query: CAES Cryptosystem: Advanced Security Tests and Results

positive: Differential Cryptanalysis of the Data Encryption Standard

positive: A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Application This paper discusses some aspects of testing random and pseudorandom number generators. A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications (NIST Special Publication) is presented. This paper also demonstrates the software implementation of the test suite with output protocols and presents experiences from testing some random and pseudorandom generators.

positive: A New Iterative Secret Key Cryptosystem Based on Reversible and Irreversible Cellular Automata Many cryptosystems have been released to secure electronic data on internet. Some data are very critical to be transmitted as plaintext. Thus, to ensure the data confidentiality and integrity, a list of cryptosystems have been elaborated. The most important ones are divided into two categories: symmetric algorithms encrypting and decrypting data in blocks using a single secret key; and asymmetric algorithms using public keys to cipher texts and secret keys to reconstruct plaintexts. The of the present work is the design and implement a new secret key cryptosystem encrypting and decrypting data in blocks according to a number of iterations. Each plaintext block is encrypted using cellular automata and a list of sub keys deduced from a secret key through cellular automata. To demonstrate the feasibility, the proposed scheme is compared with AES algorithm, the well-known symmetric block cipher. We prove that our algorithm resists against statistical attacks and it is faster than AES-256 achieving good confusion and diffusion tests.

positive: Differential Cryptanalysis of Feal and N-Hash In 1,2] we introduced the notion of diierential cryptanalysis and described its application to DESS11] and several of its variants. In this paper we show the applicability of diierential cryptanalysis to the Feal family of encryption algorithms and to the N-Hash hash function. In addition, we show how to transform diierential cryptanalytic chosen plaintext attacks into known plaintext attacks.

positive: A new kind of science The distinctive idea is easy to state, though it needs some examples to appreciate. It is that simple rules can generate complex very complex outcomes when they are repeatedly applied. Isolated examples of this phenomenon have been known for centuries. For example, the number p has a simple definition the ratio of a circle's circumference to its diameter and there is a quite simple formula to calculate its digits. The result begins: 3.14159265358979323846264338327950288419716939937510582097494459230781640 6286208998628034825342117068...

negative: Fixed-point algorithms for learning determinantal point processes Determinantal point processes (DPPs) offer an elegant tool for encoding probabilities over subsets of a ground set. Discrete DPPs are parametrized by a positive semidefinite matrix (called the DPP kernel), and estimating this kernel is key to learning DPPs from observed data. We consider the task of learning the DPP kernel, and develop for it a surprisingly simple yet effective new algorithm. Our algorithm offers the following benefits over previous approaches: (a) it is much simpler; (b) it yields equally good and sometimes even better local maxima; and (c) it runs an order of magnitude faster on large problems. We present experimental results on both real and simulated data to illustrate the numerical performance of our technique.

negative: Quantifying Mental Health from Social Media with Neural User Embeddings Mental illnesses adversely affect a significant proportion of the population worldwide. However, the typical methods to estimate and characterize the prevalence of mental health conditions are time-consuming and expensive. Consequently, best-available estimates concerning the prevalence of these conditions are often years out of date. Automated approaches that supplement traditional methods with broad, aggregated information derived from social media provide a potential means of furnishing near real-time estimates at scale. These may in turn provide grist for supporting, evaluating and iteratively improving public health programs and interventions. We propose a novel approach for mental health quantification that leverages user embeddings induced from social media post histories. Recent work showed that learned user representations capture latent aspects of individuals (e.g., political leanings). This paper investigates whether these representations also correlate with mental health statuses. To this end, we induced embeddings for a set of users known to be affected by depression and post-traumatic stress disorder, and for a set of demographically matched ‘control’ users. We then evaluated the induced user representations with respect to: (i) their ability to capture homophilic relations with respect to mental health statuses; and (ii) their predictive performance in downstream mental health models. Our experimental results demonstrate that learned user embeddings capture relevant signals for mental health quantification.

negative: Stock Market Prediction based on Time Series Data and Market Sentiment In this project, we would like to create a system that predicts stock market movements on a given day, based on time series data and market sentiment analysis. We will use Twitter data on that day to predict the market sentiment and S&P 500 values to perform analysis on historical data.

negative: Trajectory Bundling for Animated Transitions Animated transition has been a popular design choice for smoothly switching between different visualization views or layouts, in which movement trajectories are created as cues for tracking objects during location shifting. Tracking moving objects, however, becomes difficult when their movement paths overlap or the number of tracking targets increases. We propose a novel design to facilitate tracking moving objects in animated transitions. Instead of simply animating an object along a straight line, we create "bundled" movement trajectories for a group of objects that have spatial proximity and share similar moving directions. To study the effect of bundled trajectories, we untangle variations due to different aspects of tracking complexity in a comprehensive controlled user study. The results indicate that using bundled trajectories is particularly effective when tracking more targets (six vs. three targets) or when the object movement involves a high degree of occlusion or deformation. Based on the study, we discuss the advantages and limitations of the new technique, as well as provide design implications.

negative: An Automated Approach for Digital Forensic Analysis of Heterogeneous Big Data The major challenges with big data examination and analysis are volume, complex interdependence across content, and heterogeneity. The examination and analysis phases are considered essential to a digital forensics process. However, traditional techniques for the forensic investigation use one or more forensic tools to examine and analyse each resource. In addition, when multiple resources are included in one case, there is an inability to cross-correlate findings which often leads to inefficiencies in processing and identifying evidence. Furthermore, most current forensics tools cannot cope with large volumes of data. This paper develops a novel framework for digital forensic analysis of heterogeneous big data. The framework mainly focuses upon the investigations of three core issues: data volume, heterogeneous data and the investigators cognitive load in understanding the relationships between artefacts. The proposed approach focuses upon the use of metadata to solve the data volume problem, semantic web ontologies to solve the heterogeneous data sources and artificial intelligence models to support the automated identification and correlation of artefacts to reduce the burden placed upon the investigator to understand the nature and relationship of the artefacts.

negative: Application of Katharine Kolkaba Comfort theory to nursing care of patient aring is in practice since existence but caring as profession named Nursing was scientifically Coined by the founder of modern nursing who in her writings “what it is and what it is not”declared the boundaries along with its Constitutional elements. Those elements with appropriate association not only revealed the nature of caring dogma it also shows that it is doing something base on thinking something or in simple words this is practice rely on theory. According to McCrae, (2011) For the sake of assuming valuable status ofprofession in society nursing struggle to legitimate its position by generation and application of theory. Nursing literature is rich regarding theory practice association. although constant body of argument, counter argument and lack of agreed conclusion regarding nursing theory creates a bit confusion among nurses but ideally nursing theory should set the code of every day practice of nurses(Giltinane, 2013) as lack of a valued unique contribution nurses make to health care based on a specialized body of knowledge will portrayed nursing role as subordinate to doctor (Mccrae, 2011) Eminent nurses’ theorist, scientist, Researchers, Clinicians, Educators and intellectual intelligentsia aid evidence to different perspectives of theory practice link. Some unanimous convincing narratives argue that the relationship is reciprocal and according to (Mckenna, 2005) Nursing theory takes its origin from practice, than reverse to practice for testing and at the end become set to guide the practice.

negative: The impact of social and conventional media on firm equity value: A sentiment analysis approach Available online 30 December 2012

negative: A 3D printed low profile magnetic dipole antenna An electrically small, low profile magnetic dipole antenna is presented. The proposed antenna is composed of multiple small twisted loops connected in parallel. The simulated radiation efficiency is 87 % at the resonant frequency of 222.5 MHz. The corresponding electrical size ka is 0.276 with the height of 0.0016λ. The prototype is built using a selective laser sintering technology and silver paste painting. The measured result is discussed.

negative: Applying autonomous sensor systems in logistics — Combining sensor networks , RFIDs and software agents New sensor, communication and software technologies are used to broaden the facilities of tracing and tracing systems for food transports. n embedded assessing unit detects from sensor data collected by a wireless network potential risks for the freight quality. The estimation of he current maturing state of agricultural products will be supported by measurements of the gaseous hormone ethylene as an indicator for the ipening processes. A miniaturized high-resolution gas chromatography is under construction. The system autonomously configures itself to a roduct specific supervision task based on data scanned by an RFID reader during freight loading. Mobile software agents accompany the freight long the supply chain. They pre-process the vast sensor data and submit only substantial changes to the freight owner. 2006 Elsevier B.V. All rights reserved.

negative: Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey This paper provides a state-of-the-art literature review on economic analysis and pricing models for data collection and wireless communication in Internet of Things (IoT). Wireless sensor networks (WSNs) are the main components of IoT which collect data from the environment and transmit the data to the sink nodes. For long service time and low maintenance cost, WSNs require adaptive and robust designs to address many issues, e.g., data collection, topology formation, packet forwarding, resource and power optimization, coverage optimization, efficient task allocation, and security. For these issues, sensors have to make optimal decisions from current capabilities and available strategies to achieve desirable goals. This paper reviews numerous applications of the economic and pricing models, known as intelligent rational decision-making methods, to develop adaptive algorithms and protocols for WSNs. Besides, we survey a variety of pricing strategies in providing incentives for phone users in crowdsensing applications to contribute their sensing data. Furthermore, we consider the use of some pricing models in machine-to-machine (M2M) communication. Finally, we highlight some important open research issues as well as future research directions of applying economic and pricing models to IoT.

negative: Near-Duplicates Detection and Elimination Based on Web Provenance for Effective Web Search Users of World Wide Web utilize search engines for information retrieval in web as search engines play a vital role in finding information on the web. However, the performance of a web search is greatly affected by flooding of search results with information that is redundant in nature i.e., existence of nearduplicates. Such near-duplicates holdup the other promising results to the users. Many of these near-duplicates are from distrusted websites and/or authors who host information on web. Such nearduplicates may be eliminated by means of Provenance. Thus, this paper proposes a novel approach to identify such near-duplicates based on provenance. In this approach a provenance model has been built using web pages which are the search results returned by existing search engine. The proposed model combines both content based and trust based factors for classifying the results as original or near-duplicates. Keywords— Web search, Near-duplicates, Provenance, Semantics,

negative: Schemaless NoSQL Data Stores - Object-NoSQL Mappers to the Rescue? NoSQL data stores are becoming increasingly popular in application development. These systems are attractive for developers due to their ability to handle large volumes of data, as well as data with a high degree of structural variety. Typically, NoSQL data stores are accessed programmatically. Due to the imminent lack of standardized query languages, building applications against the native interfaces of NoSQL data stores creates an unfortunate technical lock-in. To re-gain platform independence, developers turn to object mapper libraries as an additional level of abstraction when accessing NoSQL data stores. The current market for Java object mappers that support NoSQL data stores is still volatile, with commercial and open source products competing for adoption. In this paper, we give an overview on the state-of-the-art in Object-Relational Mappers that can handle also NoSQL data stores, as well as dedicated Object-NoSQL Mappers. We are able to show that choosing the right object mapper library is a strategic decision with far reaching consequences: Current mappers diverge in the NoSQL data stores that they support, in their features, their robustness, their truthfulness to the documentation and query standards, and ultimately, in the runtime overhead that they introduce. Especially in web development, runtime overhead is a crucial aspect contributing to the application latency, and ultimately, the user experience. By shedding light on the current market, we intend to provide software architects with the necessary information to make informed decisions.

negative: Efficient Image Dehazing with Boundary Constraint and Contextual Regularization Images captured in foggy weather conditions often suffer from bad visibility. In this paper, we propose an efficient regularization method to remove hazes from a single input image. Our method benefits much from an exploration on the inherent boundary constraint on the transmission function. This constraint, combined with a weighted L1-norm based contextual regularization, is modeled into an optimization problem to estimate the unknown scene transmission. A quite efficient algorithm based on variable splitting is also presented to solve the problem. The proposed method requires only a few general assumptions and can restore a high-quality haze-free image with faithful colors and fine image details. Experimental results on a variety of haze images demonstrate the effectiveness and efficiency of the proposed method.

negative: Trust in Online Shopping: The Korean Student Experience E-commerce has become an important part of business. In South Korea, the market size of online shopping malls was 13,460 billion Korean Won in 2006, and this figure keeps growing. Thus, gaining loyal customers has become a rising concern. In this study, we adopted Lewicki and Bunker's three different types of trust, namely, calculus-based trust, knowledge-based trust, and identification-based trust, in order to investigate their hierarchical relationships in e-commerce and their impacts on customer satisfaction and loyalty. A total of 104 responses from university students were analyzed to test the proposed model and its hypotheses using PLS. The results showed that hierarchical relationships between different types of trust exist in the online environment, and among them, knowledge-based trust has the strongest impact on customer satisfaction. This finding implied that practitioners should focus on developing an appropriate online strategy in terms of how to build up trust-based relationships with online customers.

negative: Music-based respiratory biofeedback in visually-demanding tasks Biofeedback tools generally use visualizations to display physiological information to the user. As such, these tools are incompatible with visually demanding tasks such as driving. While auditory or haptic biofeedback may be used in these cases, the additional sensory channels can increase workload or act as a nuisance to the user. A number of studies, however, have shown that music can improve mood and concentration, while also reduce aggression and boredom. Here, we propose an intervention that combines the benefits of biofeedback and music to help users regulate their stress response while performing a visual task (driving a car simulator). Our approach encourages slow breathing by adjusting the quality of the music in response to the user’s breathing rate. We evaluate the intervention on a design with music and auditory biofeedback as independent variables. Our results indicate that our music-biofeedback intervention leads to lower arousal (reduced electrodermal activity and increased heart rate variability) than music alone, auditory biofeedback alone and a control condition.

negative: Graph databases: A survey In the era of big data, data analytics, business intelligence database management plays a vital role from technical business management and research point of view. Over many decades, database management has been a topic of active research. There are different type of database management system have been proposed over a period of time but Relational Database Management System (RDBMS) is the one which has been most popularly used in academic research as well as industrial setup[1]. In recent years, graph databases regained interest among the researchers for certain obvious reasons. One of the most important reasons for such an interest in a graph database is because of the inherent property of graphs as a graph structure. Graphs are present everywhere in the data structure, which represents the strong connectivity within the data. Most of the graph database models are defined in which data-structure for schema and instances are modeled as graph or generalization of a graph. In such graph database models, data manipulations are expressed by graph-oriented operations and type constructors [9]. Now days, most of the real world applications can be modeled as a graph and one of the best real world examples is social or biological network. This paper gives an overview of the different type of graph databases, applications, and comparison between their models based on some properties.

negative: Fundamentals of Wearable Computers and Augmented Reality, Second Edition The best ebooks about Fundamentals Of Wearable Computers And Augmented Reality Second Edition that you can get for free here by download this Fundamentals Of Wearable Computers And Augmented Reality Second Edition and save to your desktop. This ebooks is under topic such as fundamentals of wearable computers and augmented reality fundamentals of wearable computers and augmented reality fundamentals of wearable computers and augmented reality fundamentals of wearable computers and augmented reality fundamentals of wearable computers and augmented reality fundamentals of wearable computers and augmented reality fundamentals of wearable computers and augmented reality 11 location-based mixed and augmented reality storytelling fundamentals of wearable computers and augmented reality forum book review mit press journals the vietnam war revised 2nd edition ebook | slangsurfing dicom to 3d holograms: use case for augmented reality in pensees de j j rousseau citoyen de geneve v1 2 1785 ebook click here to access this book : free download amendment xivequal protection constitutional amendments solutions manual to fundamentals of engineering document about distant mirrors america as a foreign regeneration of peasants slangsurfing the cell ebook | esmajorelojes il buddhismo contemporaneo italian edition ebook | slanggeek proceedings of a court of inquiryconvened at the navy answer coming soonmore blog postings on arts letters the travel and tropical medicine manual 4e ebook fundamentals of sql server 2012 replication

negative: The State of Public Infrastructure-as-a-Service Cloud Security The public Infrastructure-as-a-Service (IaaS) cloud industry has reached a critical mass in the past few years, with many cloud service providers fielding competing services. Despite the competition, we find some of the security mechanisms offered by the services to be similar, indicating that the cloud industry has established a number of “best-practices,” while other security mechanisms vary widely, indicating that there is also still room for innovation and experimentation. We investigate these differences and possible underlying reasons for it. We also contrast the security mechanisms offered by public IaaS cloud offerings and with security mechanisms proposed by academia over the same period. Finally, we speculate on how industry and academia might work together to solve the pressing security problems in public IaaS clouds going forward.

negative:
Researching Mental Health Disorders in the Era of Social Media: Systematic Review BACKGROUND
Mental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose.

OBJECTIVE
The objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research.

METHODS
We performed a systematic literature review in March 2017, using keywords to search articles on data mining of social network data in the context of common mental health disorders, published between 2010 and March 8, 2017 in medical and computer science journals.

RESULTS
The initial search returned a total of 5386 articles. Following a careful analysis of the titles, abstracts, and main texts, we selected 48 articles for review. We coded the articles according to key characteristics, techniques used for data collection, data preprocessing, feature extraction, feature selection, model construction, and model verification. The most common analytical method was text analysis, with several studies using different flavors of image analysis and social interaction graph analysis.

CONCLUSIONS
Despite an increasing number of studies investigating mental health issues using social network data, some common problems persist. Assembling large, high-quality datasets of social media users with mental disorder is problematic, not only due to biases associated with the collection methods, but also with regard to managing consent and selecting appropriate analytics techniques.

negative: Advances in geometric modeling and feature extraction on pots This paper outlines progress on the 3DK Knowledge and Distributed Intelligence project undertaken by the Partnership for Research In Stereo Modeling (PRISM) at Arizona State University. Three of the six 3DK pilot projects of this National Science Foundation funded project involve archaeological and biological material, namely ceramic vessels, lithic artifacts, and bones. These projects are: (1) “3D Morphology of Ceramic Vessels” which aims to learn about vessel uniformity and proportionality as indicators of developing craft specialization and complex social organization among prehistoric cultures; (2) “Lithic Refitting” which intends to develop the algorithms required to (partially) automate the refitting process through 3D scanning and surface modeling; and finally (3) “3D Topography of Joint Surfaces” which investigates the ability of human ancestors to make tools and walk upright by developing software that will automate segmentation of osteological features and quantify the surface area and curvature of joint surfaces and the congruency between reciprocal joint surfaces, allowing for the development of biomechanical models. This paper discusses the links between these three unique projects and explores the underlying research aspects common to all: geometric modeling, feature recognition, and the development of a database structure aimed at making 3D models of these artifacts available online for query.

negative: A Comparison of Knives for Bread Slicing Vertical partitioning is a crucial step in physical database design in row-oriented databases. A number of vertical partitioning algorithms have been proposed over the last three decades for a variety of niche scenarios. In principle, the underlying problem remains the same: decompose a table into one or more vertical partitions. However, it is not clear how good different vertical partitioning algorithms are in comparison to each other. In fact, it is not even clear how to experimentally compare different vertical partitioning algorithms. In this paper, we present an exhaustive experimental study of several vertical partitioning algorithms. We categorize vertical partitioning algorithms along three dimensions. We survey six vertical partitioning algorithms and discuss their pros and cons. We identify the major differences in the use-case settings for different algorithms and describe how to make an apples-to-apples comparison of different vertical partitioning algorithms under the same setting. We propose four metrics to compare vertical partitioning algorithms. We show experimental results from the TPC-H and SSB benchmark and present four key lessons learned: (1) we can do four orders of magnitude less computation and still find the optimal layouts, (2) the benefits of vertical partitioning depend strongly on the database buffer size, (3) HillClimb is the best vertical partitioning algorithm, and (4) vertical partitioning for TPC-H-like benchmarks can improve over column layout by only up to 5%.

negative: Large-scale PV system based on the multiphase isolated DC/DC converter The large-scale photovoltaic (PV) systems have already been reached 200 MW power level and they will continue to grow in size in the upcoming years. This trend will challenge the existing PV system architectures by requiring power converters with a higher power rating and a higher voltage level at the point of common coupling (PCC). The cascaded H-bridge (CHB) multilevel converter is one of the solutions which could deal with the aforementioned challenges. However, the topology based on the CHB converter faces the issue of leakage current that flows through the solar panel parasitic capacitance to ground which could damage the PV panels and pose safety problems. This paper proposes a multiphase isolated DC/DC converter for the CHB topology for a large-scale PV system which eliminates the leakage current issue. At the same time, the multiphase structure of the DC/DC converter helps to increase the power rating of the converter and to reduce the PV voltage and current ripples. A 0.54 MW rated seven-level CHB converter using multiphase isolated DC/DC converters has been modeled and simulated using MATLAB/Simulink and PLECS Blockset. Simulation results of different case studies are presented to evaluate the performance of the proposed PV system configuration.

negative: Word Sense Induction for Novel Sense Detection We apply topic modelling to automatically induce word senses of a target word, and demonstrate that our word sense induction method can be used to automatically detect words with emergent novel senses, as well as token occurrences of those senses. We start by exploring the utility of standard topic models for word sense induction (WSI), with a pre-determined number of topics (=senses). We next demonstrate that a non-parametric formulation that learns an appropriate number of senses per word actually performs better at the WSI task. We go on to establish state-of-the-art results over two WSI datasets, and apply the proposed model to a novel sense detection task.

negative: A theory of visual attention. A unified theory of visual recognition and attentional selection is developed by integrating the biased-choice model for single-stimulus recognition (Luce, 1963; Shepard, 1957) with a choice model for selection from multielement displays (Bundesen, Pedersen, & Larsen, 1984) in a race model framework. Mathematically, the theory is tractable, and it specifies the computations necessary for selection. The theory is applied to extant findings from a broad range of experimental paradigms. The findings include effects of object integrality in selective report, number and spatial position of targets in divided-attention paradigms, selection criterion and number of distracters in focused-attention paradigms, delay of selection cue in partial report, and consistent practice in search. On the whole, the quantitative fits are encouraging.

negative:
Exercise prescription for overhead athletes with shoulder pathology: a systematic review with best evidence synthesis. OBJECTIVE
To produce a best evidence synthesis of exercise prescription used when treating shoulder pathology in the overhead athlete.

DESIGN
A systematic review of exercises used in overhead athletes including case studies and clinical commentaries.

DATA SOURCES
MEDLINE, PubMed, SPORTDiscus and CINAHL from database inception through July 8, 2016.

METHODS
We examined data from randomised controlled trials and prospective cohort (level I-IV evidence) studies that addressed exercise intervention in the rehabilitation of the overhead athlete with shoulder pathology. Case studies and clinical commentaries (level V evidence) were examined to account for expert opinion-based research. Data were combined using best evidence synthesis and graded (A-F) recommendations (Centre for Evidence-Based Medicine).

RESULTS
There were 33 unique exercises in six level I-IV studies that met our inclusion criteria. Most exercises were single-plane, upper extremity exercises performed below 90o of elevation. There were 102 unique exercises in 33 level V studies that met our inclusion criteria. These exercises emphasised plyometrics, kinetic chain and sport-specific training.

CONCLUSIONS AND RELEVANCE
Overall, evidence for exercise interventions in overhead athletes with shoulder pathology is dominated by expert opinion (grade D). There is great variability between exercise approaches suggested by experts and those investigated in research studies and the overall level of evidence is low. The strongest available evidence (level B) supports the use of single-plane, open chain upper extremity exercises performed below 90° of elevation and closed chain upper extremity exercises. Clinical expert pieces support a more advanced, global treatment approach consistent with the complex, multidimensional nature of sport.

negative: Multibiometric Cryptosystems Based on Feature-Level Fusion Multibiometric systems are being increasingly de- ployed in many large-scale biometric applications (e.g., FBI-IAFIS, UIDAI system in India) because they have several advantages such as lower error rates and larger population coverage compared to unibiometric systems. However, multibiometric systems require storage of multiple biometric templates (e.g., fingerprint, iris, and face) for each user, which results in increased risk to user privacy and system security. One method to protect individual templates is to store only the secure sketch generated from the corresponding template using a biometric cryptosystem. This requires storage of multiple sketches. In this paper, we propose a feature-level fusion framework to simultaneously protect multiple templates of a user as a single secure sketch. Our main contributions include: (1) practical implementation of the proposed feature-level fusion framework using two well-known biometric cryptosystems, namery,fuzzy vault and fuzzy commitment, and (2) detailed analysis of the trade-off between matching accuracy and security in the proposed multibiometric cryptosystems based on two different databases (one real and one virtual multimodal database), each containing the three most popular biometric modalities, namely, fingerprint, iris, and face. Experimental results show that both the multibiometric cryptosystems proposed here have higher security and matching performance compared to their unibiometric counterparts.

negative: Some Basic Cryptographic Requirements for Chaos-Based Cryptosystems In recent years, a large amount of work on chaos-based cryptosystems have been published. However many of the proposed schemes fail to explain or do not possess a number of features that are fundamentally important to all kind of cryptosystems. As a result, many proposed systems are difficult to implement in practice with a reasonable degree of security. Likewise, they are seldom accompanied by a thorough security analysis. Consequently, it is difficult for other researchers and end users to evaluate their security and performance. This work is intended to provide a common framework of basic guidelines that, if followed, every new cryptosystem would benefit from. The suggested guidelines address three main issues: implementation, key management, and security analysis, aiming at assisting designers of new cryptosystems to present their work in a more systematic and rigorous way to fulfill some basic cryptographic requirements. Meanwhile, several recommendations are made regarding some practical aspects of analog chaos-based secure communications, such as channel noise, limited bandwith, and attenuation.

negative: A Finger-Vein Based Cancellable Bio-cryptosystem Irrevocability is one major issue in existing bio-cryptosystems. In this paper, we proposed a cancellable bio-cryptosystem by taking the full advantage of cancellable and non-invertible properties of bio-hashing biometrics. Specifically, two transformed templates are generated by using the bio-hashing algorithm and applied into two different secure sketches, fuzzy commitment sketch and fuzzy vault sketch, respectively. These two secure sketches can be fused in two different ways: AND fusion and OR fusion, so as to emphasis either on the recognition accuracy or the security level of the system. Experimental results and security analysis show the validity of the proposed scheme.

negative: A novel secure hash algorithm for public key digital signature schemes Hash functions are the most widespread among all cryptographic primitives, and are currently used in multiple cryptographic schemes and in security protocols. This paper presents a new Secure Hash Algorithm called (SHA-192). It uses a famous secure hash algorithm given by the National Institute of Standard and Technology (NIST).The basic design of SHA192 is to have the output length of 192.The SHA-192 has been designed to satisfy the different level of enhanced security and to resist the advanced SHA attacks. The security analysis of the SHA-192 is compared to the old one given by NIST and gives more security and excellent results as shown in our discussion. In this paper the digital signature algorithm which is given by NIST has been modified using the proposed algorithms SHA-192. Using proposed SHA-192 hash algorithm a new digital signature schemes is also proposed. The SHA-192 can be used in many applications such s public key cryptosystem, digital signcryption, message authentication code, random generator and in security architecture of upcoming wireless devices like software defined radio etc.

negative: Hybrid encryption/decryption technique using new public key and symmetric key algorithm This research study proposes Hybrid Encryption System using new public key algorithm and private key algorithm. A hybrid cryptosystem is one which combines the convenience of a public-key cryptosystem with the efficiency of a symmetrickey cryptosystem. Here, we propose a provably two way secured data encryption system, which addresses the concerns of user’s privacy, authentication and accuracy. This system has two different encryption algorithms have been used both in the Encryption and decryption sequence. One is public key cryptography based on linear block cipher another one is private key cryptography based on simple symmetric algorithm. This cryptography algorithm provides more security as well as authentication comparing to other existing hybrid algorithm.

negative: A public key cryptosystem and a signature scheme based on discrete logarithms A new signature scheme is proposed, together with an implementation of the Diffie-Hellman key distribution scheme that achieves a public key cryptosystem. The security of both systems relies on the difficulty of computing discrete logarithms over finite fields.

negative: Multivariate lattices for encrypted image processing Images are inherently sensitive signals that require privacy-preserving solutions when processed in an untrusted environment, but their efficient encrypted processing is particularly challenging due to their structure and size. This work introduces a new cryptographic hard problem called m-RLWE (multivariate Ring Learning with Errors) extending RLWE. It gives support to lattice cryptosystems that allow for encrypted processing of multidimensional signals. We show an example cryptosystem and prove that it outperforms its RLWE counterpart in terms of security against basis-reduction attacks, efficiency and cipher expansion for encrypted image processing.


query: PKOM: A tool for clustering, analysis and comparison of big chemical collections

positive: Voronoi treemaps for the visualization of software metrics In this paper we present a hierarchy-based visualization approach for software metrics using Treemaps. Contrary to existing rectangle-based Treemap layout algorithms, we introduce layouts based on arbitrary polygons that are advantageous with respect to the aspect ratio between width and height of the objects and the identification of boundaries between and within the hierarchy levels in the Treemap. The layouts are computed by the iterative relaxation of Voronoi tessellations. Additionally, we describe techniques that allow the user to investigate software metric data of complex systems by utilizing transparencies in combination with interactive zooming.

positive: Chemoinformatics and Drug Discovery This article reviews current achievements in the field of chemoinformatics and their impact on modern drug discovery processes. The main data mining approaches used in cheminformatics, such as descriptor computations, structural similarity matrices, and classification algorithms, are outlined. The applications of cheminformatics in drug discovery, such as compound selection, virtual library generation, virtual high throughput screening, HTS data mining, and in silico ADMET are discussed. At the conclusion, future directions of chemoinformatics are suggested.

positive: Hierarchical Grouping to Optimize an Objective Function Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

positive: A Hybrid Reverse Engineering Approach Combining Metrics and Program Visualization Surprising as it may seem, many of the early adopters of the object-oriented paradigm already face a number of problems typically encountered in large-scale legacy systems. Consequently, reverse engineering techniques are relevant in an object-oriented context as well. This paper investigates a hybrid approach, combining the immediate appeal of visualisations with the scalability of metrics. We validate such a hybrid approach by showing how CodeCrawler —the experimental platform we built— allowed us to understand the program structure of, and identify potential design anomalies in a public domain software system.

positive: Centroidal Voronoi Tessellations: Applications and Algorithms A centroidal Voronoi tessellation is a Voronoi tessellation whose generating points are the centroids (centers of mass) of the corresponding Voronoi regions. We give some applications of such tessellations to problems in image compression, quadrature, finite difference methods, distribution of resources, cellular biology, statistics, and the territorial behavior of animals. We discuss methods for computing these tessellations, provide some analyses concerning both the tessellations and the methods for their determination, and, finally, present the results of some numerical experiments.

negative: Graph-based forensic investigation of Bitcoin transactions This thesis illustrates forensic research work on Bitcoin, an innovative Internet based global transaction system that attracts ascending popularity during the recent few years. As an open, public and scalable distributed payment system, Bitcoin brings forward significant economic and technological impact to our world. Meanwhile, a new notion of virtual currency, “Bitcoin” comes into existence such that Bitcoin currency can be “mined” from all over world complying with specific algorithms. Mined bit “coins” has practical monetary values that turn the Bitcoin system into a digital currency circulation system. Due to Bitcoin’s decentralized semantics, Bitcoin transaction and currency are not subject to control and censorship from any single authority. Therefore, Bitcoin brings out various security concerns about its application as a long-term reliable system. The research in the thesis focuses on forensic study on Bitcoin. It covers experimental study on the Bitcoin network as a peer-to-peer system and a graph-based forensic approach against Bitcoin’s transaction data. Major contributions include network data evaluation and transaction history analysis. In case of forensic investigation is needed against criminal incidents such as fraud, false transactions and money theft, which are commonly seen in commonly used digital payment systems, the research provides a guidance of efficient information collection and framework of evidence data processing and extraction.

negative: Antecedents and consequences of customer-company identification: expanding the role of relationship marketing. This article presents an empirical test of organizational identification in the context of customer-company (C-C) relationships. It investigates whether customers identify with companies and what the antecedents and consequences of such identification are. The model posits that perceived company characteristics, construed external image, and the perception of the company's boundary-spanning agent lead to C-C identification. In turn, such identification is expected to impact both in-role behavior (i.e., product utilization) as well as extra-role behavior (i.e., citizenship). The model was tested in a consultative selling context of pharmaceutical sales reps calling on physicians. Results from the empirical test indicated that customers do indeed identify with organizations and that C-C identification positively impacts both product utilization behavior and extra-role behavior even when the effect of brand perception is accounted for. Second, the study found that the organization's characteristics as well as the salesperson's characteristics contributed to the development of C-C identification.

negative: WAVES: A Wearable Asymmetric Vibration Excitation System for Presenting Three-Dimensional Translation and Rotation Cues WAVES, a Wearable Asymmetric Vibration Excitation System, is a novel wearable haptic device for presenting three dimensions of translation and rotation guidance cues. In contrast to traditional vibration feedback, which usually requires that users learn to interpret a binary cue, asymmetric vibrations have been shown to induce a pulling sensation in a desired direction. When attached to the fingers, a single voicecoil actuator presents a translation guidance cue and a pair of voicecoil actuators presents a rotation guidance cue. The directionality of mechanoreceptors in the skin led to our choice of the location and orientation of the actuators in order to elicit very strong sensations in certain directions. For example, users distinguished a "left" cue versus a "right" cue 94.5% of the time. When presented with one of six possible direction cues, users on average correctly identified the direction of translation cues 86.1% of the time and rotation cues 69.0% of the time.

negative: Automated detection of vulnerabilities in privileged programs by execution monitoring We present a method for detecting exploitations of vulnerabilities in privileged programs by monitoring their execution using audit trials, where the monitoring is with respect to specifications of the security-relevant behavior of the programs. Our work is motivated by the intrusion detection paradigm, but is a n attempt to avoid a d hoc approaches to codifying misuse behavior. Our approach as based on the observation that although privileged programs can be exploited (due to errors) to cause security compromise in systems because of the privileges accorded to them, the intended behavior of privileged programs is, of course, limited a n d benign. The key, then i s to specify the intended behavior (Le., the program policy) and to detect any action by privileged program that is outside the intended behavior a n d that imperils security. We describe a program policy specification language, which is based on simple predicate logic and regular expressions. I n addition, we present specifications of privileged programs in Unix, and Q prototype execution monitor for analyzing audit trails with respect to these specifications. The program policies are surprisingly concise and clear, a n d an addition, capable of detecting exploitations of known vulnerabilities in these programs. Although our work has been motivated by the known vulnerabilities in Unix, we believe that by tightly restricting the behavior of all privileged programs, exploitations of unknown vulnerabilities can be detected. A s Q check on the specifications, work is in progress on verifying them with respect to a n abstract security policy. *This work is funded in part by the National Security Agency University Research Program under Contract No. DOD-MDA904-93-C4083 and by ARPA under Contract No. USNN00014-94-1-0065.

negative: A review and outlook for a 'Building Information Model' (BIM): A multi-standpoint framework for technological development 1474-0346/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.aei.2010.06.003 * Tel.: +386 1 4768 521; fax: +386 1 4250 681. E-mail address: tomo.cerovsek@fgg.uni-lj.si This study provides a review of important issues for ‘Building Information Modelling’ (BIM) tools and standards and comprehensive recommendations for their advancement and development that may improve BIM technologies and provide a basis for inter-operability, integration, model-based communication, and collaboration in building projects. Based on a critical review of Building Product Modelling, including the development of standards for exchange and the features of over 150 AEC/O (Architecture, Engineering, Construction, and Operation) tools and digital models, a methodological framework is proposed for improvements to both BIM tools and schemata. The features relevant to the framework were studied using a conceptual process model and a ‘BIM System-of-Systems’ (BIM-SoS) model. The development, implementation, and use of the BIM Schema are analysed from the standpoint of standardisation. The results embrace the requirements for a BIM research methodology, with an example of methods and procedures, an R&D review with critique, and a multi-standpoint framework for developments with concrete recommendations, supported by BIM metrics, upon which the progress of tools, models, and standards may be measured, evaluated, streamlined, and judged. It is also proposed that any BIM Schema will never be ‘completed’ but should be developed as evolutionary ontology by ‘segmented standpoint models’ to better account for evolving tools and AEC/O practices. 2010 Elsevier Ltd. All rights reserved.

negative: Privacy protection issues in social networking sites Social Networking Sites (SNS) have become very popular during the past few years, as they allow users to both express their individuality and meet people with similar interests. Nonetheless, there are also many potential threats to privacy associated with these SNS such as identity theft and disclosure of sensitive information. However, many users still are not aware of these threats and the privacy settings provided by SNS are not flexible enough to protect user data. In addition, users do not have any control over what others reveal about them. As such, we conduct a preliminary study which examines the privacy protection issues on Social Networking Sites (SNS) such as MySpace, Facebook and LinkedIn. Based on this study, we identify three privacy problems in SNS and propose a Privacy Framework as a foundation to cope with these problems.

negative: Hesitant Fuzzy Sets: State of the Art and Future Directions The necessity of dealing with uncertainty in real wold problems has been a long-term research challenge that has originated different methodologies and theories. Fuzzy sets along with their extensions such as, type-2 fuzzy sets, interval valued fuzzy sets, Atanassov’s intuitionistic fuzzy sets, etc., have provided a wide range of tools able to deal with uncertainty in different type of problems. Recently, a new extension of fuzzy sets so-called hesitant fuzzy sets has been introduced to deal with hesitant situations which were not well managed by the previous tools. Hesitant fuzzy sets have attracted very quickly the attention of many researchers that have proposed diverse extensions, several type of operators to compute with such type of information, and eventually some applications have been developed. Because of such a growth, this paper presents an overview on hesitant fuzzy sets with the aim of providing a clear perspective on the different concepts, tools and trends related to this extension of fuzzy sets.

negative: Violence Detection in Movies As violence in movies has harmful influence on children, in this paper, we propose an algorithm to detect violent scene in movies. Under our definition of violence, the task of violent scene detection is decomposed into action scene detection and bloody frame detection. While previous approaches addressed on shot level of video structure only, our approach works on more semantic-complete scene structure of video. The input video (digital movie) is first segmented into several scenes. Based on the filmmaking characteristics of action scene, some features of the scene are extracted to feed into the support vector machine for classification. Finally, the face, blood and motion information are integrated to determine whether the action scene has violent content. Experimental results show that the proposed approach works reasonably well in detecting most of the violent scenes. Compared with related work, our approach is computationally simple yet effective.

negative: A comparative study of metamodeling methods for multiobjective crashworthiness optimization The response surface methodology (RSM), which typically uses quadratic polynomials, is predominantly used for metamodeling in crashworthiness optimization because of the high computational cost of vehicle crash simulations. Research shows, however, that RSM may not be suitable for modeling highly nonlinear responses that can often be found in impact related problems, especially when using limited quantity of response samples. The radial basis functions (RBF) have been shown to be promising for highly nonlinear problems, but no application to crashworthiness problems has been found in the literature. In this study, metamodels by RSM and RBF are used for multiobjective optimization of a vehicle body in frontal collision, with validations by finite element simulations using the full-scale vehicle model. The results show that RSM is able to produce good approximation models for energy absorption, and the model appropriateness can be well predicted by ANOVA. However, in the case of peak acceleration, RBF is found to generate better models than RSM based on the same number of response samples, with the multiquadric function identified to be the most stable RBF. Although RBF models are computationally more expensive, the optimization results of RBF models are found to be more accurate. 2005 Elsevier Ltd. All rights reserved.

negative:
Accuracy of Ultrasound-Guided Genicular Nerve Block: A Cadaveric Study. BACKGROUND
Genicular nerve block has recently emerged as a novel alternative treatment in chronic knee pain. The needle placement for genicular nerve injection is made under fluoroscopic guidance with reference to bony landmarks.

OBJECTIVE
To investigate the anatomic landmarks for medial genicular nerve branches and to determine the accuracy of ultrasound-guided genicular nerve block in a cadaveric model.

STUDY DESIGN
Cadaveric accuracy study.

SETTING
University hospital anatomy laboratory.

METHODS
Ten cadaveric knee specimens without surgery or major procedures were used in the study. The anatomic location of the superior medial genicular nerve (SMGN) and the inferior medial genicular nerve (IMGN) was examined using 4 knee dissections. The determined anatomical sites of the genicular nerves in the remaining 6 knee specimens were injected with 0.5 mL red ink under ultrasound guidance. The knee specimens were subsequently dissected to assess for accuracy. If the nerve was dyed with red ink, it was considered accurate placement. All other locations were considered inaccurate.

RESULTS
The course of the SMGN is that it curves around the femur shaft and passes between the adductor magnus tendon and the femoral medial epicondyle, then descends approximately one cm anterior to the adductor tubercle. The IMGN is situated horizontally around the tibial medial epicondyle and passes beneath the medial collateral ligament at the midpoint between the tibial medial epicondyle and the tibial insertion of the medial collateral ligament. The adductor tubercle for the SMGN and the medial collateral ligament for the IMGN were determined as anatomic landmarks for ultrasound. The bony cortex one cm anterior to the peak of the adductor tubercle and the bony cortex at the midpoint between the peak of the tibial medial epicondyle and the initial fibers inserting on the tibia of the medial collateral ligament were the target points for the injections of SMGN and IMGN, respectively. In the cadaver dissections both genicular nerves were seen to be dyed with red ink in all the injections of the 6 knees.

LIMITATIONS
The small number of cadavers might have led to some anatomic variations of genicular nerves being overlooked.

CONCLUSIONS
The result of this cadaveric study suggests that ultrasound-guided medial genicular nerve branch block can be performed accurately using the above-stated anatomic landmarks.

negative:
#Proana: Pro-Eating Disorder Socialization on Twitter. PURPOSE
Pro-eating disorder (ED) online movements support engagement with ED lifestyles and are associated with negative health consequences for adolescents with EDs. Twitter is a popular social media site among adolescents that provides a unique setting for Pro-ED content to be publicly exchanged. The purpose of this study was to investigate Pro-ED Twitter profiles' references to EDs and how their social connections (followers) reference EDs.

METHODS
A purposeful sample of 45 Pro-ED profiles was selected from Twitter. Profile information, all tweets, and a random sample of 100 of their followers' profile information were collected for content analysis using the Twitter Application Programming Interface. A codebook based on ED screening guidelines was applied to evaluate ED references. For each Pro-ED profile, proportion of tweets with ED references and proportion of followers with ED references in their own profile were evaluated.

RESULTS
In total, our 45 Pro-ED profiles generated 4,245 tweets for analysis. A median of 36.4% of profiles' tweets contained ED references. Pro-ED profiles had a median of 173 followers, and a median of 44.5% of followers had ED references. Pro-ED profiles with more tweets with ED references also tended to have more followers with ED references (β = .37, p < .01).

CONCLUSIONS
Findings suggest that profiles which self-identify as Pro-ED express disordered eating patterns through tweets and have an audience of followers, many of whom also reference ED in their own profiles. ED socialization on Twitter might provide social support, but in the Pro-ED context this activity might also reinforce an ED identity.

negative: Phased array systems and technologies in SELEX-Sistemi Integrati: State of art and new challenges This paper is a follow-on of [1] and [2], presented at 1996 and 2003 IEEE Intl. Symposium on Phased Array Systems and Technology, respectively. After a brief recall of 3D Long Range phased array concepts, already presented in [2], the focus is on the G-band multifunction radar systems and on the technological capabilities that SELEX-Sistemi Integrati is developing by taking advantage of its Research and Development facilities. At the end, future trends about new challenging applications and technological issues are presented and discussed.

negative: Morpho-syntactic Lexical Generalization for CCG Semantic Parsing In this paper, we demonstrate that significant performance gains can be achieved in CCG semantic parsing by introducing a linguistically motivated grammar induction scheme. We present a new morpho-syntactic factored lexicon that models systematic variations in morphology, syntax, and semantics across word classes. The grammar uses domain-independent facts about the English language to restrict the number of incorrect parses that must be considered, thereby enabling effective learning from less data. Experiments in benchmark domains match previous models with one quarter of the data and provide new state-of-the-art results with all available data, including up to 45% relative test-error reduction.

negative: A 2.4 GHz Interferer-Resilient Wake-Up Receiver Using A Dual-IF Multi-Stage N-Path Architecture A 2.4 GHz interferer-resilient wake-up receiver for ultra-low power wireless sensor nodes uses an uncertain-IF dualconversion topology, combining a distributed multi-stage N-path filtering technique with an unlocked low-Q resonator-referred local oscillator. This structure provides narrow-band selectivity and strong immunity against interferers, while avoiding expensive external resonant components such as BAW resonators or crystals. The 65 nm CMOS receiver prototype provides a sensitivity of -97 dBm and a carrier-to-interferer ratio better than -27 dB at 5 MHz offset, for a data rate of 10 kb/s at a 10-3 bit error rate, while consuming 99 μW from a 0.5 V voltage supply under continuous operation.

negative: Risk indicators and psychopathology in traumatised children and adolescents with a history of sexual abuse Childhood sexual abuse (CSA) is widespread amongst South African (SA) children, yet data on risk factors and psychiatric consequences are limited and mixed. Traumatised children and adolescents referred to our Youth Stress Clinic were interviewed to obtain demographic, sexual abuse, lifetime trauma and psychiatric histories. Data for 94 participants (59 female, 35 male; mean age 14.25 [8.25–19] years) exposed to at least one lifetime trauma were analysed. Sexual abuse was reported in 53% of participants (42.56% females, 10.63% males) with 64% of violations committed by perpetrators known to them. Multinomial logistic regression analysis revealed female gender (P = 0.002) and single-parent families (P = 0.01) to be significant predictors of CSA (62.5%). CSA did not predict exposure to other traumas. Sexually abused children had significantly higher physical and emotional abuse subscale scores and total CTQ scores than non-abused children. Depression (33%, X 2 = 10.89, P = 0.001) and PTSD (63.8%, X 2 = 4.79, P = 0.034) were the most prevalent psychological consequences of trauma and both were significantly associated with CSA. High rates of CSA predicted high rates of PTSD in this traumatised sample. Associations we found appear consistent with international studies of CSA and, should be used to focus future social awareness, prevention and treatment strategies in developing countries.

negative: Factors affecting the Choice of High Tech Engineering majors for University women and Men in Bangladesh In contrast to western countries which are experiencing declines in female enrollment in high tech engineering (HTE), Bangladesh has experienced a continuous increase in female enrollment in engineering. In order to better understand the factors driving the choice of HTE as a major, survey data was collected from 590 male and female students in HTE majors in Bangladesh. Hypotheses related to gender differences in the influence of various factors on major choice were examined using t-tests. Results revealed that, while several factors had a similar impact for women and men, gender differences in selfefficacy and socio-economic status persist and may represent barriers to women’s participation.

negative: Learning long-range vision for autonomous off-road driving Most vision-based approaches to mobile robotics suffer from the limitations imposed by stereo obstacle detection, which is short-range and prone to failure. We present a self-supervised learning process for long-range vision that is able to accurately classify complex terrain at distances up to the horizon, thus allowing superior strategic planning. The success of the learning process is due to the self-supervised training data that is generated on every frame: robust, visually consistent labels from a stereo module, normalized wide-context input windows, and a discriminative and concise feature representation. A deep hierarchical network is trained to extract informative and meaningful features from an input image, and the features are used to train a realtime classifier to predict traversability. The trained classifier sees obstacles and paths from 5 to over 100 meters, far beyond the maximum stereo range of 12 meters, and adapts very quickly to new environments. The process was developed and tested on the LAGR mobile robot. Results from a ground truth dataset are given as well as field test results.

negative: Hybrid encryption/decryption technique using new public key and symmetric key algorithm This research study proposes Hybrid Encryption System using new public key algorithm and private key algorithm. A hybrid cryptosystem is one which combines the convenience of a public-key cryptosystem with the efficiency of a symmetrickey cryptosystem. Here, we propose a provably two way secured data encryption system, which addresses the concerns of user’s privacy, authentication and accuracy. This system has two different encryption algorithms have been used both in the Encryption and decryption sequence. One is public key cryptography based on linear block cipher another one is private key cryptography based on simple symmetric algorithm. This cryptography algorithm provides more security as well as authentication comparing to other existing hybrid algorithm.

negative: A One-Layer Recurrent Neural Network with a Discontinuous Activation Function for Linear Programming A one-layer recurrent neural network with a discontinuous activation function is proposed for linear programming. The number of neurons in the neural network is equal to that of decision variables in the linear programming problem. It is proven that the neural network with a sufficiently high gain is globally convergent to the optimal solution. Its application to linear assignment is discussed to demonstrate the utility of the neural network. Several simulation examples are given to show the effectiveness and characteristics of the neural network.

negative: Works for me! characterizing non-reproducible bug reports Bug repository systems have become an integral component of software development activities. Ideally, each bug report should help developers to find and fix a software fault. However, there is a subset of reported bugs that is not (easily) reproducible, on which developers spend considerable amounts of time and effort. We present an empirical analysis of non-reproducible bug reports to characterize their rate, nature, and root causes. We mine one industrial and five open-source bug repositories, resulting in 32K non-reproducible bug reports. We (1) compare properties of non-reproducible reports with their counterparts such as active time and number of authors, (2) investigate their life-cycle patterns, and (3) examine 120 Fixed non-reproducible reports. In addition, we qualitatively classify a set of randomly selected non-reproducible bug reports (1,643) into six common categories. Our results show that, on average, non-reproducible bug reports pertain to 17% of all bug reports, remain active three months longer than their counterparts, can be mainly (45%) classified as "Interbug Dependencies'', and 66% of Fixed non-reproducible reports were indeed reproduced and fixed.

negative: Anatomic Study of the Retaining Ligaments of the Face and Applications for Facial Rejuvenation Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of

negative: A stereo display prototype with multiple focal distances Typical stereo displays provide incorrect focus cues because the light comes from a single surface. We describe a prototype stereo display comprising two independent fixed-viewpoint volumetric displays. Like autostereoscopic volumetric displays, fixed-viewpoint volumetric displays generate near-correct focus cues without tracking eye position, because light comes from sources at the correct focal distances. (In our prototype, from three image planes at different physical distances.) Unlike autostereoscopic volumetric displays, however, fixed-viewpoint volumetric displays retain the qualities of modern projective graphics: view-dependent lighting effects such as occlusion, specularity, and reflection are correctly depicted; modern graphics processor and 2-D display technology can be utilized; and realistic fields of view and depths of field can be implemented. While not a practical solution for general-purpose viewing, our prototype display is a proof of concept and a platform for ongoing vision research. The design, implementation, and verification of this stereo display are described, including a novel technique of filtering along visual lines using 1-D texture mapping.

negative: UnTran: Recognizing Unseen Activities with Unlabeled Data Using Transfer Learning The success and impact of activity recognition algorithms largely depends on the availability of the labeled training samples and adaptability of activity recognition models across various domains. In a new environment, the pre-trained activity recognition models face challenges in presence of sensing bias- ness, device heterogeneities, and inherent variabilities in human behaviors and activities. Activity Recognition (AR) system built in one environment does not scale well in another environment, if it has to learn new activities and the annotated activity samples are scarce. Indeed building a new activity recognition model and training the model with large annotated samples often help overcome this challenging problem. However, collecting annotated samples is cost-sensitive and learning activity model at wild is computationally expensive. In this work, we propose an activity recognition framework, UnTran that utilizes source domains' pre-trained autoencoder enabled activity model that transfers two layers of this network to generate a common feature space for both source and target domain activities. We postulate a hybrid AR framework that helps fuse the decisions from a trained model in source domain and two activity models (raw and deep-feature based activity model) in target domain reducing the demand of annotated activity samples to help recognize unseen activities. We evaluated our framework with three real-world data traces consisting of 41 users and 26 activities in total. Our proposed UnTran AR framework achieves ≈ 75% F1 score in recognizing unseen new activities using only 10% labeled activity data in the target domain. UnTran attains ≈ 98% F1 score while recognizing seen activities in presence of only 2-3% of labeled activity samples.

negative: Feature-Oriented Software Product Lines Inevitably, reading is one of the requirements to be undergone. To improve the performance and quality, someone needs to have something new every day. It will suggest you to have more inspirations, then. However, the needs of inspirations will make you searching for some sources. Even from the other people experience, internet, and many books. Books and internet are the recommended media to help you improving your quality and performance.

negative: Rice quality analysis using image processing techniques In agricultural industries grain quality evaluation is very big challenge. Quality control is very important in food industry because after harvesting, based on quality parameters food products are classified and graded into different grades. Grain quality evaluation is done manually but it is relative, time consuming, may be varying results and costly. To overcome these limitations and shortcoming image processing techniques is the alternative solution can be used for grain quality analysis. Rice quality is nothing but the combination of physical and chemical characteristics. Grain size and shape, chalkiness, whiteness, milling degree, bulk density and moisture content are some physical characteristics while amylose content, gelatinization temperature and gel consistency are chemical characteristics of rice. The paper presents a solution of grading and evaluation of rice grains on the basis of grain size and shape using image processing techniques. Specifically edge detection algorithm is used to find out the region of boundaries of each grain. In this technique we find the endpoints of each grain and after using caliper we can measure the length and breadth of rice. This method requires minimum time and it is low in cost.

negative: A study of big data processing constraints on a low-power Hadoop cluster Big Data processing with Hadoop has been emerging recently, both on the computing cloud and enterprise deployment. However, wide-spread security exploits may hurt the reputation of public clouds. If Hadoop on the cloud is not an option, an organization has to build its own Hadoop clusters. But having a data center is not worth for a small organization both in terms of building and operating costs. Another viable solution is to build a cluster with low-cost ARM system-on-chip boards. This paper presents a study of a Hadoop cluster for processing Big Data built atop 22 ARM boards. The Hadoop's MapReduce was replaced by Spark and experiments on three different hardware configurations were conducted to understand limitations and constraints of the cluster. From the experimental results, it can be concluded that processing Big Data on an ARM cluster is highly feasible. The cluster could process a 34 GB Wikipedia article file in acceptable time, while generally consumed the power 0.061-0.322 kWh for all benchmarks. It has been found that I/O of the hardware is fast enough, but the power of CPUs is inadequate because they are largely spent for the Hadoop's I/O.

negative: Big Data Mobile Services for New York City Taxi Riders and Drivers In recent years, taxis in New York City have been equipped with GPS sensors to record the start and end locations of every trip, in conjunction with fare collection equipment recording trip time, fare, and distance. This paper analyzes this vast dataset with big data technologies. We use MapReduce and Hive in order to understand traffic and travel patterns, discover relationships among the data, and make predictions on the taxi network. We also propose a service model combining the results of our data analysis with smartphone applications to offer users various taxi-related services based on results of our big data analysis.

negative: Software developers' perceptions of productivity The better the software development community becomes at creating software, the more software the world seems to demand. Although there is a large body of research about measuring and investigating productivity from an organizational point of view, there is a paucity of research about how software developers, those at the front-line of software construction, think about, assess and try to improve their productivity. To investigate software developers' perceptions of software development productivity, we conducted two studies: a survey with 379 professional software developers to help elicit themes and an observational study with 11 professional software developers to investigate emergent themes in more detail. In both studies, we found that developers perceive their days as productive when they complete many or big tasks without significant interruptions or context switches. Yet, the observational data we collected shows our participants performed significant task and activity switching while still feeling productive. We analyze such apparent contradictions in our findings and use the analysis to propose ways to better support software developers in a retrospection and improvement of their productivity through the development of new tools and the sharing of best practices.

negative: Graphene-based single-bacterium resolution biodevice and DNA transistor: interfacing graphene derivatives with nanoscale and microscale biocomponents. Establishing "large-contact-area" interfaces of sensitive nanostructures with microbes and mammalian cells will lead to the development of valuable tools and devices for biodiagnostics and biomedicine. Chemically modified graphene (CMG) nanostructures with their microscale area, sensitive electrical properties, and modifiable chemical functionality are excellent candidates for such biodevices at both biocellular and biomolecular scale. Here, we report on the fabrication and functioning of a novel CMG-based (i) single-bacterium biodevice, (ii) label-free DNA sensor, and (iii) bacterial DNA/protein and polyelectrolyte chemical transistor. The bacteria biodevice was highly sensitive with a single-bacterium attachment generating approximately 1400 charge carriers in a p-type CMG. Similarly, single-stranded DNA tethered on graphene hybridizes with its complementary DNA strand to reversibly increase the hole density by 5.61 x 1012 cm(-2). We further demonstrate (a) a control on the device sensitivity by manipulating surface groups, (b) switching of polarity specificity by changing surface polarity, and (c) a preferential attachment of DNA on thicker CMG surfaces and sharp CMG wrinkles.

negative: Competitive intelligence process and tools for intelligence analysis Purpose – The purpose of this survey research is twofold. First, to study and report the process that is commonly used to create and maintain a competitive intelligence (CI) program in organizations. And second, to provide an analysis of several emergent text mining, web mining and visualization-based CI tools, which are specific to collection and analysis of intelligence. Design/methodology/approach – A range of recently published research literature on CI processes, applications, tools and technologies to collect and analyze competitive information within organizations is reviewed to explore their current state, issues and challenges learned from their practice. Findings – The paper provides executive decision makers and strategic managers a better understanding of what methods are available and appropriate to the decisions they must make and the steps involved in CI undertaking. Originality/value – The findings of this research provide the managers of CI programs a context for understanding which tools and techniques are better suited to their specific types of problems; and help them develop and evaluate a usable set of tools and best practices to apply to their industry.

negative: Empowering personalized medicine with big data and semantic web technology: Promises, challenges, and use cases In healthcare, big data tools and technologies have the potential to create significant value by improving outcomes while lowering costs for each individual patient. Diagnostic images, genetic test results and biometric information are increasingly generated and stored in electronic health records presenting us with challenges in data that is by nature high volume, variety and velocity, thereby necessitating novel ways to store, manage and process big data. This presents an urgent need to develop new, scalable and expandable big data infrastructure and analytical methods that can enable healthcare providers access knowledge for the individual patient, yielding better decisions and outcomes. In this paper, we briefly discuss the nature of big data and the role of semantic web and data analysis for generating “smart data” which offer actionable information that supports better decision for personalized medicine. In our view, the biggest challenge is to create a system that makes big data robust and smart for healthcare providers and patients that can lead to more effective clinical decision-making, improved health outcomes, and ultimately, managing the healthcare costs. We highlight some of the challenges in using big data and propose the need for a semantic data-driven environment to address them. We illustrate our vision with practical use cases, and discuss a path for empowering personalized medicine using big data and semantic web technology.