (p<0.0001) features for Probabilistic Neural Network (PNN), Decision Tree (DT) C4.5, and Support Vector Machine (SVM) to select the best classifier. The best model parameter (@s) for which the PNN classifier performed best was identified using global optimization techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). We demonstrated an average classification accuracy of 96.15%, sensitivity of 96.27% and specificity of 96.08% for @s=0.0104 using threefold cross validation using PNN classifier. The computer-aided diagnosis (CAD) results were validated by comparing with expert ophthalmologists. The proposed automated system can aid clinicians to make a faster DR diagnosis during the mass screening of normal/DR images.


title: The broaden-and-build theory of positive emotions

abstract: Selective investment theory (SIT) challenges us to take a fresh look at bonding and attachment. It envisions them not as special cases of reinforcement learning, but as functional and motivational forces in their own right. Over the past 10 years, our lab has taken a similar approach to the study of positive emotions such as joy, contentment, and gratitude: Rather than seeing positive emotions as mere rewards or signals of desirable circumstances, we argue that they are complex phenomena that help create adaptive behavior. Much like Brown and Brown’s (this issue) social bonds, positive emotions orient our physiology, attention, and cognition away from short-term personal survival and toward cumulative long-term benefits. We welcome Brown and Brown’s work for its contributions to the emerging study of long-term resource building, and suggest ways in which our two programs of research support, enlarge, and intertwine with one another.


title: StORMeD: stack overflow ready made data

abstract: Stack Overflow is the de facto Question and Answer (Q&A) website for developers, and it has been used in many approaches by software engineering researchers to mine useful data. However, the contents of a Stack Overflow discussion are inherently heterogeneous, mixing natural language, source code, stack traces and configuration files in XML or JSON format. We constructed a full island grammar capable of modeling the set of 700,000 Stack Overflow discussions talking about Java, building a heterogeneous abstract syntax tree (H-AST) of each post (question, answer or comment) in a discussion. The resulting dataset models every Stack Overflow discussion, providing a full H-AST for each type of structured fragment (i.e., JSON, XML, Java, Stack traces), and complementing this information with a set of basic meta-information like term frequency to enable natural language analyses. Our dataset allows the end-user to perform combined analyses of the Stack Overflow by visiting the H-AST of a discussion.


title: Modeling the anisotropy and creep in orthotropic aluminum-silicon carbide composite rotating disc

abstract: Processing of composites containing whiskers and short fibers often result in anisotropy due to preferential alignment of reinforcing element during flow as in extrusion. Analysis of steady state creep in a rotating disc made of composites containing SiC whiskers has been carried out using Hill yield criterion. The results obtained have been compared with the results obtained using von Mises yield criterion for the isotropic composites. The material parameters characterizing anisotropy have been determined from yield stresses taken from experimental results of other studies. It is observed that the tangential stress distribution is lower in the middle of the disc but higher near the inner and the outer radius but the radial stress distribution does not get significantly affected due to anisotropy. In the disc following Hill yield criterion there is small lowering of the stress beyond the point of maxima in the middle of the disc. When one compares the consequences of these changes in the stress distribution on the creep behaviour, it is observed that anisotropy has helped to reduce the tangential strain rate significantly, more near inner radius and the strain rate distribution in the orthotropic disc is lower than that in the isotropic disc following von Mises criterion. It should be noted that the anisotropy constants have been taken from the experimental results of other studies and the lowering of tangential creep rate may be significant in the context of real life engineering. The compressive radial strain rate also reduces in the disc following Hill criterion of yield plasticity as compared to that in isotropic disc. Thus, anisotropy appears to help in restraining creep response both in the tangential and in the radial directions.


title: Balancing opportunities and risks in teenagers’ use of the internet: the role of online skills and internet self-efficacy

abstract: Many hopes exist regarding the opportunities that the internet can offer to young people as well as fears about the risks it may bring. Informed by research on media literacy, this article examines the role of selected measures of internet literacy in relation to teenagers’ online experiences. Data from a national survey of teenagers in the UK (N = 789) are analyzed to examine: first, the demographic factors that influence skills in using the internet; and, second (the main focus of the study), to ask whether these skills make a difference to online opportunities and online risks. Consistent with research on the digital divide, path analysis showed the direct influence of age and socioeconomic status on young people’s access, the direct influence of age and access on their use of online opportunities, and the direct influence of gender on online risks. The importance of online skills was evident insofar as online access, use and skills were found to mediate relations between demographic variables and young people’s experience of online opportunities and risks. Further, an unexpected positive relationship between online opportunities and risks was found, with implications for policy interventions aimed at reducing the risks of internet use.


title: A survey of large-scale analytical query processing in MapReduce

abstract: Enterprises today acquire vast volumes of data from different sources and leverage this information by means of data analysis to support effective decision-making and provide new functionality and services. The key requirement of data analytics is scalability, simply due to the immense volume of data that need to be extracted, processed, and analyzed in a timely fashion. Arguably the most popular framework for contemporary large-scale data analytics is MapReduce, mainly due to its salient features that include scalability, fault-tolerance, ease of programming, and flexibility. However, despite its merits, MapReduce has evident performance limitations in miscellaneous analytical tasks, and this has given rise to a significant body of research that aim at improving its efficiency, while maintaining its desirable properties. This survey aims to review the state of the art in improving the performance of parallel query processing using MapReduce. A set of the most significant weaknesses and limitations of MapReduce is discussed at a high level, along with solving techniques. A taxonomy is presented for categorizing existing research on MapReduce improvements according to the specific problem they target. Based on the proposed taxonomy, a classification of existing research is provided focusing on the optimization objective. Concluding, we outline interesting directions for future parallel data processing systems.


Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models.


title: Contactless EMG sensors embroidered onto textile

abstract: To obtain maximum unobtrusiveness with sensors for monitoring health parameters on the human body, two technical solutions are combined. First we propose contactless sensors for capacitive electromyography measurements. Secondly, the sensors are integrated into textile, so complete fusion with a wearable garment is enabled. We are presenting the first successful measurements with such sensors.


title: Comparison of Contaminant Dispersion Modeling Approaches for Swine Housing

abstract: Two unique models, the Lam-Bremhorst low Reynolds number turbulence model (LBLR) and a multiple airflow regions model (MARM), were compared in describing the distribution of carbon dioxide (CO2) in a simulated swine grower pen used in a previous experimental study (Brannigan and McQuitty, 1971). The results from this comparative study indicate that the LBLR model’s ability to predict airflow patterns greatly enhanced prediction of carbon dioxide distribution. The MARM and LBLR models predicted normalized CO2 levels within 4% of each other in airflow regions composed mainly of the ventilating airjet (the primary zone), but were in disagreement by as much as 48% in regions furthest removed from the airjet. In a region defined as the general building air zone, differences in predicted normalized CO2 between models ranged from 0 to 50%. The MARM model requires a prescribed airflow pattern and a detailed knowledge of entrainment ratios that reduced the predictive ability of overall contaminant dispersion.


title: doi:10.1093/brain/awn136 Brain (2008), 131,2084^2093 Abnormal temporal difference reward-learning signals in major depression

abstract: Anhedonia is a core symptom of major depressive disorder (MDD), long thought to be associated with reduced dopaminergic function. However, most antidepressants do not act directly on the dopamine system and all antidepressants have a delayed full therapeutic effect. Recently, it has been proposed that antidepressants fail to alter dopamine function in antidepressant unresponsive MDD. There is compelling evidence that dopamine neurons code a specific phasic (short duration) reward-learning signal, described by temporal difference (TD) theory. There is no current evidence for other neurons coding a TD reward-learning signal, although such evidence may be found in time. The neuronal substrates of the TD signal were not explored in this study. Phasic signals are believed to have quite different properties to tonic (long duration) signals. No studies have investigated phasic reward-learning signals in MDD. Therefore, adults with MDD receiving long-term antidepressant medication, and comparison controls both unmedicated and acutely medicated with the antidepressant citalopram, were scanned using fMRI during a reward-learning task. Three hypotheses were tested: first, patients with MDD have blunted TD reward-learning signals; second, controls given an antidepressant acutely have blunted TD reward-learning signals; third, the extent of alteration in TD signals in major depression correlates with illness severity ratings. The results supported the hypotheses. Patients with MDD had significantly reduced reward-learning signals in many non-brainstem regions: ventral striatum (VS), rostral and dorsal anterior cingulate, retrosplenial cortex (RC), midbrain and hippocampus. However, the TD signal was increased in the brainstem of patients. As predicted, acute antidepressant administration to controls was associated with a blunted TD signal, and the brainstem TD signal was not increased by acute citalopram administration. In a number of regions, the magnitude of the abnormal signals in MDD correlated with illness severity ratings. The findings highlight the importance of phasic reward-learning signals, and are consistent with the hypothesis that antidepressants fail to normalize reward-learning function in antidepressant-unresponsive MDD. Whilst there is evidence that some antidepressants acutely suppress dopamine function, the long-term action of virtually all antidepressants is enhanced dopamine agonist responsiveness. This distinction might help to elucidate the delayed action of antidepressants. Finally, analogous to recent work in schizophrenia, the finding of abnormal phasic reward-learning signals in MDD implies that an integrated understanding of symptoms and treatment mechanisms is possible, spanning physiology, phenomenology and pharmacology.


title: Model Driven Software Security Architecture of Systems-of-Systems

abstract: Recently, there is a growing interest in Systems of Systems (SoS), their architecture, security and application domains. However, their specific characteristics such as the operational independence of SoS constituent systems (CS), the absence of central authority and their emergent behavior make the modeling of their structure, behavior and security a complex task. One of the current main security challenges in the context of SoS is the cascading attack problem. The challenge is to predict the concatenation/sequence of CS's vulnerabilities that could be triggered resulting in destructive cascading failures and take corrective actions to reduce the cost, development time and effect of later changes. In this paper, we propose a domain specific modeling language (DSML) to represent SoS security rchitecture. Having SoS security models will enable the discovery, analysis and resolution of cascading attacks, in the architecture phase, preventing development time and cost wastage. Following a Model Driven Engineering (MDE) approach, we generate a graphical editor for our DSML and use it to model a Smart Campus case study.


title: Using niche-based GIS modeling to test geographic predictions of competitive exclusion and competitive release in South American pocket mice

abstract: Geographic studies addressing the role of competition in determining species’ macrodistributions have been limited by only simple or subjective means of identifying regions of suitable habitat. Now, ecological-niche models of species’ potential distributions present a possible approach to testing for the geographic patterns predicted under competitive exclusion and competitive release. Previously, we modeled the potential distributions of two spiny pocket mice (Heteromys australis and H. anomalus) in northwestern South America using specimen localities, environmental data, and the Genetic Algorithm for Rule-Set Prediction (GARP). Here we superimpose the models to examine known distributional records in areas of potential sympatry between the two species, thus testing the geographic predictions of competitive exclusion. In addition, we examine environmental characteristics of known localities, testing for data consistent with competitive release. Areas of potential sympatry are minimal, lying in regions of intermediate water balance. Only records of H. australis are known from areas of potential sympatry in regions where the species’ ranges meet, consistent with exclusion of H. anomalus by H. australis. Heteromys anomalus inhabits areas ecologically suitable for both species only in the isolated Sierra Nevada de Santa Marta, in which H. australis is not present (most likely for historical reasons). Furthermore, environmental characteristics of localities of H. anomalus in biogeographic regions where H. australis is absent fit the pattern predicted under competitive release. In contrast, localities of H. australis show no indication of competitive release. Although the results of present analyses do not conclusively demonstrate competitive exclusion or release, they provide directional hypotheses that can now be tested in experimental field and laboratory studies.


title: Animat vision: Active vision in artificial animals

abstract: We propose and demonstrate a new paradigm for active vision research that draws upon recent advances in the fields of artificial life and computer graphics. A software alternative to the prevailing hardware vision mindset, animat vision prescribes artificial animals, or animats, situated in physics-based virtual worlds as autonomous virtual robots possessing active perception systems. To be operative in its world, an animat must autonomously control its eyes and muscle-actuated body, applying computer vision algorithms to continuously analyze the retinal image streams acquired by its eyes in order to locomote purposefully through its world. We describe an initial animat vision implementation within lifelike artificial fishes inhabiting a physics-based, virtual marine world. Emulating the appearance, motion, and behavior of real fishes in their natural habitats, these animats are capable of spatially nonuniform retinal imaging, foveation, retinal image stabilization, color object recognition, and perceptually-guided navigation. These capabilities allow them to pursue moving targets such as fellow artificial fishes. Animat vision offers a fertile approach to the development, implementation, and evaluation of computational theories that profess sensorimotor competence for animal or robotic situated agents.<<ETX>>


title: A Secure Device-to-Device Link Establishment Scheme for LoRaWAN

abstract: In the Internet of Things (IoT) era, the importance of communication technology to support communication between the various types of devices has become greater than ever before. Long Range (LoRa) and Long Range Wide Area Network (LoRaWAN) are newly designed communication technologies for low-power long-distance communication in the IoT environment. Recently, LoRa and LoRaWAN have received much attention in academia and industry because low-power long-distance communication has not been supported by traditional communication technologies. Current LoRaWAN supports only communication between the end node and the central server. A recently introduced study enables device-to-device (D2D) communication in the LoRaWAN environment, which is not supported by the current LoRaWAN standard. This paper shows that direct communication between devices can reduce battery consumption. However, security is not considered in this paper and the security features of the LoRaWAN standard are also not applicable, which means that LoRaWAN D2D communication is exposed to a security threat. Therefore, we propose a secure link establishment scheme to protect the LoRaWAN D2D communication. With our scheme, both D2D nodes can securely share cryptographic keys for protecting the D2D communication. Through the security analysis, we prove that our scheme guarantees mutual authentication, confidentiality, and integrity. Performance evaluation shows that our scheme increases power consumption by approximately 5% compared with the basic scheme without security, but has little impact on overall battery lifetime. Consequently, the proposed scheme guarantees fundamental security requirements with sufficient feasibility.


title: Spatio-Temporal Anomaly Detection for Industrial Robots through Prediction in Unsupervised Feature Space

abstract: Spatio-temporal anomaly detection by unsupervised learning have applications in a wide range of practical settings. In this paper we present a surveillance system for industrial robots using a monocular camera. We propose a new unsupervised learning method to train a deep feature extractor from unlabeled images. Without any data augmentation, the algorithm co-learns the network parameters on different pseudo-classes simultaneously to create unbiased feature representation. Combining the learned features with a prediction system, we can detect irregularities in high dimensional data feed (e.g. video of a robot performing pick and place task). The results show how the proposed approach can detect previously unseen anomalies in the robot surveillance video. Although the technique is not designed for classification, we show the use of the learned features in a more traditional classification application for CIFAR-10 dataset.


title: SandBlaster: Reversing the Apple Sandbox

abstract: In order to limit the damage of malware on Mac OS X and iOS, Apple uses sandboxing, a kernel-level security layer that provides tight constraints for system calls. Particularly used for Apple iOS, sandboxing prevents apps from executing potentially dangerous actions, by defining rules in a sandbox profile. Investigating Apple's built-in sandbox profiles is difficult as they are compiled and stored in binary format. We present SandBlaster, a software bundle that is able to reverse/decompile Apple binary sandbox profiles to their original human readable SBPL (SandBox Profile Language) format. We use SandBlaster to reverse all built-in Apple iOS binary sandbox profiles for iOS 7, 8 and 9. Our tool is, to the best of our knowledge, the first to provide a full reversing of the Apple sandbox, shedding light into the inner workings of Apple sandbox profiles and providing essential support for security researchers and professionals interested in Apple security mechanisms.


title: Quality-Aware Subgraph Matching Over Inconsistent Probabilistic Graph Databases

abstract: Resource Description Framework (RDF) has been widely used in the Semantic Web to describe resources and their relationships. The RDF graph is one of the most commonly used representations for RDF data. However, in many real applications such as the data extraction/integration, RDF graphs integrated from different data sources may often contain uncertain and inconsistent information (e.g., uncertain labels or that violate facts/rules), due to the unreliability of data sources. In this paper, we formalize the RDF data by inconsistent probabilistic RDF graphs , which contain both inconsistencies and uncertainty. With such a probabilistic graph model, we focus on an important problem, quality-aware subgraph matching over inconsistent probabilistic RDF graphs (QA-gMatch), which retrieves subgraphs from inconsistent probabilistic RDF graphs that are isomorphic to a given query graph and with high quality scores (considering both consistency and uncertainty). In order to efficiently answer QA-gMatch queries, we provide two effective pruning methods, namely adaptive label pruning and quality score pruning , which can greatly filter out false alarms of subgraphs. We also design an effective index to facilitate our proposed pruning methods, and propose an efficient approach for processing QA-gMatch queries. Finally, we demonstrate the efficiency and effectiveness of our proposed approaches through extensive experiments.


title: Identifying Ideological Perspectives in Text and Video

abstract: Polarizing opinions about political and social controversies take place commonly in mass and more recently user-generated media. A functional democratic society builds on civic discussions among people holding different beliefs on an issue. However, so far, few computer technologies have been devoted to facilitate mutual understanding, and arguably could have worsened the situation. ::: We envision a computer system that can automatically understand different ideological viewpoints on an issue and identify biased news stories, blog posts, and television news. Such a computer system will raise news readers' awareness of individual sources' biases and encourage them to seek news stories from different viewpoints. ::: · Computer understanding of ideological perspectives, however, has been long considered almost impossible. In this thesis, we show that ideology, although very abstract, exhibits a concrete pattern when it is communicated among a group of people who share similar beliefs in written text, spoken text, television news production, and web video folksonomies. This emphatic pattern in ideological discourse opens up a new field of automatic ideological analysis, and enables a large amount of ideological text and video to be automatically analyzed. ::: · We develop a new statistical model, called Joint Topic and Perspective Models, based on the emphatic pattern in ideological discourse. The model combines two essential aspects of ideological discourse: topic matters and ideological biases. The simultaneous inference on topics and ideological emphasis, however, poses a computational challenge. We thus develop an approximate inference algorithm for the model based on variational methods. ::: · The emphatic pattern in ideological discourse and the Joint Topic and Perspective Model enable many interesting applications in text analysis and multimedia content understanding. At the corpus level, we show that ideological discourse can be reliably distinguished from non-ideological discourse. At the document level, we show that the perspective from which a document is written or a video is produced can be identified with high accuracy. At the sentence level, we extend the model to summarize an ideological document by selecting sentences that strongly express a particular perspective.


title: The Transport Layer Security (TLS) Protocol Version 1.3

abstract: This document specifies version 1.3 of the Transport Layer Security ::: (TLS) protocol. TLS allows client/server applications to communicate ::: over the Internet in a way that is designed to prevent eavesdropping, ::: tampering, and message forgery. This document updates RFCs 4492, 5705, ::: and 6066 and it obsoletes RFCs 5077, 5246, and 6961. This document ::: also specifies new requirements for TLS 1.2 implementations.


Software Application of the New Algorithm for Restoration of Faded Photographs and Maps in Geology


title: Color image segmentation using histogram thresholding - Fuzzy C-means hybrid approach

abstract: This paper presents a novel histogram thresholding - fuzzy C-means hybrid (HTFCM) approach that could find different application in pattern recognition as well as in computer vision, particularly in color image segmentation. The proposed approach applies the histogram thresholding technique to obtain all possible uniform regions in the color image. Then, the Fuzzy C-means (FCM) algorithm is utilized to improve the compactness of the clusters forming these uniform regions. Experimental results have demonstrated that the low complexity of the proposed HTFCM approach could obtain better cluster quality and segmentation results than other segmentation approaches that employing ant colony algorithm.


title: TPP: Traceable Privacy-Preserving Communication and Precise Reward for Vehicle-to-Grid Networks in Smart Grids

abstract: In vehicle-to-grid (V2G) networks, service providers are battery-powered vehicles, and the service consumer is the power grid. Security and privacy concerns are major obstacles for V2G networks to be extensively deployed. In 2011, Yang et al. proposed a very interesting privacy-preserving communication and precise reward architecture for V2G networks in smart grids. In this paper, we enhance Yang et al. ’s framework with the formal definitions of unforgeability and restrictiveness. Then, we propose a new traceable privacy-preserving communication and precise reward scheme with available cryptographic primitives. The proposed scheme is formally proven secure with well-established assumptions in the random oracle model. Thorough theoretical and experimental analyses demonstrate that our scheme is efficient and practical for secure V2G networks in smart grids.


title: Comprehensible and Interactive Visualizations of GIS Data in Augmented Reality

abstract: Most civil engineering tasks require accessing, surveying and modifying geospatial data in the field and referencing this virtual, geospatial information to the real world situation. Augmented Reality (AR) can be a useful tool to create, edit and update geospatial data representing real world artifacts by interacting with the 3D graphical representation of the geospatial data augmented in the user’s view.


title: A Study of Genetic Algorithms to Solve the School Timetabling Problem

abstract: This paper examines the use of genetic algorithms (GAs) to solve the school timetabling problem. The school timetabling problem falls into the category of NP-hard problems. Instances of this problem vary drastically from school to school and country to country. Previous work in this area has used genetic algorithms to solve a particular school timetabling problem and has not evaluated the performance of a GA on different problems. Furthermore, GAs have not previously been applied to solving the South African primary or high school timetabling problem. The paper presents a two-phased genetic algorithm approach to solving the school timetabling problem and provides an analysis of the effect of different low-level construction heuristics, selection methods and genetic operators on the success of the GA approach in solving these problems with respect to feasibility and timetable quality. The GA approach is tested on a benchmark set of “hard” school timetabling problems, the Greek high school timetabling problem and a South African primary and high school timetabling problem. The performance of the GA approach was found to be comparable to other methods applied to the same problems. This study has also revealed that different combinations of low-level construction heuristics, selection methods and genetic operators are needed to produce feasible timetables of good quality for the different school timetabling problems. Future work will investigate methods for the automatic configuration of GA architectures of both phases.


title: Continuous control with deep reinforcement learning

abstract: We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. Using the same learning algorithm, network architecture and hyper-parameters, our algorithm robustly solves more than 20 simulated physics tasks, including classic problems such as cartpole swing-up, dexterous manipulation, legged locomotion and car driving. Our algorithm is able to find policies whose performance is competitive with those found by a planning algorithm with full access to the dynamics of the domain and its derivatives. We further demonstrate that for many of the tasks the algorithm can learn policies end-to-end: directly from raw pixel inputs.


title: Aligning where to see and what to tell: image caption with region-based attention and scene factorization

abstract: Recent progress on automatic generation of image captions has shown that it is possible to describe the most salient information conveyed by images with accurate and meaningful sentences. In this paper, we propose an image caption system that exploits the parallel structures between images and sentences. In our model, the process of generating the next word, given the previously generated ones, is aligned with the visual perception experience where the attention shifting among the visual regions imposes a thread of visual ordering. This alignment characterizes the flow of "abstract meaning", encoding what is semantically shared by both the visual scene and the text description. Our system also makes another novel modeling contribution by introducing scene-specific contexts that capture higher-level semantic information encoded in an image. The contexts adapt language models for word generation to specific scene types. We benchmark our system and contrast to published results on several popular datasets. We show that using either region-based attention or scene-specific contexts improves systems without those components. Furthermore, combining these two modeling ingredients attains the state-of-the-art performance.


title: Explanation-based learning: a problem solving perspective

abstract: Abstract This article outlines explanation-based learning (EBL) and its role in improving problem solving performance through experience. Unlike inductive systems, which learn by abstracting common properties from multiple examples, EBL systems explain why a particular example is an instance of a concept. The explanations are then converted into operational recognition rules. In essence, the EBL approach is analytical and knowledge-intensive, whereas inductive methods are empirical and knowledge-poor. This article focuses on extensions of the basic EBL method and their integration with the prodigy problem solving system. prodigy 's EBL method is specifically designed to acquire search control rules that are effective in reducing total search time for complex task domains. Domain-specific search control rules are learned from successful problem solving decisions, costly failures, and unforeseen goal interactions. The ability to specify multiple learning strategies in a declarative manner enables EBL to serve as a general technique for performance improvement. prodigy 's EBL method is analyzed, illustrated with several examples and performance results, and compared with other methods for integrating EBL and problem solving.


title: Your Cart tells You: Inferring Demographic Attributes from Purchase Data

abstract: Demographic attributes play an important role in retail market to characterize different types of users. Such signals however are often only available for a small fraction of users in practice due to the difficulty in manual collection process by retailers. In this paper, we aim to harness the power of big data to automatically infer users' demographic attributes based on their purchase data. Typically, demographic prediction can be formalized as a multi-task multi-class prediction problem, i.e., multiple demographic attributes (e.g., gender, age and income) are to be inferred for each user where each attribute may belong to one of N possible classes (N-2). Most previous work on this problem explores different types of features and usually predicts different attributes independently. However, modeling the tasks separately may lose the ability to leverage the correlations among different attributes. Meanwhile, manually defined features require professional knowledge and often suffer from under specification. To address these problems, we propose a novel Structured Neural Embedding (SNE) model to automatically learn the representations from users' purchase data for predicting multiple demographic attributes simultaneously. Experiments are conducted on a real-world retail dataset where five attributes (gender, marital status, income, age, and education level) are to be predicted. The empirical results show that our SNE model can improve the performance significantly compared with state-of-the-art baselines.


title: The Palladio component model for model-driven performance prediction

abstract: One aim of component-based software engineering (CBSE) is to enable the prediction of extra-functional properties, such as performance and reliability, utilising a well-defined composition theory. Nowadays, such theories and their accompanying prediction methods are still in a maturation stage. Several factors influencing extra-functional properties need additional research to be understood. A special problem in CBSE stems from its specific development process: Software components should be specified and implemented independently from their later context to enable reuse. Thus, extra-functional properties of components need to be specified in a parametric way to take different influencing factors like the hardware platform or the usage profile into account. Our approach uses the Palladio component model (PCM) to specify component-based software architectures in a parametric way. This model offers direct support of the CBSE development process by dividing the model creation among the developer roles. This paper presents our model and a simulation tool based on it, which is capable of making performance predictions. Within a case study, we show that the resulting prediction accuracy is sufficient to support the evaluation of architectural design decisions.


title: CREAM: An Infrastructure for Distributed, Heterogeneous Event-Based Applications

abstract: Applications ranging from event-based supply chain management to enterprise application integration and pervasive computing depend on the timely detection and notification of events. We present Cream the event-based reactive component of the Dream middleware platform. Here we address four key issues in distributed and heterogeneous environments: event detection and notification, event composition, an active functionality service, and ontology support. We show the need for ontology support at all levels in heterogeneous environments and present a distributed active functionality service that addresses the difficult issues of event composition in widely distributed environments. We illustrate the practicality of the proposed approach through two prototypes that are based on this infrastructure: a meta-auction service and a personalized service offering in Internet-enabled vehicles.


title: Radio resource management for 5G mobile communication systems with massive antenna structure

abstract: In order to accommodate for the emerging mobile data traffic, millimetre-wave mmWave band in 30-300GHz range is being considered for the fifth generation 5G mobile networks. This mmWave band can potentially fulfil the bandwidth requirement of the next generation mobile networks for the next few decades and beyond. In this article, we propose a novel resource allocation scheme for mmWave in 5G mobile networks. The concept of multi-beam coordination is adopted to efficiently utilise the radio resource and improve the network performance in beam boundary regions. Moreover, because the mmWave signal has poor propagation characteristics, a massive antenna structure with beamforming is envisioned in this article. Our system-level simulation results indicate that the proposed scheme improves the resource utilization and network performance, compared with those of Li's and conventional schemes. Copyright © 2015 John Wiley & Sons, Ltd.


title: Opening black box Data Mining models using Sensitivity Analysis

abstract: There are several supervised learning Data Mining (DM) methods, such as Neural Networks (NN), Support Vector Machines (SVM) and ensembles, that often attain high quality predictions, although the obtained models are difficult to interpret by humans. In this paper, we open these black box DM models by using a novel visualization approach that is based on a Sensitivity Analysis (SA) method. In particular, we propose a Global SA (GSA), which extends the applicability of previous SA methods (e.g. to classification tasks), and several visualization techniques (e.g. variable effect characteristic curve), for assessing input relevance and effects on the model's responses. We show the GSA capabilities by conducting several experiments, using a NN ensemble and SVM model, in both synthetic and real-world datasets.


title: Virtual machine placement strategies in cloud computing

abstract: The prominent technology that drives the industry now-a-days is cloud computing. The growth of cloud computing has resulted in the setup of large number of data centers around the world. The data centers consume more power making it source for the carbon dioxide emission and a major contributor to greenhouse effect. This led to the deployment of virtualization. Infrastructure as a Service is one of the important services offered by cloud computing that allows virtualization and hardware to get virtualized by creating many instances of Virtual Machine (VM) on a single Physical Machine (PM) and helps in improving utilization of resources. VM consolidation includes method of choosing the more appropriate algorithm for migration of VM's and placement of VMs to the most suitable host. VM placement is a part of VM migration. The effective placement of VM is aimed to improve performance, resource utilization and reduce the energy consumption in data centers without SLA violation. This paper aims to focus on various VM placement schemes.


title: Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models of Meaning

abstract: Deep compositional models of meaning acting on distributional representations of words in order to produce vectors of larger text constituents are evolving to a popular area of NLP research. We detail a compositional distributional framework based on a rich form of word embeddings that aims at facilitating the interactions between words in the context of a sentence. Embeddings and composition layers are jointly learned against a generic objective that enhances the vectors with syntactic information from the surrounding context. Furthermore, each word is associated with a number of senses, the most plausible of which is selected dynamically during the composition process. We evaluate the produced vectors qualitatively and quantitatively with positive results. At the sentence level, the effectiveness of the framework is demonstrated on the MSRPar task, for which we report results within the state-of-the-art range.


title: Waveform-aware ambient RF energy harvesting

abstract: In this paper we suggest a new class of RF energy harvesters, which we call “waveform aware harvesters”. In contrast to traditional rectenna designs, which are usually designed for high efficiency with continuous wave (CW) signals, waveform aware harvesters are RF to DC converters which are optimized for their performance with non-CW signals. We suggest that waveform aware harvesters may have significant advantages in ambient energy harvesting, where the available RF energy is in the form of communication waveforms of a variety of types. We present an initial proof-of-concept demonstration of a waveform aware harvester optimized for harvesting energy from 2.4 GHz Wi-Fi (802.11b/g) signals with a realistic traffic model. Under realistic traffic conditions, 802.11b/g client transmissions are bursty, with a high peak-to-average ratio and a low duty cycle. We demonstrate optimized recovery of harvested energy from single 802.11b/g transmission bursts on the order of 1 ms in duration. We present an expression for maximizing usable energy stored in an energy reservoir given a signal model and parameters of the energy-harvester circuit. In contrast to other work where assumptions of CW sources lead to the desirability of a large storage capacitor, our approach considers the existing communication signal model and optimizes capacitor size to maximize the stored usable energy for a short transmission burst.


title: Quantified self and human movement: A review on the clinical impact of wearable sensing and feedback for gait analysis and intervention

abstract: Abstract The proliferation of miniaturized electronics has fueled a shift toward wearable sensors and feedback devices for the mass population. Quantified self and other similar movements involving wearable systems have gained recent interest. However, it is unclear what the clinical impact of these enabling technologies is on human gait. The purpose of this review is to assess clinical applications of wearable sensing and feedback for human gait and to identify areas of future research. Four electronic databases were searched to find articles employing wearable sensing or feedback for movements of the foot, ankle, shank, thigh, hip, pelvis, and trunk during gait. We retrieved 76 articles that met the inclusion criteria and identified four common clinical applications: (1) identifying movement disorders, (2) assessing surgical outcomes, (3) improving walking stability, and (4) reducing joint loading. Characteristics of knee and trunk motion were the most frequent gait parameters for both wearable sensing and wearable feedback. Most articles performed testing on healthy subjects, and the most prevalent patient populations were osteoarthritis, vestibular loss, Parkinson's disease, and post-stroke hemiplegia. The most widely used wearable sensors were inertial measurement units (accelerometer and gyroscope packaged together) and goniometers. Haptic (touch) and auditory were the most common feedback sensations. This review highlights the current state of the literature and demonstrates substantial potential clinical benefits of wearable sensing and feedback. Future research should focus on wearable sensing and feedback in patient populations, in natural human environments outside the laboratory such as at home or work, and on continuous, long-term monitoring and intervention.


title: Device Circuit Co Design of FEFET Based Logic for Low Voltage Processors

abstract: Ferroelectric FETs (FEFETs) are emerging devices with potential for low power applications. The unique feature which makes these devices suitable for ultra-low voltage operation is the steep slope achieved by negative capacitance of the ferroelectric oxide based gate stack. This property is being actively explored to overcome the fundamental 60 mV/decade sub threshold swing limit associated with conventional MOSFETs. In this paper, we focus on the circuit implications of the steep slope behavior of the FEFETs. We analyze the characteristics of FEFETs to get insights into their performance, and show both higher ON current and higher gate capacitance compared to standard transistors. We design and simulate a ring oscillator and a Kogge Stone adder using FEFET devices and evaluate the impact of ferroelectric layer thickness on the performance. Our analysis shows that FEFET based circuits consume lower energy compared to CMOS circuits at VDD.


title: Development of inverted pendulum robot capable of climbing stairs using planetary wheel mechanism

abstract: Improved robot mobility is necessary in order to realize the expected uses of robots in the human life environment, which includes obstacles such as stairs and narrow passages. Because objects which humans require are often located on desks or tables, which are at some height above ground level, a long vertical dimension is also necessary in the robot design. To meet these requirements, this study focused on a type of mobile robot using an inverted pendulum, which enables a robot with a long vertical dimension to negotiate narrow passages. Since it is difficult for inverted pendulum robots to climb steps, this study proposes a planetary wheel mechanism which makes it possible for an inverted pendulum robot to ascend differences in level. This mechanism has an extremely simple structure, comprising a belt, pulley, and arm, and has the advantage of not requiring addition of a new actuator. This paper describes the composition of the proposed mechanism and its principle of operation, and also describes the control method. A prototype of the robot was produced, and it was shown experimentally that the robot was capable of climbing stairs with a height of 120 mm-130 mm at a rate of approximately 2.4 s per one step.


title: Geometric structures for three-dimensional shape representation

abstract: Different geometric structures are investigated in the context of discrete surface representation. It is shown that minimal representations (i.e., polyhedra) can be provided by a surface-based method using nearest neighbors structures or by a volume-based method using the Delaunay triangulation. Both approaches are compared with respect to various criteria, such as space requirements, computation time, constraints on the distribution of the points, facilities for further calculations, and agreement with the actual shape of the object.


title: Ecological and Evolutionary Forces Shaping Microbial Diversity in the Human Intestine

abstract: The human gut is populated with as many as 100 trillion cells, whose collective genome, the microbiome, is a reflection of evolutionary selection pressures acting at the level of the host and at the level of the microbial cell. The ecological rules that govern the shape of microbial diversity in the gut apply to mutualists and pathogens alike.


title: B2B e-commerce adoption decisions in Taiwan: The interaction of cultural and other institutional factors

abstract: Business-to-business (B2B) e-commerce has become an important initiative among firms in the last few years. This study is based on data derived from over 20 interviews with CEOs, CIOs, and MIS managers in electronics and textile companies in Taiwan. This study contributes to existing literature by describing the degree to which various organizational, industrial, governmental and cultural factors influence B2B e-commerce adoption decisions in Taiwan. It is one of the first studies investigating the interaction of B2B e-commerce adoption decision factors and provides insights into the impact of Chinese culture on B2B adoption. Results indicate that organizational, industrial, governmental, and cultural factors do indeed influence B2B e-commerce adoption decisions. In addition, the nature of the cultural influence is dictated by industry conditions.


title: Improving SIMD code generation in QEMU

abstract: Modern processors are often enhanced using SIMD instructions, such as the MMX, SSE, and AVX instructions set in the x86 architecture, or the NEON instruction set in the ARM architecture. Using these SIMD instructions could significantly increase application performance, hence in application binaries a significant proportion of instructions are likely to be SIMD instructions. However, Dynamic Binary Translation (DBT) has largely overlooked SIMD instruction translation. For example, in the popular QEMU system emulator, guest SIMD instructions are often emulated with a sequence of scalar instructions even when the host machines have SIMD instructions to support such parallel computation, leaving significant potential for performance enhancement. In this paper, we propose two approaches, one leveraging the existing helper function implementation in QEMU, and the other using a newly introduced vector IR (Intermediate Representation) to enhance the performance of SIMD instruction translation in DBT of QEMU. Both approaches were implemented in the QEMU to support ARM and IA32 frontend and x86-64 backend. Preliminary experiments show that adding vector IR can significantly enhance the performance of guest applications containing SIMD instructions for both ARM and IA32 architectures when running with QEMU on the x86-64 platform.


title: Long Short Term Memory Recurrent Neural Network based Multimodal Dimensional Emotion Recognition

abstract: This paper presents our effort to the Audio/Visual+ Emotion Challenge (AV+EC2015), whose goal is to predict the continuous values of the emotion dimensions arousal and valence from audio, visual and physiology modalities. The state of art classifier for dimensional recognition, long short term memory recurrent neural network (LSTM-RNN) is utilized. Except regular LSTM-RNN prediction architecture, two techniques are investigated for dimensional emotion recognition problem. The first one is e -insensitive loss is utilized as the loss function to optimize. Compared to squared loss function, which is the most widely used loss function for dimension emotion recognition, e -insensitive loss is more robust for the label noises and it can ignore small errors to get stronger correlation between predictions and labels. The other one is temporal pooling. This technique enables temporal modeling in the input features and increases the diversity of the features fed into the forward prediction architecture. Experiments results show the efficiency of key points of the proposed method and competitive results are obtained.