Reviewerly/exHarmony huggingface dataset:
A Topic-Aware Reinforced Model for Weakly Supervised Stance Detection

Analyzing public attitudes plays an important role in opinion mining systems. Stance detection aims to determine from a text whether its author is in favor of, against, or neutral towards a given target. One challenge of this task is that a text may not explicitly express an attitude towards the target, but existing approaches utilize target content alone to build models. Moreover, although weakly supervised approaches have been proposed to ease the burden of manually annotating largescale training data, such approaches are confronted with noisy labeling problem. To address the above two issues, in this paper, we propose a Topic-Aware Reinforced Model (TARM) for weakly supervised stance detection. Our model consists of two complementary components: (1) a detection network that incorporates target-related topic information into representation learning for identifying stance effectively; (2) a policy network that learns to eliminate noisy instances from auto-labeled data based on off-policy reinforcement learning. Two networks are alternately optimized to improve each other’s performances. Experimental results demonstrate that our proposed model TARM outperforms the state-of-the-art approaches.
Preservation of semantic properties in collective argumentation: The case of aggregating abstract argumentation frameworks

An abstract argumentation framework can be used to model the argumentative stance of an agent at a high level of abstraction, by indicating for every pair of arguments that is being considered in a debate whether the first attacks the second.When modelling a group of agents engaged in such a debate, we may wish to aggregate their individual argumentation frameworks to obtain a single such framework that reflects the consensus of the group.While agents typically will not agree on every single attack, there may well be high-level agreement on semantic properties, such as whether a given argument should be accepted or whether there are any acceptable arguments at all.Using techniques from social choice theory, we analyse the circumstances under which such semantic properties agreed upon by the individual agents will be preserved under aggregation.Our results cover semantic properties formulated in terms of six of the most widely used extensionbased semantics for abstract argumentation and range from positive results that show that certain aggregation rules can provide the desired preservation guarantees to impossibility theorems that show that certain combinations of requirements cannot be met by any reasonable aggregation rule.
The history of cancer screening

One of the main challenges in breast cancer risk assessment is to provide a patient with an easily interpretable perspective on her disease's situation. This paper proposes a new method for accessing breast cancer risk, called Hybrid High-order Type-2 Fuzzy Cognitive Map Improved Random Forest Classification (H-HT2 FCM IRFC, in which, by taking an exact analysis, the disease risk is qualitatively obtained in three modes, i.e., each optimistic, realistic and pessimistic. Using either FCM or high-order FCM does not make a favorable response in uncertain situations where applying type-2 fuzzy to obtain the weights of FCM will have much better answers. A hybrid version of high-order type-2 FCM, proposed in this work, enables us to assess the breast cancer risk in three modes of optimistic, realistic, and pessimistic. The proposed method has three levels; at the first level, patient profile, family history, and inherited factors are tested by high-order FCM. At the second level, by examining the mass characteristics obtained from the mammograms, the disease risk is achieved by hybrid high-order type-2 FCM in three modes of optimistic, realistic, and pessimistic. The position of the tumor's effect on breast cancer is obtained in the third level by the fuzzy method. Finally, an overall breast cancer risk is predicted by a new algorithm, called Improved Random Forest Classification, which results in superior performance. Compared with the existing methods, the accuracy of the results obtained from the proposed method is desirable. The three-mode assessment will help the patients and their physician (oncologist) run the best treatment. Finally, the proposed method is successfully tested on an actual medical dataset.
Off-page SEO and link building: General strategies and authority transfer in the digital news media

In recent years, a number of digital news media outlets have begun to include paid links in their content. This study seeks to identify and analyse this content whose sole purpose is to improve the website authority of the advertisers and their search engine rankings. To do so, it employs two basic methodologies: first, it undertakes a systematic review of off-page SEO practices, the digital press and native advertising; and, second, it reports a case study based on the identification and analysis of 150 news items that contain specially commissioned links resulting from a commercial transaction. The study provides evidence of a new revenue stream for the digital news media, one that is not clearly disclosed and which is based on the sale of links. The article includes a discussion of the case study findings, and presents future guidelines for the use of paid links based on the emerging concept of ‘native advertising’.
On the Capabilities and Limitations of Reasoning for Natural Language Understanding.

Recent systems for natural language understanding are strong at overcoming linguistic variability for lookup style reasoning. Yet, their accuracy drops dramatically as the number of reasoning steps increases. We present the first formal framework to study such empirical observations, addressing the ambiguity, redundancy, incompleteness, and inaccuracy that the use of language introduces when representing a hidden conceptual space. Our formal model uses two interrelated spaces: a conceptual meaning space that is unambiguous and complete but hidden, and a linguistic symbol space that captures a noisy grounding of the meaning space in the symbols or words of a language. We apply this framework to study the connectivity problem in undirected graphs---a core reasoning problem that forms the basis for more complex multi-hop reasoning. We show that it is indeed possible to construct a high-quality algorithm for detecting connectivity in the (latent) meaning graph, based on an observed noisy symbol graph, as long as the noise is below our quantified noise level and only a few hops are needed. On the other hand, we also prove an impossibility result: if a query requires a large number (specifically, logarithmic in the size of the meaning graph) of hops, no reasoning system operating over the symbol graph is likely to recover any useful property of the meaning graph. This highlights a fundamental barrier for a class of reasoning problems and systems, and suggests the need to limit the distance between the two spaces, rather than investing in multi-hop reasoning with many hops.
Defense Methods Against Adversarial Examples for Recurrent Neural Networks

Adversarial examples are known to mislead deep learning models to incorrectly classify them, even in domains where such models achieve state-of-the-art performance. Until recently, research on both attack and defense methods focused on image recognition, primarily using convolutional neural networks (CNNs). In recent years, adversarial example generation methods for recurrent neural networks (RNNs) have been published, demonstrating that RNN classifiers are also vulnerable to such attacks. In this paper, we present a novel defense method, termed sequence squeezing, to make RNN classifiers more robust against such attacks. Our method differs from previous defense methods which were designed only for non-sequence based models. We also implement four additional RNN defense methods inspired by recently published CNN defense methods. We evaluate our methods against state-of-the-art attacks in the cyber security domain where real adversaries (malware developers) exist, but our methods can be applied against other discrete sequence based adversarial attacks, e.g., in the NLP domain. Using our methods we were able to decrease the effectiveness of such attack from 99.9% to 15%.
Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise

In this paper, we propose a unified view of gradient-based algorithms for stochastic convex composite optimization by extending the concept of estimate sequence introduced by Nesterov. More precisely, we interpret a large class of stochastic optimization methods as procedures that iteratively minimize a surrogate of the objective, which covers the stochastic gradient descent method and variants of the incremental approaches SAGA, SVRG, and MISO/Finito/SDCA. This point of view has several advantages: (i) we provide a simple generic proof of convergence for all of the aforementioned methods; (ii) we naturally obtain new algorithms with the same guarantees; (iii) we derive generic strategies to make these algorithms robust to stochastic noise, which is useful when data is corrupted by small random perturbations. Finally, we propose a new accelerated stochastic gradient descent algorithm and an accelerated SVRG algorithm with optimal complexity that is robust to stochastic noise.
Extending 2D Deep Learning Architectures to 3D Image Segmentation Problems

Several deep learning architectures are combined for brain tumor segmentation. All the architectures are inspired on recent 2D models where 2D convolution have been replaced by 3D convolutions. The key differences between the architectures are the size of the receptive field and the number of feature maps on the final layers. The obtained results are comparable to the top methods of previous Brats Challenges when median is use to average the results. Further investigation is still needed to analyze the outlier patients.
Pulsed Quantum-State Reconstruction of Dark Systems

We propose a novel strategy to reconstruct the quantum state of dark systems, i.e., degrees of freedom that are not directly accessible for measurement or control. Our scheme relies on the quantum control of a two-level probe that exerts a state-dependent potential on the dark system. Using a sequence of control pulses applied to the probe makes it possible to tailor the information one can obtain and, for example, allows us to reconstruct the density operator of a dark spin as well as the Wigner characteristic function of a harmonic oscillator. Because of the symmetry of the applied pulse sequence, this scheme is robust against slow noise on the probe. The proof-of-principle experiments are readily feasible in solid-state spins and trapped ions.
Earth scientists plan a ‘geological Google’

China is backing an international effort to meld geoscience databases into a one-stop data shop.
Histogram Shape-Based Scene-Change Detection Algorithm

This paper proposes histogram shape-based scene-change detection to automatically recognize the changing content in a moving image for frame rate up-conversion; in the frame rate up-conversion, it is important to detect both local and global scene changes. In addition, a threshold value should be fixed regardless of the image characteristics that requires low computation. The proposed algorithm simply extracts the shape of a two-dimensional histogram using point-based distance calculation. It also uses block merging and blocks smoothing to further improve the accuracy of the scene change detection. In the experimental results, the proposed algorithm enhanced the average F1 score to 0.607 (a 189.63% improvement) as compared with the benchmark methods. The average computation time of the proposed algorithm also decreased to 1.696 ms (a 65.81% reduction) compared with the benchmark algorithms.
Completeness of Graphical Languages for Mixed States Quantum Mechanics

There exist several graphical languages for quantum information processing, like quantum circuits, ZX-Calculus, ZW-Calculus, etc. Each of these languages forms a †-symmetric monoidal category (†-SMC) and comes with an interpretation functor to the †-SMC of (finite dimension) Hilbert spaces. In the recent years, one of the main achievements of the categorical approach to quantum mechanics has been to provide several equational theories for most of these graphical languages, making them complete for various fragments of pure quantum mechanics. We address the question of the extension of these languages beyond pure quantum mechanics, in order to reason on mixed states and general quantum operations, i.e. completely positive maps. Intuitively, such an extension relies on the axiomatisation of a discard map which allows one to get rid of a quantum system, operation which is not allowed in pure quantum mechanics. We introduce a new construction, the discard construction, which transforms any †-symmetric monoidal category into a symmetric monoidal category equipped with a discard map. Roughly speaking this construction consists in making any isometry causal. Using this construction we provide an extension for several graphical languages that we prove to be complete for general quantum operations. However this construction fails for some fringe cases like the Clifford+T quantum mechanics, as the category does not have enough isometries.
Scaling and crossover behaviour in a truncated long range quantum walk

We consider a discrete time quantum walker in one dimension, where at each step, the step length ℓ is chosen from a distribution P(ℓ)∝ℓ−δ−1 with ℓ≤ℓmax. We evaluate the probability f(x,t) that the walker is at position x at time t and its first two moments. As expected, the disorder effectively localizes the walk even for large values of δ. Asymptotically, 〈x2〉∝t3∕2 and 〈x〉∝t1∕2 independent of δ and ℓ, both finite. The scaled distribution f(x,t)t1∕2 plotted versus x∕t1∕2 shows a data collapse for x∕t<α(δ,ℓmax)∼O(1) indicating the existence of a universal scaling function. The scaling function is shown to have a crossover behaviour at δ=δ∗≈4.0 beyond which the results are independent of ℓmax. We also calculate the von Neumann entropy of entanglement which gives a larger asymptotic value compared to the quantum walk with unique step length even for large δ, with negligible dependence on the initial condition.
Quantum leakage detection using a model-independent dimension witness

Users of quantum computers must be able to confirm they are functioning as intended, even when they are remotely accessed. In particular, if the Hilbert space dimension of the quantum computer components is not as advertised---for instance, if they suffer leakage---errors can ensue and protocols may be rendered insecure. We refine the method of delayed vectors, adapted from classical chaos theory to quantum systems, and apply it remotely on the IBMQ platform---a quantum computer composed of transmon qubits. The method witnesses, in a model-independent fashion, dynamical signatures of higher-dimensional processes. We present evidence, under mild assumptions, that the IBMQ transmons suffer state leakage, with a $p$ value no larger than $5\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}4}$ under a single-qubit operation. We also estimate the number of shots necessary for revealing leakage in a two-qubit system.
Mental landscapes

This forum highlights conversations at the intersection of design methods and social studies of technology. By highlighting a diversity of perspectives on design interventions and programs, we aim to forge new connections between HCI design and communication, science and technology studies, and media studies scholarship. --- Daniela Rosner, Editor
Adaptive Generalized Gaussian Distribution Oriented Thresholding Function for Image De-Noising

In this paper, an Adaptive Generalized Gaussian Distribution (AGGD) oriented thresholding function for image de-noising is proposed.This technique utilizes a unique threshold function derived from the generalized Gaussian function obtained from the HH sub-band in the wavelet domain.Twodimensional discrete wavelet transform is used to generate the decomposition.Having the threshold function formed by using the distribution of the high frequency wavelet HH coefficients makes the function data dependent, hence adaptive to the input image to be de-noised.Thresholding is performed in the high frequency sub-bands of the wavelet transform in the interval [-t, t], where t is calculated in terms of the standard deviation of the coefficients in the HH sub-band.After thresholding, inverse wavelet transform is applied to generate the final de-noised image.Experimental results show the superiority of the proposed technique over other alternative state-of-the-art methods in the literature.
Towards Everlasting Privacy and Efficient Coercion Resistance in Remote Electronic Voting

In this work, we propose a first version of an e-voting scheme that achieves end-to-end verifiability, everlasting privacy and efficient coercion resistance in the JCJ setting. Everlasting privacy is achieved assuming an anonymous channel, without resorting to dedicated channels between the election authorities to exchange private data. In addition, the proposed scheme achieves coercion resistance under standard JCJ assumptions. As a core building block of our scheme, we also propose a new primitive called publicly auditable conditional blind signature (PACBS), where a client receives a token from the signing server after interaction; the token is a valid signature only if a certain condition holds and the validity of the signature can only be checked by a designated verifier. We utilize this primitive to blindly mark votes under coercion in an auditable manner.
Change Detection in SAR Images Based on the ROF Model Semi-Implicit Denoising Method

The explicit solution of the traditional ROF model in image denoising has the disadvantages of unstable results and requiring many iterations. To solve the problem, a new method, ROF model semi-implicit denoising, is proposed in this paper and applied to change detections of synthetic aperture radar (SAR) images. All remote sensing images used in this article have been calibrated by ENVI software. First, the ROF model semi-implicit denoising method is used to denoise the remote sensing images. Second, for the denoised images, difference images are obtained by the logarithmic ratio and mean ratio methods. The final difference image is obtained by principal component analysis fusion (PCA fusion) of the two difference images. Finally, the final difference image is clustered by fuzzy local information C-means clustering (FLICM) to obtain the change regions. The research results show that the proposed method has high detection accuracy and time operation efficiency.
Generation of ice states through deep reinforcement learning

We present a deep reinforcement learning framework where a machine agent is trained to search for a policy to generate a ground state for the square ice model by exploring the physical environment. After training, the agent is capable of proposing a sequence of local moves to achieve the goal. Analysis of the trained policy and the state value function indicates that the ice rule and loop-closing condition are learned without prior knowledge. We test the trained policy as a sampler in the Markov chain Monte Carlo and benchmark against the baseline loop algorithm. This framework can be generalized to other models with topological constraints where generation of constraint-preserving states is difficult.
Mallows and generalized Mallows model for matchings

The Mallows and Generalized Mallows Models are two of the most popular probability models for distributions on permutations.In this paper, we consider both models under the Hamming distance.This models can be seen as models for matchings instead of models for rankings.These models cannot be factorized, which contrasts with the popular MM and GMM under Kendall's-τ and Cayley distances.In order to overcome the computational issues that the models involve, we introduce a novel method for computing the partition function.By adapting this method we can compute the expectation, joint and conditional probabilities.All these methods are the basis for three sampling algorithms, which we propose and analyze.Moreover, we also propose a learning algorithm.All the algorithms are analyzed both theoretically and empirically, using synthetic and real data from the context of e-learning and Massive Open Online Courses (MOOC).
Trends in Malware Attacks against United States Healthcare Organizations, 2016-2017

Introduction: The healthcare industry has begun seeing a new hazard develop against them- the threat of cyberattack. Beginning in 2016, healthcare organizations in the United States have been targeted for malware attacks, a specific type of cyberattack. During malware incidents hackers can lock users out of their own network to gain access to information or to hold the organization for ransom. With the increase in medical technology and the need for access to this information to provide critical care, this type of incident has the potential to put patient lives and safety at risk. Methods: A content analysis was conducted to assess the trend of attacks on healthcare organizations. U.S. Healthcare IT News and Becker's Hospital Review were used to collect all publicly reported malware attacks against U.S. healthcare organizations between 2016 and 2017. A fault-tree diagram was also developed to illustrate how hackers gain access to a healthcare network using malware. Results: There were 49 cases of malware attacks against U.S. HCOs identified. The attacks occurred across 27 states, and they took place during 18 out of 24 months. Six of the organizations reported paying ransom, whereas 43 organizations did not pay or did not report payment to the press. Impacts of these attacks range from network downtime to patient and staff records being breached. Discussion: Malware attacks have the potential to impact care delivery as well as the healthcare facility itself. Even though this study identified 49 malware attacks, we know this number is significantly higher based on data from HIMSS and the FBI. A reporting loophole exists in that hospitals are only required to report attacks in the case of breached protected health or financial data. For HCOs to fully understand the risk cyberthreats pose, it is important for attacks to become public information and for lessons learned to be shared. Future research reviewing identified attacks could help identify best practices for the healthcare industry to better prepare for cyberattacks.
Formation Optimization and Control for Maneuvering Target Tracking by Mobile Sensing Agents

This paper presents a geometric formation control strategy of multiple sensing agents for maneuvering target tracking, which ensures agents to track the target with optimal target state estimation. Three sub-problems are solved for achieving the target tracking. First, an IMMCEKF algorithm is proposed to estimate the maneuvering target state by fusing multiple bearings-only measurements. This algorithm combines the interacting multiple-model estimator and the extended Kalman filter-based augmented measurement fusion algorithm, which is a centralized filter. Second, the algorithm of constructing optimal configuration for target tracking is proved and verified by maximizing the determinant of the Fisher information matrix. Third, we transform the problem of target tracking with optimal configuration into the problem of formation control. A geometric formation control approach based on Jacobi vectors is proposed. The formation shape controller and the formation tracking controller are decoupled because we use the formation center to describe the formation motion which is not relative to the formation shape described by the Jacobi vectors. The simulation results show that multiple sensing agents can track the moving target with optimal configuration such that the estimation error is obviously reduced.