title: A Real-Time Bus Positioning System Based on LoRa Technology

abstract: With the development of the Internet of Things technology, smart transportation has been continuously studied in recent years. In order to help people identify the real-time location and expected arrival time of buses, this paper proposes a real-time bus positioning system based on Long Range (LoRa) technology. The system can ease the anxiety of people waiting for buses and make the travel smarter and more convenient. The system is composed of terminal devices, data concentrators, cloud servers, and user interface. The terminal device installed on the bus broadcasts its position data to the data concentrators. Then the data concentrators upload data to the cloud server and present it to users. Compared with the traditional real-time bus positioning system, our system operates in the unlicensed frequency band and has a long transmission distance, which does not require any communication costs and repeaters. Experimental results show that our system has the advantages of low power consumption, low packet loss rate and short time delay.


title: Automatic TFT-LCD mura defect inspection using discrete cosine transform-based background filtering and 'just noticeable difference' quantification strategies

abstract: An innovative mura defect detection methodology for a thin-film transistor liquid crystal display (TFT-LCD) is developed for automatic inspection of mura defects using the discrete cosine transform (DCT) principle and background image filtering strategy. Efficient and accurate surface defect detection on flat panel display (FPD) panels has never been so important in achieving a high yield rate of FPD manufacturing. Detecting blob-mura defects in an LCD panel can be difficult due to non-uniform brightness background and slightly different brightness levels between the defect region and the background. To overcome this problem, a DCT-based background reconstruction algorithm was developed to establish the background image separated from the defects. The significant level of mura defects can be rationally quantified using the just noticeable difference (JND) definition. Actual performance of the developed method was evaluated on industrial LCD panels containing natural mura defects. Results of experimental tests verified that the proposed algorithm has a superior capability for detecting mura defects efficiently and accurately.


title: MAdFraud: investigating ad fraud in android applications

abstract: Many Android applications are distributed for free but are supported by advertisements. Ad libraries embedded in the app fetch content from the ad provider and display it on the app's user interface. The ad provider pays the developer for the ads displayed to the user and ads clicked by the user. A major threat to this ecosystem is ad fraud, where a miscreant's code fetches ads without displaying them to the user or "clicks" on ads automatically. Ad fraud has been extensively studied in the context of web advertising but has gone largely unstudied in the context of mobile advertising. We take the first step to study mobile ad fraud perpetrated by Android apps. We identify two fraudulent ad behaviors in apps: 1) requesting ads while the app is in the background, and 2) clicking on ads without user interaction. Based on these observations, we developed an analysis tool, MAdFraud, which automatically runs many apps simultaneously in emulators to trigger and expose ad fraud. Since the formats of ad impressions and clicks vary widely between different ad providers, we develop a novel approach for automatically identifying ad impressions and clicks in three steps: building HTTP request trees, identifying ad request pages using machine learning, and detecting clicks in HTTP request trees using heuristics. We apply our methodology and tool to two datasets: 1) 130,339 apps crawled from 19 Android markets including Play and many third-party markets, and 2) 35,087 apps that likely contain malware provided by a security company. From analyzing these datasets, we find that about 30% of apps with ads make ad requests while in running in the background. In addition, we find 27 apps which generate clicks without user interaction. We find that the click fraud apps attempt to remain stealthy when fabricating ad traffic by only periodically sending clicks and changing which ad provider is being targeted between installations.


title: Some Key Emerging Trends in Statistical and Graphical Software for the Social Scientist

abstract: In this article, we identify key trends in the development of statistical, graphical, and word processing software for the social scientist. We examine the trends from the perspective of the social scientist with a typical background in applied statistics, intent on developing a high-power, low-cost personal workstation for data analysis and manuscript production. Keywords: statistics, graphics, word processing, microcomputer software.


title: Knowledge Base Population: Successful Approaches and Challenges

abstract: In this paper we give an overview of the Knowledge Base Population (KBP) track at the 2010 Text Analysis Conference. The main goal of KBP is to promote research in discovering facts about entities and augmenting a knowledge base (KB) with these facts. This is done through two tasks, Entity Linking -- linking names in context to entities in the KB -- and Slot Filling -- adding information about an entity to the KB. A large source collection of newswire and web documents is provided from which systems are to discover information. Attributes ("slots") derived from Wikipedia infoboxes are used to create the reference KB. In this paper we provide an overview of the techniques which can serve as a basis for a good KBP system, lay out the remaining challenges by comparison with traditional Information Extraction (IE) and Question Answering (QA) tasks, and provide some suggestions to address these challenges.


title: Topology analysis of a switched-inductor Z-source inverter

abstract: This paper presents a developed impedance-type power inverter that is termed switched-inductor Z-source inverter. To enlarge voltage adjustable ability, the proposed inverter employs a unique switched-inductor impedance network to couple the main circuit and power source. Compared with the classical Z-source inverter, the proposed inverter increases the voltage boost inversion ability significantly, and only a very short shoot-through zero state is required to obtain high voltage conversion ratios. In addition, the voltage buck inversion ability is also provided in the proposed inverter for those applications that need low ac voltages. Same to the classical Z-source inverter, the proposed concepts of switched-inductor Z-source inverter can be applied to various applications of dc-ac, ac-ac, dc-dc and ac-dc power conversion. A detailed theoretical analysis and the general discussion are given. Both simulation and experimental results verify the main analytical results.


title: YOLO-Based Three-Stage Network for Bangla License Plate Recognition in Dhaka Metropolitan City

abstract: Real-time Automatic License Plate Recognition (ALPR) is a challenging task in computer vision since license plates in the real-world scenarios are difficult to recognize due to the presence of various factors like transparent background, occlusion, existence of multiple plates in an image, variance in illumination and viewing angle, etc. In most of the previous works related to Bangla license plate, images used for ALPR were captured in ideal conditions due to unavailability of dataset representing non-ideal conditions. In this paper, we present an approach for real-time Bangla license plate detection that is robust to enormous variations in complex real-world environment. A novel license plate recognition method is also presented making it a complete end-to-end deep learning-based ALPR system. We introduce a dataset by collecting 1, 500 different Bangladeshi vehicular license plate images that are captured manually from the street resembling various real-world scenarios. In this work, we have employed YOLOv3 algorithm to successfully localize the license plate and recognize the digits. To recognize the character, we have also built a Bangla scene character dataset containing more than 6, 400 characters, with which we have trained a ResNet-20-based deep Convolutional Neural Network (CNN). Our proposed method achieves more than 85% Intersection over Union (IoU) in digit recognition. The ResNet-20-based CNN model achieves 92.7% accuracy in recognizing the Bangla character present in the license plate.


title: A three-phase ZVS PWM DC/DC converter with asymmetrical duty cycle associated with a three-phase version of the hybridge rectifier

abstract: This paper proposes the use of a three-phase version of the hybridge rectifier in the three-phase zero-voltage switch (ZVS) DC/DC converter with asymmetrical duty cycle. The use of this new rectifier improves the efficiency of the converter because only three diodes are responsible for the conduction losses in the secondary side. The current in the secondary side of the transformer is half the output current. In addition to this, all the advantages of the three-phase DC/DC converter, i.e., the increased frequency of the output and input currents, the improved distribution of the losses, as well as the soft commutation for a wide load range, are preserved. Therefore, the resulting topology is capable of achieving high efficiency and high power density at high power levels. The theoretical analysis, simulation, and experimental results obtained from a 6-kW prototype, and also a comparison of the efficiency of this converter with the full-bridge rectifier are presented.


peepHole : Securing your homes with Raspberry Pi and Amazon Echo


title: Prefrontal cortical function and anxiety: controlling attention to threat-related stimuli

abstract: Threat-related stimuli are strong competitors for attention, particularly in anxious individuals. We used functional magnetic resonance imaging (fMRI) with healthy human volunteers to study how the processing of threat-related distractors is controlled and whether this alters as anxiety levels increase. Our work builds upon prior analyses of the cognitive control functions of lateral prefrontal cortex (lateral PFC) and anterior cingulate cortex (ACC). We found that rostral ACC was strongly activated by infrequent threat-related distractors, consistent with a role for this area in responding to unexpected processing conflict caused by salient emotional stimuli. Participants with higher anxiety levels showed both less rostral ACC activity overall and reduced recruitment of lateral PFC as expectancy of threat-related distractors was established. This supports the proposal that anxiety is associated with reduced top-down control over threat-related distractors. Our results suggest distinct roles for rostral ACC and lateral PFC in governing the processing of task-irrelevant, threat-related stimuli, and indicate reduced recruitment of this circuitry in anxiety.


title: Virtual Peers As Partners In Storytelling And Literacy Learning

abstract: Abstract Literacy learning — learning how to read and write — begins long before children enter school. One of the key skills to reading and writing is the ability to represent thoughts symbolically and share them in language with an audience who may not necessarily share the same temporal and spatial context. Children learn and practice these important language skills everyday, telling stories with the peers and adults around them. In particular, storytelling in the context of peer collaboration provides a key environment for children to learn language skills important for literacy. In light of this, an embodied conversational agent, Sam, who tells stories collaboratively with children was designed. Sam looks like a peer for pre-school children, but tells stories in a developmentally advanced way, modelling narrative skills important for literacy. Results demonstrated that children who played with the virtual peer told stories that more closely resembled the virtual peer's linguistically advanced stories: using more quoted speech and temporal and spatial expressions. In addition, children listened to Sam's stories carefully, assisting her and suggesting improvements. The potential benefits of having technology play a social role in young children's literacy learning is discussed.


title: Marine caves of the Southern Tyrrhenian Sea: a First Census of Benthic Biodiversity

abstract: This is the first paper documenting research on a selection of marine caves located along the coast of Capo Milazzo in the southern Tyrrhenian Sea. Three submarine and one semi-submerged caves were surveyed and sampled using underwater photo sampling. Surveys have only taken into account the sessile species belonging to the main taxa: Porifera, Anthozoa, Bryozoa and Polychaeta. Diversity indices and abundances were calculated for three sections within each explored cave: the Entrance Zone, Intermediate Zone and Bottom Zone. The richest group was Porifera with 21 taxa, followed by cnidarians, (Anthozoa), with 8 taxa, Polychaeta (5 taxa), and Bryozoa (5 taxa). Among Porifera, the presence of Petrobiona massiliana, a protected species according to SPA/BIO Protocol and the Bern Convention, must be highlighted. The encrusting forms were dominant in the Bottom Zone, the massive forms in the Intermediate Zone and the arborescent forms in the Entrance Zone. Generally, the percentage coverage of each morphological group showed a decline in the Intermediate Zone and a general increase in the Dark Zone within each cave. The S, H’ and J values showed different trends in the five caves. These differences, also evidenced by Permanova analysis, depend on the topographic specificity of each cave which, in turn, affects the gradients of the biotic and abiotic parameters. Finally, no horizontal gradient of rarefaction of the benthic sessile fauna has been detected. This study represents an important step for the management and conservation practices of these fragile ecosystems, especially in view of the forthcoming establishment of the Marine Protected Area.


title: A Noise-Filtered Under-Sampling Scheme for Imbalanced Classification

abstract: Under-sampling is a popular data preprocessing method in dealing with class imbalance problems, with the purposes of balancing datasets to achieve a high classification rate and avoiding the bias toward majority class examples. It always uses full minority data in a training dataset. However, some noisy minority examples may reduce the performance of classifiers. In this paper, a new under-sampling scheme is proposed by incorporating a noise filter before executing resampling. In order to verify the efficiency, this scheme is implemented based on four popular under-sampling methods, i.e., Undersampling + Adaboost, RUSBoost, UnderBagging, and EasyEnsemble through benchmarks and significance analysis. Furthermore, this paper also summarizes the relationship between algorithm performance and imbalanced ratio. Experimental results indicate that the proposed scheme can improve the original undersampling-based methods with significance in terms of three popular metrics for imbalanced classification, i.e., the area under the curve, ${F}$ -measure, and ${G}$ -mean.


Beyond Clothing Ontologies : Modeling Fashion with Subjective Influence Networks


A SIMPLE HYPOTHESIS FOR THE DEVELOPMENT OF OBESITY


title: Thermal Driven Placement for Island-style MTCMOS FPGAs

abstract: Rapid increase in transistor density and operating frequency has led to the increase in power densities, exhibiting itself as a high temperature profile. The high temperature spots over an FPGA impact the power, performance, and reliability of the chip, hence should be addressed during the design process. The logic block placement is targeted as the natural starting point to address the non-uniform thermal profile problem. The proposed placer simultaneously accounts for conventional placement objectives (routability and timing) while increases the temperature profile uniformity by optimizally spreading the power sources. As a measure of thermal uniformity in the simulation annealing core of the placer, a cost function is derived by adapting the concept of maximum entropy in a dual electrostatic charge model. The runtime complexity of this cost function is linear with respect to the number of used blocks, regardless of the size of the FPGA, and there is no need to perform the time-consuming thermal extractions. Results show an average of 73% and 51% reductions in the standard deviation and maximum gradient of temperature with less than 4% average wiring and delay penalty.


title: Separability of EEG signals recorded during right and left motor imagery using adaptive autoregressive parameters

abstract: Electroencephalogram (EEG) recordings during right and left motor imagery can be used to move a cursor to a target on a computer screen. Such an EEG-based brain-computer interface (BCI) can provide a new communication channel to replace an impaired motor function. It can be used by, e.g., patients with amyotrophic lateral sclerosis (ALS) to develop a simple binary response in order to reply to specific questions. Four subjects participated in a series of on-line sessions with an EEG-based cursor control. The EEG was recorded from electrodes overlying sensory-motor areas during left and right motor imagery. The EEG signals were analyzed in subject-specific frequency bands and classified on-line by a neural network. The network output was used as a feedback signal. The on-line error (100%-perfect classification) was between 10.0 and 38.1%. In addition, the single-trial data were also analyzed off-line by using an adaptive autoregressive (AAR) model of order 6. With a linear discriminant analysis the estimated parameters for left and right motor imagery were separated. The error rate, obtained varied between 5.8 and 32.8% and was, on average, better than the on-line results. By using the AAR-model for on-line classification an improvement in the error rate can be expected, however, with a classification delay around 1 s.


title: Maternal Care, Gene Expression, and the Transmission of Individual Differences in Stress Reactivity Across Generations

abstract: ▪ Abstract Naturally occurring variations in maternal care alter the expression of genes that regulate behavioral and endocrine responses to stress, as well as hippocampal synaptic development. These effects form the basis for the development of stable, individual differences in stress reactivity and certain forms of cognition. Maternal care also influences the maternal behavior of female offspring, an effect that appears to be related to oxytocin receptor gene expression, and which forms the basis for the intergenerational transmission of individual differences in stress reactivity. Patterns of maternal care that increase stress reactivity in offspring are enhanced by stressors imposed on the mother. These findings provide evidence for the importance of parental care as a mediator of the effects of environmental adversity on neural development.


title: Voice Conversion Using Sequence-to-Sequence Learning of Context Posterior Probabilities

abstract: Voice conversion (VC) using sequence-to-sequence learning of context posterior probabilities is proposed. Conventional VC using shared context posterior probabilities predicts target speech parameters from the context posterior probabilities estimated from the source speech parameters. Although conventional VC can be built from non-parallel data, it is difficult to convert speaker individuality such as phonetic property and speaking rate contained in the posterior probabilities because the source posterior probabilities are directly used for predicting target speech parameters. In this work, we assume that the training data partly include parallel speech data and propose sequence-to-sequence learning between the source and target posterior probabilities. The conversion models perform non-linear and variable-length transformation from the source probability sequence to the target one. Further, we propose a joint training algorithm for the modules. In contrast to conventional VC, which separately trains the speech recognition that estimates posterior probabilities and the speech synthesis that predicts target speech parameters, our proposed method jointly trains these modules along with the proposed probability conversion modules. Experimental results demonstrate that our approach outperforms the conventional VC.


Drowsiness Detection based on EEG Signal analysis using EMD and trained Neural Network


title: Enhanced home automation system using Internet of Things

abstract: The more the technology advances into the future, the more it makes the life of people depending on it easier and one of such technologies is Automation. The term Automation can be coupled with-no effort, ease of performing a task and less human involvement. Internet of Things is such other term which envisions every object around us as an integral part of the Internet. In this paper, we suggest a highly intractable and environmentally sustainable form of Home Automation System using Internet of Things a means to control the appliances at home via a device with access to the Internet. The key components of this system are a pocket sized microprocessor-Raspberry Pi and a microcontroller-Arduino Uno and an Android application to visualize the data provided by the Raspberry Pi and also to send, receive and process the requests. The Raspberry Pi acts as the brain of this system, processing the requests, responding to the requests made by the Android application, communicating with the Arduino and also acts as a server to store the data given by the sensors. All the sensors and actuators are connected to the Arduino which is connected to the Pi using a USB cable. Our main objective of developing this model is to create a home automation system which interacts with the user through various push notifications based on concerned parameters which is also eco-friendly.


title: Density-Based 3 D Shape Descriptors

abstract: We propose a novel probabilistic framework for the extraction of density-based 3D shape descriptors using kernel density estimation. Our descriptors are derived from the probability density functions (pdf) of local surface features characterizing the 3D object geometry. Assuming that the shape of the 3D object is represented as a mesh consisting of triangles with arbitrary size and shape, we provide effcient means to approximate the moments of geometric features on a triangle basis. Our framework produces a number of 3D shape descriptors that prove to be quite discriminative in retrieval applications. We test our descriptors and compare them with several other histogram-based methods on two 3D model databases, Princeton Shape Benchmark and Sculpteur, which are fundamentally different in semantic content and mesh quality. Experimental results show that our methodology not only improves the performance of existing descriptors, but also provides a rigorous framework to advance and to test new ones.


title: Leadership in Academic Health Centers: Transactional and Transformational Leadership

abstract: Leadership is a crucial component to the success of academic health science centers (AHCs) within the shifting U.S. healthcare environment. Leadership talent acquisition and development within AHCs is immature and approaches to leadership and its evolution will be inevitable to refine operations to accomplish the critical missions of clinical service delivery, the medical education continuum, and innovations toward discovery. To reach higher organizational outcomes in AHCs requires a reflection on what leadership approaches are in place and how they can better support these missions. Transactional leadership approaches are traditionally used in AHCs and this commentary suggests that movement toward a transformational approach is a performance improvement opportunity for AHC leaders. This commentary describes the transactional and transformational approaches, how they complement each other, and how to access the transformational approach. Drawing on behavioral sciences, suggestions are made on how a transactional leader can change her cognitions to align with the four dimensions of the transformational leadership approach.


title: Reconfigurable Antennas

abstract: Reconfigurable antennas change polarization, operating frequency, or far-field pattern in order to cope with changing system parameters. This paper reviews some of the past and current technology applicable to reconfigurable antennas, with several examples of implementations. Mechanically movable parts and arrays are discussed, as well as more-recent semiconductor-component and tunable-material technologies applicable to reconfigurable antennas.


title: A local expansion propagation algorithm for social link identification

abstract: Social link identification, that is to identify accounts across different online social networks that belong to the same user, is an important task in social network applications. Most existing methods to solve this problem directly applied machine learning classifiers on features extracted from user’s rich information. In practice, however, only some limited user information can be obtained because of privacy concerns. In addition, we observe that the existing methods cannot handle huge amount of potential account pairs from different online social networks. In this paper, we propose an effective method to address the above two challenges by expanding known anchor links (seed account pairs belonging to the same person). In particular, we leverage potentially useful information possessed by the existing anchor link and then develop a local expansion propagation model to identify new social links, which are taken as a generated anchor link to be used for iteratively identifying additional new social link. We evaluate our method on two most popular Chinese social networks. Experimental results show our proposed method can quickly find most of identity account pairs across different online social networks.


title: Solution-Focused Coaching in Pediatric Rehabilitation: An Integrated Model for Practice

abstract: ABSTRACTThis article describes the conceptual basis and key elements of a transdisciplinary model for solution-focused coaching in pediatric rehabilitation (SFC-peds). The model exemplifies a strengths-based, relational, and goal-oriented approach to clinical practice. It provides a distinct shift from a problem-oriented, therapist-directed approach to a possibilities-oriented approach where client empowerment takes precedence. The model facilitates client change through a method of working with client strengths and resources that involves the use of strategic questions to co-construct therapy intervention. Through client–therapist collaboration, therapy goals and plans are developed that align with client hopes, priorities, and readiness for change. SFC supports client self-determination and capacity for change through customized therapy goals and plans that are meaningful for the child and family. Implications for therapists include the need for relational expertise, practical coaching skills, and exper...


title: Program Anomaly Detection: Methodology and Practices

abstract: This tutorial will present an overview of program anomaly detection, which analyzes normal program behaviors and discovers aberrant executions caused by attacks, misconfigurations, program bugs, and unusual usage patterns. It was first introduced as an analogy between intrusion detection for programs and the immune mechanism in biology. Advanced models have been developed in the last decade and comprehensive techniques have been adopted such as hidden Markov model and machine learning. We will introduce the audience to the problem of program attacks and the anomaly detection approach against threats. We will give a general definition for program anomaly detection and derive model abstractions from the definition. The audience will be walked through the development of program anomaly detection methods from early-age n-gram approaches to complicated pushdown automata and probabilistic models. Some lab tools will be provided to help understand primitive detection models. This procedure will help the audience understand the objectives and challenges in designing program anomaly detection models. We will discuss the attacks that subvert anomaly detection mechanisms. The field map of program anomaly detection will be presented. We will also briefly discuss the applications of program anomaly detection in Internet of Things security. We expect the audience to get an idea of unsolved challenges in the field and develop a sense of future program anomaly detection directions after attending the tutorial.


title: Trastuzumab uptake and its relation to efficacy in an animal model of HER2-positive breast cancer brain metastasis

abstract: Purpose ::: The extent to which efficacy of the HER2 antibody Trastuzumab in brain metastases is limited by access of antibody to brain lesions remains a question of significant clinical importance. We investigated the uptake and distribution of trastuzumab in brain and mammary fat pad grafts of HER2-positive breast cancer to evaluate the relationship of these parameters to the anti-tumor activity of trastuzumab and trastuzumab emtansine (T-DM1).


title: Follow the Leader with Dropout Perturbations

abstract: We consider online prediction with expert advice. Over the course of many trials, the goal of the learning algorithm is to achieve small additional loss (i.e. regret) compared to the loss of the best from a set of K experts. The two most popular algorithms are Hedge/Weighted Majority and Follow the Perturbed Leader (FPL). The latter algorithm first perturbs the loss of each expert by independent additive noise drawn from a fixed distribution, and then predicts with the expert of minimum perturbed loss (“the leader”) where ties are broken uniformly at random. To achieve the optimal worst-case regret as a function of the lossL of the best expert in hindsight, the two types of algorithms need to tune their learning rate or noise magnitude, respectively, as a function ofL . Instead of perturbing the losses of the experts with additive noise, we randomly set them to 0 or 1 before selecting the leader. We show that our perturbations are an instance of dropout — because experts may be interpreted as features — although for non-binary losses the dropout probability needs to be made dependent on the losses to get good regret bounds. We show that this simple, tuning-free version of the FPL algorithm achieves two feats: optimal worst-case O( p L lnK + lnK) regret as a function ofL , and optimalO(lnK) regret when the loss vectors are drawn i.i.d. from a fixed distribution and there is a gap between the expected loss of the best expert and all others. A number of recent algorithms from the Hedge family (AdaHedge and FlipFlop) also achieve this, but they employ sophisticated tuning regimes. The dropout perturbation of the losses of the experts result in different noise distributions for each expert (because they depend on the expert’s total loss) and curiously enough no additional tuning is needed: the choice of dropout probability only affects the constants.


title: How Viral Are Viral Videos

abstract: Within only a few years after the launch of video sharing platforms, viral videos have become a pervasive Internet phenomenon. Yet, notwithstanding growing scholarly interest, the suitability of the viral metaphor seems not to have been studied so far. In this paper, we therefore investigate the attention dynamics of viral videos from the point of view of mathematical epidemiology. We introduce a novel probabilistic model of the progression of infective diseases and use it to analyze time series of YouTube view counts and Google searches. Our results on a data set of almost 800 videos show that their attention dynamics are indeed well accounted for by our epidemic model. In particular, we find that the vast majority of videos considered in this study show very high infection rates.


title: Human Identity and Gender Recognition From Gait Sequences With Arbitrary Walking Directions

abstract: We investigate the problem of human identity and gender recognition from gait sequences with arbitrary walking directions. Most current approaches make the unrealistic assumption that persons walk along a fixed direction or a pre-defined path. Given a gait sequence collected from arbitrary walking directions, we first obtain human silhouettes by background subtraction and cluster them into several clusters. For each cluster, we compute the cluster-based averaged gait image as features. Then, we propose a sparse reconstruction based metric learning method to learn a distance metric to minimize the intra-class sparse reconstruction errors and maximize the inter-class sparse reconstruction errors simultaneously, so that discriminative information can be exploited for recognition. The experimental results show the efficacy of our approach.


title: Detecting Threats of Violence in Online Discussions Using Bigrams of Important Words

abstract: Making violent threats towards minorities like immigrants or homosexuals is increasingly common on the Internet. We present a method to automatically detect threats of violence using machine learning. A material of 24,840 sentences from YouTube was manually annotated as violent threats or not, and was used to train and test the machine learning model. Detecting threats of violence works quit well with an error of classifying a violent sentence as not violent of about 10% when the error of classifying a non-violent sentence as violent is adjusted to 5%. The best classification performance is achieved by including features that combine specially chosen important words and the distance between those in the sentence.


title: Project Aura: Toward Distraction-Free Pervasive Computing

abstract: The most precious resource in a computer system is no longer its processor, memory, disk, or network, but rather human attention. Aura aims to minimize distractions on a user's attention, creating an environment that adapts to the user's context and needs. Aura is specifically intended for pervasive computing environments involving wireless communication, wearable or handheld computers, and smart spaces. Human attention is an especially scarce resource in such environments, because the user is often preoccupied with walking, driving, or other real-world interactions. In addition, mobile computing poses difficult challenges such as intermittent and variable-bandwidth connectivity, concern for battery life, and the client resource constraints that weight and size considerations impose. To accomplish its ambitious goals, research in Aura spans every system level: from the hardware, through the operating system, to applications and end users. Underlying this diversity of concerns, Aura applies two broad concepts. First, it uses proactivity, which is a system layer's ability to anticipate requests from a higher layer. In today's systems, each layer merely reacts to the layer above it. Second, Aura is self-tuning: layers adapt by observing the demands made on them and adjusting their performance and resource usage characteristics accordingly. Currently, system-layer behavior is relatively static. Both of these techniques will help lower demand for human attention.


GPS receiver architectures and measurements


title: Human intention inference and motion modeling using approximate E-M with online learning

abstract: In this paper, we present an algorithm to infer the intent of a human operator's arm movements based on the observations from a Microsoft Kinect sensor. Intentions are modeled as goal locations in 3-dimensional (3D) space where the human is intending to reach. Human intention inference is a critical step towards realizing safe human-robot collaboration. This work models the human arm's nonlinear motion dynamics using an unknown nonlinear function with intentions modeled as parameters. The unknown model is learned using a neural network (NN). Based on the learned model, an approximate expectation-maximization (E-M) algorithm is developed to infer human intentions. Furthermore, an identifier-based online model learning algorithm is developed to adapt to variations in the arm motion dynamics, trajectory of motion, goal locations, and initial conditions of different human subjects. We show the results of our algorithm using two sets of experiments conducted on data obtained from different users.


title: Efficiency Study of a 2.2 kV, 1 ns, 1 MHz Pulsed Power Generator Based on a Drift-Step-Recovery Diode

abstract: Nanosecond pulsed-power generators featuring high peak and average power are attractive for ultra-wideband radars in applications such as through-wall detection and ground penetration. The efficiency of the pulse production scheme has a major importance in battery operated devices. This paper presents an efficiency study of a 2.2-kV, 1 ns pulsed power circuit. The circuit consists of a power MOSFET serving as a prime switch and a fast drift-step recovery diode (DSRD) connected in parallel to the load. The MOSFET utilizes a low-voltage DC power supply to pump and pulse the DSRD in the forward and reverse directions. An additional low-current DC power supply is used to provide a voltage bias in order to balance the DSRD forward with respect to its reverse charge. In order to study the circuit's efficiency, it was operated at a wide range of operating parameters, including the main and bias source voltages, and the trigger duration of the prime switch. A peak voltage of 2.2 kV with a rise-time of less than 1 ns and a rise-rate of 3 kV/ns was obtained, where the efficiency was 24%. A higher efficiency of 52% was obtained when the circuit was optimized to an output peak voltage of 1.15 kV. The circuit was operated in single-shot mode, as well as in bursts of up to 100 pulses at a repetition rate of 1 MHz. Thus, during the burst a peak and average power of 96.8 kW and 155 W, respectively, were obtained.


Region-Object Relevance-Guided Visual Relationship Detection


title: Design-oriented Small-signal Modeling of Primary-Side Regulated Flyback Converters

abstract: Primary-Side Regulation (PSR) is a popular technique used today in many low-power high-volume applications, such as travel adapters and chargers for portable equipment, to reduce their size and cost. At these power levels the flyback converter is the topology of choice and several PSR methodologies to control both its output voltage (CV-PSR, Constant Voltage PSR) and its output current (CC-PSR, Constant Current PSR) are available. As to a flyback converter with CV-PSR, the implementation of the S&H circuit, a fundamental building block of the CV-PSR loop, plays a key role in the output-to-control transfer function. This paper, after briefly reviewing the concepts related to CV-PSR, describes a typical, silicon efficient implementation of an S&H circuit specific for CV-PSR usage. Then, it illustrates how its model in the frequency domain is determined with a system identification approach in the time domain. The model is verified and validated by means of PSIM simulations.


A Decomposed Dual-Cross Generative Adversarial Network for Image Rain Removal


Multi-Scale Recurrent Tracking via Pyramid Recurrent Network and Optical Flow


Modeling a DFIG Wind Turbine System using PLECS


title: Learning Fingerprint Reconstruction: From Minutiae to Image

abstract: The set of minutia points is considered to be the most distinctive feature for fingerprint representation and is widely used in fingerprint matching. It was believed that the minutiae set does not contain sufficient information to reconstruct the original fingerprint image from which minutiae were extracted. However, recent studies have shown that it is indeed possible to reconstruct fingerprint images from their minutiae representations. Reconstruction techniques demonstrate the need for securing fingerprint templates, improving the template interoperability, and improving fingerprint synthesis. But, there is still a large gap between the matching performance obtained from original fingerprint images and their corresponding reconstructed fingerprint images. In this paper, the prior knowledge about fingerprint ridge structures is encoded in terms of orientation patch and continuous phase patch dictionaries to improve the fingerprint reconstruction. The orientation patch dictionary is used to reconstruct the orientation field from minutiae, while the continuous phase patch dictionary is used to reconstruct the ridge pattern. Experimental results on three public domain databases (FVC2002 DB1_A, FVC2002 DB2_A, and NIST SD4) demonstrate that the proposed reconstruction algorithm outperforms the state-of-the-art reconstruction algorithms in terms of both: 1) spurious minutiae and 2) matching performance with respect to type-I attack (matching the reconstructed fingerprint against the same impression from which minutiae set was extracted) and type-II attack (matching the reconstructed fingerprint against a different impression of the same finger).


title: A Survey of Semantic Image and Video Annotation Tools

abstract: The availability of semantically annotated image and video assets constitutes a critical prerequisite for the realisation of intelligent knowledge management services pertaining to realistic user needs. Given the extend of the challenges involved in the automatic extraction of such descriptions, manually created metadata play a significant role, further strengthened by their deployment in training and evaluation tasks related to the automatic extraction of content descriptions. The different views taken by the two main approaches towards semantic content description, namely the Semantic Web and MPEG-7, as well as the traits particular to multimedia content due to the multiplicity of information levels involved, have resulted in a variety of image and video annotation tools, adopting varying description aspects. Aiming to provide a common framework of reference and furthermore to highlight open issues, especially with respect to the coverage and the interoperability of the produced metadata, in this chapter we present an overview of the state of the art in image and video annotation tools.


title: Bistatic Synthetic Aperture Radar Imaging Using UltraNarrowband Continuous Waveforms

abstract: We consider synthetic aperture radar (SAR) imaging using ultra-narrowband continuous waveforms (CW). Due to the high Doppler resolution of CW signals, we refer to this imaging modality as Doppler Synthetic Aperture Radar (DSAR). We present a novel model and an image formation method for the bistatic DSAR for arbitrary imaging geometries. Our bistatic DSAR model is formed by correlating the translated version of the received signal with a scaled or frequencyshifted version of the transmitted CW signal over a finite time window. High frequency analysis of the resulting model shows that the correlated signal is the projections of the scene reflectivity onto the bistatic iso-Doppler curves. We next use microlocal techniques to develop a filtered-backprojection (FBP) type image reconstruction method. The FBP inversion results in backprojection of the correlated signal onto the bistatic iso- Doppler curves as opposed to the bistatic iso-range curves, performed in the traditional wideband SAR imaging. We show that our method takes advantage of the velocity, as well as the acceleration of the antennas in certain directions to form a high resolution SAR image. Our bistatic DSAR imaging method is applicable for arbitrary flight trajectories, nonflat topography, and can accommodate system related parameters. We present resolution analysis and extensive numerical experiments to demonstrate the performance of our imaging method.


title: Learning document image binarization from data

abstract: In this paper we present a fully trainable binarization solution for degraded document images. Unlike previous attempts that often used simple features with a series of pre- and post-processing, our solution encodes all heuristics about whether or not a pixel is foreground text into a high-dimensional feature vector and learns a more complicated decision function. In particular, we prepare features of three types: 1) existing features for binarization such as intensity [1], contrast [2], [3], and Laplacian [4], [5]; 2) reformulated features from existing binarization decision functions such those in [6] and [7]; and 3) our newly developed features, namely the Logarithm Intensity Percentile (LIP) and the Relative Darkness Index (RDI). Our initial experimental results show that using only selected samples (about 1.5% of all available training data), we can achieve a binarization performance comparable to those fine-tuned (typically by hand), state-of-the-art methods. Additionally, the trained document binarization classifier shows good generalization capabilities on out-of-domain data.


title: Selective removal of the autoantibodies from rheumatoid arthritis patient plasma using protein A carrying affinity cryogels

abstract: Rheumatoid arthritis is a chronic, progressive, deabilitating autoimmune disease that occurs in approx- imately 1% of adults. Rheumatoid arthritis is characterized by chronic polyarthritis and destruction of multiple joints. In this study, IgM-antibody removal from human plasma with supermacroporous poly(hydroxyethyl methacrylate) (PHEMA) cryogel carrying protein A has been evaluated. The PHEMA cryogel was prepared by bulk polymerization which proceeds in an aqueous solution of monomer frozen inside a plastic syringe (cryo-polymerization). After thawing, the PHEMA cryogel contains a continuous matrix having interconnected macropores of 10-200m size. Pore volume in the PHEMA cryogel was 71.6%. Protein A molecules were covalently immobilized onto the PHEMA cryogel via cyanogen bromide (CNBr) activation. The PHEMA cryogel was contacted with blood in in vitro system for the determina- tion of blood-compatibility. The supermacroporous structure of the PHEMA cryogel makes it possible to process blood cells without blocking the cryogel column. IgM-antibody adsorption capacity decreased significantly with the increase of the plasma flow-rate. The maximum IgM-antibody adsorption amount was 42.7 mg/g. IgM-antibody molecules could be repeatedly adsorbed and eluted without noticeable loss in the IgM-antibody adsorption amount. © 2010 Elsevier B.V. All rights reserved.


title: Robust fingerprint authentication using local structural similarity

abstract: Fingerprint matching is challenging as the matcher has to minimize two competing error rates: the False Accept Rate and the False Reject Rate. We propose a novel, efficient, accurate and distortion-tolerant fingerprint authentication technique based on graph representation. Using the fingerprint minutiae features, a labeled, and weighted graph of minutiae is constructed for both the query fingerprint and the reference fingerprint. In the first phase, we obtain a minimum set of matched node pairs by matching their neighborhood structures. In the second phase, we include more pairs in the match by comparing distances with respect to matched pairs obtained in first phase. An optional third phase, extending the neighborhood around each feature, is entered if we cannot arrive at a decision based on the analysis in first two phases. The proposed algorithm has been tested with excellent results on a large private livescan database obtained with optical scanners.


title: Example overflow: using social media for code recommendation

abstract: Modern Q&A websites, such as Stack Overflow, use social media to provide concise answers, and offer rich technical context with quality assessment capabilities. Although some of the answers may include executable code snippets, they are entangled in free text and are not easily extracted. Q&A websites are not designed for such direct code reuse. We present Example Overflow, a code search and recommendation tool which brings together social media and code recommendation systems. Example Overflow enables crowd-sourced software development by utilizing both textual and social information, which accompany source code on the Web. Its browsing mechanism minimizes the context switch associated with other code search tools. In this paper we describe the development of the tool, provide preliminary evaluation, and discuss its contribution to an example centric programming paradigm.


title: A reconfigurable fault-tolerant deflection routing algorithm based on reinforcement learning for network-on-chip

abstract: We propose a reconfigurable fault-tolerant deflection routing algorithm (FTDR) based on reinforcement learning for NoC. The algorithm reconfigures the routing table through a kind of reinforcement learning---Q-learning using 2-hop fault information. It is topology-agnostic and insensitive to the shape of the fault region. In order to reduce the routing table size, we also propose a hierarchical Q-learning based deflection routing algorithm (FTDR-H) with area reduction up to 27% for a switch in an 8 x 8 mesh compared to the original FTDR. Experimental results show that in the presence of faults, FTDR and FTDR-H are better than other fault-tolerant deflection routing algorithms and a turn model based fault-tolerant routing algorithm.


title: An UWB Wall Scanner Based on a Shape Estimating SAR Algorithm

abstract: This paper describes a complete pulse based high-resolution radar system. This includes the transmitting and receiving unit as well as the signal processing of the down-sampled echoes. The main focus of the hardware design was on the usage of low-cost components. The signal evaluation is based on the shape reconstruction of simple targets in order to use the proposed radar system for wall scanning applications, e. g. the detection of water pipes.


title: Security and Enforcement in Spectrum Sharing

abstract: When different stakeholders share a common resource, such as the case in spectrum sharing, security and enforcement become critical considerations that affect the welfare of all stakeholders. Recent advances in radio spectrum access technologies, such as cognitive radios, have made spectrum sharing a viable option for significantly improving spectrum utilization efficiency. However, those technologies have also contributed to exacerbating the difficult problems of security and enforcement. In this paper, we review some of the critical security and privacy threats that impact spectrum sharing. We propose a taxonomy for classifying the various threats, and describe representative examples for each threat category. We also discuss threat countermeasures and enforcement techniques, which are discussed in the context of two different approaches: ex ante (preventive) and ex post (punitive) enforcement.


title: Hypnotic aspects of spiritual experience

abstract: This article presents hypnosis as a concept, process, and perspective from which to view religious and spiritual events. Spiritual experiences are seen as internally-based occurrences which bear noticeable similarity to the characteristics of self-hypnosis. This is considered in reference to biblical accounts, mysticism, contemplation and the Jungian “collective unconscious.” Finally, the unconscious, which is accessed through hypnosis, is explored with regards to its role in spiritual experience.


title: Sensory integration dysfunction affects efficacy of speech therapy on children with functional articulation disorders

abstract: Background: Articulation disorders in young children are due to defects occurring at a certain stage in sensory and motor development. Some children with functional articulation disorders may also have sensory integration dysfunction (SID). We hypothesized that speech therapy would be less efficacious in children with SID than in those without SID. Hence, the purpose of this study was to compare the efficacy of speech therapy in two groups of children with functional articulation disorders: those without and those with SID. Method: A total of 30 young children with functional articulation disorders were divided into two groups, the no-SID group (15 children) and the SID group (15 children). The number of pronunciation mistakes was evaluated before and after speech therapy. Results: There were no statistically significant differences in age, sex, sibling order, education of parents, and pretest number of mistakes in pronunciation between the two groups (P. 0.05). The mean and standard deviation in the pre- and post-test number of mistakes in pronunciation were 10.5 ± 3.2 and 3.3 ± 3.3 in the no-SID group, and 10.1 ± 2.9 and 6.9 ± 3.5 in the SID group, respectively. Results showed great changes after speech therapy treatment (F = 70.393; P , 0.001) and interaction between the pre/post speech therapy treatment and groups (F = 11.119; P = 0.002). Conclusions: Speech therapy can improve the articulation performance of children who have functional articulation disorders whether or not they have SID, but it results in significantly greater improvement in children without SID. SID may affect the treatment efficiency of speech therapy in young children with articulation disorders.


Network Intrusion Detection Using Machine Learning


title: Using the Nyström Method to Speed Up Kernel Machines

abstract: A major problem for kernel-based predictors (such as Support Vector Machines and Gaussian processes) is that the amount of computation required to find the solution scales as O(n3), where n is the number of training examples. We show that an approximation to the eigendecomposition of the Gram matrix can be computed by the Nystrom method (which is used for the numerical solution of eigenproblems). This is achieved by carrying out an eigendecomposition on a smaller system of size m < n, and then expanding the results back up to n dimensions. The computational complexity of a predictor using this approximation is O(m2n). We report experiments on the USPS and abalone data sets and show that we can set m ≪ n without any significant decrease in the accuracy of the solution.


title: Language support for fast and reliable message-based communication in singularity OS

abstract: Message-based communication offers the potential benefits of providing stronger specification and cleaner separation between components. Compared with shared-memory interactions, message passing has the potential disadvantages of more expensive data exchange (no direct sharing) and more complicated programming.In this paper we report on the language, verification, and run-time system features that make messages practical as the sole means of communication between processes in the Singularity operating system. We show that using advanced programming language and verification techniques, it is possible to provide and enforce strong system-wide invariants that enable efficient communication and low-overhead software-based process isolation. Furthermore, specifications on communication channels help in detecting programmer mistakes early---namely at compile-time---thereby reducing the difficulty of the message-based programming model.The paper describes our communication invariants, the language and verification features that support them, as well as implementation details of the infrastructure. A number of benchmarks show the competitiveness of this approach.


title: Learning Language-Visual Embedding for Movie Understanding with Natural-Language

abstract: Learning a joint language-visual embedding has a number of very appealing properties and can result in variety of practical application, including natural language image/video annotation and search. In this work, we study three different joint language-visual neural network model architectures. We evaluate our models on large scale LSMDC16 movie dataset for two tasks: 1) Standard Ranking for video annotation and retrieval 2) Our proposed movie multiple-choice test. This test facilitate automatic evaluation of visual-language models for natural language video annotation based on human activities. In addition to original Audio Description (AD) captions, provided as part of LSMDC16, we collected and will make available a) manually generated re-phrasings of those captions obtained using Amazon MTurk b) automatically generated human activity elements in "Predicate + Object" (PO) phrases based on "Knowlywood", an activity knowledge mining model. Our best model archives Recall@10 of 19.2% on annotation and 18.9% on video retrieval tasks for subset of 1000 samples. For multiple-choice test, our best model achieve accuracy 58.11% over whole LSMDC16 public test-set.


title: Two epoch algorithms for disaster recovery

abstract: Remote backup copies of databases are often maintained to ensure availability of data even in the presence of extensive failures, for which local replication mechanisms may be inadequate. We present two versions of an epoch algorithm for maintaining a consistent remote backup copy of a database. The algorithms ensure scalability, which makes them suitable for very large databases. The correctness and the performance of the algorithms are discussed, and an additional application for distributed group commit is given.


Reduction of calcium release site models via optimized state aggregation


title: CScrypt: A Compressive-Sensing-Based Encryption Engine for the Internet of Things: Demo Abstract

abstract: Internet of Things (IoT) have been connecting the physical world seamlessly and provides tremendous opportunities to a wide range of applications. However, potential risks exist when IoT system collects local sensor data and uploads to the Cloud. The private data leakage can be severe with curious database administrator or malicious hackers who compromise the Cloud. In this demo, we solve this problem of guaranteeing the user data privacy and security using compressive sensing based cryptographic method. We present CScrypt, a compressive-sensing-based encryption engine for the Cloud-enabled IoT systems to secure the interaction between the IoT devices and the Cloud. Our system exploits the fact that each individual's biometric data can be trained to a unique dictionary which can be used as an encryption key meanwhile to compress the original data. We will demonstrate a functioning prototype of our system using live data stream when attending the conference.


title: Automatic recognition of speakers from their voices

abstract: This paper presents a survey of automatic speaker recognition techniques. The paper indudes a discussion of the speaker-dependent properties of the speech signal, methods for selecting an efficient set of speech measurements, results of experimental studies illustrating the performance of various methods of speaker recognition, and a comparision of the performance of automatic methods with that of human listeners. Both text-dependent as well as text-independent speaker-recognition techniques are discussed.


title: Multivariate Analysis as a Means of Comparing Growth in Fish

abstract: The Hotelling's T2 is described as a test for differences between like von Bertalanffy growth parameters estimated from two fish groups when these parameters are correlated. Equations to calculate T2 are presented as are equations to modify T2 to an F statistic for use in testing. Simultaneous confidence intervals and critical F values are described to separate effects of parameters on observed differences in growth. Assumptions made by the T2 technique are discussed, as are ways in which any bias caused by violating these assumptions is lessened. Two numerical examples concerning growth differences among groups of Pacific ocean perch (Sebastes alutus) are provided. Growth of male and of female perch captured off the Columbia River is different with L∞ being the most statistically significant parameter and t0 the least. Growth of male perch captured off Vancouver Island and off the Columbia River is also different with only L∞ statistically significant.Key words: Hotelling's T2, multivariate statistics, P...


title: Linear pattern matching algorithms

abstract: In 1970, Knuth, Pratt, and Morris [1] showed how to do basic pattern matching in linear time. Related problems, such as those discussed in [4], have previously been solved by efficient but sub-optimal algorithms. In this paper, we introduce an interesting data structure called a bi-tree. A linear time algorithm for obtaining a compacted version of a bi-tree associated with a given string is presented. With this construction as the basic tool, we indicate how to solve several pattern matching problems, including some from [4] in linear time.


title: perf fuzzer: Targeted Fuzzing of the perf event open() System Call

abstract: Fuzzing is a process where random, almost valid, input streams are automatically generated and fed into computer systems in order to test the robustness of userexposed interfaces. We fuzz the Linux kernel system call interface; unlike previous work that attempts to generically fuzz all of an operating system’s system calls, we explore the effectiveness of using specific domain knowledge and focus on finding bugs and security issues related to a single Linux system call. The perf event open() system call was introduced in 2009 and has grown to be a complex interface with over 40 arguments that interact in subtle ways. By using detailed knowledge of typical perf event usage patterns we develop a custom tool, perf fuzzer, that has found bugs that more generic, system-wide, fuzzers have missed. Numerous crashing bugs have been found, including a local root exploit. Fixes for these bugs have been merged into the main Linux source tree. Testing continues to find new bugs, although they are increasingly hard to isolate, requiring development of new isolation techniques and helper utilities. We describe the development of perf fuzzer, examine the bugs found, and discuss ways that this work can be extended to find more bugs and cover other system calls.


title: Soft robotic glove for hand rehabilitation and task specific training

abstract: This paper presents advancements in the design of a portable, soft robotic glove for individuals with functional grasp pathologies. The robotic glove leverages soft material actuator technology to safely distribute forces along the length of the finger and provide active flexion and passive extension. These actuators consist of molded elastomeric bladders with anisotropic fiber reinforcements that produce specific bending, twisting, and extending trajectories upon fluid pressurization. In particular, we present a method for customizing a soft actuator to a wearer's biomechanics and demonstrate in a motion capture system that the ranges of motion (ROM) of the two are nearly equivalent. The active ROM of the glove is further evaluated using the Kapandji test. Lastly, in a case study, we present preliminary results of a patient with very weak hand strength performing a timed Box-and-Block test with and without the soft robotic glove.


title: Pull-in time dynamics as a measure of absolute pressure

abstract: The squeezed-film damping component of the pull-in time of an electrostatically-actuated micromechanical fixed-fixed beam is shown to be a sensitive, and nearly linear function of ambient air pressure in the measured range of 0.1 mbar to 1013 mbar (1 atm or 760 Torr). Pull-in time simulations, based on a one-dimensional macromodel using a damping constant proportional to pressure, are in good agreement with measured data. The data and simulations show that pull-in type devices will make excellent microelectromechanical systems (MEMS) sensors for broad-range absolute pressure measurements and for in situ leak monitoring of hermetically scaled packages containing other sensors or IC's. The pull-in sensors are compatible with any MEMS fabrication processes that allow out-of-plane electrostatic actuation, including surface micromachining and silicon wafer-bonding, and they do not require a cavity sealed at vacuum or at a reference air pressure.


Decision-theoretic planning for content planning in ITS


title: White spot analysis: The potential of patent information for research and development

abstract: Patents do not only offer legal protection, but also provide an extensive source of technical information: pre-processed in “technology maps” or “patent maps”, interesting technical details can be identified for the own development of new product ideas and business opportunities.


title: Toward cost effective and optimal selection of IT disaster recovery cloud solution

abstract: Many organizations rely on Disaster Recovery services to ensure availability and continuity of their operations during undesired circumstances and outages. Business Impact Analysis concept enables adequate planning for business continuation as it delivers essential information on services' criticality levels and their impact on business. The advancement in communication and technology has reshaped the development of Disaster Recovery solutions over cloud that become an attractive approach and fundamental part to sustain business operations. Organizations should select the model that best balances the level of control, software configuration, maintenance, and cost. In this paper, we explore Disaster Recovery architecture in term of hardware, software, data replication, networking, and access points. Further, the paper identifies key challenges, benefits, security concerns, and protections methodologies for Disaster Recovery cloud solution. Additionally, we present our illustrative cost analysis of different type of Disaster Recovery solutions based on cost of assets, operating cost, and cost of failures.


title: High-Velocity Oxy-Fuel (HVOF) Suspension Spraying of Mullite Coatings

abstract: Mullite coatings (3Al2O3 AE 2SiO2) were deposited by suspension thermal spraying of micron-sized (D50=1.8 lm) feedstock powders, using a high-velocity oxy-fuel gun (HVOF) operated on propylene (DJ-2700) and hydrogen fuels (DJ-2600). The liquid carrier employed in this approach allows for controlled injection of much finer particles than in conventional thermal spraying, leading to coatings with low porosity and fine and homogeneous porosity distribution, making this process potentially suitable for creating thin layers with low gas permeability. In-flight particle states were measured for a number of spray conditions of varying fuel-to-oxygen ratios and standoff distances and related to the resulting


title: MSFCN-multiple supervised fully convolutional networks for the osteosarcoma segmentation of CT images

abstract: Abstract Background and objective Automatic osteosarcoma tumor segmentation on computed tomography (CT) images is a challenging problem, as tumors have large spatial and structural variabilities. In this study, an automatic tumor segmentation method, which was based on a fully convolutional networks with multiple supervised side output layers (MSFCN), was presented. Methods Image normalization is applied as a pre-processing step for decreasing the differences among images. In the frame of the fully convolutional networks, supervised side output layers were added to three layers in order to guide the multi-scale feature learning as a contracting structure, which was then able to capture both the local and global image features. Multiple feature channels were used in the up-sampling portion to capture more context information, for the assurance of accurate segmentation of the tumor, with low contrast around the soft tissue. The results of all the side outputs were fused to determine the final boundaries of the tumors. Results A quantitative comparison of the 405 osteosarcoma manual segmentation results from the CT images showed that the average Dice similarity coefficient (DSC), average sensitivity, average Hammoude distance (HM) and F1-measure were 87.80%, 86.88%, 19.81% and 0.908, respectively. It was determined that, when compared with the other learning-based algorithms (for example, the fully convolution networks (FCN), U-Net method, and holistically-nested edge detection (HED) method), the MSFCN had the best performances in terms of DSC, sensitivity, HM and F1-measure. Conclusion The results indicated that the proposed algorithm contributed to the fast and accurate delineation of tumor boundaries, which could potentially assist doctors in making more precise treatment plans.


title: A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based On Minimum Cuts

abstract: Sentiment analysis seeks to identify the viewpoint(s) underlying a text span; an example application is classifying a movie review as "thumbs up" or "thumbs down". To determine this sentiment polarity, we propose a novel machine-learning method that applies text-categorization techniques to just the subjective portions of the document. Extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints.


title: Artificial Neural Networks and their Applications

abstract: The Artificial Neural network is a functional imitation of simplified model of the biological neurons and their goal is to construct useful computers for real world problems. The ANN applications have increased dramatically in the last few years fired by both theoretical and practical applications in a wide variety of applications. A brief theory of ANN is presented and potential areas are identified and future trends are discussed.


title: Transitive Text Mining for Information Extraction and Hypothesis Generation

abstract: Transitive text mining - also named Swanson Linking (SL) after its primary and principal researcher - tries to establish meaningful links between literature sets which are virtually disjoint in the sense that each does not mention the main concept of the other. If successful, SL may give rise to the development of new hypotheses. In this communication we describe our approach to transitive text mining which employs co-occurrence analysis of the medical subject headings (MeSH), the descriptors assigned to papers indexed in PubMed. In addition, we will outline the current state of our web-based information system which will enable our users to perform literature-driven hypothesis building on their own.


title: Women's Underrepresentation in Science: Sociocultural and Biological Considerations

abstract: The underrepresentation of women at the top of math-intensive fields is controversial, with competing claims of biological and sociocultural causation. The authors develop a framework to delineate possible causal pathways and evaluate evidence for each. Biological evidence is contradictory and inconclusive. Although cross-cultural and cross-cohort differences suggest a powerful effect of sociocultural context, evidence for specific factors is inconsistent and contradictory. Factors unique to underrepresentation in math-intensive fields include the following: (a) Math-proficient women disproportionately prefer careers in non–math-intensive fields and are more likely to leave math-intensive careers as they advance; (b) more men than women score in the extreme math-proficient range on gatekeeper tests, such as the SAT Mathematics and the Graduate Record Examinations Quantitative Reasoning sections; (c) women with high math competence are disproportionately more likely to have high verbal competence, allowing greater choice of professions; and (d) in some math-intensive fields, women with children are penalized in promotion rates. The evidence indicates that women’s preferences, potentially representing both free and constrained choices, constitute the most powerful explanatory factor; a secondary factor is performance on gatekeeper tests, most likely resulting from sociocultural rather than biological causes.


title: A high-resolution time-to-digital converter implemented in field-programmable-gate-arrays

abstract: A high-resolution time-to-digital converter (TDC) implemented in a general purpose field-programmable-gate-array (FPGA) is presented. Dedicated carry lines of an FPGA are used as delay cells to perform time interpolation within the system clock period and to realize the fine time measurement. Two Gray-code counters, working on in-phase and out-of-phase system clocks respectively, are designed to get the stable value of the coarse time measurement. The fine time code and the coarse time counter value, along with the channel identifier, are then written into a first-in first-out (FIFO) buffer. Tests have been done to verify the performance of the TDC. The resolution after calibration can reach 50 ps


title: MV-YOLO: Motion Vector-Aided Tracking by Semantic Object Detection

abstract: Object tracking is the cornerstone of many visual analytics systems. While considerable progress has been made in this area in recent years, robust, efficient, and accurate tracking in real-world video remains a challenge. In this paper, we present a hybrid tracker that leverages motion information from the compressed video stream and a general-purpose semantic object detector acting on decoded frames to construct a fast and efficient tracking engine. The proposed approach is compared with several well-known recent trackers on the OTB tracking dataset. The results indicate advantages of the proposed method in terms of speed and/or accuracy.Other desirable features of the proposed method are its simplicity and deployment efficiency, which stems from the fact that it reuses the resources and information that may already exist in the system for other reasons.


title: CDRM: A Cost-Effective Dynamic Replication Management Scheme for Cloud Storage Cluster

abstract: Data replication has been widely used as a mean of increasing the data availability of large-scale cloud storage systems where failures are normal. Aiming to provide cost-effective availability, and improve performance and load-balancing of cloud storage, this paper presents a cost-effective dynamic replication management scheme referred to as CDRM. A novel model is proposed to capture the relationship between availability and replica number. CDRM leverages this model to calculate and maintain minimal replica number for a given availability requirement. Replica placement is based on capacity and blocking probability of data nodes. By adjusting replica number and location according to workload changing and node capacity, CDRM can dynamically redistribute workloads among data nodes in the heterogeneous cloud. We implemented CDRM in Hadoop Distributed File System (HDFS) and experiment results conclusively demonstrate that our CDRM is cost effective and outperforms default replication management of HDFS in terms of performance and load balancing for large-scale cloud storage.


title: Mechanical design and robust tracking control of a class of antagonistic variable stiffness actuators based on the equivalent nonlinear torsion springs

abstract: The novel conceptual model of the antagonistic variable stiffness actuator based on the equivalent nonlinear torsion spring and the friction damper is demonstrated. For the dynamic model of the antagonistic variable stiffness actuator in the presence of parametric uncertainties, unknown bounded friction torques, unknown bounded external disturbance, and input saturation constraints, using the coordinate transformation, the state space model of the antagonistic variable stiffness actuator with composite disturbances and input saturation constraints is transformed into an extended integral chain–type pseudo-linear system with input saturation constraints. Subsequently, a combination of the linear extended state observer, sliding mode control, and adaptive input saturation compensation law is adopted for the design of the robust tracking controller that simultaneously regulates the position and stiffness of the antagonistic equivalent nonlinear torsion spring-based variable stiffness actuator. Under the proposed controller, the semi-global uniformly ultimately bounded stability of the closed-loop system has been proved via Lyapunov stability analysis. Simulation studies demonstrate the effectiveness and the robustness of the proposed robust adaptive tracking control method for the antagonistic variable stiffness actuator.


title: A Framework for Analysis of Road Accidents

abstract: The road accident data analysis use data mining and machine learning techniques, focusing on identifying factors that affect the severity of an accident. There are a variety of reasons that contribute to accidents. Some of them are internal to the driver but many are external. For example, adverse weather conditions like fog, rainfall or snowfall cause partial visibility and it may become difficult as well as risky to drive on such roads. It is expected that the findings from this paper would help civic authorities to take proactive actions on likely crash prone weather and traffic conditions.


title: A Trigraph Based Centrality Approach Towards Text Summarization

abstract: As the electronic documents are increasing due to the revolution of information there is an urgent need for summarizing the text documents. From the previous works we observed that there is no generalized graph model for text summarization and low order ngrams could not preserve the contextual meaning. This paper focuses on an extractive based graphical approach for text summarization, based on trigrams and graph based centrality measure. Trigraph is generated and the centrality of the connected trigraph is taken to extract the important trigrams. A mapping is done between the original words and the trigrams to regain the link between the words. And after comparing the centrality from the graph, the summary is extracted. The ROUGE-SU4 F-measure obtained for the proposed approach is 0.036 which is significantly better than the previous approaches.


title: Should the parameters of a BCI translation algorithm be continually adapted

abstract: Abstract People with or without motor disabilities can learn to control sensorimotor rhythms (SMRs) recorded from the scalp to move a computer cursor in one or more dimensions or can use the P300 event-related potential as a control signal to make discrete selections. Data collected from individuals using an SMR-based or P300-based BCI were evaluated offline to estimate the impact on performance of continually adapting the parameters of the translation algorithm during BCI operation. The performance of the SMR-based BCI was enhanced by adaptive updating of the feature weights or adaptive normalization of the features. In contrast, P300 performance did not benefit from either of these procedures.


title: Neural Network-Based Optimal Adaptive Output Feedback Control of a Helicopter UAV

abstract: Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking.


Iron deficiency and the cognitive and psychomotor development of children: A pilot study with institutionalised children


title: Tightly Packed Tries: How to Fit Large Models into Memory, and Make them Load Fast, Too

abstract: We present Tightly Packed Tries (TPTs), a compact implementation of read-only, compressed trie structures with fast on-demand paging and short load times. ::: ::: We demonstrate the benefits of TPTs for storing n-gram back-off language models and phrase tables for statistical machine translation. Encoded as TPTs, these databases require less space than flat text file representations of the same data compressed with the gzip utility. At the same time, they can be mapped into memory quickly and be searched directly in time linear in the length of the key, without the need to decompress the entire file. The overhead for local decompression during search is marginal.


title: Decoder-side HEVC quality enhancement with scalable convolutional neural network

abstract: The latest High Efficiency Video Coding (HEVC) has been increasingly used to generate video streams over Internet. However, the decoded HEVC video streams may incur severe quality degradation, especially at low bit-rates. Thus, it is necessary to enhance visual quality of HEVC videos at the decoder side. To this end, we propose in this paper a Decoder-side Scalable Convolutional Neural Network (DS-CNN) approach to achieve quality enhancement for HEVC, which does not require any modification of the encoder. In particular, our DS-CNN approach learns a model of Convo-lutional Neural Network (CNN) to reduce distortion of both I and B/P frames in HEVC. It is different from the existing CNN-based quality enhancement approaches, which only handle intra coding distortion, thus not suitable for B/P frames. Furthermore, a scalable structure is included in our DS-CNN, suchthat the computational complexity of our DS-CNN approach is adjustable to the changing computational resources. Finally, the experimental results show the effectiveness of our DS-CNN approach in enhancing quality for both I and B/P frames of HEVC.


title: Camera calibration and 3D reconstruction from a single view based on scene constraints

abstract: This paper mainly focuses on the problem of camera calibration and 3D reconstruction from a single view of structured scene. It is well known that three constraints on the intrinsic parameters of a camera can be obtained from the vanishing points of three mutually orthogonal directions. However, there usually exist one or several pairs of line segments, which are mutually orthogonal and lie in the pencil of planes defined by two of the vanishing directions in the structured scenes. It is proved in this paper that a new independent constraint to the image of the absolute conic can be obtained if the pair of line segments is of equal length or with known length ratio in space. The constraint is further studied both in terms of the vanishing points and the images of circular points. Hence, four independent constraints on a camera are obtained from one image, and the camera can be calibrated under the widely accepted assumption of zero-skew. This paper also presents a simple method for the recovery of camera extrinsic parameters and projection matrix with respect to a given world coordinate system. Furthermore, several methods are presented to estimate the positions and poses of space planar surfaces from the recovered projection matrix and scene constraints. Thus, a scene structure can be reconstructed by combining the planar patches. Extensive experiments on simulated data and real images, as well as a comparative test with other methods in the literature, validate our proposed methods. (C) 2004 Elsevier B.V. All rights reserved.


title: 19.3 A 50-to-66GHz 65nm CMOS all-digital fractional-N PLL with 220fsrms jitter

abstract: Digital-PLL frequency synthesizers for wireless applications have become popular in the sub-10GHz range. However, mm-wave synthesizers still rely on analog PLLs, predominantly of the integer-N type [1]. This is due to limited DCO tuning resolution and challenges in phase digitization including limited resolution, linearity, and input frequency of the TDC. The 60GHz ADPLL in [2] uses a 32× CML/injection-locked-frequency-divider chain in the DCO-TDC interface, which results in high power consumption (>0.5 of total power), large area, and high in-band phase noise. This paper presents a 60GHz phase-domain ADPLL with a 16GHz tuning range, digital phase extraction at f DCO /4, and several on-chip calibration techniques that enable low in-band phase noise and spurs.


title: Three Visions of Liberty: John Stuart Mill, Isaiah Berlin, Quentin Skinner

abstract: In this chapter, I wish to examine John Stuart Mill’s concept of liberty as it emerges especially from On Liberty (1859) in the light of Isaiah Berlin’s and Quentin Skinner’s interpretations. Mill’s view of liberty as absence of constraints in self-regarding actions has been hailed by Berlin as the quintessential notion of “negative liberty” (an expression originally coined by Samuel Pufendorf and then used by Jeremy Bentham).1 On the other hand, Skinner has emphasized Mill’s belief that a minimum level of interference from the government, together with a maximum of social tolerance, was needed to promote “individuality” and “eccentricity"; in this respect Mill’s view of liberty cannot be described as merely negative for it has a positive goal. After examining the respective merits of their interpretations, I conclude that both Berlin and Skinner had a political agenda in their interpretation of Mill which was dictated by the practical circumstances as well as by their different interpretative sensibilities. What emerges clearly from this comparison and study in reception is the complexity and comprehensiveness of Mill’s notion of liberty, which cannot be easily pinpointed and hastily attributed to a political or philosophical camp: according to the aspects of his thought emphasized by the authors, Mill’s concept of liberty can be characterized either as “negative” or as “positive.”


Algorithms in C++ ,Part 5: Graph Algorithms -3/E.


Artificial Neural Networks and Machine Learning – ICANN 2017


title: Deep appearance and motion learning for egocentric activity recognition

abstract: Abstract Egocentric activity recognition has recently generated great popularity in computer vision due to its widespread applications in egocentric video analysis. However, it poses new challenges comparing to the conventional third-person activity recognition tasks, which are caused by significant body shaking, varied lengths, and poor recoding quality, etc. To handle these challenges, in this paper, we propose deep appearance and motion learning (DAML) for egocentric activity recognition, which leverages the great strength of deep learning networks in feature learning. In contrast to hand-crafted visual features or pre-trained convolutional neural network (CNN) features with limited generality to new egocentric videos, the proposed DAML is built on the deep autoencoder (DAE), and directly extracts appearance and motion feature, the main cue of activities, from egocentric videos. The DAML takes advantages of the great effectiveness and efficiency of the DAE in unsupervised feature learning, which provides a new representation learning framework of egocentric videos. The learned appearance and motion features by the DAML are seamlessly fused to accomplish a rich informative egocentric activity representation which can be readily fed into any supervised learning models for activity recognition. Experimental results on two challenging benchmark datasets show that the DAML achieves high performance on both short- and long-term egocentric activity recognition tasks, which is comparable to or even better than the state-of-the-art counterparts.


title: Preparation of thermo-responsive acrylic hydrogels useful for the application in transdermal drug delivery systems

abstract: Abstract In this research, hydrogel membranes mainly composed of three kinds of latex particles within carboxymethyl cellulose (CMC) matrix were prepared for the purpose of transdermal drug release. All three latex particles were acrylic systems and synthesized by emulsion polymerization with the addition of an initiator, potassium persulfate, and a crosslinking agent, methylene bisacrylamide. Among them, poly(acrylic acid-co-sodium acrylate) copolymer was applied to increase the swelling ability of hydrogel membrane. Degree of neutralization or the amount of added crosslinking agent was changed to give the optimum properties endowed to the membrane. Poly(acrylic acid-co-2-ethylhexyl acrylate) copolymer latex was used to provide the adhesive ability for the membrane. Finally, to give a thermo-responsive behavior in swelling, poly(N-isopropyl acrylamide) latex and its copolymers were synthesized by polymerization of N-isopropyl acrylamide with different amounts of acrylic acid, in which lower critical solution temperature (LCST) could be modulated. Besides blending these microgel particles with CMC and several other compounds such as tartaric acid and PAA oligomer used to adjust pH value of doping solution, aluminum ion (Al3+) was applied to give ionic interaction with carboxylate groups distributed in all three latex particles, thus giving interconnected interactions among all microgel particles and in turn a good membrane formability. Morphology, structures and swelling capability of prepared hydrogel membranes were then examined. Caffeine, used as the model drug, was incorporated into membranes and the drug release behavior at different temperatures was evaluated. These prepared hydrogel membranes have potential in the application of transdermal drug delivery system.


title: A novel buck-boost AC-AC converter with inverting and non-inverting operation and no commutation problem

abstract: In this paper, a new type of ac-ac converter is proposed which can operate as traditional non-inverting buck and boost converter or inverting buck-boost converter. The proposed converter is immune from shorting of voltage source even when all switches are turned on simultaneously, and therefore, PWM switching dead-times are eliminated to produce high quality output waveform. Owning to the benefit that only two switches are switching at high frequency, the switching losses are reduced which allow the high frequency operation with reduced passive filter requirements. Compared to the traditional non-inverting buck and boost ac-ac converter which can only step-down or step-up the input voltage respectively, the proposed topology has both step-down and step-up operations with or without voltage inversion. Moreover, while operating as non-inverting buck and boost converter, it has lower switch voltage stresses, lower inductor current stress and ripple current than the traditional inverting buck-boost ac-ac converter. A detailed topology analysis is presented followed by experimental results to prove the advantages of the proposed converter.


title: Credit Card Fraud Detection: Personalized or Aggregated Model

abstract: Banking industry suffers lost in millions of dollars each year caused by credit card fraud. Tremendous effort, time and money have been spent to detect fraud where there are studies done on creating personalized model for each credit card holder to identify fraud. These studies claimed that each card holder carries different spending behavior which necessitates personalized model. However, to the best of our knowledge, there has not been any study conducted to verify this hypothesis. Hence, in this paper, we investigate the effectiveness of personalized models compared to the aggregated models in identify fraud for different individuals. For this purpose, we have collected some actual transactions and some other data through an online questionnaire. We have then constructed personalized and aggregated models. The performance of these models is evaluated using test data set to compare their accuracy in identifying fraud for different individuals. To our surprise, the experimental results show that aggregated models outperforms personalized models. Besides, we have also compared the performance of the random forest and Naive Bayes in creating the models for fraud detection. Generally, random forest performs better than the Naive Bayes for the aggregated model while Naive Bayes performs better in the personalized models.


title: The Disablement in the Physically Active Scale, Part II: The Psychometric Properties of an Outcomes Scale for Musculoskeletal Injuries

abstract: Abstract Context: Outcomes assessment is an integral part of ensuring quality in athletic training, but few generic instruments have been specifically designed to measure disablement in the physica...


title: Assessing the Market Benefits of Reducing Deforestation: A General Equilibrium Investigation

abstract: Deforestation is one of the major sources of greenhouse gases (GHG) emissions, accounting for around 17% of total global GHG discharges. As the role forests play in the global carbon cycle has been widely recognized, several studies analysing the potential contribution of avoided deforestation credits in a carbon market have already been performed, showing that these credits should play a substantial role in an overall portfolio of mitigation strategies. Using a dynamic, multiregional Computable General Equilibrium (CGE) model, the ICES model (Intertemporal Computable Equilibrium System), this paper follows a novel approach, since deforestation emission reductions are not linked to a global carbon market, as commonly used. Instead, we use a global warming approach which provides an additional valuation criteria: the market general equilibrium value of halting/reducing deforestation, based on climate change impacts. This exercise consists on the formulation of a scenario where carbon emissions from deforestation are reduced by 50% and 100%, thereby producing a different CO2 concentration levels in the atmosphere and a corresponding variation in temperature. Those changes in temperature in turn impact the economy at various levels, and the corresponding indirect effects are then assessed. Moreover, such an exercise may provide additional information to policy makers considering the creation/participation of an international fund to protect tropical forests. In fact, market valuation for avoided deforestation can be provided not only for a complete halt of deforestation but also for different reduction targets. Finally, and taking advantage of the dynamic feature of the model, different paths for a same global level of reduced deforestation are simulated, shedding new lights on the temporal value of reduced deforestation.


title: Combining Domain Knowledge and Machine Learning for Robust Fall Detection

abstract: This paper presents a method for combining domain knowledge and machine learning CDKML for classifier generation and online adaptation. The method exploits advantages in domain knowledge and machine learning as complementary information sources. Whereas machine learning may discover patterns in interest domains that are too subtle for humans to detect, domain knowledge may contain information on a domain not present in the available domain dataset. CDKML has three steps. First, prior domain knowledge is enriched with relevant patterns obtained by machine learning to create an initial classifier. Second, genetic algorithms refine the classifier. Third, the classifier is adapted online on the basis of user feedback using the Markov decision process. CDKML was applied in fall detection. Tests showed that the classifiers developed by CDKML have better performance than machine-learning classifiers generated on a training dataset that does not adequately represent all real-life cases of the learned concept. The accuracy of the initial classifier was 10 percentage points higher than the best machine-learning classifier and the refinement added 3 percentage points. The online adaptation improved the accuracy of the refined classifier by an additional 15 percentage points.


title: An 8.3 nW −72 dBm event driven IoE wake up receiver RF front end

abstract: This work presents an ultra-low power event driven wake-up receiver (WuRx) fabricated in a RF CMOS 130 nm process. The receiver consists of an off-chip lumped element matching network, an envelope detector, a decision circuit capable of detecting sub-mV baseband signal voltages and a clock source consuming 1.3 nW. This receiver has demonstrated a sensitivity of −72 dBm while consuming a total of 8.3 nW from 1 V and 0.65 V sources.


title: From ankylosis to pencil-in-cup deformity in psoriatic arthritis: a case report.

abstract: A 56-year-old woman with psoriatic arthritis is presented whose initially ankylosed digit was later found to develop pencil-in-cup change. The patient was treated over that period with etanercept and had no signs of active arthritis. The possible mechanisms for these changes are discussed.


title: Detecting a trojan die in 3D stacked integrated circuits

abstract: While 3D integrated circuits provide many security advantages, one disadvantage is the insertion of a Trojan die into the stack. In this paper, we explore a technique to detect an extra die through delay analysis.


title: Compound facial expressions of emotion

abstract: Understanding the different categories of facial expressions of emotion regularly used by us is essential to gain insights into human cognition and affect as well as for the design of computational models and perceptual interfaces. Past research on facial expressions of emotion has focused on the study of six basic categories—happiness, surprise, anger, sadness, fear, and disgust. However, many more facial expressions of emotion exist and are used regularly by humans. This paper describes an important group of expressions, which we call compound emotion categories. Compound emotions are those that can be constructed by combining basic component categories to create new ones. For instance, happily surprised and angrily surprised are two distinct compound emotion categories. The present work defines 21 distinct emotion categories. Sample images of their facial expressions were collected from 230 human subjects. A Facial Action Coding System analysis shows the production of these 21 categories is different but consistent with the subordinate categories they represent (e.g., a happily surprised expression combines muscle movements observed in happiness and surprised). We show that these differences are sufficient to distinguish between the 21 defined categories. We then use a computational model of face perception to demonstrate that most of these categories are also visually discriminable from one another.