title: Original Contribution: Optical character recognition by a neural network

abstract: An optical character recognition (OCR) system, which uses a multilayer perceptron (MLP) neural network classifier, is described. The neural network classifier has the advantage of being fast (highly parallel), easily trainable, and capable of creating arbitrary partitions of the input feature space. Issues in design of the neural network that we examine include the selection of input features, the choice of network learning and momentum parameters, and the selection of training patterns. We also provide a detailed analysis of the learning parameters to provide insight into the MLP, and to suggest a mechanism to automatically tune these parameters. An OCR neural network classifier was trained to recognize characters from a large number of fonts, thereby approaching an omnifont environment. Samples were selected from over 200 fonts and 50 typical office documents, for a total of 110,000 training patterns. In order to evaluate the performance of the MLP classifier, a comparison is made with a high performance dynamic contour warping (DCW) classifier. The base recognition rate on the test set is 96.7% for the neural network classifier, compared to 95.9% for the DCW classifier.


title: The impact of emotion on creativity in organizations

abstract: Describes the impact of individuals on organizations, specifically, how a person’s emotional state influences the workplace. Uses two case studies to demonstrate how an emotionally healthy person has a positive impact on empowerment and creativity, and how an emotionally negative person impedes creativity and disempowers others. Stresses the importance of attention to the affective dimension of employees’ lives as a factor in empowerment and creativity.


title: Adwords management for third-parties in SEM: An optimisation model and the potential of Twitter

abstract: In Search Engine Marketing (SEM), “third-party” partners play an important intermediate role by bridging the gap between search engines and advertisers in order to optimise advertisers' campaigns in exchange of a service fee. In this paper, we present an economic analysis of the market involving a third-party broker in Google AdWords and the broker's customers. We show that in order to optimise his profit, a third-party broker should minimise the weighted average Cost Per Click (CPC) of the portfolio of keywords attached to customer's ads while still satisfying the negotiated customer's demand. To help the broker build and manage such portfolio of keywords, we develop an optimisation framework inspired from the classical Markowitz portfolio management which integrates the customer's demand constraint and enables the broker to manage the tradeoff between return on investment and risk through a single risk aversion parameter. We then propose a method to augment the keywords portfolio with relevant keywords extracted from trending and popular topics on Twitter. Our evaluation shows that such a keywords-augmented strategy is very promising and enables the broker to achieve, on average, four folds larger return on investment than with a non-augmented strategy, while still maintaining the same level of risk.


title: FPDetective: dusting the web for fingerprinters

abstract: In the modern web, the browser has emerged as the vehicle of choice, which users are to trust, customize, and use, to access a wealth of information and online services. However, recent studies show that the browser can also be used to invisibly fingerprint the user: a practice that may have serious privacy and security implications. In this paper, we report on the design, implementation and deployment of FPDetective, a framework for the detection and analysis of web-based fingerprinters. Instead of relying on information about known fingerprinters or third-party-tracking blacklists, FPDetective focuses on the detection of the fingerprinting itself. By applying our framework with a focus on font detection practices, we were able to conduct a large scale analysis of the million most popular websites of the Internet, and discovered that the adoption of fingerprinting is much higher than previous studies had estimated. Moreover, we analyze two countermeasures that have been proposed to defend against fingerprinting and find weaknesses in them that might be exploited to bypass their protection. Finally, based on our findings, we discuss the current understanding of fingerprinting and how it is related to Personally Identifiable Information, showing that there needs to be a change in the way users, companies and legislators engage with fingerprinting.


title: Stability Ball Versus Office Chair: Comparison of Muscle Activation and Lumbar Spine Posture During Prolonged Sitting

abstract: Objective: The objective of the study was to evaluate the differences between sitting on a stability ball and in an office chair in terms of trunk muscle activation and lumbar spine posture. Background: Stability balls have become increasingly popular as an alternative to office chairs to help reduce the prevalence of low back pain; however, little research has been conducted on their use as office chairs. Methods: The 14 participants (7 men, 7 women) were required to sit on both a stability ball and an office chair for 1 hour each while performing various computer workstation tasks throughout the sitting periods. The activation of eight muscles and lumbar spine posture were measured and analyzed. Results: Increased muscle activation in thoracic erector spinae (p = .0352), decreased pelvic tilt (p = .0114), and increased perceived discomfort (p < .0001) while sitting on the stability ball were observed. Conclusions: The small changes in biological responses when sitting on a stability ball as compared with an office chair, combined with the increased reported discomfort while on the ball, suggests its use for prolonged sitting may not be advantageous. Application: Prolonged sitting on a stability ball does not greatly alter the manner in which an individual sits, yet it appears to increase the level of discomfort. Therefore, it is important to fully explore a new chair design and consult scientific research before implementing its use.


title: Predicting food nutrition facts using pocket-size near-infrared sensor

abstract: Diet monitoring is one of the most important aspects in preventative health care that aims to reduce various health risks. Manual recording has been a prevalence among all approaches yet it is tedious and often end up with a low adherence rate. Several existing techniques that have been developed to monitor food intake suffer too with accuracy, efficiency, and user acceptance rate. In this paper we propose a novel approach on measuring food nutrition facts, through a pocket-size non-intrusive near-infrared (NIR) scanner. We build efficient regression models that can make quantitative prediction on food nutrition contents, such as energy and carbohydrate. Our extensive experiments on off-the-shelf liquid foods demonstrates the accuracy of these regression models and proves the applicability of using NIR spectra that are collected by small hand-held scanner, on food nutrition prediction.


title: Understanding pedestrian behaviors from stationary crowd groups

abstract: Pedestrian behavior modeling and analysis is important for crowd scene understanding and has various applications in video surveillance. Stationary crowd groups are a key factor influencing pedestrian walking patterns but was largely ignored in literature. In this paper, a novel model is proposed for pedestrian behavior modeling by including stationary crowd groups as a key component. Through inference on the interactions between stationary crowd groups and pedestrians, our model can be used to investigate pedestrian behaviors. The effectiveness of the proposed model is demonstrated through multiple applications, including walking path prediction, destination prediction, personality classification, and abnormal event detection. To evaluate our model, a large pedestrian walking route dataset 1 is built. The walking routes of 12, 684 pedestrians from a one-hour crowd surveillance video are manually annotated. It will be released to the public and benefit future research on pedestrian behavior analysis and crowd scene understanding.


title: Longitudinal and lateral control for automated lane change maneuvers

abstract: This paper considers the trajectory planning problem of a vehicle system for automated lane change maneuvers. By considering a lane change maneuver as primarily a longitudinal planning problem, the proposed trajectory planning algorithm determines whether there exists a longitudinal trajectory which allows the ego vehicle to safely position itself in a gap between surrounding vehicles in the target lane. If such a longitudinal trajectory exists, the algorithm plans the corresponding lateral trajectory. The lane change trajectory planning problem is thereby reduced to solving low-complexity model predictive control problems resulting in loosely coupled longitudinal and lateral motion trajectories. Simulation results demonstrate the ability of the proposed algorithm to generate smooth collision-free trajectories for lane change maneuvers.


title: Generic Application-Level Protocol Analyzer and its Language

abstract: The Shield project relied on application protocol analyzers to detect potential exploits of application vulnerabilities. We present the design of a second-generation generic application-level protocol analyzer (GAPA) that encompasses a domain-specific language and the associated run-time. We designed GAPA to satisfy three important goals: safety, real-time analysis and response, and rapid development of analyzers. We have found that these goals are relevant for many network monitors that implement protocol analysis. Therefore, we built GAPA to be readily integrated into tools such as Ethereal as well as Shield. GAPA preserves safety through the use of a memorysafe language for both message parsing and analysis, and through various techniques to reduce the amount of state maintained in order to avoid denial-of-service attacks. To support online analysis, the GAPA runtime uses a streamprocessing model with incremental parsing. In order to speed protocol development, GAPA uses a syntax similar to many protocol RFCs and other specifications, and incorporates many common protocol analysis tasks as built-in abstractions. We have specified 10 commonly used protocols in the GAPA language and found it expressive and easy to use. We measured our GAPA prototype and found that it can handle an enterprise client HTTP workload at up to 60 Mbps, sufficient performance for many end-host firewall/IDS scenarios. At the same time, the trusted code base of GAPA is an order of magnitude smaller than Ethereal.


A pictorial guide for the second trimester ultrasound


title: An accurate and fast point-to-plane registration technique

abstract: This paper addresses a registration refinement problem and presents an accurate and fast point-to-(tangent) plane technique. Point-to-plane approach is known to be very accurate for registration refinment of partial 3D surfaces. However, the computation complexity for finding the intersection point on a destination surface from a source control point is hindering the algorithm from real-time applications. We introduce a novel point-to-plane registration technique by combining the high-speed advantage of point-to-projection technique. In order to find the intersection point fast and accurately, we forward-project the source point to the destination surface and reproject the projection point to the normal vector of the source point. We show that iterative projections of the projected destination point to the normal vector converge to the intersection point. By assuming the destination surface to be a monotonic function in a new 2D coordinate system, we show contraction mapping properties of our iterative projection technique. Experimental results for several objects are presented for both pair-wise and multi-view registrations.


title: Occupancy detection via iBeacon on Android devices for smart building management

abstract: Building heating, ventilation, and air conditioning (HVAC) systems are considered to be the main target for energy reduction due to their significant contribution to commercial buildings' energy consumption. Knowing a building's occupancy plays a crucial role in implementing demand-response HVAC. In this paper we propose a new solution based on the iBeacon technology. This solution is different from the previous ones because it leverages on the Bluetooth Low Energy standard, which provides lower power consumption. Moreover, the iBeacon protocol can be used both on iOs systems and Android ones, making this new approach portable. Differently from our previous work based on iOS devices, in this paper we focus on an Android based solution with the aim of increasing the accuracy of the location and the energy efficiency of the entire system. We increased the accuracy by 10% and the energy efficiency by 15%.


title: Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning

abstract: The ability to act in multiple environments and transfer previous knowledge to new situations can be considered a critical aspect of any intelligent agent. Towards this goal, we define a novel method of multitask and transfer learning that enables an autonomous agent to learn how to behave in multiple tasks simultaneously, and then generalize its knowledge to new domains. This method, termed "Actor-Mimic", exploits the use of deep reinforcement learning and model compression techniques to train a single policy network that learns how to act in a set of distinct tasks by using the guidance of several expert teachers. We then show that the representations learnt by the deep policy network are capable of generalizing to new tasks with no prior expert guidance, speeding up learning in novel environments. Although our method can in general be applied to a wide range of problems, we use Atari games as a testing environment to demonstrate these methods.


title: Matching HCI Methods and Developers Values in eXtreme Programming Development Processes

abstract: Here within we describe our practical experience with the orchestration of human computer interaction (HCI) methods and extreme programming (XP) software development processes. We show how we selected the applied methods based on the motivating goals and values of developers by using a means-end approach. We discuss our experiences with the applied methods and conclude with some advice on which HCI methods are optimally supporting extreme programming developers. Author


title: Chatbot using TensorFlow for small Businesses

abstract: Chatbots are software used in entertainment industry, businesses and user support. Chatbots are modeled on various techniques such as knowledge base, machine learning based. Machine learning based chatbots yields more practical results. Chatbot which gives responses based on the context of conversation tends to be more user friendly. The chatbot we are proposing demonstrates a method of developing chatbot which can follow the context of the conversation. This method uses TensorFlow for developing the neural network model of the chatbot and uses the nlp techniques to maintain the context of the conversation. This chatbots can be used in small industries or business for automating customer care as user queries will be handled by chatbots thus reducing need of human labour and expenditure.


title: Architecture of Images: An Investigation of Architectural Representations and the Visual Perception of Three-Dimensional Space

abstract: At the end of the millennium, architecture faces diverse challenges. Its identity as a science and an art dealing with physical space needs to be redefined in the electronic era. The complexity of information space, which often takes over physical space in our daily lives, will broaden architecture's role-once well defined-in the shaping of the physical environment. Our sense of space and time in a place is not determined as much by the physical space we occupy as by the remote places connected to us by the web of communications. Telecommunications extend our existential space far beyond our physical space. Telephones and televisions are the most common devices of such sensual extension, removing the object of our perceptions from the house or street we occupy and bringing our ears and eyes to places thousands of miles distant from us [2]. Interwoven with these conditions is another phenomenon contributing to the transformation of traditional architecture: that of the reduction of architectural and urban space to a series of images. Today's city streets, buildings and homes are not merely a collection of sidewalks, walls, floors and roofs. In other words, they are more than the physical, threedimensional elements of which they are composed. The visual communications giving life to our cities and homes happen through light and images, both of them divorced from the solidity and three-dimensionality of the media traditionally used by architecture. Electric lights, billboards, liquidcrystal displays and neon signs in the urban space and TV and computer screens in domestic space are all legible expressions of the fragmentation of the three-dimensional solidity of our living environment, which loses its identity as a result of the ascendancy of two-dimensional images. The forms of these ephemeral architectures are often expressions of the


title: Matrix analysis

abstract: Linear algebra and matrix theory are fundamental tools in mathematical and physical science, as well as fertile fields for research. This new edition of the acclaimed text presents results of both classic and recent matrix analyses using canonical forms as a unifying theme, and demonstrates their importance in a variety of applications. The authors have thoroughly revised, updated, and expanded on the first edition. The book opens with an extended summary of useful concepts and facts and includes numerous new topics and features, such as: - New sections on the singular value and CS decompositions - New applications of the Jordan canonical form - A new section on the Weyr canonical form - Expanded treatments of inverse problems and of block matrices - A central role for the Von Neumann trace theorem - A new appendix with a modern list of canonical forms for a pair of Hermitian matrices and for a symmetric-skew symmetric pair - Expanded index with more than 3,500 entries for easy reference - More than 1,100 problems and exercises, many with hints, to reinforce understanding and develop auxiliary themes such as finite-dimensional quantum systems, the compound and adjugate matrices, and the Loewner ellipsoid - A new appendix provides a collection of problem-solving hints.


title: Virtual Network Embedding: A Survey

abstract: Network virtualization is recognized as an enabling technology for the future Internet. It aims to overcome the resistance of the current Internet to architectural change. Application of this technology relies on algorithms that can instantiate virtualized networks on a substrate infrastructure, optimizing the layout for service-relevant metrics. This class of algorithms is commonly known as "Virtual Network Embedding (VNE)" algorithms. This paper presents a survey of current research in the VNE area. Based upon a novel classification scheme for VNE algorithms a taxonomy of current approaches to the VNE problem is provided and opportunities for further research are discussed.


Poster: Lightweight Content-based Phishing Detection


title: and www.blackwell-synergy.com. Interactive Volume Rendering with Dynamic Ambient Occlusion and Color Bleeding

abstract: We propose a method for rendering volumetric data sets at interactive frame rates while supporting dynamic ambient occlusion as well as an approximation to color bleeding. In contrast to ambient occlusion approaches for polygonal data, techniques for volumetric data sets have to face additional challenges, since by changing rendering parameters, such as the transfer function or the thresholding, the structure of the data set and thus the light interactions may vary drastically. Therefore, during a preprocessing step which is independent of the rendering parameters we capture light interactions for all combinations of structures extractable from a volumetric data set. In order to compute the light interactions between the different structures, we combine this preprocessed information during rendering based on the rendering parameters defined interactively by the user. Thus our method supports interactive exploration of a volumetric data set but still gives the user control over the most important rendering parameters. For instance, if the user alters the transfer function to extract different structures from a volumetric data set the light interactions between the extracted structures are captured in the rendering while still allowing interactive frame rates. Compared to known local illumination models for volume rendering our method does not introduce any substantial rendering overhead and can be integrated easily into existing volume rendering applications. In this paper we will explain our approach, discuss the implications for interactive volume rendering and present the achieved results.


Judgment of Information Quality and Cognitive Authority in the Web


title: Clustering-Based Method for Positive and Unlabeled Text Categorization Enhanced by Improved TFIDF

abstract: PU learning occurs frequently in Web pages classification and text retrieval applications because users may be interested in information on the same topic. Collecting reliable negative examples is a key step in PU (Positive and Unlabeled) text classification, which solves a key problem in machine learning when no labeled negative examples are available in the training set or negative examples are difficult to collect. Thus, this paper presents a novel clustering-based method for collecting reliable negative examples (CCRNE). Different from traditional methods, we remove as many probable positive examples from unlabeled set as possible, which results that more reliable negative examples are found out. During the process of building classifier, a novel TFIDF-improved feature weighting approach, which reflects the importance of the term in the positive and negative training examples respectively, is presented to describe documents in the Vector Space Model. We also build a weighted voting classifier by iteratively applying the SVM algorithm and implement OCS (One-class SVM), PEBL (Positive Example Based Learning) and 1-DNFII (Constrained 1-DNF) methods used for comparison. Experimental results on three real-world datasets (Reuters Corpus Volume 1 (RCV1), Reuters-21578 and 20 Newsgroups) show that our proposed C-CRNE extracts more reliable negative examples than the baseline algorithms with very low error rates. And our classifier outperforms other state-of-art classification methods from the perspective of traditional performance metrics.


title: RTOG 0438: A phase I trial of highly conformal radiation therapy for patients with liver metastases.

abstract: 257 Background: This multi-institutional phase I trial was conducted to determine the maximally tolerated dose (MTD) of hypofractionated, highly conformal radiation therapy (RT) in patients (pts) with liver metastases. Methods: Eligibility criteria included non-surgical pts with ≤ 5 liver metastases; total measurement for all lesions ≤ 8 cm. There were four dose levels (DLs) escalating from 35 Gy to 50 Gy in 5 Gy increments given in 10 fractions with defined normal tissue dose limits. Accrual began at 40 Gy. The clinical target volume (CTV) included all metastases identified on contrast CT/MRI with a 5 mm margin. The planning target margin ranged from 4 to 30 mm around the CTV. For quality assurance the Image-Guided Therapy Center (ITC) remote review tool was used to evaluate treatment planning images and dosimetry information. Dose limiting toxicities (DLTs) were defined as treatment-related grade (Gr) ≥ 4 hepatic, gastrointestinal (GI), thrombocytopenia, or radiation induced liver disease (RILD) within ...


title: Transformational and Transactional Leadership: A Meta-Analytic Test of Their Relative Validity.

abstract: This study provided a comprehensive examination of the full range of transformational, transactional, and laissez-faire leadership. Results (based on 626 correlations from 87 sources) revealed an overall validity of .44 for transformational leadership, and this validity generalized over longitudinal and multisource designs. Contingent reward (.39) and laissez-faire (.37) leadership had the next highest overall relations; management by exception (active and passive) was inconsistently related to the criteria. Surprisingly, there were several criteria for which contingent reward leadership had stronger relations than did transformational leadership. Furthermore, transformational leadership was strongly correlated with contingent reward (.80) and laissez-faire (.65) leadership. Transformational and contingent reward leadership generally predicted criteria controlling for the other leadership dimensions, although transformational leadership failed to predict leader job performance. In the past 20 years, a substantial body of research has accumulated on transformational–transactional leadership theory. Burns (1978) first introduced the concepts of transformational and transactional leadership in his treatment of political leadership. As Conger and Kanungo (1998) noted, to Burns the difference between transformational and transactional leadership is in terms of what leaders and followers offer one another. Transformational leaders offer a purpose that transcends short-term goals and focuses on higher order intrinsic needs. Transactional leaders, in contrast, focus on the proper exchange of resources. If transformational leadership results in followers identifying with the needs of the leader, the transactional leader gives followers something they want in exchange for something the leader wants (Kuhnert & Lewis, 1987). To Burns, transactional leadership is more commonplace than is transformational leadership, if less dramatic in its consequences. Bass (1985) based his theory of transformational leadership on Burns’s (1978) conceptualization, with several modifications or elaborations. First, Bass did not agree with Burns that transformational and transactional leadership represent opposite ends of a single continuum. Bass argued that transformational and transactional leadership are separate concepts, and further argued that the best leaders are both transformational and transactional. Second, Bass elaborated considerably on the behaviors that manifest transformational and transactional leadership. Although the theory has undergone several revisions, in the most recent version there are four dimensions of transformational leadership, three dimensions of transactional leadership, and a nonleadership dimension. The four dimensions of transformational leadership are charisma or idealized influence, inspirational motivation, intellectual


title: Normalizing the Shadows – The Role of Symbolic Models for Individuals’ Shadow IT Usage

abstract: Employees increasingly use cloud services of third party providers to efficiently perform their job. Interestingly, the majority adopts the cloud without approval of the organization. IT resources like that are commonly referred to as shadow IT. Although widespread in practice, the present literature on shadow IT is scarce lacking a clear definition of the phenomenon and exclusively focusing on the organizational perspective. This research addresses the issue at the individual level. We define shadow IT usage as voluntary usage of any IT resource violating injunctive IT norms at work as reaction to perceived situational constraints with the intent to enhance work performance, but not to harm the organization. Building furthermore on theories of organizational frustration, social learning, and norm salience, we develop a research model of individuals’ shadow IT usage to analyze the inhibiting effect of explicit IT usage restrictions on users’ deviant behaviors in a future laboratory experiment.


title: A Fully Integrated Dual-Band WLP CMOS Power Amplifier for 802.11n WLAN Applications

abstract: A fully integrated dual-band CMOS power amplifier (PA) is developed for 802.11n WLAN applications using wafer-level package (WLP) technology. This paper presents a detailed design for the optimal impedance of dual-band PA (2 GHz/5 GHz PA) output transformers with low loss, which is provided by using 2:2 and 2:1 output transformers for the 2 GHz PA and the 5 GHz PA, respectively. In addition, several design issues in the dual-band PA design using WLP technology are addressed, and a design method is proposed. All considerations for the design of dual-band WLP PA are fully reflected in the design procedure. The 2 GHz WLP CMOS PA produces a saturated power of 26.3 dBm with a peak power-added efficiency (PAE) of 32.9%. The 5 GHz WLP CMOS PA produces a saturated power of 24.7 dBm with a PAE of 22.2%. The PA is tested using an 802.11n signal, which satisfies the stringent error vector magnitude (EVM) and mask requirements. It achieved an EVM of -28 dB at an output power of 19.5 dBm with a PAE of 13.1% at 2.45 GHz and an EVM of -28 dB at an output power of 18.1 dBm with a PAE of 8.9% at 5.8 GHz.


title: Secure and Efficient Handover Authentication Based on Bilinear Pairing Functions

abstract: Seamless handover over multiple access points is highly desirable to mobile nodes, but ensuring security and efficiency of this process is challenging. This paper shows that prior handover authentication schemes incur high communication and computation costs, and are subject to a few security attacks. Further, a novel handover authentication protocol named PairHand is proposed. PairHand uses pairing-based cryptography to secure handover process and to achieve high efficiency. Also, an efficient batch signature verification scheme is incorporated into PairHand. Experiments using our implementation on laptop PCs show that PairHand is feasible in real applications.


title: The weight consistency matrix framework for general non-binary LDPC code optimization: Applications in flash memories

abstract: Transmission channels underlying modern memory systems, e.g., Flash memories, possess a significant amount of asymmetry. While existing LDPC codes optimized for symmetric, AWGN-like channels are being actively considered for Flash applications, we demonstrate that, due to channel asymmetry, such approaches are fairly inadequate. We propose a new, general, combinatorial framework for the analysis and design of non-binary LDPC (NB-LDPC) codes for asymmetric channels. We introduce a refined definition of absorbing sets, which we call general absorbing sets (GASs), and an important subclass of GASs, which we refer to as general absorbing sets of type two (GASTs). Additionally, we study the combinatorial properties of GASTs. We then present the weight consistency matrix (WCM), which succinctly captures key properties in a GAST. Based on these new concepts, we then develop a general code optimization framework, and demonstrate its effectiveness on the realistic highly-asymmetric normal-Laplace mixture (NLM) Flash channel. Our optimized codes enjoy over one order (resp., half of an order) of magnitude performance gain in the uncorrectable BER (UBER) relative to the unoptimized codes (resp. the codes optimized for symmetric channels).


title: Drosophila dec-1 Eggshell Proteins Are Differentially Distributed via a Multistep Extracellular Processing and Localization Pathway

abstract: Abstract In Drosophila the multilayered eggshell forms during late oogenesis between the oocyte and the overlaying follicle cells. Proper eggshell assembly requires wild-type dec-1 gene function. Alternatively spliced dec-1 transcripts encode three proproteins that are cleaved extracellularly in a stage-specific manner to at least five distinct derivatives. Using polyclonal antibodies raised against fusion proteins containing different regions of the dec-1 proteins, we have localized several dec-1 derivatives in the assembling and completed eggshell. Although all of the dec-1 derivatives are generated in the oocyte proximal vitelline membrane layer, they are differentially distributed in the mature egg. Some derivatives are gradually released from the vitelline membrane and become localized within distinct regions of the chorion, while others are taken up by the oocyte or become concentrated in the endochorionic spaces or cavities. The diverse distributions of the dec-1 derivatives suggest that each derivative plays a distinct role in eggshell assembly. These results also suggest that the vitelline membrane layer, by acting as a transient storage site, may control the availability of molecules active in eggshell assembly and by extension perhaps other follicle cell products important in early embryonic pattern formation.


title: Predicting development of dementia in the elderly with the Selective Reminding Test

abstract: Abstract The ability to predict the development of dementia through the detection of memory impairment in nondemented individuals was assessed with the Selective Reminding Test (SR), a popular test of verbal memory functioning in the elderly. The SR was administered to 385 nondemented volunteer subjects (mean age=80.4 years) enrolled in a longitudinal study of risk factors in the development of dementia. Of these, 36 subjects ultimately became demented. SR scores obtained from 1 to 2 years prior to the diagnosis of dementia were compared with a set of previously established cutoff scores derived from a cognitively normal elderly sample. The results demonstrated that sum of recall and delayed recall were the SR measures best able to predict dementia with sensitivities of 47% and 44%, respectively. The predictive values were 37% and 40%, respectively, or better than two-and-one-half times the base rate. The contributions of both the SR Test and the Fuld Object-Memory Test (OM) were discussed in terms of the...


title: An Online Transformerless Uninterruptible Power Supply (UPS) System With a Smaller Battery Bank for Low-Power Applications

abstract: Uninterruptible power supplies (UPS) are widely used to provide reliable and high-quality power to critical loads in all grid conditions. This paper proposes a nonisolated online UPS system. The proposed system consists of bridgeless PFC boost rectifier, battery charger/discharger, and an inverter. A new battery charger/discharger has been implemented which ensures the bidirectional flow of power between dc link and battery bank, reducing the battery bank voltage to only 24V, and regulates the dc-link voltage during the battery power mode. Operating batteries in parallel improves the battery performance and resolve the problems related to conventional battery banks that arrange batteries in series. A new control method, integrating slide mode and proportional-resonant control, for the inverter has been proposed which regulates the output voltage for both linear and nonlinear loads. The controller exhibits excellent performance during transients and step changes in load. The operating principle and experimental results of 1-kVA prototype have been presented for validation of the proposed system.


A Unified Model for Cross-Domain and Semi-Supervised Named Entity Recognition in Chinese Social Media.


title: A Unified approach for Conventional Zero-shot, Generalized Zero-shot and Few-shot Learning

abstract: Prevalent techniques in zero-shot learning do not generalize well to other related problem scenarios. Here, we present a unified approach for conventional zero-shot, generalized zero-shot, and few-shot learning problems. Our approach is based on a novel class adapting principal directions’ (CAPDs) concept that allows multiple embeddings of image features into a semantic space. Given an image, our method produces one principal direction for each seen class. Then, it learns how to combine these directions to obtain the principal direction for each unseen class such that the CAPD of the test image is aligned with the semantic embedding of the true class and opposite to the other classes. This allows efficient and class-adaptive information transfer from seen to unseen classes. In addition, we propose an automatic process for the selection of the most useful seen classes for each unseen class to achieve robustness in zero-shot learning. Our method can update the unseen CAPD taking the advantages of few unseen images to work in a few-shot learning scenario. Furthermore, our method can generalize the seen CAPDs by estimating seen–unseen diversity that significantly improves the performance of generalized zero-shot learning. Our extensive evaluations demonstrate that the proposed approach consistently achieves superior performance in zero-shot, generalized zero-shot, and few/one-shot learning problems.


title: Design, Modeling, and Validation of a Soft Magnetic 3-D Force Sensor

abstract: Recent advances in robotics promise a future where robots co-exist and collaborate with humans in unstructured environments, which will require frequent physical interactions where accurate tactile information will be crucial for performance and safety. This article describes the design, fabrication, modeling, and experimental validation of a soft-bodied tactile sensor that accurately measures the complete 3-D force vector for both normal and shear loading conditions. Our research considers the detection of changes in the magnetic field vector due to the motion of a miniature magnet in a soft substrate to measure normal and shear forces with high accuracy and bandwidth. The proposed sensor is a pyramid-shaped tactile unit with a tri-axis Hall element and a magnet embedded in a silicone rubber substrate. The non-linear mapping between the 3-D force vector and the Hall effect voltages is characterized by training a neural network. We validate the proposed soft force sensor over static and dynamic loading experiments and obtain a mean absolute error below 11.7 mN or 2.2% of the force range. These results were obtained for a soft force sensor prototype and loading conditions not included in the training process, indicating strong generalization of the model. To demonstrate its utility, the proposed sensor is used in a force-controlled pick-and-place experiment as a proof-of-concept case study.


Testing of a life saving device for scuba divers (Buddy Buzzer)


title: Blood diseases detection using data mining techniques

abstract: In healthcare systems, there is huge medical data collected from many medical tests which conducted in many domains. Much research has been done to generate knowledge from medical data by using data mining techniques. However, there still needs to extract hidden information in the medical data, which can help in detecting diseases in the early stage or even before happening. In this study, we apply three data mining classifiers; Decision Tree, Rule Induction, and Naive Bayes, on a test blood dataset which has been collected from Europe Gaza Hospital, Gaza Strip. The classifiers utilize the CBC characteristics to predict information about possible blood diseases in early stage, which may enhance the curing ability. Three experiments are conducted on the test blood dataset, which contains three types of blood diseases; Hematology Adult, Hematology Children and Tumor. The results show that Naive Bayes classifier has the ability to predict the Tumor of blood disease better than the other two classifiers with accuracy of 56%, Rule induction classifier gives better result in predicting Hematology (Adult, Children) with accuracy of (57%–67%) respectively, while Decision Tree has the Lowest accuracy rate for detecting the three types of diseases in our dataset.


title: Fast suboptimal algorithms for the computation of graph edit distance

abstract: Graph edit distance is one of the most flexible mechanisms for error-tolerant graph matching. Its key advantage is that edit distance is applicable to unconstrained attributed graphs and can be tailored to a wide variety of applications by means of specific edit cost functions. Its computational complexity, however, is exponential in the number of vertices, which means that edit distance is feasible for small graphs only. In this paper, we propose two simple, but effective modifications of a standard edit distance algorithm that allow us to suboptimally compute edit distance in a faster way. In experiments on real data, we demonstrate the resulting speedup and show that classification accuracy is mostly not affected. The suboptimality of our methods mainly results in larger inter-class distances, while intra-class distances remain low, which makes the proposed methods very well applicable to distance-based graph classification.


title: Deep inferior epigastric perforator flap demonstrating the triple response of Lewis phenomenon – A case report and insights into aetiology

abstract: Abstract Dermatographism, also known as skin writing, is an immunological response that remains an enigma. It occurs when normal skin is stroked with a dull object causing it to become raised and inflamed, and assume the shape of the stroke. Here we present a case of dermatographia in a denervated deep inferior epigastric perforator flap used to reconstruct breast tissue following mastectomy.


title: A Reference Guide to Cloud Computing Dimensions: Infrastructure as a Service Classification Framework

abstract: Recently, a growing development and use of Cloud computing services has been observed generally and also in the area of government. Despite initial positive results, it is challenging in theory and practice to find an appropriate provider matching the individual requirements of a company or a government. Moreover, the number of new entrants as well as non-transparent service offers, which sometimes differ significantly, make it difficult to migrate into the Cloud. Due to the lack of adequate possibilities to compare and classify Cloud providers we are presenting in this paper a provider independent classification framework for Infrastructure as a Service (IaaS) which can be used in e-Government. For this purpose, the target dimensions for Cloud Computing from a customer perspective were defined, based on expert interviews and an international literature review. The relevance of the target dimensions was evaluated with an additional survey conducted among IT managers. Extended by a provider market analysis the classification framework was designed and finally checked for applicability and can be used to create concrete cloud procurement processes, refine Cloud strategies or develop migration requirements for governments.


title: Architecture of autonomous vehicle simulation and control framework

abstract: With the current advances in Autonomous Navigation Systems for Self Driving Vehicles, there is a need for specialized and focused software architecture for ease in simulating the vehicle behavior and associated modules such as perception, drive by wire system, obstacle detection and path planning. In addition, testing algorithms on real vehicles during development takes enormous effort, time and also contains risk to the vehicle and its corresponding surroundings. Having a software architecture that is very similar to the target autonomous system combined with a fairly accurate simulator, reduces this risk and eases the development process. In this paper, we propose an integrated software architecture using ROS and Gazebo that is compliant to JAUS standards, which is scalable and easy to use. The proposed architecture uses Gazebo that is equipped with the Open Dynamics Engine providing realism to the simulation along with sensors perception, path planning and vehicle control using a Drive-by-Wire system. The results of using the proposed software architecture are shown for Autonomous and Tele-operated navigation missions on an all-terrain vehicle.


title: Update of Neuropathology and Neurological Recovery After Traumatic Brain Injury

abstract: This review focuses on the potential for traumatic brain injury to evoke both focal and diffuse changes within the brain parenchyma, while considering the cellular constituents involved and the subcellular perturbations that contribute to their dysfunction. New insight is provided on the pathobiology of traumatically induced cell body injury and diffuse axonal damage. The consequences of axonal damage in terms of subsequent deafferentation and any potential retrograde cell death and atrophy are addressed. The regional and global metabolic sequelae are also considered. This detailed presentation of the neuropathological consequences of traumatic brain injury is used to set the stage for better appreciating the neurological recovery occurring after traumatic injury. Although the pathological and clinical effects of focal and diffuse damage are usually intermingled, the different clinical manifestations of recovery patterns associated with focal versus diffuse injuries are presented. The recognizable patterns of recovery, involving unconsciousness, posttraumatic confusion/amnesia, and postconfusional restoration, that typically occur across the full spectrum of diffuse injury are described, recognizing that the patient’s long-term recovery may involve more idiosyncratic combinations of dysfunction. The review highlights the relationship of focal lesions to localizing syndromes that may be embedded in the evolving natural history of diffuse pathology. It is noted that injuries with primarily focal pathology do not necessarily follow a comparable pattern of recovery with distinct phases. Potential linkages of these recovery patterns to the known neuropathological sequelae of injury and various reparative mechanisms are considered and it is proposed that potential biological markers and newer imaging technologies will better define these linkages. Key words: coma, diffuse and focal brain injury, diffuse axonal injury, metabolic change, neuronal cell body damage, posttraumatic amnesia, recovery, traumatic brain injury


title: A Survey of Context Modelling and Reasoning Techniques

abstract: Development of context-aware applications is inherently complex. These applications adapt to changing context information: physical context, computational context, and user context/tasks. Context information is gathered from a variety of sources that differ in the quality of information they produce and that are often failure prone. The pervasive computing community increasingly understands that developing context-aware applications should be supported by adequate context information modelling and reasoning techniques. These techniques reduce the complexity of context-aware applications and improve their maintainability and evolvability. In this paper we discuss the requirements that context modelling and reasoning techniques should meet, including the modelling of a variety of context information types and their relationships, of situations as abstractions of context information facts, of histories of context information, and of uncertainty of context information. This discussion is followed by a description and comparison of current context modelling and reasoning techniques.


title: A new single phase to three phase converter with active input current shaping for low cost AC motor drives

abstract: A single-phase-to-three-phase converter for a low-cost AC motor drive is proposed. The converter employs only six switches and incorporates a front-end half-bridge active rectifier structure that provides the DC link with an active input current shaping feature, which results in sinusoidal input current at close to unity power factor. The front-end rectifier in the converter permits bidirectional power flow and provides for excellent regulation against fluctuations in source voltage, facilitating regenerative braking of the AC motor drive. A control strategy that maintains a near-unity power factor over the full operating range and is easy to implement is described. Suitable design guides for the selection of filter components are presented. Simulation and experimental results that verify the developed theoretical models are also presented. >


title: Time Provisioning Evaluation of KVM, Docker and Unikernels in a Cloud Platform

abstract: Unikernels are a promising alternative for application deployment in cloud platforms. They comprise a very small footprint, providing better deployment agility and portability among virtualization platforms. Similar to Linux containers, they are a lightweight alternative for deploying distributed applications based on microservices. However, the comparison of unikernels with other virtualization options regarding the concurrent provisioning of instances, as in the case of microservices-based applications, is still lacking. This paper provides an evaluation of KVM (Virtual Machines), Docker (Containers), and OSv (Unikernel), when provisioning multiple instances concurrently in an OpenStack cloud platform. We confirmed that OSv outperforms the other options and also identified opportunities for optimization.


title: Factors Influencing the Adoption of Internet Banking in Tunisia

abstract: The purpose of this paper is to determine those factors that influence the adoption of internet banking services in Tunisia. A theoretical model is provided that conceptualizes and links different factors influencing the adoption of internet banking. A total of 253 respondents in Tunisia were sampled for responding: 95 were internet bank users, 158 were internet bank non users. Factor analyses and regression technique are employed to study the relationship. The results of the model tested clearly that use of internet banking in Tunisia is influenced most strongly by convenience, risk, security and prior internet knowledge. Only information on online banking did not affect intention to use internet banking service in Tunisia. The results also propose that demographic factors impact significantly internet banking behaviour, specifically, occupation and instruction. Finally, this paper suggests that an understanding the factors affecting intention to use internet banking is very important to the practitioners who plan and promote new forms of banking in the current competitive market.


A Dataset for Detecting Stance in Tweets.


title: Performance of VoLTE and data traffic in LTE heterogeneous networks

abstract: VoLTE (Voice over LTE) is expected to be deployed in the near future to provide voice services in LTE networks. There is also an increasing need for much higher data capacity as a result of smartphone proliferation. Therefore, it is important to investigate and optimize the LTE networks in the presence of voice and data traffic. In this paper, we evaluate the performance of LTE Downlink when both VoLTE and data users are present. Both Macro only (homogenous) and HetNet (heterogeneous with Macro and small cells) scenarios are considered. It is shown that HetNet with Pico cell range expansion significantly improves both the system data performance and VoLTE capacity.


title: Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks

abstract: Recommender systems enable users to access products or articles that they would otherwise not be aware of due to the wealth of information to be found on the Internet. The two traditional recommendation techniques are content-based and collaborative filtering. While both methods have their advantages, they also have certain disadvantages, some of which can be solved by combining both techniques to improve the quality of the recommendation. The resulting system is known as a hybrid recommender system. In the context of artificial intelligence, Bayesian networks have been widely and successfully applied to problems with a high level of uncertainty. The field of recommendation represents a very interesting testing ground to put these probabilistic tools into practice. This paper therefore presents a new Bayesian network model to deal with the problem of hybrid recommendation by combining content-based and collaborative features. It has been tailored to the problem in hand and is equipped with a flexible topology and efficient mechanisms to estimate the required probability distributions so that probabilistic inference may be performed. The effectiveness of the model is demonstrated using the MovieLens and IMDB data sets.


title: Accelerating loss of seagrasses across the globe threatens coastal ecosystems

abstract: Coastal ecosystems and the services they provide are adversely affected by a wide variety of human activities. In particular, seagrass meadows are negatively affected by impacts accruing from the billion or more people who live within 50 km of them. Seagrass meadows provide important ecosystem services, including an estimated $1.9 trillion per year in the form of nutrient cycling; an order of magnitude enhancement of coral reef fish productivity; a habitat for thousands of fish, bird, and invertebrate species; and a major food source for endangered dugong, manatee, and green turtle. Although individual impacts from coastal development, degraded water quality, and climate change have been documented, there has been no quantitative global assessment of seagrass loss until now. Our comprehensive global assessment of 215 studies found that seagrasses have been disappearing at a rate of 110 km2 yr−1 since 1980 and that 29% of the known areal extent has disappeared since seagrass areas were initially recorded in 1879. Furthermore, rates of decline have accelerated from a median of 0.9% yr−1 before 1940 to 7% yr−1 since 1990. Seagrass loss rates are comparable to those reported for mangroves, coral reefs, and tropical rainforests and place seagrass meadows among the most threatened ecosystems on earth.


title: Twitter and Polls: Analyzing and estimating political orientation of Twitter users in India General #Elections2014

abstract: This year (2014) in the month of May, the tenure of the 15th Lok Sabha was to end and the elections to the 543 parliamentary seats were to be held. A whooping $5 billion were spent on these elections, which made us stand second only to the US Presidential elections in terms of money spent. Swelling number of Internet users and Online Social Media (OSM) users could effect 3-4% of urban population votes as per a report of IAMAI (Internet & Mobile Association of India). Our count of tweets related to elections from September 2013 to May 2014, was close to 18.21 million. We analyzed the complete dataset and found that the activity on Twitter peaked during important events. It was evident from our data that the political behavior of the politicians affected their followers count. Yet another aim of our work was to find an efficient way to classify the political orientation of the users on Twitter. We used four different techniques: two were based on the content of the tweets, one on the user based features and another based on community detection algorithm on the retweet and user mention networks. We found that the community detection algorithm worked best. We built a portal to show the analysis of the tweets of the last 24 hours. To the best of our knowledge, this is the first academic pursuit to analyze the elections data and classify the users in the India General Elections 2014.


title: Towards Principled Methods for Training Generative Adversarial Networks

abstract: The goal of this paper is not to introduce a single algorithm or method, but to make theoretical steps towards fully understanding the training dynamics of generative adversarial networks. In order to substantiate our theoretical analysis, we perform targeted experiments to verify our assumptions, illustrate our claims, and quantify the phenomena. This paper is divided into three sections. The first section introduces the problem at hand. The second section is dedicated to studying and proving rigorously the problems including instability and saturation that arize when training generative adversarial networks. The third section examines a practical and theoretically grounded direction towards solving these problems, while introducing new tools to study them.


New Hybrid Approach to Solve the 0 / 1 , Bounded Knapsack Problem


title: Humans Require Context to Infer Ironic Intent (so Computers Probably do, too)

abstract: Automatically detecting verbal irony (roughly, sarcasm) is a challenging task because ironists say something other than ‐ and often opposite to ‐ what they actually mean. Discerning ironic intent exclusively from the words and syntax comprising texts (e.g., tweets, forum posts) is therefore not always possible: additional contextual information about the speaker and/or the topic at hand is often necessary. We introduce a new corpus that provides empirical evidence for this claim. We show that annotators frequently require context to make judgements concerning ironic intent, and that machine learning approaches tend to misclassify those same comments for which annotators required additional context.


title: Analysis and Decoupling Design of a 30 MHz Resonant SEPIC Converter

abstract: This paper presents the analysis and design of a 30 MHz resonant SEPIC converter. With the conventional design method, tuning the amplitude and phase of the fundamental input voltage and current of the rectifier stage affect each other so that the design procedure is coupled seriously. Based on the circuit duality theory, an improved design method is proposed by redividing the resonant SEPIC topology to realize the independent tuning of the amplitude and phase. The implementation and loss analysis of the SEPIC with the new design are provided in details. Moreover, a novel structure of the PCB embedded inductors is introduced and the comparison among the discrete inductor, the planar spiral inductor and the four-layered solenoid PCB inductor is given in terms of the loss. A 15 V input, 25 W/28 V output/30 MHz SEPIC converter was built to verify the proposed design method and the benefit of the new structure of the PCB embedded inductors. The power density of the power stage is realized over 200 W/in3 and the efficiency is 82.5% at the rated output. The temperature of the proposed PCB embedded inductor is reduced from 102.6 °C (the discrete inductor) to 64.8 °C (a reduction of 37%) and the efficiency is improved 2% over the discrete solution at the full load.


title: A Survey on Efficient Extraction of Named Entities from New Domains Using Big Data Analytics

abstract: Named Entity Recognition (NER) is an important step in text mining. This paper proposes a survey on extracting named entities from new domains using big data analytics. The survey shows the methods and challenges applied for the efficient extraction of named entities from various fields having large corpus of data such as banking, medical and social networks. Most of NER methods discussed below are normally based on supervised learning techniques which often require a large amount of training dataset to train a good classifier. However, many applications in Information Retrieval (IR) and Natural Language Processing (NLP) suffer high from the noisy and short nature of texts.


title: Grammatical gender effects on cognition: Implications for language learning and language use

abstract: In 4 experiments, the authors addressed the mechanisms by which grammatical gender (in Italian and German) may come to affect meaning. In Experiments I (similarity judgments) and 2 (semantic substitution errors), the authors found Italian gender effects for animals but not for artifacts; Experiment 3 revealed no comparable effects in German. These results suggest that gender effects arise as a generalization from an established association between gender of nouns and sex of human referents, extending to nouns referring to sexuated entities. Across languages, such effects are found when the language allows for easy mapping between gender of nouns and sex of human referents (Italian) but not when the mapping is less transparent (German). A final experiment provided further constraints: These effects during processing arise at a lexical-semantic level rather than at a conceptual level.


title: A single-switch high voltage quasi-resonant DC-DC converter for a pulsed plasma thruster

abstract: In nano-satellites, the pulsed plasma thruster (PPT) is a suitable electronic propulsion system for drag compensation or attitude control. In this paper, a new quasi-resonant converter topology is proposed for the PPT igniter. It consists of a resonant tank, a step-up transformer and a Cockcroft-Walton voltage multiplier circuit. As compared to other resonant converters, the proposed topology uses a single switch. This improves the reliability and eases switching control. The resonant tank is driven to work at quasi-resonant state to obtain zero-current-switching (ZCS). This reduces switching noise and voltage spike. A prototype circuit has been developed to validate the concept. The output voltage is set to 4kV from a 5V power supply, and the preliminary experimental results show that the circuit works as analyzed.


title: Multi-Task Neural Models for Translating Between Styles Within and Across Languages

abstract: Generating natural language requires conveying content in an appropriate style. We explore two related tasks on generating text of varying formality: monolingual formality transfer and formality-sensitive machine translation. We propose to solve these tasks jointly using multi-task learning, and show that our models achieve state-of-the-art performance for formality transfer and are able to perform formality-sensitive translation without being explicitly trained on style-annotated translation examples.


title: Security and Privacy in Smart Cities: Challenges and Opportunities

abstract: Smart cities are expected to improve the quality of daily life, promote sustainable development, and improve the functionality of urban systems. Now that many smart systems have been implemented, security and privacy issues have become a major challenge that requires effective countermeasures. However, traditional cybersecurity protection strategies cannot be applied directly to these intelligent applications because of the heterogeneity, scalability, and dynamic characteristics of smart cities. Furthermore, it is necessary to be aware of security and privacy threats when designing and implementing new mechanisms or systems. Motivated by these factors, we survey the current situations of smart cities with respect to security and privacy to provide an overview of both the academic and industrial fields and to pave the way for further exploration. Specifically, this survey begins with an overview of smart cities to provide an integrated context for readers. Then, we discuss the privacy and security issues in current smart applications along with the corresponding requirements for building a stable and secure smart city. In the next step, we summarize the existing protection technologies. Finally, we present open research challenges and identify some future research directions.


title: Differential pattern of functional brain plasticity after compassion and empathy training

abstract: Although empathy is crucial for successful social interactions, excessive sharing of othersnegative emotions may be maladaptive and constitute a source of burnout. To investigate functional neural plasticity underlying the augmentation of empathy and to test the counteracting potential of compassion, one group of participants was first trained in empathic resonance and subsequently in compassion. In response to videos depicting human suffering, empathy training, but not memory training (control group), increased negative affect and brain activations in anterior insula and anterior midcingulate cortexbrain regions previously associated with empathy for pain. In contrast, subsequent compassion training could reverse the increase in negative effect and, in contrast, augment self-reports of positive affect. In addition, compassion training increased activations in a non- overlapping brain network spanning ventral striatum, pregenual anterior cingulate cortex and medial orbitofrontal cortex. We conclude that training compassion may reflect a new coping strategy to overcome empathic distress and strengthen resilience.


title: Effect of Thermal Aging on the Electrical Characteristics of XLPE for HV Cables

abstract: This work highlights the effect of thermal aging on the commonly studied electrical properties of cross-linked polyethylene devoted to be used in the HV cables insulation. The obtained results show the great effect of thermal aging on the electrical properties of the polymer especially for aging temperatures situated above the melting temperature of the material. On the other part, it is believed that the degradation degree is related to the applied temperature. Hence, the degradation process has been occurred in fast way as the temperature is higher. The almost deterioration markers in the dielectric behavior can be attributed to both the chemical and physical changes. The main chemical changes are the bulk thermo-oxidation degradation leading to the enhancement of dipoles and carbonyl groups concentration which disturbs the dielectric properties leading to the failure of the material. The physical changes result from the changes in the ratio of crystalline to amorphous parts in the material. Both changes have something to do it together in the life time reduction of the XLPE insulation.


title: Characterization of a DC to 40 GHz SPDT switch based on GaAs mHEMT technology at cryogenic temperature

abstract: In this paper a DC to 40 GHz single pole double throw (SPDT) switch based on GaAs metamorphic high electron-mobility transistors (mHEMTs) is presented. Characterization is carried out at ambient temperature and at 15 K. The insertion loss from DC to 40 GHz is smaller than 2.2 dB and 1.5 dB at 295 K and 15 K, respectively. The isolation is better than 17 dB. The switch with a size of 1 × 1 mm2 is dedicated to multi-purpose measurement setups at cryogenic temperature, but also operates at room temperature. Bias dependency, switch design and influence of illumination are discussed additionally.


title: 40Ar/39Ar Geochronology of Subaerial Ascension Island and a Re-evaluation of the Temporal Progression of Basaltic to Rhyolitic Volcanism

abstract: Ar/Ar geochronology of basaltic to rhyolitic lavas, domes, and pyroclastic deposits from Ascension Island indicates that the maximum age of subaerially exposed samples is 1094 ka. Thirty-eight Ar/Ar ages, coupled with new and existing geochemical data, constrain the eruptive histories of the four distinct mafic magma types (high Zr/Nb, low Zr/Nb, intermediate Zr/Nb, and Dark Slope Crater) and document temporal variations in magma sources. Lavas from the eastern felsic complex, previously assumed to be as old as or slightly younger than the 602^1094 ka Middleton Ridge complex, are as young as 52 ka. Basaltic to benmoreitic scoria cones and associated flows of the intermediate Zr/Nb magma type have been inferred to be the most recent eruptive products, yet their eruptive histories extend back to 705 ka. These intermediate Zr/ Nb magmas are likely to be parental to the abundant trachytic to rhyolitic lavas and domes, which contradicts previous interpretations that call upon a high Zr/Nb parental basalt. Two distinct fractionation trends are observed in the trace element variations of Ascension trachytes and rhyolites. Ar/Ar ages of the samples defining the two trends suggest that ilmenite fractionation dominated the Nb budget and thus controlled Zr/Nb ratios in early (4931ka) Ascension evolved magmas, whereas zircon^titanite fractionation was predominant in younger felsic magmas.The eruptive sequence and compositions of the subaerial lavas and domes at Ascension Island are unique in comparison with other ocean island volcanoes because of its on-axis location and eruptions of highSiO2 trachyte and rhyolite.


title: Rules for the addition of O-linked N-acetylglucosamine to secreted proteins in Dictyostelium discoideum--in vivo studies on glycosylation of mucin MUC1 and MUC2 repeats.

abstract: One class of O-glycosylation in the simple eukaryote Dictyostelium discoideum involves the addition of a single N-acetylglucosamine residue to Ser and Thr residues on secreted or membrane-bound proteins at an early stage of development. A previously developed in vivo approach for the identification of acceptor sites for O-glycosylation was used to further characterise the specificity of the UDP-GlcNAc :polypeptide N-acetylglucosaminyltransferase(s). Glutathione S-transferase fusion proteins were constructed to express and secrete the mucin peptide repeat for MUC1 (PDT1RPAPGS1T2APPAHGVT3S2A) and a MUC2-like peptide (PT1T2T3PIT4T5T6T7T8T9VT10PT11PT12PT13GT14QT15), respectively (superscript numbers indicate residues with the potential to be glycosylated). Monosaccharide analysis, electrospray-ionisation mass spectrometry and protein sequencing showed that the modification is a single N-acetylglucosamine attached to certain Thr residues. The MUC1 repeat was glycosylated on T2 and T3 and there were no modifications on T1 or on S1 and S2. The MUC2 glycopeptide was glycosylated on T1, T3, T5, T7, T9, T10, T11, T12, T13 and T14. Our results show that the D. discoideum glycosylation apparatus incorporates GlcNAc residues into peptide sequences similar to those reported for the addition of GalNAc residues in mammalian tissues. The anomeric linkage of the GlcNAc residues to the polypeptide chain was shown to be in alpha configuration as determined by NMR studies.


title: Darwin: a neuromorphic hardware co-processor based on Spiking Neural Networks

abstract: Broadly speaking, the goal of neuromorphic engineering is to build computer systems that mimic the brain. Spiking Neural Network (SNN) is a type of biologically-inspired neural networks that perform information processing based on discrete-time spikes, different from traditional Artificial Neural Network (ANN). Hardware implementation of SNNs is necessary for achieving high-performance and low-power. We present the Darwin Neural Processing Unit (NPU), a neuromorphic hardware co-processor based on SNN implemented with digitallogic, supporting a maximum of 2048 neurons, 20482 = 4194304 synapses, and 15 possible synaptic delays. The Darwin NPU was fabricated by standard 180 nm CMOS technology with an area size of 5 ×5 mm2 and 70 MHz clock frequency at the worst case. It consumes 0.84 mW/MHz with 1.8 V power supply for typical applications. Two prototype applications are used to demonstrate the performance and efficiency of the hardware implementation.


title: Simple Recurrent Units for Highly Parallelizable Recurrence.

abstract: Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations. In this work, we propose the Simple Recurrent Unit (SRU), a light recurrent unit that balances model capacity and scalability. SRU is designed to provide expressive recurrence, enable highly parallelized implementation, and comes with careful initialization to facilitate training of deep models. We demonstrate the effectiveness of SRU on multiple NLP tasks. SRU achieves 5--9x speed-up over cuDNN-optimized LSTM on classification and question answering datasets, and delivers stronger results than LSTM and convolutional models. We also obtain an average of 0.7 BLEU improvement over the Transformer model on translation by incorporating SRU into the architecture.


title: Morphable Counters: Enabling Compact Integrity Trees For Low-Overhead Secure Memories

abstract: Securing off-chip main memory is essential for protection from adversaries with physical access to systems. However, current secure-memory designs incur considerable performance overheads – a major cause being the multiple memory accesses required for traversing an integrity-tree, that provides protection against man-in-the-middle attacks or replay attacks. In this paper, we provide a scalable solution to this problem by proposing a compact integrity tree design that requires fewer memory accesses for its traversal. We enable this by proposing new storage-efficient representations for the counters used for encryption and integrity-tree in secure memories. Our Morphable Counters are more cacheable on-chip, as they provide more counters per cacheline than existing split counters. Additionally, they incur lower overheads due to counter-overflows, by dynamically switching between counter representations based on usage pattern. We show that using Morphable Counters enables a 128-ary integrity-tree, that can improve performance by 6.3% on average (up to 28.3%) and reduce system energy-delay product by 8.8% on average, compared to an aggressive baseline using split counters with a 64-ary integrity-tree. These benefits come without any additional storage or reduction in security and are derived from our compact counter representation, that reduces the integrity-tree size for a 16GB memory from 4MB in the baseline to 1MB. Compared to recently proposed VAULT, our design provides a speedup of 13.5% on average (up to 47.4%).


title: Synthetic Oversampling with the Majority Class: A New Perspective on Handling Extreme Imbalance

abstract: The class imbalance problem is a pervasive issue in many real-world domains. Oversampling methods that inflate the rare class by generating synthetic data are amongst the most popular techniques for resolving class imbalance. However, they concentrate on the characteristics of the minority class and use them to guide the oversampling process. By completely overlooking the majority class, they lose a global view on the classification problem and, while alleviating the class imbalance, may negatively impact learnability by generating borderline or overlapping instances. This becomes even more critical when facing extreme class imbalance, where the minority class is strongly underrepresented and on its own does not contain enough information to conduct the oversampling process. We propose a novel method for synthetic oversampling that uses the rich information inherent in the majority class to synthesize minority class data. This is done by generating synthetic data that is at the same Mahalanbois distance from the majority class as the known minority instances. We evaluate over 26 benchmark datasets, and show that our method offers a distinct performance improvement over the existing state-of-the-art in oversampling techniques.


title: Development of magnet connection of modular units for soft robotics

abstract: Field of soft robotics has been widely researched. Modularization of soft robots is one of the effort to expand the field. In this paper, we introduce a magnet connection for modularized soft units which were introduced in our previous research. The magnet connector was designed with off the shelf magnets. Thanks to the magnet connection, it was simpler and more intuitive than the connection method that we used in previous research. Connecting strength of the magnet connection and bending performance of a soft bending actuator assembled with the units were tested. Connecting strength and air leakage prevention of the connector was affordable in a range of actuating pneumatic pressure. We hope that this magnet connector enables modularized soft units being used as a daily item in the future.


title: BRAINSUP: Brainstorming Support for Creative Sentence Generation

abstract: We present BRAINSUP, an extensible framework for the generation of creative sentences in which users are able to force several words to appear in the sentences and to control the generation process across several semantic dimensions, namely emotions, colors, domain relatedness and phonetic properties. We evaluate its performance on a creative sentence generation task, showing its capability of generating well-formed, catchy and effective sentences that have all the good qualities of slogans produced by human copywriters.


title: Question Answering and Question Generation as Dual Tasks

abstract: We study the problem of joint question answering (QA) and question generation (QG) in this paper. ::: Our intuition is that QA and QG have intrinsic connections and these two tasks could improve each other. ::: On one side, the QA model judges whether the generated question of a QG model is relevant to the answer. ::: On the other side, the QG model provides the probability of generating a question given the answer, which is a useful evidence that in turn facilitates QA. ::: In this paper we regard QA and QG as dual tasks. ::: We propose a training framework that trains the models of QA and QG simultaneously, and explicitly leverages their probabilistic correlation to guide the training process of both models. ::: We implement a QG model based on sequence-to-sequence learning, and a QA model based on recurrent neural network. ::: As all the components of the QA and QG models are differentiable, all the parameters involved in these two models could be conventionally learned with back propagation. ::: We conduct experiments on three datasets. Empirical results show that our training framework improves both QA and QG tasks. ::: The improved QA model performs comparably with strong baseline approaches on all three datasets.


title: Automated PCB inspection in small series production based on SIFT algorithm

abstract: There has long been a concern to improve the techniques of image processing for PCB's inspection. However, it is noticed the focus is mainly on the inspection of PCB in mass production. Even with a low volume of production, the small series production has become increasingly relevant. This work presents an image processing pipeline for mounted PCB inspection in small series production, strongly based on SIFT (Scale Invariant Feature Transform) algorithm. The defect types covered are: absent, rotated/inverted, misplaced and wrong. Experiments were performed using a software developed with the proposed approach. That software runs in an automatic optical inspection machine with an industrial camera and controlled illumination. The overall accuracy of the inspections is around 80%.


title: Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression

abstract: Although fully generative models have been successfully used to model the contents of text documents, they are often awkward to apply to combinations of text data and document metadata. In this paper we propose a Dirichlet-multinomial regression (DMR) topic model that includes a log-linear prior on document-topic distributions that is a function of observed features of the document, such as author, publication venue, references, and dates. We show that by selecting appropriate features, DMR topic models can meet or exceed the performance of several previously published topic models designed for specific data.


title: Combining CPU and GPU architectures for fast similarity search

abstract: The Signature Quadratic Form Distance on feature signatures represents a flexible distance-based similarity model for effective content-based multimedia retrieval. Although metric indexing approaches are able to speed up query processing by two orders of magnitude, their applicability to large-scale multimedia databases containing billions of images is still a challenging issue. In this paper, we propose a parallel approach that balances the utilization of CPU and many-core GPUs for efficient similarity search with the Signature Quadratic Form Distance. In particular, we show how to process multiple distance computations and other parts of the search procedure in parallel, achieving maximal performance of the combined CPU/GPU system. The experimental evaluation demonstrates that our approach implemented on a common workstation with 2 GPU cards outperforms traditional parallel implementation on a high-end 48-core NUMA server in terms of efficiency almost by an order of magnitude. If we consider also the price of the high-end server that is ten times higher than that of the GPU workstation then, based on price/performance ratio, the GPU-based similarity search beats the CPU-based solution by almost two orders of magnitude. Although proposed for the SQFD, our approach of fast GPU-based similarity search is applicable for any distance function that is efficiently parallelizable in the SIMT execution model.


title: Computationally Efficient Simulation of Queues: The R Package queuecomputer

abstract: Large networks of queueing systems model important real-world systems such as MapReduce clusters, web-servers, hospitals, call centers and airport passenger terminals. To model such systems accurately, we must infer queueing parameters from data. Unfortunately, for many queueing networks there is no clear way to proceed with parameter inference from data. Approximate Bayesian computation could offer a straightforward way to infer parameters for such networks if we could simulate data quickly enough. ::: We present a computationally efficient method for simulating from a very general set of queueing networks with the R package queuecomputer. Remarkable speedups of more than 2 orders of magnitude are observed relative to the popular DES packages simmer and simpy. We replicate output from these packages to validate the package. ::: The package is modular and integrates well with the popular R package dplyr. Complex queueing networks with tandem, parallel and fork/join topologies can easily be built with these two packages together. We show how to use this package with two examples: a call center and an airport terminal.


title: In situ measurement of collagen synthesis by human bone cells with a Sirius Red-based colorimetric microassay: effects of transforming growth factor β2 and ascorbic acid 2-phosphate

abstract: Staining of collagens by Sirius Red, a standard histological procedure, was applied to quantify collagen synthesis in human osteoblast-like cell cultures in situ. After morphological analysis of the deposited material, the stain was dissolved and its optical density determined spectrophotometrically using a microtiter plate assay system. The method was sensitive with a detection limit for collagen synthesized by 3000 normal human periosteal cells. The assay is easy to perform and specific with respect to different extracellular materials, for example, collagen types I and III were well stained, collagen type IV and laminin exhibited only low staining, and fibronectin, chondroitin sulfate, dermatan sulfate, and amyloid β were negative. A major advantage of the method is the combination of identification of collagen-producing cells in situ with subsequent spectrophotometric quantification of the dissolved stain. Thus it is possible to obtain information on cell morphology, active sites of collagen deposition in a cell culture, microscopic detection of high-and low-producer cells prior to dissolution and quantification of the deposited material. In this regard the assay is superior to either radioactive labeling, hydroxyproline determination, or Sirius Red-based colorimetric assays with cell lysates. Since the quantification is based on microtiter plate reading, the method can be recommended for the screening of large quantities of samples.


title: Effects of Conversational Agents on Human Communication in Thought-Evoking Multi-Party Dialogues

abstract: This paper presents an experimental study that analyzes how conversational agents activate human communication in thought-evoking multi-party dialogues between multi-users and multi-agents. A thought-evoking dialogue, which is a kind of interaction in which agents act on user willingness to provoke user thinking, has the potential to stimulate multi-party interaction. In this paper, we focus on quiz-style multi-party dialogues between two users and two agents as an example of a thought-evoking multi-party dialogue. The experiment results showed that the presence of a peer agent significantly improved user satisfaction and increased the number of user utterances. We also found that agent empathic expressions significantly improved user satisfaction, raised user ratings of a peer agent, and increased user utterances. Our findings will be useful for stimulating multi-party communication in various applications such as educational agents and community facilitators.


title: IHS_RD: Lexical Normalization for English Tweets

abstract: This paper describes the Twitter lexical normalization system submitted by IHS R&D Belarus team for the ACL 2015 workshop on noisy user-generated text. The proposed system consists of two components: a CRFbased approach to identify possible normalization candidates, and a post-processing step in an attempt to normalize words that do not have normalization variants in the lexicon. Evaluation on the test data set showed that our unconstrained system achieved the Fmeasure of 0.8272 (rank 1 out of 5 submissions for the unconstrained mode, rank 2 out of all 11 submissions).


title: IKBT: solving closed-form Inverse Kinematics with Behavior Tree

abstract: Serial robot arms have complicated kinematic equations which must be solved to write effective arm planning and control software (the Inverse Kinematics Problem). Existing software packages for inverse kinematics often rely on numerical methods which have significant shortcomings. Here we report a new symbolic inverse kinematics solver which overcomes the limitations of numerical methods, and the shortcomings of previous symbolic software packages. We integrate Behavior Trees, an execution planning framework previously used for controlling intelligent robot behavior, to organize the equation solving process, and a modular architecture for each solution technique. The system successfully solved, generated a LaTex report, and generated a Python code template for 18 out of 19 example robots of 4-6 DOF. The system is readily extensible, maintainable, and multi-platform with few dependencies. The complete package is available with a Modified BSD license on Github.


title: Transformative Learning as Discourse

abstract: This article presents an elaboration of the epistemology of transformative learning in adult education. Transformative learning is understood as a uniquely adult form of metacognitive reasoning. Reasoning is the process of advancing and assessing reasons, especially those that provide arguments supporting beliefs resulting in decisions to act. Beliefs are justified when they are based on good reasons. The process of reasoning may involve such tacit knowledge as aptitudes, skills, and competencies. The article examines the nature of reasoning within the context of critical-dialectical discourse—the intersubjective process of communicative learning by which adults assess beliefs—and its implications for democratic citizenship and adult education.


title: A qualitative evaluation of a learning dashboard to support advisor-student dialogues

abstract: This paper presents an evaluation of a learning dashboard that supports the dialogue between a student and a study advisor. The dashboard was designed, developed, and evaluated in collaboration with study advisers. To ensure scalability to other contexts, the dashboard uses data that is commonly available at any higher education institute. It visualizes the grades of the student, an overview of the progress through the year, his/her position in comparison with peers, sliders to plan the next years and a prediction of the length of the bachelor program for this student in years based on historic data. The dashboard was deployed at KU Leuven, Belgium and used in September 2017 to support 224 sessions between students and study advisers. We observed twenty of these conversations. We also collected feedback from 101 students with questionnaires. Results of our observations indicate that the dashboard primarily triggers insights at the beginning of a conversation. The number of insights and the level of these insights (factual, interpretative and reflective) depends on the context of the conversation. Most insights were triggered in conversations with students doubting to continue the program, indicating that our dashboard is useful to support difficult decision-making processes.


title: IoT security: challenges and solutions for mining

abstract: The Internet of Things (IoT) paradigm with its vast range of heterogeneous connecting technologies heralds a new era for internet research, especially given that this explosion in connectivity for devices or `things' is not without risk. Scholars recognise that IoT security concerns persist and that evidence highlighting increasing cyber-security vulnerabilities requires attention. Currently IoT security literature confirms: industry confusion, lack of clear standards, interoperability fears and security problems (with reference to identity, authentication, access control, protocol and network security, privacy, and trust and governance difficulties all within the IoT technology realm). In short, there is urgent need for governance in IoT to avoid unstructured fragmentation of architectures, protocols and identification systems, and responsibilities. To address anticipated future numbers of IoT devices, a secure, scalable, yet flexible solution is needed to work across a range of technologies and dynamic environments. Specific industries such as the Western Australian (WA) Mining and Resource sector, whilst recognised as an early adopter of technology/ IoT applications, is also currently seeking security solutions that provide competitive advantage. To this end, the research being conducted here is utilising qualitative methodologies (alongside document analyses), and specific real-world/live case-studies towards relevant organisations' IoT cyber-security decision making, with a view to developing best-practice cross-party guidance(s). Work here is at its early stages; ultimately variables identified and subsequently validated shall go towards a new developed design guide for the deployment of Information Technology/ Operational Technology and IoT environments, to address IoT security concerns, applicable to (major WA mining and resources companies and) the energy and resources generally.


title: Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

abstract: Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.


title: Occlusion-Free Visual Servoing for the Shared Autonomy Teleoperation of Dual-Arm Robots

abstract: Visual servoing in telerobotics provides information to the operator about the remote location and assists in the task execution to reduce stress on the user. Occlusions in this scenario can, on the one hand, lead to the visual servoing failure, and, on the other hand, degrade the user experience and navigation performance due to the obstructed vision of elements of interest. In this letter, we consider a teleoperation system composed of two robot arms where one is remotely operated, while the other is autonomous and equipped with an eye-in-hand camera sensor. We propose a reactive unified convex optimization based controller that allows the execution of occlusion-free tasks by autonomously adjusting the camera robot, so as to keep the teleoperated one in the field of view. The occlusion avoidance is formulated inside the optimization as a constraint in the image space, it is formally derived from a collision avoidance analogy and made robust against noisy measurements and dynamic environment. A state machine is used to switch the control policy whenever an occlusion might occur. We validate our approach with experiments on a 14 d.o.f. dual-arm ABB YuMi robot equipped with an red, green, blue (RGB) camera and teleoperated by a 3 d.o.f. Novint Falcon device.


title: A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecological modelling 168

abstract: Natural complexity can best be explored using spatial analysis tools based on concepts of landscape as process continuums that can be partially decomposed into objects or patches. We introduce a five-step methodology based on multi-scale segmentation and object relationship modelling. Hierarchical patch dynamics (HPD) is adopted as the theoretical framework to address issues of heterogeneity, scale, connectivity and quasi-equilibriums in landscapes. Remote sensing has emerged as the most useful data source for characterizing land use/land cover but a vast majority of applications rely on basic image processing concepts developed in the 1970s: one spatial scale, per-pixel classification of a multi-scale spectral feature space. We argue that this methodology does not make sufficient use of spatial concepts of neighbourhood, proximity or homogeneity. In contrast, the authors demonstrate in this article the utility of the HPD framework as a theoretical basis for landscape analysis in two different projects using alternative image processing methodologies, which try to overcome the ‘pixel-centred’ view. The first project focuses on habitat mapping using a high dimension multi-scale GIS database. Focal patches are derived through aggregating automatically generated landscape segments using sub-patch information including dominant tree crown densities and species. The second project uses fractal-based segmentation to produce multiple candidate segmented agricultural scenes, and then develops a decision framework to choose the combination of segmentation levels best suited to identifying shrub encroachment. The challenge and flexibility of the multi-scale segmentation/object relationship modellingapproach lies in the defining of the semantic rules which relate the lower level landscape units or holons to higher levels of organization. We seek to embrace the challenges of scale and hierarchy in landscapes and have tested two different ways to decompose complex natural environments into focal units utilising topological relations to model between the smallest units of differentiation and the focal level. We believe the use of a HPD theoretical framework will help development of better tools for characterizing the patterns and processes, acting through a range of scales, which make up landscapes. © 2003 Elsevier Science B.V. All rights reserved.


Compositionality from Reinforcement Learning ∗


title: The Venturia Apple Pathosystem: Pathogenicity Mechanisms and Plant Defense Responses

abstract: Venturia inaequalis is the causal agent of apple scab, a devastating disease of apple. We outline several unique features of this pathogen which are useful for molecular genetics studies intended to understand plant-pathogen interactions. The pathogenicity mechanisms of the pathogen and overview of apple defense responses, monogenic and polygenic resistance, and their utilization in scab resistance breeding programs are also reviewed.


title: Workflow Automation: Overview and Research Issues

abstract: Workflow management systems, a relatively recent technology, are designed to make work more efficient, integrate heterogeneous application systems, and support interorganizational processes in electronic commerce applications. In this paper, we introduce the field of workflow automation, the subject of this special issue of Information Systems Frontiers. In the first part of the paper, we provide basic definitions and frameworks to aid understanding of workflow management technologies. In the remainder of the paper, we discuss technical and management research opportunities in this field and discuss the other contributions to the special issue.


title: Calibrating the sociometer: the relationship between interpersonal appraisals and state self-esteem.

abstract: Four experiments examined the functional relationship between interpersonal appraisal and subjective feelings about oneself. Participants imagined receiving one of several positive or negative reactions from another person (Experiments 1, 2, and 3) or actually received interpersonal evaluations (Experiment 4), then completed measures relevant to state self-esteem. All 4 studies showed that subjective feelings were a curvilinear, ogival function of others' appraisals. Although trait self-esteem correlated with state reactions as a main effect, it did not moderate participants' reactions to interpersonal feedback.


Sleep Paralysis in Brazilian Folklore and Other Cultures: A Brief Review