title: Automatic Spatially-Aware Fashion Concept Discovery

abstract: This paper proposes an automatic spatially-aware concept discovery approach using weakly labeled image-text data from shopping websites. We first fine-tune GoogleNet by jointly modeling clothing images and their corresponding descriptions in a visual-semantic embedding space. Then, for each attribute (word), we generate its spatially-aware representation by combining its semantic word vector representation with its spatial representation derived from the convolutional maps of the fine-tuned network. The resulting spatially-aware representations are further used to cluster attributes into multiple groups to form spatially-aware concepts (e.g., the neckline concept might consist of attributes like v-neck, round-neck, etc). Finally, we decompose the visual-semantic embedding space into multiple concept-specific subspaces, which facilitates structured browsing and attribute-feedback product retrieval by exploiting multimodal linguistic regularities. We conducted extensive experiments on our newly collected Fashion200K dataset, and results on clustering quality evaluation and attribute-feedback product retrieval task demonstrate the effectiveness of our automatically discovered spatially-aware concepts.


title: KinectFusion: real-time dynamic 3D surface reconstruction and interaction

abstract: We present KinectFusion, a system that takes live depth data from a moving Kinect camera and in real-time creates high-quality, geometrically accurate, 3D models. Our system allows a user holding a Kinect camera to move quickly within any indoor space, and rapidly scan and create a fused 3D model of the whole room and its contents within seconds. Even small motions, caused for example by camera shake, lead to new viewpoints of the scene and thus refinements of the 3D model, similar to the effect of image super-resolution. As the camera is moved closer to objects in the scene more detail can be added to the acquired 3D model.


title: Scaling up dynamic time warping for datamining applications

abstract: There has been much recent interest in adapting data mining algorithms to time series databases. Most of these algorithms need to compare time series. Typically some variation of Euclidean distance is used. However, as we demonstrate in this paper, Euclidean distance can be an extremely brittle distance measure. Dynamic time warping (DTW) has been suggested as a technique to allow more robust distance calculations, however it is computationally expensive. In this paper we introduce a modification of DTW which operates on a higher level abstraction of the data, in particular, a Piecewise Aggregate Approximation (PAA). Our approach allows us to outperform DTW by one to two orders of magnitude, with no loss of accuracy.


title: A Review of Heterogeneous Photocatalysis for Water and Surface Disinfection

abstract: Photo-excitation of certain semiconductors can lead to the production of reactive oxygen species that can inactivate microorganisms. The mechanisms involved are reviewed, along with two important applications. The first is the use of photocatalysis to enhance the solar disinfection of water. It is estimated that 750 million people do not have accessed to an improved source for drinking and many more rely on sources that are not safe. If one can utilize photocatalysis to enhance the solar disinfection of water and provide an inexpensive, simple method of water disinfection, then it could help reduce the risk of waterborne disease. The second application is the use of photocatalytic coatings to combat healthcare associated infections. Two challenges are considered, i.e., the use of photocatalytic coatings to give “self-disinfecting” surfaces to reduce the risk of transmission of infection via environmental surfaces, and the use of photocatalytic coatings for the decontamination and disinfection of medical devices. In the final section, the development of novel photocatalytic materials for use in disinfection applications is reviewed, taking account of materials, developed for other photocatalytic applications, but which may be transferable for disinfection purposes.


title: STRUCTURING THE UNSTRUCTURED DATA: THE USE OF CONTENT ANALYSIS

abstract: Content analysis is a research technique used to organise large amounts of ::: textual data into standardised formats which allows arriving at suggestions/conclusions. ::: Content analysis can be carried out quantitatively by counting the words or qualitatively by ::: coding. The former approach refers to counting the frequency of the keywords and the later ::: refers to identifying similar themes or concepts from the data set. This paper discusses the use ::: of conceptual content analysis by using computerised software to analyse data gathered from ::: semi-structured interviews. The context of the research within which content analysis is used ::: is to identify the influence of performance measurement towards construction research ::: activities. The paper first explains the research methodology pertaining to this study by ::: reasoning out the selection of case study research approach coupled with semi-structured ::: interviews. The paper then discusses how the information gathered from semi-structured ::: interviews is fed into the computerised software to identify and generate main concepts of the ::: study.


title: X-Band SIW cavity-backed patch antenna for radar applications

abstract: A Substrate Integrated Waveguide (SIW) cavity-backed microstrip patch antenna for radar applications operating in X Band is designed. In order to increase the bandwidth, the SIW technology is used to build a circular cavity which surrounds a modified rectangular patch. The modified patch has truncated corners and two cuts in the lateral sides. Accurate electromagnetic investigation has been performed using CST Microwave Studio© in terms of radiation pattern, bandwidth, and matching. The prototype employs a Taconic RF-35 substrate with er= 3.5 and thickness of 1.52 mm. A good agreement between simulations and experiments is obtained. The proposed antenna is a promising candidate for a low-cost implementation of radar antenna arrays.


title: Research Trends in Lego and Robotic Usage in Education: A Document Analysis

abstract: The purpose of this study is to examine Lego and robotics interventions in education. For this aim, 45 master's theses and doctoral dissertations published between 1991 and 2014 with Lego, robotic, and first Lego league keywords were examined. Data were classified according to methods, sample sizes, and variables. Results and suggestions from the manuscripts were also categorized. Document analysis was conducted in order to identify and show general trends. Findings are presented descriptively with charts and tables. The topic was most popular in 2013, and almost half of the studies preferred quantitative methods. Surveys were the most popular data collecting tool, and interviews were second. Sample size changed according to research method, and secondary school students were the most common participants. Surprisingly, most researchers omitted details about data collection time and process. Of those who responded, most dedicated 6 to 8 weeks to data collection. Generally, results showed that incorporating Lego and robotics leads to increased motivation and self-confidence, just like every new technology. Some researchers investigated Lego and robotic usage and perception of gender stereotypes with positive outcomes. Using Lego and robotics also reduced anxiety towards math and science in women. Nearly all researchers suggested longer term studies to gain a clearer vision about this topic. The results of this study will be helpful for guiding future research in this area. Keywords: Lego, robotic, document analysis


title: Oruta: Privacy-Preserving Public Auditing for Shared Data in the Cloud

abstract: With cloud storage services, it is commonplace for data to be not only stored in the cloud, but also shared across multiple users. However, public auditing for such shared data --- while preserving identity privacy --- remains to be an open challenge. In this paper, we propose the first privacy-preserving mechanism that allows public auditing on shared data stored in the cloud. In particular, we exploit ring signatures to compute the verification information needed to audit the integrity of shared data. With our mechanism, the identity of the signer on each block in shared data is kept private from a third party auditor (TPA), who is still able to verify the integrity of shared data without retrieving the entire file. Our experimental results demonstrate the effectiveness and efficiency of our proposed mechanism when auditing shared data.


title: Software process: a roadmap

abstract: Software process research deals with the methods and technologies used to assess, support, and improve software development activities. The field has grown up during the 80s to address the increasing complexity and criticality of software development activities. This paper aims to briefly present the history and achievements of software process research, some critical evaluation of the results produced so far, and possible directions for future work.


title: What Size Test Set Gives Good Error Rate Estimates?

abstract: We address the problem of determining what size test set guarantees statistically significant results in a character recognition task, as a function of the expected error rate. We provide a statistical analysis showing that if, for example, the expected character error rate is around 1 percent, then, with a test set of at least 10,000 statistically independent handwritten characters (which could be obtained by taking 100 characters from each of 100 different writers), we guarantee, with 95 percent confidence, that: (1) the expected value of the character error rate is not worse than 1.25 E, where E is the empirical character error rate of the best recognizer, calculated on the test set; and (2) a difference of 0.3 E between the error rates of two recognizers is significant. We developed this framework with character recognition applications in mind, but it applies as well to speech recognition and to other pattern recognition problems.


title: 60 GHz antennas in package for portable applications

abstract: This paper presents a low-cost antenna-in-package (AiP) design that supports broadside and end-fire direction radiation suitable for future 60 GHz portable applications. The package is based on multilayer organic technology, so it can be fabricated by PCB manufacturers.


title: Additive sequential evolutionary design of experiments

abstract: Process models play important role in computer aided pro- cess engineering. Although the structure of these models are a priori known, model parameters should be estimated based on experiments. The accuracy of the estimated parameters largely depends on the information content of the experimental data presented to the parameter identification algorithm. Optimal experiment design (OED) can maximize the confidence on the model parameters. The paper proposes a new additive sequential evolutionary experiment design approach to maximize the amount of information content of experiments. The main idea is to use the identified models to design new experiments to gradually improve the model accuracy while keeping the collected information from previous experiments. This scheme requires an effective optimization algorithm, hence the main contribution of the paper is the incorporation of Evolutionary Strategy (ES) into a new iterative scheme of optimal experiment design (AS-OED). This paper illustrates the applicability of AS-OED for the design of feeding profile for a fed-batch biochemical reactor.


title: Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning

abstract: Reinforcement learning algorithms for real-world robotic applications must be able to handle complex, unknown dynamical systems while maintaining data-efficient learning. These requirements are handled well by model-free and model-based RL approaches, respectively. In this work, we aim to combine the advantages of these approaches. By focusing on time-varying linear-Gaussian policies, we enable a model-based algorithm based on the linear-quadratic regulator that can be integrated into the model-free framework of path integral policy improvement. We can further combine our method with guided policy search to train arbitrary parameterized policies such as deep neural networks. Our simulation and real-world experiments demonstrate that this method can solve challenging manipulation tasks with comparable or better performance than model-free methods while maintaining the sample efficiency of model-based methods.


title: Classification and Detection of Micro-Level Impact of Issue-Focused Documentary Films based on Reviews

abstract: We present novel research at the intersection of review mining and impact assessment of issue-focused information products, namely documentary films. We develop and evaluate a theoretically grounded classification schema, related codebook, corpus annotation, and prediction model for detecting multiple types of impact that documentaries can have on individuals, such as change versus reaffirmation of behavior, cognition, and emotions, based on user-generated content, i.e., reviews. This work broadens the scope of review mining tasks, which typically comprise the prediction of ratings, helpfulness, and opinions. Our results suggest that documentaries can change or reinforce peoples' conception of an issue. We perform supervised learning to predict impact on the sentence level by using data driven as well as predefined linguistic, lexical, and psychological features; achieving an accuracy rate of 81% (F1) when using a Random Forest classifier, and 73% with a Support Vector Machine.


title: - 1-AUDIO FEATURE EXTRACTION AND ANALYSIS FOR SCENE SEGMENTATION AND CLASSIFICATION

abstract: Understanding of the scene content of a video sequence is very important for content-based indexing and retrieval of multimedia databases. Research in this area in the past several years has focused on the use of speech recognition and image analysis techniques. As a complimentary effort to the prior work, we have focused on using the associated audio information (mainly the nonspeech portion) for video scene analysis. As an example, we consider the problem of discriminating five types of TV programs, namely commercials, basketball games, football games, news reports, and weather forecasts. A set of low-level audio features are proposed for characterizing semantic contents of short audio clips. The linear separability of different classes under the proposed feature space is examined using a clustering analysis. The effective features are identified by evaluating the intracluster and intercluster scattering matrices of the feature space. Using these features, a neural net classifier was successful in separating the above five types of TV programs. By evaluating the changes between the feature vectors of adjacent clips, we also can identify scene breaks in an audio sequence quite accurately. These results demonstrate the capability of the proposed audio features for characterizing the semantic content of an audio sequence.


title: Testing Predictions From Personality Neuroscience Brain Structure and the Big Five

abstract: We used a new theory of the biological basis of the Big Five personality traits to generate hypotheses about the association of each trait with the volume of different brain regions. Controlling for age, sex, and whole-brain volume, results from structural magnetic resonance imaging of 116 healthy adults supported our hypotheses for four of the five traits: Extraversion, Neuroticism, Agreeableness, and Conscientiousness. Extraversion covaried with volume of medial orbitofrontal cortex, a brain region involved in processing reward information. Neuroticism covaried with volume of brain regions associated with threat, punishment, and negative affect. Agreeableness covaried with volume in regions that process information about the intentions and mental states of other individuals. Conscientiousness covaried with volume in lateral prefrontal cortex, a region involved in planning and the voluntary control of behavior. These findings support our biologically based, explanatory model of the Big Five and demonstra...


title: Landscape analysis system using 3D stereoscopic for drone

abstract: Autonomous drone is the Unmanned Aerial Vehicle (UAV) that did not have human control when operated. But if the drone encounter problem and cannot continue the mission, it has to land and wait for recover. It need to analyze the safety of landing area. This paper proposes the method to analyze the landing area of the drone using cross-correlation of the 3D stereoscopic image. The output of cross-correlation will be taken by polynomial equation. The polynomial coefficient is considered as the parameters for analyzing the landing Landscape. 120 images are used to train and set the parameters. To evaluate the system performance, 40 images from real environments are tested. The experimental result shows that the proposed system can correctly analyze the landing landscape as 92.5%.


title: Single-Phase PLLs: A Review of Recent Advances

abstract: Single-phase phase-locked loops (PLLs) are popular for the synchronization and control of single-phase grid-connected converters. They are also widely used for monitoring and diagnostic purposes in the power and energy areas. In recent years, a large number of single-phase PLLs with different structures and properties have been proposed in the literature. The main aim of this paper is to provide a review of these PLLs. To this end, the single-phase PLLs are first classified into two major categories: 1) power-based PLLs and 2) quadrature signal generation-based PLLs. The members of each category are then described and their pros and cons are discussed. This paper provides a deep insight into characteristics of different single-phase PLLs, and therefore, can be considered as a reference for researchers and engineers.


title: When to Invest in Security? Empirical Evidence and a Game-Theoretic Approach for Time-Based Security

abstract: Games of timing aim to determine the optimal defense against a strategic attacker who has the technical capability to breach a system in a stealthy fashion. Key questions arising are when the attack takes place, and when a defensive move should be initiated to reset the system resource to a known safe state. ::: In our work, we study a more complex scenario called Time-Based Security in which we combine three main notions: protection time, detection time, and reaction time. Protection time represents the amount of time the attacker needs to execute the attack successfully. In other words, protection time represents the inherent resilience of the system against an attack. Detection time is the required time for the defender to detect that the system is compromised. Reaction time is the required time for the defender to reset the defense mechanisms in order to recreate a safe system state. ::: In the first part of the paper, we study the VERIS Community Database (VCDB) and screen other data sources to provide insights into the actual timing of security incidents and responses. While we are able to derive distributions for some of the factors regarding the timing of security breaches, we assess the state-of-the-art regarding the collection of timing-related data as insufficient. ::: In the second part of the paper, we propose a two-player game which captures the outlined Time-Based Security scenario in which both players move according to a periodic strategy. We carefully develop the resulting payoff functions, and provide theorems and numerical results to help the defender to calculate the best time to reset the defense mechanism by considering protection time, detection time, and reaction time.


Clustering and the continuous k-means algorithm


title: A Novel Approach to Multiple Sequence Alignment Using Multiobjective Evolutionary Algorithm Based on Decomposition

abstract: Multiple sequence alignment (MSA) is a fundamental and key step for implementing other tasks in bioinformatics, such as phylogenetic analyses, identification of conserved motifs and domains, structure prediction, etc. Despite the fact that there are many methods to implement MSA, biologically perfect alignment approaches are not found hitherto. This paper proposes a novel idea to perform MSA, where MSA is treated as a multiobjective optimization problem. A famous multiobjective evolutionary algorithm framework based on decomposition is applied for solving MSA, named MOMSA. In the MOMSA algorithm, we develop a new population initialization method and a novel mutation operator. We compare the performance of MOMSA with several alignment methods based on evolutionary algorithms, including VDGA, GAPAM, and IMSA, and also with state-of-the-art progressive alignment approaches, such as MSAprobs, Probalign, MAFFT, Procons, Clustal omega, T-Coffee, Kalign2, MUSCLE, FSA, Dialign, PRANK, and CLUSTALW. These alignment algorithms are tested on benchmark datasets BAliBASE 2.0 and BAliBASE 3.0. Experimental results show that MOMSA can obtain the significantly better alignments than VDGA, GAPAM on the most of test cases by statistical analyses, produce better alignments than IMSA in terms of TC scores, and also indicate that MOMSA is comparable with the leading progressive alignment approaches in terms of quality of alignments.


title: Two-Stage Monte Carlo Tree Search for Connect6

abstract: Recently, Monte Carlo tree search (MCTS) has become a well-known game search method, and has been successfully applied to many games. This method performs well in solving search trees with numerous branches, such as Go, Havannah, etc. Connect6 is a game involving a search tree with numerous branches, and it is also one of the sudden-death games. This paper thus proposes a new MCTS variant related to Connect6, called two-stage MCTS. The first stage focuses on threat space search (TSS), which is designed to solve the sudden-death problem. For the double-threat TSS in Connect6, this study proposes an algorithm called iterative threat space search (ITSS) which combines normal TSS with conservative threat space search (CTSS). The second stage uses MCTS to estimate the game-theoretic value of the initial position. This stage aims at finding the most promising move. The experimental result shows that two-stage MCTS is considerably more efficient than traditional MCTS on those positions with TSS solution in Connect6. Furthermore, according to Connect6 heuristic knowledge, this paper uses relevance-zone search to accelerate identifying winning and losing moves.


title: Indirect Content Privacy Surveys: Measuring Privacy Without Asking About It

abstract: The strong emotional reaction elicited by privacy issues is well documented (e.g., [12, 8]). The emotional aspect of privacy makes it difficult to evaluate privacy concern, and directly asking about a privacy issue may result in an emotional reaction and a biased response. This effect may be partly responsible for the dramatic privacy concern ratings coming from recent surveys, ratings that often seem to be at odds with user behavior. In this paper we propose indirect techniques for measuring content privacy concerns through surveys, thus hopefully diminishing any emotional response. We present a design for indirect surveys and test the design's use as (1) a means to measure relative privacy concerns across content types, (2) a tool for predicting unwillingness to share content (a possible indicator of privacy concern), and (3) a gauge for two underlying dimensions of privacy - content importance and the willingness to share content. Our evaluation consists of 3 surveys, taken by 200 users each, in which privacy is never asked about directly, but privacy warnings are issued with increasing escalation in the instructions and individual question-wording. We demonstrate that this escalation results in statistically and practically significant differences in responses to individual questions. In addition, we compare results against a direct privacy survey and show that rankings of privacy concerns are increasingly preserved as privacy language increases in the indirect surveys, thus indicating our mapping of the indirect questions to privacy ratings is accurately reflecting privacy concerns.


title: Automatic defect identification and grading system for ‘Jonagold’ apples

abstract: A method to grade ‘Jonagold’ apples based on features extracted from defects is described. Database consisting of multi-spectral images of Jonagold apples is used for the work. Fuzzy C-Means (FCM) clustering method is used for defect segmentation, features from defect part is extracted using Histogram of Oriented Gradients (HOG) method and Apple classification is performed by using Multi-Class Support Vector Machine (MSVM) with accuracy of 97.5% for two category grading (healthy and defected) and 94.66% for multi-category grading (healthy apples, slightly defected apples and seriously defected apples).


title: Seeing, sensing, and scrutinizing

abstract: Large changes in a scene often become difficult to notice if made during an eye movement, image flicker, movie cut, or other such disturbance. It is argued here that this change blindness can serve as a useful tool to explore various aspects of vision. This argument centers around the proposal that focused attention is needed for the explicit perception of change. Given this, the study of change perception can provide a useful way to determine the nature of visual attention, and to cast new light on the way that it is — and is not — involved in visual perception. To illustrate the power of this approach, this paper surveys its use in exploring three different aspects of vision. The first concerns the general nature of seeing. To explain why change blindness can be easily induced in experiments but apparently not in everyday life, it is proposed that perception involves a6irtual representation, where object representations do not accumulate, but are formed as needed. An architecture containing both attentional and nonattentional streams is proposed as a way to implement this scheme. The second aspect concerns the ability of observers to detect change even when they have no visual experience of it. This sensing is found to take on at least two forms: detection without visual experience (but still with conscious awareness), and detection without any awareness at all. It is proposed that these are both due to the operation of a nonattentional visual stream. The final aspect considered is the nature of visual attention itself — the mechanisms involved when scrutinizing items. Experiments using controlled stimuli show the existence of various limits on visual search for change. It is shown that these limits provide a powerful means to map out the attentional mechanisms involved. © 2000 Elsevier Science Ltd. All rights reserved.


title: Extending touch-less interaction on vision based wearable device

abstract: A touch-less interaction technology on vision based wearable device is designed and evaluated. Users interact with the application with dynamic hands/feet gestures in front of the camera. Several proof-of-concept prototypes with eleven dynamic gestures are developed based on the touch-less interaction. At last, a comparing user study evaluation is proposed to demonstrate the usability of the touch-less approach, as well as the impact on user's emotion, running on a wearable framework or Google Glass.


title: Indoor 3D navigation and positioning of vehicles in multi-storey parking garages

abstract: We propose a novel method for three-dimensional navigation and localization of a land vehicle in a multi-storey parking-garage. In order to navigate or localize in 3D space we also need height information in addition to 2D position. Conventionally, an altimeter is used to get the floor level/height information. The solution presented in this paper uses low cost gyro and odometer sensors, combined with a 3D map by means of particle filtering and collision detection techniques to localize the vehicle in a parking garage. This eliminates the necessity of an altimeter or other additional aiding sources such as radio signaling. Thus the proposed solution can be used without any additional infrastructure devices. Other sources of information, such as WLAN signals, can be used to complement the solution if and when available.


title: A hybrid approach to offloading mobile image classification

abstract: Current mobile devices are unable to execute complex vision applications in a timely and power efficient manner without offloading some of the computation. This paper examines the tradeoffs that arise from executing some of the workload onboard and some remotely. Feature extraction and matching play an essential role in image classification and have the potential to be executed locally. Along with advances in mobile hardware, understanding the computation requirements of these applications is essential to realize their full potential in mobile environments. We analyze the ability of a mobile platform to execute feature extraction and matching, and prediction workloads under various scenarios. The best configuration for optimal runtime (11% faster) executes feature extraction with a GPU onboard and offloads the rest of the pipeline. Alternatively, compressing and sending the image over the network achieves lowest data transferred (2.5× better) and lowest energy usage (3.7× better) than the next best option.


title: Development of a Bendable Permanent-Magnet Tubular Linear Motor

abstract: This paper presents a new flexible linear actuator. It is a two-phase permanent-magnet tubular linear motor. An elastomer offers the desired actuator flexibility both between the coils of the stator and between the magnets in the mover. We determined the motor dimensions in a numerical parameter optimization and used various finite element models. The novel artificial muscle actuator is designed for tensile loads. It is electrically powered and obtains variable forces. Weights up to 750 g were lifted with a minimal bending radius of 200 mm. The outer diameter of the flexible linear direct drive measures about 30 mm.


title: Medical Applications of Wireless Body Area Networks

abstract: Wireless Body Area Networks (WBANs) provide efficient communication solutions to the ubiquitous healthcare systems. Health monitoring, telemedicine, military, interactive entertainment, and portable audio/video systems are some of the applications where WBANs can be used. The miniaturized sensors together with advance micro-electro-mechanical systems (MEMS) technology create a WBAN that continuously monitors the health condition of a patient. This paper presents a comprehensive discussion on the applications of WBANs in smart healthcare systems. We highlight a number of projects that enable WBANs to provide unobtrusive long-term healthcare monitoring with real-time updates to the health center. In addition, we list many potential medical applications of a WBAN including epileptic seizure warning, glucose monitoring, and cancer detection.


title: Approximation algorithms for classification problems with pairwise relationships: metric labeling and Markov random fields

abstract: In a traditional classification problem, we wish to assign one of k labels (or classes) to each of n objects, in a way that is consistent with some observed data that we have about the problem. An active line of research in this area is concerned with classification when one has information about pairwise relationships among the objects to be classified; this issue is one of the principal motivations for the framework of Markov random fields, and it arises in areas such as image processing, biometry, and document analysis. In its most basic form, this style of analysis seeks to find a classification that optimizes a combinatorial function consisting of assignment costs---based on the individual choice of label we make for each object---and separation costs---based on the pair of choices we make for two "related" objects.We formulate a general classification problem of this type, the metric labeling problem; we show that it contains as special cases a number of standard classification frameworks, including several arising from the theory of Markov random fields. From the perspective of combinatorial optimization, our problem can be viewed as a substantial generalization of the multiway cut problem, and equivalent to a type of uncapacitated quadratic assignment problem.We provide the first nontrivial polynomial-time approximation algorithms for a general family of classification problems of this type. Our main result is an O(log k log log k)-approximation algorithm for the metric labeling problem, with respect to an arbitrary metric on a set of k labels, and an arbitrary weighted graph of relationships on a set of objects. For the special case in which the labels are endowed with the uniform metric---all distances are the same---our methods provide a 2-approximation algorithm.


SECURING THE INTERNET OF THINGS : A REVIEW


title: High voltage, high performance switch using series connected IGBTs

abstract: A high performance, high voltage switch obtained by a series connection of 3300 V IGBT modules is presented. The correct voltage sharing between the devices is ensured by the presence of a simple and reliable auxiliary circuit, which acts as feedback on the gate terminal. A comparison in terms of power losses, output currents and switching performances to a single 6500 V IGBT module is also presented. An extended experimental analysis demonstrates strong advantages in terms of switching losses (up to 67%) and current and voltage gradients. Use of trench-gate IGBT's, available in 3300 V size, but not in 6500 V one, allows extra performance to series connected circuit, as experimental results show. Such circuit is suitable to increase performances of heavy traction inverters. Multiple series connection allows to obtain very high rated voltage, low-losses switches.


title: Proximal Tibiofibular Joint Instability and Treatment Approaches: A Systematic Review of the Literature.

abstract: PURPOSE ::: To evaluate the treatment options, outcomes, and complications associated with proximal tibiofibular joint (PTFJ) instability, which will aim to improve surgical treatment of PTFJ instability and aid surgeons in their decision making and treatment selection. ::: ::: ::: METHODS ::: A systematic review was performed according to Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines. Inclusion criteria were as follows: PTFJ instability treatment techniques, PTFJ surgical outcomes, English language, and human studies. Exclusion criteria were cadaveric studies, animal studies, basic science articles, editorial articles, review articles, and surveys. Furthermore, we excluded studies that did not report patient follow-up time and studies without any patient-reported, clinical or radiographic outcomes at the final follow-up. ::: ::: ::: RESULTS ::: The systematic review identified 44 studies (96 patients) after inclusion and exclusion criteria application. For the treatment of PTFJ instability, there were 18 studies (35 patients) describing nonoperative management, 3 studies (4 patients) reported on open reduction, 11 studies (25 patients) reported on fixation, 4 studies (10 patients) that described proximal fibula resection, 3 studies (11 patients) reported on adjustable cortical button repair, 2 studies (3 patients) reported on ligament reconstructions, and 5 (8 patients) studies reported on biceps femoris tendon rerouting. The most (77% to 90%) PTFJ dislocations and instability were anterolateral/unspecified anterior dislocation or instability. Improved outcomes after all forms of PTFJ instability treatment were reported; however, high complication rates were associated with both PTFJ fixation (28%) and fibular head resection (20%). ::: ::: ::: CONCLUSIONS ::: Improved outcomes can be expected after surgical treatment of PTFJ instability. Proximal tibiofibular ligament reconstruction, specifically biceps rerouting and anatomic graft reconstruction, leads to improved outcomes with low complication rates. Nonoperative treatment is associated with persistent symptoms, whereas both fixation and fibular head resection are associated with high complication rates. ::: ::: ::: LEVEL OF EVIDENCE ::: Level IV, systematic review of level IV studies.


title: Delayed “Choice” Quantum Eraser

abstract: This paper reports a "delayed choice quantum eraser" experiment proposed by Scully and Druhl in 1982. The experimental results demonstrated the possibility of simultaneously observing both particle-like and wave-like behavior of a quantum via quantum entanglement. The which-path or both-path information of a quantum can be erased or marked by its entangled twin even after the registration of the quantum.


title: Improving Fairness, Efficiency, and Stability in HTTP-Based Adaptive Video Streaming With Festive

abstract: Modern video players today rely on bit-rate adaptation in order to respond to changing network conditions. Past measurement studies have identified issues with today's commercial players when multiple bit-rate-adaptive players share a bottleneck link with respect to three metrics: fairness, efficiency, and stability. Unfortunately, our current understanding of why these effects occur and how they can be mitigated is quite limited. In this paper, we present a principled understanding of bit-rate adaptation and analyze several commercial players through the lens of an abstract player model consisting of three main components: bandwidth estimation, bit-rate selection, and chunk scheduling. Using framework, we identify the root causes of several undesirable interactions that arise as a consequence of overlaying the video bit-rate adaptation over HTTP. Building on these insights, we develop a suite of techniques that can systematically guide the tradeoffs between stability, fairness, and efficiency and thus lead to a general framework for robust video adaptation. We pick one concrete instance from this design space and show that it significantly outperforms today's commercial players on all three key metrics across a range of experimental scenarios.


title: Partition based hash tree — An efficient certificate revocation system

abstract: A critical check in the certificate validation process is to determine whether a certificate has been revoked or not. Revocation of a certificate is the objective of invalidation of a certificate before its operational lifetime, which was set during its creation. Traditional certificate revocation systems include Certificate Revocation List (CRL) which requires all users to download the list of revoked certificates and store it locally that is subsequently used, to check the revocation status of the certificate prior to usage of the certificate. Another more modern approach is Online Certificate Status Protocol (OCSP) that allows for an online check of a certificate status from a well-known server before a certificate is processed. Searching for the revocation status of a certificate is commonly done with a Merkle hash tree algorithm, in which revoked certificates are arranged in the form of binary tree and binary searching algorithm is used to search for the target certificate. Existing approaches based on the Merkle Hash tree have their own drawbacks and, in this paper, we propose a different type of search algorithm, the partition based hash tree, to improve searches for revoked certificates. We also identify how this proposed approach improves efficiency and scalability issues over traditional approaches of certificate search.


title: Programming Literacy: Computational Thinking in Brazilian Public Schools

abstract: Bringing Programming to K-12 Education has been a strong tendency in Brazil and in the world. In this work-in-progress, we describe an experience in teacher training with the goal of promoting the practice of programming and the development of computational thinking in students of Brazilian public schools, through an initiative called Programming Literacy. Here we present strategies used in the implementation of the initiative, design of a curricular structure and training program, as well as monitoring and evaluation strategies. The results and findings of the initiative, involving more than 500 students from four cities in 2016, are shared as well as the perspectives on next steps.


title: Indoor inertial waypoint navigation for the blind

abstract: Indoor navigation technology is needed to support seamless mobility for the visually impaired. This paper describes the construction and evaluation of an inertial dead reckoning navigation system that provides real-time auditory guidance along mapped routes. Inertial dead reckoning is a navigation technique coupling step counting together with heading estimation to compute changes in position at each step. The research described here outlines the development and evaluation of a novel navigation system that utilizes information from the mapped route to limit the problematic error accumulation inherent in traditional dead reckoning approaches. The prototype system consists of a wireless inertial sensor unit, placed at the users' hip, which streams readings to a smartphone processing a navigation algorithm. Pilot human trials were conducted assessing system efficacy by studying route-following performance with blind and sighted subjects using the navigation system with real-time guidance, versus offline verbal directions.


title: Acoustic analysis of the infant cry: classical and new methods

abstract: In the last few decades many papers have written about the analysis of the infant cry. The acoustic analysis has a shorter history than emotional, physiological, etc. investigations. This paper deals with classical and new methods of acoustic analysis of the infant cry. The final goal is to detect hearing disorders according to the crying at the earliest possible moment. Classical acoustic methods were reproduced and compared with solutions, which were not available before. This paper most of all deals with the characteristics of the fundamental frequency of the cry in the time and in the frequency domain.


title: Crime Information Extraction from Police and Witness Narrative Reports

abstract: To solve crimes, investigators often rely on interviews with witnesses, victims, or criminals themselves. The interviews are transcribed and the pertinent data is contained in narrative form. To solve one crime, investigators may need to interview multiple people and then analyze the narrative reports. There are several difficulties with this process: interviewing people is time consuming, the interviews - sometimes conducted by multiple officers - need to be combined, and the resulting information may still be incomplete. For example, victims or witnesses are often too scared or embarrassed to report or prefer to remain anonymous. We are developing an online reporting system that combines natural language processing with insights from the cognitive interview approach to obtain more information from witnesses and victims. We report here on information extraction from police and witness narratives. We achieved high precision, 94% and 96% and recall, 85% and 90%, for both narrative types.


title: A software architecture for generic human activity recognition from smartphone sensor data

abstract: In these last few years, several recent studies have demonstrated the possibility to perform Human Activity Recognition (HAR) by smartphone sensor data, enabling in this way a new generation of context-aware mobile applications. Smartphone-based HAR systems can exploit the full set of embedded sensors beside the accelerometer in order to increase the accuracy of the detection process. At the same time, the practical deployment of such systems can result highly challenging since it must cope with the limited computational resources and the battery constraints of the mobile devices. In this paper, we address such issues by proposing a novel, generic HAR architecture for Android devices. The system design takes into account the energy constraints by: (i) limiting the number of sensors/features used for the recognition process, while still guaranteeing satisfactory performance in terms of activity detection; (ii) allocating the most CPU intensive tasks, like the training and data processing phases, on an external server. At the same time, the system operations automatize the full learning process, simply notifying the user when a new classification model has been installed on the client. We validate the proposed HAR systems on two use-cases, i.e. transportation mode detection and walking mode detection, and we describe an application for indoor floor detection. Measurements show that the overall accuracy of the activity recognition process can be up to 90% for both the use-cases.


Can Intensive Exposure to Foreign Language Sounds Affect the Perception of Native Sounds


title: Active Rotary-Legs Mechanism for Stair-Climbing Mobility Vehicle

abstract: This study describes a novel active rotary-legs mechanism for a stair-climbing mobility vehicle. We have previously developed a stair-climbing up and down wheelchair with lever propelled rotary-legs operated using the human upper body. The previous results indicated that the required torque for the stair-climbing up and down procedure can be reduced based on the user's posture transition, which was achieved via the procedure using only the human upper body. The design principle of the proposed active rotary-legs mechanism in this study is to develop a more compact and lightweight mechanism for a stair-climbing up and down mobility vehicle. We achieve this objective based on the previous observations by appropriately combining the active and passive components. The developed mechanism consists of a four-bar linkage mechanism with motors and gas springs as active and passive components, respectively. The gas springs are connected to the linkage mechanism as parallel elastic actuators, which can reduce the required motor torque and make the complete mechanism compact and lightweight. We describe herein a detailed design of the active rotary-legs mechanism and conduct simulations and preliminary experiments to investigate the effectiveness of our proposed methodology.


Computational Models for Spatial Prepositions


title: Adversarial Neural Networks for Cross-lingual Sequence Tagging

abstract: We study cross-lingual sequence tagging with little or no labeled data in the target language. Adversarial training has previously been shown to be effective for training cross-lingual sentence classifiers. However, it is not clear if language-agnostic representations enforced by an adversarial language discriminator will also enable effective transfer for token-level prediction tasks. Therefore, we experiment with different types of adversarial training on two tasks: dependency parsing and sentence compression. We show that adversarial training consistently leads to improved cross-lingual performance on each task compared to a conventionally trained baseline.


title: Small Sample Learning in Big Data Era

abstract: As a promising area in artificial intelligence, a new learning paradigm, called Small Sample Learning (SSL), has been attracting prominent research attention in the recent years. In this paper, we aim to present a survey to comprehensively introduce the current techniques proposed on this topic. Specifically, current SSL techniques can be mainly divided into two categories. The first category of SSL approaches can be called "concept learning", which emphasizes learning new concepts from only few related observations. The purpose is mainly to simulate human learning behaviors like recognition, generation, imagination, synthesis and analysis. The second category is called "experience learning", which usually co-exists with the large sample learning manner of conventional machine learning. This category mainly focuses on learning with insufficient samples, and can also be called small data learning in some literatures. More extensive surveys on both categories of SSL techniques are introduced and some neuroscience evidences are provided to clarify the rationality of the entire SSL regime, and the relationship with human learning process. Some discussions on the main challenges and possible future research directions along this line are also presented.


title: Where, When and Why Brain Activation Differs for Bilinguals and Monolinguals during Picture Naming and Reading Aloud

abstract: Using functional magnetic resonance imaging, we found that when bilinguals named pictures or read words aloud, in their native or nonnative language, activation was higher relative to monolinguals in 5 left hemisphere regions: dorsal precentral gyrus, pars triangularis, pars opercularis, superior temporal gyrus, and planum temporale. We further demonstrate that these areas are sensitive to increasing demands on speech production in monolinguals. This suggests that the advantage of being bilingual comes at the expense of increased work in brain areas that support monolingual word processing. By comparing the effect of bilingualism across a range of tasks, we argue that activation is higher in bilinguals compared with monolinguals because word retrieval is more demanding; articulation of each word is less rehearsed; and speech output needs careful monitoring to avoid errors when competition for word selection occurs between, as well as within, language.


title: Distance metric learning with eigenvalue fine tuning

abstract: Distance metric learning focuses on learning one global or multiple local distance functions to draw similar instances close to each other and push away dissimilar ones. Most existing work has to do matrix projection to learn distance functions. In this paper, we present a novel distance function learning model which is based on eigenvalue fine tuning. Our model not only is able to learn the global distance function but also can be easily adopted into local metric learning tasks. From the perspective of dimension reduction, the proposed model can measure how much information has been preserved after feature transformation. Moreover, we connect our model with principal components analysis to improve its performance by introducing the label information. Experimental results have demonstrated the effectiveness of the proposed method.


title: Liquid Crystal Polymer (LCP) based antenna for flexible system on package (SoP) applications

abstract: The design, fabrication and measurement of a bowtie antenna on a flexible Liquid Crystal Polymer (LCP) substrate is reported in this paper. The antenna is fed by a balun transition which helps improve the gain up to 5.1 dB. The antenna performance is analyzed for both planar and curved substrates. The comparison between simulation and measurements shows a good agreement. This structure can either be used to sense the bending of the substrate or use the bending to tilt the beam.


title: Augmented reality without fiducial markers

abstract: This work presents an implementation of an augmented reality system that does not use fiducial markers for neither estimating the camera pose nor inserting a virtual object but natural marks such as corners of objects that are present in the scene. The system uses a RGB-D dataset for positioning the camera which moves with 6 DOF, for defining a work plane and for superimposing the object on the video frames at real time. The user can select through a graphical interface the work plane and the virtual object, and modify its size, position and orientation. The system is robust to fast an complex camera motion, comparable to the most outstanding works of augmented reality.


title: The Architecture and Datasets of Docear's Research Paper Recommender System

abstract: In the past few years, we have developed a research paper recommender system for our reference management software Docear. In this paper, we introduce the architecture of the recommender system and four datasets. The architecture comprises of multiple components, e.g. for crawling PDFs, generating user models, and calculating content‐based recommendations. It supports researchers and developers in building their own research paper recommender systems, and is, to the best of our knowledge, the most comprehensive architecture that has been released in this field. The four datasets contain metadata of 9.4 million academic articles, including 1.8 million articles publicly available on the Web; the articles' citation network; anonymized information on 8,059 Docear users; information about the users' 52,202 mind‐maps and personal libraries; and details on the 308,146 recommendations that the recommender system delivered. The datasets are a unique source of information to enable, for instance, research on collaborative filtering, content‐based filtering, and the use of reference‐management and mind‐mapping software.


title: Certainty and Sensitive Knowledge

abstract: In‘Is Locke’s Theory o fKnowledge Inconsistent?’ and‘Locke’s ‘Sensitive Knowledge’: Knowledge or Assurance’ Samuel Rickless has reopened the issue of whether Locke believes that what he calls sensitive knowledge really counts as knowledge. Rickless maintains that, having argued that knowledge requires certainty and having admitted that our awareness of objects external to the mind falls short of the certainty of intuition and demonstration, Locke is forced to concede that beliefs about the existence of objects external to the mind do not satisfy his criterion of knowledge; while some such beliefs reach such a high level of probability that we may be assured of them and they may be allowed to ‘pass under’ the name of knowledge, such beliefs are not really knowledge. In this case, calling such beliefs sensitive knowledge is to use ‘knowledge’ as an honorific; even though they fall short of the criterion of knowledge, for all practical purposes, we will let them pass as knowledge. Rickless adduces three considerations in support of this interpretation: first, certainty does not admit of degrees so, in conceding that beliefs about the existence of extra-mental objects fall short of the certainty of intuition and demonstration, Locke is conceding that such beliefs literally do not constitute knowledge; second, Locke tends to speak of our assurance of the existence of extra-mental objects and ‘assurance’ is a technical term indicating an epistemic level somewhat less than certainty; third, to say that something ‘passes under’ the name of a sort is to indicate that it is not really of that sort but that the name of the sort will be applied to it for practical purposes.


title: Multi-spatial Classifier for Blue Whale Images using Photo-identification Method

abstract: The process of photo-identification images of the blue whale (Balaenoptera musculus), is made manually; this process classifies images blue whale through its dorsal fin characteristics. The features are extracted visually, which can generate errors at the moment of classified. In this work an image classifier blue whale is presented, which have features such as color pigmentation, background image, type of dorsal fin, among others; these common characteristics generate high statistical dependence. This statistical dependence causes the data extracted through a segmented image of the blue whale, are to be observed through a hyperplane. Using statistical techniques and the K-Nearest Neighbors classifier, the classification of three types of dorsal fin is obtained with an accuracy of 71.66%, assessing the value of K = 7, taking reference catalog of species CICIMAR- IPN.


title: Commonsense reasoning, commonsense knowledge, and the SP theory of intelligence

abstract: Commonsense reasoning (CSR) and commonsense knowledge (CSK) (together abbreviated as CSRK) are areas of study concerned with problems which are trivially easy for adults but which are challenging for artificial systems. This paper describes how the SP System -- meaning the "SP Theory of Intelligence" and its realisation in the "SP Computer Model" -- has strengths and potential in several aspects of CSRK. Some shortcomings of the system in that area may be overcome with planned future developments. A particular strength of the SP System is that it shows promise as an overarching theory for four areas of relative success with CSRK problems -- described by other authors -- which have been developed without any integrative theory. How the SP System may help to solve four other kinds of CSRK problem is described: 1) how the strength of evidence for a murder may be influenced by the level of lighting of the murder as it was witnessed; 2) how people may arrive at the commonly-accepted interpretation of phrases like "water bird"; 3) the interpretation of the horse's head scene in "The Godfather" film; and how the SP System may help to resolve the reference of an ambiguous pronoun in sentences in the format of a 'Winograd schema'. Also described is why a fifth CSRK problem -- modelling how a chef may crack an egg into a bowl -- is beyond the capabilities of the SP System as it is now and how those deficiencies may be overcome via planned developments of the system.


title: Bridging Music and Image via Cross-Modal Ranking Analysis

abstract: Human perceptions of music and image are closely related to each other, since both can inspire similar human sensations, such as emotion, motion, and power. This paper aims to explore whether and how music and image can be automatically matched by machines. The main contributions are three aspects. First, we construct a benchmark dataset composed of more than $45\,000$ music-image pairs. Human labelers are recruited to annotate whether these pairs are well-matched or not. The results show that they generally agree with each other on the matching degree of music-image pairs. Secondly, we investigate suitable semantic representations of music and image for this cross-modal matching task. In particular, we adopt lyrics as a middle-media to connect music and image, and design a set of lyric-based attributes for image representation. Thirdly, we propose cross-modal ranking analysis (CMRA) to learn the semantic similarity between music and image with ranking labeling information. CMRA aims to find the optimal embedding spaces for both music and image in the sense of maximizing the ordinal margin between music–image pairs. The proposed method is able to learn the non-linear relationship between music and image, and to integrate heterogeneous ranking data from different modalities into a unified space. Experimental results demonstrate that the proposed method outperforms state-of-the-art cross-modal methods in the music–image matching task, and achieves a consistency rate of $91.5\%$ with human labelers.


title: Attribution Modeling Increases Efficiency of Bidding in Display Advertising

abstract: Predicting click and conversion probabilities when bidding on ad exchanges is at the core of the programmatic advertising industry. Two separated lines of previous works respectively address i) the prediction of user conversion probability and ii) the attribution of these conversions to advertising events (such as clicks) after the fact. We argue that attribution modeling improves the efficiency of the bidding policy in the context of performance advertising. Firstly we explain the inefficiency of the standard bidding policy with respect to attribution. Secondly we learn and utilize an attribution model in the bidder itself and show how it modifies the average bid after a click. Finally we produce evidence of the effectiveness of the proposed method on both offline and online experiments with data spanning several weeks of real traffic from Criteo, a leader in performance advertising.


title: Quantum Neuron: an elementary building block for machine learning on quantum computers

abstract: Even the most sophisticated artificial neural networks are built by aggregating substantially identical units called neurons. A neuron receives multiple signals, internally combines them, and applies a non-linear function to the resulting weighted sum. Several attempts to generalize neurons to the quantum regime have been proposed, but all proposals collided with the difficulty of implementing non-linear activation functions, which is essential for classical neurons, due to the linear nature of quantum mechanics. Here we propose a solution to this roadblock in the form of a small quantum circuit that naturally simulates neurons with threshold activation. Our quantum circuit defines a building block, the"quantum neuron", that can reproduce a variety of classical neural network constructions while maintaining the ability to process superpositions of inputs and preserve quantum coherence and entanglement. In the construction of feedforward networks of quantum neurons, we provide numerical evidence that the network not only can learn a function when trained with superposition of inputs and the corresponding output, but that this training suffices to learn the function on all individual inputs separately. When arranged to mimic Hopfield networks, quantum neural networks exhibit properties of associative memory. Patterns are encoded using the simple Hebbian rule for the weights and we demonstrate attractor dynamics from corrupted inputs. Finally, the fact that our quantum model closely captures (traditional) neural network dynamics implies that the vast body of literature and results on neural networks becomes directly relevant in the context of quantum machine learning.


title: A Hardware Design Language for Efficient Control of Timing Channels

abstract: Information security can be compromised by leakage via low-level hardware features. One recently prominent example is cache probing attacks, which rely on timing channels created by caches. We introduce a hardware design language, SecVerilog, which makes it possible to statically analyze information flow at the hardware level. With SecVerilog, systems can be built with verifiable control of timing channels and other information channels. SecVerilog is Verilog, extended with expressive type annotations that enable precise reasoning about information flow. It also comes with rigorous formal assurance: we prove that SecVerilog enforces timing-sensitive noninterference and thus ensures secure information flow. By building a secure MIPS processor and its caches, we demonstrate that SecVerilog makes it possible to build complex hardware designs with verified security, yet with low overhead in time, space, and HW designer effort.


title: Exploiting Image-trained CNN Architectures for Unconstrained Video Classification

abstract: We conduct an in-depth exploration of different strategies for doing event detection in videos using convolutional neural networks (CNNs) trained for image classification. We study different ways of performing spatial and temporal pooling, feature normalization, choice of CNN layers as well as choice of classifiers. Making judicious choices along these dimensions led to a very significant increase in performance over more naive approaches that have been used till now. We evaluate our approach on the challenging TRECVID MED'14 dataset with two popular CNN architectures pretrained on ImageNet. On this MED'14 dataset, our methods, based entirely on image-trained CNN features, can outperform several state-of-the-art non-CNN models. Our proposed late fusion of CNN- and motion-based features can further increase the mean average precision (mAP) on MED'14 from 34.95% to 38.74%. The fusion approach achieves the state-of-the-art classification performance on the challenging UCF-101 dataset.


ECONOMIC DIAGNOSTICS IN ENVIRONMENT OF STATICIZED RELATIONSHIP MARKETING


title: A Simple Proof of the Restricted Isometry Property for Random Matrices

abstract: We give a simple technique for verifying the Restricted Isometry Property (as introduced by Candes and Tao) for random matrices that underlies Compressed Sensing. Our approach has two main ingredients: (i) concentration inequalities for random inner products that have recently provided algorithmically simple proofs of the Johnson–Lindenstrauss lemma; and (ii) covering numbers for finite-dimensional balls in Euclidean space. This leads to an elementary proof of the Restricted Isometry Property and brings out connections between Compressed Sensing and the Johnson–Lindenstrauss lemma. As a result, we obtain simple and direct proofs of Kashin’s theorems on widths of finite balls in Euclidean space (and their improvements due to Gluskin) and proofs of the existence of optimal Compressed Sensing measurement matrices. In the process, we also prove that these measurements have a certain universality with respect to the sparsity-inducing basis.


title: Beyond Memorability: Visualization Recognition and Recall

abstract: In this paper we move beyond memorability and investigate how visualizations are recognized and recalled. For this study we labeled a dataset of 393 visualizations and analyzed the eye movements of 33 participants as well as thousands of participant-generated text descriptions of the visualizations. This allowed us to determine what components of a visualization attract people's attention, and what information is encoded into memory. Our findings quantitatively support many conventional qualitative design guidelines, including that (1) titles and supporting text should convey the message of a visualization, (2) if used appropriately, pictograms do not interfere with understanding and can improve recognition, and (3) redundancy helps effectively communicate the message. Importantly, we show that visualizations memorable “at-a-glance” are also capable of effectively conveying the message of the visualization. Thus, a memorable visualization is often also an effective one.


title: Simplifying Mined Process Models: An Approach Based on Unfolding

abstract: Process models discovered using process mining tend to be complex and have problems balancing between overfitting and underfitting. Overfitting models are not general enough while underfitting models allow for too much behavior. This paper presents a post-processing approach to simplify discovered process models while controlling the balance between overfitting and underfitting. The discovered process model, expressed in terms of a Petri net, is unfolded into a branching process using the event log. Subsequently, the resulting branching process is folded into a simpler process model capturing the desired behavior.


title: Social network analysis: A methodological introduction

abstract: Social network analysis is a large and growing body of research on the measurement and analysis of relational structure. Here, we review the fundamental concepts of network analysis, as well as a range of methods currently used in the field. Issues pertaining to data collection, analysis of single networks, network comparison, and analysis of individual-level covariates are discussed, and a number of suggestions are made for avoiding common pitfalls in the application of network methods to substantive questions. those seeking to add a structural component to their own work. Although many classical methods are discussed, more emphasis is placed on recent, statistical approaches to network analysis, as these are somewhat less well covered by existing reviews. Finally, an effort has been made throughout to highlight common pitfalls which can await the unwary researcher, and to suggest how these may be avoided. The result, it is hoped, is a basic reference that offers a rigorous treatment of essential concepts and methods, without assuming prior background in this area. The overall structure of this paper is as follows. After a brief comment on some things which are not discussed here (the field being too large to admit treatment in a single paper), an overview of core concepts and notation is pre- sented. Following this is a discussion of network data, including basic issues involving representation, boundary definition, sampling schemes, instruments, and visualiza- tion. I then proceed to an overview of common approaches to the measurement and modelling of structural properties within single networks, followed by sections on methods for network comparison and modelling of individual attributes. Finally, I conclude with a discussion of some additional issues which affect the use of network analysis in practical settings. Topics not discussed


title: Semi-Supervised Autoencoder: A Joint Approach of Representation and Classification

abstract: Recent years have witnessed the significant success of representation learning and deep learning in various prediction and recognition applications. Most of these previous studies adopt the two-phase procedures, namely the first step of representation learning and then the second step of supervised learning. In this process, to fit the training data the initial model weights, which inherits the good properties from the representation learning in the first step, will be changed in the second step. In other words, the second step leans better classification models at the cost of the possible deterioration of the effectiveness of representation learning. Motivated by this observation we propose a joint framework of representation and supervised learning. It aims to learn a model, which not only guarantees the "semantics" of the original data from representation learning but also fit the training data well via supervised learning. Along this line we develop the model of semi-supervised Auto encoder under the spirit of the joint learning framework. The experiments on various data sets for classification show the significant effectiveness of the proposed model.


title: ASTRO: A Datalog System for Advanced Stream Reasoning

abstract: The rise of the Internet of Things (IoT) and the recent focus on a gamut of 'Smart City' initiatives world-wide have pushed for new advances in data stream systems to (1) support complex analytics and evolving graph applications as continuous queries, and (2) deliver fast and scalable processing on large data streams. Unfortunately current continuous query languages (CQL) lack the features and constructs needed to support the more advanced applications. For example recursive queries are now part of SQL, Datalog, and other query languages, but they are not supported by most CQLs, a fact that caused a significant loss of expressive power, which is further aggravated by the limitation that only non-blocking queries can be supported. To overcome these limitations we have developed an a dvanced st ream r easo ning system ASTRO that builds on recent advances in supporting aggregates in recursive queries. In this demo, we will briefly elucidate the formal Streamlog semantics, which combined with the Pre-Mappability (PreM) concept, allows the declarative specification of many complex continuous queries, which are then efficiently executed in real-time by the portable ASTRO architecture. Using different case studies, we demonstrate (i) the ease-of-use, (ii) the expressive power and (iii) the robustness of our system, as compared to other state-of-the-art declarative CQL systems.


title: Speech Emotion Recognition Based on Improved MFCC

abstract: Speech1 Emotion Recognition SER uses the Berlin EMO-DB database, seven emotions. Traditional emotional features and their statistics are used in SER. Two improved Mel Frequency Cepstrum Coefficients MFCC features are added to this experiment, which extract MFCC parameters from the energy curve and the fundamental frequency curve, that are energy MFCC EEMFCC and fundamental frequency MFCC F0MFCC, using Support Vector Machines SVM as the recognition machine, we obtained the highest average recognition rate of 85.37% for the seven categories and 100% for sad.


title: Knowledge-aware Multimodal Fashion Chatbot

abstract: Multimodal fashion chatbot provides a natural and informative way to fulfill customers' fashion needs. However, making it 'smart' in generating substantive responses remains a challenging problem. In this paper, we present a multimodal domain knowledge enriched fashion chatbot. It forms a taxonomy-based learning module to capture the fine-grained semantics in images and leverages an end-to-end neural conversational model to generate responses based on the conversation history, visual semantics, and domain knowledge. To avoid inconsistent dialogues, deep reinforcement learning method is used to further optimize the model.


title: The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices

abstract: This paper proposes scalable and fast algorithms for solving the Robust PCA problem, namely recovering a low-rank matrix with an unknown fraction of its entries being arbitrarily corrupted. This problem arises in many applications, such as image processing, web data ranking, and bioinformatic data analysis. It was recently shown that under surprisingly broad conditions, the Robust PCA problem can be exactly solved via convex optimization that minimizes a combination of the nuclear norm and the $\ell^1$-norm . In this paper, we apply the method of augmented Lagrange multipliers (ALM) to solve this convex program. As the objective function is non-smooth, we show how to extend the classical analysis of ALM to such new objective functions and prove the optimality of the proposed algorithms and characterize their convergence rate. Empirically, the proposed new algorithms can be more than five times faster than the previous state-of-the-art algorithms for Robust PCA, such as the accelerated proximal gradient (APG) algorithm. Moreover, the new algorithms achieve higher precision, yet being less storage/memory demanding. We also show that the ALM technique can be used to solve the (related but somewhat simpler) matrix completion problem and obtain rather promising results too. We further prove the necessary and sufficient condition for the inexact ALM to converge globally. Matlab code of all algorithms discussed are available at http://perception.csl.illinois.edu/matrix-rank/home.html


title: Physiological employment standards I. Occupational fitness standards: objectively subjective?

abstract: This paper examines the processes involved in the establishment of a minimum occupational fitness standard, with particular reference to the interplay that inevitably occurs between objective measurements and subjective decisions. The areas considered include: the determination of the critical task on which to base a standard; establishing minimum acceptable performance and methods of best practice for the execution of these tasks; determining the physical demands of a task and a reasonable relative workload; producing the final standard. Finally, the impact of the subjective component of the development of an occupational fitness standard on its defensibility is discussed. It is concluded that all standards involve some subjective aspects; the extent of these could be reduced by further research. In the meantime, it would be prudent for those developing standards to detail the rationale, methods and evidence by which subjective decisions were reached, to provide an audit trail for subsequent investigation.


title: Virtual Device Passthrough for High Speed VM Networking

abstract: Supporting network I/O at high packet rates in virtual machines is fundamental for the deployment of Cloud data centers and Network Function Virtualization. Historically, SR-IOV and hardware passthrough were thought as the only viable solution to reduce the high cost of virtualization. In previous work [15] we showed how even plain device emulation can achieve VM-to-VM speeds of millions of packets per second (Mpps), though still at least 3 times slower than bare metal. In this paper, to fill this gap, we present ptnetmap, a virtual passthrough network device (based on the netmap framework). ptnetmap allows VMs to connect to any netmap port (physical devices, software switches, netmap pipes), conserving the speed and isolation of the native netmap system, and removing the constraints of hardware passthrough. Our work includes two key features not present in previous proposals: we provide a high speed path also to untrusted VMs, and do not require dedicated polling cores/threads, which is fundamental to achieve an efficient use of resources. Besides these features, our speed is also beyond previously published values. Running on top of ptnetmap, VMs can saturate a 10 Gbit link at 14.88 Mpps, talk at over 20 Mpps to untrusted VMs, and over 70 Mpps to trusted VMs. ptnetmap extends the netmap framework, and currently supports Linux and FreeBSD guests, and QEMU/KVM host. Support for bhyve/FreeBSD host is under development.


Context-Aware Insider Threat Detection Alex Memory


title: Scheduling jobs across geo-distributed datacenters

abstract: With growing data volumes generated and stored across geo-distributed datacenters, it is becoming increasingly inefficient to aggregate all data required for computation at a single datacenter. Instead, a recent trend is to distribute computation to take advantage of data locality, thus reducing the resource (e.g., bandwidth) costs while improving performance. In this trend, new challenges are emerging in job scheduling, which requires coordination among the datacenters as each job runs across geo-distributed sites. In this paper, we propose novel job scheduling algorithms that coordinate job scheduling across datacenters with low overhead, while achieving near-optimal performance. Our extensive simulation study with realistic job traces shows that the proposed scheduling algorithms result in up to 50% improvement in average job completion time over the Shortest Remaining Processing Time (SRPT) based approaches.


title: Design and analysis of a decoupled XY micro compliant parallel manipulator

abstract: In this paper, a new compliant parallel mechanism driven by piezoelectric actuators is designed, which is a modified structure from our previous designed stage. In order to compensate the limited stroke of PZT, the secondary amplification mechanism was adopted in this design, and also the matrix method is utilized to calculate its stiffness and compliance for this compliant mechanism. In practical application, the accuracy of mechanism is an essential index that we wish to achieve, meanwhile the parasitic motion analysis, dynamic and static analysis are also presented in this paper. The method is validated by finite element analysis to demonstrate its effectiveness.


Computation of the Salience of Features


title: Lessons and Challenges from Mining Retail E-Commerce Data

abstract: The architecture of Blue Martini Software's e-commerce suite has supported data collection, data transformation, and data mining since its inception. With clickstreams being collected at the application-server layer, high-level events being logged, and data automatically transformed into a data warehouse using meta-data, common problems plaguing data mining using weblogs (e.g., sessionization and conflating multi-sourced data) were obviated, thus allowing us to concentrate on actual data mining goals. The paper briefly reviews the architecture and discusses many lessons learned over the last four years and the challenges that still need to be addressed. The lessons and challenges are presented across two dimensions: business-level vs. technical, and throughout the data mining lifecycle stages of data collection, data warehouse construction, business intelligence, and deployment. The lessons and challenges are also widely applicable to data mining domains outside retail e-commerce.


title: On separation of platform-independent particles in user interfaces

abstract: The complexity of user interface (UI) design grows quickly with the number of application concerns. Such complexity compounds with additional requirement of contextual-awareness (i.e., adapt to user location, skill level, etc.) and support of heterogeneous devices and platforms (e.g., web, mobile app). Implementation support of such a wide-range of orthogonal concerns often results in restatement of a significant portion of the UI description using platform-specific components. Replication requires repeated implementation decision, greatly increasing development costs since each version/context variant may need separate development. Naturally, such replication also produces error prone maintenance because code updates must correlate among all replicas. Using separation of concerns, the application can be decomposed into fine-grain fragments, which we call particles, some of which are platform independent and others are not. Using this decomposition, this paper addresses the above inefficiency by dynamically composing particles at runtime that match user demands, context, and target platform.


title: Head Pose Estimation and Augmented Reality Tracking: An Integrated System and Evaluation for Monitoring Driver Awareness

abstract: Driver distraction and inattention are prominent causes of automotive collisions. To enable driver-assistance systems to address these problems, we require new sensing approaches to infer a driver's focus of attention. In this paper, we present a new procedure for static head-pose estimation and a new algorithm for visual 3-D tracking. They are integrated into the novel real-time (30 fps) system for measuring the position and orientation of a driver's head. This system consists of three interconnected modules that detect the driver's head, provide initial estimates of the head's pose, and continuously track its position and orientation in six degrees of freedom. The head-detection module consists of an array of Haar-wavelet Adaboost cascades. The initial pose estimation module employs localized gradient orientation (LGO) histograms as input to support vector regressors (SVRs). The tracking module provides a fine estimate of the 3-D motion of the head using a new appearance-based particle filter for 3-D model tracking in an augmented reality environment. We describe our implementation that utilizes OpenGL-optimized graphics hardware to efficiently compute particle samples in real time. To demonstrate the suitability of this system for real driving situations, we provide a comprehensive evaluation with drivers of varying ages, race, and sex spanning daytime and nighttime conditions. To quantitatively measure the accuracy of system, we compare its estimation results to a marker-based cinematic motion-capture system installed in the automotive testbed.


title: Relational Frame Theory: A Post-Skinnerian Account of Human Language and Cognition

abstract: Part I: The Basic Account. 1. Language and Cognition: Constructing an Alternative Approach Within the Behavioral Tradition S.C. Hayes, et al. 2. Derived Relational Responding as Learned Behavior S.C. Hayes, et al. 3. Multiple Stimulus Relations and the Transformation of Stimulus Functions D. Barnes-Holmes, et al. 4. Relations Among Relations: Analogies, Metaphors, and Stories I. Stewart, et al. 5. Thinking, Problem-Solving, and Pragmatic Verbal Analysis S.C. Hayes, et al. 6. Understanding and Verbal Regulation D. Barnes-Holmes, et al. 7. Self and Self-Directed Rules D. Barnes-Holmes, et al. 8. Relational Frame Theory: A Precis S.C. Hayes, et al. Part II: Extensions and Applications. 9. Psychological Development Y. Barnes-Holmes, et al. 10. Education Y. Barnes-Holmes, et al. 11. Social Processes B. Roche, et al. 12. Psychopathology and Psychotherapy K.G. Wilson, et al. 13. Religion, Spirituality, and Transcendence D. Barnes-Holmes, et al. Epilogue. References. Index.


title: An accurate and efficient solar tracking system using image processing and LDR sensor

abstract: The use of renewable energy resources is increasing for the purpose of producing electricity in modern world due to lack of non-renewable sources. As solar panels are becoming more popular day by day, it is a crying need to maximize the solar panel efficiency. The efficiency of solar panels can be increased to a great extent if the solar panels continuously rotate in the direction of sun. Microcontroller and an arrangement of LDR sensors can be used for the purpose of tracking the sun. But the system is less efficient because of the low sensitivity and disturbance of light dependent resistors. In this paper, a methodology of an automatic solar tracker is proposed by means of both sensors and image processing simultaneously. The mechanism of solar tracking was implemented by the use of an image processing software which combines the effect of sensors and processed image of sun and controls the solar panel accordingly. The methodology is a combination of hardware and software which can be used to control hundreds of solar panels in a solar power plant with more accuracy. It can be concluded that the proposed system is more accurate and efficient than the conventional solar tracking systems which can optimize the power requirement.


title: Large-scale Analysis of Spoken Free-verse Poetry

abstract: Most modern and post-modern poems have developed a post-metrical idea of lyrical prosody that employs rhythmical features of everyday language and prose instead of a strict adherence to rhyme and metrical schemes. This development is subsumed under the term free verse prosody. We present our methodology for the large-scale analysis of modern and post-modern poetry in both their written form and as spoken aloud by the author. We employ language processing tools to align text and speech, to generate a null-model of how the poem would be spoken by a naive reader, and to extract contrastive prosodic features used by the poet. On these, we intend to build our model of free verse prosody, which will help to understand, differentiate and relate the different styles of free verse poetry. We plan to use our processing scheme on large amounts of data to iteratively build models of styles, to validate and guide manual style annotation, to identify further rhythmical categories, and ultimately to broaden our understanding of free verse poetry. In this paper, we report on a proof-of-concept of our methodology using smaller amounts of poems and a limited set of features. We find that our methodology helps to extract differentiating features in the authors’ speech that can be explained by philological insight. Thus, our automatic method helps to guide the literary analysis and this in turn helps to improve our computational models.


title: Extended Transconjunctival Lower Eyelid Blepharoplasty with Release of the Tear Trough Ligament and Fat Redistribution

abstract: BACKGROUND ::: The transconjunctival lower eyelid blepharoplasty is advantageous for its quick recovery and low complication rates. Conventional techniques rely on fat removal to contour the lower eyelid. This article describes the authors' extended transconjunctival lower eyelid blepharoplasty technique that takes dissection beyond the orbital rim to address aging changes on the midcheek. ::: ::: ::: METHODS ::: From December of 2012 to December of 2015, 54 patients underwent this procedure. Through a transconjunctival incision, the preseptal space was entered and excess orbital fat pads were excised. Medially, the origins of the palpebral part of the orbicularis oculi, the tear trough ligament, and orbital part of the orbicularis oculi were sequentially released, connecting the dissection with the premaxillary space. More laterally, the orbicularis retaining ligament was released, connecting the dissection with the prezygomatic space. Excised orbital fat was then grafted under the released tear trough ligament to correct the tear trough deformity. When the patients had significant maxillary retrusion, structural fat grafting was performed at the same time. ::: ::: ::: RESULTS ::: The mean follow-up was 10 months. High satisfaction was noted among the patients treated with this technique. The revision rate was 2 percent. Complication rates were low. No chemosis, prolonged swelling, lower eyelid retraction, or ectropion was seen in any patients. ::: ::: ::: CONCLUSION ::: The extended transconjunctival lower blepharoplasty using the midcheek soft-tissue spaces is a safe and effective approach for treating patients presenting with eye bags and tear trough deformity. ::: ::: ::: CLINICAL QUESTION/LEVEL OF EVIDENCE ::: Therapeutic, V.


title: Traffic Sign Detection and Recognition using Features Combination and Random Forests

abstract: In this paper, we present a computer vision based system for fast robust Traffic Sign Detection and Recognition (TSDR), consisting of three steps. The first step consists on image enhancement and thresholding using the three components of the Hue Saturation and Value (HSV) space. Then we refer to distance to border feature and Random Forests classifier to detect circular, triangular and rectangular shapes on the segmented images. The last step consists on identifying the information included in the detected traffic signs. We compare four features descriptors which include Histogram of Oriented Gradients (HOG), Gabor, Local Binary Pattern (LBP), and Local Self-Similarity (LSS). We also compare their different combinations. For the classifiers we have carried out a comparison between Random Forests and Support Vector Machines (SVMs). The best results are given by the combination HOG with LSS together with the Random Forest classifier. The proposed method has been tested on the Swedish Traffic Signs Data set and gives satisfactory results.


title: CCD and CMOS Imaging Array Technologies - Technology Review

abstract: This paper provides an overview of both CCD (charged coupled device) and CMOS (complimentary metal oxide semiconductor) imaging array technologies. CCDs have been in existence for nearly 30 years and the technology has matured to the point where very large, consistent (low numbers of defects) devices can now be produced. However, CCDs suffer from a number of drawbacks, including cost, complex power supplies and support electronics. CMOS imaging arrays, on the other hand, are still in their infancy, but are set to develop rapidly and offer a number of potential benefits over CCDs. This review provides an overview of both CCD and CMOS imaging technology, and includes explanations of how images are captured and read out from the imaging arrays. Also covered are issues such as performance characteristics, cost considerations and the future of imaging arrays. This review does not provide details of colour sensors, colour filter arrays and colour interpolation, etc., as these will be the subject of a separate report. Introduction to CCDs The Charged Coupled Device (CCD) was invented in 1970 by Willard Boyle and George Smith at Bell Laboratories, USA [Sharma97]. The idea originated from research into magnetic bubble memories, and as with many great inventions, Smith is quoted as saying “[we] invented charged-coupled devices in an hour” [Lucent96]. In the intervening twentyeight years, CCDs have found their way into a huge range of products including fax machines, photocopiers, cameras, scanners and even children’s toys. CCDs consist of thousands (or millions) of light sensitive cells or pixels that are capable of producing an electrical charge proportional to the amount of light they receive. Typically, the pixels are arranged in either a single line (linear array CCDs) or in a two-dimensional grid (area array CCDs). The particular application will, in general, dictate the type of CCD that is used. Flatbed scanners, for example, use linear array CCDs and, in this case, it is necessary to progressively move the CCD over the object being imaged (or vice versa) while capturing multiple one-dimensional images in order to build up the final two-dimensional image. Digital cameras, on the other hand, normally use area array CCDs, thus allowing the full twodimensional image to be captured within a single exposure. One of the fundamental parameters of a CCD is resolution, which is equal to the total number of pixels that makes up the light sensitive area of the device. One of the first area array CCDs, manufactured by Fairchild in 1974, had a resolution of 100x100 [Oregon97]. Today, the largest commercially available device is approximately 9000x7000 or roughly 63 million pixels [Pixelv97]. Other parameters that characterise CCDs will be discussed in more detail later.


title: Network security analysis SCADA system automation on industrial process

abstract: Supervisory Control and Data Acquisition (SCADA) is a unit control system that has been used almost in various industries around the world in terms of process automation. This system delineates the real infrastructure and provides ease in operations and monitoring, but this system has vulnerability in the security aspects of data communications connected between SCADA support devices. This can have a major impact on industry and the economy. This research was conducted by designing and building SCADA infrastructure and analyzing vulnerability threats to SCADA network security. This research penetrates SCADA network using Kali Linux and data traffic analysis on SCADA network using Wireshark. From the results of Wireshark analysis got the attacker with User Anonymous. Analysis performed with normal and abnormal data traffic conditions. The result of this research is penetration of SCADA network using Kali Linux, which is used to attack and make data traffic between Programable Logic Controller (PLC) with Human Machine Interface (HMI) becomes solid, and result from penetration testing, SCADA system become down due of data traffic on a dense network, thereby indicating that SCADA networks are vulnerable to malware threats and attacks, the results of this study are recommendations and network security strategy SCADA system.


title: Efficient Subgraph Matching on Billion Node Graphs

abstract: The ability to handle large scale graph data is crucial to an increasing number of applications. Much work has been dedicated to supporting basic graph operations such as subgraph matching, reachability, regular expression matching, etc. In many cases, graph indices are employed to speed up query processing. Typically, most indices require either super-linear indexing time or super-linear indexing space. Unfortunately, for very large graphs, super-linear approaches are almost always infeasible. In this paper, we study the problem of subgraph matching on billion-node graphs. We present a novel algorithm that supports efficient subgraph matching for graphs deployed on a distributed memory store. Instead of relying on super-linear indices, we use efficient graph exploration and massive parallel computing for query processing. Our experimental results demonstrate the feasibility of performing subgraph matching on web-scale graph data.


title: A Seven-Switch Five-Level Active-Neutral-Point-Clamped Converter and Its Optimal Modulation Strategy

abstract: Multilevel inverters are receiving more attentions nowadays as one of preferred solutions for medium- and high-power applications. As one of the most popular hybrid multilevel inverter topologies, the five-level active-neutral-point-clamped inverter (5L-ANPC) combines the features of the conventional flying-capacitor type and neutral-point-clamped (NPC) type inverter and was commercially used for industrial applications. In order to further decrease the number of active switches, this paper proposes a seven-switch 5L-ANPC (7S-5L-ANPC) topology, which employs only seven active switches and two discrete diodes. The analysis has shown a lower current rating can be selected for the seventh switch under high power factor condition, which is verified by simulation results. The modulation strategy for 7S-5L-ANPC inverter is discussed. A 1 kVA single-phase experimental prototype is built to verify the validity and flexibility of the proposed topology and modulation method.


title: A Reliable Open-Source System Architecture for the Fast Designing and Prototyping of Autonomous Multi-UAV Systems: Simulation and Experimentation

abstract: During the process of design and development of an autonomous Multi-UAV System, two main problems appear. The first one is the difficulty of designing all the modules and behaviors of the aerial multi-robot system. The second one is the difficulty of having an autonomous prototype of the system for the developers that allows to test the performance of each module even in an early stage of the project. These two problems motivate this paper. A multipurpose system architecture for autonomous multi-UAV platforms is presented. This versatile system architecture can be used by the system designers as a template when developing their own systems. The proposed system architecture is general enough to be used in a wide range of applications, as demonstrated in the paper. This system architecture aims to be a reference for all designers. Additionally, to allow for the fast prototyping of autonomous multi-aerial systems, an Open Source framework based on the previously defined system architecture is introduced. It allows developers to have a flight proven multi-aerial system ready to use, so that they can test their algorithms even in an early stage of the project. The implementation of this framework, introduced in the paper with the name of “CVG Quadrotor Swarm”, which has also the advantages of being modular and compatible with different aerial platforms, can be found at https://github.com/Vision4UAV/cvg_quadrotor_swarm with a consistent catalog of available modules. The good performance of this framework is demonstrated in the paper by choosing a basic instance of it and carrying out simulation and experimental tests whose results are summarized and discussed in this paper.