Query: When 25 Cents is Too Much: An Experiment on Willingness-To-Sell and Willingness-To-Protect Personal Information

Relevant passages:
Privacy and rationality in individual decision making <=SEP=> Traditional theory suggests consumers should be able to manage their privacy. Yet, empirical and theoretical research suggests that consumers often lack enough information to make privacy-sensitive decisions and, even with sufficient information, are likely to trade off long-term privacy for short-term benefits

Personalization versus Privacy: An Empirical Examination of the Online Consumer's Dilemma <=SEP=> Personalization refers to the tailoring of products and purchase experience to the tastes of individual consumers based upon their personal and preference information. Recent advances in information acquisition and processing technologies have allowed online vendors to offer varieties of web-based personalization that not only increases switching costs, but also serves as important means of acquiring valuable customer information. However, investments in online personalization may be severely undermined if consumers do not use these services due to privacy concerns. In the absence of any empirical evidence that seeks to understand this consumer dilemma, our research develops a parsimonious model to predict consumers’ usage of online personalization as a result of the tradeoff between their value for personalization and concern for privacy. In addition to this tradeoff, we find that a consumer’s intent to use personalization services is positively influenced by her trust in the vendor. Our findings suggest that: 1. online vendors can improve their abilities to acquire and use customer information through trust building activities; 2. it is of critical importance that vendors understand and evaluate the different values consumers may place in enjoying various types of personalization.

Conditioning Prices on Purchase History <=SEP=> T rapid advance in information technology now makes it feasible for sellers to condition their price offers on consumers’ prior purchase behavior. In this paper we examine when it is profitable to engage in this form of price discrimination when consumers can adopt strategies to protect their privacy. Our baseline model involves rational consumers with constant valuations for the goods being sold and a monopoly merchant who can commit to a pricing policy. Applying results from the prior literature, we show that although it is feasible to price so as to distinguish high-value and low-value consumers, the merchant will never find it optimal to do so. We then consider various generalizations of this model, such as allowing the seller to offer enhanced services to previous customers, making the merchant unable to commit to a pricing policy, and allowing competition in the marketplace. In these cases we show that sellers will, in general, find it profitable to condition prices on purchase history.

Privacy in electronic commerce and the economics of immediate gratification <=SEP=> Dichotomies between privacy attitudes and behavior have been noted in the literature but not yet fully explained. We apply lessons from the research on behavioral economics to understand the individual decision making process with respect to privacy in electronic commerce. We show that it is unrealistic to expectindividual rationality in this context. Models of self-control problems and immediate gratification offer more realistic descriptions of the decision process and are more consistent with currently available data. In particular, we show why individuals who may genuinely want to protect their privacy might not do so because of psychological distortions well documented in the behavioral literature; we show that these distortions may affect not only 'naive' individuals but also 'sophisticated' ones; and we prove that this may occur also when individuals perceive the risks from not protecting their privacy as significant.

Privacy in e-commerce: examining user scenarios and privacy preferences <=SEP=> Privacy is a necessary concern in electronic commerce. It is difficult, if not impossible, to complete a transaction without revealing some personal data – a shipping address, billing information, or product preference. Users may be unwilling to provide this necessary information or even to browse online if they believe their privacy is invaded or threatened. Fortunately, there are technologies to help users protect their privacy. P3P (Platform for Privacy Preferences Project) from the World Wide Web Consortium is one such technology. However, there is a need to know more about the range of user concerns and preferences about privacy in order to build usable and effective interface mechanisms for P3P and other privacy technologies. Accordingly, we conducted a survey of 381 U.S. Net users, detailing a range of commerce scenarios and examining the participants' concerns and preferences about privacy. This paper presents both the findings from that study as well as their design implications.

Characterization studies of fluorine-induced corrosion crystal defects on microchip Al bondpads using X-ray photoelectron spectroscopy <=SEP=> In wafer fabrication, Fluorine (F) contamination may cause F-induced corrosion and defects on microchip Al bondpad, resulting in bondpad discoloration or non-stick on pad (NSOP). In the previous paper [1], the authors studied the F-induced corrosion and defects, characterized the composition of the “flower-like” defects and determined the binding energy of Al fluoride [AlF<sub>6</sub>]<sup>3-</sup> using X-ray Photoelectron Spectroscopy (XPS) and Time of Flight Secondary Ion Mass Spectrometry (TOF-SIMS) techniques. In this paper, we further studied F-induced corrosion and defects, and characterized the composition of the “crystal-like” defects using XPS. The experimental results showed that the major component of the “crystal-like” defect was Al fluoride of AlF<sub>3</sub>. The percentages of the components of the “crystal-like” defects on the affected bondpad are: Al (22.2%), Al<sub>2</sub>O<sub>3</sub> (5.4%), AlF<sub>3</sub>(70.0%) and [AlF<sub>6</sub>]<sup>3-</sup> (2.4%). During high-resolution fitting, the binding energies of Al (72.8eV)Al<sub>2</sub>O<sub>3</sub> (74.5eV), AlF<sub>3</sub> (76.3eV) and [AlF<sub>6</sub>]<sup>3-</sup> (78.7eV) were used.

User-oriented Bayesian identification and its configuration <=SEP=> Identification of tracked objects is a key capability of automated surveillance and information systems for air, surface and subsurface (maritime), and ground environments, improving situational awareness and offering decision support to operational users. The Bayesian-based identification data combining process (IDCP) provides an effective instrument for fusion of uncertain identity indications from various sources. A user-oriented approach to configuration of the process is introduced, which enables operators to adapt the IDCP to changing identification needs in varying operational scenarios. Application of results from cognitive psychology and decision theory provides good access to retrieval of Bayesian data and makes configuration easily feasible to operational experts.

Probabilistic Models for Expert Finding <=SEP=> A common task in many applications is to find persons who are knowledgeable about a given topic (i.e., expert finding). In this paper, we propose and develop a general probabilistic framework for studying expert finding problem and derive two families of generative models (candidate generation models and topic generation models) from the framework. These models subsume most existing language models proposed for expert finding. We further propose several techniques to improve the estimation of the proposed models, including incorporating topic expansion, using a mixture model to model candidate mentions in the supporting documents, and defining an email count-based prior in the topic generation model. Our experiments show that the proposed estimation strategies are all effective to improve retrieval accuracy.

GENIES: a natural-language processing system for the extraction of molecular pathways from journal articles <=SEP=> Systems that extract structured information from natural language passages have been highly successful in specialized domains. The time is opportune for developing analogous applications for molecular biology and genomics. We present a system, GENIES, that extracts and structures information about cellular pathways from the biological literature in accordance with a knowledge model that we developed earlier. We implemented GENIES by modifying an existing medical natural language processing system, MedLEE, and performed a preliminary evaluation study. Our results demonstrate the value of the underlying techniques for the purpose of acquiring valuable knowledge from biological journals.

The Myth of the "Biphasic" Hyaluronic Acid Filler. <=SEP=> BACKGROUND
The terms "biphasic" and "monophasic" have been used frequently as a means of differentiating hyaluronic acid (HA) fillers. This type of categorization is based on misinterpretations of the term "phase" and provides no help to the practitioner when selecting the most appropriate product for each indication, patient, and injection technique.


OBJECTIVE
The purpose of this study was to analyze the properties of 2 HA filler families; Juvederm (JUV) (Allergan), often stated to be monophasic and Restylane (RES) (Galderma), often stated to be biphasic, and discuss what properties may have led to the use of the terms monophasic and biphasic.


MATERIALS AND METHODS
Three different methods were used for JUV and RES: determination of extractable HA; determination of water uptake; and microscopy.


RESULTS
The analyzed products were shown to contain both observable gel particles and extractable HA and have the ability to absorb added water.


CONCLUSION
The categorization of HA fillers as biphasic or monophasic was shown to be scientifically incorrect and should therefore be avoided. Further analytical measurement of the properties leading to this misinterpretation can provide information to discriminate and categorize HA fillers on a sounder scientific basis.

Link-level access cloud architecture design based on SDN for 5G networks <=SEP=> The exponential growth of data traffic and connected devices, and the reduction of latency and costs, are considered major challenges for future mobile communication networks. The satisfaction of these challenges motivates revisiting the architecture of these networks. We propose an SDN-based design of a hierarchical architecture for the 5G packet core. In this article we focus on the design of its access cloud with the goal of providing low latency and scalable Ethernet-like support to terminals and MTC devices including mobility management. We examine and address its challenges in terms of network scalability and support for link-level mobility. We propose a link-level architecture that forwards frames from and to edge network elements (AP and routers) with a label that identifies the APs through which the terminal is reachable. An SDN local controller tracks and updates the users' location information at the edge network elements. Additionally, we propose to delegate in SDN local controllers the handling of non-scalable operations, such as broadcast and multicast messages, and network management procedures.

Product Adoption Rate Prediction in a Competitive Market <=SEP=> As the worlds of commerce and the Internet technology become more inextricably linked, a large number of user consumption series become available for online market intelligence analysis. A critical demand along this line is to predict the future product adoption state of each user, which enables a wide range of applications such as targeted marketing. Nevertheless, previous works only aimed at predicting if a user would adopt a particular product or not with a binary buy-or-not representation. The problem of tracking and predicting users’ adoption rates, i.e., the frequency and regularity of using each product over time, is still under-explored. To this end, we present a comprehensive study of product adoption rate prediction in a competitive market. This task is nontrivial as there are three major challenges in modeling users’ complex adoption states: the heterogeneous data sources around users, the unique user preference and the competitive product selection. To deal with these challenges, we first introduce a flexible factor-based decision function to capture the change of users’ product adoption rate over time, where various factors that may influence users’ decisions from heterogeneous data sources can be leveraged. Using this factor-based decision function, we then provide two corresponding models to learn the parameters of the decision function with both generalized and personalized assumptions of users’ preferences. We further study how to leverage the competition among different products and simultaneously learn product competition and users’ preferences with both generalized and personalized assumptions. Finally, extensive experiments on two real-world datasets show the superiority of our proposed models.

Child abuse, neglect, and adult behavior: research design and findings on criminality, violence, and child abuse. <=SEP=> Using a prospective cohorts design, a large sample of physical and sexual abuse cases was compared to a matched control group. Overall, abused and neglected subjects had higher rates than did controls for adult criminality and arrests for violent offenses, but not for adult arrests for child abuse or neglect. Findings are discussed in the context of intergenerational transmission of violence, and directions for future research are suggested.

"GrabCut": interactive foreground extraction using iterated graph cuts <=SEP=> The problem of efficient, interactive foreground/background segmentation in still images is of great practical importance in image editing. Classical image segmentation tools use either texture (colour) information, e.g. Magic Wand, or edge (contrast) information, e.g. Intelligent Scissors. Recently, an approach based on optimization by graph-cut has been developed which successfully combines both types of information. In this paper we extend the graph-cut approach in three respects. First, we have developed a more powerful, iterative version of the optimisation. Secondly, the power of the iterative algorithm is used to simplify substantially the user interaction needed for a given quality of result. Thirdly, a robust algorithm for "border matting" has been developed to estimate simultaneously the alpha-matte around an object boundary and the colours of foreground pixels. We show that for moderately difficult examples the proposed method outperforms competitive tools.

A novel hybrid MCDM model combining the SAW, TOPSIS and GRA methods based on experimental design <=SEP=> Multiple criteria decision-making (MCDM) is a difficult task because the existing alternatives are frequently in conflict with each other. This study presents a hybrid MCDM method combining simple additive weighting (SAW), techniques for order preference by similarity to an ideal solution (TOPSIS) and grey relational analysis (GRA) techniques. A feature of this method is that it employs an experimental design technique to assign attribute weights and then combines different MCDM evaluation methods to construct the hybrid decision-making model. This model can guide a decision maker in making a reasonable judgment without requiring professional skills or extensive experience. The ranking results agreed upon by multiple MCDM methods are more trustworthy than those generated by a single MCDM method. The proposed method is illustrated in a practical application scenario involving an IC packaging company. Four additional numerical examples are provided to demonstrate the applicability of the proposed method. In all of the cases, the results obtained using the proposed method were highly similar to those derived by previous studies, thus proving the validity and capability of this method to solve real-life MCDM problems. © 2016 Elsevier Inc. All rights reserved.

Semi-supervised hashing for scalable image retrieval <=SEP=> Large scale image search has recently attracted considerable attention due to easy availability of huge amounts of data. Several hashing methods have been proposed to allow approximate but highly efficient search. Unsupervised hashing methods show good performance with metric distances but, in image search, semantic similarity is usually given in terms of labeled pairs of images. There exist supervised hashing methods that can handle such semantic similarity but they are prone to overfitting when labeled data is small or noisy. Moreover, these methods are usually very slow to train. In this work, we propose a semi-supervised hashing method that is formulated as minimizing empirical error on the labeled data while maximizing variance and independence of hash bits over the labeled and unlabeled data. The proposed method can handle both metric as well as semantic similarity. The experimental results on two large datasets (up to one million samples) demonstrate its superior performance over state-of-the-art supervised and unsupervised methods.

A Hierarchical Security Framework for Defending Against Sophisticated Attacks on Wireless Sensor Networks in Smart Cities <=SEP=> In smart cities, wireless sensor networks (WSNs) act as a type of core infrastructure that collects data from the city to implement smart services. The security of WSNs is one of the key issues of smart cities. In resource-restrained WSNs, dynamic ongoing or unknown attacks usually steer clear of isolated defense components. Therefore, to resolve this problem, we propose a hierarchical framework based on chance discovery and usage control (UCON) technologies to improve the security of WSNs while still taking the low-complexity and high security requirements of WSNs into account. The features of continuous decision and dynamic attributes in UCON can address ongoing attacks using advanced persistent threat detection. In addition, we use a dynamic adaptive chance discovery mechanism to detect unknown attacks. To design and implement a system using the mechanism described above, a unified framework is proposed in which low-level attack detection with simple rules is performed in sensors, and high-level attack detection with complex rules is performed in sinks and at the base station. Moreover, software-defined networking and network function virtualization technologies are used to perform attack mitigation when either low-level or high-level attacks are detected. An experiment was performed to acquire an attack data set for evaluation. Then, a simulation was created to evaluate the resource consumption and attack detection rate. The results demonstrate the feasibility and efficiency of the proposed scheme.

Factorized graph matching <=SEP=> Graph matching plays a central role in solving correspondence problems in computer vision. Graph matching problems that incorporate pair-wise constraints can be cast as a quadratic assignment problem (QAP). Unfortunately, QAP is NP-hard and many algorithms have been proposed to solve different relaxations. This paper presents factorized graph matching (FGM), a novel framework for interpreting and optimizing graph matching problems. In this work we show that the affinity matrix can be factorized as a Kronecker product of smaller matrices. There are three main benefits of using this factorization in graph matching: (1) There is no need to compute the costly (in space and time) pair-wise affinity matrix; (2) The factorization provides a taxonomy for graph matching and reveals the connection among several methods; (3) Using the factorization we derive a new approximation of the original problem that improves state-of-the-art algorithms in graph matching. Experimental results in synthetic and real databases illustrate the benefits of FGM. The code is available at http://humansensing.cs.cmu.edu/fgm.

Complete Analytical Forward and Inverse Kinematics for the NAO Humanoid Robot <=SEP=> Articulated robots with multiple degrees of freedom, such as humanoid robots, have become popular research platforms in robotics and artificial intelligence. Such robots can perform complex motions, including the balancing, walking, and kicking skills required in the RoboCup robot soccer competition. The design of complex dynamic motions is achievable only through the use of robot kinematics, which is an application of geometry to the study of arbitrary robotic chains. This thesis studies the problems of forward and inverse kinematics for the Aldebaran NAO humanoid robot and presents for the first time a complete analytical solution to both problems with no approximations, including an implementation of a software library for real-time execution. The forward kinematics allow NAO developers to map any configuration of the robot from its own joint space to the three-dimensional physical space, whereas the inverse kinematics provide closedform solutions to finding joint configurations that drive the end effectors of the robot to desired points in the three-dimensional space. The proposed solution was made feasible through a decomposition into five independent problems (head, two arms, two legs), the use of the Denavit-Hartenberg method, and the analytical solution of a non-linear system of equations. The main advantage of the proposed inverse kinematics compared to existing numerical approaches is its accuracy, its efficiency, and the elimination of singularities. The implemented NAO kinematics library, which additionally offers centerof-mass calculation, is demonstrated in two motion design tasks: pointing to the ball and basic balancing. The library has been integrated into the software architecture of the RoboCup team “Kouretes” and is currently being used in various motion design problems, such as dynamic balancing, trajectory following, dynamic kicking, and omnidirectional walking.

Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. <=SEP=> The complex language of eukaryotic gene expression remains incompletely understood. Despite the importance suggested by many noncoding variants statistically associated with human disease, nearly all such variants have unknown mechanisms. Here, we address this challenge using an approach based on a recent machine learning advance-deep convolutional neural networks (CNNs). We introduce the open source package Basset to apply CNNs to learn the functional activity of DNA sequences from genomics data. We trained Basset on a compendium of accessible genomic sites mapped in 164 cell types by DNase-seq, and demonstrate greater predictive accuracy than previous methods. Basset predictions for the change in accessibility between variant alleles were far greater for Genome-wide association study (GWAS) SNPs that are likely to be causal relative to nearby SNPs in linkage disequilibrium with them. With Basset, a researcher can perform a single sequencing assay in their cell type of interest and simultaneously learn that cell's chromatin accessibility code and annotate every mutation in the genome with its influence on present accessibility and latent potential for accessibility. Thus, Basset offers a powerful computational approach to annotate and interpret the noncoding genome.

Fast Exact Inference with a Factored Model for Natural Language Parsing <=SEP=> We presenta novel generati ve model for natural languagetree structuresin whichsemantic(lexical dependenc y) andsyntacticstructuresare scoredwith separatemodels.Thisfactorizationprovidesconceptual simplicity, straightforwardopportunitiesfor separatelyimproving the componentmodels,anda level of performancealreadycloseto thatof similar, non-factoredmodels.Most importantly, unlikeothermodernparsing models,thefactoredmodeladmitsanextremelyeffectiveA parsingalgorithm,which makesefficient,exactinferencefeasible.

A stochastic control strategy for hybrid electric vehicles <=SEP=> The supervisory control strategy of a hybrid vehicle coordinates the operation of vehicle sub-systems to achieve performance targets such as maximizing fuel economy and reducing exhaust emissions. This high-level control problem is commonly referred as the power management problem. In the past, many supervisory control strategies were developed on the basis of a few pre-defined driving cycles, using intuition and heuristics. The resulting control strategy was often inherently cycle-beating and lacked a guaranteed level of optimality. In this study, the power management problem is tackled from a stochastic viewpoint. An infinite-horizon stochastic dynamic optimization problem is formulated. The power demand from the driver is modeled as a random Markov process. The optimal control strategy is then obtained by using stochastic dynamic programming (SDP). The obtained control law is in the form of a stationary full-state feedback and can be directly implemented. Simulation results over standard driving cycles and random driving cycles are presented to demonstrate the effectiveness of the proposed stochastic approach. It was found that the obtained SDP control algorithm outperforms a sub-optimal rule-based control strategy trained from deterministic DP results.

Snort Intrusion Detection System with Intel Software Guard Extension (Intel SGX) <=SEP=> Network Function Virtualization (NFV) promises the benefits of reduced infrastructure, personnel, and management costs by outsourcing network middleboxes to the public or private cloud. Unfortunately, running network functions in the cloud entails security challenges, especially for complex stateful services. In this paper, we describe our experiences with hardening the king of middleboxes - Intrusion Detection Systems (IDS) - using Intel Software Guard Extensions (Intel SGX) technology. Our IDS secured using Intel SGX, called SEC-IDS, is an unmodified Snort 3 with a DPDK network layer that achieves 10Gbps line rate. SEC-IDS guarantees computational integrity by running all Snort code inside an Intel SGX enclave. At the same time, SEC-IDS achieves near-native performance, with throughput close to 100 percent of vanilla Snort 3, by retaining network I/O outside of the enclave. Our experiments indicate that performance is only constrained by the modest Enclave Page Cache size available on current Intel SGX Skylake based E3 Xeon platforms. Finally, we kept the porting effort minimal by using the Graphene-SGX library OS. Only 27 Lines of Code (LoC) were modified in Snort and 178 LoC in Graphene-SGX itself.

Probabilistic reasoning in intelligent systems - networks of plausible inference <=SEP=> Probabilistic methods to create the areas, of computational tools. But I needed to get canned, bayesian networks worked recently strongly. Recently I tossed this book was published. In intelligent systems is researchers in, ai operations research excellence award for graduate. Too concerned about how it i've been. Apparently daphne koller and learning structures evidential reasoning. Pearl is a language for i've. Despite its early publication date it, is not great give the best references.

Comparing textual descriptions to process models - The automatic detection of inconsistencies <=SEP=> Many organizations maintain textual process descriptions alongside graphical process models. The purpose is to make process information accessible to various stakeholders, including those who are not familiar with reading and interpreting the complex execution logic of process models. Despite this merit, there is a clear risk that model and text become misaligned when changes are not applied to both descriptions consistently. For organizations with hundreds of di↵erent processes, the e↵ort required to identify and clear up such conflicts is considerable. To support organizations in keeping their process descriptions consistent, we present an approach to automatically identify inconsistencies between a process model and a corresponding textual description. Our approach detects cases where the two process representations describe activities in di↵erent orders and detect process model activities not contained in the textual description. A quantitative evaluation with 53 real-life model-text pairs demonstrates that our approach accurately identifies inconsistencies between model and text.

Approximating Polyhedra with Spheres for Time-Critical Collision Detection <=SEP=> This article presentsa method for approximatingpolyhedralobjects to support a time-critical collision-detectionalgorithm. The approximationsare hierarchies of spheres, and they allow the time-critical algorithm to progressivelyrefine the accuracy of its detection, stopping as needed to maintain the real-time performanceessential for interactive applications.The key to this approachis a preprocessthat automaticallybuilds tightly fitting hierarchies for rigid and articulatedobjects.The preprocessuses medial-axis surfaces, which are skeletal representations of objects. These skeletonsguide an optimizationtechniquethat gives the hierarchies accuracy properties appropriate for collision detection. In a sample application, hierarchies built this way allow the time-criticalcollision-detectionalgorithmto have acceptableaccuracy, improving significantly on that possible with hierarchies built by previous techniques. The performanceof the time-critical algorithmin this application is consistently 10 to 100 times better than a previous collision-detection algorithm, maintaining low latency and a nearIy constant frame rate of 10 frames per second on a conventional graphics workstation. The time-critical algorithm maintains its real-time performanceas objects become more complicated, even as they exceed previouslyreported complexitylevels by a factor of more than 10.

Reward Shaping via Meta-Learning <=SEP=> Reward shaping is one of the most effective methods to tackle the crucial yet challenging problem of credit assignment in Reinforcement Learning (RL). However, designing shaping functions usually requires much expert knowledge and handengineering, and the difficulties are further exacerbated given multiple similar tasks to solve. In this paper, we consider reward shaping on a distribution of tasks, and propose a general meta-learning framework to automatically learn the efficient reward shaping on newly sampled tasks, assuming only shared state space but not necessarily action space. We first derive the theoretically optimal reward shaping in terms of credit assignment in model-free RL. We then propose a value-based meta-learning algorithm to extract an effective prior over the optimal reward shaping. The prior can be applied directly to new tasks, or provably adapted to the task-posterior while solving the task within few gradient updates. We demonstrate the effectiveness of our shaping through significantly improved learning efficiency and interpretable visualizations across various settings, including notably a successful transfer from DQN to DDPG.

ADDIS ABABA UNIVERSITY SCHOOL OF GRADUATE STUDIES FACULTY OF INFORMATICS DEPARTMENT OF INFORMATION SCIENCE DESIGNING A STEMMER FOR GE’EZ TEXT USING RULE BASED APPROACH <=SEP=> 

A compact dual-band half-mode SIW based semi-circular antenna <=SEP=> In this paper, a half mode SIW based semi-circular antenna with co-axial feed has been designed for dual band operation. The fundamental resonating frequency is found to operate at 4.66 GHz while the next higher mode is at 8.3 GHz. The antenna performance is optimized through parametric studies and the dual band antenna is designed using Arlon AD270 substrate. Simulation studies have been carried out to study the various antenna parameters.

A novel coaxial probe waveguide to microstrip transition <=SEP=> In this paper, a modified coaxial probe waveguide-to-microstrip transition structure at K-band is proposed. The transition uses two kinds of coaxial probes and has hermetic characteristics. It can also be used in other band, such as Ka-band. The structure is demonstrated for designing the transition. HFSS simulation and measurement results for the transition structure have been compared in terms of |S/sub 21/|. The comparison shows that the measurement agrees well with the simulation result. The measurement has shown 0.5 dB insertion loss.