Reviewerly/exHarmony huggingface dataset:
Quantifying Privacy Vulnerability to Socialbot Attacks: An Adaptive Non-Submodular Model

Privacy breaches are one of the biggest concerns on Online Social Networks (OSNs), especially with an introduction of automated attacks by socialbots, which can automatically extract victims' private content by exploiting social behavior to befriend them. The key insight of this attack is that by intelligently sending friend requests to a small subset of users, called the Critical Friending Set (CFS), such a bot can evade current defense mechanisms. We study the vulnerability of OSNs to socialbot attacks. Specifically, we introduce a new optimization problem, Min-Friending, which identifies a minimum CFS to friend in order to obtain at least Q benefit, which quantifies the amount of private information the bot obtains. The two main challenges of this problem are how to cope with incomplete knowledge of network topology and how to model users' responses to friend requests. In this paper, we show that Min-Friending is inapproximable within a factor of (1-o(1)) ln Q and present an adaptive approximation algorithm using adaptive stochastic optimization. The key feature of our solution lies in the adaptive method, where partial network topology is revealed after each successful friend request. Thus the decision of whom to send a friend request to next is made with the outcomes of past decisions taken into account. Traditional tools break down when attempting to place a bound on the performance of this technique with realistic user models. Therefore, we additionally introduce a novel curvature-based technique to construct an approximation ratio of ln Q for a model of user behavior learned from empirical measurements on Facebook.
Robots that maintain equilibrium: Proactivity by reasoning about user intentions and preferences

Robots need to exhibit proactive behavior if they are to be accepted in human-centered environments. A proactive robot must reason about the actions it can perform, the state of the environment, the state and the intentions of its users, and what the users deem desirable. This paper proposes a computational framework for proactive robot behavior that formalizes the above ingredients. The framework is grounded on the notion of Equilibrium Maintenance: current and future states are continuously evaluated to identify opportunities for acting that steer the system into more desirable states. We show that this process leads a robot to proactively generate its own goals and enact them, and that the obtained behavior depends on a model of user intentions, preferences, and the temporal horizon used in prediction. A number of examples show that our framework accounts for even slight variations in user preference models and perceived user intentions. We also show how the level of informedness of the system is easily customizable.
A parallel computing architecture for high-performance OWL reasoning

The Web Ontology Language (OWL) is a widely used knowledge representation language for describing knowledge in application domains by using classes, properties, and individuals. Ontology classification is an important and widely used service that computes a taxonomy of all classes occurring in an ontology. It can require significant amounts of runtime, but most OWL reasoners do not support any kind of parallel processing. We present a novel thread-level parallel architecture for ontology classification, which is ideally suited for shared-memory SMP servers, but does not rely on locking techniques and thus avoids possible race conditions. We evaluated our prototype implementation with a set of real-world ontologies. Our experiments demonstrate a very good scalability resulting in a speedup that is linear to the number of available cores.
Influence of impurity on the rate of single photon superradiance and photon transport in disordered N qubit chain

We investigate the rate of superradiant emission for a number of artificial atoms (qubits) embedded in a one-dimensional open waveguide. More specifically, we study the 1D (N+1)- qubit chain where N qubits are identical with respect to their excitation frequency Ω but have different rates of spontaneous emission Γn, and a single impurity qubit which is different from N qubits by its excitation frequency ΩP and the rate of spontaneous emission ΓP. We performed exact diagonalization of Hamiltonian and showed that the system has two hybridized collective states one of which accumulates the widths of all qubits. The energy spectrum of these states and corresponding probabilities are investigated as a function of the frequency detuning between the impurity and other qubits in a chain. It is shown that the inclusion of the impurity qubit alters the resonance widths of the system if the total width overlaps the frequency detuning between qubits and the impurity. In this case the resonance widths experience a significant repulsion. The photon transmission through disordered N- qubit chain with the impurity qubit is also considered. It is shown that a single photon transport through this system is described by a simple expression which predicts for specific photon frequency the existence of a complete transmission peak and transparency window between frequencies Ω and ΩP.
Maximum <i>a posteriori</i> probability estimates for quantum tomography

Using a Bayesian methodology, we introduce the maximum a posteriori~(MAP) estimator for quantum state and process tomography. The maximum likelihood, hedged maximum likelihood, maximum likelihood-maximum entropy estimator, and estimators of this general type, can be viewed as special cases of the MAP estimator. The MAP, like the Bayes mean estimator includes prior knowledge, however for cases of interest to tomography can take advantage of convex optimization tools making it numerically easier to compute. We show how the MAP and other Bayesian quantum state estimators can be corrected for noise produced if the quantum state passes through a noisy quantum channel prior to measurement.
Continuously non-malleable codes with split-state refresh

Non-malleable codes for the split-state model allow to encode a message into two parts, such that arbitrary independent tampering on each part, and subsequent decoding of the corresponding modified codeword, yields either the same as the original message, or a completely unrelated value.Continuously non-malleable codes further allow to tolerate an unbounded (polynomial) number of tampering attempts, until a decoding error happens.The drawback is that, after an error happens, the system must self-destruct and stop working, otherwise generic attacks become possible.In this paper we propose a solution to this limitation, by leveraging a split-state refreshing procedure.Namely, whenever a decoding error happens, the two parts of an encoding can be locally refreshed (i.e., without any interaction), which allows to avoid the self-destruct mechanism in some applications.Additionally, the refreshing procedure can be exploited in order to obtain security against continual leakage attacks.We give an abstract framework for building refreshable continuously non-malleable codes in the common reference string model, and provide a concrete instantiation based on the external Diffie-Hellman assumption.Finally, we explore applications in which our notion turns out to be essential.The first application is a signature scheme tolerating an arbitrary polynomial number of split-state tampering attempts, without requiring a self-destruct capability, and in a model where refreshing of the memory happens only after an invalid output is produced.This circumvents an impossibility result from a recent work by Fuijisaki and Xagawa (Asiacrypt 2016).The second application is a compiler for tamper-resilient read-only RAM programs.In comparison to other tamper-resilient RAM compilers, ours has several advantages, among which the fact that, in some cases, it does not rely on the self-destruct feature.
Scheduling Inter-Datacenter Video Flows for Cost Efficiency

As video streaming applications are deployed on the cloud, cloud providers are charged by ISPs for inter-datacenter transfers under the dominant percentile-based charging models. In order to minimize the payment costs, existing works aim to keep the traffic on each link under the charging volume. However, these methods cannot fully utilize each link's available bandwidth capacity. As a solution, we propose an economical and deadline-driven video flow scheduling system, called EcoFlow. Considering that different video flows have different transmission deadlines, EcoFlow transmits videos in the order of their deadline tightness and postpones the deliveries of later-deadline videos to later time slots. The flows that are expected to miss their deadlines are divided into subflows to be rerouted to other under-utilized links. We also propose setting each link's initial charging volume to reduce the scheduling latency at the beginning of the charging period and discuss how to deal with issues such as the prediction errors of link available bandwidth and the lack of charging volume's prior knowledge. Furthermore, we designed implementation strategies for using EcoFlow in both centralized and distributed situations. Experimental results demonstrate that EcoFlow achieves lower bandwidth costs and higher video flow transmission rates when compared to existing methods.
An improved diagnosis technique for breast cancer using LCFS and TreeHiCARe classifier model

Purpose One of the main reasons for increase in mortality rate in woman is breast cancer. Accurate early detection of breast cancer seems to be the only solution for diagnosis. In the field of breast cancer research, many new computer-aided diagnosis systems have been developed to reduce the diagnostic test false positives because of the subtle appearance of breast cancer tissues. The purpose of this study is to develop the diagnosis technique for breast cancer using LCFS and TreeHiCARe classifier model. Design/methodology/approach The proposed diagnosis methodology initiates with the pre-processing procedure. Subsequently, feature extraction is performed. In feature extraction, the image features which preserve the characteristics of the breast tissues are extracted. Consequently, feature selection is performed by the proposed least-mean-square (LMS)-Cuckoo search feature selection (LCFS) algorithm. The feature selection from the vast range of the features extracted from the images is performed with the help of the optimal cut point provided by the LCS algorithm. Then, the image transaction database table is developed using the keywords of the training images and feature vectors. The transaction resembles the itemset and the association rules are generated from the transaction representation based on a priori algorithm with high conviction ratio and lift. After association rule generation, the proposed TreeHiCARe classifier model emanates in the diagnosis methodology. In TreeHICARe classifier, a new feature index is developed for the selection of a central feature for the decision tree centered on which the classification of images into normal or abnormal is performed. Findings The performance of the proposed method is validated over existing works using accuracy, sensitivity and specificity measures. The experimentation of proposed method on Mammographic Image Analysis Society database resulted in classification of normal and abnormal cancerous mammogram images with an accuracy of 0.8289, sensitivity of 0.9333 and specificity of 0.7273. Originality/value This paper proposes a new approach for the breast cancer diagnosis system by using mammogram images. The proposed method uses two new algorithms: LCFS and TreeHiCARe. LCFS is used to select optimal feature split points, and TreeHiCARe is the decision tree classifier model based on association rule agreements.
Using Chinese dark chess endgame databases to validate and fine-tune game evaluation functions1

Alpha–beta based game playing programs usually contain an evaluation function f to assess the value of every legal position by considering the location and material values of each piece on the board. Normally, a manually designed f is imperfect and requires a great amount of expert knowledge. Theor etically speaking, f can be a giant table which gives value for every position. However, this table has too many entries. Hence it takes huge space and is also time-consuming to construct. Note that when the number of pieces left on the board is small, say 6 for Chinese dark chess, such a giant table already exists and is called an endgame database. Endgame databases provide game-theoretical, i.e., perfect, values for all legal positions in the included endgame. Due to resource constraints, only a very selected number of endgame databases are built. Observing the strength of having perfect information on selected positions and the limitation of not being able to have information on all positions by using endgame databases to derive f, we propose a scheme to better design f. First, an f is manually designed, and then is validated and fine-tuned using abstracted critical information from existing endgame databases. In the process, we begin by validating and correcting f on positions when they are contained in endgame databases. Using this information, we then discover meta-knowledge to fine-tune and revise f so that f better evaluates a position, even if it is not in any given endgame database. The experiment results show that our approach is successful.
An investigation of strength analysis metrics for game-playing programs: A case study in Chinese dark chess

For developers of game-playing programs, it is an important but non-trivial task to measure the strengths of the programs, especially with respect to the theoretically optimal players. The optimal players are hard to obtain in practice since many real-world games are too hard to solve entirely. In this paper, a reduced version of Chinese dark chess (CDC), 2×4 CDC, which has been solved, is used as a testbed to analyze the strengths of game-playing programs for CDC, a non-deterministic game. Absolute strength is then defined to be the win rates against the optimal player. Experiment results show that win rates against designated baseline programs are a good metric, since these win rates are highly correlated to absolute strengths. The stronger the designated baseline programs are, the higher the correlations of win rates are to the absolute strengths. In addition, it is shown that prediction rates and mean squared errors are other good metrics and have high correlations to the absolute strengths. The experiment results also show that the win rates obtained in 2×4 CDC have a high correlation to those in the original 4×8 CDC, except for those using the technique of progressive bias.
MR-I MaxMin-scalable two-phase border based knowledge hiding technique using MapReduce

Border based Knowledge hiding techniques (BB-KHT) are widely adopted form of privacy preservation techniques of data mining. These approaches are used to hide sensitive knowledge (confidential information) present in a dataset before sharing or analyzing it. BB-KHT primarily rely on border theory and maximum criterion method for preserving privacy and perpetuating good data quality of sanitized dataset but costs high computational complexity. Further, due to sequential nature, these approaches are particularly felicitous for small datasets and become infeasible while dealing with large scale datasets. Therefore, to subjugate the identified challenges of infeasibility and high computational complexity, a scalable two-phase improved MaxMin BB-KHT using MapReduce framework (MR-I MaxMin) is proposed. The proposed scheme requires only two database scans throughout the hiding process and hence, is computationally inexpensive. Moreover, the scheme also commits to preserve good data quality of sanitized dataset. The MapReduce version of proposed approach helps in achieving the feasibility by processing large voluminous data in a parallel fashion. Quantitative experiments and evaluations have been performed over a number of real and synthetically generated large-scale datasets. It is shown that the proposed MR-I MaxMin technique outperforms the similar existing approaches and vanquishes the identified challenges along with much-needed privacy preservation.
Determination of 3D curve skeleton of a digital object

The 3D curve skeleton of a digital object, constructed as a connected set of voxels spanning its interior, captures an approximate but truthful representation of the object shape. An efficient and robust technique for extraction of the 3D curve skeleton of a digital object is presented in this paper. The 3D isothetic inner cover of the object, obtained in a preprocessing step, is utilized to design a combinatorial algorithm in the orthogonal domain that also provides further results in the general domain. The voxels representing the surface of the inner cover are represented in a topological space. They are completely enclosed by the inner cover that satisfies certain conditions expressed in another topological space. The homotopy equivalence of topological spaces is used to report the 3D curve skeleton. Also, the homology groups of the topological spaces are proved to be isomorphic. The resulting skeleton is connected, single voxel thick, and centered with respect to the object volume. It further preserves the topology of the inner cover and may be modified to preserve the object topology in most of the cases. The accuracy of shape representation by the skeleton can be controlled based on the requirement. Experimental results on a wide range of objects demonstrate the efficacy and robustness of the method.
SynBF: A New Bilateral Filter for Postremoval of Noise From Synthesis Views in 3-D Video

In 3-D video systems, noise in the texture and depth videos of reference views may not be removed (Scenario 1) or not fully removed (Scenario 2) by prefiltering methods before the view synthesis procedure. In these scenarios, the noise is transferred to the generated synthesis view. After investigating the noise model of the synthesis view, we conclude that the noise in the synthesis view not only causes fluctuation in the photometric values of pixels in the range domain but also additionally shifts the positions of neighbor pixels in the spatial domain compared to that in natural images. It consequently damages the textural content near edges in the synthesis view, for which the popular local filters of natural images, that is, the bilateral filter (BF) and the guided filter, do not work well. In this paper, we develop a new local filter for the synthesis view (named SynBF) after it has been generated, which has a similar expression as that of the BF but not the exact same weight terms. On one hand, the spatial term in the classical BF is directly reused due to its robustness to noise, which gives high weights to spatially closed pixels of to-be-filtered pixels. On the other hand, a reliability term is designed that gives high weights to pixels that are unlikely to be affected by noise. It is inspired by the finding that not all pixels are significantly affected by noise in the synthesis view. In this way, true edge profiles are protected in the filtering process. Experiments are conducted on a set of synthesis views for both scenarios above and compared to the two local filters, which verifies its effectiveness in removing noise and protecting edge profiles. The proposed method can be considered as a supplement to prefiltering methods of texture/depth videos in 3-D video systems.
Detecting proper nouns in indonesian-language translation of the quran using a guided method

Proper nouns (often abbreviated PN or NNP) are a class of words important in labelling and subsequent text processing, especially in natural language processing (NLP). Name entity recognition (NER) is one study that requires PN. The lack of labeled data for Indonesian text, especially the PN label, may be attributed for the lack of NER research in the Indonesian language. This study aims to detect PN in Indonesian-language translations of the Quran guided by deriving location information from the Quran as its source text. In the Indonesian language, PN are written using initial capital letters, which are used to determine and guide PN location. This article proposes that PN in Indonesian-language translations of the Quran can be determined based on PoS Information of Quranic text by developing a certain production rule. The results of this research showed that the proposed method has promising results for further research.
Photo2Silhouette: Transferring silhouette photo style for images

Silhouette photos are attractive visual artworks. We propose a photo style transfer approach that can generate realistic silhouette images. The motivation for this approach is to help ordinary users to gain silhouette photos which are difficult to shoot because of the limitations of cameras, weather and skills. Our approach consists of two major steps. The first step is an automatic sky segmentation to divide an input image into an object foreground and a sky background. The core step is the second one, in which the colors of the foreground and background are tuned according to the characteristics of silhouette photos respectively. Specifically, a statistic color transfer method is applied to the sky background using a perfect sky photo taken at dusk or dawn, and an adaptive color correction approach is further presented for the foreground to ensure a harmonious aesthetic effect. Extensive experiments show that our method can achieve satisfactory sky segmentation results and generate aesthetically pleasing silhouette photos.
Distinguishing unitary gates on the IBM quantum processor

An unknown unitary gate, which is secretly chosen from several known ones, can always be distinguished perfectly. In this paper, we implement such a task on IBM’s quantum processor. More precisely, we experimentally demonstrate the discrimination of two qubit unitary gates, the identity gate and the ${2 \over 3}\pi$ -phase shift gate, using two discrimination schemes — the parallel scheme and the sequential scheme. We program these two schemes on the ibmqx4, a 5-qubit superconducting quantum processor via IBM cloud, with the help of the QSI modules. We report that both discrimination schemes achieve success probabilities at least 85%.
The Similarity of Causal Structure

Whether y obtains under the counterfactual supposition that x is thought to depend on whether y obtains in the most similar world(s) in which x obtains. Graphical causal models have proved useful in developing a principled notion of similarity between worlds, but this notion is limited insofar as it does not apply to counterfactual suppositions about causal structure. Here, we explore the possibility of filling this lacuna by introducing a notion of similarity between causal graphs. Since there are multiple principled senses in which graphs can be similar, we introduce multiple similarity metrics and multiple ways to prioritize these metrics.
ENG: End-to-End Neural Geometry for Robust Depth and Pose Estimation Using CNNs

Recovering structure and motion parameters given a image pair or a sequence of images is a well studied problem in computer vision. This is often achieved by employing Structure from Motion (SfM) or Simultaneous Localization and Mapping (SLAM) algorithms based on the real-time requirements. Recently, with the advent of Convolutional Neural Networks (CNNs) researchers have explored the possibility of using machine learning techniques to reconstruct the 3D structure of a scene and jointly predict the camera pose. In this work, we present a framework that achieves state-of-the-art performance on single image depth prediction for both indoor and outdoor scenes. The depth prediction system is then extended to predict optical flow and ultimately the camera pose and trained end-to-end. Our framework outperforms previous deep-learning based motion prediction approaches, and we also demonstrate that the state-of-the-art metric depths can be further improved using the knowledge of pose.