Reviewerly/exHarmony huggingface dataset:
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning

Effective coordination is crucial to solve multi-agent collaborative (MAC) problems. While centralized reinforcement learning methods can optimally solve small MAC instances, they do not scale to large problems and they fail to generalize to scenarios different from those seen during training. In this paper, we consider MAC problems with some intrinsic notion of locality (e.g., geographic proximity) such that interactions between agents and tasks are locally limited. By leveraging this property, we introduce a novel structured prediction approach to assign agents to tasks. At each step, the assignment is obtained by solving a centralized optimization problem (the inference procedure) whose objective function is parameterized by a learned scoring model. We propose different combinations of inference procedures and scoring models able to represent coordination patterns of increasing complexity. The resulting assignment policy can be efficiently learned on small problem instances and readily reused in problems with more agents and tasks (i.e., zero-shot generalization). We report experimental results on a toy search and rescue problem and on several target selection scenarios in StarCraft: Brood War, in which our model significantly outperforms strong rule-based baselines on instances with 5 times more agents and tasks than those seen during training.
Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration

Algorithm configuration methods optimize the performance of a parameterized heuristic algorithm on a given distribution of problem instances. Recent work introduced an algorithm configuration procedure (``Structured Procrastination'') that provably achieves near optimal performance with high probability and with nearly minimal runtime in the worst case. It also offers an anytime property: it keeps tightening its optimality guarantees the longer it is run. Unfortunately, Structured Procrastination is not adaptive to characteristics of the parameterized algorithm: it treats every input like the worst case. Follow-up work (``LeapsAndBounds'') achieves adaptivity but trades away the anytime property. This paper introduces a new algorithm, ``Structured Procrastination with Confidence'', that preserves the near-optimality and anytime properties of Structured Procrastination while adding adaptivity. In particular, the new algorithm will perform dramatically faster in settings where many algorithm configurations perform poorly. We show empirically both that such settings arise frequently in practice and that the anytime property is useful for finding good configurations quickly.
Burst Ranking for Blind Multi-Image Deblurring

We propose a new incremental aggregation algorithm for multi-image deblurring with automatic image selection. The primary motivation is that current burst deblurring methods do not handle well situations in which misalignment or out-of-context frames are present in the burst. These real-life situations result in poor reconstructions or manual selection of the images that are used to deblur. Automatically selecting the best frames within the burst to improve the base reconstruction is challenging because the number of possible images fusions is equal to the power set cardinal. Here, we approach the multi-image deblurring problem as a two steps process. First, we successfully learn a comparison function to rank a burst of images using a deep convolutional neural network. Then, an incremental Fourier burst accumulation with a reconstruction degradation mechanism is applied fusing only less blurred images that are sufficient to maximize the reconstruction quality. Experiments with the proposed algorithm have shown superior results when compared to other similar approaches, outperforming other methods described in the literature in previously described situations. We validate our findings on several synthetic and real datasets.
Tunneling in projective quantum Monte Carlo simulations with guiding wave functions

Quantum tunneling is a valuable resource exploited by quantum annealers to solve complex optimization problems. Tunneling events also occur during projective quantum Monte Carlo (PQMC) simulations, and in a class of problems characterized by a double-well energy landscape their rate was found to scale linearly with the first energy gap, i.e., even more favorably than in physical quantum annealers, where the rate scales with the gap squared. Here we investigate how a guiding wave function---which is essential to make many-body PQMC simulations computationally feasible---affects the tunneling rate. The chosen test beds are a continuous-space double-well problem, the ferromagnetic quantum Ising chain, and the recently introduced shamrock model. As guiding wave function, we consider an approximate Boltzmann-type ansatz, the numerically exact ground state of the double-well model, and a neural-network wave function based on a Boltzmann machine. Remarkably, for each ansatz we find the same asymptotic linear scaling of the tunneling rate that was previously found in the PQMC simulations performed without a guiding wave function. We also provide a semiclassical theory for the double-well with exact guiding wave function that explains the observed linear scaling. These findings suggest that PQMC simulations guided by an accurate ansatz represent a valuable benchmark for physical quantum annealers and a potentially competitive quantum-inspired optimization technique.
Learning in Text Streams: Discovery and Disambiguation of Entity and Relation Instances

We consider a scenario where an artificial agent is reading a stream of text composed of a set of narrations, and it is informed about the identity of some of the individuals that are mentioned in the text portion that is currently being read. The agent is expected to learn to follow the narrations, thus disambiguating mentions and discovering new individuals. We focus on the case in which individuals are entities and relations, and we propose an end-to-end trainable memory network that learns to discover and disambiguate them in an online manner, performing one-shot learning, and dealing with a small number of sparse supervisions. Our system builds a not-given-in-advance knowledge base, and it improves its skills while reading unsupervised text. The model deals with abrupt changes in the narration, taking into account their effects when resolving co-references. We showcase the strong disambiguation and discovery skills of our model on a corpus of Wikipedia documents and on a newly introduced dataset, that we make publicly available.
Hybrid Active Contours Driven by Edge and Region Fitting Energies Based on p-Laplace Equation

By using local and global image information, a novel active contour method based on the p-Laplace equation for image segmentation is proposed in this paper. The force term in the evolution equation incorporates global, local and edge information of the image. The global and local terms drag the contour towards object boundaries precisely and take care of the contour movement when it is away from the object. Meanwhile, the integration of edge stopping function in this force term ensures the stoppage of contour at object boundaries to avoid boundary leakage problem. The variable exponent p-Laplace energy is used for smoothness of the level set to detect the exact object boundaries in the presence of complex topological changes and deep depression. Finally, the adaptive force and p-Laplace energy term are jointly integrated into a level set by using a simple finite difference scheme to build the final evolution equation for the method. The proposed method has strong capability to accurately segment the images having noise, intensity inhomogeneity, and complex object boundaries. Moreover, the proposed method overcomes the problem arise from contour initialization as the evolution of contour is independent of the initialization of level set function. Experimental results on synthetic, real and medical images along with two publicly available databases validate the robustness and effectiveness of the proposed method.
A New Date-Balanced Method Based on Adaptive Asymmetric and Diversity Regularization in Person Re-Identification

Person re-identification (person re-ID) is a challenging task which aims at spotting same persons among disjoint camera views. It has certainly generated a lot of attention in the field of computer vision, but it remains a challenging task due to the complexity of person appearances from different camera views. To solve this challenging problem, many excellent methods have been proposed, especially metric learning-based algorithms. However, most of them suffer from the problem of data imbalance. To solve this problem, in the paper we proposed a new data-balanced method and named it Enhanced Metric Learning (EML) based on adaptive asymmetric and diversity regularization for person re-ID. Metric learning is important for person re-ID because it can eliminate the negative effects caused by camera differences to a certain extent. But most metric learning approaches often neglect the problem of data imbalance caused by too many negative samples but few positive samples. And they often treat all negative samples the same as positive ones, which can lead to the loss of important information. Our approach pays different attention to the positive samples and negative ones. Firstly, we classified negative samples into three groups adaptively, and then paid different attention to them using adaptive asymmetric strategy. By treating samples differently, the proposed method can better exploit the discriminative information between positive and negative samples. Furthermore, we also proposed to impose a diversity regularizer to avoid over-fitting when the training sets are small or medium-sized. Finally, we designed a series of experiments on four challenging databases (VIPeR, PRID450S, CUHK01 and GRID), to compare with some excellent metric learning methods. Experimental results show that the rank-1 matching rate of the proposed method has outperformed the state-of-the-art by 3.64%, 4.2%, 3.13% and 2.83% on the four databases, respectively.
Semi-Supervised Human Detection via Region Proposal Networks Aided by Verification

In this paper, we explore how to leverage readily available unlabeled data to improve semi-supervised human detection performance. For this purpose, we specifically modify the region proposal network (RPN) for learning on a partially labeled dataset. Based on commonly observed false positive types, a verification module is developed to assess foreground human objects in the candidate regions to provide an important cue for filtering the RPN's proposals. The remaining proposals with high confidence scores are then used as pseudo annotations for re-training our detection model. To reduce the risk of error propagation in the training process, we adopt a self-paced training strategy to progressively include more pseudo annotations generated by the previous model over multiple training rounds. The resulting detector re-trained on the augmented data can be expected to have better detection performance. The effectiveness of the main components of this framework is verified through extensive experiments, and the proposed approach achieves state-of-the-art detection results on multiple scene-specific human detection benchmarks in the semi-supervised setting.
Patch Based Texture Classification of Thyroid Ultrasound Images using Convolutional Neural Network

Ultrasound (US) is an affordable and important imaging modality in medical imaging without potential hazards for patients and medical practitioners as compared to computed tomography which uses X-rays, magnetic resonance imaging which uses magnetic field and radio waves that could heat up the patient's body during long examinations, nuclear imaging, etc. Texture classification of anatomical structures in US images is an essential step for disease diagnosis and monitoring. In this work, we employed a convolutional neural network to segment thyroid gland in US images. This is particularly important for thyroid diseases diagnosis as they involve changes in the shape and size of the thyroid over time. The training of the Convolutional Neural Network (CNN) was not done directly on the acquired US images but on texture database that is created by dividing the thyroid US images of size 760 × 500 pixels into smaller texture patches of size 20 × 20 pixels. We obtained a Dice coefficient (DC) of 0.876 and Hausdorff Distance (HD) of 7.3 using the trained CNN that classifies the thyroid tissues as thyroid or non-thyroid. This approach was compared to the classic image processing approaches like active contours with edges (ACWE), graph cut (GC) and pixel-based classifier (PBC) which obtained a DC of 0.805, 0.745 and 0.666 respectively and Volumetric and Mass-Spring Models which obtained a HD of 11.1 and 9.8 respectively.
Designated Server Certificateless Deniably Authenticated Encryption With Keyword Search

In email system, the cryptography technology has been used to defend email secrets, so it is important to search specific encrypted emails on cloud sever without local decryption. The Public key encryption with keyword search (PEKS) might is a suitable way to perform the email ciphertext search. However, most existing PEKS schemes cannot protect the identity privacy of data sender. Deniably authenticated encryption (DAE) technique allows the data sender to deny his/her involvement after the communication. Moreover, the receiver can verify the authenticity of ciphertext in DAE, which assures the identity privacy of data sender. In this paper, so as to solve the above shortages in existing PEKS schemes, we introduce an original scheme called designated server certificateless deniably authenticated encryption with keyword search (dCLDAEKS), where leverages the techniques of DAE and designated server. In dCLDAEKS, data sender authenticates the messages and simultaneously encrypt them. Meanwhile, only designated server has ability to execute search ciphertext operation for receivers. So there is no adversary including the server can launch inside or outside offline KGA. Therefore dCLDAEKS scheme can better protect the identity privacy of data sender. In addition, compared the related schemes in the literature, dCLDAEKS scheme perform less efficient in some procedure, but it can against inside KGA and better protect the sender's identity privacy.
Minimal Solvers for Rectifying From Radially-Distorted Conjugate Translations

This paper introduces minimal solvers that jointly solve for radial lens undistortion and affine-rectification using local features extracted from the image of coplanar translated and reflected scene texture, which is common in man-made environments.The proposed solvers accommodate different types of local features and sampling strategies, and three of the proposed variants require just one feature correspondence.State-of-the-art techniques from algebraic geometry are used to simplify the formulation of the solvers.The generated solvers are stable, small and fast.Synthetic and real-image experiments show that the proposed solvers have superior robustness to noise compared to the state of the art.The solvers are integrated with an automated system for rectifying imaged scene planes from coplanar repeated texture.Accurate rectifications on challenging imagery taken with narrow to wide field-of-view lenses demonstrate the applicability of the proposed solvers.
Dataset for file fragment classification of video file formats

File fragment classification of video file formats is a topic of interest in network forensics. There are some publicly available datasets for file fragments of various file types such as textual, audio, and image file formats. However, there is no public dataset for file fragments of video file formats. So, in order to evaluate and compare the performance of the classification methods, a challenge is the need to have such datasets.In this study, we present a dataset that contains file fragments of 10 video file formats: 3GP, AVI, ASF, FLV, MKV, MOV, MP4, WebM, OGV, and RMVB. Corresponding to each format, the dataset contains the file fragments of video files with different video codec types: H.263, MPEG-4, WMV, H.264, FLV1, H.265, VP8, VP9, Theora, and RealVideo. Totally, 20 different pairs of video format and codec are employed. For each pair of video format and codec, 30,000 file fragments are provided. Totally, the dataset contains 600,000 file fragments.
Joint Light Field Spatial and Angular Super-Resolution From a Single Image

Synthesizing a densely sampled light field from a single image is highly beneficial for many applications. Moreover, jointly solving both angular and spatial super-resolution problem also introduces new possibilities in light field imaging. The conventional method relies on physical-based rendering and a secondary network to solve the angular super-resolution problem. In addition, pixel-based loss limits the network capability to infer scene geometry globally. In this paper, we show that both super-resolution problems can be solved jointly from a single image by proposing a single end-to-end deep neural network that does not require a physical-based approach. Two novel loss functions based on known light field domain knowledge are proposed to enable the network to consider the relation between sub-aperture images. Experimental results show that the proposed model successfully synthesizes dense high resolution light field and it outperforms the state-of-the-art method in both quantitative and qualitative criteria. The method can be generalized to various scenes, rather than focusing on a particular subject. The synthesized light field can be used as if it has been captured by a light field camera, such as depth estimation and refocusing.
Formula size games for modal logic and μ-calculus

We propose a new version of formula size game for modal logic. The game characterizes the equivalence of pointed Kripke-models up to formulas of given numbers of modal operators and binary connectives. Our game is similar to the well-known Adler-Immerman game. However, due to a crucial difference in the definition of positions of the game, its winning condition is simpler, and the second player does not have a trivial optimal strategy. Thus, unlike the Adler-Immerman game, our game is a genuine two-person game. We illustrate the use of the game by proving a non-elementary succinctness gap between bisimulation invariant first-order logic $\mathrm{FO}$ and (basic) modal logic $\mathrm{ML}$. We also present a version of the game for the modal $\mu$-calculus $\mathrm{L}_\mu$ and show that $\mathrm{FO}$ is also non-elementarily more succinct than $\mathrm{L}_\mu$.
Identification of Daily Activites and Environments Based on the AdaBoost Method Using Mobile Device Data: A Systematic Review

Using the AdaBoost method may increase the accuracy and reliability of a framework for daily activities and environment recognition. Mobile devices have several types of sensors, including motion, magnetic, and location sensors, that allow accurate identification of daily activities and environment. This paper focuses on the review of the studies that use the AdaBoost method with the sensors available in mobile devices. This research identified the research works written in English about the recognition of daily activities and environment recognition using the AdaBoost method with the data obtained from the sensors available in mobile devices that were published between 2012 and 2018. Thus, 13 studies were selected and analysed from 151 identified records in the searched databases. The results proved the reliability of the method for daily activities and environment recognition, highlighting the use of several features, including the mean, standard deviation, pitch, roll, azimuth, and median absolute deviation of the signal of motion sensors, and the mean of the signal of magnetic sensors. When reported, the analysed studies presented an accuracy higher than 80% in recognition of daily activities and environments with the Adaboost method.
Cyberbullying Checker: Online Bully Content Detection Using Hybrid Supervised Learning

Cyberbullying is primarily associated with the utilization of digital media in order to bully someone. It has grown to a level where it is seriously affecting and damaging individual’s lives where social media plays a key role by providing a fecund means for bullies and the ones using such portals are more vulnerable to attacks. This online detrimental behavior has instigated a need for devising an automated mechanism using data-driven methodologies for critically analyzing and detecting such unfavorable activities. This work presents the application of eight supervised learning techniques, namely, Naïve Bayes, Support Vector Machines, Projective Adaptive Resonance Theory, JRIP is RIPPER Algorithm, K-Nearest Neighbor, Decision Tree, Logistic Regression, Artificial Neural Networks for identification and detection of textual cyberbullying content on Formspring.me, MySpace, and Ask.fm datasets. The study was carried out using the Weka tool. The results were critically analyzed using Accuracy, Precision, Recall, and F-Measure as efficacy criteria. Among the aforesaid techniques, JRIP outperforms the existing methods with around 91–96% accuracy and can enhance the performance of cyberbullying classification task cogently.
Facilitating transfer through incorrect examples and explanatory feedback

This article examines whether studying correct versus incorrect examples produces differential learning. A prediction that follows from behaviourism is that learning should be best from studying correct examples. A contrasting prediction is that incorrect examples can highlight a concept's properties that are missing in the example, and thereby enable concept learning. We test these ideas across two experiments, wherein subjects were shown hypothetical study scenarios and were asked to determine whether each was a true experiment. In Experiment 1, some subjects were only presented correct examples, some were only presented incorrect examples, and others were presented both. In addition, example type was crossed with feedback type: Some subjects were given explanatory feedback and some were not given any feedback; a control condition was also included, wherein subjects were not shown any study scenarios. All subjects completed a posttest involving novel scenarios; some questions asked subjects to indicate whether they were true experiments (classification questions), and some asked them to specify what was lacking in the design or to indicate how it could be fixed (application questions). Experiment 2 used a similar design, but compared explanatory feedback with corrective feedback. In both experiments, as long as some form of feedback was provided, subjects in the mixed example condition achieved the best classification performance. Furthermore, subjects in the incorrect and mixed example conditions performed best on application questions, particularly when explanatory feedback was provided. These findings offer insights into the mechanisms that might underlie learning from incorrect examples.