title: The Relevance Vector Machine

abstract: The support vector machine (SVM) is a state-of-the-art technique for regression and classification, combining excellent generalisation properties with a sparse kernel representation. However, it does suffer from a number of disadvantages, notably the absence of probabilistic outputs, the requirement to estimate a trade-off parameter and the need to utilise 'Mercer' kernel functions. In this paper we introduce the Relevance Vector Machine (RVM), a Bayesian treatment of a generalised linear model of identical functional form to the SVM. The RVM suffers from none of the above disadvantages, and examples demonstrate that for comparable generalisation performance, the RVM requires dramatically fewer kernel functions.


title: Automated SQL tuning through trial and (sometimes) error

abstract: SQL tuning---the attempt to improve a poorly-performing execution plan produced by the database query optimizer---is a critical aspect of database performance tuning. Ironically, as commercial databases strive to improve on the manageability front, SQL tuning is becoming more of a black art. It requires a high level of expertise in areas like (i) query optimization, run-time execution of query plan operators, configuration parameter settings, and other database internals; (ii) identification of missing indexes and other access structures; (iii) statistics maintained about the data; and (iv) characteristics of the underlying storage system. Since database systems, their workloads, and the data that they manage are not getting any simpler, database users and administrators often rely on trial and error for SQL tuning. In this paper, we take the position that the trial-and-error (or, experiment-driven) process of SQL tuning can be automated by the database system in an efficient manner; freeing the user or administrator from this burden in most cases. A number of current approaches to SQL tuning indeed take an experiment-driven approach. We are prototyping a tool, called zTuned, that automates experiment-driven SQL tuning. This paper describes the design choices in zTuned to address three nontrivial issues: (i) how is the SQL tuning logic integrated with the regular query optimizer, (ii) how to plan the experiments to conduct so that a satisfactory (new) plan can be found quickly, and (iii) how to conduct experiments with minimal impact on the user-facing production workload. We conclude with a preliminary empirical evaluation and outline promising new directions in automated SQL tuning.


title: Self-sovereign Identity $-$ Opportunities and Challenges for the Digital Revolution

abstract: The interconnectedness of people, services and devices is a defining aspect of the digital revolution, and, secure digital identities are an important prerequisite for secure and legally compliant information exchange. Existing approaches to realize a secure identity management focus on central providers of identities such as national authorities or online service providers. Hence, changing residence or service provider often means to start over and creating new identities, because procedures for data portability are missing. Self-sovereign digital identities are instead created and managed by individuals, and enable them to maintain their digital identities independent from residence, national eID infrastructure and market-dominating service providers.


title: A Novel Printed UWB Slot Antenna With Reconfigurable Band-Notch Characteristics

abstract: A novel printed ultrawideband (UWB) slot antenna with reconfigurable band-notch characteristics is presented in this letter. The proposed antenna consists of a modified rectangular radiation patch with a circular and three rectangular-shaped slots etched on it. Additionally, by cutting two triangular-shaped slits on a modified ground plane, practical fractional bandwidth of more than 120% (3.12-12.51 GHz), defined by -10 dB return loss, is achieved. In order to generate single and dual band-notch characteristics, two p-i-n diodes are mounted across the circular slot. The designed antenna has small size of 20 × 20 mm2 with switchable single notched bands of 3.12-3.84 GHz WiMAX and 5-6.07 GHz WLAN, when diode 1 and diode 2 are respectively on. Also, dual bands of 3.12-3.82 and 4.9-6.06 GHz are eliminated from whole frequency band when both diodes are concurrently on.


title: For Submission as a regular paper : Title : An Approximate Bayesian Method for Simultaneous Localisation and Mapping

abstract: This paper describes a Bayesian formulation of the Simultaneous Localisation and Mapping (SLAM) problem. Previously, the SLAM problem could only be solved in real time through the use of the Kalman Filter. This generally restricts the application of SLAM methods to domains with straight-forward (analytic) environment and sensor models. In this paper the Sum-of-Gaussian (SOG) method is used to approximate more general (arbitrary) probability distributions. This representation permits the generalizations made possible by Monte-Carlo methods, while inheriting the real-time computational advantages of the Kalman filter. The method is demonstrated by its application to sub-sea field data. The sub-sea data consists of both sonar and visual information of near-field landmarks. This is a particularly challenging problem incorporating diverse sensing modalities, amorphous environment features, and poorly known vehicle dynamics; none of which can be easily handled by Kalman filter-based SLAM algorithms.


title: Semantic Regularities in Document Representations

abstract: Recent work exhibited that distributed word representations are good at capturing linguistic regularities in language. This allows vector-oriented reasoning based on simple linear algebra between words. Since many different methods have been proposed for learning document representations, it is natural to ask whether there is also linear structure in these learned representations to allow similar reasoning at document level. To answer this question, we design a new document analogy task for testing the semantic regularities in document representations, and conduct empirical evaluations over several state-of-the-art document representation models. The results reveal that neural embedding based document representations work better on this analogy task than conventional methods, and we provide some preliminary explanations over these observations.


title: RGB-(D) scene labeling: Features and algorithms

abstract: Scene labeling research has mostly focused on outdoor scenes, leaving the harder case of indoor scenes poorly understood. Microsoft Kinect dramatically changed the landscape, showing great potentials for RGB-D perception (color+depth). Our main objective is to empirically understand the promises and challenges of scene labeling with RGB-D. We use the NYU Depth Dataset as collected and analyzed by Silberman and Fergus [30]. For RGB-D features, we adapt the framework of kernel descriptors that converts local similarities (kernels) to patch descriptors. For contextual modeling, we combine two lines of approaches, one using a superpixel MRF, and the other using a segmentation tree. We find that (1) kernel descriptors are very effective in capturing appearance (RGB) and shape (D) similarities; (2) both superpixel MRF and segmentation tree are useful in modeling context; and (3) the key to labeling accuracy is the ability to efficiently train and test with large-scale data. We improve labeling accuracy on the NYU Dataset from 56.6% to 76.1%. We also apply our approach to image-only scene labeling and improve the accuracy on the Stanford Background Dataset from 79.4% to 82.9%.


title: Male preference for female pubic hair: an evolutionary view.

abstract: SUMMARY ::: The ectoparasite avoidance hypothesis proposes that human hairlessness was favoured by sexual selection, because hairless individuals suffered from lower parasite loads. Females have seemingly less hairy bodies than men suggesting that the selection toward hairlessness is more intense in females than in males. This study examined male preference for hairy and shaved female genitalia. Pubic hair, although still functional in terms of dissipation of phermononal secretions, was perceived by heterosexual males recruited in the university (age range: 19-38 years, N = 96) as much less attractive as shaved female genitalia. Males who were more disgust sensitive and those who were sexually unrestricted showed a stronger preference for shaved genitalia than others. Self-reported frequency of pornography consumption was associated, contrary to expectations, with a stronger preference for hairy genitalia which suggests that this may be a result of negative frequency dependent selection. Older males also preferred hairy genitalia more than younger males. Overall, these results suggest that a preference for shaved genitalia may be explained by the superficial resemblance of pubic hair with chest hair, which is less developed as in our evolutionary past, perhaps due to the benefits associated with ectoparasite avoidance.


title: Semantic conflict resolution ontology (SCROL): an ontology for detecting and resolving data and schema-level semantic conflicts

abstract: Establishing semantic interoperability among heterogeneous information sources has been a critical issue in the database community for the past two decades. Despite the critical importance, current approaches to semantic interoperability of heterogeneous databases have not been sufficiently effective. We propose a common ontology called semantic conflict resolution ontology (SCROL) that addresses the inherent difficulties in the conventional approaches, i.e., federated schema and domain ontology approaches. SCROL provides a systematic method for automatically detecting and resolving various semantic conflicts in heterogeneous databases. SCROL provides a dynamic mechanism of comparing and manipulating contextual knowledge of each information source, which is useful in achieving semantic interoperability among heterogeneous databases. We show how SCROL is used for detecting and resolving semantic conflicts between semantically equivalent schema and data elements. In addition, we present evaluation results to show that SCROL can be successfully used to automate the process of identifying and resolving semantic conflicts.


title: Low cost silver ink RFID tag antennas

abstract: We discuss silver ink as a low cost option for manufacturing RFID tags at ultra high frequencies (UHF). An analysis of two different RFID tag antennas, made from silver ink and from copper, is presented at UHF. The influence of each material on tag performance is discussed along with simulation results and measurement data which are in good agreement. It is observed that RFID tag performance depends both on material and on the shape of the antenna. For some classes of antennas, silver ink with higher conductivity performs as well as copper, which makes it an attractive low cost alternative material to copper for RFID tag antennas.


title: Powering Pervasive Computing Systems

abstract: In this article, the authors discuss new trends in powering pervasive computing systems and review what we have learned in the last 15 years of building such systems. Major advances in cloud computing, RF, power harvesting, and networking have enabled new asymmetric architectures that reduce our reliance on batteries. Although the authors see a slowing of Moore's law, and although battery density technology has remained relatively flat, there are still opportunities in improving energy efficiency. This article is part of a special issue on pervasive computing revisited.


title: Throughput calculation in a HIPERLAN type 2 network considering power control and link adaptation

abstract: This paper deals with the combination of transmit power control and link adaptation in wireless data networks. The tools for engineering wireless voice networks are not necessarily suited for data communications due to the smooth transition between good and bad states in data networks. This paper proposes a theoretical model to calculate the throughput in a wireless data network using HIPERLAN type 2 as an example system. It is based on the computation of the probability density function of the signal-to-interference ratio (SIR) at the receiver and the calculation of the expected throughput value based on known throughput curves versus SIR. The results from the theoretical model are presented and compared to the results from a simulation model. An important outcome is that maximum throughput in the network is not necessarily achieved at maximum transmit powers.


Report of the Ad Hoc Committee on Open Access


title: Temporal Pattern and Association Discovery of Diagnosis Codes Using Deep Learning

abstract: Longitudinal health records contain data on patients' visits, condition, treatment, and test results representing progression of their health status over time. In poorly understood patient populations, such data are particularly helpful in characterizing disease progression and early detection. In this work we developed a deep learning algorithm for temporal pattern discovery over Rochester Epidemiology Project data. We modeled each patient's records as a matrix of temporal clinical events with ICD9 and HCUP CSS diagnosis codes as rows and years of diagnosis as columns. Patients aged 18 or younger at the time of diagnosis were selected. A deep Boltzmann machine network with three hidden layers was constructed with each patient's diagnosis matrix values as visible nodes. The final weights of the network model were analyzed as the common features among patients' records.


title: Particle swarm optimization with spatially meaningful neighbours

abstract: Neighbourhood topologies in particle swarm optimization (PSO) are typically random in terms of the spatial positions of connected neighbours. This study explores the use of spatially meaningful neighbours for PSO. An approach is designed which uses heuristics to leverage the natural neighbours computed with Delaunay triangulation. The approach is compared to standard PSO sociometries and fitness distance ratio approaches. Although intrinsic properties of Delaunay triangulation limit the practical application of this approach to low dimensions results show that it is a successful particle swarm optimizer.


title: A Measurement Study of Server Utilization in Public Clouds

abstract: Due to a server's non-proportional energy consumption, it is highly desirable to increase server utilization in order to lower energy consumption and minimize environmental impact. To increase the utilization level, we must first understand the current utilization level in the state-of-the-art cloud environment. Towards that goal, we devise a new technique which can be used to measure the CPU utilization in a cloud. We sampled two different infrastructure cloud providers (IaaS or Infrastructure as a Service) on their server utilization. Surprisingly, our results show that there is a significant room for improvement.


title: Partial preprocessing C code for variability analysis

abstract: The C preprocessor is commonly used to implement variability. Given a feature selection, code fragments can be excluded from compilation with #ifdef and similar directives. However, the token-based nature of the C preprocessor makes variability implementation difficult and error-prone. Additionally, variability mechanisms are intertwined with macro definitions, macro expansion, and file inclusion. To determine whether a code fragment is compiled, the entire file must be preprocessed. We present a partial preprocessor that preprocesses file inclusion and macro expansion, but retains variability information for further analysis. We describe the mechanisms of the partial preprocessor, provide a full implementation, and present some initial experimental results. The partial preprocessor is part of a larger endeavor in the TypeChef project to check variability implementations (syntactic correctness, type correctness) in C projects such as the Linux kernel.


title: NetSearch: Googling large-scale network management data

abstract: In order to ensure the service quality, modern Internet Service Providers (ISPs) invest tremendously on their network monitoring and measurement infrastructure. Vast amount of network data, including device logs, alarms, and active/passive performance measurement across different network protocols and layers, are collected and stored for analysis. As network measurement grows in scale and sophistication, it becomes increasingly challenging to effectively “search” for the relevant information that best support the needs of network operations. In this paper, we look into techniques that have been widely applied in the information retrieval and search engine domain and explore their applicability in network management domain. We observe that unlike the textural information on the Internet, network data are typically annotated with time and location information, which can be further augmented using information based on network topology, protocol and service dependency. We design NetSearch, a system that pre-processes various network data sources on data ingestion, constructs index that matches both the network spatial hierarchy model and the inherent timing/textual information contained in the data, and efficiently retrieves the relevant information that network operators search for. Through case study, we demonstrate that NetSearch is an important capability for many critical network management functions such as complex impact analysis.


title: Automatic classi fi cation of patients with Alzheimer ' s disease from structural MRI : A comparison of ten methods using the ADNI database

abstract: Abstract Recently, several high dimensional classification methods have been proposed to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls (CN) based on T1-weighted MRI. However, these methods were assessed on different populations, making it difficult to compare their performance. In this paper, we evaluated the performance of ten approaches (five voxel-based methods, three methods based on cortical thickness and two methods based on the hippocampus) using 509 subjects from the ADNI database. Three classification experiments were performed: CN vs AD, CN vs MCIc (MCI who had converted to AD within 18 months, MCI converters — MCIc) and MCIc vs MCInc (MCI who had not converted to AD within 18 months, MCI non-converters — MCInc). Data from 81 CN, 67 MCInc, 39 MCIc and 69 AD were used for training and hyperparameters optimization. The remaining independent samples of 81 CN, 67 MCInc, 37 MCIc and 68 AD were used to obtain an unbiased estimate of the performance of the methods. For AD vs CN, whole-brain methods (voxel-based or cortical thickness-based) achieved high accuracies (up to 81% sensitivity and 95% specificity). For the detection of prodromal AD (CN vs MCIc), the sensitivity was substantially lower. For the prediction of conversion, no classifier obtained significantly better results than chance. We also compared the results obtained using the DARTEL registration to that using SPM5 unified segmentation. DARTEL significantly improved six out of 20 classification experiments and led to lower results in only two cases. Overall, the use of feature selection did not improve the performance but substantially increased the computation times.


title: Reversible Data Hiding in Encrypted Image

abstract: This work proposes a novel reversible data hiding scheme for encrypted image. After encrypting the entire data of an uncompressed image by a stream cipher, the additional data can be embedded into the image by modifying a small proportion of encrypted data. With an encrypted image containing additional data, one may firstly decrypt it using the encryption key, and the decrypted version is similar to the original image. According to the data-hiding key, with the aid of spatial correlation in natural image, the embedded data can be successfully extracted and the original image can be perfectly recovered.


title: Predicting Grades

abstract: To increase efficacy in traditional classroom courses as well as in Massive Open Online Courses (MOOCs), automated systems supporting the instructor are needed. One important problem is to automatically detect students that are going to do poorly in a course early enough to be able to take remedial actions. Existing grade prediction systems focus on maximizing the accuracy of the prediction while overseeing the importance of issuing timely and personalized predictions. This paper proposes an algorithm that predicts the final grade of each student in a class. It issues a prediction for each student individually, when the expected accuracy of the prediction is sufficient. The algorithm learns online what is the optimal prediction and time to issue a prediction based on past history of students' performance in a course. We derive a confidence estimate for the prediction accuracy and demonstrate the performance of our algorithm on a dataset obtained based on the performance of approximately 700 UCLA undergraduate students who have taken an introductory digital signal processing over the past 7 years. We demonstrate that for 85% of the students we can predict with 76% accuracy whether they are going do well or poorly in the class after the 4th course week. Using data obtained from a pilot course, our methodology suggests that it is effective to perform early in-class assessments such as quizzes, which result in timely performance prediction for each student, thereby enabling timely interventions by the instructor (at the student or class level) when necessary.


title: Systems engineering challenges and MBSE opportunities for automotive system design

abstract: Many industries are facing rapid growth in the complexity of the systems they develop. In the automotive domain, vehicles are essentially becoming high performance computing platforms, comprised of dozens of electronic control systems (ECUs) that process over 100 million of lines of software code. In addition, Tens of thousands of vehicle product variants must be managed. One of the primary ways problems associated with increased system complexity can be mitigated so that products are delivered on time, on budget, and with minimal quality issues, is for engineering organizations to utilize the most effective systems engineering methodologies. Model based systems engineering (MBSE), implemented using languages like SysML, is one such methodology that has gained a foothold over the past 10–15 years and continues to be refined and improved today. This paper provides an overview of the general challenges systems engineering faces today including those associated with the automotive industry. Approaches based on MBSE are considered to address these challenges. Next, an enterprise-wide model-based engineering (MBE) strategy proposed by the aerospace and defense industry that could also be effectively utilized by automotive OEMs is discussed. Finally, a set of specific automotive applications of MBSE is described.


Secondary carcinoma of the penis. A review of the literature and a report of nine new cases


title: Applications of Secure Location Sensing in Healthcare

abstract: Secure location sensing has the potential to improve healthcare processes regarding security, efficiency, and safety. For example, enforcing close physical proximity to a patient when using a barcode medication administration system (BCMA) can mitigate the consequences of unsafe barcode scanning workarounds. We present Beacon+, a Bluetooth Low Energy (BLE) device that extends the design of Apple's popular iBeacon specification with unspoofable, temporal, and authenticated advertisements. Our prototype Beacon+ design enables secure location sensing applications such as real-time tracking of hospital assets (e.g., infusion pumps). We implement this exact real-time tracking system and use it as a foundation for a novel application that applies location-based restrictions on access control.


title: A New Ultralight Antropomorphic Hand

abstract: In this paper a very lightweight artificial hand is presented that approximates the manipulation abilities of a human hand very well. A large variety of different objects can be grasped reliably and the movements of the hand appear to be very natural. This five finger hand has 13 independent degrees of freedom driven by a new type of powerful small size flexible fluidic actuator. The actuators are completely integrated in the fingers which made possible the design of a very compact and lightweight hand that can either be used as a prosthetic hand or as a humanoid robot hand. A mathematical model for the expansion of a flexible fluidic actuator is given and the mechanical construction and features of the new anthropomorphic hand are illustrated.


title: A Siamese Long Short-Term Memory Architecture for Human Re-Identification

abstract: Matching pedestrians across multiple camera views known as human re-identification (re-identification) is a challenging problem in visual surveillance. In the existing works concentrating on feature extraction, representations are formed locally and independent of other regions. We present a novel siamese Long Short-Term Memory (LSTM) architecture that can process image regions sequentially and enhance the discriminative capability of local feature representation by leveraging contextual information. The feedback connections and internal gating mechanism of the LSTM cells enable our model to memorize the spatial dependencies and selectively propagate relevant contextual information through the network. We demonstrate improved performance compared to the baseline algorithm with no LSTM units and promising results compared to state-of-the-art methods on Market-1501, CUHK03 and VIPeR datasets. Visualization of the internal mechanism of LSTM cells shows meaningful patterns can be learned by our method.


title: A Robot Localization Framework Using CNNs for Object Detection and Pose Estimation

abstract: External localization is an essential part for the indoor operation of small or cost-efficient robots, as they are used, for example, in swarm robotics. We introduce a two-stage localization and instance identification framework for arbitrary robots based on convolutional neural networks. Object detection is performed on an external camera image of the operation zone providing robot bounding boxes for an identification and orientation estimation convolutional neural network. Additionally, we propose a process to generate the necessary training data. The framework was evaluated with 3 different robot types and various identification patterns. We have analyzed the main framework hyperparameters providing recommendations for the framework operation settings. We achieved up to 98% mAP@IOU0.5 and only 1.6{\deg} orientation error, running with a frame rate of 50 Hz on a GPU.


title: Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding

abstract: We marry two powerful ideas: deep representation learning for visual recognition and language understanding, and symbolic program execution for reasoning. Our neural-symbolic visual question answering (NS-VQA) system first recovers a structural scene representation from the image and a program trace from the question. It then executes the program on the scene representation to obtain an answer. Incorporating symbolic structure as prior knowledge offers three unique advantages. First, executing programs on a symbolic space is more robust to long program traces; our model can solve complex reasoning tasks better, achieving an accuracy of 99.8% on the CLEVR dataset. Second, the model is more data- and memory-efficient: it performs well after learning on a small number of training data; it can also encode an image into a compact representation, requiring less storage than existing methods for offline question answering. Third, symbolic program execution offers full transparency to the reasoning process; we are thus able to interpret and diagnose each execution step.


title: Motorization for an Electric Scooter by Using Permanent-Magnet Machines Optimized Based on a Hybrid Metaheuristic Algorithm

abstract: This paper deals with the proposition of a permanent-magnet (PM) machine used to motorize an electric scooter. Several electrical machine configurations are discussed, and the best solution is chosen. The power density and the energetic performances of the chosen machine (with a fractional-slot configuration) are improved using a hybrid optimization algorithm, which combines simulated annealing (SA) and particle swarm optimization (PSO) techniques. The obtained results are numerically and experimentally validated to prove that the proposed solution suites the scooter application.


title: A Colorization Based Animation Broadcast System with Traitor Tracing Capability

abstract: Distributing video contents via broadcasting network mechanisms has become a promising business opportunity for the entertainment industry. However, since content piracy is always a serious problem, broadcasted contents must be adequately protected. Rather than implementing sophisticate key-management schemes for access control, an animation broadcast system based on colorization techniques is proposed. In the proposed system, gray-level animation video sequences are delivered via broadcast mechanisms, such as multicast, to reduce the overhead in server processing and network bandwidth. Moreover, color seeds labeled with fingerprint codes are delivered to each client through low-bandwidth auxiliary connections and then used to generate high-quality full-color animations with slight differences between versions received by each client-side device. When a user illegally duplicates and distributes the received video, his identity can be easily found out by examining features extracted from the pirated video. The proposed scheme also shows good resistance to collusion attacks where two or more users cooperate to generate an illegal copy in expectation of getting rid of legal responsibility. The proposed scheme exhibits advantages in network bandwidth, system performance and content security.


title: Extracting psychiatric stressors for suicide from social media using deep learning

abstract: BACKGROUND ::: Suicide has been one of the leading causes of deaths in the United States. One major cause of suicide is psychiatric stressors. The detection of psychiatric stressors in an at risk population will facilitate the early prevention of suicidal behaviors and suicide. In recent years, the widespread popularity and real-time information sharing flow of social media allow potential early intervention in a large-scale population. However, few automated approaches have been proposed to extract psychiatric stressors from Twitter. The goal of this study was to investigate techniques for recognizing suicide related psychiatric stressors from Twitter using deep learning based methods and transfer learning strategy which leverages an existing annotation dataset from clinical text. ::: ::: ::: METHODS ::: First, a dataset of suicide-related tweets was collected from Twitter streaming data with a multiple-step pipeline including keyword-based retrieving, filtering and further refining using an automated binary classifier. Specifically, a convolutional neural networks (CNN) based algorithm was used to build the binary classifier. Next, psychiatric stressors were annotated in the suicide-related tweets. The stressor recognition problem is conceptualized as a typical named entity recognition (NER) task and tackled using recurrent neural networks (RNN) based methods. Moreover, to reduce the annotation cost and improve the performance, transfer learning strategy was adopted by leveraging existing annotation from clinical text. ::: ::: ::: RESULTS & CONCLUSIONS ::: To our best knowledge, this is the first effort to extract psychiatric stressors from Twitter data using deep learning based approaches. Comparison to traditional machine learning algorithms shows the superiority of deep learning based approaches. CNN is leading the performance at identifying suicide-related tweets with a precision of 78% and an F-1 measure of 83%, outperforming Support Vector Machine (SVM), Extra Trees (ET), etc. RNN based psychiatric stressors recognition obtains the best F-1 measure of 53.25% by exact match and 67.94% by inexact match, outperforming Conditional Random Fields (CRF). Moreover, transfer learning from clinical notes for the Twitter corpus outperforms the training with Twitter corpus only with an F-1 measure of 54.9% by exact match. The results indicate the advantages of deep learning based methods for the automated stressors recognition from social media.


THE ECONOMIC EFFECTS OF MULTIPLE INCENTIVES


title: Deep, Narrow Sigmoid Belief Networks Are Universal Approximators

abstract: In this note, we show that exponentially deep belief networks can approximate any distribution over binary vectors to arbitrary accuracy, even when the width of each layer is limited to the dimensionality of the data. We further show that such networks can be greedily learned in an easy yet impractical way.


title: Context and leadership: an examination of the nine-factor full-range leadership theory using the Multifactor Leadership Questionnaire ☆

abstract: In this study, we examined the validity of the measurement model and factor structure of Bass and Avolio’s Multifactor Leadership Questionnaire (MLQ) (Form 5X). We hypothesized that evaluations of leadership—and hence the psychometric properties of leadership instruments—may be affected by the context in which leadership is observed and evaluated. Using largely homogenous business samples consisting of 2279 pooled male and 1089 pooled female raters who evaluated same-gender leaders, we found support for the nine-factor leadership model proposed by Bass and Avolio. The model was configurally and partially metrically invariant—suggesting that the same constructs were validly measured in the male and female groups. Mean differences were found between the male and female samples on four leadership factors (Study 1). Next, using factor-level data of 18 independently gathered samples (N=6525 raters) clustered into prototypically homogenous contexts, we tested the nine-factor model and found it was stable (i.e., fully invariant) within homogenous contexts (Study 2). The contextual factors comprised environmental risk, leader–follower gender, and leader hierarchical level. Implications for use of the MLQ and nine-factor model are discussed.


title: A Novel Evolutionary Data Mining Algorithm With Applications to Churn Prediction

abstract: Classification is an important topic in data mining research. Given a set of data records, each of which belongs to one of a number of predefined classes, the classification problem is concerned with the discovery of classification rules that can allow records with unknown class membership to be correctly classified. Many algorithms have been developed to mine large data sets for classification models and they have been shown to be very effective. However, when it comes to determining the likelihood of each classification made, many of them are not designed with such purpose in mind. For this, they are not readily applicable to such problems as churn prediction. For such an application, the goal is not only to predict whether or not a subscriber would switch from one carrier to another, it is also important that the likelihood of the subscriber's doing so be predicted. The reason for this is that a carrier can then choose to provide a special personalized offer and services to those subscribers who are predicted with higher likelihood to churn. Given its importance, we propose a new data mining algorithm, called data mining by evolutionary learning (DMEL), to handle classification problems of which the accuracy of each predictions made has to be estimated. In performing its tasks, DMEL searches through the possible rule space using an evolutionary approach that has the following characteristics: 1) the evolutionary process begins with the generation of an initial set of first-order rules (i.e., rules with one conjunct/condition) using a probabilistic induction technique and based on these rules, rules of higher order (two or more conjuncts) are obtained iteratively; 2) when identifying interesting rules, an objective interestingness measure is used; 3) the fitness of a chromosome is defined in terms of the probability that the attribute values of a record can be correctly determined using the rules it encodes; and 4) the likelihood of predictions (or classifications) made are estimated so that subscribers can be ranked according to their likelihood to churn. Experiments with different data sets showed that DMEL is able to effectively discover interesting classification rules. In particular, it is able to predict churn accurately under different churn rates when applied to real telecom subscriber data.


title: Rehabilitation Management of the Charcot–Marie–Tooth Syndrome: A Systematic Review of the Literature

abstract: The Charcot-Marie-Tooth disease (CMT) causes significant muscular deficits in the affected patients, restricts daily activities (ADL), and involves a severe disability. Although the conservative intervention is the only treatment for the disease, there is no scientific evidence so far on rehabilitation treatment. Objectives of the review are: research the best literary evidence so far on the rehabilitation treatment of CMT; critically analyze the outcome, to build an evidence-based work protocol.A systematic review of the rehabilitation of a patient with CMT, including the results from the following databases: Pubmed, Medline, Embase, Pedro, Cinahl, Ebsco discovery. Criteria for inclusion: randomized/controlled studies, analytic studies, transversal studies on a cohort of at least 10 individuals; medium/long-term report of the results.Eleven studies in total have been admitted to the final review phase; trials about physiotherapy CMT treatment (5), about orthosis treatment (6). Despite the wide range of outcomes and proposed interventions, the data points to the following: strength or endurance trainings improve functionality and ADLs of affected patients, while orthotic role is, at the moment, not completely clear.Physiotherapy treatment is a useful tool to manage CMT; more studies on a larger number of cases are needed to define orthosis utility and to establish the gold standard of the treatment.


title: Vision-guided flight stability and control for micro air vehicles

abstract: Substantial progress has been made recently towards designing, building and test-flying remotely piloted Micro Air Vehicles (MAVs) and small UAVs. We seek to complement this progress in overcoming the aerodynamic obstacles to flight at very small scales with a vision-guided flight stability and autonomy system, based on a robust horizon detection algorithm. In this paper, we first motivate the use of computer vision for MAV autonomy, arguing that given current sensor technology, vision may be the only practical approach to the problem. We then describe our statistical vision-based horizon detection algorithm, which has been demonstrated at 30 Hz with over 99.9% correct horizon identification. Next, we develop robust schemes for the detection of extreme MAV attitudes, where no horizon is visible, and for the detection of horizon estimation errors, due to external factors such as video transmission noise. Finally, we discuss our feedback controller for self-stabilized flight, and report results on vision-based autonomous flights of duration exceeding ten minutes.


title: Vision: smart home control with head-mounted sensors for vision and brain activity

abstract: Today, an increasing number of household appliances is being connected to the Internet to form a smart home. Intelligent control algorithms in the cloud adapt the configuration of this Internet-of-Things to our daily routines and personal preferences. Frequently, there are unforeseen situations where the control algorithms will not capture the actual desired configuration. In these cases, the user must intervene in the control algorithms and manually adjust the connected object's setting. Browsing to the appropriate web service or launching the vendor-specific companion app for even a simple interaction like lowering the temperature setting is a tedious process. In this paper, we report on our early insights in building a mobile system that provides a common, intuitive interface to all actuators in the smart home. Using a head-mounted camera and a commercial Emotiv EEG neuro-headset, we let the user configure the IoT by merely looking at an object and performing a related facial expression. This way, users only need to look at an object and think about the desired action. We leverage on the home cloudlet for the compute-intensive signal processing for object detection.


title: Learning multiple collaborative tasks with a mixture of Interaction Primitives

abstract: Robots that interact with humans must learn to not only adapt to different human partners but also to new interactions. Such a form of learning can be achieved by demonstrations and imitation. A recently introduced method to learn interactions from demonstrations is the framework of Interaction Primitives. While this framework is limited to represent and generalize a single interaction pattern, in practice, interactions between a human and a robot can consist of many different patterns. To overcome this limitation this paper proposes a Mixture of Interaction Primitives to learn multiple interaction patterns from unlabeled demonstrations. Specifically the proposed method uses Gaussian Mixture Models of Interaction Primitives to model nonlinear correlations between the movements of the different agents. We validate our algorithm with two experiments involving interactive tasks between a human and a lightweight robotic arm. In the first, we compare our proposed method with conventional Interaction Primitives in a toy problem scenario where the robot and the human are not linearly correlated. In the second, we present a proof-of-concept experiment where the robot assists a human in assembling a box.


title: YAF : Yet Another Flowmeter

abstract: A flow meter generates flow data - which contains information about each connection observed on a network - from a stream of observed packets. Flow meters can be implemented in standalone measurement devices or inline on packet forwarding devices, such as routers. YAF (Yet Another Flowmeter) was created as a reference implementation of an IPFIX Metering and Exporting Process, and to provide a platform for experimentation and rapid deployment of new flow meter capabilities. Significant engineering effort has also gone into ensuring that YAF is a high performance, flexible, stable, and capable flow collector. This paper describes the some of the issues we encountered in designing and implementing YAF, along with some background on some of the technologies that we chose for implementation. In addition we will describe some of our experiences in deploying and operating YAF in large-scale networks.


title: Structured Descriptions of Complex Objects

abstract: Experimental techniques are demonstrated which generate segmented symbolic descriptions for complex objects with joints, such as a hammer or a glove. Complete "descriptions with relationship of parts at joints and descriptions of joints are presented. These techniques are elements of a larger scheme for description mechanisms for hypotheses, and for visual memory and recognition.


title: Lexical Disambiguation in Natural Language Questions (NLQs)

abstract: Question processing is a fundamental step in a question answering (QA) application, and its quality impacts the performance of QA application. The major challenging issue in processing question is how to extract semantic of natural language questions (NLQs). A human language is ambiguous. Ambiguity may occur at two levels; lexical and syntactic. In this paper, we propose a new approach for resolving lexical ambiguity problem by integrating context knowledge and concepts knowledge of a domain, into shallow natural language processing (SNLP) techniques. Concepts knowledge is modeled using ontology, while context knowledge is obtained from WordNet, and it is determined based on neighborhood words in a question. The approach will be applied to a university QA system.


title: DC Proximal Newton for Nonconvex Optimization Problems

abstract: We introduce a novel algorithm for solving learning problems where both the loss function and the regularizer are nonconvex but belong to the class of difference of convex (DC) functions. Our contribution is a new general purpose proximal Newton algorithm that is able to deal with such a situation. The algorithm consists in obtaining a descent direction from an approximation of the loss function and then in performing a line search to ensure a sufficient descent. A theoretical analysis is provided showing that the iterates of the proposed algorithm admit as limit points stationary points of the DC objective function. Numerical experiments show that our approach is more efficient than the current state of the art for a problem with a convex loss function and a nonconvex regularizer. We have also illustrated the benefit of our algorithm in high-dimensional transductive learning problem where both the loss function and regularizers are nonconvex.


title: On the ion-scale structure of thin current sheets in the magnetotail

abstract: A numerical and analytical investigation of the formation and structure of thin current sheets which form in a magnetotail-like plasma model is presented. Current sheet formation is driven by the application of a boundary electric field similar to what would be expected from solar wind driving. As a result, a thin current sheet forms which involves strong Hall-type electric fields, which, by means of electric field drifts, strongly increase the electron contribution, and reduce the ion contribution to the cross-tail current density. This result is also supported by analytical investigations of the total plasma momentum. The thin current sheet forms on strong gradients in the ion density and the magnetic field. The simulation leads to a well defined equilibrium state.


title: Emotion expression in body action and posture.

abstract: Emotion communication research strongly focuses on the face and voice as expressive modalities, leaving the rest of the body relatively understudied. Contrary to the early assumption that body movement only indicates emotional intensity, recent studies have shown that body movement and posture also conveys emotion specific information. However, a deeper understanding of the underlying mechanisms is hampered by a lack of production studies informed by a theoretical framework. In this research we adopted the Body Action and Posture (BAP) coding system to examine the types and patterns of body movement that are employed by 10 professional actors to portray a set of 12 emotions. We investigated to what extent these expression patterns support explicit or implicit predictions from basic emotion theory, bidimensional theory, and componential appraisal theory. The overall results showed partial support for the different theoretical approaches. They revealed that several patterns of body movement systematically occur in portrayals of specific emotions, allowing emotion differentiation. Although a few emotions were prototypically expressed by one particular pattern, most emotions were variably expressed by multiple patterns, many of which can be explained as reflecting functional components of emotion such as modes of appraisal and action readiness. It is concluded that further work in this largely underdeveloped area should be guided by an appropriate theoretical framework to allow a more systematic design of experiments and clear hypothesis testing.


Distributed video game streaming system for pervasive gaming


title: Developing criteria for establishing interrater reliability of specific items : applications to assessment of adaptive behavior

abstract: Abstract A set of criteria based upon biostatistical considerations for determining the interrater reliability of specific adaptive behavior items in a given setting was presented. The advantages and limitations of extant statistical assessment procedures were discussed. Also, a set of guidelines for differentiating type of adaptive behavior that are statistically reliable from those that are reliable in a clinical or practical sense was delineated. Data sets were presented throughout in order to illustrate the advantages of recommended statistical procedures over other available ones.


title: Spatially Resolved Raman Spectroscopy of Single- and Few-Layer Graphene

abstract: We present Raman spectroscopy measurements on single- and few-layer graphene flakes. By using a scanning confocal approach, we collect spectral data with spatial resolution, which allows us to directly compare Raman images with scanning force micrographs. Single-layer graphene can be distinguished from double- and few-layer by the width of the D‘ line: the single peak for single-layer graphene splits into different peaks for the double-layer. These findings are explained using the double-resonant Raman model based on ab initio calculations of the electronic structure and of the phonon dispersion. We investigate the D line intensity and find no defects within the flake. A finite D line response originating from the edges can be attributed either to defects or to the breakdown of translational symmetry.


title: Adaptive Morphology: A Design Principle for Multimodal and Multifunctional Robots

abstract: Morphology plays an important role in behavioral and locomotion strategies of living and artificial systems. There is biological evidence that adaptive morphological changes can not only extend dynamic performances by reducing tradeoffs during locomotion but also provide new functionalities. In this article, we show that adaptive morphology is an emerging design principle in robotics that benefits from a new generation of soft, variable-stiffness, and functional materials and structures. When moving within a given environment or when transitioning between different substrates, adaptive morphology allows accommodation of opposing dynamic requirements (e.g., maneuverability, stability, efficiency, and speed). Adaptive morphology is also a viable solution to endow robots with additional functionalities, such as transportability, protection, and variable gearing. We identify important research and technological questions, such as variable-stiffness structures, in silico design tools, and adaptive control systems to fully leverage adaptive morphology in robotic systems.


title: Generative replay with feedback connections as a general strategy for continual learning

abstract: Standard artificial neural networks suffer from the well-known issue of catastrophic forgetting, making continual or lifelong learning problematic. Recently, numerous methods have been proposed for continual learning, but due to differences in evaluation protocols it is difficult to directly compare their performance. To enable more meaningful comparisons, we identified three distinct continual learning scenarios based on whether task identity is known and, if it is not, whether it needs to be inferred. Performing the split and permuted MNIST task protocols according to each of these scenarios, we found that regularization-based approaches (e.g., elastic weight consolidation) failed when task identity needed to be inferred. In contrast, generative replay combined with distillation (i.e., using class probabilities as"soft targets") achieved superior performance in all three scenarios. In addition, we reduced the computational cost of generative replay by integrating the generative model into the main model by equipping it with generative feedback connections. This Replay-through-Feedback approach substantially shortened training time with no or negligible loss in performance. We believe this to be an important first step towards making the powerful technique of generative replay scalable to real-world continual learning applications.


title: Vocal activity informed singing voice separation with the iKala dataset

abstract: A new algorithm is proposed for robust principal component analysis with predefined sparsity patterns. The algorithm is then applied to separate the singing voice from the instrumen tal accompaniment using vocal activity information. To eval uate its performance, we construct a new publicly available iKala dataset that features longer durations and higher quality than the existing MIR-IK dataset for singing voice separation. Part of it will be used in the MIREX Singing Voice Separa tion task. Experimental results on both the MIR-IK dataset and the new iKala dataset confirmed that the more informed the algorithm is, the better the separation results are.


title: Segmentation of stock trading customers according to potential value

abstract: In this article, we use three clustering methods (K-means, self-organizing map, and fuzzy K-means) to find properly graded stock market brokerage commission rates based on the 3-month long total trades of two different transaction modes (representative assisted and online trading system). Stock traders for both modes are classified in terms of the amount of the total trade as well as the amount of trade of each transaction mode, respectively. Results of our empirical analysis indicate that fuzzy K-means cluster analysis is the most robust approach for segmentation of customers of both transaction modes. We then propose a decision tree based rule to classify three groups of customers and suggest different brokerage commission rates of 0.4, 0.45, and 0.5% for representative assisted mode and 0.06, 0.1, and 0.18% for online trading system, respectively.


An Integrated Survey of Project Scheduling


title: Efficient Algorithms for Convolutional Sparse Representations

abstract: When applying sparse representation techniques to images, the standard approach is to independently compute the representations for a set of overlapping image patches. This method performs very well in a variety of applications, but results in a representation that is multi-valued and not optimized with respect to the entire image. An alternative representation structure is provided by a convolutional sparse representation, in which a sparse representation of an entire image is computed by replacing the linear combination of a set of dictionary vectors by the sum of a set of convolutions with dictionary filters. The resulting representation is both single-valued and jointly optimized over the entire image. While this form of a sparse representation has been applied to a variety of problems in signal and image processing and computer vision, the computational expense of the corresponding optimization problems has restricted application to relatively small signals and images. This paper presents new, efficient algorithms that substantially improve on the performance of other recent methods, contributing to the development of this type of representation as a practical tool for a wider range of problems.


title: STT-RAM Energy Reduction Using Self-Referenced Differential Write Termination Technique

abstract: Spin-transfer torque random access memory (STT-RAM) has emerged as an attractive candidate for future nonvolatile memories. It advantages the benefits of current state-of-the-art memories including high-speed read operation (of static RAM), high density (of dynamic RAM), and nonvolatility (of flash memories). However, the write operation in the 1T-1MTJ STT-RAM bitcell is asymmetric and stochastic, which leads to high energy consumption and long latency. In this paper, a new write assist technique is proposed to terminate the write operation immediately after switching takes place in the magnetic tunneling junction (MTJ). As a result, both the write time and write energy consumption of 1T-1MTJ bitcells improves. Moreover, the proposed write assist technique leads to an error-free write operation. The simulation results using a 65-nm CMOS access transistor and a 40-nm MTJ technology confirm that the proposed write assist technique results in three orders of magnitude improvement in bit error rate compared with the best existing techniques. Moreover, the proposed write assist technique leads to 81% energy saving compared with a cell without write assist and adds only 9.6% area overhead to a 16-kbit STT-RAM array.


title: The security of RFID readers with IDS/IPS solution using Raspberry Pi

abstract: This paper presents developed detection rules for LLRP (Low Level Reader Protocol) interface, implementation of IDS (Intrusion Detection System) and IPS (Intrusion Prevention System) solution for UHF (Ultra High Frequency) RFID (Radio Frequency Identification) reader using Raspberry Pi 3. Developed security solution is used for securing current RFID readers supporting LLRP. The paper also deals with a comparison of IPS/IDS solutions and subsequently selection of the appropriate one for the implementation using Raspberry Pi. Main contribution is the securing of RFID readers which are usually available without any encryption support on the market. The functionality is tested with software metasploit framework.


title: Customer Relationship Management and Firm Performance

abstract: In this paper, we examine the impact of customer relationship management (CRM) on firm performance using a hierarchical construct model. Using the resource-based view (RBV) of the firm, strategic CRM is conceptualized as an endogenously determined function of the organization’s ability to harness and orchestrate lower order capabilities that comprise physical assets such as IT and organizational capabilities. The results reveal a positive and significant path between a superior CRM capability and firm performance. It is shown that CRM initiatives that jointly emphasize customer intimacy, cost reduction and analytic intelligence outperform those that take a less balanced approach. The results help to explain why CRM programs can be successful and what capabilities are required to support success.


title: Distilling an Ensemble of Greedy Dependency Parsers into One MST Parser

abstract: We introduce two first-order graph-based dependency parsers achieving a new state of the art. The first is a consensus parser built from an ensemble of independently trained greedy LSTM transition-based parsers with different random initializations. We cast this approach as minimum Bayes risk decoding (under the Hamming cost) and argue that weaker consensus within the ensemble is a useful signal of difficulty or ambiguity. The second parser is a "distillation" of the ensemble into a single model. We train the distillation parser using a structured hinge loss objective with a novel cost that incorporates ensemble uncertainty estimates for each possible attachment, thereby avoiding the intractable cross-entropy computations required by applying standard distillation objectives to problems with structured outputs. The first-order distillation parser matches or surpasses the state of the art on English, Chinese, and German.


A walkthrough of Requirement Elicitation Techniques


title: How evaluation guides AI research

abstract: Evaluation should be a mechanism of progress both within and across AI research projects. For the individual, evaluation can tell us how and why our methods and programs work and, so, tell us how our research should proceed. For the community, evaluation expedites the understanding of available methods and, so, their integration into further research. In this article, we present a five-stage model of AI research and describe guidelines for evaluation that are appropriate for each stage. These guidelines, in the form of evaluation criteria and techniques, suggest how to perform evaluation. We conclude with a set of recommendations that suggest how to encourage the evaluation of AI research.


title: Enhanced bag-of-words model for phrase-level sentiment analysis

abstract: We propose a novel rule-based model to incorporate contextual information and effect of negation that enhances the performance of sentiment classification performed using bag-of-words models. We employed morphological analysis in feature extraction to ensure feature vector contains only opinionated words in a textual review. Also it reduces the dimensionality of feature vector and, eventually improves the efficiency of the classification algorithm. Further, we consider grammatical relationships to incorporate the context of adjectives and scope of negations within a phrase, to the feature vector. This enables our model to capture contextual polarity of adjectives and impact of negation words. For the morphological analysis we mainly employ Part Of Speech taggers (POS taggers) and grammatical relationships which are obtained using typed dependency parsers. By using dependency-based rules, we relax the conditional independent assumption of bag-of-words models by way of combining adjectives and negations to identified target words and, hence obtain a sentiment classification accuracy that significantly better than baseline performance.


title: RiMOM: A Dynamic Multistrategy Ontology Alignment Framework

abstract: Ontology alignment identifies semantically matching entities in different ontologies. Various ontology alignment strategies have been proposed; however, few systems have explored how to automatically combine multiple strategies to improve the matching effectiveness. This paper presents a dynamic multistrategy ontology alignment framework, named RiMOM. The key insight in this framework is that similarity characteristics between ontologies may vary widely. We propose a systematic approach to quantitatively estimate the similarity characteristics for each alignment task and propose a strategy selection method to automatically combine the matching strategies based on two estimated factors. In the approach, we consider both textual and structural characteristics of ontologies. With RiMOM, we participated in the 2006 and 2007 campaigns of the Ontology Alignment Evaluation Initiative (OAEI). Our system is among the top three performers in benchmark data sets.


title: Cooperative Access Class Barring for Machine-to-Machine Communications

abstract: Supporting trillions of devices is the critical challenge in machine-to-machine (M2M) communications, which results in severe congestions in random access channels of cellular systems that have been recognized as promising scenarios enabling M2M communications. 3GPP thus developed the access class barring (ACB) for individual stabilization in each base station (BS). However, without cooperations among BSs, devices within dense areas suffer severe access delays. To facilitate devices escaping from continuous congestions, we propose the cooperative ACB for global stabilization and access load sharing to eliminate substantial defects in the ordinary ACB, thus significantly improving access delays.


title: From text question-answering to multimedia QA on web-scale media resources

abstract: With the proliferation of text and multimedia information, users are now able to find answers to almost any questions on the Web. Meanwhile, they are also bewildered by the huge amount of information routinely presented to them. Question-answering (QA) is a natural direction to address this information over-loading problem. The aim of QA is to return precise answers to users' questions. Text-based QA research has been carried out for the past 15 years with good success especially for answering fact-based questions. The aim of this paper is to extend the text-based QA research to multimedia QA to tackle a range of factoid, definition and "how-to" QA in a common framework. The system will be designed to find multimedia answers from Web-scale media resources such as Flicker and YouTube. This paper describes the architecture and our recent research on various types of multimedia QA for a range of applications. The paper also discusses directions for future research.


title: Binocular photometric stereo acquisition and reconstruction for 3d talking head applications.

abstract: In order to render a high quality, versatile 3D talking head, a stable, high frame rate AV data acquisition system is constructed. It can capture 3D position, surface orientation and albedo texture of the talking head video images along with the corresponding speech signals. The system consists of a computer controlled LED lighting subsystem; high speed stereo cameras; a microphone; and a computer for synchronous recording of multi-stream AV data. The visual image data collected is processed through a binocular photometric stereo 3D reconstruction pipeline. The pipeline automatically segments out the face; computes the depth map with binocular stereo; computes the normal map with photometric stereo; generates albedo texture; and finally constructs a high-detailed 3d model with depth and normal cues as constraints. By using the data collected with the built system, we can capture high quality dynamic facial performance, synchronized with the subject’s uttered speech.


title: Generic and unified model of Switched Capacitor Converters

abstract: A generic modeling methodology that analyzes the losses in Switched Capacitors Converters (SCC) was developed and verified by simulation and experiments. The proposed analytical approach is unified, covering both hard and soft switched SCC topologies. The major advantage of the proposed model is that it expresses the losses as a function of the currents passing through each flying capacitor. Since these currents are linearly proportional to the output current, the model is also applicable to SCC with multiple capacitors. The proposed model provides an insight into the expected losses in SCC and the effects of their operational conditions such as duty cycle. As such, the model can help in the optimization of SCC systems and their control to achieve desired regulations.


title: Patch antenna with reduced size for 400MHz transmission in confined surroundings

abstract: This paper relates to the reduction of the size of a patch antenna to achieve a transmission by coupling with another antenna at a frequency close to 400 MHz (radio-control frequency), with dimensional constraints and in a closed environment. It introduces in particular the calculation results concerning the reduction of the patch antenna, as well as the experimental validation. It also shows the calculation and experimental results of transmission by coupling with the antenna of the reception system.


Recursion in language, theory-of-mind inference, and arithmetic


title: Robust Object Tracking via Sparse Collaborative Appearance Model

abstract: In this paper, we propose a robust object tracking algorithm based on a sparse collaborative model that exploits both holistic templates and local representations to account for drastic appearance changes. Within the proposed collaborative appearance model, we develop a sparse discriminative classifier (SDC) and sparse generative model (SGM) for object tracking. In the SDC module, we present a classifier that separates the foreground object from the background based on holistic templates. In the SGM module, we propose a histogram-based method that takes the spatial information of each local patch into consideration. The update scheme considers both the most recent observations and original templates, thereby enabling the proposed algorithm to deal with appearance changes effectively and alleviate the tracking drift problem. Numerous experiments on various challenging videos demonstrate that the proposed tracker performs favorably against several state-of-the-art algorithms.


title: Bayesian probabilistic tensor factorization for malicious tweets in trending topics

abstract: Online social networks (OSNs) are widely known interactive communication tools for millions of users and their friends. But, in recent years, these tools become the platforms for executing spam messages and malwares. It is obvious that a user reacts to the message of an OSN friend than from an unknown user which makes the social spam more vulnerable and popular than conventional email. Twitter spam detection has become an attractive research topic in recent years in which the majority of earlier works focuses upon identifying malicious user accounts and based on using honeypot detection schemes. Conventional approaches uses Twitter trending topics without interaction of user activity and receiver. In order to handle this issue, this work proposes a new social interaction Tweet Trending Bayesian Probabilistic Tensor Factorization (TTBPTF) based tweet trending topics spammers' detection in social networks that exploits social activities, spammer activity and user activity in Twitter. These interaction activities are analyzed in TTBPTF and features such as user profile features, semantic features, user activity and location based features are extracted. Then, the extracted features are represented in matrix format to analyze the results of tweet trending topics spam user with Bayesian Probabilistic learning methods. The extracted text features are analyzed using Quadratic entropy measure in which each labeled tweet is represented through natural language models. Moreover, Bayesian probabilistic learning method is applied with the goal of examining the emergent characteristics of spam in social networks. The proposed approach detects the spammers collectively based on the users' social actions, spammer activity and social relations. It is observed that when the Bayesian Probabilistic Tensor Factorization is adjusted for the feature vector learning, the results of spammer activity and received user results are analyzed in an efficient manner.


title: Managing elasticity across multiple cloud providers

abstract: In the context of cloud computing, elasticity is the capacity to scale computing resources up and down easily. Currently, most Platforms as a Service (PaaS) manage application elasticity within a single cloud provider. However, the not so infrequent issue of cloud outages has become a concern that hinders the availability of cloud-based applications. The most promising solutions to this issue are those based on the federation of multiple clouds. In this paper, we present a Multi-Cloud-PaaS architecture. We show how this architecture can be used for managing elasticity across multiple cloud providers.


title: Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contest

abstract: The New Yorker publishes a weekly captionless cartoon. More than 5,000 readers submit captions for it. The editors select three of them and ask the readers to pick the funniest one. We describe an experiment that compares a dozen automatic methods for selecting the funniest caption. We show that negative sentiment, human-centeredness, and lexical centrality most strongly match the funniest captions, followed by positive sentiment. These results are useful for understanding humor and also in the design of more engaging conversational agents in text and multimodal (vision+text) systems. As part of this work, a large set of cartoons and captions is being made available to the community.


title: Secure Iris Authentication Using Visual Cryptography

abstract: Biometrics deal with automated methods of identifying a person or verifying the identity of a person based on physiological or behavioral characteristics. Visual cryptography is a secret sharing scheme where a secret image is encrypted into the shares which independently disclose no information about the original secret image. As biometric template are stored in the centralized database, due to security threats biometric template may be modified by attacker. If biometric template is altered authorized user will not be allowed to access the resource. To deal this issue visual cryptography schemes can be applied to secure the iris template. Visual cryptography provides great means for helping such security needs as well as extra layer of authentication.


title: Motivational, emotional, and behavioral correlates of fear of missing out

abstract: Social media utilities have made it easier than ever to know about the range of online or offline social activities one could be engaging. On the upside, these social resources provide a multitude of opportunities for interaction; on the downside, they often broadcast more options than can be pursued, given practical restrictions and limited time. This dual nature of social media has driven popular interest in the concept of Fear of Missing Out – popularly referred to as FoMO. Defined as a pervasive apprehension that others might be having rewarding experiences from which one is absent, FoMO is characterized by the desire to stay continually connected with what others are doing. The present research presents three studies conducted to advance an empirically based understanding of the fear of missing out phenomenon. The first study collected a diverse international sample of participants in order to create a robust individual differences measure of FoMO, the Fear of Missing Out scale (FoMOs); this study is the first to operationalize the construct. Study 2 recruited a nationally representative cohort to investigate how demographic, motivational and well-being factors relate to FoMO. Study 3 examined the behavioral and emotional correlates of fear of missing out in a sample of young adults. Implications of the FoMOs measure and for the future study of FoMO are discussed.


title: Automeris io (caterpillar) dermatitis.

abstract: Four subjects were exposed to dorsal spines of the Automeris io caterpillar. The experimental presented herein indicates that there is a substantial variability in response between human subjects. The urticarial reaction produced can be categorized broadly as contact urticarda and most probably is nonimmunologic, depending on mediator release.


title: Detection and recognition of traffic signs based on RGB to red conversion

abstract: This paper presents a different approach for detecting and recognizing traffic signs. Traffic signs are designed such that they can be easily observed by humans because of their shape and shading which are not precisely the same as surrounding environment. Traffic sign recognition is a fundamental component in autonomous cars and it can similarly be utilized as an integral part of driver assistance frameworks. The system is designed to perform two tasks, traffic sign detection and recognition. The algorithms demonstrated include RGB to Red conversion for getting the red component in the image, filters which perform the task of noise reduction as well as edge detection, thresholding and segmentation to get the desired traffic sign from image and then an artificial neural network for recognition. The neural network used here is a Single Layer Perceptron neural network. Implementation of the system has been successfully done in Matlab and it works quite productively and efficiently with the database of Indian traffic signs created as a part of the project.


title: Applying Deep Learning to Airbnb Search

abstract: The application to search ranking is one of the biggest machine learning success stories at Airbnb. Much of the initial gains were driven by a gradient boosted decision tree model. The gains, however, plateaued over time. This paper discusses the work done in applying neural networks in an attempt to break out of that plateau. We present our perspective not with the intention of pushing the frontier of new modeling techniques. Instead, ours is a story of the elements we found useful in applying neural networks to a real life product. Deep learning was steep learning for us. To other teams embarking on similar journeys, we hope an account of our struggles and triumphs will provide some useful pointers. Bon voyage!


A Computational Approach to Perceived Trustworthiness of Airbnb Host Profiles.


title: Visualizing Deep Neural Networks for Text Analytics

abstract: Deep neural networks (DNNs) have made tremendous progress in many different areas in recent years. How these networks function internally, however, is often not well understood. Advances in under-standing DNNs will benefit and accelerate the development of the field. We present TNNVis, a visualization system that supports un-derstanding of deep neural networks specifically designed to analyze text. TNNVis focuses on DNNs composed of fully connected and convolutional layers. It integrates visual encodings and interaction techniques chosen specifically for our tasks. The tool allows users to: (1) visually explore DNN models with arbitrary input using a combination of node–link diagrams and matrix representation; (2) quickly identify activation values, weights, and feature map patterns within a network; (3) flexibly focus on visual information of interest with threshold, inspection, insight query, and tooltip operations; (4) discover network activation and training patterns through animation; and (5) compare differences between internal activation patterns for different inputs to the DNN. These functions allow neural network researchers to examine their DNN models from new perspectives, producing insights on how these models function. Clustering and summarization techniques are employed to support large convolutional and fully connected layers. Based on several part of speech models with different structure and size, we present multiple use cases where visualization facilitates an understanding of the models.


title: Lurasidone Monotherapy in the Treatment of Bipolar I Depression: A Randomized, Double-Blind, Placebo-Controlled Study

abstract: In July 2013, the Food and Drug Administration approved lurasidone for the treatment of bipolar I depression. Two studies discussing its monotherapy and adjunctive use that were pivotal in obtaining this approval are featured in this issue.


title: Barcode-Assisted Planar Object Tracking Method for Mobile Augmented Reality

abstract: In this paper, we propose a planar target tracking method that exploits a barcode containing information about a target. Our method combines both barcode detection and natural feature tracking methods to track a planar object efficiently on mobile devices. A planar target is detected by recognizing the barcode located near the target, and the target's keypoints are tracked in video sequences. We embed the information related to a planar object into the barcode, and the information is used to limit image regions to perform keypoint matching between consecutive frames. We show how to detect a barcode robustly and what information is embedded for efficient tracking. Our detection method runs at 30 fps on modern mobile devices, and it can be used for mobile augmented reality applications using planar targets.


title: ZaliQL: Causal Inference from Observational Data at Scale

abstract: Causal inference from observational data is a subject of active research and development in statistics and computer science. Many statistical software packages have been developed for this purpose. However, these toolkits do not scale to large datasets. We propose and demonstrate ZaliQL: a SQL-based framework for drawing causal inference from observational data. ZaliQL supports the state-of-the-art methods for causal inference and runs at scale within PostgreSQL database system. In addition, we built a visual interface to wrap around ZaliQL. In our demonstration, we will use this GUI to show a live investigation of the causal effect of different weather conditions on flight delays.


Artificial Immune System Classification of Multiple-class Problems


title: Molecular systematic study of Cardamine glechomifolia Levl. (Brassicaceae) using internal transcribed spacer sequence of nuclear ribosomal DNA (ITS) and chloroplast trnL and trnL-F sequences

abstract: Abstract The internal transcribed spacer (ITS) region of nuclear ribosomal DNA, trn L and trn L-F genes of Cardamine glechomifolia Levl. (family Brassicaceae) were sequenced and analyzed with the sequence of related Cardamine species retrieved from NCBI GenBank to detect pattern of evolutionary differentiation. All trees resulting from combined sequence analyses data of ITS, trn L and trn L-F gene resolve that C. glechomifolia – an endemic species to South Korea clade with Cardamine microzyga (100% bootstrap support). The evolutionary history was inferred using the Maximum Parsimony method. The consistency index is (0.588235), the retention index is (0.687500), and the composite index is 0.519622 (0.404412) for all sites and parsimony-informative sites (in parentheses). The result of the analysis using Maximum Parsimony was found congruence with Maximum Likelihood method and in Baseyan analysis.


title: Approximate Computing for Long Short Term Memory (LSTM) Neural Networks

abstract: Long Short Term Memory (LSTM) networks are a class of recurrent neural networks that are widely used for machine learning tasks involving sequences, including machine translation, text generation, and speech recognition. Large-scale LSTMs, which are deployed in many real-world applications, are highly compute intensive. To address this challenge, we propose AxLSTM, an application of approximate computing to improve the execution efficiency of LSTMs. An LSTM is composed of cells, each of which contains a cell state along with multiple gating units that control the addition and removal of information from the state. The LSTM execution proceeds in timesteps, with a new symbol of the input sequence processed at each timestep. AxLSTM consists of two techniques—Dynamic Timestep Skipping (DTS) and Dynamic State Reduction (DSR). DTS identifies, at runtime, input symbols that are likely to have little or no impact on the cell state and skips evaluating the corresponding timesteps. In contrast, DSR reduces the size of the cell state in accordance with the complexity of the input sequence, leading to a reduced number of computations per timestep. We describe how AxLSTM can be applied to the most common application of LSTMs, viz. , sequence-to-sequence learning. We implement AxLSTM within the TensorFlow deep learning framework and evaluate it on 3 state-of-the-art sequence-to-sequence models. On a 2.7 GHz Intel Xeon server with 128 GB memory and 32 processor cores, AxLSTM achieves $ {1.08\times -1.31 \times }$ speedups with minimal loss in quality, and $ {1.12 \times -1.37 \times }$ speedups when moderate reductions in quality are acceptable.


title: The UCR time series archive

abstract: The UCR time series archive–introduced in 2002, has become an important resource in the time series data mining community, with at least one thousand published papers making use of at least one data set from the archive. The original incarnation of the archive had sixteen data sets but since that time, it has gone through periodic expansions. The last expansion took place in the summer of 2015 when the archive grew from 45 to 85 data sets. This paper introduces and will focus on the new data expansion from 85 to 128 data sets. Beyond expanding this valuable resource, this paper offers pragmatic advice to anyone who may wish to evaluate a new algorithm on the archive. Finally, this paper makes a novel and yet actionable claim: of the hundreds of papers that show an improvement over the standard baseline ( 1-nearest neighbor classification ), a fraction might be mis-attributing the reasons for their improvement. Moreover, the improvements claimed by these papers might have been achievable with a much simpler modification, requiring just a few lines of code.


title: From Fabula to Fabulous Using Discourse Structure Relations to Separate Paragraphs in Automatically Generated Stories

abstract: To form a MOS transistor with a LDD structure, the transistor is formed in a well region. There is formed a gate oxide layer on a silicon substrate and an N+ type poly-silicon layer serving as a gate electrode is formed on the gate oxide layer. The poly-silicon layer is doped with phosphorus atoms. Then, a surface of the silicon substrate is exposed in a LDD region serving as a source/drain region as formed with phosphorus implantation. The LDD region of the transistor is implanted with phosphorus ions. Subsequently, a side wall is formed on the gate electrode. To enhance the adherence of the side wall material and activate the phosphorus ions implanted in the previous step, annealing and formation of an oxide film are effected. This thermal treatment prevents the phosphorus atoms from deporting the gate electrode of the poly-silicon layer and self diffusing into the LDD region. This thermal treatment is performed with nitrogen gas containing 1 to 5 volume % at the initial stage thereof.


title: Learning and inference via maximum inner product search

abstract: A large class of commonly used probabilistic models known as log-linear models are defined up to a normalization constant. Typical learning algorithms for such models require solving a sequence of probabilistic inference queries. These inferences are typically intractable, and are a major bottleneck for learning models with large output spaces. In this paper, we provide a new approach for amortizing the cost of a sequence of related inference queries, such as the ones arising during learning. Our technique relies on a surprising connection with algorithms developed in the past two decades for similarity search in large data bases. Our approach achieves improved running times with provable approximation guarantees. We show that it performs well both on synthetic data and neural language models with large output spaces.


title: Multiport UHF RFID-Tag Antenna for Enhanced Energy Harvesting of Self-Powered Wireless Sensors

abstract: This paper presents the design and experimental evaluation of a long-range solar powered sensor-enhanced radio frequency identification (RFID) tag. The tag antenna is a multiport microstrip patch with an overlay of thin-film solar cells for energy harvesting. A second port is allocated on the patch antenna for supplementary energy harvesting from the RF signal transmitted by the reader. An $I^{2}C$ -RFID chip along with a microcontroller unit (MCU) and temperature and humidity sensor are incorporated into the tag design to implement a low-cost wireless sensor using a commercial RFID reader. The measurements of the fabricated RFID-tag sensor demonstrate that a maximum sensing/reading range of 27 m is achieved when all the circuits are powered using solar cells, while it is 7.48 m with only the secondary option of energy harvesting. The proposed sensor with dual energy harvesting achieves both a longer range and a lifetime compared with similar battery-powered sensor-enhanced RFID tags. The RFID sensor is also evaluated in a climate chamber and the sensor data (temperature/humidity) were remotely recorded with an excellent accuracy using a commercial ultra high frequency (UHF) RFID reader. In addition, the sensor can be programmed for the temperature/humidity surveillance of sensitive items, such as those found in various supply chain and transportation applications.


title: ConceptNet 5.5: An Open Multilingual Graph of General Knowledge

abstract: Machine learning about language can be improved by supplying it with specific knowledge and sources of external information. We present here a new version of the linked open data resource ConceptNet that is particularly well suited to be used with modern NLP techniques such as word embeddings. ::: ConceptNet is a knowledge graph that connects words and phrases of natural language with labeled edges. Its knowledge is collected from many sources that include expert-created resources, crowd-sourcing, and games with a purpose. It is designed to represent the general knowledge involved in understanding language, improving natural language applications by allowing the application to better understand the meanings behind the words people use. ::: When ConceptNet is combined with word embeddings acquired from distributional semantics (such as word2vec), it provides applications with understanding that they would not acquire from distributional semantics alone, nor from narrower resources such as WordNet or DBPedia. We demonstrate this with state-of-the-art results on intrinsic evaluations of word relatedness that translate into improvements on applications of word vectors, including solving SAT-style analogies.


title: A Review of the Elements of Human Well-Being with an Emphasis on the Contribution of Ecosystem Services

abstract: Natural ecosystems perform fundamental life-support services upon which human civilization depends. However, many people believe that nature provides these services for free and therefore, they are of little or no value. While we do not pay for them, we pay significantly for their loss in terms of wastewater treatment facilities, moratoriums on greenhouse gases, increased illnesses, reduced soil fertility and losses in those images of nature that contribute to our basic happiness. Little is understood about the well-being benefits of the natural environment and its ecosystem services. The interwoven relationship of ecosystems and human well-being is insufficiently acknowledged in the wider philosophical, social, and economic well-being literature. In this article, we discuss an approach to examine human well-being and the interactions of its four primary elements-basic human needs, economic needs, environmental needs, and subjective well-being-and ecosystem services.


title: Visual-vestibular and postural analysis of motion sickness, panic, and acrophobia

abstract: National funds and co-financed by FEDER through COMPETE2020 under the PT2020 Partnership Agreement (POCI-01-0145-FEDER-007653). FCT/3599-PPCDT/121494/PT


title: Curcumin, Inflammation, and Chronic Diseases: How Are They Linked?

abstract: It is extensively verified that continued oxidative stress and oxidative damage may lead to chronic inflammation, which in turn can mediate most chronic diseases including cancer, diabetes, cardiovascular, neurological, inflammatory bowel disease and pulmonary diseases. Curcumin, a yellow coloring agent extracted from turmeric, shows strong anti-oxidative and anti-inflammatory activities when used as a remedy for the prevention and treatment of chronic diseases. How oxidative stress activates inflammatory pathways leading to the progression of chronic diseases is the focus of this review. Thus, research to date suggests that chronic inflammation, oxidative stress, and most chronic diseases are closely linked, and the antioxidant properties of curcumin can play a key role in the prevention and treatment of chronic inflammation diseases.


title: MeshFlow: Minimum Latency Online Video Stabilization

abstract: Many existing video stabilization methods often stabilize videos off-line, i.e. as a postprocessing tool of pre-recorded videos. Some methods can stabilize videos online, but either require additional hardware sensors (e.g., gyroscope) or adopt a single parametric motion model (e.g., affine, homography) which is problematic to represent spatially-variant motions. In this paper, we propose a technique for online video stabilization with only one frame latency using a novel MeshFlow motion model. The MeshFlow is a spatial smooth sparse motion field with motion vectors only at the mesh vertexes. In particular, the motion vectors on the matched feature points are transferred to their corresponding nearby mesh vertexes. The MeshFlow is produced by assigning each vertex an unique motion vector via two median filters. The path smoothing is conducted on the vertex profiles, which are motion vectors collected at the same vertex location in the MeshFlow over time. The profiles are smoothed adaptively by a novel smoothing technique, namely the Predicted Adaptive Path Smoothing (PAPS), which only uses motions from the past. In this way, the proposed method not only handles spatially-variant motions but also works online in real time, offering potential for a variety of intelligent applications (e.g., security systems, robotics, UAVs). The quantitative and qualitative evaluations show that our method can produce comparable results with the state-of-the-art off-line methods.


title: Porous PDMS force sensitive resistors.

abstract: Here we present an elastomeric force sensitive resistor (FSR) made from a porous matrix of polydimethylsiloxane (PDMS) filled with carbon black. The fabrication process is based on the use of a low cost sacrificial sugar cube scaffold which leads to a highly porous and compressible material. By filling this porous matrix with carbon black we can achieve typical resistance changes from 20 kΩ to 100 Ω for an applied 95% compressive strain. This material is suitable for a wide variety of sensing applications which include tactile artificial skin for robotics and solvent detection.


title: Comparison of Cimetidine and Placebo for the Prophylaxis of Upper Gastrointestinal Bleeding Due to Stress-related Gastric Mucosal Damage in the Intensive Care Unit

abstract: A multicenter, randomized, double-blind, placebo- controlled study was conducted with 87 patients in in tensive care units to study the effectiveness of constant infusions of cimetidine (50 mg/hr) in the prophylaxis of stress-related mucosal bleeding. Fifty-four patients re ceived cimetidine and 33 received placebo. The groups were comparable by age, sex, and severity of illness. One (2%) of the 54 patients receiving cimetidine had upper gastrointestinal hemorrhage and 7 (21%) of the 33 patients receiving placebo had upper gastrointestinal hemorrhage (p = 0.002). The risk of bleeding for every 100 patient days in intensive care units was reduced by 94% in the patients receiving cimetidine. Constant infu sion cimetidine was well tolerated. Only one patient (cimetidine) developed pneumonia during the study, but it was not considered to be related to drug therapy. No patients experienced adverse drug interactions. Two patients (4%) experienced reversible side effects from treatment. Cimetidine, administered ...


title: Coconut: Threshold Issuance Selective Disclosure Credentials with Applications to Distributed Ledgers

abstract: Coconut is a novel selective disclosure credential scheme supporting distributed threshold issuance, public and private attributes, re-randomization, and multiple unlinkable selective attribute revelations. Coconut integrates with blockchains to ensure confidentiality, authenticity and availability even when a subset of credential issuing authorities are malicious or offline. We implement and evaluate a generic Coconut smart contract library for Chainspace and Ethereum; and present three applications related to anonymous payments, electronic petitions, and distribution of proxies for censorship resistance. Coconut uses short and computationally efficient credentials, and our evaluation shows that most Coconut cryptographic primitives take just a few milliseconds on average, with verification taking the longest time (10 milliseconds).


title: Predicting Socio-Economic Indicators using News Events

abstract: Many socio-economic indicators are sensitive to real-world events. Proper characterization of the events can help to identify the relevant events that drive fluctuations in these indicators. In this paper, we propose a novel generative model of real-world events and employ it to extract events from a large corpus of news articles. We introduce the notion of an event class, which is an abstract grouping of similarly themed events. These event classes are manifested in news articles in the form of event triggers which are specific words that describe the actions or incidents reported in any article. We use the extracted events to predict fluctuations in different socio-economic indicators. Specifically, we focus on food prices and predict the price of 12 different crops based on real-world events that potentially influence food price volatility, such as transport strikes, festivals etc. Our experiments demonstrate that incorporating event information in the prediction tasks reduces the root mean square error (RMSE) of prediction by 22% compared to the standard ARIMA model. We also predict sudden increases in the food prices (i.e. spikes) using events as features, and achieve an average 5-10% increase in accuracy compared to baseline models, including an LDA topic-model based predictive model.


title: Human identification using pyroelectric infrared sensors

abstract: Human Identification Using Pyroelectric Infrared Sensors by Aron E. Suppes Dr. Evangelos Yfantis, Examination Committee Chair Professor of Computer Science University of Nevada, Las Vegas The objective of this thesis is to discuss the viability of using pyroelectric infrared (PIR) sensors as a biometric system for human identification. The human body emits infrared radiation, the distribution of which varies throughout the body, and depends upon the shape and composition of the particular body part. A PIR sensor utilizing a Fresnel lens will respond to this infrared radiation. When a human walks, the motion of the body's individual components form a characteristic gait that is likely to affect a PIR sensor field in a unique way. A statistical model, such as a Hidden Markov Model, could be used for the identification process. The model would consist of two phases; learning and testing. The learning phase would train the model on a particular feature or signature. The testing phase would take a signature as input and determine which of the trained models it matches with the highest probability.


title: Component design and implementation of a 60 kW full SiC traction inverter with boost converter

abstract: In electric vehicular (EV) traction drives, Silicon Carbide (SiC) devices promise large savings in volume and weight of the converter system, especially when an inverter is combined with a boost DC/DC converter. Such a system has been designed, implemented and tested as a laboratory prototype. Detailed information on the component design is given and the prototypes characteristics are compared with a conventional Silicon IGBT based industrial system.