title: TwitterNews: Real time event detection from the Twitter data stream

abstract: Research in event detection from the Twitter stream- ing data has been gaining momentum in the last couple of years. Although such data is noisy and often contains mislead- ing information, Twitter can be a rich source of information if harnessed properly. In this paper, we propose a scalable event detection system, TwitterNews, to detect and track newsworthy events in real time from Twitter. TwitterNews provides a novel approach, by combining random indexing based term vector model with locality sensitive hashing, that aids in performing incremental clustering of tweets related to various events within a fixed time. TwitterNews also incorporates an effective strategy to deal with the cluster fragmentation issue prevalent in in- cremental clustering. The set of candidate events generated by TwitterNews are then filtered, to report the newsworthy events along with an automatically selected representative tweet from each event cluster. Finally, we evaluate the effectiveness of TwitterNews, in terms of the recall and the precision, using a publicly available corpus.


Participation Good News for the Future ? Young People , Internet Use , and Political


title: Open and closed innovation – different innovation cultures for different strategies

abstract: Although strategies, processes, or the role of business models have been addressed in the open innovation literature, the people side of the equation – i.e., the underlying innovation culture – has been neglected so far. Whereas cultural requirements of open innovation have been mentioned, such as the need to overcome the not-invented-here (NIH) syndrome, there exists, to the best of our knowledge, no study that empirically examines open innovation cultures. We attempt to fill this research gap by focusing on innovation cultures within three business units (two follow a closed and one follows an open innovation approach) of a leading multinational company within the specialty chemicals industry. Employing an overall sample of 109 respondents, we focus on the cultural dimensions of NIH syndrome, risk-taking, and management support of innovative behaviour and provide first evidence of cultural differences between Open and Closed Innovation units.


title: Reconfigurable RFICs in Si-based technologies for a compact intelligent RF front-end

abstract: This paper presents reconfigurable RF integrated circuits (ICs) for a compact implementation of an intelligent RF front-end for multiband and multistandard applications. Reconfigurability has been addressed at each level starting from the basic elements to the RF blocks and the overall front-end architecture. An active resistor tunable from 400 to 1600 /spl Omega/ up to 10 GHz has been designed and an equivalent model has been extracted. A fully tunable active inductor using a tunable feedback resistor has been proposed that provides inductances between 0.1-15 nH with Q>50 in the C-band. To demonstrate reconfigurability at the block level, voltage-controlled oscillators with very wide tuning ranges have been implemented in the C-band using the proposed active inductor, as well as using a switched-spiral resonator with capacitive tuning. The ICs have been implemented using 0.18-/spl mu/m Si-CMOS and 0.18-/spl mu/m SiGe-BiCMOS technologies.


title: Norm of Reciprocity, Reciprocal Benefits, and Reciprocal Relationships: A Revisit of the Role of Reciprocity in Knowledge Sharing

abstract: Reciprocity has been considered as one of the most important constructs in knowledge sharing literature. However, prior studies have defined and measured this construct in different ways, leading to mixed research findings about its role. To solve the controversy, based on prior reciprocity literature, we differentiate three relevant concepts namely norm of reciprocity, reciprocal benefits, and reciprocal relationships and propose the causal relationships between these constructs according to the norm internalization theory. A field survey with 386 employees in a Chinese organization is conducted to test the proposed hypotheses. The results show that reciprocal benefits and reciprocal relationships fully mediate the impacts of norm of reciprocity on knowledge sharing intention. These findings suggest that the internalization mechanism (e.g., indirect effect) works better than the compliance mechanism (e.g., direct effect) under the voluntary knowledge sharing context. This study enriches the knowledge sharing literature and provides suggestions on organizational knowledge sharing practices.


title: A Natural Language Query Interface to Structured Information

abstract: Accessing structured data such as that encoded in ontologies and knowledge bases can be done using either syntactically complex formal query languages like SPARQL or complicated form interfaces that require expensive customisation to each particular application domain. This paper presents the QuestIO system - a natural language interface for accessing structured information, that is domain independent and easy to use without training. It aims to bring the simplicity of Google's search interface to conceptual retrieval by automatically converting short conceptual queries into formal ones, which can then be executed against any semantic repository. ::: ::: QuestIO was developed specifically to be robustwith regard to language ambiguities, incomplete or syntactically ill-formed queries, by harnessing the structure of ontologies, fuzzy stringmatching, and ontology-motivated similarity metrics.


title: Combating the Bandits in the Cloud: A Moving Target Defense Approach

abstract: Security and privacy in cloud computing are critical components for various organizations that depend on the cloud in their daily operations. Customers' data and the organizations' proprietary information have been subject to various attacks in the past. In this paper, we develop a set of Moving Target Defense (MTD) strategies that randomize the location of the Virtual Machines (VMs) to harden the cloud against a class of Multi-Armed Bandit (MAB) policy-based attacks. These attack policies capture the behavior of adversaries that seek to explore the allocation of VMs in the cloud and exploit the ones that provide the highest rewards (e.g., access to critical datasets, ability to observe credit card transactions, etc). We assess through simulation experiments the performance of our MTD strategies, showing that they can make MAB policy-based attacks no more effective than random attack policies. Additionally, we show the effects of critical parameters -- such as discount factors, the time between randomizing the locations of the VMs and variance in the rewards obtained -- on the performance of our defenses. We validate our results through simulations and a real OpenStack system implementation in our lab to assess migration times and down times under different system loads.


title: The design of ISM-band radar antenna for small boat's trajectory tracking

abstract: This paper discusses about the design of a specific radar antenna at ISM frequency band. The overall radar system will be utilized as a trajectory generator for a developed autonomous chaser boat prototype in which the radar antenna was designed to meet this requirement. Fresnel Zone free-space calculation revealed that the radar antenna should be placed at the lake side with a minimum height of 2.5 m. By considering the possible size and distance of the tracked boat, the required radar antenna vertical beamwidth for sufficient elevation coverage is at least 26°. Further design process in relation to desired azimuth tracking resolution revealed that the required horizontal beamwidth is less than 15°. A 4×8 microstrip array antenna was designed by simulations to meet these requirements. The simulation results revealed that the 3dB horizontal beamwidth was 12.2° and the 3dB vertical beamwidth was 160°. Two 4×8 microstrip array antennas were fabricated by utilizing the simulated design, one as transmitting antenna and the other as receiving antenna. Then, the overall performances were justified by experimental antenna measurements. The simulation and measurement results showed that the designed radar antenna can operate at ISM band with sufficient 3dB horizontal beamwidth of less than 15°.


Image Quality Assessment for Fake Biometric Detection : Application to Iris , Fingerprint , and Face Recognition


title: The Biometric Passport: The Technical Requirements and Possibilities of Using

abstract: This paper discusses the requirements, national regulations, and application requirements to introduction of a new biometric passport in the European Union (EU), especially in the Republic of Poland. The biometric passport is sometimes called e-passport. The biometric systems depend highly on the existing hardware biometrical technologies and its software components. Some important national solutions will be also described in this article.


title: Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media

abstract: Social media is often viewed as a sensor into various societal events such as disease outbreaks, protests, and elections. We describe the use of social media as a crowdsourced sensor to gain insight into ongoing cyber-attacks. Our approach detects a broad range of cyber-attacks (e.g., distributed denial of service (DDoS) attacks, data breaches, and account hijacking) in a weakly supervised manner using just a small set of seed event triggers and requires no training or labeled samples. A new query expansion strategy based on convolution kernels and dependency parses helps model semantic structure and aids in identifying key event characteristics. Through a large-scale analysis over Twitter, we demonstrate that our approach consistently identifies and encodes events, outperforming existing methods.


title: ClioPatria: A SWI-Prolog infrastructure for the Semantic Web

abstract: ClioPatria is a comprehensive semantic web development framework based on SWI-Prolog. SWI-Prolog provides an efficient C-based main-memory RDF store that is designed to cooperate naturally and efficiently with Prolog, realizing a flexible RDF-based environment for rule based programming. ClioPatria extends this core with a SPARQL and LOD server, an extensible web frontend to manage the server, browse the data, query the data using SPARQL and Prolog and a Git-based plugin manager. The ability to query RDF using Prolog provides query composition and smooth integration with application logic. ClioPatria is primarily positioned as a prototyping platform for exploring novel ways of reasoning with RDF data. It has been used in several research projects in order to perform tasks such as data integration and enrichment and semantic search.


title: A robot navigation method based on RFID and QR code in the warehouse

abstract: With the rapid development of e-commerce, warehousing and logistics industry has entered a period of rapid development. New logistics demand is not different from traditional commodity transport, with the features of small batches, variety, high speed and low cost. Flexible warehousing technology based on small mobile handing robots is rising rapidly. The article proposes a flexible warehouse space model based on RFID and QR code on the basis of analyzing e-commerce logistics. In the warehouse space model, the article analyses logistics processes, considering time and traffic conflict, and uses the A∗ algorithm for robot path planning. The article verifies the feasibility of navigation based RFID and QR code through simulation.


title: Dynamic Searchable Encryption in Very-Large Databases: Data Structures and Implementation

abstract: We design and implement dynamic symmetric searchable encryption schemes that efficiently and privately search server-held encrypted databases with tens of billions of record-keyword pairs. Our basic theoretical construction supports single-keyword searches and offers asymptotically optimal server index size, fully parallel searching, and minimal leakage. Our implementation effort brought to the fore several factors ignored by earlier coarse-grained theoretical performance analyses, including lowlevel space utilization, I/O parallelism and goodput. We accordingly introduce several optimizations to our theoretically optimal construction that model the prototype’s characteristics designed to overcome these factors. All of our schemes and optimizations are proven secure and the information leaked to the untrusted server is precisely quantified. We evaluate the performance of our prototype using two very large datasets: a synthesized census database with 100 million records and hundreds of keywords per record and a multi-million webpage collection that includes Wikipedia as a subset. Moreover, we report on an implementation that uses the dynamic SSE schemes developed here as the basis for supporting recent SSE advances, including complex search queries (e.g., Boolean queries) and richer operational settings (e.g., query delegation), in the above terabyte-scale databases.


title: Automatic Quantification of Spinal Curvature in Scoliotic Radiograph using Image Processing

abstract: Choosing the most suitable treatment for the scoliosis relies heavily on accurate and reproducible spinal curvature measurement from radiographs. Our objective is to reduce the variability in spinal curvature measurement by reducing the user intervention and bias. In order to determine the reliability of the spinal curvature measurement as it is in the clinical measurement of scoliosis a methodological survey has been carried out that concludes with inter and intra observer error variation. The proposed method list out horizontal inclination of all the vertebrae's in terms of slopes using active contour models and morphological operators. This facilitates the radiologist to decide end vertebrae and hence inter/intra observer variation is completely eliminated. Tables 1 and 2 shows the observer error variation between manual and proposed methods in terms of mean and standard deviation.


title: Robust Keyframe-based Dense SLAM with an RGB-D Camera

abstract: In this paper, we present RKD-SLAM, a robust keyframe-based dense SLAM approach for an RGB-D camera that can robustly handle fast motion and dense loop closure, and run without time limitation in a moderate size scene. It not only can be used to scan high-quality 3D models, but also can satisfy the demand of VR and AR applications. First, we combine color and depth information to construct a very fast keyframe-based tracking method on a CPU, which can work robustly in challenging cases (e.g.~fast camera motion and complex loops). For reducing accumulation error, we also introduce a very efficient incremental bundle adjustment (BA) algorithm, which can greatly save unnecessary computation and perform local and global BA in a unified optimization framework. An efficient keyframe-based depth representation and fusion method is proposed to generate and timely update the dense 3D surface with online correction according to the refined camera poses of keyframes through BA. The experimental results and comparisons on a variety of challenging datasets and TUM RGB-D benchmark demonstrate the effectiveness of the proposed system.


title: Dynamic Manga: Animating Still Manga via Camera Movement

abstract: We propose a method for animating still manga imagery through camera movements. Given a series of existing manga pages, we start by automatically extracting panels, comic characters, and balloons from the manga pages. Then, we use a data-driven graphical model to infer per-panel motion and emotion states from low-level visual patterns. Finally, by combining domain knowledge of film production and characteristics of manga, we simulate camera movements over the manga pages, yielding an animation. The results augment the still manga contents with animated motion that reveals the mood and tension of the story, while maintaining the original narrative. We have tested our method on manga series of different genres, and demonstrated that our method can generate animations that are more effective in storytelling and pacing, with less human efforts, as compared with prior works. We also show two applications of our method, mobile comic reading, and comic trailer generation.


Leading change with emotional intelligence.


title: Performance comparison of ad hoc wireless network routing protocols

abstract: In recent years many protocols for ad hoc wireless networks have been developed but very little information is available on the performance of these protocols. After describing the desirable characteristics of an ad hoc network routing protocol this paper provides an overview of four existing ad hoc wireless routing protocols, which are distance sequence distance vector (DSDV), ad hoc on demand distance vector (AODV), dynamic source routing (DSR) and temporally ordered routing algorithm (TORA). The performance comparison of these four protocols is based on simulations performed using network simulator-2. The performance parameters analyzed are the mobility rate, network load and network size. The paper describes all the parameters used for the simulations in detail and then compares each routing protocol's simulation results before arriving at a conclusion as to which is the best one for ad hoc networks.


title: Femtocaching and device-to-device collaboration: A new architecture for wireless video distribution

abstract: We present a new architecture to handle the ongoing explosive increase in the demand for video content in wireless networks. It is based on distributed caching of the content in femtobasestations with small or non-existing backhaul capacity but with considerable storage space, called helper nodes. We also consider using the wireless terminals themselves as caching helpers, which can distribute video through device-todevice communications. This approach allows an improvement in the video throughput without deployment of any additional infrastructure. The new architecture can improve video throughput by one to two orders-of-magnitude.


title: lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits

abstract: The paper proposes a novel upper confidence bound (UCB) procedure for identifying the arm with the largest mean in a multi-armed bandit game in the fixed confidence setting using a small number of total samples. The procedure cannot be improved in the sense that the number of samples required to identify the best arm is within a constant factor of a lower bound based on the law of the iterated logarithm (LIL). Inspired by the LIL, we construct our confidence bounds to explicitly account for the infinite time horizon of the algorithm. In addition, by using a novel stopping time for the algorithm we avoid a union bound over the arms that has been observed in other UCB-type algorithms. We prove that the algorithm is optimal up to constants and also show through simulations that it provides superior performance with respect to the state-of-the-art.


title: Topography and location of the depressor anguli oris muscle with a reference to the mental foramen

abstract: The labiomandibular fold (LMF) is the area of the face that extends from the mouth corner to the mandibular border, and its prominence tends to increase with age. The LMF can be formed by the medial or lateral border of the depressor anguli oris (DAO). The aim of this study was to demonstrate the topographical anatomy between the DAO and mental foramen, thereby providing critical information for the safest and most effective site at which to inject botulinum toxin type A (BTX-A). Thirty-four hemifaces from Korean adult cadavers were dissected. The maximum width between the medial borders of the bilateral DAO, parallel to the intercheilion horizontal line, was 59.9 ± 4.6 (mean ± SD) mm below the lower lip. The minimum width between the medial borders of the attachment of bilateral DAO was 29.7 ± 4.8 mm at the mandibular border. The mental foramen was located in the middle third from the cheilion to the mandibular border in 28 cases (90.3%), and it was mostly confined within the DAO muscle coverage in 21 cases (67.7%). The buccal branch of the facial nerve entered through the middle third of the lateral border of DAO and then distributed. Concomitantly, the marginal mandibular branch of the facial nerve entered through the lower third of the lateral border of DAO in 17 cases (60.7%). These results represent additional reference data for identifying the position of the mental foramen on the facial skin, and will be useful for providing criteria for the most effective site for injecting BTX-A when treating the LMF.


Sensorimotor Psychotherapy Group Therapy in the Treatment of Complex PTSD


title: RIC: Relaxed Inclusion Caches for Mitigating LLC Side-Channel Attacks

abstract: Recently, side-channel attacks on Last Level Caches (LLCs) were demonstrated. The attacks require the ability to evict critical data from the cache hierarchy, making future accesses visible. We propose Relaxed Inclusion Caches (RIC), a low-complexity cache design protecting against LLC side channel attacks. RIC relaxes inclusion when it is not needed, preventing the attacker from replacing the victim's data from the local core caches thus protecting critical data from leakage. RIC improves performance (by about 10%) and retains snoop filtering capabilities of inclusive cache hierarchies, while requiring only minimal changes to the cache.


title: Label Refinery: Improving ImageNet Classification through Label Progression

abstract: Among the three main components (data, labels, and models) of any supervised learning system, data and models have been the main subjects of active research. However, studying labels and their properties has received very little attention. Current principles and paradigms of labeling impose several challenges to machine learning algorithms. Labels are often incomplete, ambiguous, and redundant. In this paper we study the effects of various properties of labels and introduce the Label Refinery: an iterative procedure that updates the ground truth labels after examining the entire dataset. We show significant gain using refined labels across a wide range of models. Using a Label Refinery improves the state-of-the-art top-1 accuracy of (1) AlexNet from 59.3 to 67.2, (2) MobileNet from 70.6 to 73.39, (3) MobileNet-0.25 from 50.6 to 55.59, (4) VGG19 from 72.7 to 75.46, and (5) Darknet19 from 72.9 to 74.47.


title: Improvement of SIW horn antenna with metallic via drilled transition

abstract: In this paper, a wideband substrate integrated waveguide (SIW) horn antenna with enhanced performance is investigated. Through adding metallic via on the edge of the parallel plates, additional inductance is introduced and can compensate the coupling capacitance in some degree, which significantly improves the impedance matching of the planar horn antenna. Moreover, the introduced via act as the extended narrow walls of SIW, which can enhance the Front-to-Back Ratio (FTBR) at the same time. Results indicate the proposed horn antenna works from 16.42 to 20.89 GHz with stable end-fire radiation patterns in the whole operating band.


title: Optical properties of human skin, subcutaneous and mucous tissues in the wavelength range from 400 to 2000 nm

abstract: The optical properties of human skin, subcutaneous adipose tissue and human mucosa were measured in the wavelength range 400–2000 nm. The measurements were carried out using a commercially available spectrophotometer with an integrating sphere. The inverse adding–doubling method was used to determine the absorption and reduced scattering coefficients from the measurements.


title: A Review on Ocular Biomechanic Models for Assessing Visual Fatigue in Virtual Reality

abstract: With the wide spread of affordable virtual reality headsets, virtual environments are rapidly changing the way humans interact with reality. Understanding the effects of virtual environments on the mental and cognitive state is essential. In addition, defining methods for measuring and assessing visual fatigue in virtual environments is still needed. While eye movements are tightly coupled to the mental state, analysis of eye movement can add insights for safer virtual environments. Biomechanical analysis has been used extensively in the analysis of human movement. Simulation of different scenarios such as injuries and surgeries provided insights and solutions to problems that were otherwise impossible. This includes understanding the effects of changing insertion points of muscle on range of motion or how muscle activation can affect the motion produced. Extending the use of biomechanical simulation analysis into eye movement can be used to deepen our understanding of how virtual environments affect our visual and mental capabilities. This paper presents a thorough review on ocular biomechanics and ocular models in literature. We start with a brief introduction on the anatomy of the eye and eye kinematics. In addition, properties of the extraocular muscles (EOM) are described and the difference between EOMs and skeletal muscle is highlighted. The challenges facing biomechanical simulation and analysis of eye movement are presented along with the role of ocular models in assessing visual fatigue. Furthermore, the compatibility of available biomechanical tools to analyze ocular movements is discussed.


title: Electricity Theft Detection and Localization in Grid-Tied Microgrids

abstract: While operating in three different modes with bi-directional power flow, electricity theft detection, and localization in a grid-tied microgrid (MG) become challenging tasks, particularly when the occurrence rate of the theft and its frequency patterns are random. To address this problem, a stochastic Petri net formalism is used in this paper to detect and localize the occurrence of theft in grid-tied MGs. The disturbance at any instance of time in the form of resistance above a threshold in the accumulated data of a smart meter, irrespective of the mode of operation, triggers a transition assigned to the arc, which alerts the transmission module. The affected transition and suspected user information are forwarded to the meter data management system (MDMS) for localization of the theft. A smart meter forwards its readings to the MDMS only when a theft is detected; otherwise readings are sent outside an MG once or twice a day depending on utility regulations. This low sampling rate significantly improves the overall privacy of customers. For line resistance measurement in the MG, singular value decomposition is used. In experiments, it calculates the technical and non-technical losses with 100% accuracy without knowing the exact topology of the power distribution network.


title: Stochastic Block Model Reveals the Map of Citation Patterns and Their Evolution in Time

abstract: In this study we map out the large-scale structure of citation networks of science journals and follow their evolution in time by using stochastic block models (SBMs). The SBM fitting procedures are principled methods that can be used to find hierarchical grouping of journals into blocks that show similar incoming and outgoing citations patterns. These methods work directly on the citation network without the need to construct auxiliary networks based on similarity of nodes. We fit the SBMs to the networks of journals we have constructed from the data set of around 630 million citations and find a variety of different types of blocks, such as clusters, bridges, sources, and sinks. In addition we use a recent generalization of SBMs to determine how much a manually curated classification of journals into subfields of science is related to the block structure of the journal network and how this relationship changes in time. The SBM method tries to find a network of blocks that is the best high-level representation of the network of journals, and we illustrate how these block networks (at various levels of resolution) can be used as maps of science.


Progressive Cognitive Human Parsing


title: Kinematic and electromyographic analysis of the Nordic Hamstring Exercise.

abstract: Abstract The Nordic Hamstring Exercise (NHE) has been introduced as a training tool to improve the efficiency of eccentric hamstring muscle contraction. The aim of this study was to perform a biomechanical analysis of the NHE. Eighteen participants (20.4±1.9years) performed two sets of five repetitions each of the NHE and maximal eccentric voluntary contraction (MEVC) of the knee flexors on an isokinetic dynamometer whilst knee angular displacement and electrical activity (EMG) of biceps femoris were measured. EMG was on average higher during the NHE (134.3% of the MEVC). During the forward fall of the NHE, the angle at which a sharp increase in downward velocity occurred varied between 47.9 and 80.5deg, while the peak knee angular velocity (pVelocity) varied between 47.7 and 132.8degs −1 . A significant negative correlation was found between pVelocity and peak EMG ( r =−0.62, p r =−0.75, p


title: DenseCap: Fully Convolutional Localization Networks for Dense Captioning

abstract: We introduce the dense captioning task, which requires a computer vision system to both localize and describe salient regions in images in natural language. The dense captioning task generalizes object detection when the descriptions consist of a single word, and Image Captioning when one predicted region covers the full image. To address the localization and description task jointly we propose a Fully Convolutional Localization Network (FCLN) architecture that processes an image with a single, efficient forward pass, requires no external regions proposals, and can be trained end-to-end with a single round of optimization. The architecture is composed of a Convolutional Network, a novel dense localization layer, and Recurrent Neural Network language model that generates the label sequences. We evaluate our network on the Visual Genome dataset, which comprises 94,000 images and 4,100,000 region-grounded captions. We observe both speed and accuracy improvements over baselines based on current state of the art approaches in both generation and retrieval settings.


Checking More and Alerting Less: Detecting Privacy Leakages via Enhanced Data-flow Analysis and Peer Voting.


title: Fault segmentation in fabric images using Gabor wavelet transform

abstract: Gabor wavelets have been successfully applied for a variety of machine vision applications such as Texture segmentation, Edge detection, Boundary detection etc. As the Fourier transform is not suitable for detecting local defects, and the Wavelet transforms posses only limited number of orientations, Gabor wavelet transform is chosen and applied to detect the defects in fabrics. Gabor filters scheme that imitates the early human vision process is applied to the sample under inspection. Defects can be automatically segmented from the regular texture by applying the proposed method. Proper thresholding ensures segmentation of the defect from the texture background. The results obtained using this method confirms its efficiency. This can also be applied to detect defects on surfaces and materials that have regular periodic texture.


title: Do you know where your group is? development of a group-as-a-whole compass. Part I

abstract: A therapist's ability to locate the essential dimensions of a group is a critical skill. The literature on group-as-a-whole theory and practice is a rich body of work that reveals a variety of discrete models and approaches with little coordination between them. This paper is an attempt at theory binding. A group-as-a-whole compass is introduced as a spherical motif to integrate varied conceptual understandings of whole-group functioning. The compass, by providing a metapsychological framework, is designed to orient the therapist and theoretician to the myriad ways that group-centered configurations come to life and can be known.


title: A secure triple level encryption method using cryptography and steganography

abstract: Cryptography is the practice for secure communication in the presence of third parties. Steganography is technique of writing hidden messages such that except the sender and receiver, no one even suspects the existence of the message. In today's hi-tech age, threats from intruders are very great such that usage of either of the above techniques separately may not be able to provide the intended protection. In order to increase the level of protection, both the techniques may be used in a combined manner. Multimedia techniques can also be used to hide the data. In this paper, we propose an encrypting system which combines techniques of cryptography and steganography with data hiding. Instead of using a single level of data encryption, the message is encrypted twice. Traditional techniques have been used for this purpose. Then the cipher is hidden inside the image in encrypted format for further use. It uses a reference matrix for selection of passwords depending on the properties of the image. The image with the hidden data is used for further purposes.


title: Scrum + Engineering Practices: Experiences of Three Microsoft Teams

abstract: The Scrum methodology is an agile software development process that works as a project management wrapper around existing engineering practices to iteratively and incrementally develop software. With Scrum, for a developer to receive credit for his or her work, he or she must demonstrate the new functionality provided by a feature at the end of each short iteration during an iteration review session. Such a short-term focus without the checks and balances of sound engineering practices may lead a team to neglect quality. In this paper we present the experiences of three teams at Microsoft using Scrum with an additional nine sound engineering practices. Our results indicate that these teams were able to improve quality, productivity, and estimation accuracy through the combination of Scrum and nine engineering practices.


Jump-Starting Concept Map Construction with Knowledge Extracted From Documents


title: Action Video Games Make Dyslexic Children Read Better

abstract: Learning to read is extremely difficult for about 10% of children; they are affected by a neurodevelopmental disorder called dyslexia [1, 2]. The neurocognitive causes of dyslexia are still hotly debated [3-12]. Dyslexia remediation is far from being fully achieved [13], and the current treatments demand high levels of resources [1]. Here, we demonstrate that only 12 hr of playing action video games-not involving any direct phonological or orthographic training-drastically improve the reading abilities of children with dyslexia. We tested reading, phonological, and attentional skills in two matched groups of children with dyslexia before and after they played action or nonaction video games for nine sessions of 80 min per day. We found that only playing action video games improved children's reading speed, without any cost in accuracy, more so than 1 year of spontaneous reading development and more than or equal to highly demanding traditional reading treatments. Attentional skills also improved during action video game training. It has been demonstrated that action video games efficiently improve attention abilities [14, 15]; our results showed that this attention improvement can directly translate into better reading abilities, providing a new, fast, fun remediation of dyslexia that has theoretical relevance in unveiling the causal role of attention in reading acquisition.


title: Learning Analytics: A Survey

abstract: Learning analytics is a research topic that is gaining increasing popularity in recent time. It analyzes the learning data available in order to make aware or improvise the process itself and/or the outcome such as student performance. In this survey paper, we look at the recent research work that has been conducted around learning analytics, framework and integrated models, and application of various models and data mining techniques to identify students at risk and to predict student performance. Learning Analytics is a research field related to Educational Data Mining (EDM), and is gaining increasing popularity since Horizon Report 2012 described it as a forthcoming trend. Learning analytics (LA) refers to the application of Big Data methodologies and techniques to improvise the learning. LA is based on analyzing the learning behavior of students using a wide data set that takes into consideration- the student enrollment data, the previous academic record of students, student surveys through questionnaires about courses and teaching methods, data from online discussion forums and such. In predicting and analyzing student performance, there are a no. of techniques that could be used such as classification algorithms - Eg: decision tree methods- C4.5, RepTree and J48, k-nearest neighbor classifier, Naive Bayes, Multi-layer perceptron (neural networks), Sequential Minimal Optimization and clustering methods - Eg: Latent Semantic Analysis and K-means clustering methods. There could be various reasons for a student to fail or drop out from the course such as financial issues, health problems, family issues, time-management, under-preparedness etc. The educational institutions are, however, interested only in the predictor variables that come under the institutional purview or control which is lack of preparation on student front. If the institution is able to identify early, the students who would need help in completing their course, it can put forth some preventive actions such as customizing the course material for the targeted students or provide additional coaching. To this front, learning analytics plays a key role in predicting student performance which would benefit both the students and educational institutions. In this survey paper, we look at the recent research work surrounding the field of learning analytics. Detailed survey follows in the next section.


title: A Contrast Enhancement Framework with JPEG Artifacts Suppression

abstract: Contrast enhancement is used for many algorithms in computer vision. It is applied either explicitly, such as histogram equalization and tone-curve manipulation, or implicitly via methods that deal with degradation from physical phenomena such as haze, fog or underwater imaging. While contrast enhancement boosts the image appearance, it can unintentionally boost unsightly image artifacts, especially artifacts from JPEG compression. Most JPEG implementations optimize the compression in a scene-dependent manner such that low-contrast images exhibit few perceivable artifacts even for relatively high-compression factors. After contrast enhancement, however, these artifacts become significantly visible. Although there are numerous approaches targeting JPEG artifact reduction, these are generic in nature and are applied either as pre- or post-processing steps. When applied as pre-processing, existing methods tend to over smooth the image. When applied as post-processing, these are often ineffective at removing the boosted artifacts. To resolve this problem, we propose a framework that suppresses compression artifacts as an integral part of the contrast enhancement procedure. We show that this approach can produce compelling results superior to those obtained by existing JPEG artifacts removal methods for several types of contrast enhancement problems.


VISUAL DATA MINING AND PIXEL-ORIENTED VISUALIZATION TECHNIQUES


title: Content-Based Image Retrieval in Medical Applications

abstract: OBJECTIVES ::: To develop a general structure for semantic image analysis that is suitable for content-based image retrieval in medical applications and an architecture for its efficient implementation. ::: ::: ::: METHODS ::: Stepwise content analysis of medical images results in six layers of information modeling incorporating medical expert knowledge (raw data layer, registered data layer, feature layer, scheme layer, object layer, knowledge layer). A reference database with 10,000 images categorized according to the image modality, orientation, body region, and biological system is used. By means of prototypes in each category, identification of objects and their geometrical or temporal relationships are handled in the object and the knowledge layer, respectively. A distributed system designed with only three core elements is implemented: (i) the central database holds program sources, processing scheme descriptions, images, features, and administrative information about the workstation cluster; (ii) the scheduler balances distributed computing; and (iii) the web server provides graphical user interfaces for data entry and retrieval, which can be easily adapted to a variety of applications for content-based image retrieval in medicine. ::: ::: ::: RESULTS ::: Leaving-one-out experiments were distributed by the scheduler and controlled via corresponding job lists offering transparency regarding the viewpoints of a distributed system and the user. The proposed architecture is suitable for content-based image retrieval in medical applications. It improves current picture archiving and communication systems that still rely on alphanumerical descriptions, which are insufficient for image retrieval of high recall and precision.


A New Control strategy to Enhance LVRT for Doubly Fed Induction Generator


title: Parallelizing hot topic detection of microblog on spark

abstract: With the emergence of the big data age, how to get valuable hot topic from the vast amount of digitized textual materials quickly and accurately has attracted more and more attention. This paper proposes a parallel Two-phase Micmac Hot Topic Detection(TMHTD) method specially design for microblogging in “Big Data” environment, which is implemented based on Apache Spark cloud computing environment. TMHTD is a distributed clustering framework for documents sets with two phases, including micro-clustering and macro-clustering. In the first phase, TMHTD partitions original data sets into a group of smaller data sets, and these data subsets are clustered into many small topics, producing intermediate results. In the second phase, the intermediate results are integrated into one, further clustered, and achieve the final hot topic sets. To improve the accuracy of the hot topic detection, an optimization of TMHTD is proposed. To handle large databases, we deliberately design a group of MapReduce jobs to concretely accomplish the hot topic detection in a highly scalable way. Extensive experimental results indicate that the accuracy and performance of TMHTD algorithm can be improved significantly over existing approaches.


title: Image-Based Synthesis for Deep 3D Human Pose Estimation

abstract: This paper addresses the problem of 3D human pose estimation in the wild. A significant challenge is the lack of training data, i.e., 2D images of humans annotated with 3D poses. Such data is necessary to train state-of-the-art CNN architectures. Here, we propose a solution to generate a large set of photorealistic synthetic images of humans with 3D pose annotations. We introduce an image-based synthesis engine that artificially augments a dataset of real images with 2D human pose annotations using 3D motion capture data. Given a candidate 3D pose, our algorithm selects for each joint an image whose 2D pose locally matches the projected 3D pose. The selected images are then combined to generate a new synthetic image by stitching local image patches in a kinematically constrained manner. The resulting images are used to train an end-to-end CNN for full-body 3D pose estimation. We cluster the training data into a large number of pose classes and tackle pose estimation as a K-way classification problem. Such an approach is viable only with large training sets such as ours. Our method outperforms most of the published works in terms of 3D pose estimation in controlled environments (Human3.6M) and shows promising results for real-world images (LSP). This demonstrates that CNNs trained on artificial images generalize well to real images. Compared to data generated from more classical rendering engines, our synthetic images do not require any domain adaptation or fine-tuning stage.


title: A Comparison of the Effect of Mobile Phone Use and Alcohol Consumption on Driving Simulation Performance

abstract: OBJECTIVE ::: The present study compared the effects of a variety of mobile phone usage conditions to different levels of alcohol intoxication on simulated driving performance and psychomotor vigilance. ::: ::: ::: METHODS ::: Twelve healthy volunteers participated in a crossover design in which each participant completed a simulated driving task on 2 days, separated by a 1-week washout period. On the mobile phone day, participants performed the simulated driving task under each of 4 conditions: no phone usage, a hands-free naturalistic conversation, a hands-free cognitively demanding conversation, and texting. On the alcohol day, participants performed the simulated driving task at four different blood alcohol concentration (BAC) levels: 0.00, 0.04, 0.07, and 0.10. Driving performance was assessed by variables including time within target speed range, time spent speeding, braking reaction time, speed deviation, and lateral lane position deviation. ::: ::: ::: RESULTS ::: In the BAC 0.07 and 0.10 alcohol conditions, participants spent less time in the target speed range and more time speeding and took longer to brake in the BAC 0.04, 0.07, and 0.10 than in the BAC 0.00 condition. In the mobile phone condition, participants took longer to brake in the natural hands-free conversation, cognitively demanding hands-free conversation and texting conditions and spent less time in the target speed range and more time speeding in the cognitively demanding, hands-free conversation, and texting conditions. When comparing the 2 conditions, the naturalistic conversation was comparable to the legally permissible BAC level (0.04), and the cognitively demanding and texting conversations were similar to the BAC 0.07 to 0.10 results. ::: ::: ::: CONCLUSION ::: The findings of the current laboratory study suggest that very simple conversations on a mobile phone may not represent a significant driving risk (compared to legally permissible BAC levels), whereas cognitively demanding, hands-free conversation, and particularly texting represent significant risks to driving.


title: Cell nuclei extraction from breast cancer histopathologyimages using colour, texture, scale and shape information

abstract: Cell nuclei extraction from Haematoxylin and Eosin (H&E) stained breast cancer slide images is a challenging task due to the high content complexity of images: nuclei have heterogeneous appearance and overlap while the background is complex and non-homogeneous. This causes standard extraction methods to perform poorly.


title: Improved SDN responsiveness to UDP flood attacks

abstract: This poster proposes the creation of actuating triggers to deal with UDP flood attacks. We focus on improving responsiveness to such attacks by adopting the actuating triggers technique proposed by Shin et al [1]. We propose two types of UDP triggers: a traffic percentage trigger, and an amplification rate trigger. These two new triggers are specific to UDP.


title: PicoCTF: A Game-Based Computer Security Competition for High School Students

abstract: The shortage of computer security experts is a critical problem. To encourage greater computer science interest among high school students, we designed and hosted a computer security competition called PicoCTF. Unlike existing competitions, PicoCTF focused primarily on offense and presented challenges in the form of a web-based game. Approximately 2,000 teams participated, with students playing for an average of 12 hours. We present the game-based competition design, an evaluation based on survey responses and website interaction statistics, and insights into the students who played. Further we have released our platform and challenges as an open source project, which has been adapted into the curricula of 40 high schools. Since its release in August of 2013, the PicoCTF platform has been used to host six other capturethe-flag competitions.


title: Ontology-Based Context Modeling

abstract: Ontologies are a widely accepted tool for the modeling of context information. We view the identification of the benefits and challenges of ontologybased models to be an important next step to further improve the usability of ontologies in context-aware applications. We outline a set of criteria with respect to ontology engineering and context modeling and discuss some recent achievements in the area of ontology-based context modeling in order to determine the important next steps necessary to fully exploit ontologies in pervasive computing.


title: Recommender Systems and their Security Concerns

abstract: Instead of simply using two-dimensional User × Item features, advanced recommender systems rely on more additional dimensions (e.g. time, location, social network) in order to provide better recommendation services. In the first part of this paper, we will survey a variety of dimension features and show how they are integrated into the recommendation process. When the service providers collect more and more personal information, it brings great privacy concerns to the public. On another side, the service providers could also suffer from attacks launched by malicious users who want to bias the recommendations. In the second part of this paper, we will survey attacks from and against recommender service providers, and existing solutions.


title: Contextual Choices in Online Physics Problems: Promising Insights Into Closing the Gender Gap

abstract: Throughout the world, female students are less likely than males to take advanced physics courses. This mixed-methods study uses a concurrent, nested design to study an online homework intervention designed to address choice and achievement. A choice of three different contexts (biological, sports, and traditional) were offered to students for each physics problem, intending to stimulate females’ interest and enhance achievement. Informed by aspects of Artino’s social-cognitive model of academic motivation and emotion, we investigated: Which context of physics problems do males and females select?; What explanations do students give for their choices?; Are there differences in the achievement of males and females?; and Is there a relationship between student achievement and the context selected? Fifty-two high school physics students from five U.S. states participated. Data included pre and post Force Concept Inventory scores, homework context choices and achievement, and rationales for choices. Findings indicate that females were most likely to select biology contexts; males, traditional. All students made more attempts on video questions over word questions, although females did not score as well. For all questions, students generally persisted until they answered them correctly, with females taking fewer attempts on problems. Context choice was mostly driven by interest, for males, and perceptions of difficulty level for females; however, rationales were indistinguishable by gender. On their first homework question attempt, females scored significantly better than the males. Initially, males had significantly higher FCI scores; post homework intervention, females increased their mean scores significantly on the FCI, erasing the initial gender gap, with no growth nor decline in males’ scores. Females with FCI growth were equally as likely to choose biology contexts as traditional contexts; males were more likely to choose biology contexts. Findings from this study suggest that modest changes to homework problems that provide choice and make the physics problems more contextually interesting - even without changes in classroom instruction - could increase interest and motivation students and increase achievement for both male and female students. Recommendations will be discussed.


title: Evolving an Expert Checkers Playing Program Without Using Human Expertise

abstract: An evolutionary algorithm has taught itself how to play the game of checkers without using features that would normally require human expertise. Using only the raw positions of pieces on the board and the piece differential, the evolutionary program optimized artificial neural networks to evaluate alternative positions in the game. Over the course of several hundred generations, the program taught itself to play at a level that is competitive with human experts (one level below human masters). This was verified by playing the best evolved neural network against 165 human players on an Internet gaming zone. The neural network's performance earned a rating that was better than 99.61% of all registered players at the Website. Control experiments between the best evolved neural network and a program that relies on material advantage indicate the superiority of the neural network both at equal levels of look ahead and CPU time. The results suggest that the principles of Darwinian evolution may he usefully applied to solving problems that have not yet been solved by human expertise.


title: Can texting be used to improve romantic relationships? - The effects of sending positive text messages on relationship satisfaction

abstract: Content of first text to romantic partner was manipulated in two experiments.Participants sent a positive text to their partner everyday for two weeks.Positive texting had a positive effect on romantic satisfaction.Positive texting had to be written in one's own words to be effective.Initiating texting also had a positive effect on romantic satisfaction. Texting has become a highly popular communication tool in college students. The current studies were designed to use texting to promote positive communication between romantic partners with the hope to enhance relationship satisfaction. In two studies, the authors experimentally manipulated the content of the first messages that participants sent out to their romantic partner everyday for two weeks. Participants' satisfaction was assessed at the beginning, in the middle, and at the end of the study. The partners, who were unaware of their partner's involvement in the study, were contacted at the end of the study and reported their satisfaction. The experimental conditions in Study 1 did not show statistically significant differences in their relationship happiness trajectory over the two weeks from the control condition, possibly due to the fact that participants sent out scripted texts provided by the researchers. In Study 2 the experimental groups wrote their own texts following instructions provided by the researchers. All experimental conditions showed a significantly different satisfaction trajectory compared to the control condition. However, partner satisfaction did not respond to the texting manipulation in either study.


title: Exploring the application of process mining to support self-regulated learning: An initial analysis with video lectures

abstract: Self-regulated learning involves students taking the responsibility of their own learning. Self-regulated learning students usually adopt a variety of learning strategies and behaviors, such as the performance of forethought-performance-reflection cycles or the regular and sequenced work over time, that eventually enable them to achieve a more significant and long-lasting learning. In this paper, we explore if these particular behaviors and strategies can be analyzed through the application of process mining techniques taking as data the events registered during the performance of learning activities. The discovery of the underlying processes followed by students can open new approaches to study the real self-regulated strategies used by students. The paper reviews the techniques and tools available to perform the process mining of events related to self-regulated learning and describes some initial works in this area. Furthermore, as an initial empirical study, we analyze the process followed by students regarding the visualization of videos provided in a first-year engineering subject. The obtained results are studied taking into account the grades obtained by the students. The results show that the students that obtained the best grades follow more varied routes than the students that obtained the worst grades. In addition, the best ones are more regular over time regarding weekly video visualization, mainly at the beginning of the term, while the worst ones visualize the videos mainly at the second part of the term.


title: Robobarista: Object Part based Transfer of Manipulation Trajectories from Crowd-sourcing in 3D Pointclouds

abstract: There is a large variety of objects and appliances in human environments, such as stoves, coffee dispensers, juice extractors, and so on. It is challenging for a roboticist to program a robot for each of these object types and for each of their instantiations. In this work, we present a novel approach to manipulation planning based on the idea that many household objects share similarly-operated object parts. We formulate the manipulation planning as a structured prediction problem and design a deep learning model that can handle large noise in the manipulation demonstrations and learns features from three different modalities: point-clouds, language and trajectory. In order to collect a large number of manipulation demonstrations for different objects, we developed a new crowd-sourcing platform called Robobarista. We test our model on our dataset consisting of 116 objects with 249 parts along with 250 language instructions, for which there are 1225 crowd-sourced manipulation demonstrations. We further show that our robot can even manipulate objects it has never seen before.


title: Authorship attribution for textual data on online social networks

abstract: Authorship Attribution, (AA) is a process of determining a particular document is written by which author among a list of suspected authors. Authorship attribution has been the problem from last six decades; when there were handwritten documents needed to be identified for the genuine author. Due to the technology advancement and increase in cybercrime and unlawful activities, this problem of AA becomes forth most important to trace out the author behind online messages. Over the past, many years research has been conducted to attribute the authorship of an author on the basis of their writing style as all authors possess different distinctiveness while writing a piece of document. This paper presents a comparative study of various machine learning approaches on different feature sets for authorship attribution on short text. The Twitter dataset has been used for comparison with varying sample size of a dataset of 10 prolific authors with various combinations of feature sets. The significance and impact of combinations of features while inferring different stylometric features has been reflected. The results of different approaches are compared based on their accuracy and precision values.


title: Monte Carlo Tree Search for Asymmetric Trees

abstract: We present an extension of Monte Carlo Tree Search (MCTS) that strongly increases its efficiency for trees with asymmetry and/or loops. Asymmetric termination of search trees introduces a type of uncertainty for which the standard upper confidence bound (UCB) formula does not account. Our first algorithm (MCTS-T), which assumes a non-stochastic environment, backs-up tree structure uncertainty and leverages it for exploration in a modified UCB formula. Results show vastly improved efficiency in a well-known asymmetric domain in which MCTS performs arbitrarily bad. Next, we connect the ideas about asymmetric termination to the presence of loops in the tree, where the same state appears multiple times in a single trace. An extension to our algorithm (MCTS-T+), which in addition to non-stochasticity assumes full state observability, further increases search efficiency for domains with loops as well. Benchmark testing on a set of OpenAI Gym and Atari 2600 games indicates that our algorithms always perform better than or at least equivalent to standard MCTS, and could be first-choice tree search algorithms for non-stochastic, fully-observable environments.


title: A Quantitative Comparison of Methods for 3D Face Reconstruction from 2D Images

abstract: In the past years, many studies have highlighted the relation between deviations from normal facial morphology (dysmorphology) and some genetic and mental disorders. Recent advances in methods for reconstructing the 3D geometry of the face from 2D images opens new possibilities for dysmorphology research without the need for specialized 3D imaging equipment. However, it is unclear whether these methods could reconstruct the facial geometry with the required accuracy. In this paper we present a comparative study of some of the most relevant approaches for 3D face reconstruction from 2D images, including photometric-stereo, deep learning and 3D Morphable Model fitting. We address the comparison in qualitatively and quantitatively terms using a public database consisting of 2D images and 3D scans from 100 people. Interestingly, we find that some methods produce quite noisy reconstructions that do not seem realistic, whereas others look more natural. However, the latter do not seem to adequately capture the geometric variability that exists between different subjects and produce reconstructions that look always very similar across individuals, thus questioning their fidelity.


title: ROCR: An Energy-Efficient Dynamic Wall-Climbing Robot

abstract: We present a novel bioinspired dynamic climbing robot, with a recursive name: ROCR is an oscillating climbing robot. ROCR, pronounced “Rocker,” is a pendular, two-link, serial-chain robot that utilizes alternating handholds and an actuated tail to propel itself upward in a climbing style based on observation of human climbers and brachiating gibbons. ROCR's bioinspired pendular climbing strategy is simple and efficient. In fact, to our knowledge, ROCR is also the first climbing robot that is designed for efficiency. ROCR is a lightweight, flexible, and self-contained robot. This robot is intended for autonomous surveillance and inspection on sheer vertical surfaces. Potential locomotion gait strategies were investigated in simulation using Working Model 2D, and were evaluated on a basis of climbing rate, energy efficiency, and whether stable open-loop climbing was achieved. We identified that the most effective climbing resulted from sinusoidal tail motions. The addition of a body stabilizer reduced the robot's out-of-plane motion at higher frequencies and promoted more reliable gripper attachment. Experimental measurements of the robot showed climbing efficiencies of over 20% and a specific resistance of 5.0, while consuming 27 J/m at a maximum climbing speed of 15.7 cm/s (0.34 body lengths/s) - setting a first benchmark for efficiency of climbing robots. Future work will include further design optimization, integration of more complex gripping mechanisms, and investigating more complex control strategies.


title: Yarn-Level Cloth Simulation with Sliding Persistent Contacts

abstract: Cloth is made of yarns that are stitched together forming semi-regular patterns. Due to the complexity of stitches and patterns, the macroscopic behavior of cloth is dictated by the contact interactions between yarns, not by the mechanical properties of yarns alone. The computation of cloth mechanics at the yarn level appears as a computationally complex and costly process at first sight, due to the need to resolve many fine-scale contact interactions. We propose instead an efficient representation of cloth at the yarn level that treats yarn-yarn contacts as persistent, but with the possibility to slide, thereby avoiding expensive contact handling altogether. We introduce a compact representation of yarn geometry and kinematics, capturing the essential deformation modes of yarn crossings, loops, stitches, and stacks, with a minimum cost. Based on this representation, we design force models that reproduce the characteristic macroscopic behavior of yarn-based fabrics. Our approach is suited for both woven and knitted fabrics. We demonstrate the efficiency of our method on simulations with millions of degrees of freedom (hundreds of thousands of yarn loops), almost one order of magnitude faster than previous techniques. We also compare the different macroscopic behavior under woven and knitted patterns with the same yarn density.


title: APTEEN: A Hybrid Protocol for Efficient Routing and Comprehensive Information Retrieval in Wireless Sensor Networks

abstract: Wireless sensor networks with thousands of tiny sensor nodes, are expected to find wide applicability and increasing deployment in coming years, as they enable reliable monitoring and analysis of the environment. In this paper, we propose a hybrid routing protocol (APTEEN) which allows for comprehensive information retrieval. The nodes in such a network not only react to time-critical situations, but also give an overall picture of the network at periodic intervals in a very energy efficient manner. Such a network enables the user to request past, present and future data from the network in the form of historical, one-time and persistent queries respectively. We evaluated the performance of these protocols and observe that these protocols are observed to outperform existing protocols in terms of energy consumption and longevity of the network.


title: Strength development, hydration reaction and pore structure of autoclaved slag cement with added silica fume

abstract: Under continuous hydrothermal treatment the strength of portland cement paste decreases with curing time and the pore structure coarsens. It was found in this study that the compressive strength of slag cement paste containing 67.5 wt.% ggbfs also decreases with time after 24 hour hydrothermal processing, but with a small addition of silica fume to the slag cement, the cement strength increases and the pore structure densifies when processed under comparable conditions. Based on observations XRD and SEM, these changes are attributed to: (a), changes in the hydration reactions and products by highly reactive silica fume, such that amorphous products dominate and the strength reducing phase α-C2SH does not form; (b), slower hydration of slag, partially caused by the decreased pH of the pore solution, favors the formation of a dense pore structure; and (c), the space filling properties of the micro particles of silica fume.


title: Workshop on Unsupervised and Transfer Learning Autoencoders, Unsupervised Learning, and Deep Architectures

abstract: Autoencoders play a fundamental role in unsupervised learning and in deep architectures for transfer learning and other tasks. In spite of their fundamental role, only linear autoencoders over the real numbers have been solved analytically. Here we present a general mathematical framework for the study of both linear and non-linear autoencoders. The framework allows one to derive an analytical treatment for the most non-linear autoencoder, the Boolean autoencoder. Learning in the Boolean autoencoder is equivalent to a clustering problem that can be solved in polynomial time when the number of clusters is small and becomes NP complete when the number of clusters is large. The framework sheds light on the different kinds of autoencoders, their learning complexity, their horizontal and vertical composability in deep architectures, their critical points, and their fundamental connections to clustering, Hebbian learning, and information theory.


title: A stochastic map for uncertain spatial relationships

abstract: In this paper we will describe a representation for spatial relationships which makes explicit their inherent uncertainty. We will show ways to manipulate them to obtain estimates of relationships and associated uncertainties not explicitly given, and show how decisions to sense or act can be made a priori based on those estimates. We will show how new constraint information, usually obtained by measurement, can be used to update the world model of relationships consistently, and in some situations, optimally. The framework we describe relies only on well-known state estimation methods.


title: Topological pattern recognition for point cloud data

abstract: In this paper we discuss the adaptation of the methods of homology from algebraic topology to the problem of pattern recognition in point cloud data sets. The method is referred to as persistent homology , and has numerous applications to scientific problems. We discuss the definition and computation of homology in the standard setting of simplicial complexes and topological spaces, then show how one can obtain useful signatures, called barcodes, from finite metric spaces, thought of as sampled from a continuous object. We present several different cases where persistent homology is used, to illustrate the different ways in which the method can be applied.


title: Porous scaffold design for tissue engineering

abstract: A paradigm shift is taking place in medicine from using synthetic implants and tissue grafts to a tissue engineering approach that uses degradable porous material scaffolds integrated with biological cells or molecules to regenerate tissues. This new paradigm requires scaffolds that balance temporary mechanical function with mass transport to aid biological delivery and tissue regeneration. Little is known quantitatively about this balance as early scaffolds were not fabricated with precise porous architecture. Recent advances in both computational topology design (CTD) and solid free-form fabrication (SFF) have made it possible to create scaffolds with controlled architecture. This paper reviews the integration of CTD with SFF to build designer tissue-engineering scaffolds. It also details the mechanical properties and tissue regeneration achieved using designer scaffolds. Finally, future directions are suggested for using designer scaffolds with in vivo experimentation to optimize tissue-engineering treatments, and coupling designer scaffolds with cell printing to create designer material/biofactor hybrids.


title: Quality-based jitter buffer algorithm using adaptive variable-size window

abstract: Jitter is one of the premier impairments which inflict a heavy damage on the quality of service in wireless VoIP. To solve this issue, this paper proposes a new quality-based jitter buffer algorithm. An adaptive windowing algorithm is introduced to dynamically adjust the window size which indicates the numbers of packets used to estimate the future network delay and loss rete. When receiving a voice packet, the receiver firstly uses the variable-size window algorithm to update the window size. Then, the histogram of delay is established according to the remaining packets in window. Finally, E-Model is applied to evaluate the speech quality based on delay histogram. By searching for the maximum speech quality, the optimal buffer delay is obtained. Owe to the variable-size statistical window, both the accuracy of network delay prediction and the ability to deal with spikes have further improved. The experiment results show that the whole VoIP communication under our proposed algorithm not only suffers the smallest average delay and lowest packet loss, but also achieves the highest speech quality.


title: Adaptive Inverse Perspective Mapping for lane map generation with SLAM

abstract: This paper proposes an adaptive Inverse Perspective Mapping (IPM) algorithm to obtain accurate bird's-eye view images from the sequential images of forward looking cameras. These images are often distorted by the motion of the vehicle; even a small motion can cause a substantial effect on bird'seye view images. In this paper, we propose an adaptive model for the IPM to accurately transform camera images to bird'seye view images by using motion information. Using motion derived from the monocular visual simultaneous localization and mapping (SLAM), experimental result shows that the proposed approaches can provide stable bird's-eye view images, even with large motion during the drive.


title: Logic Machine Architecture: Inference Mechanisms

abstract: Logic Machine Architecture (LMA) is a layered implementation of theorem-proving tools. The kernel of formula storage, retrieval, and manipulation primitives (layers 0 and 1) is described elsewhere[2]. The layer described here (layer 2) contains resolution- and equality-based inference rules, subsumption, and demodulation. It is designed to provide all of the tools required to create a theorem-prover with minimal effort. Although LMA is currently implemented in Pascal, an interface to LISP will be included in the original release. This paper includes the design principles and techniques used in layer 2, as well as two simple theorem provers which illustrate the services of layer 2 — one written in LISP and the other in Pascal.


title: Fit for Continuous Integration: How Organizations Assimilate an Agile Practice

abstract: Despite being a cornerstone of agile development, surprisingly little is known about how organizations assimilate continuous integration (CI) and what organizational changes the practice implies. Through a systematic literature review complemented by case study research we address this gap and develop a conceptual framework describing organizational implications of CI assimilation. We employ adoption theory of technology-induced innovation as theoretical lens and argue that as organizations transform through the assimilation stages, ambiguity increases, forcing them to think in alternatives and take tradeoffs. To the existing body of knowledge on agile development assimilation we add an in-depth study of a distinct agile practice.


title: 5G New Radio Base-Station Sensitivity and Performance

abstract: In this paper, we address and analyze the receiver reference sensitivity requirements for the 5G New Radio (NR) wireless communications systems, which relate to the SNR requirements at the base station to reach 95 % of the maximum throughput defined for fixed reference channels. Based on the latest 3GPP specifications and evaluation assumptions agreed for Release 15, a wide set of different transmission bandwidths and radio interface numerologies are investigated, at sub-6GHz and millimeter-wave frequency ranges, covering both AWGN and fading channel scenarios as well as varying mobility conditions. The performance results in terms of the relative throughput and block error rate using LDPC coding scheme are presented and analyzed, while for comparison purposes also LTE turbo code based results are provided. The results show that in frequency-selective channels, the reference sensitivity and UL radio link performance are systematically better with LDPC code compared to turbo code. The results also indicate that the purely front-loaded demodulation reference signal (DM-RS) based system can outperform the corresponding two DM-RS based system even at higher velocities and high center frequencies, allowing low decoding latency and efficient pipelined receiver processing.


title: Fluvastatin treatment inhibits leucocyte adhesion and extravasation in models of complement-mediated acute inflammation

abstract: Complement activation plays a relevant role in the development of tissue damage under inflammatory conditions, and clinical and experimental observations emphasize its contribution to inflammatory vasculitides. Statins have recently been shown to reduce cardiovascular morbidity independently of plasma cholesterol lowering and in vitro studies support a direct anti-inflammatory action of these drugs. The aim of this study was to verify the in vivo effect of fluvastatin on complement-mediated acute peritoneal inflammation. The effect of oral treatment with fluvastatin was investigated in normo-cholesterolaemic rats that received intraperitoneal injection of either yeast-activated rat serum (Y-act RS) or lipopolysaccharide to induce peritoneal inflammation monitored by the number of PMN recruited in peritoneal fluid washes. In addition, vascular adherence and extravasation of leucocytes were evaluated by direct videomicroscopy examination on mesentery postcapillary venules topically exposed to Y-act RS. The number of PMN in the peritoneal washes of rats treated with fluvastatin was 38% lower than that of untreated animals (P < 0.05) 12 h after LPS injection, and was even lower (56%) in rats treated with Y-act RS already 8 h after injection (P < 0.02). Firm adhesion to endothelium and extravasation of leucocytes evaluated under direct videomicroscopy observation were significantly inhibited in fluvastatin treated rats (77% and 72%, respectively; P < 0.01), 120 min after treatment with Y-act RS. Our results demonstrate that fluvastatin inhibits in vivo complement-dependent acute peritoneal inflammation and suggest a role for statins in preventing the inflammatory flares usually associated with complement activation in chronic diseases, such as SLE or rheumatoid arthritis.


title: Optical fiber connected millimeter-wave radar for FOD detection on runway

abstract: Since the Concorde supersonic aircraft accident in 2000, the threat of Foreign Object Debris (FOD) on airport runways has been focused on. The FOD detection system is expected to prevent such the tragedy. In order to keep the airport runways clean and safe, authors are developing radar to detect small metallic objects on the airport runway surface. Millimeter-wave radar is the first candidate for this purpose because of its high detection resolution. However, the radar surveillance range is generally limited due to the high frequency attenuation and the low power devices. This paper proposes a new concept of FOD detection millimeter-wave radar using a Radio over Fiber (RoF) system. The concept overcomes the attenuation problems by connecting a local signal generator and the millimeter-wave radar module with optical fibers. The optical fiber propagation loss is much less than the free space propagation loss of radio frequency (RF), thus the completely new FOD detection system can be composed by applying the RoF system. Measurements of a prototype system, 94 GHz millimeter-wave radar connected by optical fiber, show the possibilities for the new airport application especially in terms of detection sensitivity.


title: Music transcription modelling and composition using deep learning

abstract: We apply deep learning methods, specifically long short-term memory (LSTM) networks, to music transcription modelling and composition. We build and train LSTM networks using approximately 23,000 music transcriptions expressed with a high-level vocabulary (ABC notation), and use them to generate new transcriptions. Our practical aim is to create music transcription models useful in particular contexts of music composition. We present results from three perspectives: 1) at the population level, comparing descriptive statistics of the set of training transcriptions and generated transcriptions; 2) at the individual level, examining how a generated transcription reflects the conventions of a music practice in the training transcriptions (Celtic folk); 3) at the application level, using the system for idea generation in music composition. We make our datasets, software and sound examples open and available: \url{this https URL}.


title: Chinese Irony Corpus Construction and Ironic Structure Analysis

abstract: Non-literal expression recognition is a challenging task in natural language processing. An ironic expression implies the opposite of the literal meaning, causing problems in opinion mining and sentiment analysis. In this paper, ironic messages are collected from microblogs to form an irony corpus based on the use of emoticons, linguistic forms, and sentiment polarity. Five linguistic patterns are mined by using the proposed bootstrapping approach. We also analyze the linguistic structure and elements used to convey irony. Based on our observations, ironic words/phrases and contextual information are the necessary elements in irony, while the contextual information can be hidden in linguistic forms. A rhetorical element, which is optional in irony, can also be used to help strengthen the effects and understandability of an ironic expression. The ironic elements in each instance of our irony corpus are labelled based on this structure. This corpus can be used to study the usage of ironic expressions and the identification of ironic elements, and thus improve the performance of irony recognition.


title: Probabilistic short-term wind power forecasting based on deep neural networks

abstract: High-precision wind power forecasting is an essential operation issue of power systems integrated with large numbers of wind farms. In addition to traditional forecasting methods, probabilistic forecasting is recognized as an optimal forecasting solution since it provides a wealth of valuable uncertainty information of wind power. In this paper, a novel approach based on deep neural networks (DNNs) for the deterministic short-term wind power forecasting of wind farms is proposed. DNN models including long short-term memory (LSTM) recurrent neural networks (RNNs) have achieved better results compared with traditional methods. Further, probabilistic forecasting based on conditional error analysis is also implemented. Favorable results of probabilistic forecasting are achieved owing to elaborate division of the conditions set based on cluster analysis. The performance of the proposed method is tested on a dataset of several wind farms in north-east China. Forecasting results are evaluated using different indices, which proves the effectiveness of the proposed method.


title: Mind Reading : Neural Mechanisms of Theory of Mind and Self-Perspective

abstract: Abstract Human self-consciousness as the metarepresentation of ones own mental states and the so-called theory of mind (TOM) capacity, which requires the ability to model the mental states of others, are closely related higher cognitive functions. We address here the issue of whether taking the self-perspective (SELF) or modeling the mind of someone else (TOM) employ the same or differential neural mechanisms. A TOM paradigm was used and extended to include stimulus material that involved TOM and SELF capacities in a two-way factorial design. A behavioral study in 42 healthy volunteers showed that TOM and SELF induced differential states of mind: subjects assigned correctly first or third person pronouns when providing responses to the stimuli. Following the behavioral study, we used functional magnetic resonance imaging (fMRI) in eight healthy, right-handed males to study the common and differential neural mechanisms underlying TOM and SELF. The main factor TOM led to increased neural activity in the anterior cingulate cortex and left temporopolar cortex. The main factor SELF led to increased neural activity in the right temporoparietal junction and in the anterior cingulate cortex. A significant interaction of both factors TOM and SELF was observed in the right prefrontal cortex. These divergent neural activations in response to TOM and SELF suggest that these important differential mental capacities of human self-consciousness are implemented at least in part in distinct brain regions.


title: Wafer defect patterns recognition based on OPTICS and multi-label classification

abstract: In the industry of integrated circuits, defect patterns shown on a wafer map contain crucial information for quality engineers to find the cause of defect to increase yield. This paper proposes a method for wafer defect pattern recognition which could recognize more than one defect patterns based on Ordering Point to Identify the Cluster Structure(OPTICS) and Support Vector Machine(SVM). The effectiveness of the proposed method has been verified from following three aspects from a real-world data set of wafer maps(WM-811K): salient defect pattern recognition accuracy up to 94.3% and the accuracy of some types has an obvious improvement, multi-patterns recognition accuracy(82.0%), and computation time has a significantly reduction.


title: Multi-Agent Actor-Critic with Generative Cooperative Policy Network

abstract: We propose an efficient multi-agent reinforcement learning approach to derive equilibrium strategies for multi-agents who are participating in a Markov game. Mainly, we are focused on obtaining decentralized policies for agents to maximize the performance of a collaborative task by all the agents, which is similar to solving a decentralized Markov decision process. We propose to use two different policy networks: (1) decentralized greedy policy network used to generate greedy action during training and execution period and (2) generative cooperative policy network (GCPN) used to generate action samples to make other agents improve their objectives during training period. We show that the samples generated by GCPN enable other agents to explore the policy space more effectively and favorably to reach a better policy in terms of achieving the collaborative tasks.


title: Chat More: Deepening and Widening the Chatting Topic via A Deep Model

abstract: The past decade has witnessed the boom of human-machine interactions, particularly via dialog systems. In this paper, we study the task of response generation in open-domain multi-turn dialog systems. Many research efforts have been dedicated to building intelligent dialog systems, yet few shed light on deepening or widening the chatting topics in a conversational session, which would attract users to talk more. To this end, this paper presents a novel deep scheme consisting of three channels, namely global, wide, and deep ones. The global channel encodes the complete historical information within the given context, the wide one employs an attention-based recurrent neural network model to predict the keywords that may not appear in the historical context, and the deep one trains a Multi-layer Perceptron model to select some keywords for an in-depth discussion. Thereafter, our scheme integrates the outputs of these three channels to generate desired responses. To justify our model, we conducted extensive experiments to compare our model with several state-of-the-art baselines on two datasets: one is constructed by ourselves and the other is a public benchmark dataset. Experimental results demonstrate that our model yields promising performance by widening or deepening the topics of interest.


title: Dynamic Contention Window Methods for Improved Coexistence Between LTE and Wi-Fi in Unlicensed Bands

abstract: Recently, the 3rd Generation Partnership Project (3GPP) announced standards that permit the Licensed-Assisted Access (LAA) of Long Term Evolution (LTE) to operate over unlicensed spectrum bands. This permission, which is a part of the 5G specifications, is due to the scarcity of the licensed spectrum and the increased use of wireless networks and services. However, these unlicensed bands are mainly occupied by 802.11 based WLAN devices. Thus, challenges arise for the efficient coexistence mechanism to share the same unlicensed band by LAA and Wi-Fi to maintain the quality of service and manage the interference among users. In this work, we propose new variable contention window (CW) methods for LAA to enable the coexistence of LTE and Wi-Fi in a fair manner based on the Wi-Fi statistics. The main novelty of this work is that the knowledge of Wi-Fi activity statistics is exploited to adapt the CW of LAA more effectively. These methods are evaluated based on the 3GPP fairness definition for such coexistence mechanisms under various traffic loads. We show that the fairness depends on the LAA CW size. Further, through simulation results, we show that the proposed schemes are more friendly to the existing Wi-Fi network, in particular for the higher traffic loads, compared with the existing Category 4 Listen-Before-Talk (Cat 4 LBT) algorithm defined in the 3GPP standard and provide higher total throughputs for both coexisting networks, improving the overall network performance.


title: Translating a Math Word Problem to an Expression Tree

abstract: Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to automatic math word problem solving. Despite its simplicity, a drawback still remains: a math word problem can be correctly solved by more than one equations. This non-deterministic transduction harms the performance of maximum likelihood estimation. In this paper, by considering the uniqueness of expression tree, we propose an equation normalization method to normalize the duplicated equations. Moreover, we analyze the performance of three popular SEQ2SEQ models on the math word problem solving. We find that each model has its own specialty in solving problems, consequently an ensemble model is then proposed to combine their advantages. Experiments on dataset Math23K show that the ensemble model with equation normalization significantly outperforms the previous state-of-the-art methods.


title: DIFusion: Fast Skip-Scan with Zero Space Overhead

abstract: Scan is a crucial operation in main-memory column-stores. It scans a column and returns a result bit vector indicating which records satisfy a filter predicate. ByteSlice is an in-memory data layout that chops data into multiple bytes and exploits early-stop capability by high-order bytes comparisons. As column widths are usually not multiples of byte, the last-byte of ByteSlice is padded with 0's, wasting memory bandwidth and computation power. To fully leverage the resources, we propose to weave a secondary index into the vacant bits (i.e., bits originally padded with 0's), forming our new layout coined DIFusion (Data Index Fusion). DIFusion enables skip-scan, a new fast scan that inherits the early-stopping capability from ByteSlice and at the same time possesses the data-skipping ability of index with zero space overhead. Empirical results show that skip-scan on DIFusion outperforms scan on ByteSlice.


Listening Comprehension over Argumentative Content


title: Text Embeddings for Retrieval From a Large Knowledge Base

abstract: Text embedding representing natural language documents in a semantic vector space can be used for document retrieval using nearest neighbor lookup. In order to study the feasibility of neural models specialized for retrieval in a semantically meaningful way, we suggest the use of the Stanford Question Answering Dataset (SQuAD) in an open-domain question answering context, where the first task is to find paragraphs useful for answering a given question. First, we compare the quality of various text-embedding methods on the performance of retrieval and give an extensive empirical comparison on the performance of various non-augmented base embedding with, and without IDF weighting. Our main results are that by training deep residual neural models specifically for retrieval purposes can yield significant gains when it is used to augment existing embeddings. We also establish that deeper models are superior to this task. The best base baseline embeddings augmented by our learned neural approach improves the top-1 paragraph recall of the system by 14%.


title: Factores de riesgo independientes para la presencia de preeclampsia.

abstract: Determine asociated factors related to preeclampsia in pregnant women at Belen Trujillo Hospital. ::: Methods: Was conduced an analitic, observational, retrospective, case and controls investigation. The study population was conformed for 176 pregnant women by inclusion and exclusion criteria distributed into 2 groups: with and without preeclampsia . ::: Results: Statistical analysis about risk factors were : teen pregnant (OR: 0.66; p>0.05), overweigh (OR: 1.99; p 0.05), low escolarity (OR: 2.99; p<0.01). ::: Conclusions: Overweigh, primigravity, low escolarity are risk factors asociated to preeclampsia. Teen pregnant and rural procedence are not risk factors asociated to preeclampsia


title: Understanding Graph Sampling Algorithms for Social Network Analysis

abstract: Being able to keep the graph scale small while capturing the properties of the original social graph, graph sampling provides an efficient, yet inexpensive solution for social network analysis. The challenge is how to create a small, but representative sample out of the massive social graph with millions or even billions of nodes. Several sampling algorithms have been proposed in previous studies, but there lacks fair evaluation and comparison among them. In this paper, we analyze the state-of art graph sampling algorithms and evaluate their performance on some widely recognized graph properties on directed graphs using large-scale social network datasets. We evaluate not only the commonly used node degree distribution, but also clustering coefficient, which quantifies how well connected are the neighbors of a node in a graph. Through the comparison we have found that none of the algorithms is able to obtain satisfied sampling results in both of these properties, and the performance of each algorithm differs much in different kinds of datasets.


title: Using eye tracking to understand learners' reading process through the concept-mapping learning strategy

abstract: The author used an eye-tracking methodology to examine the influence of the concept-mapping learning strategy on learners performing an English reading task. Eighty-six freshmen enrolled in English courses participated in this control-group pretest-posttest experiment, and received either traditional or concept-mapping instruction for learning English reading skills. A concept-mapping strategy was introduced to the learners in the experimental group to improve their reading ability. The results of independent t tests, in which eye-tracking data on fixation time and fixation path were used, indicated that the participants who received concept-mapping instruction exhibited shorter fixation times on the core concept and other content in the text, compared with the group that received traditional instruction. Moreover, the experimental group demonstrated longer and irregular rereading paths than the control group (CG) did. These results indicated that concept mapping serves as a reference to assist average readers to improve and to identify primary ideas that clarify the meaning of an article.


title: A novel attribute weighting algorithm for clustering high-dimensional categorical data

abstract: Due to data sparseness and attribute redundancy in high-dimensional data, clusters of objects often exist in subspaces rather than in the entire space. To effectively address this issue, this paper presents a new optimization algorithm for clustering high-dimensional categorical data, which is an extension of the k-modes clustering algorithm. In the proposed algorithm, a novel weighting technique for categorical data is developed to calculate two weights for each attribute (or dimension) in each cluster and use the weight values to identify the subsets of important attributes that categorize different clusters. The convergence of the algorithm under an optimization framework is proved. The performance and scalability of the algorithm is evaluated experimentally on both synthetic and real data sets. The experimental studies show that the proposed algorithm is effective in clustering categorical data sets and also scalable to large data sets owning to its linear time complexity with respect to the number of data objects, attributes or clusters.


title: Harmonic Suppressed Dual-Band Bandpass Filters With Tunable Passbands

abstract: This paper presents a novel approach to the design of tunable dual-band bandpass filter with broadband harmonic suppression characteristics. The proposed filter structure offers the possibility of two tunable passbands, as well as a fixed first passband and controllable second passband. The tunable passband frequency usually causes a shift of the harmonics, which need to be suppressed to improve out-of the passband characteristics. In order to suppress the harmonics over a broad bandwidth, defected ground structures are used at input and output feeding lines without degrading the passbands characteristics. Both theory and experiment are provided to validate the proposed filter. From the experimental results, it is found that the proposed filter exhibits a first passband center frequency tunable range of 34.14% from 0.85 to 1.2 GHz with the almost constant 3-dB fractional bandwidth (FBW) of 13% and second passband center frequency tunable range of 41.81% from 1.40 to 2.14 GHz with the 3-dB FBW of 11%. The measured results of the proposed filters show a rejection level of 20 dB up to more than ten times of second passband frequency can be obtained, thereby ensuring broad harmonics rejection characteristics without degradation of passbands. The measurement data have good agreement with the simulation.


title: Traitor Tracing and Revocation for Secure Decoders in File Syncing-and-Sharing Service

abstract: Today, many cloud storage services have been available to small-to-medium business and individuals by file syncing-and-sharing (FSS) service. To meet the security requirement of FSS, we present a new architecture based on secure Player/Reader box with RBAC-compatible cryptosystem, which supports to access the encrypted data in the cloud, as well as traitor tracing and revocation mechanisms for pirate box. We improve a cryptosystem, called Partially-ordered Hierarchical Encryption (PHE) to realize this architecture. In this system, two security mechanisms, traitor tracing and revocation, are provided to support efficient digital forensics. The result of performance evaluation shows that our scheme is more efficient than the existing schemes with traitor tracing and revocation.


Aesthetics in Information Visualization