Query: Enhancing Authorship Attribution By Utilizing Syntax Tree Profiles

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A Comparison of Event Models for Naive Bayes Text Classification <=SEP=> Recent approaches to text classification have used two different first-order probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multi-variate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e.g. Larkey and Croft 1996; Koller and Sahami 1997). Others use a multinomial model, that is, a uni-gram language model with integer word counts (e.g. Lewis and Gale 1994; Mitchell 1997). This paper aims to clarify the confusion by describing the differences and details of these two models, and by empirically comparing their classification performance on five text corpora. We find that the multi-variate Bernoulli performs well with small vocabulary sizes, but that the multinomial performs usually performs even better at larger vocabulary sizes—providing on average a 27% reduction in error over the multi-variate Bernoulli model at any vocabulary size.

Building a Large Annotated Corpus of English: The Penn Treebank <=SEP=> In this paper, we review our experience with constructing one such large annotated corpus--the Penn Treebank, a corpus consisting of over 4.5 million words of American English. During the first three-year phase of the Penn Treebank Project (1989-1992), this corpus has been annotated for part-of-speech (POS) information. In addition, over half of it has been annotated for skeletal syntactic structure. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-93-87. This technical report is available at ScholarlyCommons: http://repository.upenn.edu/cis_reports/237 Building A Large Annotated Corpus of English: The Penn Treebank MS-CIS-93-87 LINC LAB 260 Mitchell P. Marcus Beatrice Santorini Mary Ann Marcinkiewicz University of Pennsylvania School of Engineering and Applied Science Computer and Information Science Department Philadelphia, PA 19104-6389

Author Identification Using Imbalanced and Limited Training Texts <=SEP=> This paper deals with the problem of author identification. The common N-grams (CNG) method [6] is a language-independent profile-based approach with good results in many author identification experiments so far. A variation of this approach is presented based on new distance measures that are quite stable for large profile length values. Special emphasis is given to the degree upon which the effectiveness of the method is affected by the available training text samples per author. Experiments based on text samples on the same topic from the Reuters Corpus Volume 1 are presented using both balanced and imbalanced training corpora. The results show that CNG with the proposed distance measures is more accurate when only limited training text samples are available, at least for some of the candidate authors, a realistic condition in author identification problems.

Bigrams of Syntactic Labels for Authorship Discrimination of Short Texts <=SEP=> We present a method for authorship discrimination that is based on the frequency of bigrams of syntactic labels that arise from partial parsing of the text. We show that this method, alone or combined with other classification features, achieves a high accuracy on discrimination of the work of Anne and Charlotte Brontë, which is very difficult to do by traditional methods. Moreover, high accuracies are achieved even on fragments of text little more than 200 words long. .................................................................................................................................................................................

A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization <=SEP=> The Rocchio relevance feedback algorithm is one of the most popular and widely applied learning methods from information retrieval. Here, a probabilistic analysis of this algorithm is presented in a text categorization framework. The analysis gives theoretical insight into the heuristics used in the Rocchio algorithm, particularly the word weighting scheme and the similarity metric. It also suggests improvements which lead to a probabilistic variant of the Rocchio classi er. The Rocchio classi er, its probabilistic variant, and a naive Bayes classi er are compared on six text categorization tasks. The results show that the probabilistic algorithms are preferable to the heuristic Rocchio classi er not only because they are more well-founded, but also because they achieve better performance.

Multiple nutrient deficiency detection in paddy leaf images using color and pattern analysis <=SEP=> Paddy being the staple food of India is majorly affected by deficiency of primary nutrient elements like nitrogen, phosphorus and potassium. Leaves can be deficient with multiple nutrient elements at a same time. This can alter natural color of paddy leaves. Such leaves are considered as defective. The proposed work is to automate multiple nutrient element deficiency identification of paddy leaves. Pattern analysis RGB color features are extracted to identify defective paddy leaves. Firstly the database of healthy, nitrogen, phosphorus and potassium defected paddy leaves are created. For any test image effective comparison at different levels are employed such as multiple color comparison, multiple pattern comparison and combination of color and patterns comparison, so that defectiveness is accurately identified for combination of deficiency such as nitrogen-phosphorus(NP), nitrogen-potassium(NK) and phosphorous-potassium (KP).

Facial Landmark Detection with Tweaked Convolutional Neural Networks <=SEP=> This paper concerns the problem of facial landmark detection. We provide a unique new analysis of the features produced at intermediate layers of a convolutional neural network (CNN) trained to regress facial landmark coordinates. This analysis shows that while being processed by the CNN, face images can be partitioned in an unsupervised manner into subsets containing faces in similar poses (i.e., 3D views) and facial properties (e.g., presence or absence of eye-wear). Based on this finding, we describe a novel CNN architecture, specialized to regress the facial landmark coordinates of faces in specific poses and appearances. To address the shortage of training data, particularly in extreme profile poses, we additionally present data augmentation techniques designed to provide sufficient training examples for each of these specialized sub-networks. The proposed Tweaked CNN (TCNN) architecture is shown to outperform existing landmark detection methods in an extensive battery of tests on the AFW, ALFW, and 300W benchmarks. Finally, to promote reproducibility of our results, we make code and trained models publicly available through our project webpage.

Undervalued or Overvalued Customers : Capturing Total Customer Engagement Value <=SEP=> Customers can interact with and create value for firms in a variety of ways. This article proposes that assessing the value of customers based solely upon their transactions with a firm may not be sufficient, and valuing this engagement correctly is crucial in avoiding undervaluation and overvaluation of customers. We propose four components of a customer’s engagement value (CEV) with a firm. The first component is customer lifetime value (the customer’s purchase behavior), the second is customer referral value (as it relates to incentivized referral of new customers), the third is customer influencer value (which includes the customer’s behavior to influence other customers, that is increasing acquisition, retention, and share of wallet through word of mouth of existing customers as well as prospects), and the fourth is customer knowledge value (the value added to the firm by feedback from the customer). CEV provides a comprehensive framework that can ultimately lead to more efficient marketing strategies that enable higher long-term contribution from the customer. Metrics to measure CEV, future research propositions regarding relationships between the four components of CEV are proposed and marketing strategies that can leverage these relationships suggested.

Wordnet Enhanced Automatic Crossword Generation <=SEP=> We report on a system for automatically generating and displaying crosswords from a system manager supplied database of potential clues and corresponding words that index those clues. The system relies on the lexical relations encoded in WordNet to enhance the aesthetics of the resulting crossword by making it easier to automatically identify a grid that may be populated with words and clues that have a thematic focus. The system architecture is provided in overview, as is empirical evaluation.

Software evolution storylines <=SEP=> This paper presents a technique for visualizing the interactions between developers in software project evolution. The goal is to produce a visualization that shows more detail than animated software histories, like code_swarm [15], but keeps the same focus on aesthetics and presentation. Our software evolution storylines technique draws inspiration from XKCD's "Movie Narrative Charts" and the aesthetic design of metro maps. We provide the algorithm, design choices, and examine the results of using the storylines technique. Our conclusion is that the it is able to show more details when compared to animated software project history videos. However, it does not scale to the largest projects, such as Eclipse and Mozilla.

Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph <=SEP=> While conversing with chatbots, humans typically tend to ask many questions, a significant portion of which can be answered by referring to large-scale knowledge graphs (KG). While Question Answering (QA) and dialog systems have been studied independently, there is a need to study them closely to evaluate such real-world scenarios faced by bots involving both these tasks. Towards this end, we introduce the task of Complex Sequential QA which combines the two tasks of (i) answering factual questions through complex inferencing over a realistic-sized KG of millions of entities, and (ii) learning to converse through a series of coherently linked QA pairs. Through a labor intensive semi-automatic process, involving in-house and crowdsourced workers, we created a dataset containing around 200K dialogs with a total of 1.6M turns. Further, unlike existing large scale QA datasets which contain simple questions that can be answered from a single tuple, the questions in our dialogs require a larger subgraph of the KG. Specifically, our dataset has questions which require logical, quantitative, and comparative reasoning as well as their combinations. This calls for models which can: (i) parse complex natural language questions, (ii) use conversation context to resolve coreferences and ellipsis in utterances, (iii) ask for clarifications for ambiguous queries, and finally (iv) retrieve relevant subgraphs of the KG to answer such questions. However, our experiments with a combination of state of the art dialog and QA models show that they clearly do not achieve the above objectives and are inadequate for dealing with such complex real world settings. We believe that this new dataset coupled with the limitations of existing models as reported in this paper should encourage further research in Complex Sequential QA.

Can we learn to gamble efficiently <=SEP=> Betting is an important problem faced by millions of sports fans each day. Presented with an upcoming matchup between team A and team B, and given the opportunity to place a 50/50 wager on either, where should a gambler put her money? This decision is not, of course, made in isolation: both teams will have played a number of decided matches with other teams throughout the season. Furthermore, a reasonable assumption to make is that the relation “A tends to beat B” is transitive. Under transitivity, the best prediction strategy is clearly to sort the teams by their abilities and predict according to this ranking. The obvious difficulty is that the best ranking of the teams is not known an advance. But there’s a more subtle issue: even with knowledge of all match outcomes in advance, i.e. a list of items of the form (team X < team Y ), it’s NP-hard to determine the best ranking of the teams when the outcomes are noisy. This is exactly the infamous Minimum Feedback Arc Set problem. The question we pose is as follows: can we design a non-trivial online prediction strategy in this setting which achieves vanishing regret relative to the best ranking in hindsight, even when the latter is computationally infeasible? It is tempting to believe this is impossible, as competing with the best ranking would appear tantamount to finding the best ranking. However, this assertion is false: the algorithm need not learn a ranking explicitly, it must only output a prediction (X < Y ) or (Y < X) when presented with a pair (X, Y ), and these predictions do not necessarily have to satisfy transitivity. Indeed, consider the following simple algorithm: treat each team pair (X, Y ) as an independent learning problem (ignoring all other matchups). In the long run, this will achieve vanishing regret with respect to the best ranking. So why is this not desirable? The trivial approach unfortunately admits a bad regret bound: the algorithm must see O(n) matches per team before it can start to make decent predictions. On the other hand, there is an information-theoretic approach that requires only O(log n) observations per team—the downside, unfortunately, is that this requires exponential computation. We would like to achieve this rate with an efficient method.

Towards Building Forensics Enabled Cloud Through Secure Logging-as-a-Service <=SEP=> Collection and analysis of various logs (e.g., process logs, network logs) are fundamental activities in computer forensics. Ensuring the security of the activity logs is therefore crucial to ensure reliable forensics investigations. However, because of the black-box nature of clouds and the volatility and co-mingling of cloud data, providing the cloud logs to investigators while preserving users' privacy and the integrity of logs is challenging. The current secure logging schemes, which consider the logger as trusted cannot be applied in clouds since there is a chance that cloud providers (logger) collude with malicious users or investigators to alter the logs. In this paper, we analyze the threats on cloud users' activity logs considering the collusion between cloud users, providers, and investigators. Based on the threat model, we propose Secure-Logging-as-a-Service ( SecLaaS), which preserves various logs generated for the activity of virtual machines running in clouds and ensures the confidentiality and integrity of such logs. Investigators or the court authority can only access these logs by the RESTful APIs provided by SecLaaS, which ensures confidentiality of logs. The integrity of the logs is ensured by hash-chain scheme and proofs of past logs published periodically by the cloud providers. In prior research, we used two accumulator schemes Bloom filter and RSA accumulator to build the proofs of past logs. In this paper, we propose a new accumulator scheme - Bloom-Tree, which performs better than the other two accumulators in terms of time and space requirement.

Cloud forensics: A research perspective <=SEP=> Cloud computing and digital forensics are both developing topics and researching these topics requires an understanding of the main aspects of both cloud computing and digital forensics. In cloud computing it is necessary not only to understand its characteristics and the different services and deployment models but also to survey the underpinning elements of cloud computing such as virtualization and the distributed computing which are important to identify its impact on current digital forensics guidelines and procedures. Unlike papers discussing the challenges and opportunities presented by cloud computing in relation to digital forensics, in this paper, we will discuss the underpinning cloud computing elements which are required to provide forensics friendly cloud services. Furthermore, we suggest a set of questions that will aid in the process of cloud forensics analysis.

Reading Between the Lines: Overcoming Data Sparsity for Accurate Classification of Lexical Relationships <=SEP=> The lexical semantic relationships between word pairs are key features for many NLP tasks. Most approaches for automatically classifying related word pairs are hindered by data sparsity because of their need to observe two words co-occurring in order to detect the lexical relation holding between them. Even when mining very large corpora, not every related word pair co-occurs. Using novel representations based on graphs and word embeddings, we present two systems that are able to predict relations between words, even when these are never found in the same sentence in a given corpus. In two experiments, we demonstrate superior performance of both approaches over the state of the art, achieving significant gains in recall.

The wisdom development scale: further validity investigations. <=SEP=> Researchers are gaining an interest in the concept of wisdom, a more holistic yet often ineffable educational outcome. Models of wisdom abound, but few have rigorously tested measures. This study looks at Brown's (2004a, 2004b) Model of Wisdom Development and its associated measure, the Wisdom Development Scale (WDS; Brown & Greene, 2006). The construct validity, measurement invariance, criterion validity, and reliability of scores from the WDS were assessed with over 3000 participants from two separate groups: one a sample of professionals and the other a sample of college students. Support for construct validity and reliability with these samples was found, along with measurement invariance. Latent means analyses showed predicted discrimination between the groups, and criterion validity evidence, with another measure of collegiate educational outcomes, was found.

The importance of drawing in the mechanical design process <=SEP=> -This paper is a study on the importance of drawing ( both formal drafting and informal sketching) during the process of mechanical design. Five hypotheses, focused on the t~es of drawings, their necessity in mechanical problem solving, and their relation to the external representation medium, are presented and supported. Support is through referenced studies in other domains and the results of protocol studies performed on five mechanical designers. Videotapes of all the marks-on-paper made by designers in representative sections of the design process were studied in detail for their type and purpose. The resulting data is supportive of the hypotheses. These results also give requirements for future computer aided design tools and graphics education, and goals for further studies. I. I N T R O D U C T I O N The goal of this paper is to study the importance of drawing (both formal drafting and informal sketching) during the process of mechanical design. This goal can be extended to state that we intend to show the necessio" of drawing during all the developmental stages of a mechanical design. Through the information presented here, the requirements for future computer aided design tools, graphics education, and further studies will be developed. All mechanical engineers are taught drafting. Thus, most engineers are skilled at making and interpreting these formal mechanical drawings. These drawings are representations of a final design (the end product of the design process) and they are intended to archive the completed design and communicate it to other designers and manufacturing personnel. Additionally, engineers are notorious for not being able to think without making "'back-of-the-envelope" sketches of rough ideas. Sometimes these informal sketches serve to communicate a concept to a colleague, but more often they just help the idea take shape on paper. It is in considering how these sketches help an idea take form that gives a hint that drawing's role in engineering is more than just to archive a concept or to communicate with others. Understanding the use of both drafting and sketching in design is important to help formulate the future development of Computer Aided Design or Drafting (CAD) systems. As CAD evolves and becomes more "'intelligent,'" the question of what attributes these systems must have becomes more important. In the past, CAD system attributes have primarily been driven from developments in the computer industry, it is only through understanding drawing's importance in the design process that these systems can be based on design needs. Additionally, the pressures of CAD tool development, faculty time demands, and course expenses cause academic institutions to reevaluate the content of their "graphics'" courses. Understanding drawing's importance in the design process helps establish what skills need to be taught to engineers during their training. This paper is organized by first, in Section 2, clarifying the types of drawings used in mechanical design. The hypotheses to be addressed in this paper are given in Section 3. A discussion of research on the understanding of visual imagery to be used as a basis for arguments in support of the hypotheses is in Section 4. In Section 5 is a discussion of the results of data taken on how mechanical engineers use drawings during design. Lastly, in Section 6, is a discussion of how well the hypotheses have been supported and the implications of our findings on CAD development, educational requirements, and future research directions. 2. TYPES OF D R A W I N G S USED IN DESIGN Engineers make many types of marks-on-paper. In research, to be described in Section 5, we have broken down these marks into two main groupings: support notation and graphic representations. Support notation includes textual notes, lists, dimensions (including leaders and arrows), and calculations. Graphic representations include drawings of objects and their functions, and plots and charts. Mechanical design graphic representations are often scale drawings made with mechanical instruments or CAD computer systems. These drawings, made in accordance with a set of widely accepted rules, are defined as having been drafted. Sketches, on the other hand, are defined as "free-hand" drawings. They are usually not to scale and may use shorthand notations to represent both objects and their function. A differentiation must be made between the act of graphic representation and the medium on which it occurs. The medium, whether it be paper and pencil, a computer stylus on a tablet, chalk on a blackboard, or other medium may put interface restrictions on the representation. The following discussions are concerned with what is being represented, not with how the representation is made. However, the discussions point to the medium's restriction on representation and the need for improved interfaces. Another aspect of drawings to be considered is the level of abstraction of the information to be represented. During the design process, the design is refined

Attention training and attention state training <=SEP=> The ability to attend and to exercise cognitive control are vital aspects of human adaptability. Several studies indicate that attention training using computer based exercises can lead to improved attention in children and adults. Randomized control studies of exposure to nature, mindfulness and integrative body-mind training (IBMT) yield improved attention and self-regulation. Here, we ask how attention training and attention state training might be similar and different in their training methods, neural mechanisms and behavioral outcomes. Together these various methods lead to practical ways of improving attention and self-regulation.

Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring <=SEP=> 1 Department of Decision Sciences and Information Management, K.U.Leuven Naamsestraat 69, B-3000 Leuven, Belgium {David.Martens;Johan.Huysmans; Bart.Baesens;Jan.Vanthienen}@econ.kuleuven.be 2 School of Computing, National University of Singapore, 3 Science Drive 2, Singapore 117543, Singapore rudys@comp.nus.edu.sg 3 University of Southampton, School of Management, Highfield Southampton, SO17 1BJ, UK Bart@soton.ac.uk

Disgust is a factor in extreme prejudice. <=SEP=> Understanding intergroup prejudice is a dominant research focus for social psychology. Prejudice is usually conceptualized as a continuum of positive/negative affect, but this has limitations. It neither reflects people's ability to maintain simultaneous positive and negative stereotypes of others nor explains extreme prejudice (bigotry). Some researchers have proposed multidimensional models of prejudice in which different negative emotions are evoked depending on the situation. Extending this to bigotry raises the question of which emotions are most relevant. Therefore, this study looked at 'anti-group' texts--writings which promote extreme intergroup hostility--and analysed the frequency of emotive language. Findings suggest that bigotry may be distinguished by high levels of disgust.

Thinking forensics: Cognitive science for forensic practitioners. <=SEP=> Human factors and their implications for forensic science have attracted increasing levels of interest across criminal justice communities in recent years. Initial interest centred on cognitive biases, but has since expanded such that knowledge from psychology and cognitive science is slowly infiltrating forensic practices more broadly. This article highlights a series of important findings and insights of relevance to forensic practitioners. These include research on human perception, memory, context information, expertise, decision-making, communication, experience, verification, confidence, and feedback. The aim of this article is to sensitise forensic practitioners (and lawyers and judges) to a range of potentially significant issues, and encourage them to engage with research in these domains so that they may adapt procedures to improve performance, mitigate risks and reduce errors. Doing so will reduce the divide between forensic practitioners and research scientists as well as improve the value and utility of forensic science evidence.

CoSIP: A Constrained Session Initiation Protocol for the Internet of Things <=SEP=> The Internet of Things (IoT) refers to the interconnection of billions of constrained devices, denoted as “smart objects” (SO), in an Internet-like structure. SOs typically feature limited capabilities in terms of computation and memory and operate in constrained environments, such low-power lossy networks. As IP has been foreseen as the standard for smart-object communication, an e↵ort to bring IP connectivity to SOs and define suitable communication protocols (i.e. CoAP) is being carried out within standardization organisms, such as IETF. In this paper, we propose a constrained version of the Session Initiation Protocol (SIP), named “CoSIP”, whose intent is to allow constrained devices to instantiate communication sessions in a lightweight and standard fashion. Session instantiation can include a negotiation phase of some parameters which will be used for all subsequent communication. CoSIP can be adopted in several application scenarios, such as service discovery and publish/subscribe applications, which are detailed. An evaluation of the proposed protocol is also presented, based on a Java implementation of CoSIP, to show the benefits that its adoption can bring about, in terms of compression rate with the existing SIP protocol and message overhead compared with the use of CoAP.

Geometric Correspondence Network for Camera Motion Estimation <=SEP=> In this paper, we propose a new learning scheme for generating geometric correspondences to be used for visual odometry. A convolutional neural network (CNN) combined with a recurrent neural network (RNN) are trained together to detect the location of keypoints as well as to generate corresponding descriptors in one unified structure. The network is optimized by warping points from source frame to reference frame, with a rigid body transform. Essentially, learning from warping. The overall training is focused on movements of the camera rather than movements within the image, which leads to better consistency in the matching and ultimately better motion estimation. Experimental results show that the proposed method achieves better results than both related deep learning and hand crafted methods. Furthermore, as a demonstration of the promise of our method we use a naive SLAM implementation based on these keypoints and get a performance on par with ORB-SLAM.

The relational model is dead, SQL is dead, and I don't feel so good myself <=SEP=> We report the opinions expressed by well-known database researchers on the future of the relational model and SQL during a panel at the International Workshop on Non-Conventional Data Access (NoCoDa 2012), held in Florence, Italy in October 2012 in conjunction with the 31st International Conference on Conceptual Modeling. The panelists include: Paolo Atzeni (Università Roma Tre, Italy), Umeshwar Dayal (HP Labs, USA), Christian S. Jensen (Aarhus University, Denmark), and Sudha Ram (University of Arizona, USA). Quotations from movies are used as a playful though effective way to convey the dramatic changes that database technology and research are currently undergoing.

Genetic and Stress-Induced Loss of NG2 Glia Triggers Emergence of Depressive-like Behaviors through Reduced Secretion of FGF2 <=SEP=> NG2-expressing glia (NG2 glia) are a uniformly distributed and mitotically active pool of cells in the central nervous system (CNS). In addition to serving as progenitors of myelinating oligodendrocytes, NG2 glia might also fulfill physiological roles in CNS homeostasis, although the mechanistic nature of such roles remains unclear. Here, we report that ablation of NG2 glia in the prefrontal cortex (PFC) of the adult brain causes deficits in excitatory glutamatergic neurotransmission and astrocytic extracellular glutamate uptake and induces depressive-like behaviors in mice. We show in parallel that chronic social stress causes NG2 glia density to decrease in areas critical to Major Depressive Disorder (MDD) pathophysiology at the time of symptom emergence in stress-susceptible mice. Finally, we demonstrate that loss of NG2 glial secretion of fibroblast growth factor 2 (FGF2) suffices to induce the same behavioral deficits. Our findings outline a pathway and role for NG2 glia in CNS homeostasis and mood disorders.

Adaptively generating high quality fixes for atomicity violations <=SEP=> It is difficult to fix atomicity violations correctly. Existing gate lock algorithm (GLA) simply inserts gate locks to serialize exe-cutions, which may introduce performance bugs and deadlocks. Synthesized context-aware gate locks (by Grail) require complex source code synthesis. We propose Fixer to adaptively fix ato-micity violations. It firstly analyses the lock acquisitions of an atomicity violation. Then it either adjusts the existing lock scope or inserts a gate lock. The former addresses cases where some locks are used but fail to provide atomic accesses. For the latter, it infers the visibility (being global or a field of a class/struct) of the gate lock such that the lock only protects related accesses. For both cases, Fixer further eliminates new lock orders to avoid introducing deadlocks. Of course, Fixer can produce both kinds of fixes on atomicity violations with locks. The experi-mental results on 15 previously used atomicity violations show that: Fixer correctly fixed all 15 atomicity violations without introducing deadlocks. However, GLA and Grail both intro-duced 5 deadlocks. HFix (that only targets on fixing certain types of atomicity violations) only fixed 2 atomicity violations and introduced 4 deadlocks. Fixer also provides an alternative way to insert gate locks (by inserting gate locks with proper visibility) considering fix acceptance.

A model for team-based access control (TMAC 2004) <=SEP=> Role based access control (RBAC) has been proved to be effective for defining access control. However, in an environment where collaborative work is needed, additional features should be added on top of RBAC to accommodate the new requirements. In this paper we describe a team access control extension model called TMAC04 (Team-Based Access Control 2004), which is built on the well-known RBAC. The TMAC04 model efficiently represents teamwork in the real world. It allows certain users to join a team based on their existing roles in an organization within limited contexts and new permissions to perform the required work.

Additively Manufactured Scaffolds for Bone Tissue Engineering and the Prediction of their Mechanical Behavior: A Review <=SEP=> Additive manufacturing (AM), nowadays commonly known as 3D printing, is a revolutionary materials processing technology, particularly suitable for the production of low-volume parts with high shape complexities and often with multiple functions. As such, it holds great promise for the fabrication of patient-specific implants. In recent years, remarkable progress has been made in implementing AM in the bio-fabrication field. This paper presents an overview on the state-of-the-art AM technology for bone tissue engineering (BTE) scaffolds, with a particular focus on the AM scaffolds made of metallic biomaterials. It starts with a brief description of architecture design strategies to meet the biological and mechanical property requirements of scaffolds. Then, it summarizes the working principles, advantages and limitations of each of AM methods suitable for creating porous structures and manufacturing scaffolds from powdered materials. It elaborates on the finite-element (FE) analysis applied to predict the mechanical behavior of AM scaffolds, as well as the effect of the architectural design of porous structure on its mechanical properties. The review ends up with the authors' view on the current challenges and further research directions.

The motivations and experiences of the on-demand mobile workforce <=SEP=> On-demand mobile workforce applications match physical world tasks and willing workers. These systems offer to help conserve resources, streamline courses of action, and increase market efficiency for micro- and mid-level tasks, from verifying the existence of a pothole to walking a neighbor's dog. This study reports on the motivations and experiences of individuals who regularly complete physical world tasks posted in on-demand mobile workforce marketplaces. Data collection included semi-structured interviews with members (workers) of two different services. The analysis revealed the main drivers for participating in an on-demand mobile workforce, including desires for monetary compensation and control over schedules and task selection. We also reveal main reasons for task selection, which involve situational factors, convenient physical locations, and task requester profile information. Finally, we discuss the key characteristics of the most worthwhile tasks and offer implications for novel crowdsourcing systems for physical world tasks.

Lumbar stabilization: a review of core concepts and current literature, part 2. <=SEP=> Lumbar-stabilization exercise programs have become increasingly popular as a treatment for low-back pain. In this article, we outline an evidence-based medicine approach to evaluating patients for a lumbar-stabilization program. We also discuss typical clinical components of this type of program and the rationale for including these particular features based on the medical literature.