Value co-creation between firms and customers: The role of big data-based cooperative assets

To better understand how big data interconnects firms and customers in promoting value co-creation, we propose a theoretical framework of big data-based cooperative assets based on evidence of multiple case studies. We identify four types of big data resources and four types of associated digital platforms, and we explore how firms develop the cooperative assets by transforming big data resources via the theoretical lens of service-dominant logic. This study offers a new theoretical perspective on value co-creation and an alternative competitive strategy in the era of big data for firms. ã 2016 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).


The TypTop System: Personalized Typo-Tolerant Password Checking

Password checking systems traditionally allow login only if the correct password is submitted. Recent work on typo-tolerant password checking suggests that usability can be improved, with negligible security loss, by allowing a small number of typographical errors. Existing systems, however, can only correct a handful of errors, such as accidentally leaving caps lock on or incorrect capitalization of the first letter in a password. This leaves out numerous kinds of typos made by users, such as transposition errors, substitutions, or capitalization errors elsewhere in a password. Some users therefore receive no benefit from existing typo-tolerance mechanisms.
 We introduce personalized typo-tolerant password checking. In our approach, the authentication system learns over time the typos made by a specific user. In experiments using Mechanical Turk, we show that 45% of users would benefit from personalization. Therefore, we design a system, called TypTop, that securely implements personalized typo-tolerance. Underlying TypTop is a new stateful password-based encryption scheme that can be used to store recent failed login attempts. Our formal analysis shows that security in the face of an attacker that obtains the state of the system reduces to the difficulty of a brute-force dictionary attack against the real password. We implement TypTop for Linux and Mac OS login and report on a proof-of-concept deployment.


Markov Logic Networks for Natural Language Question Answering

Our goal is to answer elementary-level science questions using knowledge extracted automatically from science textbooks, expressed in a subset of first-order logic. Given the incomplete and noisy nature of these automatically extracted rules, Markov Logic Networks (MLNs) seem a natural model to use, but the exact way of leveraging MLNs is by no means obvious. We investigate three ways of applying MLNs to our task. In the first, we simply use the extracted science rules directly as MLN clauses. Unlike typical MLN applications, our domain has long and complex rules, leading to an unmanageable number of groundings. We exploit the structure present in hard constraints to improve tractability, but the formulation remains ineffective. In the second approach, we instead interpret science rules as describing prototypical entities, thus mapping rules directly to grounded MLN assertions, whose constants are then clustered using existing entity resolution methods. This drastically simplifies the network, but still suffers from brittleness. Finally, our third approach, called Praline, uses MLNs to align the lexical elements as well as define and control how inference should be performed in this task. Our experiments, demonstrating a 15% accuracy boost and a 10x reduction in runtime, suggest that the flexibility and different inference semantics of Praline are a better fit for the natural language question answering task.


The Acquisition , Transfer , and Depreciation of Knowledge in Service Organizations : Productivity in Franchises

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Massively Parallel NUMA-aware Hash Joins

Driven by the two main hardware trends increasing main memory and massively parallel multi-core processing in the past few years, there has been much research e ort in parallelizing well-known join algorithms. However, the non-uniform memory access (NUMA) of these architectures to main memory has only gained limited attention in the design of these algorithms. We study recent proposals of main memory hash join implementations and identify their major performance problems on NUMA architectures. We then develop a NUMA-aware hash join for massively parallel environments, and show how the speci c implementation details a ect the performance on a NUMA system. Our experimental evaluation shows that a carefully engineered hash join implementation outperforms previous high performance hash joins by a factor of more than two, resulting in an unprecedented throughput of 3/4 billion join argument tuples per second.


Reducing the effect of email interruptions on employees

It is generally assumed that because it is not necessary to react to email messages when they arrive, employees will read their messages in their own time with minimum interruption to their work. This research has shown that email messages do have some disruptive effect by interrupting the user. Employees at the Danwood Group in the UK were monitored to see how they used email. It was found that most employees had their email software check for incoming messages every 5min and responded to the arrival of a message within 6 s. A recovery time between finishing reading the email and returning to normal work also existed though it was shorter than published recovery times for a telephone interrupt. This analysis has suggested that a number of methods can be employed to reduce this interrupt effect. Employee training, changing the settings and modes of using the email software and the introduction of a one line email facility are all shown to have beneficial effects. This has led to a series of recommendations that will enable the Danwood Group to make better use of email communication and increase employee effectiveness. r 2003 Elsevier Science Ltd. All rights reserved.


Immersion, presence and performance in virtual environments: an experiment with tri-dimensional chess

This paper describes an experiment to assess the influence of immersion on performance in immersive virtual environments. The task involved Tri-Dimensional Chess, and required subjects to reproduce on a real chess board the state of the board learned from a sequence of moves witnessed in a virtual environment. Twenty four subjects were allocated to a factorial design consisting of two levels of immersion (exocentric screen based, and egocentric HMD based), and two kinds of environment (plain and realistic. The results suggest that egocentric subjects performed better than exocentric, and those in the more realistic environment performed better than those in the less realistic environment. Previous knowledge of chess, and amount of virtual practice were also significant, and may be considered as control variables to equalise these factors amongst the subjects. Other things being equal, males remembered the moves better than females, although female performance improved with higher spatial ability test score. The paper also attempts to clarify the relationship between immersion, presence and performance, and locates the experiment within such a theoretical framework.


Probabilistic graph and hypergraph matching

We consider the problem of finding a matching between two sets of features, given complex relations among them, going beyond pairwise. Each feature set is modeled by a hypergraph where the complex relations are represented by hyper-edges. A match between the feature sets is then modeled as a hypergraph matching problem. We derive the hyper-graph matching problem in a probabilistic setting represented by a convex optimization. First, we formalize a soft matching criterion that emerges from a probabilistic interpretation of the problem input and output, as opposed to previous methods that treat soft matching as a mere relaxation of the hard matching problem. Second, the model induces an algebraic relation between the hyper-edge weight matrix and the desired vertex-to-vertex probabilistic matching. Third, the model explains some of the graph matching normalization proposed in the past on a heuristic basis such as doubly stochastic normalizations of the edge weights. A key benefit of the model is that the global optimum of the matching criteria can be found via an iterative successive projection algorithm. The algorithm reduces to the well known Sinkhorn [15] row/column matrix normalization procedure in the special case when the two graphs have the same number of vertices and a complete matching is desired. Another benefit of our model is the straight-forward scalability from graphs to hyper-graphs.


A Review of Non-Invasive Sensory Feedback Methods for Transradial Prosthetic Hands

Any implant or prosthesis replacing a function or functions of an organ or group of organs should be biologically and sensorily integrated with the human body in order to increase their acceptance with their user. If this replacement is for a human hand, which is an important interface between humans and their environment, the acceptance issue and developing sensory-motor embodiment will be more challenging. Despite progress in prosthesis technologies, 50–60% of hand amputees wear a prosthetic device. One primary reason for the rejection of the prosthetic hands is that there is no or negligibly small feedback or tactile sensation from the hand to the user, making the hands less functional. In fact, the loss of a hand means interrupting the closed-loop sensory feedback between the brain (motor control) and the hand (sensory feedback through the nerves). The lack of feedback requires significant cognitive efforts from the user in order to do basic gestures and daily activities. To this aim, recently, there has been significant development in the provision of sensory feedback from transradial prosthetic hands, to enable the user take part in the control loop and improve user embodiment. Sensory feedback to the hand users can be provided via invasive and non-invasive methods. The latter includes the use of temperature, vibration, mechanical pressure and skin stretching, electrotactile stimulation, phantom limb stimulation, audio feedback, and augmented reality. This paper provides a comprehensive review of the non-invasive methods, performs their critical evaluation, and presents challenges and opportunities associated with the non-invasive sensory feedback methods.


Intrusion Detection with Neural Networks

With the rapid expansion of computer networks during the past few years, security has become a crucial issue for modern computer systems. A good way to detect illegitimate use is through monitoring unusual user activity. Methods of intrusion detection based on hand-coded rule sets or predicting commands on-line are laborous to build or not very reliable. This paper proposes a new way of applying neural networks to detect intrusions. We believe that a user leaves a ’print’ when using the system; a neural network can be used to learn this print and identify each user much like detectives use thumbprints to place people at crime scenes. If a user’s behavior does not match his/her print, the system administrator can be alerted of a possible security breech. A backpropagation neural network called NNID (Neural Network Intrusion Detector) was trained in the identification task and tested experimentally on a system of 10 users. The system was 96% accurate in detecting unusual activity, with 7% false alarm rate. These results suggest that learning user profiles is an effective way for detecting intrusions.


Network intrusion detection

Intrusion detection is a new, retrofit approach for providing a sense of security in existing computers and data networks, while allowing them to operate in their current "open" mode. The goal of intrusion detection is to identify unauthorized use, misuse, and abuse of computer systems by both system insiders and external penetrators. The intrusion detection problem is becoming a challenging task due to the proliferation of heterogeneous computer networks since the increased connectivity of computer systems gives greater access to outsiders and makes it easier for intruders to avoid identification. Intrusion detection systems (IDSs) are based on the beliefs that an intruder's behavior will be noticeably different from that of a legitimate user and that many unauthorized actions are detectable. Typically, IDSs employ statistical anomaly and rulebased misuse models in order to detect intrusions. A number of prototype IDSs have been developed at several institutions, and some of them have also been deployed on an experimental basis in operational systems. In the present paper, several host-based and network-based IDSs are surveyed, and the characteristics of the corresponding systems are identified. The host-based systems employ the host operating system's audit trails as the main source of input to detect intrusive activity, while most of the network-based IDSs build their detection mechanism on monitored network traffic, and some employ host audit trails as well. An outline of a statistical anomaly detection algorithm employed in a typical IDS is also included.<<ETX>>


Comparison of learning algorithms for handwritten digit recognition

This paper compares the performance of several classi er algorithms on a standard database of handwritten digits. We consider not only raw accuracy, but also rejection, training time, recognition time, and memory requirements.


Efficient Progressive Sampling

Having access to massive amounts of data does not necessarily imply that induction algorithms must use them all. Samples often provide the same accuracy with far less computational cost. However, the correct sample size rarely is obvious. We analyze methods for progressive samplingusing progressively larger samples as long as model accuracy improves. We explore several notions of efficient progressive sampling. We analyze efficiency relative to induction with all instances; we show that a simple, geometric sampling schedule is asymptotically optimal, and we describe how best to take into account prior expectations of accuracy convergence. We then describe the issues involved in instantiating an efficient progressive sampler, including how to detect convergence. Finally, we provide empirical results comparing a variety of progressive sampling methods. We conclude that progressive sampling can be remarkably efficient .


Feature Extraction Methods for Time Series Data in SAS ® Enterprise Miner TM

Because time series data have a unique data structure, it is not easy to apply some existing data mining tools directly to the data. For example, in classification and clustering problems, each time point is often considered a variable and each time series is considered an observation. As the time dimension increases, the number of variables also increases, in proportion to the time dimension. Therefore, data mining tasks require some feature extraction techniques to summarize each time series in a form that has a significantly lower dimension. This paper describes various feature extraction methods for time series data that are implemented in SAS Enterprise MinerTM. INTRODUCTION Time series data mining has four major tasks: clustering, indexing, classification, and segmentation. Clustering finds groups of time series that have similar patterns. Indexing finds similar time series in order, given a query series. Classification assigns each time series to a known category by using a trained model. Segmentation partitions time series. Time series data can be considered multidimensional data; this means that there is one observation per time unit, and each time unit makes one dimension. Some common data mining techniques are used for the tasks. However, in the real world, each time series is usually a high-dimensional sequence, such as stock market data whose prices change over time or data collected by a medical device. When the time unit is seconds, the dimensions of data that accumulate in just one hour are 3,600. Moreover, the dimensions are highly correlated to one another. The number of variables in training data increases proportionally as the time dimension increases. Most existing data mining tools cannot be used efficiently on time series data without a dimension reduction. Therefore, a dimension reduction is required through feature extraction techniques that map each time series to a lower-dimensional space. The most common techniques of dimension reduction in time series are singular value decomposition (SVD), discrete Fourier transformation (DFT), discrete wavelet transformation (DWT), and line segment methods. Some classical time series analyses, such as seasonal, trend, seasonal decomposition, and correlation analyses, are also used for feature extraction. This paper shows how to use SAS Enterprise Miner to implement the feature extraction techniques. But it does not attempt to explain these time series techniques in detail and instead invites the interested reader to explore some of the literature on the subject: Keogh and Pazzani (2000a), Keogh et al. (2000), and Gavrilov et al. (2000) for time series dimensionality reduction and the TIMESERIES procedure documentation in SAS/ETS for the classical time series analyses. This paper is organized as follows. First, it explains feature extraction and dimension reduction techniques that use classical time series analysis, and then it describes feature extraction techniques that use some well-known mathematical functions for dimension reduction. Next, it demonstrates the performance of dimension reduction techniques by presenting some examples. The paper uses SAS Enterprise Miner 13.1 to demonstrate these techniques.


On Evaluation of 6D Object Pose Estimation

A pose of a rigid object has 6 degrees of freedom and its full knowledge is required in many robotic and scene understanding applications. Evaluation of 6D object pose estimates is not straightforward. Object pose may be ambiguous due to object symmetries and occlusions, i.e. there can be multiple object poses that are indistinguishable in the given image and should be therefore treated as equivalent. The paper defines 6D object pose estimation problems, proposes an evaluation methodology and introduces three new pose error functions that deal with pose ambiguity. The new error functions are compared with functions commonly used in the literature and shown to remove certain types of non-intuitive outcomes. Evaluation tools are provided at: https : //github.com/thodan/obj pose eval


The Lone-Actor Terrorist and the TRAP-18

An open source sample of 111 lone-actor terrorists from the United States and Europe were studied through the lens of the Terrorist Radicalization Assessment Protocol (TRAP-18). This investigative template consists of 8 proximal warning behaviors and 10 distal characteristics for active risk management or active monitoring, respectively, by national security threat assessors. Several aspects of criterion validity were tested in this known outcome sample. Seventy percent of the terrorists were positive for at least half or more of the indicators. When the sample was divided into Islamic extremists, right-wing extremists, and single-issue terrorists, there were no significant differences across all 18 indicators except for 4. When the sample was divided according to successful versus thwarted attackers, the successful attackers were significantly more fixated, creative, and innovative, and failed to have a prior sexually intimate pair bond. They were significantly less likely to have displayed pathway warning behavior and be dependent on a virtual community of likeminded true believers. Effect sizes were small to medium (! " 0.190–0.317). The TRAP-18 appears to have promise as an eventual structured professional judgment risk assessment instrument according to some of the individual terrorist content domains outlined by Monahan (2012, 2016).


Word and Phrase Translation with word2vec

Word and phrase tables are key inputs to machine translations, but costly to produce. New unsupervised learning methods represent words and phrases in a high-dimensional vector space, and these monolingual embeddings have been shown to encode syntactic and semantic relationships between language elements. The information captured by these embeddings can be exploited for bilingual translation by learning a transformation matrix that allows matching relative positions across two monolingual vector spaces. This method aims to identify high-quality candidates for word and phrase translation more costeffectively from unlabeled data. This paper expands the scope of previous attempts of bilingual translation to four languages (English, German, Spanish, and French). It shows how to process the source data, train a neural network to learn the high-dimensional embeddings for individual languages and expands the framework for testing their quality beyond the English language. Furthermore, it shows how to learn bilingual transformation matrices and obtain candidates for word and phrase translation, and assess their quality.


Feature Selection Methods: Genetic Algorithms vs. Greedy-like Search

This paper presents a comparison between two feature selection methods, the Importance Score (IS) which is based on a greedy-like search and a genetic algorithm-based (GA) method, in order to better understand their strengths and limitations and their area of application. The results of our experiments show a very strong relation between the nature of the data and the behavior of both systems. The Importance Score method is more efficient when dealing with little noise and small number of interacting features, while the genetic algorithms can provide a more robust solution at the expense of increased computational effort.


Fast texture synthesis using tree-structured vector quantization

Texture synthesis is important for many applications in computer graphics, vision, and image processing. However, it remains difficult to design an algorithm that is both efficient and capable of generating high quality results. In this paper, we present an efficient algorithm for realistic texture synthesis. The algorithm is easy to use and requires only a sample texture as input. It generates textures with perceived quality equal to or better than those produced by previous techniques, but runs two orders of magnitude faster. This permits us to apply texture synthesis to problems where it has traditionally been considered impractical. In particular, we have applied it to constrained synthesis for image editing and temporal texture generation. Our algorithm is derived from Markov Random Field texture models and generates textures through a deterministic searching process. We accelerate this synthesis process using tree-structured vector quantization.


ClothCap: seamless 4D clothing capture and retargeting

Designing and simulating realistic clothing is challenging. Previous methods addressing the capture of clothing from 3D scans have been limited to single garments and simple motions, lack detail, or require specialized texture patterns. Here we address the problem of capturing regular clothing on fully dressed people in motion. People typically wear multiple pieces of clothing at a time. To estimate the shape of such clothing, track it over time, and render it believably, each garment must be segmented from the others and the body. Our ClothCap approach uses a new multi-part 3D model of clothed bodies, automatically segments each piece of clothing, estimates the minimally clothed body shape and pose under the clothing, and tracks the 3D deformations of the clothing over time. We estimate the garments and their motion from 4D scans; that is, high-resolution 3D scans of the subject in motion at 60 fps. ClothCap is able to capture a clothed person in motion, extract their clothing, and retarget the clothing to new body shapes; this provides a step towards virtual try-on.


Representation and Mimesis in Generative Art : Creating Fifty Sisters

Fifty Sisters is a generative artwork commissioned for the Ars Electronica Museum in Linz. The work consists of fifty 1m ∞ 1m images of computer-synthesized plant-forms, algorithmically ‘grown’ from computer code using artificial evolution and generative grammars. Each plant-like form is derived from the primitive graphic elements of oil company logos. The title of the work refers to the original ‘Seven Sisters’ — a cartel of seven oil companies that dominated the global petrochemical industry and Middle East oil production from the mid–1940s until the oil crisis of the 1970s. In this paper I discuss the issue of representation in generative art and how dialogues in mimesis inform the production of a generative artwork, using Fifty Sisters as an example. I also provide information on how these concepts translate into the technical and how issues of representation necessarily pervade all computer-based generative art. C o m p u t i n g C o m m u n i c a t i o n A e s t h e t i c s a n d X . B e r g a m o , I t a l y . x c o a x . o r g


Arrays in Blitz++

Numeric arrays in Blitz++ rival the eeciency of Fortran, but without any extensions to the C++ language. Blitz++ has features unavailable in Fortran 90/95, such as arbitrary transpose operations, array renaming, tensor notation, partial reductions, multicomponent arrays and stencil operators. The library handles parsing and analysis of array expressions on its own using the expression templates technique, and performs optimizations (such as loop transformations) which have until now been the responsibility of compilers.


Integration of a Wireless I/O Interface for PROFIBUS and PROFINET for Factory Automation

Cost related to the wiring of sensors and actuators in the manufacturing industry is rapidly increasing with the number of field devices. Complex engineering as well as cable wear and tear are important factors. For over a decade, fieldbuses have been the dominating approach to simplify installation, reduce engineering efforts, and increase reliability at the field level. The next step in this process is to introduce wireless communication in certain problematic parts of the field network. In order to do this, a reliable real-time communication system, designed to cope with the harsh environments where factory automation systems are employed, is required. The wireless interface for sensors and actuators (WISA), with its two subconcepts WISA-power and WISA-com, is such a system. This paper discusses how the WISA concept can be efficiently integrated into wired field networks for factory automation-both state-of-the-art [process fieldbus decentral peripherals (PROFIBUS DP)] and new (PROFINET IO) networks. We propose amendments to WISA, which will improve the 802.11b/g coexistence and harmonize the integration of WISA for sensor/actuator communication in, e.g., PROFINET IO systems. The suggested improvements relate to both the radio protocol in order to improve coexistence with other wireless technologies as well as concepts for the integration of WISA into the field network of PROFINET/PROFIBUS.


Dual-band circularly polarized microstrip antenna

A new dual-band circularly polarized microstrip antenna is presented experimentally. In this paper, using dual-radiator a new design dual-band antenna can be obtained. The frequency ratio of this design is flexible which more than 1.4.


A Transceiver Module for FMCW Radar Sensors Using 94-GHz Dot-Type Schottky Diode Mixer

In this study, we fabricated a 94-GHz transceiver module for a millimeter-wave (MMW) frequency modulation continuous wave (FMCW) radar sensor. The transceiver modules consist of a waveguide voltage-controlled oscillator (VCO) and Rx module using a single balanced mixer. We designed a mixer with a conversion loss of 6.4 dB, without using an amplifier. Also, the waveguide VCO consisted of an InP Gunn diode, a varactor diode, two bias posts with LPF, and a Magic Tee for the MMW radar transceiver. The fabricated VCO has a tuning range of 1280 MHz by a varactor bias of 0~20 V, 1.69% linearity range of 680 MHz, and current consumption of 154 to 157 mA. The completed module has a good conversion loss of 10.6 dB with an LO power of 11.4 dBm at 94 GHz. With this RF and LO input power, the conversion loss was maintained between 10.2-11.5 dB in the RF frequency range of 93.67-94.95 GHz.


A half-bridge resonant inverter with three-phase PWM rectifier for induction heating is proposed in this paper. An adjustable frequency limit is introduced to maintain the inverter's switching frequency at a value higher than the resonant frequency for zero voltage operation. In case of no work piece in the induction coil, the dc bus can be adjusted while the switching frequency of the inverter remains at the limit. The important advantages of the proposed system are reduced switching loss, small size of filter and faster response at a no load condition. The proposed technique is implemented on the created hardware setup. The inverter operates at in a frequency range of 20–40 kHz, while the three-phase PWM rectifier runs at 23.8 kHz to deliver output power at 1.5 kW.


Predicting Friends and Foes in Signed Networks Using Inductive Inference and Social Balance Theory

Besides the notion of friendship, trust or support in social networking sites (SNSs), quite often social interactions also reflect users' antagonistic attitude towards each other. Thus, the hidden knowledge contained in social network data can be considered as an important resource to discover the formation of such positive and negative links. In this work, an inductive learning framework is presented to suggest 'friends' and 'foes' links to individuals which envisage the social balance among users in the corresponding friends and foes networks (FFN). First we learn a model by applying C4.5, the most widely adopted decision tree based classification algorithm, to exploit the feature patterns presented in the users' FFN and utilizing it to further predict friend/foe relationship of unknown links. Secondly, a quantitative measure of social balance, balance index, is used to support our decision on the recommendation of new friends and foes links (FFL) to avoid possible imbalance in the extended FFN with newly suggested links. The proposed scheme ensures that the recommendation of new FFLs either maintains or enhances the balancing factor of the existing FFN of an individual. Experimental results show the effectiveness of our proposed schemes.


A review on 3 D micro-additive manufacturing technologies

New microproducts need the utilization of a diversity of materials and have complicated three-dimensional (3D) microstructures with high aspect ratios. To date, many micromanufacturing processes have been developed but specific class of such processes are applicable for fabrication of functional and true 3D microcomponents/assemblies. The aptitude to process a broad range of materials and the ability to fabricate functional and geometrically complicated 3D microstructures provides the additive manufacturing (AM) processes some profits over traditional methods, such as lithography-based or micromachining approaches investigated widely in the past. In this paper, 3D micro-AM processes have been classified into three main groups, including scalable micro-AM systems, 3D direct writing, and hybrid processes, and the key processes have been reviewed comprehensively. Principle and recent progress of each 3D micro-AM process has been described, and the advantages and disadvantages of each process have been presented.


Semantic audio content-based music recommendation and visualization based on user preference examples

Preference elicitation is a challenging fundamental problem when designing recommender systems. In the present work we propose a content-based technique to automatically generate a semantic representation of the user’s musical preferences directly from audio. Starting from an explicit set of music tracks provided by the user as evidence of his/her preferences, we infer high-level semantic descriptors for each track obtaining a user model. To prove the benefits of our proposal, we present two applications of our technique. In the first one, we consider three approaches to music recommendation, two of them based on a semantic music similarity measure, and one based on a semantic probabilistic model. In the second application, we address the visualization of the user’s musical preferences by creating a humanoid cartoon-like character – the Musical Avatar – automatically inferred from the semantic representation. We conducted a preliminary evaluation of the proposed technique in the context of these applications with 12 subjects. The results are promising: the recommendations were positively evaluated and close to those coming from state-ofthe-art metadata-based systems, and the subjects judged the generated visualizations to capture their core preferences. Finally, we highlight the advantages of the proposed semantic user model for enhancing the user interfaces of information filtering systems. 2012 Elsevier Ltd. All rights reserved.


An estimation of the number of cells in the human body.

BACKGROUND
All living organisms are made of individual and identifiable cells, whose number, together with their size and type, ultimately defines the structure and functions of an organism. While the total cell number of lower organisms is often known, it has not yet been defined in higher organisms. In particular, the reported total cell number of a human being ranges between 10(12) and 10(16) and it is widely mentioned without a proper reference.

AIM
To study and discuss the theoretical issue of the total number of cells that compose the standard human adult organism.

SUBJECTS AND METHODS
A systematic calculation of the total cell number of the whole human body and of the single organs was carried out using bibliographical and/or mathematical approaches.

RESULTS
A current estimation of human total cell number calculated for a variety of organs and cell types is presented. These partial data correspond to a total number of 3.72 × 10(13).

CONCLUSIONS
Knowing the total cell number of the human body as well as of individual organs is important from a cultural, biological, medical and comparative modelling point of view. The presented cell count could be a starting point for a common effort to complete the total calculation.


Galvanic Skin Response : A Physiological Sensor System for Affective Computing

system is the change in electrical properties of skin due to variation in physiological and psychological conditions. The change is caused by the degree to which a person's sweat glands are active. Psychological status of a person tends to make the glands active and this change the skin resistance. Drier the skin is, higher will be the skin resistance. This variation in skin resistance ranges from 5kΩ to 25kΩ. In the current work, a subject whose Galvanic skin response (GSR) is to be measured, has been shown/played a movie clipping, images or recorded audio signals. Depending upon the theme, emotion will be evoked in the subject. Due to the change in emotion, GSR varies. This variation in GSR is recorded for a fixed time interval. In the present work, a total of 75 subjects are selected. People from different age groups and social background, both male and female have been carefully considered. Data acquisition and analysis is carried out by using LabVIEW. The results obtained are convincing and follows the ground reality with a high level of accuracy. As the subject also gives the feedback about the emotions he/she experienced, the results obtained are validated.


VANET-cloud: a generic cloud computing model for vehicular Ad Hoc networks

Cloud computing is a network access model that aims to transparently and ubiquitously share a large number of computing resources. These are leased by a service provider to digital customers, usually through the Internet. Due to the increasing number of traffic accidents and dissatisfaction of road users in vehicular networks, the major focus of current solutions provided by intelligent transportation systems is on improving road safety and ensuring passenger comfort. Cloud computing technologies have the potential to improve road safety and traveling experience in ITSs by providing flexible solutions (i.e., alternative routes, synchronization of traffic lights, etc.) needed by various road safety actors such as police, and disaster and emergency services. In order to improve traffic safety and provide computational services to road users, a new cloud computing model called VANET-Cloud applied to vehicular ad hoc networks is proposed. Various transportation services provided by VANET-Cloud are reviewed, and some future research directions are highlighted, including security and privacy, data aggregation, energy efficiency, interoperability, and resource management.


A dual-polarity grappa kernel for the robust reconstruction of accelerated EPI data

The quality of high-resolution Echo Planar images of the human brain has improved greatly in recent years, enabled by novel multi-channel receiver coil arrays and parallel imaging. However, in regions with local field inhomogeneity, EPI artifacts limit which parts of the brain can be imaged successfully. In this work, we present evidence that certain image artifacts can be attributed to nonlinear phase errors that are present in regions of local susceptibility gradients and certain coil array elements. Because these phase errors cannot be corrected with conventional Nyquist ghost correction, we propose a new method that integrates ghost correction with parallel imaging reconstruction. The proposed Dual-Polarity GRAPPA method operates directly on raw EPI data to estimate k-space data from the under-sampled acquisition while simultaneously correcting inherent EPI phase errors. We present examples of this method successfully removing strong phase-error artifacts in high-resolution 7T EPI data.


Marble: high-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization

The rapidly increasing availability of electronic health records (EHRs) from multiple heterogeneous sources has spearheaded the adoption of data-driven approaches for improved clinical research, decision making, prognosis, and patient management. Unfortunately, EHR data do not always directly and reliably map to phenotypes, or medical concepts, that clinical researchers need or use. Existing phenotyping approaches typically require labor intensive supervision from medical experts. We propose Marble, a novel sparse non-negative tensor factorization method to derive phenotype candidates with virtually no human supervision. Marble decomposes the observed tensor into two terms, a bias tensor and an interaction tensor. The bias tensor represents the baseline characteristics common amongst the overall population and the interaction tensor defines the phenotypes. We demonstrate the capability of our proposed model on both simulated and patient data from a publicly available clinical database. Our results show that Marble derived phenotypes provide at least a 42.8% reduction in the number of non-zero element and also retains predictive power for classification purposes. Furthermore, the resulting phenotypes and baseline characteristics from real EHR data are consistent with known characteristics of the patient population. Thus it can potentially be used to rapidly characterize, predict, and manage a large number of diseases, thereby promising a novel, data-driven solution that can benefit very large segments of the population.


Using Augmented Reality to Visualise Architecture Designs in an Outdoor Environment

This paper presents the use of a wearable computer system to visualise outdoor architectural features using augmented reality. The paper examines the question How does one visualise a design for a building, modification to a building, or extension to an existing building relative to its physical surroundings? The solution presented to this problem is to use a mobile augmented reality platform to visualise the design in spatial context of its final physical surroundings. The paper describes the mobile augmented reality platform TINMITH2 used in the investigation. The operation of the system is described through a detailed example of the system in operation. The system was used to visualise a simple extension to a building on one of the University of South Australia campuses.


Technologies for Augmented Reality Systems: Realizing Ultrasound-Guided Needle Biopsies

We present a real-time stereoscopic video-see-through augmented reality (AR) system applied to the medical procedure known as ultrasound-guided needle biopsy of the breast. The AR system was used by a physician during procedures on breast models and during non-invasive examinations of human subjects. The system merges rendered live ultrasound data and geometric elements with stereo images of the patient acquired through head-mounted video cameras and presents these merged images to the physician in a head-mounted display. The physician sees a volume visualization of the ultrasound data directly under the ultrasound probe, properly registered within the patient and with the biopsy needle. Using this system, a physician successfully guided a needle into an artificial tumor within a training phantom of a human breast. We discuss the construction of the AR system and the issues and decisions which led to the system architecture and the design of the video see-through head-mounted display. We designed methods to properly resolve occlusion of the real and synthetic image elements. We developed techniques for realtime volume visualization of timeand position-varying ultrasound data. We devised a hybrid tracking system which achieves improved registration of synthetic and real imagery and we improved on previous techniques for calibration of a magnetic tracker. CR


Improving static and dynamic registration in an optical see-through HMD

In Augmented Reality, see-through HMDs superimpose virtual 3D objects on the real world. This technology has the potential to enhance a user's perception and interaction with the real world. However, many Augmented Reality applications will not be accepted until we can accurately register virtual objects with their real counterparts. In previous systems, such registration was achieved only from a limited range of viewpoints, when the user kept his head still. This paper offers improved registration in two areas. First, our system demonstrates accurate static registration across a wide variety of viewing angles and positions. An optoelectronic tracker provides the required range and accuracy. Three calibration steps determine the viewing parameters. Second, dynamic errors that occur when the user moves his head are reduced by predicting future head locations. Inertial sensors mounted on the HMD aid head-motion prediction. Accurate determination of prediction distances requires low-overhead operating systems and eliminating unpredictable sources of latency. On average, prediction with inertial sensors produces errors 2-3 times lower than prediction without inertial sensors and 5-10 times lower than using no prediction at all. Future steps that may further improve registration are outlined.


A wearable computer system with augmented reality to support terrestrial navigation

To date augmented realities are typically operated in only a small defined area, in the order of a large room. This paper reports on our investigation into expanding augmented realities to outdoor environments. The project entails providing visual navigation aids to users. A wearable computer system with a see-through display, digital compass, and a differential GPS are used to provide visual cues while performing a standard orienteering task. This paper reports the outcomes of a set of trials using an off the shelf wearable computer, equipped with a custom built navigation software package, "map-in-the-hat".


Benchmarking of Document Image Analysis Tasks for Palm Leaf Manuscripts from Southeast Asia

This paper presents a comprehensive test of the principal tasks in document image analysis (DIA), starting with binarization, text line segmentation, and isolated character/glyph recognition, and continuing on to word recognition and transliteration for a new and challenging collection of palm leaf manuscripts from Southeast Asia. This research presents and is performed on a complete dataset collection of Southeast Asian palm leaf manuscripts. It contains three different scripts: Khmer script from Cambodia, and Balinese script and Sundanese script from Indonesia. The binarization task is evaluated on many methods up to the latest in some binarization competitions. The seam carving method is evaluated for the text line segmentation task, compared to a recently new text line segmentation method for palm leaf manuscripts. For the isolated character/glyph recognition task, the evaluation is reported from the handcrafted feature extraction method, the neural network with unsupervised learning feature, and the Convolutional Neural Network (CNN) based method. Finally, the Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) based method is used to analyze the word recognition and transliteration task for the palm leaf manuscripts. The results from all experiments provide the latest findings and a quantitative benchmark for palm leaf manuscripts analysis for researchers in the DIA community.


Frostbite following cryolipolysis treatment in a beauty salon: a case study.

This case study describes frostbite, a previously unreported complication following cryolipolysis, which resulted in substantial necrosis of the flank. Medical attention was not sought until one week after treatment. On examination, two distinct areas of significant frostbite in the left flank with surrounding erythema were revealed. Surgical intervention was avoided, as is recommended in cases of frostbite, and conservative treatment resulted in recovery of the affected area. Here, the authors highlight the adverse effects related to cryolipolysis, analysing the pathogenesis, clinical manifestations and management of this injury. The necessity of regulation within the cosmetic sector and the challenges associated with its implementation are also described. The authors believe emphasis must be placed on increasing patient awareness on the potential hazards of seeking cosmetic treatment from unregulated providers.


Generation of Pencil Sketch Drawing

This paper proposes a pencil sketch generating system that can create various pencil sketching styles for images, to satisfy different needs of users. The pencil sketch generating process includes process of three parts: line drawing, tone adjustment, and texture rendering. Some operations needed are first proposed, and various pencil sketching styles that generated by the proposed system are illustrated.


Security Risk Assessment of Cloud Carrier

Cloud computing based delivery model has been adopted by end-users and enterprises to reduce IT costs and complexities. The ability to offload user software and data to cloud data centers has raised many security and privacy concerns over the cloud computing model. Significant research efforts have focused on hyper visor security and low-layer operating system implementations in cloud data centers. Unfortunately, the role of cloud carrier in the security and privacy of user software and data has not been well studied. Cloud carrier represents the wide area network that provides the connectivity and transport of cloud services between cloud consumers and cloud providers. In this paper, we present a risk assessment framework to study the security risk of the cloud carrier between cloud consumers and cloud providers. The risk assessment framework leverages the National Vulnerability Database (NVD) to examine the security vulnerabilities of operating systems of routers within the cloud carrier. This framework provides quantifiable security metrics for cloud carrier, which enables cloud consumers to establish the quality of security services among cloud providers. Such security metric information is very useful in the Service Level Agreement (SLA) negotiation between a cloud consumer and a cloud provider. It can be also be used to build a tool to verify SLA compliance. Furthermore, we implement this framework for the cloud carriers of Amazon Web Services and Windows Azure Platform. Our experiments show that the security risks of cloud carriers on these two commercial clouds are significantly different. This finding provides guidance for a network provider to improve the security of cloud carriers.


Embedded 3D printing of strain sensors within highly stretchable elastomers.

A new method, embedded-3D printing (e-3DP), is reported for fabricating strain sensors within highly conformal and extensible elastomeric matrices. e-3DP allows soft sensors to be created in nearly arbitrary planar and 3D motifs in a highly programmable and seamless manner. Several embodiments are demonstrated and sensor performance is characterized.


Free viewpoint video (FVV) survey and future research direction

Free viewpoint video (FVV) is one of the new trends in the development of advanced visual media type that aims to provide a new immersive user experience and interactivity that goes beyond higher image quality (HD/4K TV) and higher realism (3D TV). Potential applications include interactive personal visualization and free viewpoint navigation. The goal of this paper is to provide an overview of the FVV system and some target application scenarios. Associated standardization activities and technological barriers to overcome are also described. This paper is organized as follows: a general description of the FVV system and functionalities is given in Section I. Since an FVV system is composed of a chain of processing modules, an in-depth functional description of each module is provided in Section II. Examples of emerging FVV applications and use cases are given in Section III. A summary of technical challenges to overcome for wider usage and market penetration of FVV is given in Section IV.


Semisupervised and Weakly Supervised Road Detection Based on Generative Adversarial Networks

Road detection is a key component of autonomous driving; however, most fully supervised learning road detection methods suffer from either insufficient training data or high costs of manual annotation. To overcome these problems, we propose a semisupervised learning (SSL) road detection method based on generative adversarial networks (GANs) and a weakly supervised learning (WSL) method based on conditional GANs. Specifically, in our SSL method, the generator generates the road detection results of labeled and unlabeled images, and then they are fed into the discriminator, which assigns a label on each input to judge whether it is labeled. Additionally, in WSL method we add another network to predict road shapes of input images and use them in both generator and discriminator to constrain the learning progress. By training under these frameworks, the discriminators can guide a latent annotation process on the unlabeled data; therefore, the networks can learn better representations of road areas and leverage the feature distributions on both labeled and unlabeled data. The experiments are carried out on KITTI ROAD benchmark, and the results show our methods achieve the state-of-the-art performances.


High performance CMOS dual supply level shifter for a 0.5V input and 1V output in standard 1.2V 65nm technology process

This paper presents the design of a highly efficient CMOS level shifter qc-level shifter. Unlike many recent level shifters, the proposed qc-level shifter does not use bootstrap capacitors to minimize active area. When implemented on a 65nm CMOS technology, under the large capacitive loading condition (2pF), qc-level shifter has a lower active area (94%), and energy-delay product (21.4%) than the reference bootstrap level shifter circuit (ts-level shifter). In comparison to a conventional shifter (c-level shifter)the corresponding reductions are 9.5% and 55%, respectively. Also qc-level shifter has very small effective input capacitance in comparison with ts-level shifter as it does not need a bootstrap capacitor connected to its input.


Application of Social Media Analytics in the Banking Sector to Drive Growth and Sustainability: A Proposed Integrated Framework

Large amounts of data sets being generated from social media platforms have led to surge in demand for social media analytics (SMA) use in business operations. Strategic operation of SMA yields a positive impact on marketing activities, customer engagement, risk analysis and assessment product or service design, credit rating of customer profile, customer education and competitive analysis. Banks have started tapping into advanced prescriptive and predictive analytics into a bid to develop insights, managing high costs of compliance and non-compliance such as financial risks and reputational risks thereby generating a significant impact in business operations. Optimisation modelling of business portfolios, products and services offerings are contributing significantly towards achieving a sustainable and profitable growth. This is however, against the background of high volatility and weakening demand for traditional products in the banking industry. Systematic literature review was used in examining how banks are applying social media analytics to improve their business operations. Findings reveals that social media analytics provide concrete solutions which helps to improve revenue streams of banks, providing a guarantee for compliance, survival, sustainability and growth objectives. As a result, a integrated framework is proposed to address the gap existing in literature which lacks a focus on an integrated framework that can assist decision makers on the social media analytics to employ in the banking operations.


Digital Heritage. Progress in Cultural Heritage: Documentation, Preservation, and Protection

Raising audience awareness over the creation and evolution of a cultural participatory digital platform is a critical point for its acceptance. The proposed platform adopts user involvement in the content collection level through the implementation of a mobile application easily downloadable to the user’s smartphone and the use of a web portal application. Complementary web portal permits the management of the collected content in a trustworthy manner adopting an extended role-based access control model for authorization purposes. Users can formulate private groups to contribute and share content. Platform guarantees the soundness of contributed content through an auditing procedure requested by the contributors and conducted by experts selected randomly. In order to stress the applicability of our platform to various cultural environments, we present a number of usage scenarios targeting various stakeholders from specialists and museum curators to students, teachers and simple enthusiasts aiming in the devel‐ opment of coherent narrations.


Topic- and Time-Oriented Visual Text Analysis

In this article, we first present the benefit of combing text analysis (topic models in particular) with interactive visualization. We then highlight examples from prior work on topic and time-oriented visual text analysis. Lastly, we present four challenges that warrant additional future research in the field of visual text analysis.


SWEET: Serving the Web by Exploiting Email Tunnels

Open communications over the Internet pose serious threats to countries with repressive regimes, leading them to develop and deploy censorship mechanisms within their networks. Unfortunately, existing censorship circumvention systems do not provide high availability guarantees to their users, as censors can easily identify, hence disrupt, the traffic belonging to these systems using today’s advanced censorship technologies. In this paper, we propose Serving the Web by Exploiting Email Tunnels SWEET, a highly available censorship-resistant infrastructure. SWEET works by encapsulating a censored user’s traffic inside email messages that are carried over public email services like Gmail and Yahoo Mail. As the operation of SWEET is not bound to any specific email provider, we argue that a censor will need to block email communications all together in order to disrupt SWEET, which is unlikely as email constitutes an important part of today’s Internet. Through experiments with a prototype of our system, we find that SWEET’s performance is sufficient for Web browsing. In particular, regular Websites are downloaded within couple of seconds.


Social Entrepreneurship: A Critique and Future Directions

Work on social entrepreneurship constitutes a field of study that intersects a number of domains, including entrepreneurial studies, social innovation, and nonprofit management. Scholars are beginning to contribute to the development of this new discipline through efforts that attempt to trace the emergence of social entrepreneurship as well as by comparing it to other organizational activities such as conventional entrepreneurship. However, as a nascent field, social entrepreneurship scholars are in the midst of a number of debates involving definitional and conceptual clarity, boundaries of the field, and a struggle to arrive at a set of relevant and meaningful research questions. This paper examines the promise of social entrepreneurship as a domain of inquiry and suggests a number of research areas and research questions for future study.


Management of trust in the e-marketplace: the role of the buyer's experience in building trust

The e-marketplace, or broker-managed online market, is one of today’s most profitable e-business models. In these marketplaces, buyers routinely engage with businesses and individual sellers with whom they have had little or no prior interaction, making trust one of the most important issues in the e-marketplaces. Therefore, a clear understanding of the trust building process in the e-marketplace is important for the success in the market. In this study, we analyze the process by which e-marketplace customers develop trust in both a market-maker and sellers. Additionally, this analysis provides a theoretical framework to identify the antecedents of trust. In the framework, we suggest that the impact of trust on transaction intention is moderated by the customer’s previous experiences in the e-marketplace. The theoretical model presented here was tested on survey and transaction data collected from 692 respondents. The results indicate that customers value the importance of trust in the market-maker and sellers differentially by the level of transaction experience. It is also shown that market-maker’s characteristics (reputation, website’s usability and security) and seller’s characteristics (expertise) play an important role in the formation and development of trust toward a market-maker and sellers, respectively. Journal of Information Technology (2007) 22, 119–132. doi:10.1057/palgrave.jit.2000095 Published online 23 January 2007


A note on the maximum flow through a network

-This note discusses the problem of maximizing the rate of flow from one terminal to another, through a network which consists of a number of branches, each of which has a !imited capacity. The main result is a theorem: The maximum possible flow from left to right through a network is equal to the minimum value among all simple cut-sets. This theorem is applied to solve a more general problem, in which a number of input nodes and a number of output nodes are used.


The Design of High-Level Features for Photo Quality Assessment

We propose a principled method for designing high level features forphoto quality assessment. Our resulting system can classify between high quality professional photos and low quality snapshots. Instead of using the bag of low-level features approach, we first determine the perceptual factors that distinguish between professional photos and snapshots. Then, we design high level semantic features to measure the perceptual differences. We test our features on a large and diverse dataset and our system is able to achieve a classification rate of 72% on this difficult task. Since our system is able to achieve a precision of over 90% in low recall scenarios, we show excellent results in a web image search application.


Proposing measures of flicker in the low frequencies for lighting applications

The IEEE Standards Working Group, IEEE PAR1789 "Recommending practices for modulating current in High Brightness LEDs for mitigating health risks to viewers" has been formed to advise the lighting industry and standards groups about the emerging concern of flicker in LED lighting. This paper intends to introduce new measures and definitions of lamp flicker in lighting. The discussion represents on-going work in IEEE PAR1789 that is vital to designing safe LED lamp drivers.


DataPlay: interactive tweaking and example-driven correction of graphical database queries

Writing complex queries in SQL is a challenge for users. Prior work has developed several techniques to ease query specification but none of these techniques are applicable to a particularly difficult class of queries: quantified queries. Our hypothesis is that users prefer to specify quantified queries interactively by trial-and-error. We identify two impediments to this form of interactive trial-and-error query specification in SQL: (i) changing quantifiers often requires global syntactical query restructuring, and (ii) the absence of non-answers from SQL's results makes verifying query correctness difficult. We remedy these issues with DataPlay, a query tool with an underlying graphical query language, a unique data model and a graphical interface. DataPlay provides two interaction features that support trial-and-error query specification. First, DataPlay allows users to directly manipulate a graphical query by changing quantifiers and modifying dependencies between constraints. Users receive real-time feedback in the form of updated answers and non-answers. Second, DataPlay can auto-correct a user's query, based on user feedback about which tuples to keep or drop from the answers and non-answers. We evaluated the effectiveness of each interaction feature with a user study and we found that direct query manipulation is more effective than auto-correction for simple queries but auto-correction is more effective than direct query manipulation for more complex queries.


Reliability of lower limb motor evoked potentials in stroke and healthy populations: How many responses are needed?

OBJECTIVE
To determine the intra- and inter-session reliability of motor evoked potential (MEP) size parameters in the lower limb of patients with stroke, focussing on the number of MEPs collected and the method of measuring MEP size.

METHODS
Transcranial magnetic stimulation was used to elicit MEPs in the soleus muscle of patients with stroke (n=13) and age-matched healthy participants (n=13) during low level muscle activation. Two sets of 10 responses were collected in the first session and a further 10 responses collected in a second session held 7 days later. Four MEP size measurements were made using 4, 6, 8, or all 10 of the MEPs collected. Intra- and inter-session reliability was examined using intraclass correlation coefficients (ICC) and typical percentage error.

RESULTS
Intrasession ICC statistics using 6 or more MEPs were >0.85 in the stroke group but intersession values were all <0.5. Reliability was best when measuring parameters from individual MEPs rather than averaged responses.

CONCLUSIONS
Reliability of intrasession MEP size is excellent in the lower limb of patients with stroke using as few as 6 MEPs but intersession reliability is poor.

SIGNIFICANCE
Comparing MEP size measures across two or more sessions is questionable in the lower limb of patients with stroke.


The Future of Memory: Remembering, Imagining, and the Brain

During the past few years, there has been a dramatic increase in research examining the role of memory in imagination and future thinking. This work has revealed striking similarities between remembering the past and imagining or simulating the future, including the finding that a common brain network underlies both memory and imagination. Here, we discuss a number of key points that have emerged during recent years, focusing in particular on the importance of distinguishing between temporal and nontemporal factors in analyses of memory and imagination, the nature of differences between remembering the past and imagining the future, the identification of component processes that comprise the default network supporting memory-based simulations, and the finding that this network can couple flexibly with other networks to support complex goal-directed simulations. This growing area of research has broadened our conception of memory by highlighting the many ways in which memory supports adaptive functioning.


A Literature Review and Classification of Recommender Systems on Academic Journals

Recommender systems have become an important research field since the emergence of the first paper on collaborative filtering in the mid‐1990s. In general, recommender systems are defined as the supporting systems which help users to find information, products, or services (such as books, movies, music, digital products, web sites, and TV programs) by aggregating and analyzing suggestions from other users, which mean reviews from various authorities, and user attributes. However, as academic researches on recommender systems have increased significantly over the last ten years, more researches are required to be applicable in the real world situation. Because research field on recommender systems is still wide and less mature than other research fields. Accordingly, the existing articles on recommender systems need to be reviewed toward the next generation of recommender systems. However, it would be not easy to confine the recommender system researches to specific disciplines, considering the nature of the recommender system researches. So, we reviewed all articles on recommender systems from 37 journals which were published from 2001 to 2010. The 37 journals are selected from top 125 journals of the MIS Journal Rankings. Also, the literature search was based on the descriptors “Recommender system”, “Recommendation system”, “Personalization system”, “Collaborative filtering” and “


Subpixel rendering without color distortions for diamond-shaped PenTile displays

A novel subpixel rendering algorithm for diamond-shaped PenTile displays, which improves the apparent display resolution and reduces color fringing artifacts, is proposed in this work. We develop two types of filters: main filters and nonnegative filters. First, the main filters are derived to minimize the error between an original input image and a perceived image. Second, the nonnegative filters are modified from the main filters. While the main filters are optimized for improving the resolution, the nonnegative filters suppress color distortions effectively. We analyze local characteristics of an image and estimate the amount of color distortions in each region. Based on the color distortion analysis, we render the image by applying adaptive combinations of the main filters and the nonnegative filters. Experimental results demonstrate that the proposed algorithm outperforms conventional algorithms, by not only improving the apparent resolution but also suppressing color distortions effectively.


Can Scientific Impact Be Predicted?

A widely used measure of scientific impact is citations. However, due to their heavy-tailed distribution, citations are fundamentally difficult to predict. Instead, to characterize scientific impact, we address two analogous questions asked by many scientific researchers: “How will my h-index evolve over time, and which of my previously or newly published papers will contribute to it?” To answer these questions, we perform two related tasks. First, we develop a model to predict authors' future h-indices based on their current scientific impact. Second, we examine the factors that drive papers-either previously or newly published-to increase their authors' predicted future h-indices. By leveraging relevant factors, we can predict an author's h-index in five years with an R2 value of 0.92 and whether a previously (newly) published paper will contribute to this future h-index with an F1 score of 0.99 (0.77). We find that topical authority and publication venue are crucial to these effective predictions, while topic popularity is surprisingly inconsequential. Further, we develop an online tool that allows users to generate informed h-index predictions. Our work demonstrates the predictability of scientific impact, and can help researchers to effectively leverage their scholarly position of “standing on the shoulders of giants”.


Early prediction of scholar popularity

Prediction of scholar popularity has become an important research topic for a number of reasons. In this paper, we tackle the problem of predicting the popularity {\it trend} of scholars by concentrating on making predictions both as earlier and accurate as possible. In order to perform the prediction task, we first extract the popularity trends of scholars from a training set. To that end, we apply a time series clustering algorithm called K-Spectral Clustering (K-SC) to identify the popularity trends as cluster centroids. We then predict trends for scholars in a test set by solving a classification problem. Specifically, we first compute a set of measures for individual scholars based on the distance between earlier points in her particular popularity curve and the identified centroids. We then combine those distance measures with a set of academic features (e.g., number of publications, number of venues, etc) collected during the same monitoring period, and use them as input to a classification method. One aspect that distinguishes our method from other approaches is that the monitoring period, during which we gather information on each scholar popularity and academic features, is determined on a per scholar basis, as part of our approach. Using total citation count as measure of scientific popularity, we evaluate our solution on the popularity time series of more than 500,000 Computer Science scholars, gathered from Microsoft Azure Marketplace (https://datamarket.azure.com/dataset/mrc/microsoftacademic). The experimental results show that the our prediction method outperforms other alternative prediction methods. We also show how to apply our method jointly with regression models to improve the prediction of scholar popularity values (e.g., number of citations) at a given future time.


Bringing PageRank to the citation analysis

The paper attempts to provide an alternative method for measuring the importance of scientific papers based on the Google’s PageRank. The method is a meaningful extension of the common integer counting of citations and is then experimented for bringing PageRank to the citation analysis in a large citation network. It offers a more integrated picture of the publications’ influence in a specific field. We firstly calculate the PageRanks of scientific papers. The distributional characteristics and comparison with the traditionally used number of citations are then analyzed in detail. Furthermore, the PageRank is implemented in the evaluation of research influence for several countries in the field of Biochemistry and Molecular Biology during the time period of 2000–2005. Finally, some advantages of bringing PageRank to the citation analysis are concluded. 2007 Elsevier Ltd. All rights reserved.


An Efficient Design Methodology for CNFET Based Ternary Logic Circuits

Ternary logic is a promising alternative to conventional binary logic. Implementation of ternary logic circuits however requires devices with multiple thresholds, which is a complex task with current CMOS technology. Carbon Nanotube based FETs (CNFETs) present an easier alternative for the implementation of ternary logic circuits since their threshold voltages can be varied by varying the diameter. In existing design methodology of ternary logic circuits, ternary signals are first converted to binary signals using ternary decoders which are then passed through binary gates and an encoder to get the final ternary output. Since every input signal requires a ternary decoder, the resulting circuits lead to increased area, power and delay. In this paper, authors suggest a new transistor-level methodology for designing CNFET-based ternary circuits without using ternary decoders. Elimination of decoders results in reduced area, power and delay. Simulation results indicate that up to 88% reduction in power consumption, 64% reduction in transistor count and 12 % reduction in delay can be achieved using the proposed approach when compared to the existing one.


Adaptive Control of an Inverted Pendulum Using Adaptive PID Neural Network

Abstract- In this paper, a new method for adaptive control of nonlinear systems using neural networks and Proportional-Integral-Derivative (PID) methodology is proposed. In this method, a PID control and adaptive linear neural network is used to control a inverted pendulum with uncertainities and external disturbances. The system consists of an inverted pole hinged on a cart which is free to move in the x direction. The performance of the proposed method is demonstrated via simulations.


Scaling HDFS to More Than 1 Million Operations Per Second with HopsFS

HopsFS is an open-source, next generation distribution of the Apache Hadoop Distributed File System(HDFS) that replaces the main scalability bottleneck in HDFS, single node in-memory metadata service, with a no-sharedstate distributed system built on a NewSQL database. By removing the metadata bottleneck in Apache HDFS, HopsFS enables significantly larger cluster sizes, more than an order of magnitude higher throughput, and significantly lower clientlatencies for large clusters. In this paper, we detail the techniques and optimizations that enable HopsFS to surpass 1 million file system operations per second - at least 16 times higher throughput than HDFS. In particular, we discuss how we exploit recent high performance features from NewSQL databases, such as application defined partitioning, partition-pruned index scans, and distribution aware transactions. Together with more traditional techniques, such as batching and write-ahead caches, we show how many incremental optimizations have enabled a revolution in distributed hierarchical file system performance.


Inpainting of Continuous Frames of Old Movies Based on Deep Neural Network

With the changes of eras, traditional movies may suffer continuous frame damage after digits due to improper preservation. In order to solve this problem, we proposed a new inpainting technique for continuous video sequences based on deep neural networks. We introduced the latest image restoration techniques for inpainting key frames of new scenes. Then we use the deep neural network interpolation algorithm to interpolate the intermediate frames, so that the video can achieve a coherent effect in time. In order to preserve the original information as much as possible, we only replace the damaged areas and preserve most of undamaged areas, and finally perform image blending. We test different types of videos and compare them with other methods. Our method has higher quality video inpainting than existing methods.


Non-Autoregressive Machine Translation with Auxiliary Regularization

As a new neural machine translation approach, NonAutoregressive machine Translation (NAT) has attracted attention recently due to its high efficiency in inference. However, the high efficiency has come at the cost of not capturing the sequential dependency on the target side of translation, which causes NAT to suffer from two kinds of translation errors: 1) repeated translations (due to indistinguishable adjacent decoder hidden states), and 2) incomplete translations (due to incomplete transfer of source side information via the decoder hidden states). In this paper, we propose to address these two problems by improving the quality of decoder hidden representations via two auxiliary regularization terms in the training process of an NAT model. First, to make the hidden states more distinguishable, we regularize the similarity between consecutive hidden states based on the corresponding target tokens. Second, to force the hidden states to contain all the information in the source sentence, we leverage the dual nature of translation tasks (e.g., English to German and German to English) and minimize a backward reconstruction error to ensure that the hidden states of the NAT decoder are able to recover the source side sentence. Extensive experiments conducted on several benchmark datasets show that both regularization strategies are effective and can alleviate the issues of repeated translations and incomplete translations in NAT models. The accuracy of NAT models is therefore improved significantly over the state-of-the-art NAT models with even better efficiency for inference.


Turing Patterns in Memristive Cellular Nonlinear Networks

The formation of ordered structures, in particular Turing patterns, in complex spatially extended systems has been observed in many different contexts, spanning from natural sciences (chemistry, physics, and biology) to technology (mechanics and electronics). In this paper, it is shown that the use of memristors in a simple cell of a spatially-extended circuit architecture allows us to design systems able to generate Turing patterns. In addition, the memristor parameters play a key role in the selection of the type and characteristics of the emerging pattern, which is also influenced by the initial conditions. The problem of finding the regions of parameters where Turing patterns may emerge in the proposed cellular architecture is solved in an analytic way, and numerical results are shown to illustrate the system behavior with respect to its parameters.


Integrated multilayered triboelectric nanogenerator for harvesting biomechanical energy from human motions.

We demonstrate a new flexible multilayered triboelectric nanogenerator (TENG) with extremely low cost, simple structure, small size (3.8 cm×3.8 cm×0.95 cm) and lightweight (7 g) by innovatively integrating five layers of units on a single flexible substrate. Owing to the unique structure and nanopore-based surface modification on the metal surface, the instantaneous short-circuit current (Isc) and the open-circuit voltage (Voc) could reach 0.66 mA and 215 V with an instantaneous maximum power density of 9.8 mW/cm2 and 10.24 mW/cm3. This is the first 3D integrated TENG for enhancing the output power. Triggered by press from normal walking, the TENG attached onto a shoe pad was able to instantaneously drive multiple commercial LED bulbs. With the flexible structure, the TENG can be further integrated into clothes or even attached onto human body without introducing sensible obstruction and discomfort to human motions. The novel design of TENG demonstrated here can be applied to potentially achieve self-powered portable electronics.


An Operational Amplifier with Recycling Folded Cascode Topology and Adaptive Biasing

This paper presents a highly adaptive operational amplifier with high gain, high bandwidth, high speed and low power consumption. By adopting the recycling folded cascode topology along with an adaptivebiasing circuit, this design achieves high performance in terms of gain-bandwidth product (GBW) and slew rate (SR). This single stage op-amp has been designed in 0.18μm technology with a power supply of 1.8V and a 5pF load. The simulation results show that the amplifier achieved a GBW of 335.5MHz, Unity Gain Bandwidth of 247.1MHz and a slew rate of 92.8V/μs.


Building Recognition From Multi-Aspect High-Resolution InSAR Data in Urban Areas

The improved ground resolution of state-of-the-art synthetic aperture radar (SAR) sensors suggests utilizing SAR data for the analysis of urban areas. The appearance of buildings in SAR or interferometric SAR (InSAR) data is characterized by the consequences of the inherent oblique scene illumination, such as layover, occlusion by radar shadow, and multipath signal propagation. Therefore, particularly in dense built-up areas, building reconstruction is often impossible from a single SAR or InSAR measurement alone. But, the reconstruction quality can be significantly improved by a combined analysis of multi-aspect data. In this paper, two approaches are proposed to detect and reconstruct buildings of different size from multi-aspect high-resolution InSAR data sets. Both approaches focus on the recognition of buildings supported by knowledge-based analysis considering the mentioned SAR-specific effects observed in urban areas. Building features are extracted independently for each direction from the magnitude and phase information of the interferometric data. Initial primitives are segmented and afterward projected from slant-range into the world coordinate system. From the fused set of primitives of both flight directions, building hypotheses are generated. The first approach exploits the frequently observed lines of bright double-bounce scattering, which are used for building reconstruction in residential districts. In the case of larger buildings, such as industrial halls, often additional features of roof and facade elements are visible. Therefore, in a second approach, extended buildings are extracted by grouping primitives of different kinds. The two approaches are demonstrated in an urban environment for an InSAR data set, which has spatial resolution of about 30 cm and was taken from two orthogonal flight directions.


Ridge gap waveguide antenna array using integrated coaxial power divider

A slot array antenna is designed using ridge gap waveguide technology in two sub arrays. Each sub array is fed through a power divider which is designed on a separate ridge- groove gap waveguide. The coupling between two gap waveguides is conducted using coaxial transmission lines. The power divider of ridge-groove gap is fed using coaxial line that can provide a bandwidth of 22% at 14 GHz. The integrated antenna operates within the frequency range of 13.5-15.6 GHz with a gain of 13.9-15.5 dB. Design of the transmission line, power divider and sub-array antenna is presented.


Video abstraction: A systematic review and classification

The demand for various multimedia applications is rapidly increasing due to the recent advance in the computing and network infrastructure, together with the widespread use of digital video technology. Among the key elements for the success of these applications is how to effectively and efficiently manage and store a huge amount of audio visual information, while at the same time providing user-friendly access to the stored data. This has fueled a quickly evolving research area known as video abstraction. As the name implies, video abstraction is a mechanism for generating a short summary of a video, which can either be a sequence of stationary images (keyframes) or moving images (video skims). In terms of browsing and navigation, a good video abstract will enable the user to gain maximum information about the target video sequence in a specified time constraint or sufficient information in the minimum time. Over past years, various ideas and techniques have been proposed towards the effective abstraction of video contents. The purpose of this article is to provide a systematic classification of these works. We identify and detail, for each approach, the underlying components and how they are addressed in specific works.


BC-PDS: Protecting Privacy and Self-Sovereignty through BlockChains for OpenPDS

In the Big Data era, personal metadata may will become a new type of corporate asset, however there have already been a growing public concern about user’s privacy mined from metadata. In this paper we address the problem of implementing the self-sovereignty of personal metadata on the existing OpenPDS/SafeAnswers framework according to the Windhover Principle. In order to do that, we propose a new framework, called BlocakChain-based Personal Data Store (BCPDS), to realize two basic properties: notary and autonomy. This framework, firstly introduces the BlockChain as a notary, into OpenPDS/SafeAnswers for secure storage of personal meta-data instead of the original database. Next, we present an AutoNomybased Access Control (ANAC) to improve the SafeAnswers module, where ANAC is a new mechanism that enforces access based on the relationship among all authorized users and metadata’s owner. In addition, we also propose General Access Structure (GAS) and threshold secret sharing scheme in BlockChain as an implementation method for our BC-PDS framework.


Feature Based Sentiment Analysis for Service Reviews

Sentiment Analysis deals with the analysis of emotions, opinions and facts in the sentences which are expressed by the people. It allows us to track attitudes and feelings of the people by analyzing blogs, comments, reviews and tweets about all the aspects. The development of Internet has strong influence in all types of industries like tourism, healthcare and any business. The availability of Internet has changed the way of accessing the information and sharing their experience among users. Social media provide this information and these comments are trusted by other users. This paper recognizes the use and impact of social media on healthcare industry by analyzing the users’ feelings expressed in the form of free text, thereby gives the quality indicators of services or features related with them. In this paper, a sentiment classifier model using improved Term Frequency Inverse Document Frequency (TFIDF) method and linear regression model has been proposed to classify online reviews, tweets or customer feedback for various features. The model involves the process of gathering online user reviews about hospitals and analyzes those reviews in terms of sentiments expressed. Information Extraction process filters irrelevant reviews, extracts sentimental words of features identified and quantifies the sentiment of features using sentiment dictionary. Emotionally expressed positive or negative words are assigned weights using the classification prescribed in the dictionary. The sentiment analysis on tweets/reviews is done for various features using Natural Language Processing (NLP) and Information Retrieval (IR) techniques. The proposed linear regression model using the senti-score predicts the star rating of the feature of service. The statistical results show that improved TF-IDF method gives better accuracy when compared with TF and TF-IDF methods, used for representing the text. The senti-score obtained as a result of text analysis (user feedback) on features gives not only the opinion summarization but also the comparative results on various features of different competitors. This information can be used by business to focus on the low scored features so as to improve their business and ensure a very high level of user satisfaction.


An AOI algorithm for PCB based on feature extraction

With the development of the micro-electronic industry, electronical components assembled on the printed circuit board (PCB) become more and more microsize and its mounted density is increasing. It is out of date to depend on manual inspection to assure the joints quality. Instead, automated optical inspection (AOI) for solder joints based on the machine vision has become more and more important. In this paper, based on the image acquired from a 3-CCD color camera and a 3-color hemispherical LED arrays light resource (red, green, and blue), the place, shape, and logical features of solder joints of chip components are extracted. The place features are related to solder joint place. As for shape features, we divide solder joint into several regions and extract its shape features by its color, occupancy ratio of area, center of gravity and continuous pixels. The logical features come from their close relationships of different regions of shape features, color distributing and place features. On the basis of the features, an AOI algorithm is developed. The defects of lacking solder, surplus solder, no solder, pseudo joints, wrong component, damaged component, component absent, shift, tomb stone, wrong polarity, etc. can be identified properly by using the proposed algorithm. Finally, some experiment results are presented to show the validity of the algorithm.


Robust Hand Detection and Classification in Vehicles and in the Wild

Robust hand detection and classification is one of the most crucial pre-processing steps to support human computer interaction, driver behavior monitoring, virtual reality, etc. This problem, however, is very challenging due to numerous variations of hand images in real-world scenarios. This work presents a novel approach named Multiple Scale Region-based Fully Convolutional Networks (MSRFCN) to robustly detect and classify human hand regions under various challenging conditions, e.g. occlusions, illumination, low-resolutions. In this approach, the whole image is passed through the proposed fully convolutional network to compute score maps. Those score maps with their position-sensitive properties can help to efficiently address a dilemma between translation-invariance in classification and detection. The method is evaluated on the challenging hand databases, i.e. the Vision for Intelligent Vehicles and Applications (VIVA) Challenge, Oxford hand dataset and compared against various recent hand detection methods. The experimental results show that our proposed MS-FRCN approach consistently achieves the state-of-the-art hand detection results, i.e. Average Precision (AP) / Average Recall (AR) of 95.1% / 94.5% at level 1 and 86.0% / 83.4% at level 2, on the VIVA challenge. In addition, the proposed method achieves the state-of-the-art results for left/right hand and driver/passenger classification tasks on the VIVA database with a significant improvement on AP/AR of ~7% and ~13% for both classification tasks, respectively. The hand detection performance of MS-RFCN reaches to 75.1% of AP and 77.8% of AR on Oxford database.


Interventions in Small Food Stores to Change the Food Environment, Improve Diet, and Reduce Risk of Chronic Disease

INTRODUCTION
Many small-store intervention trials have been conducted in the United States and other countries to improve the food environment and dietary behaviors associated with chronic disease risk. However, no systematic reviews of the methods and outcomes of these trials have been published. The objective of this study was to identify small-store interventions and to determine their impact on food availability, dietary behaviors, and psychosocial factors that influence chronic disease risk.

METHODS
From May 2009 through September 2010, we used PubMed, web-based searches, and listservs to identify small-store interventions that met the following criteria: 1) a focus on small food stores, 2) a completed impact evaluation, and 3) English-written documentation (peer-reviewed articles or other trial documents). We initially identified 28 trials; 16 met inclusion criteria and were used for analysis. We conducted interviews with project staff to obtain additional information. Reviewers extracted and reported data in a table format to ensure comparability between data.

RESULTS
Reviewed trials were implemented in rural and urban settings in 6 countries and primarily targeted low-income racial/ethnic minority populations. Common intervention strategies included increasing the availability of healthier foods (particularly produce), point-of-purchase promotions (shelf labels, posters), and community engagement. Less common strategies included business training and nutrition education. We found significant effects for increased availability of healthy foods, improved sales of healthy foods, and improved consumer knowledge and dietary behaviors.

CONCLUSION
Trial impact appeared to be linked to the increased provision of both healthy foods (supply) and health communications designed to increase consumption (demand).


Detection of money laundering groups using supervised learning in networks

Money laundering is a major global problem, enabling criminal organisations to hide their ill-gotten gains and to finance further operations. Prevention of money laundering is seen as a high priority by many governments, however detection of money laundering without prior knowledge of predicate crimes remains a significant challenge. Previous detection systems have tended to focus on individuals, considering transaction histories and applying anomaly detection to identify suspicious behaviour. However, money laundering involves groups of collaborating individuals, and evidence of money laundering may only be apparent when the collective behaviour of these groups is considered. In this paper we describe a detection system that is capable of analysing group behaviour, using a combination of network analysis and supervised learning. This system is designed for real-world application and operates on networks consisting of millions of interacting parties. Evaluation of the system using real-world data indicates that suspicious activity is successfully detected. Importantly, the system exhibits a low rate of false positives, and is therefore suitable for use in a live intelligence environment.


The CL-SciSumm Shared Task 2018: Results and Key Insights

This overview describes the official results of the CL-SciSumm Shared Task 2018 – the first medium-scale shared task on scientific document summarization in the computational linguistics (CL) domain. This year, the dataset comprised 60 annotated sets of citing and reference papers from the open access research papers in the CL domain. The Shared Task was organized as a part of the 41 Annual Conference of the Special Interest Group in Information Retrieval (SIGIR), held in Ann Arbor, USA in July 2018. We compare the participating systems in terms of two evaluation metrics. The annotated dataset and evaluation scripts can be accessed and used by the community from: https://github.com/WING-NUS/scisumm-corpus.


Monocyte and macrophage plasticity in tissue repair and regeneration.

Heterogeneity and high versatility are the characteristic features of the cells of monocyte-macrophage lineage. The mononuclear phagocyte system, derived from the bone marrow progenitor cells, is primarily composed of monocytes, macrophages, and dendritic cells. In regenerative tissues, a central role of monocyte-derived macrophages and paracrine factors secreted by these cells is indisputable. Macrophages are highly plastic cells. On the basis of environmental cues and molecular mediators, these cells differentiate to proinflammatory type I macrophage (M1) or anti-inflammatory or proreparative type II macrophage (M2) phenotypes and transdifferentiate into other cell types. Given a central role in tissue repair and regeneration, the review focuses on the heterogeneity of monocytes and macrophages with current known mechanisms of differentiation and plasticity, including microenvironmental cues and molecular mediators, such as noncoding RNAs.


A survey of traffic sign recognition

Advanced Driver Assistance Systems (ADAS) refer to various high-tech in-vehicle systems that are designed to increase road traffic safety by helping drivers gain better awareness of the road and its potential hazards as well as other drivers around them. The design of traffic sign recognition, one important subsystem of ADAS, has been a challenge problem for many years and hence become an important and active research topic in the area of intelligent transport systems. The realization of a real-time traffic sign recognition system is usually divided into three stages: detection, tracking and classification. This paper introduces the main difficulties in road sign recognition and briefly surveys the state-of-the-art technologies in this field with further discussions on the potential trend of development of road sign recognition.


Analysis and design of proximity fuze based on waveguide discontinuity

In order to satisfy the special requirement of antenna directivity base on proximity Fuze, the concept of the waveguide discontinuity was introduced which could be a method of designing antenna. The beam of radiation should deviate from axis direction of bomb in a certain angle The frequency was between 24 GHz and 25 GHz.Circular waveguide was connected to coaxial waveguide. This form could inspire high model in discontinuity place. Electric field of coupling model could lead to a oblique radiation pattern. In the meanwhile, two symmetrical metal column was loaded in waveguide in purpose of changing impedance of antenna. The test results demonstrated the veracity of the analysis.