title: Nurses discuss bedside handover and using written handover sheets

abstract: BACKGROUND ::: The analysis of nursing errors in clinical management highlighted that clinical handover plays a pivotal role in patient safety. Changes to handover including conducting handover at the bedside and the use of written handover summary sheets were subsequently implemented. ::: ::: ::: AIM ::: The aim of the study was to explore nurses' perspectives on the introduction of bedside handover and the use of written handover sheets. ::: ::: ::: METHOD ::: Using a qualitative approach, data were obtained from six focus groups containing 30 registered and enrolled (licensed practical) nurses. Thematic analysis revealed several major themes. ::: ::: ::: FINDINGS ::: Themes identified included: bedside handover and the strengths and weaknesses; patient involvement in handover, and good communication is about good communicators. Finally, three sources of patient information and other issues were also identified as key aspects. ::: ::: ::: CONCLUSIONS ::: How bedside handover is delivered should be considered in relation to specific patient caseloads (patients with cognitive impairments), the shift (day, evening or night shift) and the model of service delivery (team versus patient allocation). ::: ::: ::: IMPLICATIONS FOR NURSING MANAGEMENT ::: Flexible handover methods are implicit within clinical setting issues especially in consideration to nursing teamwork. Good communication processes continue to be fundamental for successful handover processes.


title: The elderly interacting with a digital agenda through an RFID pen and a touch screen

abstract: Ambient Assisted Living (AAL) aims to enhance quality of life of elder and impaired people. Thanks to the advances in Information and Communication Technologies (ICT), it is now possible not only to support new home services to improve their quality of life, but also to provide them with more natural, simple and multimodal interaction styles. It enables introducing new better-suited interaction styles for performing tasks according to user need, abilities and the usage conditions. In this paper, a digital agenda application is described and tested by a group of elderly users. This customized personal agenda allows elders to create agenda entries, to view calendar entries and a person's telephone without typing any letter or number. Through observation and the capture of data we studied how six participants (ages from 66 to 74) interacted with the personal agenda through a touch screen and an RFID-based interface. The RFID interaction consisted of a sheet-like physical object' the RFID board', and a pen-like object 'the IDBlue pen' that the users used to choose the desired option in the board. Beside the users' satisfaction, interesting results have been found that make us aware of valuable information to enhance their interaction with the digital world, as well as, their quality of life. Moreover, it is shown how a pen-like object and a sheet-like one can be used by the elderly to interact with the digital world.


title: GPTIPS 2: an open-source software platform for symbolic data mining

abstract: GPTIPS is a free, open source MATLAB based software platform for symbolic data mining (SDM). It uses a multigene variant of the biologically inspired machine learning method of genetic programming (MGGP) as the engine that drives the automatic model discovery process. Symbolic data mining is the process of extracting hidden, meaningful relationships from data in the form of symbolic equations. In contrast to other data-mining methods, the structural transparency of the generated predictive equations can give new insights into the physical systems or processes that generated the data. Furthermore, this transparency makes the models very easy to deploy outside of MATLAB.


title: Enterprise Information Systems Architecture—Analysis and Evaluation

abstract: Numerous software architecture proposals are available to industrial information engineers in developing their enterprise information systems. While those proposals and corresponding methodologies are helpful to engineers in determining appropriate architecture, the systematic methods for the evaluation of software architecture are scarce. To select appropriate software architecture from various alternatives appropriately, a scenario-based method has been proposed to assess how software architecture affects the fulfillment of business requirements. The empirical evaluation on the selection of a supply chain software tool has shown that the developed method offers remarkable insights of software development and can be incorporated into the industrial informatics practice of an organization with a moderate cost.


title: Threat is in the Sex of the Beholder: Men Find Weapons Faster than do Women:

abstract: In visual displays, people locate potentially threatening stimuli, such as snakes, spiders, and weapons, more quickly than similar benign stimuli, such as beetles and gadgets. Such biases are likely adaptive, facilitating fast responses to potential threats. Currently, and historically, men have engaged in more weapons-related activities (fighting and hunting) than women. If biases of visual attention for weapons result from selection pressures related to these activities, then we would predict such biases to be stronger in men than in women. The current study reports the results of two visual search experiments, in which men showed a stronger bias of attention toward guns and knives than did women, whether the weapons were depicted wielded or not. When the weapons were depicted wielded, both sexes searched for them with more caution than when they were not. Neither of these effects extended reliably to syringes, a non-weapon—yet potentially threatening—object. The findings are discussed with respect to t...


title: Multichannel Convolutional Neural Network for Biological Relation Extraction

abstract: The plethora of biomedical relations which are embedded in medical logs (records) demands researchers' attention. Previous theoretical and practical focuses were restricted on traditional machine learning techniques. However, these methods are susceptible to the issues of "vocabulary gap" and data sparseness and the unattainable automation process in feature extraction. To address aforementioned issues, in this work, we propose a multichannel convolutional neural network (MCCNN) for automated biomedical relation extraction. The proposed model has the following two contributions: (1) it enables the fusion of multiple (e.g., five) versions in word embeddings; (2) the need for manual feature engineering can be obviated by automated feature learning with convolutional neural network (CNN). We evaluated our model on two biomedical relation extraction tasks: drug-drug interaction (DDI) extraction and protein-protein interaction (PPI) extraction. For DDI task, our system achieved an overall f-score of 70.2% compared to the standard linear SVM based system (e.g., 67.0%) on DDIExtraction 2013 challenge dataset. And for PPI task, we evaluated our system on Aimed and BioInfer PPI corpus; our system exceeded the state-of-art ensemble SVM system by 2.7% and 5.6% on f-scores.


title: Switch-controlled multi-octave bandwidth radial power divider/combiner

abstract: Summary form only given. An improved very wideband radial waveguide-based power divider/combiner is presented, which uses switching to compensate for cavity resonance. The combiner is implemented with broadband probes composed of cylindrical conductors and dielectric spacers, arranged on a rod for mechanical stability. The proposed switch-controlled radial power combiner provides low loss ( 1 dB), broad bandwidth (400 MHz∼2000 MHz), and high power capability.


title: CRT-based Fully Homomorphic Encryption over the Integers ⋆

abstract: In 1978, Rivest, Adleman and Dertouzos introduced the basic concept of privacy homomorphism that allows computation on encrypted data without decryption. It was an interesting work whose idea precedes the recent development of fully homomorphic encryption, although actual example schemes proposed in the paper are all susceptible to simple known-plaintext attacks.In this paper, we revisit one of their proposals, in particular the third scheme which is based on the Chinese Remainder Theorem and is ring homomorphic. It is known that only a single pair of known plaintext/ciphertext is needed to break this scheme. However, by exploiting the standard technique to insert an error to a message before encryption, we can cope with this problem. We present a secure modification of their proposal by showing that the proposed scheme is fully homomorphic and secure against the chosen plaintext attacks under the approximate GCD assumption and the sparse subset sum assumption when the message space is restricted to Z 2 k .Interestingly, the proposed scheme can be regarded as a generalization of the DGHV scheme with larger plaintext space. Our scheme has O ~ ( λ 5 ) ciphertext expansion overhead while the DGHV has O ~ ( λ 8 ) for the security parameter λ . When restricted to the homomorphic encryption scheme with depth of O ( log λ ) , the overhead is reduced to O ~ ( λ ) . Our scheme can be used in applications requiring a large message space Z Q for log Q = O ( λ 4 ) , or SIMD style operations on Z Q k for log Q = O ( λ ) , k = O ( λ 3 ) , with O ~ ( λ 5 ) ciphertext size as in the DGHV.


title: Safety critical systems: challenges and directions

abstract: Safety-critical systems are those systems whose failure could result in loss of life, significant property damage or damage to the environment. There are many well-known examples in application areas such as medical devices, aircraft flight control, weapons and nuclear systems. Many modern information systems are becoming safety-critical in a general sense because financial loss and even loss of life can result from their failure. Future safety-critical systems will be more common and more powerful. From a software perspective, developing safety-critical systems in the numbers required and with adequate dependability is going to require significant advances in areas such as specification, architecture, verification and the software process. The very visible problems that have arisen in the area of information system security suggests that security is a major challenge too.


title: Signal Processing Techniques Restructure The Big Data Era

abstract: Big data science has been developed into a topic that attracts attention from industry, academia and governments. The main objective in Big Data science is to recognize and extract meaningful information from huge amounts of heterogeneous data and unstructured data (which constitute 95% of big data). Signal Processing (SP) techniques and related statistical learning (SL) tools such as Principal Component Analysis (PCA), R-PCA (Robust PCA), Compressive Sampling (CS), convex optimization (CO), stochastic approximation (SA), kernel based learning (KBL) tasks are used for robustness, compression and dimensionality reduction in Big Data arising challenges. This review paper introduces Big Data related SP techniques and presents applications of this emerging field.


title: Probabilistic Multi-Task Learning for Visual Saliency Estimation in Video

abstract: In this paper, we present a probabilistic multi-task learning approach for visual saliency estimation in video. In our approach, the problem of visual saliency estimation is modeled by simultaneously considering the stimulus-driven and task-related factors in a probabilistic framework. In this framework, a stimulus-driven component simulates the low-level processes in human vision system using multi-scale wavelet decomposition and unbiased feature competition; while a task-related component simulates the high-level processes to bias the competition of the input features. Different from existing approaches, we propose a multi-task learning algorithm to learn the task-related "stimulus-saliency" mapping functions for each scene. The algorithm also learns various fusion strategies, which are used to integrate the stimulus-driven and task-related components to obtain the visual saliency. Extensive experiments were carried out on two public eye-fixation datasets and one regional saliency dataset. Experimental results show that our approach outperforms eight state-of-the-art approaches remarkably.


title: A procedure for analyzing unbalanced datasets

abstract: This paper describes a procedure for analyzing unbalanced datasets that include many nominal- and ordinal-scale factors. Such datasets are often found in company datasets used for benchmarking and productivity assessment. The two major problems caused by lack of balance are that the impact of factors can be concealed and that spurious impacts can be observed. These effects are examined with the help of two small artificial datasets. The paper proposes a method of forward pass residual analysis to analyze such datasets. The analysis procedure is demonstrated on the artificial datasets and then applied to the COCOMO dataset. The paper ends with a discussion of the advantages and limitations of the analysis procedure.


title: Training and testing object detectors with virtual images

abstract: In the area of computer vision, deep learning has produced a variety of state-of-the-art models that rely on massive labeled data. However, collecting and annotating images from the real world is too demanding in terms of labor and money investments, and is usually inflexible to build datasets with specific characteristics, such as small area of objects and high occlusion level. Under the framework of Parallel Vision, this paper presents a purposeful way to design artificial scenes and automatically generate virtual images with precise annotations. A virtual dataset named ParallelEye is built, which can be used for several computer vision tasks. Then, by training the DPM U+0028 Deformable parts model U+0029 and Faster R-CNN detectors, we prove that the performance of models can be significantly improved by combining ParallelEye with publicly available real-world datasets during the training phase. In addition, we investigate the potential of testing the trained models from a specific aspect using intentionally designed virtual datasets, in order to discover the flaws of trained models. From the experimental results, we conclude that our virtual dataset is viable to train and test the object detectors.


title: The 2007 MIREX Audio Mood Classification Task: Lessons Learned

abstract: Recent music information retrieval (MIR) research pays increasing attention to music classification based on moods expressed by music pieces. The first Audio Mood Classification (AMC) evaluation task was held in the 2007 running of the Music Information Retrieval Evaluation eXchange (MIREX). This paper describes important issues in setting up the task, including dataset construction and ground-truth labeling, and analyzes human assessments on the audio dataset, as well as system performances from various angles. Interesting findings include system performance differences with regard to mood clusters and the levels of agreement amongst human judgments regarding mood labeling. Based on these analyses, we summarize experiences learned from the first community scale evaluation of the AMC task and propose recommendations for future AMC and similar evaluation tasks.


title: Integrating technology into K-12 teaching and learning: current knowledge gaps and recommendations for future research

abstract: Although research studies in education show that use of technol- ogy can help student learning, its use is generally affected by certain barriers. In this paper, we first identify the general barriers typically faced by K-12 schools, both in the United States as well as other countries, when integrating technology into the curriculum for instructional purposes, namely: (a) resources, (b) institution, (c) subject culture, (d) attitudes and beliefs, (e) knowledge and skills, and (f) assessment. We then describe the strategies to overcome such barriers: (a) having a shared vision and technology integration plan, (b) overcoming the scarcity of resources, (c) changing attitudes and beliefs, (d) conducting professional development, and (e) reconsidering assessments. Finally, we identify several current knowledge gaps pertaining to the barriers and strategies of technology integration, and offer pertinent recommendations for future research.


title: Container oriented job scheduling using linear programming model

abstract: With Docker gaining widespread popularity in the recent years, the container scheduler becomes a crucial role for the exploding containerized applications and services. In this work, the container host energy conservation, the container image pulling costs from the image registry to the container hosts and the workload network transition costs from the clients to the container hosts are evaluated in combination. By modeling the scheduling problem as an integer linear programming, an effective and adaptive scheduler is proposed. Impressive cost savings were achieved compared to Docker Swarm scheduler. Moreover, it can be easily integrated into the open-source container orchestration frameworks.


title: Ripple joins for online aggregation

abstract: We present a new family of join algorithms, called ripple joins, for online processing of multi-table aggregation queries in a relational database management system (DBMS). Such queries arise naturally in interactive exploratory decision-support applications. Traditional offline join algorithms are designed to minimize the time to completion of the query. In contrast, ripple joins are designed to minimize the time until an acceptably precise estimate of the query result is available, as measured by the length of a confidence interval. Ripple joins are adaptive, adjusting their behavior during processing in accordance with the statistical properties of the data. Ripple joins also permit the user to dynamically trade off the two key performance factors of on-line aggregation: the time between successive updates of the running aggregate, and the amount by which the confidence-interval length decreases at each update. We show how ripple joins can be implemented in an existing DBMS using iterators, and we give an overview of the methods used to compute confidence intervals and to adaptively optimize the ripple join “aspect-ratio” parameters. In experiments with an initial implementation of our algorithms in the POSTGRES DBMS, the time required to produce reasonably precise online estimates was up to two orders of magnitude smaller than the time required for the best offline join algorithms to produce exact answers.


title: The Melanogenesis Alteration Effects of Achillea millefolium L. Essential Oil and Linalyl Acetate: Involvement of Oxidative Stress and the JNK and ERK Signaling Pathways in Melanoma Cells

abstract: The mitogen-activated protein kinase (MAPK) family, including extracellular signal-regulated kinase (ERK)1/2, c-Jun N-terminal kinase (JNK)1/2 and p38 MAPK, is known to be activated by ultraviolet (UV) radiation in melanocytes to regulate melanin production. Reactive oxygen species (ROS) play important roles in the pathway of ERK and JNK activation. It has been established that the essential oil of Achillea millefolium L. (AM-EO) has activities that suppress the oxidative stress and inflammatory responses. Thus, we analyzed the effects of AM-EO on melanogenesis in melanocyte stimulating hormone (α-MSH) treated melanoma cells. The results demonstrated that AM-EO suppresses melanin production by decreasing tyrosinase activity through the regulation of the JNK and ERK signaling pathways. This effect might be associated with the AM-EO activity leading to the suppression of ROS, and linalyl acetate is its major functional component. Therefore, we propose that AM-EO has the potential to treat hyperpigmentation in the future.


title: Control of Beam Direction for Substrate-Integrated Waveguide Slot Array Antenna Using Metasurface

abstract: The method to control the aperture phase distribution of antenna using a metasurface is proposed to point the antenna beam to desired directions. A $1\times 10$ substrate-integrated waveguide (SIW) slot array antenna directly integrated with the metasurface of double-layer miniaturized framed Jerusalem crosses is, for example, designed to achieve the oblique radiation beam in 30° at $Ka$ -bands. In order to achieve a full 360° transmission phase range, part of SIW slots are inverted to obtain additional 180° phase shift so that more than 400° phase shift range is realized. The measured results show that the beam directions are within 29.3 ± 1° from 34.25 to 35.75 GHz with a return loss better than 15 dB. The measured maximum gain is 12.1 dBi at 34.75 GHz. The combination of the array antenna and the metasurface features a low profile, simple structure, and flexible design for oblique beam application.


title: Compact SRR Loaded UWB Circular Monopole Antenna With Frequency Notch Characteristics

abstract: This paper presents the design of a compact split ring resonator (SRR) loaded coplanar waveguide (CPW) fed ultrawideband circular monopole antenna having frequency notch characteristics. The electromagnetic coupling of the SRR with the CPW yields the frequency notch. Fabricated prototypes were measured and compared with simulations and good agreement was obtained. The impedance and radiation plots confirm the suppression of the desired notch frequency. A theoretical formulation to calculate the notch frequency is also proposed and validated.


title: Abstract Automatic Color Calibration for Large Camera Arrays

abstract: A vane-type rotary actuator in which a disc like seal member made of an elastic material with a hole provided in the middle thereof is pressed in place in contact with a flange surface. With seal material thus somewhat deformed in the radial direction thereof, pressure required for sealing is generated at the inner surface of a cylinder and the outer surface of a shaft cylinder.


title: An Automatic Number Plate Recognition of Bangladeshi Vehicles

abstract: a-day, the importance of automatic traffic monitoring has drawn the attention to the intelligent transport systems. A number plate is the unique identification of a vehicle. Automatic Number Plate Recognition (ANPR) is designed to locate and recognize the number plate of a moving vehicle automatically. For Bangladeshi Vehicles there are no fixed rules for number plates like other countries. Because of the variety of conditions and patterns, the recognition process of number plates for Bangladeshi vehicles is so difficult. We develop a ANPR system which is divided into three part such as number plate Localization, Extraction and Recognition. After successfully complete localization and extraction of number plate we send that portion to a Bangla OCR to fetch the text of the number plate for further processing. In this paper we present a complete ANPR system for Bangladesh Vehicles using standard algorithms with some customization and with our own algorithm for Number Plate extraction which can detect Number Plate region and successfully extract detected region and then able to recognize that number plate text. KeywordsSobel Operator; Outsu threshold; Hough transform; Bangladeshi vehicles number plate


title: Assessing the bias in samples of large online networks

abstract: We consider the sampling bias introduced in the study of online networks when collecting data through publicly available APIs (application programming interfaces). We assess differences between three samples of Twitter activity; the empirical context is given by political protests taking place in May 2012. We track online communication around these protests for the period of one month, and reconstruct the network of mentions and re-tweets according to the search and the streaming APIs, and to different filtering parameters. We find that smaller samples do not offer an accurate picture of peripheral activity; we also find that the bias is greater for the network of mentions, partly because of the higher influence of snowballing in identifying relevant nodes. We discuss the implications of this bias for the study of diffusion dynamics and political communication through social media, and advocate the need for more uniform sampling procedures to study online communication.


title: A lifelong learning perspective for mobile robot control

abstract: Designing robots that learn by themselves to perform complex real-world tasks is a still-open challenge for the field of robotics and artificial intelligence. In this paper the author presents the robot learning problem as a lifelong problem, in which a robot faces a collection of tasks over its entire lifetime. Such a scenario provides the opportunity to gather general-purpose knowledge that transfers across tasks. The author illustrates a particular leaning mechanism, explanation-based neural network learning, that transfers knowledge between related tasks via neural network action models. The learning approach is illustrated using a mobile robot, equipped with visual, ultrasonic and laser sensors. In less than 10 minutes operation time, the robot is able to learn to navigate to a marked target object in a natural office environment. >


REDUCTION OF THE SPARK IGNITION ENGINE EMISSIONS USING LIMESTONE FILTER


title: Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models

abstract: The Flickr30k dataset has become a standard benchmark for sentence-based image description. This paper presents Flickr30k Entities, which augments the 158k captions from Flickr30k with 244k coreference chains, linking mentions of the same entities across different captions for the same image, and associating them with 276k manually annotated bounding boxes. Such annotations are essential for continued progress in automatic image description and grounded language understanding. They enable us to define a new benchmark for localization of textual entity mentions in an image. We present a strong baseline for this task that combines an image-text embedding, detectors for common objects, a color classifier, and a bias towards selecting larger objects. While our baseline rivals in accuracy more complex state-of-the-art models, we show that its gains cannot be easily parlayed into improvements on such tasks as image-sentence retrieval, thus underlining the limitations of current methods and the need for further research.


title: Model-based traceability

abstract: Many organizations invest considerable cost and effort in building traceability matrices in order to comply with regulatory requirements or process improvement initiatives. Unfortunately, these matrices are frequently left un-used and project stakeholders continue to perform critical software engineering activities such as change impact analysis or requirements satisfaction assessment without benefit of the established traces. A major reason for this is the lack of a process framework and associated tools to support the use of these trace matrices in a strategic way. In this position paper, we present a model-based approach designed to help organizations gain full benefit from the traces they develop and to allow project stakeholders to plan, generate, and execute trace strategies in a graphical modeling environment. The approach includes a standard notation for capturing strategic traceability decisions in the form of a graph, and also notation for modeling reusable trace queries using augmented sequence diagrams. All of the model elements, including project specific data, are represented using XML. The approach is demonstrated through examples from a traffic simulator project composed of requirements, UML class diagrams, code, test cases, and test case results.


title: Predictive Trip Planning - Smart Routing in Smart Cities

abstract: Smart route planning gathers increasing interest as cities become crowded and jammed. We present a system for individual trip planning that incorporates future traffic hazards in routing. Future traffic conditions are computed by a Spatio-Temporal Random Field based on a stream of sensor readings. In addition, our approach estimates traffic flow in areas with low sensor coverage using a Gaussian Process Regression. The conditioning of spatial regression on intermediate predictions of a discrete probabilistic graphical model allows to incorporate historical data, streamed online data and a rich dependency structure at the same time. We demonstrate the system and test model assumptions with a real-world use-case from Dublin city, Ireland.


title: Stochastic Pooling for Regularization of Deep Convolutional Neural Networks

abstract: We introduce a simple and effective method for regularizing large convolutional neural networks. We replace the conventional deterministic pooling operations with a stochastic procedure, randomly picking the activation within each pooling region according to a multinomial distribution, given by the activities within the pooling region. The approach is hyper-parameter free and can be combined with other regularization approaches, such as dropout and data augmentation. We achieve state-of-the-art performance on four image datasets, relative to other approaches that do not utilize data augmentation.


title: A Systematic Survey of Program Comprehension through Dynamic Analysis

abstract: Program comprehension is an important activity in software maintenance, as software must be sufficiently understood before it can be properly modified. The study of a program's execution, known as dynamic analysis, has become a common technique in this respect and has received substantial attention from the research community, particularly over the last decade. These efforts have resulted in a large research body of which currently there exists no comprehensive overview. This paper reports on a systematic literature survey aimed at the identification and structuring of research on program comprehension through dynamic analysis. From a research body consisting of 4,795 articles published in 14 relevant venues between July 1999 and June 2008 and the references therein, we have systematically selected 176 articles and characterized them in terms of four main facets: activity, target, method, and evaluation. The resulting overview offers insight in what constitutes the main contributions of the field, supports the task of identifying gaps and opportunities, and has motivated our discussion of several important research directions that merit additional consideration in the near future.


The Current State of StarCraft AI Competitions and Bots


title: Big data analytics for supply chain management

abstract: A high number of business cases are characterized by an expanded complexity. This is based on increased collaboration between companies, customers and governmental organizations on one hand and more individual products and services on the other hand. Due to that, companies are planning to address these issues with Big Data solutions. This paper deals with Big Data solutions focusing on Supply Chains, which represents a key discipline for handling the increased collaboration next to vast amounts of exchanged data. Today, the main focus lays on optimizing Supply Chain Visibility to handle complexity and to support decision making for handling risks and interruptions along supply chains. Therefore, Big Data concepts and technologies will play a key role. This paper describes the current skituation, actual solutions and presents exemplary use-cases for illustration. A classification regarding the area of application and potential benefits arising from Big Data Analytics are also given. Furthermore, this paper outlines general technologies to show capabilities of Big Data analytics.


title: Cultivating the sense of belonging and motivating user participation in virtual communities: A social capital perspective

abstract: Abstract Virtual communities (VCs) are attracting more attention as they provide a platform for people to share experiences and knowledge, which may further impact their purchase decisions. From a social capital perspective, this study investigated factors that cultivate a VC member's sense of belonging and their effects on facilitating his participation in the VC in terms of the intentions to get and share experiences and knowledge. The results indicated that three factors that relate to three dimensions of social capital in the VC – familiarity with members in the VC from the structural dimension, perceived similarity with other members from the cognitive dimension, and trust in other members from the relational dimension – are all positively related to the sense of belonging, which affects intentions to get and share knowledge and mediates the relationships between social capital factors and a VC member's intentions to participate.


Three smart spectrophotometric methods for resolution of severely overlapped binary mixture of Ibuprofen and Paracetamol in pharmaceutical dosage form


title: Leaf area index estimation in vineyards using a ground-based LiDAR scanner

abstract: Estimation of grapevine vigour using mobile proximal sensors can provide an indirect method for determining grape yield and quality. Of the various indexes related to the characteristics of grapevine foliage, the leaf area index (LAI) is probably the most widely used in viticulture. To assess the feasibility of using light detection and ranging (LiDAR) sensors for predicting the LAI, several field trials were performed using a tractor-mounted LiDAR system. This system measured the crop in a transverse direction along the rows of vines and geometric and structural parameters were computed. The parameters evaluated were the height of the vines (H), the cross-sectional area (A), the canopy volume (V) and the tree area index (TAI). This last parameter was formulated as the ratio of the crop estimated area per unit ground area, using a local Poisson distribution to approximate the laser beam transmission probability within vines. In order to compare the calculated indexes with the actual values of LAI, the scanned vines were defoliated to obtain LAI values for different row sections. Linear regression analysis showed a good correlation (R 2 = 0.81) between canopy volume and the measured values of LAI for 1 m long sections. Nevertheless, the best estimation of the LAI was given by the TAI (R 2 = 0.92) for the same length, confirming LiDAR sensors as an interesting option for foliage characterization of grapevines. However, current limitations exist related to the complexity of data process and to the need to accumulate a sufficient number of scans to adequately estimate the LAI.


title: On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality

abstract: The classification problem is considered in which an output variable y assumes discrete values with respective probabilities that depend upon the simultaneous values of a set of input variables x = {x_1,....,x_n}. At issue is how error in the estimates of these probabilities affects classification error when the estimates are used in a classification rule. These effects are seen to be somewhat counter intuitive in both their strength and nature. In particular the bias and variance components of the estimation error combine to influence classification in a very different way than with squared error on the probabilities themselves. Certain types of (very high) bias can be canceled by low variance to produce accurate classification. This can dramatically mitigate the effect of the bias associated with some simple estimators like “naive” Bayes, and the bias induced by the curse-of-dimensionality on nearest-neighbor procedures. This helps explain why such simple methods are often competitive with and sometimes superior to more sophisticated ones for classification, and why “bagging/aggregating” classifiers can often improve accuracy. These results also suggest simple modifications to these procedures that can (sometimes dramatically) further improve their classification performance.


A SYSTEMATIC LITERATURE REVIEW ON AGILE PROJECT MANAGEMENT


title: Semantic smoothing for text clustering

abstract: In this paper we present a new semantic smoothing vector space kernel (S-VSM) for text documents clustering. In the suggested approach semantic relatedness between words is used to smooth the similarity and the representation of text documents. The basic hypothesis examined is that considering semantic relatedness between two text documents may improve the performance of the text document clustering task. For our experimental evaluation we analyze the performance of several semantic relatedness measures when embedded in the proposed (S-VSM) and present results with respect to different experimental conditions, such as: (i) the datasets used, (ii) the underlying knowledge sources of the utilized measures, and (iii) the clustering algorithms employed. To the best of our knowledge, the current study is the first to systematically compare, analyze and evaluate the impact of semantic smoothing in text clustering based on 'wisdom of linguists', e.g., WordNets, 'wisdom of crowds', e.g., Wikipedia, and 'wisdom of corpora', e.g., large text corpora represented with the traditional Bag of Words (BoW) model. Three semantic relatedness measures for text are considered; two knowledge-based (Omiotis[1] that uses WordNet, and WLM[2] that uses Wikipedia), and one corpus-based (PMI[3] trained on a semantically tagged SemCor version). For the comparison of different experimental conditions we use the BCubed F-Measure evaluation metric which satisfies all formal constraints of good quality cluster. The experimental results show that the clustering performance based on the S-VSM is better compared to the traditional VSM model and compares favorably against the standard GVSM kernel which uses word co-occurrences to compute the latent similarities between document terms.


title: Timing-IdeaGraph: A directed cognition graph approach for decision making based on temporal event sequences

abstract: Sequence pattern mining is an important mining task in data mining. However, most researches focus on improving the efficiency of algorithms, more and more attentions are paid to independently analyzing each frequent sequential pattern, which could bring biases to the final decisions. To understand the whole situation with sequence patterns, this paper proposes a systematic approach called Timing-IdeaGraph to build a directed cognition graph. Firstly, with consideration of big data on event sequences, an efficient algorithm is applied to capture frequent sequential patterns. Next, duplicate patterns are removed. After that, we merge relevant patterns and visualize them into a directed cognition graph. In order to make the approach human-centric, we propose an algorithm to identify bridge events and patterns which would proactively trigger human's deep cognition, e.g., creative design, for better decision making. Two real case studies are introduced to show how to use Timing-IdeaGraph in a computer supported cooperative environment.


title: Pass Transistor Adiabatic Logic with NMOS pull-down configuration in stand-by mode

abstract: This paper presents design of 2∶1 MUX using adiabatic logic style PAL2NSM and the results of energy dissipation are compared with simple CMOS 2∶1 MUX. PAL2NSM is a dual rail logic and it works on single phase AC supply voltage. Hence, instead of using constant supply voltage like in conventional CMOS circuits, power-clock supply (PC) which is in the form of sinusoidal or ramp signal is used as supply voltage. The simulated results are obtained using BSIM3v3 Spectre models in standard 0.18µm CMOS technology using Cadence design environment. The delay comparison is performed for pre-layout (schematic view) and post-layout simulations under different operating conditions.


title: Wide-Loading-Range Fully Integrated LDR With a Power-Supply Ripple Injection Filter

abstract: A low-dropout regulator for on-chip application with a power-supply rejection (PSR) boosting filter circuit for enhancing supply noise rejection at middle-to-high frequency over a wide loading range is presented in this brief. The idea has been analytically modeled and experimentally verified with a standard 0.13- μm CMOS process. The on-chip compensation capacitance is 1 pF. For the PSR filter design, the total on-chip capacitance is 20 pF. From the experimental results, the implemented regulator can operate with supply voltage of 1.2 V with nominal dropout voltage of 0.2 V at maximum load of 50 mA and IQ of 37.32 μA.


title: Recommendations on Queue Management and Congestion Avoidance

abstract: This memo presents two recommendations to the Internet community concerning measures to improve and preserve Internet performance. It presents a strong recommendation for testing, standardization, and widespread deployment of active queue management in routers, to improve the performance of today's Internet. It also urges a concerted effort of research, measurement, and ultimate deployment of router mechanisms to protect the Internet from flows that are not sufficiently responsive to congestion notification.


title: Parametric and Non-parametric User-aware Sentiment Topic Models

abstract: The popularity of Web 2.0 has resulted in a large number of publicly available online consumer reviews created by a demographically diverse user base. Information about the authors of these reviews, such as age, gender and location, provided by many on-line consumer review platforms may allow companies to better understand the preferences of different market segments and improve their product design, manufacturing processes and marketing campaigns accordingly. However, previous work in sentiment analysis has largely ignored these additional user meta-data. To address this deficiency, in this paper, we propose parametric and non-parametric User-aware Sentiment Topic Models (USTM) that incorporate demographic information of review authors into topic modeling process in order to discover associations between market segments, topical aspects and sentiments. Qualitative examination of the topics discovered using USTM framework in the two datasets collected from popular online consumer review platforms as well as quantitative evaluation of the methods utilizing those topics for the tasks of review sentiment classification and user attribute prediction both indicate the utility of accounting for demographic information of review authors in opinion mining.


title: FanStore: Enabling Efficient and Scalable I/O for Distributed Deep Learning

abstract: Emerging Deep Learning (DL) applications introduce heavy I/O workloads on computer clusters. The inherent long lasting, repeated, and random file access pattern can easily saturate the metadata and data service and negatively impact other users. In this paper, we present FanStore, a transient runtime file system that optimizes DL I/O on existing hardware/software stacks. FanStore distributes datasets to the local storage of compute nodes, and maintains a global namespace. With the techniques of system call interception, distributed metadata management, and generic data compression, FanStore provides a POSIX-compliant interface with native hardware throughput in an efficient and scalable manner. Users do not have to make intrusive code changes to use FanStore and take advantage of the optimized I/O. Our experiments with benchmarks and real applications show that FanStore can scale DL training to 512 compute nodes with over 90\% scaling efficiency.


title: UVM Based Reusable Verification IP for Wishbone Compliant SPI Master Core

abstract: The System on Chip design industry relies heavily on functional verification to ensure that the designs are bug-free. As design engineers are coming up with increasingly dense chips with much functionality, the functional verification field has advanced to provide modern verification techniques. In this paper, we present verification of a wishbone compliant Serial Peripheral Interface (SPI) Master core using a System Verilog based standard verification methodology, the Universal Verification Methodology (UVM). The reason for using UVM factory pattern with parameterized classes is to develop a robust and reusable verification IP. SPI is a full duplex communication protocol used to interface components most likely in embedded systems. We have verified an SPI Master IP core design that is wishbone compliant and compatible with SPI protocol and bus and furnished the results of our verification. We have used QuestaSim for simulation and analysis of waveforms, Integrated Metrics Center, Cadence for coverage analysis. We also propose interesting future directions for this work in developing reliable systems.


title: Deep Residual Convolutional Network for Natural Image Denoising and Brightness Enhancement

abstract: Because of the low-light shooting environment, the camera sensor will loss huge details and fuzzy edge. A deep low-light residual convolutional network (LRCNN) is proposed in this paper, which utilizes the sparse coding feature to get the true signal and adaptively adjusts the image exposure in the low-light state. The residual connections in LRCNN help us preserve more potential detail information in the original picture and accelerate the training speed of the network. Many existing image enhancement algorithms only are able to address one aspect of image problems. We designed a neural network system which could deal with many image processing problems at the same time. The experimental results show that our neural network system well optimizes the images that affected by darkness and noise. It also avoids an artificial appearance in generating the image patches.


title: A single supply biopotential amplifier

abstract: A biopotential amplifier for single supply operation is presented. It uses a Driven Right Leg Circuit (DRL) to drive the patient's body to a DC common mode voltage, centering biopotential signals with respect to the amplifier's input voltage range. This scheme ensures proper range operation when a single power supply is used. The circuit described is especially suited for low consumption, battery-powered applications, requiring a single battery and avoiding switching voltage inverters to achieve dual supplies. The generic circuit is described and, as an example, a biopotential amplifier with a gain of 60 dB and a DC input range of ±200 mV was implemented using low power operational amplifiers. A Common Mode Rejection Ratio (CMRR) of 126 dB at 50 Hz was achieved without trimming.


title: Probabilistic (logic) programming concepts

abstract: A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. This makes it hard to understand the underlying programming concepts and appreciate the differences between the different languages. To obtain a better understanding of probabilistic programming, we identify a number of core programming concepts underlying the primitives used by various probabilistic languages, discuss the execution mechanisms that they require and use these to position and survey state-of-the-art probabilistic languages and their implementation. While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been considered for over 20 years.


title: What Is a Case Study and What Is It Good for

abstract: This paper aims to clarify the meaning, and explain the utility, of the case study method, a method often practiced but little understood. A “case study,” I argue, is best defined as an intensive study of a single unit with an aim to generalize across a larger set of units. Case studies rely on the same sort of covariational evidence utilized in non-case study research. Thus, the case study method is correctly understood as a particular way of defining cases, not a way of analyzing cases or a way of modeling causal relations. I show that this understanding of the subject illuminates some of the persistent ambiguities of case study work, ambiguities that are, to some extent, intrinsic to the enterprise. The travails of the case study within the discipline of political science are also rooted in an insufficient appreciation of the methodological tradeoffs that this method calls forth. This paper presents the familiar contrast between case study and non-case study work as a series of characteristic strengths and weaknesses— affinities —rather than as antagonistic approaches to the empirical world. In the end, the perceived hostility between case study and non-case study research is largely unjustified and, perhaps, deserves to be regarded as a misconception. Indeed, the strongest conclusion to arise from this methodological examination concerns the complementarity of single-unit and cross-unit research designs.


title: Big five personality and adolescent Internet addiction: The mediating role of coping style.

abstract: Abstract This study examined the unique associations between big five personality traits and adolescent Internet addiction (IA), as well as the mediating role of coping style underlying these relations. Our theoretical model was tested with 998 adolescents. Participants provided self-report data on demographic variables, big five personality traits, coping style, and IA. After controlling for demographic variables, it was found that agreeableness and conscientiousness were negatively associated with IA, whereas extraversion, neuroticism, and openness to experience were positively associated with IA. Mediation analyses further indicated that conscientiousness had an indirect impact on adolescent IA through decreased emotion-focused coping, whereas extraversion, neuroticism, openness to experience had indirect impacts on adolescent IA through increased emotion-focused coping. In contrast, problem-focused coping had no mediating role. These findings suggest that emotion-focused coping may, in part, account for the association between big five personality and adolescent IA.


title: Allergenicity and cross-reactivity of cat and dog allergenic extracts.

abstract: This study aims to confirm that cat allergen 1 (CAT-1) is a major allergenic determinant in cat-sensitive patients, and to further define the role of other determinants, as well as to identify the determinants responsible for the cross-reactivity between cat and dog extracts. Firstly, the allergenic determinant with an electrophoretic mobility of 18 kD (corresponding to CAT-1) is indeed a major allergenic determinant being recognized by the majority (75%) of cat-sensitive subjects. Secondly, the cross-reactivity between the two species was confirmed by RAST inhibition. Cat and dog soluble allergens could inhibit, to variable degrees, the binding of serum IgE from cat- and dog-sensitive patients to insolubilized allergens. binding of serum IgE from subjects sensitive only to cats was inhibited by cat extracts only. These observations suggest the presence of determinants common to the two sources of extracts, and others specific for each species. These data were confirmed by immunoblot analysis. Indeed, an allergenic determinant of 69 kD was found in both cat and dog extracts. Conversely the allergenic determinants with an electrophoretic mobility of 18 and 32 kD were found only in cat extracts, and those at 22 and 24 kD were dog specific. However, surprisingly, serum IgE antibodies from patients sensitive only to cats reacted on immunoblot differently from those of both cat- and dog-allergic subjects. Indeed, the 18 kD determinant was the only one recognized by serum IgE antibodies from subjects sensitive to cats only, as opposed to the patients allergic to both species: then, the 69 kD determinant was strongly recognized and the 18 kD only slightly recognized.(ABSTRACT TRUNCATED AT 250 WORDS)


title: Standards based generation of a virtual factory model

abstract: Developing manufacturing simulation models usually requires experts with knowledge of multiple areas including manufacturing, modeling, and simulation software. The expertise requirements increase for virtual factory models that include representations of manufacturing at multiple resolution levels. This paper reports on an initial effort to automatically generate virtual factory models using manufacturing configuration data in standard formats as the primary input. The execution of the virtual factory generates time series data in standard formats mimicking a real factory. Steps are described for auto-generation of model components in a software environment primarily oriented for model development via a graphic user interface. Advantages and limitations of the approach and the software environment used are discussed. The paper concludes with a discussion of challenges in verification and validation of the virtual factory prototype model with its multiple hierarchical models and future directions.


title: Schema Extraction for Tabular Data on the Web

abstract: Tabular data is an abundant source of information on the Web, but remains mostly isolated from the latter's interconnections since tables lack links and computer-accessible descriptions of their structure. In other words, the schemas of these tables -- attribute names, values, data types, etc. -- are not explicitly stored as table metadata. Consequently, the structure that these tables contain is not accessible to the crawlers that power search engines and thus not accessible to user search queries. We address this lack of structure with a new method for leveraging the principles of table construction in order to extract table schemas. Discovering the schema by which a table is constructed is achieved by harnessing the similarities and differences of nearby table rows through the use of a novel set of features and a feature processing scheme. The schemas of these data tables are determined using a classification technique based on conditional random fields in combination with a novel feature encoding method called logarithmic binning, which is specifically designed for the data table extraction task. Our method provides considerable improvement over the well-known WebTables schema extraction method. In contrast with previous work that focuses on extracting individual relations, our method excels at correctly interpreting full tables, thereby being capable of handling general tables such as those found in spreadsheets, instead of being restricted to HTML tables as is the case with the WebTables method. We also extract additional schema characteristics, such as row groupings, which are important for supporting information retrieval tasks on tabular data.


title: Centralized Path Planning for Multiple Robots: Optimal Decoupling into Sequential Plans

abstract: We develop an algorithm to decouple a multi-robot path planning problem into subproblems whose solutions can be executed sequentially. Given an external path planner for general configuration spaces, our algorithm finds an execution sequence that minimizes the dimension of the highest-dimensional subproblem over all possible execution sequences. If the external planner is complete (at least up to this minimum dimension), then our algorithm is complete because it invokes the external planner only for spaces of dimension at most this minimum. Our algorithm can decouple and solve path planning problems with many robots, even with incomplete external planners. We show scenarios involving 16 to 65 robots, where our algorithm solves planning problems of dimension 32 to 130 using a PRM planner for at most eight dimensions. 1


title: High-level speaker verification with support vector machines

abstract: Recently, high-level features such as word idiolect, pronunciation, phone usage, prosody, etc., have been successfully used in speaker verification. The benefit of these features was demonstrated in the NIST extended data task for speaker verification; with enough conversational data, a recognition system can become "familiar" with a speaker and achieve excellent accuracy. Typically, high-level-feature recognition systems produce a sequence of symbols from the acoustic signal and then perform recognition using the frequency and co-occurrence of symbols. We propose the use of support vector machines for performing the speaker verification task from these symbol frequencies. Support vector machines have been applied to text classification problems with much success. A potential difficulty in applying these methods is that standard text classification methods tend to "smooth" frequencies which could potentially degrade speaker verification. We derive a new kernel based upon standard log likelihood ratio scoring to address limitations of text classification methods. We show that our methods achieve significant gains over standard methods for processing high-level features.


title: The trade-off between taxi time and fuel consumption in airport ground movement

abstract: Environmental issues play an important role across many sectors. This is particularly the case in the air transportation industry. One area which has remained relatively unexplored in this context is the ground movement problem for aircraft on the airport’s surface. Aircraft have to be routed from a gate to a runway and vice versa and a key area of study is whether fuel burn and environmental impact improvements will best result from purely minimising the taxi times or whether it is also important to avoid multiple acceleration phases. This paper presents a newly developed multi-objective approach for analysing the trade-off between taxi time and fuel consumption during taxiing. The approach consists of a combination of a graph-based routing algorithm and a population adaptive immune algorithm to discover different speed profiles of aircraft. Analysis with data from a European hub airport has highlighted the impressive performance of the new approach. Furthermore, it is shown that the trade-off between taxi time and fuel consumption is very sensitive to the fuel-related objective function which is used.


title: SDR-based LFM signal generator for radar/SAR systems

abstract: In this paper design and implementing process of a compact, low-cost and user-friendly probe signal generator for pulse radars and constant wave radars based on software-defined radio (SDR) are presented [1]. In his research the author uses the Universal Software Radio Peripheral (USRP) as SDR developed by the National Instruments [2]. Proposed device was designed for a wide range of the radar systems, however, the first tests were held in conjunction with the synthetic aperture radar in the continuous wave mode [3], [4]. This application required a base band up to 40MHz, which after amplification in analog front-end x24 gave the band of 960 MHz on the output transmitting antenna, providing a range resolution of about 15cm.


title: Advanced interconnect and antenna-in-package design for millimeter-wave 5G communications

abstract: This paper presents a high-gain millimeter-wave array antenna highly integrated on a single organic chip package. The multi-phased beam steering capability is enabled to reduce multipath fading and over-the-air applications around 30GHz. To improve antenna bandwidth, gain and radiation efficiency, a dual-patch antenna is built on a superstrate structure. A number of design attributes, such as patch, dipole, and slot sizes have been studied. Advanced interconnect between die and antennas in multi-layered dielectric is optimized for transceiver integration and packaging with minimal insertion loss and coupling between the antenna feed-lines. Good correlation is reached among measurement and full-wave electromagnetic simulations. System-level characterization of a mechanically assembled transceiver module is presented with antenna radiation patterns.


title: Digital beethoven — An android based virtual piano

abstract: Smart phones with high resolution cameras have enabled innovative computer vision applications that apply techniques like real-time image processing and virtual reality to provide a close approximation of reality. In this paper, we present an Android application that allows users to play a virtual piano by using a keyboard drawn on a piece of paper. The application allows the user to point the camera of a hand held device towards the keyboard, and process image of the paper keyboard in real time. The application then detects fingers placed on a key and after key detection plays the corresponding sounds. Initial results demonstrate the potential of such an application in providing a viable replacement for heavy and expensive instruments.


title: A Revision Restoring Projection after Nipple Reconstruction by Burying Four Triangular Dermal Flaps

abstract: Numerous techniques have been used to achieve long-term projection of the nipple following nipple-areola reconstruction. However, the reconstructed nipple loses projection over time. We describe a technique that uses local flaps to improve the lost projection of reconstructed nipples.Between November 2013 and March 2015, nine patients (11 nipples) underwent revisional nipple reconstruction for lost projection. Only C-H nipple reconstructions were included in this study. The medical history of each patient was reviewed and photographs were taken in front and lateral views. All patients attended routine follow-up visits. Deepithelialized triangular flaps were made on all four sides of the nipple and buried in the opposite corners in order to augment the volume of the nipple. Anchoring sutures were used to attach each triangular flap on the side opposite their point of origin, and the resulting defects were closed directly.This procedure was used successfully in nine patients (11 nipples). Adequate projection was achieved in all patients and no complications occurred. The average nipple height was 3 mm before operation, 7 mm one day after operation, 5 mm at the six-month follow-up, and 5 mm at the 12-month follow-up. The average nipple-areolar angle was 164° before the operation, 111° one day after the operation, 130° at the six-month follow-up, and 133° at the 12-month follow-up.The method described provides a solution to the loss of projection in reconstructed nipples. We recommend this technique because it leads to better projection, greater volume, and a more natural shape.


title: Exploiting behaviors of communities of twitter users for link prediction

abstract: Currently, online social networks and social media have become increasingly popular showing an exponential growth. This fact have attracted increasing research interest and, in turn, facilitating the emergence of new interdisciplinary research directions, such as social network analysis. In this scenario, link prediction is one of the most important tasks since it deals with the problem of the existence of a future relation among members in a social network. Previous techniques for link prediction were based on structural (or topological) information. Nevertheless, structural information is not enough to achieve a good performance in the link prediction task on large-scale social networks. Thus, the use of additional information, such as interests or behaviors that nodes have into their communities, may improve the link prediction performance. In this paper, we analyze the viability of using a set of simple and non-expensive techniques that combine structural with community information for predicting the existence of future links in a large-scale online social network, such as Twitter. Twitter, a microblogging service, has emerged as a useful source of informative data shared by millions of users whose relationships require no reciprocation. Twitter network was chosen because it is not well understood, mainly due to the occurrence of directed and asymmetric links yet. Experiments show that our proposals can be used efficiently to improve unsupervised and supervised link prediction task in a directed and asymmetric large-scale network.


Image Segmentation : A Survey Neelesh


title: Failure prediction for HPC systems and applications

abstract: As large-scale systems evolve towards post-petascale computing, it is crucial to focus on providing fault-tolerance strategies that aim to minimize fault's effects on applications. By far the most popular technique is the checkpoint-restart strategy. A complement to this classical approach is failure avoidance, by which the occurrence of a fault is predicted and proactive measures are taken. This requires a reliable prediction system to anticipate failures and their locations. One way of offering prediction is by the analysis of system logs generated during production by large-scale systems. Current research in this field presents a number of limitations that make them unusable for running on real production high-performance computing (HPC) systems. Based on our observations that different failures have different distributions and behaviours, we propose a novel hybrid approach that combines signal analysis with data mining in order to overcome current limitations. We show that by analysing each event according to its specific behaviour, our prediction provides a precision of over 90% and its able to discover about 50% of all failures in a system, result which allows its integration in proactive fault tolerance protocols.


title: EmojiNet: An Open Service and API for Emoji Sense Discovery

abstract: This paper presents the release of EmojiNet, the largest machine-readable emoji sense inventory that links Unicode emoji representations to their English meanings extracted from the Web. EmojiNet is a dataset consisting of: (i) 12,904 sense labels over 2,389 emoji, which were extracted from the web and linked to machine-readable sense definitions seen in BabelNet, (ii) context words associated with each emoji sense, which are inferred through word embedding models trained over Google News corpus and a Twitter message corpus for each emoji sense definition, and (iii) recognizing discrepancies in the presentation of emoji on different platforms, specification of the most likely platform-based emoji sense for a selected set of emoji. The dataset is hosted as an open service with a REST API and is available at this http URL The development of this dataset, evaluation of its quality, and its applications including emoji sense disambiguation and emoji sense similarity are discussed.


title: 5GNOW: non-orthogonal, asynchronous waveforms for future mobile applications

abstract: This article provides some fundamental indications about wireless communications beyond LTE/LTE-A (5G), representing the key findings of the European research project 5GNOW. We start with identifying the drivers for making the transition to 5G networks. Just to name one, the advent of the Internet of Things and its integration with conventional human-initiated transmissions creates a need for a fundamental system redesign. Then we make clear that the strict paradigm of synchronism and orthogonality as applied in LTE prevents efficiency and scalability. We challenge this paradigm and propose new key PHY layer technology components such as a unified frame structure, multicarrier waveform design including a filtering functionality, sparse signal processing mechanisms, a robustness framework, and transmissions with very short latency. These components enable indeed an efficient and scalable air interface supporting the highly varying set of requirements originating from the 5G drivers.


title: A Biologically Inspired System for Action Recognition

abstract: We present a biologically-motivated system for the recognition of actions from video sequences. The approach builds on recent work on object recognition based on hierarchical feedforward architectures [25, 16, 20] and extends a neurobiological model of motion processing in the visual cortex [10]. The system consists of a hierarchy of spatio-temporal feature detectors of increasing complexity: an input sequence is first analyzed by an array of motion- direction sensitive units which, through a hierarchy of processing stages, lead to position-invariant spatio-temporal feature detectors. We experiment with different types of motion-direction sensitive units as well as different system architectures. As in [16], we find that sparse features in intermediate stages outperform dense ones and that using a simple feature selection approach leads to an efficient system that performs better with far fewer features. We test the approach on different publicly available action datasets, in all cases achieving the highest results reported to date.


title: On the "naturalness" of buggy code

abstract: Real software, the kind working programmers produce by the kLOC to solve real-world problems, tends to be “natural”, like speech or natural language; it tends to be highly repetitive and predictable. Researchers have captured this naturalness of software through statistical models and used them to good effect in suggestion engines, porting tools, coding standards checkers, and idiom miners. This suggests that code that appears improbable, or surprising, to a good statistical language model is “unnatural” in some sense, and thus possibly suspicious. In this paper, we investigate this hypothesis. We consider a large corpus of bug fix commits (ca. 7,139), from 10 different Java projects, and focus on its language statistics, evaluating the naturalness of buggy code and the corresponding fixes. We find that code with bugs tends to be more entropic (i.e. unnatural), becoming less so as bugs are fixed. Ordering files for inspection by their average entropy yields cost-effectiveness scores comparable to popular defect prediction methods. At a finer granularity, focusing on highly entropic lines is similar in cost-effectiveness to some well-known static bug finders (PMD, FindBugs) and or- dering warnings from these bug finders using an entropy measure improves the cost-effectiveness of inspecting code implicated in warnings. This suggests that entropy may be a valid, simple way to complement the effectiveness of PMD or FindBugs, and that search-based bug-fixing methods may benefit from using entropy both for fault-localization and searching for fixes.


title: Applications of machine learning in animal behaviour studies

abstract: In many areas of animal behaviour research, improvements in our ability to collect large and detailed data sets are outstripping our ability to analyse them. These diverse, complex and often high-dimensional data sets exhibit nonlinear dependencies and unknown interactions across multiple variables, and may fail to conform to the assumptions of many classical statistical methods. The field of machine learning provides methodologies that are ideally suited to the task of extracting knowledge from these data. In this review, we aim to introduce animal behaviourists unfamiliar with machine learning (ML) to the promise of these techniques for the analysis of complex behavioural data. We start by describing the rationale behind ML and review a number of animal behaviour studies where ML has been successfully deployed. The ML framework is then introduced by presenting several unsupervised and supervised learning methods. Following this overview, we illustrate key ML approaches by developing data analytical pipelines for three different case studies that exemplify the types of behavioural and ecological questions ML can address. The first uses a large number of spectral and morphological characteristics that describe the appearance of pheasant, Phasianus colchicus, eggs to assign them to putative clutches. The second takes a continuous data stream of feeder visits from PIT (passive integrated transponder)-tagged jackdaws, Corvus monedula, and extracts foraging events from it, which permits the construction of social networks. Our final example uses aerial images to train a classifier that detects the presence of wildebeest, Connochaetes taurinus, to count individuals in a population. With the advent of cheaper sensing and tracking technologies an unprecedented amount of data on animal behaviour is becoming available. We believe that ML will play a central role in translating these data into scientific knowledge and become a useful addition to the animal behaviourist's analytical toolkit.


title: Between-Class Learning for Image Classification

abstract: In this paper, we propose a novel learning method for image classification called Between-Class learning (BC learning). We generate between-class images by mixing two images belonging to different classes with a random ratio. We then input the mixed image to the model and train the model to output the mixing ratio. BC learning has the ability to impose a constraint on the shape of the feature distributions, and thus the generalization ability is improved. BC learning is originally a method developed for sounds, which can be digitally mixed. Mixing two image data does not appear to make sense; however, we argue that because convolutional neural networks have an aspect of treating input data as waveforms, what works on sounds must also work on images. First, we propose a simple mixing method using internal divisions, which surprisingly proves to significantly improve performance. Second, we propose a mixing method that treats the images as waveforms, which leads to a further improvement in performance. As a result, we achieved 19.4% and 2.26% top-1 errors on ImageNet-1K and CIFAR-10, respectively.


title: Survey on Computer Vision for UAVs: Current Developments and Trends

abstract: During last decade the scientific research on Unmanned Aerial Vehicless (UAVs) increased spectacularly and led to the design of multiple types of aerial platforms. The major challenge today is the development of autonomously operating aerial agents capable of completing missions independently of human interaction. To this extent, visual sensing techniques have been integrated in the control pipeline of the UAVs in order to enhance their navigation and guidance skills. The aim of this article is to present a comprehensive literature review on vision based applications for UAVs focusing mainly on current developments and trends. These applications are sorted in different categories according to the research topics among various research groups. More specifically vision based position-attitude control, pose estimation and mapping, obstacle detection as well as target tracking are the identified components towards autonomous agents. Aerial platforms could reach greater level of autonomy by integrating all these technologies onboard. Additionally, throughout this article the concept of fusion multiple sensors is highlighted, while an overview on the challenges addressed and future trends in autonomous agent development will be also provided.


title: Dynamic Resource Allocation Algorithm for Container-Based Service Computing

abstract: Cloud computing and virtualization technologies play important roles in modern service-oriented computing paradigm. More conventional services are being migrated to virtualized computing environments to achieve flexible deployment and high availability. We introduce a schedule algorithm based on fuzzy inference system (FIS), for global container resource allocation by evaluating nodes' statuses using FIS. We present the approaches to build containerized test environment and validates the effectiveness of the resource allocation policies by running sample use cases. Experiment results show that the presented infrastructure and schema derive optimal resource configurations and significantly improves the performance of the cluster.


Education and Development: Moving Beyond Labour Productivity


title: 2005a), ‘The benefits of frequent positive affect: Does happiness lead to success

abstract: Numerous studies show that happy individuals are successful across multiple life domains, including marriage, friendship, income, work performance, and health. The authors suggest a conceptual model to account for these findings, arguing that the happiness–success link exists not only because success makes people happy, but also because positive affect engenders success. Three classes of evidence — crosssectional, longitudinal, and experimental — are documented to test their model. Relevant studies are described and their effect sizes combined meta-analytically. The results reveal that happiness is associated with and precedes numerous successful outcomes, as well as behaviors paralleling success. Furthermore, the evidence suggests that positive affect — the hallmark of well-being — may be the cause of many of the desirable characteristics, resources, and successes correlated with happiness. Limitations, empirical issues, and important future research questions are discussed.


title: A core library for robust numeric and geometric computation

abstract: Nonrobustness is a well-known problem in many areas of computational science. Until now, robustness techniques and the construction of robust algorithms have been the province of experts in this field of research. We describe a new C/C++ library (CORE) for robust numeric and geometric computation based on the principles of Exact Geometric Computation (EGC). Through our library, for the first time, any programmer can write robust and efficient algorithms. The Core Library is based on a novel numerical core that is powerful enough to support EGC for algebraic problems. This is coupled with a simple delivery mechanism which transparently extends conventional C/C++ programs into robust codes. We are currently addressing efficiency issues in our library: (a) at the compiler and language level, (b) at the level of incorporating EGC techniques, as well as the (c) the system integration of both (a) and (b). Pilot experimental results are described. The basic library is availableathttp://cs.nyu.edu/exact/core/andthe C++-to-C compiler is under development.


title: Design of Efficient Microstrip Linear Antenna Array for 5G Communications Systems

abstract: This paper presents design of an efficient microstrip antenna array for 5G communication systems. The antenna operates at 28 GHz and it is formed of 16 elements of rectangular patches arranged in a linear configuration. The single element has gap-coupled feeder and the antenna is designed on low-loss Teflon based RT/duroid 5880 substrate with dielectric constant of 2.2 and substrate thickness of 0.381 mm. A defected ground structure (DGS) in the form of rectangular slots is employed in the array design to reduce the mutual coupling between adjacent elements. The antenna is simulated using CST microwave studio and the results show that the 16-element array has a 10-dB bandwidth greater than 2 GHz and a maximum gain of 17.4 dBi at 28.4 GHz. The performance of the proposed antenna satisfies the requirements of 5G communications systems in terms of high gain, high radiation efficiency and adequate bandwidth.


title: Identifying and Exploiting Windows Kernel Race Conditions via Memory Access Patterns

abstract: The overall security posture of operating systems’ kernels – and specifically the Microsoft Windows NT kernel – against both local and remote attacks has visibly improved throughout the last decade. In our opinion, this is primarily due to the increasing interest in kernel-mode vulnerabilities by both white and black-hat parties, as they ultimately allow attackers to subvert the currently widespread defense-in-depth technologies implemented on operating system level, such as sandboxing, or other features enabling better management of privileges within the execution environment (e.g. Mandatory Integrity Control). As a direct outcome, Microsoft has invested considerable resources in both improving the development process with programs like Secure Development Lifecycle, and explicitly hardening the kernel against existing attacks; the latter was particularly characteristic to Windows 8, which introduced more kernel security improvements than any NT-family system thus far[11]. In this paper, we discuss the concept of employing CPU-level operating system instrumentation to identify potential instances of local race conditions in fetching user-mode input data within system call handlers and other user-facing ring-0 code, and how it was successfully implemented in the Bochspwn project. Further in the document, we present a number of generic techniques easing the exploitation of timing bound kernel vulnerabilities and show how these techniques can be employed in practical attacks against three exemplary vulnerabilities discovered by Bochspwn. In the last sections, we conclusively provide some suggestions on related research areas that haven’t been fully explored and require further development.


title: Investigating the Characteristics of Open Spaces to Enhance Social Interactions in Neighborhood Environments

abstract: In recent decades, stability and sustainable development have been proposed as one of the key areas to improve human living conditions. Meanwhile, increasing social interactions as one of social sustainability measures in the neighborhood environments has been considered because the residential environments are parts of larger urban areas and play an important role in the sustainability of megacities. In this study, according to the importance of neighborhood environments, measures to design open space in order to promote social sustainability have been discussed. The method used in this study was cross-sectional and to gather data, library-documentary sources and field observations were used. Initially, social interactions criteria in neighborhoods were recognized, then effective characteristics in designing open spaces including caring traffic restrictions, promoting walking, and considering the residents in different ages were investigated.


title: STRADS: a distributed framework for scheduled model parallel machine learning

abstract: Machine learning (ML) algorithms are commonly applied to big data, using distributed systems that partition the data across machines and allow each machine to read and update all ML model parameters --- a strategy known as data parallelism. An alternative and complimentary strategy, model parallelism, partitions the model parameters for non-shared parallel access and updates, and may periodically repartition the parameters to facilitate communication. Model parallelism is motivated by two challenges that data-parallelism does not usually address: (1) parameters may be dependent, thus naive concurrent updates can introduce errors that slow convergence or even cause algorithm failure; (2) model parameters converge at different rates, thus a small subset of parameters can bottleneck ML algorithm completion. We propose scheduled model parallelism (SchMP), a programming approach that improves ML algorithm convergence speed by efficiently scheduling parameter updates, taking into account parameter dependencies and uneven convergence. To support SchMP at scale, we develop a distributed framework STRADS which optimizes the throughput of SchMP programs, and benchmark four common ML applications written as SchMP programs: LDA topic modeling, matrix factorization, sparse least-squares (Lasso) regression and sparse logistic regression. By improving ML progress per iteration through SchMP programming whilst improving iteration throughput through STRADS we show that SchMP programs running on STRADS outperform non-model-parallel ML implementations: for example, SchMP LDA and SchMP Lasso respectively achieve 10x and 5x faster convergence than recent, well-established baselines.


title: Eyewear Computing - Augmenting the Human with Head-mounted Wearable Assistants (Dagstuhl Seminar 16042).

abstract: The seminar was composed of workshops and tutorials on head-mounted eye tracking, egocentric vision, optics, and head-mounted displays. ::: The seminar welcomed 30 academic and industry researchers from Europe, the US, and Asia with a diverse background, including wearable and ubiquitous computing, computer vision, developmental psychology, optics, and human-computer interaction. ::: In contrast to several previous Dagstuhl seminars, we used an ignite talk format to reduce the time of talks to one half-day and to leave the rest of the week for hands-on sessions, group work, general discussions, and socialising. The key results of this seminar are 1) the identification of key research challenges and summaries of breakout groups on multimodal eyewear computing, egocentric vision, security and privacy issues, skill augmentation and task guidance, eyewear computing for gaming, as well as prototyping of VR applications, 2) a list of datasets and research tools for eyewear computing, 3) three small-scale datasets recorded during the seminar, 4) an article in ACM Interactions entitled "Eyewear Computers for Human-Computer Interaction", as well as 5) two follow-up workshops on "Egocentric Perception, Interaction, and Computing" at the European Conference on Computer Vision (ECCV) as well as "Eyewear Computing" at the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp).


title: ALTRUISM OR BENEFIT SEEKING? EXAMINING THE EFFECTS OF PERSONAL CHARACTERISTICS AND BEHAVIORAL BELIEFS ON ONLINE SHOPPER’S ATTITUDE AND INTENTION TO BE AN ONLINE GROUP BUYING INITIATOR

abstract: Among the various online shopping business models, one that specifically attracts the attention of Internet users is “online group buying. which is characterized by group negotiation benefits and online word-of-mouth effects. Extant online group buying research has examined topics covering pricing mechanisms, pricing models, sales performance, and online users’ motivation to participation. Whereas online group buying requires close collaboration among the group buying website, the supplier, the initiator, and the participants, little attention has been paid on the role of the initiator. This study investigates the antecedents of behavioral intention of online group buying initiators from the social exchange theory perspective. The aim is to understand the influence of social exchange factors and the effects of cognitive psychology determinants on the behavioral intention to be an online group buying initiator. This study employed an online survey method, in which a total of 428 valid responses were acquired. Results indicated that self-efficacy, altruism, perceived benefits, and perceived risks significantly affect the attitude toward being an online group buying initiator. Moreover, perceived behavioral control, subjective norm, and attitude positively affect the behavioral intention to be an online group buying initiator. Further analysis also showed that the experience of being an online group buying initiator plays an important moderating role on certain paths between research constructs. For Internet users who had experience in being online group buying initiators, altruism positively and perceived risks negatively affect attitude. In addition, subjective norm positively influences the behavioral intention to be online group buying initiators for inexperienced Internet users. Besides the discussion of research findings, we offer suggested directions for future research.


title: Real-time Upper-body Human Pose Estimation using a Depth Camera

abstract: Automatic detection and pose estimation of humans is an important task in Human-Computer Interaction (HCI), user interaction and event analysis. This paper presents a model based approach for detecting and estimating human pose by fusing depth and RGB color data from monocular view. The proposed system uses Haar cascade based detection and template matching to perform tracking of the most reliably detectable parts namely, head and torso. A stick figure model is used to represent the detected body parts. The fitting is then performed independently for each limb, using the weighted distance transform map. The fact that each limb is fitted independently speeds-up the fitting process and makes it robust, avoiding the combinatorial complexity problems that are common with these types of methods. The output is a stick figure model consistent with the pose of the person in the given input image. The algorithm works in real-time and is fully automatic and can detect multiple non-intersecting people.


title: Efficient protocols for deterministic secure quantum communication using GHZ-like states

abstract: Two protocols for deterministic secure quantum communication (DSQC) using GHZ-like states have been proposed. It is shown that one of these protocols can be modified to an equivalent but more efficient protocol of quantum secure direct communication (QSDC). Security and efficiency of the proposed protocols are analyzed in detail and are critically compared with the existing protocols. It is shown that the proposed protocols are highly efficient. It is also shown that all the physical systems where dense coding is possible can be used to design maximally efficient protocol of DSQC and QSDC. Further, it is shown that dense coding is sufficient but not essential for DSQC and QSDC protocols of the present kind. We have shown that there exist a large class of quantum state which can be used to design maximally efficient DSQC and QSDC protocols of the present kind. It is further, observed that maximally efficient QSDC protocols are more efficient than their DSQC counterparts. This additional efficiency arises at the cost of message transmission rate.


title: Seamless Transfer of Single-Phase Grid-Interactive Inverters Between Grid-Connected and Stand-Alone Modes

abstract: This paper presents a novel seamless transfer of single-phase grid-interactive inverters between grid-connected and stand-alone modes. The grid-connected inverter should operate in grid-tied and off-grid modes in order to provide power to the emergency load during system outages. However, the grid current controller and the output voltage controller are switched between the two modes, so the outputs of both controllers may not be equal during the transfer instant, which will cause the current or voltage spikes during the switching process. The transfer between the two controllers does not exist in the proposed method. In grid-tied mode, the voltage controller is used for compensating the filter capacitor current, and the current controller is used to control the grid current. In stand-alone mode, the voltage controller is used to regulate the output voltage, whereas the output of the current controller is zero. With the proposed control method, the seamless transfer can be achieved between both modes, even in polluted grid voltage. The principle and realization conditions of the control methods at both modes are analyzed. The detailed process of the seamless transfer between the two modes is illustrated. Finally, the simulation and experimental results verify the theoretical analysis.


title: Emotional labor and burnout: Comparing two perspectives of "people work".

abstract: Abstract Although it has often been presumed that jobs involving “people work” (e.g., nurses, service workers) are emotionally taxing (Maslach & Jackson, 1982), seldom is the emotional component of these jobs explicitly studied. The current study compared two perspectives of emotional labor as predictors of burnout beyond the effects of negative affectivity: job-focused emotional labor (work demands regarding emotion expression) and employee-focused emotional labor (regulation of feelings and emotional expression). Significant differences existed in the emotional demands reported by five occupational groupings. The use of surface-level emotional labor, or faking, predicted depersonalization beyond the work demands. Perceiving the demand to display positive emotions and using deep-level regulation were associated with a heightened sense of personal accomplishment, suggesting positive benefits to this aspect of work. These findings suggest new antecedents of employee burnout and clarify the emotional labor literature by comparing different conceptualizations of this concept.


title: Support structures and their impacts on employee outcomes: a longitudinal field study of an enterprise system implementation

abstract: Despite the impressive progress in understanding the benefits and challenges related to enterprise system (ES) implementations--such as enterprise resource planning (ERP) systems--little is known about how the support structures traditionally used by organizations to help employees cope with a new ES affect employee outcomes related to the system and their jobs. Likewise, little is known about how existing peer advice ties in the business unit influence these outcomes after an ES implementation. Understanding employee outcomes is critical because of their ramifications for long-term ES success. This paper examines the impacts of four traditional support structures (namely, training, online support, help desk support, and change management support), and peer advice ties on four key employee outcomes (namely, system satisfaction, job stress, job satisfaction, and job performance). This paper also seeks to show that it is peer advice ties that best fill the complex informational needs of employees after an ES implementation by providing the right information at the right time and in the right context. The proposed model was tested in a field study conducted in one business unit of a large telecommunications company and gathered data from 120 supplier liaisons over the course of a year. Both traditional support structures and peer advice ties were found to influence the various outcomes, even after controlling for pre-implementation levels of the dependent variables. In all cases, peer advice ties was the strongest predictor, thus underscoring the importance of this critical internal resource.


title: Cahn–Hilliard Inpainting and a Generalization for Grayvalue Images

abstract: The Cahn-Hilliard equation is a nonlinear fourth order diffusion equation originating in material science for modeling phase separation and phase coarsening in binary alloys. The inpainting of binary images using the Cahn-Hilliard equation is a new approach in image processing. In this paper we discuss the stationary state of the proposed model and introduce a generalization for grayvalue images of bounded variation. This is realized by using subgradients of the total variation functional within the flow, which leads to structure inpainting with smooth curvature of level sets.


title: 2D motion detection bounded hand 3D trajectory tracking and gesture recognition under complex background

abstract: In this paper, a 2D motion detection bounded hand 3D trajectory tracking and gesture recognition system is proposed for virtual reality interactions. First, the Bayes decision rule for classification of background and foreground is utilized to automatically locate the hand that bounded within a rectangle, and then the trajectory of the hand in 3D space is tracked by mean shift particle filter and stereo imaging. The skin color feature is exploited for image matting that effectively segment the hand contour in video sequence automatically. Finally the hand gesture is recognized by the connected component analysis and line approximation. The proposed technique works without any markers or constraints, overcomes the disturbance of arms and faces in the scene, and can recognize multiple hands with different gestures under complex background.


title: Impact of mobile technology on student attitudes, engagement, and learning

abstract: Mobile devices and collaborative learning environments are common tools in education but not all collaborative learning is structured the same. This study evaluated student learning in three different collaborative learning environments, both with and without mobile technology, to assess students' engagement, critical thinking, and attitudes toward collaborative learning. The results indicate that mobile technology is associated with positive student perceptions of collaborative learning but with increased disengagement by students during class. In addition, the level of students' critical thinking was more closely associated with the tools used to construct written responses than with the collaborative learning environment style. Students constructing paragraph responses on a mobile device demonstrated significantly less critical thinking than those who used a computer keyboard or wrote responses by hand. Speech and eye contact are strong indicators of student engagement in groups.Technology serves important functions but often distracts from engagement.Critical thinking is more evident in typed or written versus texted essays.


title: Context Dependent Movie Recommendations Using a Hierarchical Bayesian Model

abstract: We use a hierarchical Bayesian approach to model user preferences in different contexts or settings. Unlike many previous recommenders, our approach is content-based. We assume that for each context, a user has a different set of preference weights which are linked by a common, "generic context" set of weights. The approach uses Expectation Maximization (EM) to estimate both the generic context weights and the context specific weights. This improves upon many current recommender systems that do not incorporate context into the recommendations they provide. In this paper, we show that by considering contextual information, we can improve our recommendations, demonstrating that it is useful to consider context in giving ratings. Because the approach does not rely on connecting users via collaborative filtering, users are able to interpret contexts in different ways and invent their own contexts.


title: Aerie: flexible file-system interfaces to storage-class memory

abstract: Storage-class memory technologies such as phase-change memory and memristors present a radically different interface to storage than existing block devices. As a result, they provide a unique opportunity to re-examine storage architectures. We find that the existing kernel-based stack of components, well suited for disks, unnecessarily limits the design and implementation of file systems for this new technology. We present Aerie, a flexible file-system architecture that exposes storage-class memory to user-mode programs so they can access files without kernel interaction. Aerie can implement a generic POSIX-like file system with performance similar to or better than a kernel implementation. The main benefit of Aerie, though, comes from enabling applications to optimize the file system interface. We demonstrate a specialized file system that reduces a hierarchical file system abstraction to a key/value store with fewer consistency guarantees but 20-109% higher performance than a kernel file system.