Query: Characterizing conflicts in fair division of indivisible goods using a scale of criteria

Relevant passages:
The combinatorial assignment problem: approximate competitive equilibrium from equal incomes <=SEP=> Impossibility theorems suggest that the only efficient and strategyproof mechanisms for the problem of combinatorial assignment - e.g., assigning schedules of courses to students - are dictatorships. Dictatorships are mostly rejected as unfair: for any two agents, one chooses all their objects before the other chooses any. Any solution will involve compromise amongst efficiency, incentive and fairness considerations.
 This paper proposes a solution to the combinatorial assignment problem. It is developed in four steps. First, I propose two new criteria of outcome fairness, the maximin share guarantee and envy bounded by a single good, which weaken well-known criteria to accommodate indivisibilities; the criteria formalize why dictatorships are unfair. Second, I prove existence of an approximation to Competitive Equilibrium from Equal Incomes in which (i) incomes are unequal but arbitrarily close together; (ii) the market clears with error, which approaches zero in the limit and is small for realistic problems. Third, I show that this Approximate CEEI satisfies the fairness criteria. Last, I define a mechanism based on Approximate CEEI that is strategyproof for the zero-measure agents economists traditionally regard as price takers. The proposed mechanism is calibrated on real data and is compared to alternatives from theory and practice: all other known mechanisms are either manipulable by zero-measure agents or unfair ex-post, and most are both manipulable and unfair.

The Santa Claus problem <=SEP=> We consider the following problem: The Santa Claus has n presents that he wants to distribute among m kids. Each kid has an arbitrary value for each present. Let p<sub>ij</sub> be the value that kid i has for present j. The Santa's goal is to distribute presents in such a way that the least lucky kid is as happy as possible, i.e he tries to maximize min<sub>i=1,...,m</sub> sum<sub>j ∈ S<sub>i</sub></sub> p<sub>ij</sub> where S<sub>i</sub> is a set of presents received by the i-th kid.Our main result is an O(log log m/log log log m) approximation algorithm for the restricted assignment case of the problem when p<sub>ij</sub> ∈ p<sub>j</sub>,0 (i.e. when present j has either value p<sub>j</sub> or 0 for each kid). Our algorithm is based on rounding a certain natural exponentially large linear programming relaxation usually referred to as the configuration LP. We also show that the configuration LP has an integrality gap of Ω(m<sup>1/2</sup>) in the general case, when p<sub>ij</sub> can be arbitrary.

On approximately fair allocations of indivisible goods <=SEP=> We study the problem of fairly allocating a set of indivisible goods to a set of people from an algorithmic perspective. fair division has been a central topic in the economic literature and several concepts of fairness have been suggested. The criterion that we focus on is envy-freeness. In our model, a monotone utility function is associated with every player specifying the value of each subset of the goods for the player. An allocation is envy-free if every player prefers her own share than the share of any other player. When the goods are divisible, envy-free allocations always exist. In the presence of indivisibilities, we show that there exist allocations in which the envy is bounded by the maximum marginal utility, and present a simple algorithm for computing such allocations. We then look at the optimization problem of finding an allocation with minimum possible envy. In the general case the problem is not solvable or approximable in polynomial time unless P = NP. We consider natural special cases (e.g.additive utilities) which are closely related to a class of job scheduling problems. Approximation algorithms as well as inapproximability results are obtained. Finally we investigate the problem of designing truthful mechanisms for producing allocations with bounded envy.

Allocating indivisible goods <=SEP=> The problem of allocating divisible goods has enjoyed a lot of attention in both mathematics (e.g. the cake-cutting problem) and economics (e.g. market equilibria). On the other hand, the natural requirement of indivisible goods has been somewhat neglected, perhaps because of its more complicated nature. In this work we study a fairness criterion, called the Max-Min Fairness problem, for k players who want to allocate among themselves m indivisible goods. Each player has a specified valuation function on the subsets of the goods and the goal is to split the goods between the players so as to maximize the minimum valuation. Viewing the problem from a game theoretic perspective, we show that for two players and additive valuations the expected minimum of the (randomized) cut-and-choose mechanism is a 1/2-approximation of the optimum. To complement this result we show that no truthful mechanism can compute the exact optimum.We also consider the algorithmic perspective when the (true) additive valuation functions are part of the input. We present a simple 1/(m - k + 1) approximation algorithm which allocates to every player at least 1/k fraction of the value of all but the k - 1 heaviest items. We also give an algorithm with additive error against the fractional optimum bounded by the value of the largest item. The two approximation algorithms are incomparable in the sense that there exist instances when one outperforms the other.

A polynomial-time approximation scheme for maximizing the minimum machine completion time <=SEP=> 

Design Considerations and Performance Evaluation of Single-Stage TAIPEI Rectifier for HVDC Distribution Applications <=SEP=> Design considerations and performance evaluations of a three-phase, four-switch, single-stage, isolated zero-voltage-switching (ZVS) rectifier are presented. The circuit is obtained by integrating the three-phase, two-switch, ZVS, discontinuous-current-mode (DCM), boost power-factorcorrection (PFC) rectifier, named for short the TAIPEI rectifier, with the ZVS full-bridge (FB) phase-shift dc/dc converter. The performance was evaluated on a three-phase 2.7-kW prototype designed for HVDC distribution applications with the line-toline voltage range from 180 VRMS to 264 VRMS and with a tightly regulated variable dc output voltage from 200 V to 300 V. The prototype operates with ZVS over the entire input-voltage and load-current range and achieves less than 5% input-current THD with the efficiency in the 95% range.

The induction of apoptosis and autophagy by Wasabia japonica extract in colon cancer. <=SEP=> PURPOSE
Wasabia japonica (wasabi) has been shown to exhibit properties of detoxification, anti-inflammation and the induction of apoptosis in cancer cells. This study aimed to investigate the molecular mechanism of the cytotoxicity of wasabi extract (WE) in colon cancer cells to evaluate the potential of wasabi as a functional food for chemoprevention.


METHODS
Colo 205 cells were treated with different doses of WE, and the cytotoxicity was analyzed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide. Apoptosis and autophagy were detected by 4',6-diamidino-2-phenylindole, 5,5',6,6'-tetrachloro-1,1',3,3'-tetraethyl-imidacarbo-yanine iodide and staining for acidic vascular organelles (AVOs), along with Western blotting.


RESULTS
The results demonstrated that WE induced the extrinsic pathway and mitochondrial death machinery through the activation of TNF-α, Fas-L, caspases, truncated Bid and cytochrome C. WE also induced autophagy by decreasing the phosphorylation of Akt and mTOR and promoting the expression of microtubule-associated protein 1 light chain 3-II and AVO formation. An in vivo xenograft model verified that tumor growth was delayed by WE treatment.


CONCLUSION
Our studies revealed that WE exhibits anti-colon cancer properties through the induction of apoptosis and autophagy. These results provide support for the application of WE as a chemopreventive functional food and as a prospective treatment of colon cancer.

Detecting Image Splicing using Geometry Invariants and Camera Characteristics Consistency <=SEP=> Recent advances in computer technology have made digital image tampering more and more common. In this paper, we propose an authentic vs. spliced image classification method making use of geometry invariants in a semi-automatic manner. For a given image, we identify suspicious splicing areas, compute the geometry invariants from the pixels within each region, and then estimate the camera response function (CRF) from these geometry invariants. The cross-fitting errors are fed into a statistical classifier. Experiments show a very promising accuracy, 87%, over a large data set of 363 natural and spliced images. To the best of our knowledge, this is the first work detecting image splicing by verifying camera characteristic consistency from a single-channel image

Internet Reflections on Teenagers <=SEP=> The Internet is a piece of today's way of life that numerous young people can't envision what the world resembled before the Internet existed. The Internet is fun, enlightening and an extraordinary wellspring of correspondence with others. It's an instructive device and clients can find out about just about anything. Sharing data through Internet is simple, shoddy and quick. Young people have admittance to billions of sites containing data as content, pictures and recordings [1-10].

Optimal Mass Transport for Registration and Warping <=SEP=> Image registration is the process of establishing a common geometric reference frame between two or more image data sets possibly taken at different times. In this paper we present a method for computing elastic registration and warping maps based on the Monge–Kantorovich theory of optimal mass transport. This mass transport method has a number of important characteristics. First, it is parameter free. Moreover, it utilizes all of the grayscale data in both images, places the two images on equal footing and is symmetrical: the optimal mapping from image A to image B being the inverse of the optimal mapping from B to A. The method does not require that landmarks be specified, and the minimizer of the distance functional involved is unique; there are no other local minimizers. Finally, optimal transport naturally takes into account changes in density that result from changes in area or volume. Although the optimal transport method is certainly not appropriate for all registration and warping problems, this mass preservation property makes the Monge–Kantorovich approach quite useful for an interesting class of warping problems, as we show in this paper. Our method for finding the registration mapping is based on a partial differential equation approach to the minimization of the L 2 Kantorovich–Wasserstein or “Earth Mover's Distance” under a mass preservation constraint. We show how this approach leads to practical algorithms, and demonstrate our method with a number of examples, including those from the medical field. We also extend this method to take into account changes in intensity, and show that it is well suited for applications such as image morphing.

Formation and suppression of acoustic memories during human sleep <=SEP=> Sleep and memory are deeply related, but the nature of the neuroplastic processes induced by sleep remains unclear. Here, we report that memory traces can be both formed or suppressed during sleep, depending on sleep phase. We played samples of acoustic noise to sleeping human listeners. Repeated exposure to a novel noise during Rapid Eye Movements (REM) or light non-REM (NREM) sleep leads to improvements in behavioral performance upon awakening. Strikingly, the same exposure during deep NREM sleep leads to impaired performance upon awakening. Electroencephalographic markers of learning extracted during sleep confirm a dissociation between sleep facilitating memory formation (light NREM and REM sleep) and sleep suppressing learning (deep NREM sleep). We can trace these neural changes back to transient sleep events, such as spindles for memory facilitation and slow waves for suppression. Thus, highly selective memory processes are active during human sleep, with intertwined episodes of facilitative and suppressive plasticity. Though memory and sleep are related, it is still unclear whether new memories can be formed during sleep. Here, authors show that people could learn new sounds during REM or light non-REM sleep, but that learning was suppressed when sounds were played during deep NREM sleep.

Shape Segmentation by Approximate Convexity Analysis <=SEP=> We present a shape segmentation method for complete and incomplete shapes. The key idea is to directly optimize the decomposition based on a characterization of the expected geometry of a part in a shape. Rather than setting the number of parts in advance, we search for the smallest number of parts that admit the geometric characterization of the parts. The segmentation is based on an intermediate-level analysis, where first the shape is decomposed into approximate convex components, which are then merged into consistent parts based on a nonlocal geometric signature. Our method is designed to handle incomplete shapes, represented by point clouds. We show segmentation results on shapes acquired by a range scanner, and an analysis of the robustness of our method to missing regions. Moreover, our method yields results that are comparable to state-of-the-art techniques evaluated on complete shapes.

A Survey Paper : Areas , Techniques and Challenges of Opinion Mining <=SEP=> Opinion Mining is a promising discipline, defined as an intersection of information retrieval and computational linguistic techniques to deal with the opinions expressed in a document. The field aims at solving the problems related to opinions about products, Politian in newsgroup posts, review sites, comments on Facebook posts and twitter etc. This paper is about to covers the techniques, applications, long and short future research areas, Research gaps and future challenges in opinion mining. Further, an attempt has been made to discuss in detail the use of supervised, unsupervised, machine learning and case based reasoning techniques in opinion mining to perform computational treatment of sentiments.

A Study of Influencing Factors for Repurchase Intention in Internet Shopping Malls <=SEP=> This research studied the effect of 15 variables on the consumers' overall satisfaction and repurchase intention in Internet shopping malls. As the value of loyal customers is incomparably high in Electronic Commerce, winning customers’ loyalty is vital to success of shopping malls. In this study, a customer is defined as one who has purchased goods or services at least once from Internet shopping malls. The research variables are demographic factors, product perceptions, customer service, perceived ease of use, site image, promotion, perceived consumer risk, personal characteristics and Internet communications environments. The outcome of the research is as follows: Perceived consumer risk shows a negative relationship with the repurchase intention, and all the other variables product perceptions, customer service, perceived ease of use, site image, promotion, communications environments are positively related with the repurchase intention. Also, the overall satisfaction level of customers for the Internet shopping malls positively influences repurchase intention.

Adding Chinese Captions to Images <=SEP=> This paper extends research on automated image captioning in the dimension of language, studying how to generate Chinese sentence descriptions for unlabeled images. To evaluate image captioning in this novel context, we present Flickr8k-CN, a bilingual extension of the popular Flickr8k set. The new multimedia dataset can be used to quantitatively assess the performance of Chinese captioning and English-Chinese machine translation. The possibility of re-using existing English data and models via machine translation is investigated. Our study reveals to some extent that a computer can master two distinct languages, English and Chinese, at a similar level for describing the visual world. Data is publicly available at http://tinyurl.com/flickr8kcn

MAGAN: Margin Adaptation for Generative Adversarial Networks <=SEP=> We propose a novel training procedure for Generative Adversarial Networks (GANs) to improve stability and performance by using an adaptive hinge loss objective function. We estimate the appropriate hinge loss margin with the expected energy of the target distribution, and derive both a principled criterion for updating the margin and an approximate convergence measure. The resulting training procedure is simple yet robust on a diverse set of datasets. We evaluate the proposed training procedure on the task of unsupervised image generation, noting both qualitative and quantitative performance improvements.

Efficient and fast multi-modal foreground-background segmentation using RGBD data <=SEP=> This paper addresses the problem of foreground and background segmentation. Multi-modal data specifically RGBD data has gain many tasks in computer vision recently. However, techniques for background subtraction based only on single-modality still report state-of-the-art results in many benchmarks. Succeeding the fusion of depth and color data for this task requires a robust formalization allowing at the same time higher precision and faster processing. To this end, we propose to make use of kernel density estimation technique to adapt multi-modal data. To speed up kernel density estimation, we explore the fast Gauss transform which allows the summation of a mixture of M kernel at N evaluation points in O(M+N) time as opposed to O(MN) time for a direct evaluation. Extensive experiments have been carried out on four publicly available RGBD foreground/background datasets. Results demonstrate that our proposal outperforms state-of-the-art methods for almost all of the sequences acquired in challenging indoor and outdoor contexts with a fast and non-parametric operation. c © 2017 Elsevier Ltd. All rights reserved.

Semi-Supervised Ensemble Ranking <=SEP=> Ranking plays a central role in many Web search and information retrieval applications. Ensemble ranking, sometimes called meta-search, aims to improve the retrieval performance by combining the outputs from multiple ranking algorithms. Many ensemble ranking approaches employ supervised learning techniques to learn appropriate weights for combining multiple rankers. The main shortcoming with these approaches is that the learned weights for ranking algorithms are query independent. This is suboptimal since a ranking algorithm could perform well for certain queries but poorly for others. In this paper, we propose a novel semi-supervised ensemble ranking (SSER) algorithm that learns query-dependent weights when combining multiple rankers in document retrieval. The proposed SSER algorithm is formulated as an SVM-like quadratic program (QP), and therefore can be solved efficiently by taking advantage of optimization techniques that were widely used in existing SVM solvers. We evaluated the proposed technique on a standard document retrieval testbed and observed encouraging results by comparing to a number of state-of-the-art techniques.

Comparative evaluation of text- and citation-based plagiarism detection approaches using guttenplag <=SEP=> Various approaches for plagiarism detection exist. All are based on more or less sophisticated text analysis methods such as string matching, fingerprinting or style comparison. In this paper a new approach called Citation-based Plagiarism Detection is evaluated using a doctoral thesis, in which a volunteer crowd-sourcing project called GuttenPlag identified substantial amounts of plagiarism through careful manual inspection. This new approach is able to identify similar and plagiarized documents based on the citations used in the text. It is shown that citation-based plagiarism detection performs significantly better than text-based procedures in identifying strong paraphrasing, translation and some idea plagiarism. Detection rates can be improved by combining citation-based with text-based plagiarism detection.

A 280-KBytes Twin-Bit-Cell Embedded NOR Flash Memory With a Novel Sensing Current Protection Enhanced Technique and High-Voltage Generating Systems <=SEP=> A novel sensing current protection enhanced (SCPE) technique and high-voltage (HV) generating systems are adopted to greatly enhance the advantages of the twin-bit cell. The SCPE technique provides the tradeoff of sensing margin loss between “1” and “0” bits sensing case to realize fast read access. The read speed is improved by 7.7%. HV generating systems with parallel-series-transform and capacitance-shared techniques are proposed to satisfy the complicated requirements of output capability and current drivability with lower area penalty. The HV periphery area decreases by 71%. A 1.5-V 280-KBytes embedded NOR flash memory IP has been fabricated in HHGrace (Shanghai Huahong Grace Semiconductor Manufacturing Corporation) 90-nm 4 poly 4 metal CMOS process. The complicated operation voltages and access time of 23.5 ns have been achieved at 1.5 V. The die size of the proposed IP is 0.437 mm<inline-formula> <tex-math notation="LaTeX">$^{2}$ </tex-math></inline-formula> and the area size of charge pump has been reduced to 0.006 mm<inline-formula> <tex-math notation="LaTeX">$^{2}$ </tex-math></inline-formula> on average.

Routing, scheduling and channel assignment in Wireless Mesh Networks: Optimization models and algorithms <=SEP=> Wireless Mesh networks (WMNs) can partially replace the wired backbone of traditional wireless access networks and, similarly, they require to carefully plan radio resource assignment in order to provide the same quality guarantees to traffic flows. In this paper we study the radio resource assignment optimization problem in Wireless Mesh Networks assuming a time division multiple access (TDMA) scheme, a dynamic power control able to vary emitted power slot-by-slot, and a rate adaptation mechanism that sets transmission rates according to the signalto-interference-and-noise ratio (SINR). The proposed optimization framework includes routing, scheduling and channel assignment. Quality requirements of traffic demands are expressed in terms of minimum bandwidth and modeled with constraints defining the number of information units (packets) that must be delivered per frame. We consider an alternative problem formulation where decision variables represent compatible sets of links active in the same slot and channel, called configurations. We propose a two phases solution approach where a set of configurations is first selected to meet traffic requirements along the best available paths, and then configurations are assigned to channels according to device characteristics and constraints. The optimization goal is to minimize the number of used slots, which is directly related to the global resource allocation efficiency. We provide a lower bound of the optimal solution solving the continuous relaxation of problem formulation. Moreover, we propose a heuristic approach to determine practical integer solutions (upper bound). Since configuration variables are exponentially many, our solution approaches are based on the Column Generation technique. In order to assess the effectiveness of the proposed algorithms we show the numerical results obtained on a set of realistic-size randomly generated instances.

A high performance 76.5 GHz FMCW RADAR for advanced driving assistance system <=SEP=> Frequency modulated Continuous Wave (FMCW) is continuous wave RADAR widely used for Intelligent Adaptive Cruise Control (ACC) and Collision Warning System (CWS). The paper presents a design and simulation of millimeter wave FMCW RADAR. Paper introduces a mathematical modeling of a FMCW RADAR including its target model. Design of a 76.5 GHz FMCW RADAR & its target model by using Advanced Design System (ADS) software and target range & velocity at various conditions are employed in the simulated FMCW RADAR system to verify the operation of the system.

CANAuth - A Simple, Backward Compatible Broadcast Authentication Protocol for CAN bus <=SEP=> The Controller-Area Network (CAN) bus protocol [1] is a bus protocol invented in 1986 by Robert Bosch GmbH, originally intended for automotive use. By now, the bus can be found in devices ranging from cars and trucks, over lightning setups to industrial looms. Due to its nature, it is a system very much focused on safety, i.e., reliability. Unfortunately, there is no build-in way to enforce security, such as encryption or authentication. In this paper, we investigate the problems associated with implementing a backward compatible message authentication protocol on the CAN bus. We show which constraints such a protocol has to meet and why this eliminates, to the best of our knowledge, all the authentication protocols published so far. Furthermore, we present a message authentication protocol, CANAuth, that meets all of the requirements set forth and does not violate any constraint of the CAN bus. Keywords—CAN bus, embedded networks, broadcast authentication, symmetric cryptography

Predictive State Temporal Difference Learning <=SEP=> We propose a new approach to value function approximation which combines linear temporal difference reinforcement learning with subspace identification. In practical applications, reinforcement learning (RL) is complicated by the fact that state is either high-dimensional or partially observable. Therefore, RL methods are designed to work with features of state rather than state itself, and the success or failure of learning is often determined by the suitability of the selected features. By comparison, subspace identification (SSID) methods are designed to select a feature set which preserves as much information as possible about state. In this paper we connect the two approaches, looking at the problem of reinforcement learning with a large set of features, each of which may only be marginally useful for value function approximation. We introduce a new algorithm for this situation, called Predictive State Temporal Difference (PSTD) learning. As in SSID for predictive state representations, PSTD finds a linear compression operator that projects a large set of features down to a small set that preserves the maximum amount of predictive information. As in RL, PSTD then uses a Bellman recursion to estimate a value function. We discuss the connection between PSTD and prior approaches in RL and SSID. We prove that PSTD is statistically consistent, perform several experiments that illustrate its properties, and demonstrate its potential on a difficult optimal stopping problem.

Finding social roles in Wikipedia <=SEP=> This paper investigates some of the social roles people play in the online community of Wikipedia. We start from qualitative comments posted on community oriented pages, wiki project memberships, and user talk pages in order to identify a sample of editors who represent four key roles: substantive experts, technical editors, vandal fighters, and social networkers. Patterns in edit histories and egocentric network visualizations suggest potential "structural signatures" that could be used as quantitative indicators of role adoption. Using simple metrics based on edit histories we compare two samples of Wikipedians: a collection of long term dedicated editors, and a cohort of editors from a one month window of new arrivals. According to these metrics, we find that the proportions of editor types in the new cohort are similar those observed in the sample of dedicated contributors. The number of new editors playing helpful roles in a single month's cohort nearly equal the number found in the dedicated sample. This suggests that informal socialization has the potential provide sufficient role related labor despite growth and change in Wikipedia. These results are preliminary, and we describe several ways that the method can be improved, including the expansion and refinement of role signatures and identification of other important social roles.

Active Magnetic Anomaly Detection Using Multiple Micro Aerial Vehicles <=SEP=> Magnetic anomaly detection (MAD) is an important problem in applications ranging from geological surveillance to military reconnaissance. MAD sensors detect local disturbances in the magnetic field, which can be used to detect the existence of and to estimate the position of buried, hidden, or submerged objects, such as ore deposits or mines. These sensors may experience false positive and false negative detections and, without prior knowledge of the targets, can only determine proximity to a target. The uncertainty in the sensors, coupled with a lack of knowledge of even the existence of targets, makes the estimation and control problems challenging. We utilize a hierarchical decomposition of the environment, coupled with an estimation algorithm based on random finite sets, to determine the number of and the locations of targets in the environment. The small team of robots follow the gradient of mutual information between the estimated set of targets and the future measurements, locally maximizing the rate of information gain. We present experimental results of a team of quadrotor micro aerial vehicles discovering and localizing an unknown number of permanent magnets.

Demand Side Management in Smart Grid Using Heuristic Optimization <=SEP=> Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.

Cookie Hijacking in the Wild : Security and Privacy Implications <=SEP=> The widespread demand for online privacy, also fueled by widely-publicized demonstrations of session hijacking attacks against popular websites, has spearheaded the increasing deployment of HTTPS. However, many websites still avoid ubiquitous encryption due to performance or compatibility issues. The prevailing approach in these cases is to force critical functionality and sensitive data access over encrypted connections, while allowing more innocuous functionality to be accessed over HTTP. In practice, this approach is prone to flaws that can expose sensitive information or functionality to third parties. In this work, we conduct an in-depth assessment of a diverse set of major websites and explore what functionality and information is exposed to attackers that have hijacked a user’s HTTP cookies. We identify a recurring pattern across websites with partially deployed HTTPS; service personalization inadvertently results in the exposure of private information. The separation of functionality across multiple cookies with different scopes and inter-dependencies further complicates matters, as imprecise access control renders restricted account functionality accessible to non-session cookies. Our cookie hijacking study reveals a number of severe flaws; attackers can obtain the user’s home and work address and visited websites from Google, Bing and Baidu expose the user’s complete search history, and Yahoo allows attackers to extract the contact list and send emails from the user’s account. Furthermore, e-commerce vendors such as Amazon and Ebay expose the user’s purchase history (partial and full respectively), and almost every website exposes the user’s name and email address. Ad networks like Doubleclick can also reveal pages the user has visited. To fully evaluate the practicality and extent of cookie hijacking, we explore multiple aspects of the online ecosystem, including mobile apps, browser security mechanisms, extensions and search bars. To estimate the extent of the threat, we run IRB-approved measurements on a subset of our university’s public wireless network for 30 days, and detect over 282K accounts exposing the cookies required for our hijacking attacks. We also explore how users can protect themselves and find that, while mechanisms such as the EFF’s HTTPS Everywhere extension can reduce the attack surface, HTTP cookies are still regularly exposed. The privacy implications of these attacks become even more alarming when considering how they can be used to deanonymize Tor users. Our measurements suggest that a significant portion of Tor users may currently be vulnerable to cookie hijacking.

Learning Profiles in Duplicate Question Detection <=SEP=> This paper presents the results of systematic and comparative experimentation with major types of methodologies for automatic duplicate question detection when these are applied to datasets of progressively larger sizes, thus allowing to study the learning profiles of this task under these different approaches and evaluate their merits. This study was made possible by resorting to the recent release for research purposes, by the Quora online question answering engine, of a new dataset with over 400,000 pairs labeled with respect to their elements being duplicate interrogative segments.

Neurobiology of Schizophrenia <=SEP=> With its hallucinations, delusions, thought disorder, and cognitive deficits, schizophrenia affects the most basic human processes of perception, emotion, and judgment. Evidence increasingly suggests that schizophrenia is a subtle disorder of brain development and plasticity. Genetic studies are beginning to identify proteins of candidate genetic risk factors for schizophrenia, including dysbindin, neuregulin 1, DAOA, COMT, and DISC1, and neurobiological studies of the normal and variant forms of these genes are now well justified. We suggest that DISC1 may offer especially valuable insights. Mechanistic studies of the properties of these candidate genes and their protein products should clarify the molecular, cellular, and systems-level pathogenesis of schizophrenia. This can help redefine the schizophrenia phenotype and shed light on the relationship between schizophrenia and other major psychiatric disorders. Understanding these basic pathologic processes may yield novel targets for the development of more effective treatments.