Reviewerly/exHarmony huggingface dataset:
Graph attention propagation for few-shot learning

Few-shot learning, which learns novel concept from very few samples given some base categories that have sufficient training samples, is a significant yet practical task since plenty of real-world categories have merely very limited samples. The main challenge is how to mine the correlations between the base and novel categories, and transfer information of based categories to help learn novel concepts. To address this work, we propose a graph attention propagation (GAP) model that introduces a graph attention mechanism to mine correlations among categories and propagate information through each other. In this way, the model can adaptively transfer knowledge of the base categories to help learn those novel categories. We conduct experiments on the ImageNet dataset and demonstrate that the GAP model show superior performance for few-shot learning.
Quantum Lovász local lemma: Shearer’s bound is tight

Lovász Local Lemma (LLL) is a very powerful tool in combinatorics and probability theory to show the possibility of avoiding all “bad” events under some “weakly dependent” condition. Over the last decades, the algorithmic aspect of LLL has also attracted lots of attention in theoretical computer science. A tight criterion under which the abstract version LLL (ALLL) holds was given by Shearer. It turns out that Shearer’s bound is generally not tight for variable version LLL (VLLL). Recently, Ambainis et al. introduced a quantum version LLL (QLLL), which was then shown to be powerful for the quantum satisfiability problem.
Boosting as a kernel-based method

Boosting combines weak (biased) learners to obtain effective learning algorithms for classification and prediction. In this paper, we show a connection between boosting and kernel-based methods, highlighting both theoretical and practical applications. In the $$\ell _2$$ context, we show that boosting with a weak learner defined by a kernel K is equivalent to estimation with a special boosting kernel. The number of boosting iterations can then be modeled as a continuous hyperparameter, and fit (along with other parameters) using standard techniques. We then generalize the boosting kernel to a broad new class of boosting approaches for general weak learners, including those based on the $$\ell _1$$ , hinge and Vapnik losses. We develop fast hyperparameter tuning for this class, which has a wide range of applications including robust regression and classification. We illustrate several applications using synthetic and real data.
Imbalanced Ensemble Classifier for Learning from Imbalanced Business School Dataset

Private business schools in India face a regular problem of picking quality students for their MBA programs to achieve the desired placement percentage. Generally, such datasets are biased towards one class, i.e., imbalanced in nature. And learning from the imbalanced dataset is a difficult proposition. This paper proposes an imbalanced ensemble classifier which can handle the imbalanced nature of the dataset and achieves higher accuracy in case of the feature selection (selection of important characteristics of students) cum classification problem (prediction of placements based on the students’ characteristics) for Indian business school dataset. The optimal value of an important model parameter is found. Experimental evidence is also provided using Indian business school dataset to evaluate the outstanding performance of the proposed imbalanced ensemble classifier.
Ultracompression: Framework For High Density Compression Of Ultrasound Volumes Using Physics Modeling Deep Neural Networks

Ultrasound image compression by preserving speckle-based key information is a challenging task. In this paper, we introduce an ultrasound image compression framework with the ability to retain realism of speckle appearance despite achieving very high-density compression factors. The compressor employs a tissue segmentation method, transmitting segments along with transducer frequency, number of samples and image size as essential information required for decompression. The decompressor is based on a convolutional network trained to generate patho-realistic ultrasound images which convey essential information pertinent to tissue pathology visible in the images. We demonstrate generalizability of the building blocks using two variants to build the compressor. We have evaluated the quality of decompressed images using distortion losses as well as perception loss and compared it with other off the shelf solutions. The proposed method achieves a compression ratio of 725:1 while preserving the statistical distribution of speckles. This enables image segmentation on decompressed images to achieve dice score of 0.89 ± 0.11, which evidently is not so accurately achievable when images are compressed with current standards like JPEG, JPEG 2000, WebP and BPG. We envision this frame work to serve as a roadmap for speckle image compression standards.
Analysis of Objects Classification Approaches Using Vectors of Inflection Points

An increase of the automated video surveillance system operational performance was proposed by reducing the amount of data using the contour inflection points for object identification. The authors developed a structural statistic method to identify facial images. It allowed obtaining a set of vectors that consist of inflection points of every hierarchical layer of the processed object. The research described in this paper shows the comparison of such vectors identifying similarities and referring them to a particular class. Three objects classification approaches were proposed and analyzed: approaches based on centroid, sum and maximum. Two alternative schemes for the calculation of distance between inflection point vectors were also proposed and analyzed: the classical Euclidean meter and the modified one, based on distances from the coordinate origins.
Adversarial attack against scene recognition system for unmanned vehicles

Unmanned scene recognition means that unmanned vehicles can collect environmental data from equipped sensors and make decisions through algorithms, in which deep learning has become one of key technologies. Especially, with the discovery of adversarial examples against deep learning, the research on offensive and defensive against adversarial examples illustrates that the deep learning model for unmanned scene recognition also has the safety vulnerability. However, as far as we know, few studies have tried to explore the adversarial example attack in this field. Therefore, we try to address this problem by generating adversarial examples againist scene recognition classification model through experiments. In addition, we also try to improve the adversarial model robustness by the adversarial training. Extensive experiments have been conducted, and experimental results show that adversarial examples have an efficient attack effect on the neural network for scene recognition.
Automatically Query Active Features Based on Pixel-Level for Facial Expression Recognition

Feature extraction-based subspace learning methods normally learn a projection that can convert the high-dimensional data to the low-dimensional representation. However, they may not be suitable for better classification since features obtained by these methods ignore discriminability of the data-pixel itself. Given this, we propose a novel approach that automatically queries active features combing sparse representation classification for the facial expression recognition. The proposed approach aims to automatically query discriminative features from raw pixels, thereby fully considering the underlying characteristics existed in the source data. Especially, the proposed approach based on pixel-level adaptively selects the most active and discriminative feature for representation and classification. The intraclass low-rank decomposition and principal feature analysis are simultaneously used to guarantee that the extracted features can capture the most active energy of the raw data, and thus, the proposed approach can be also applied for other feature extraction and selection tasks. We conduct comprehensive experiments on four public datasets, and the results show superior performance than some state-of-the-art methods.
An Experiment with Statistical Region Merging and Seeded Region Growing Image Segmentation Techniques

This paper puts forth our observations from the experiments conducted on interactive segmentation techniques - Statistical Region Merging and Seeded Region Growing, both based on Region Growing methods, using Matlab software on selected natural images. Both these techniques meant to solve segmentation challenges posed by medical images and were effective as well; however, in this experiment, we have subjected natural images to these techniques to assess the effectiveness. We have chosen the images such that those have complexities and pose segmentation challenges. The objective of this experiment is to understand and assess the effectiveness of these techniques on select natural images, which have complex image composition in terms of intensity, color mix, indistinct object boundary, low contrast, etc. We have used Jaccard Index, Dice Coefficient and Hausdorff Distance as measures to assess the accuracy, besides visual assessment. We have compared the segmented images with ground truth using these accuracy measures. While both the techniques failed to give acceptable results on most of the images, relatively, we have found Statistical Region Merging to be slightly better than Seeded Region Growing method.
Structured Decomposition for Reversible Boolean Functions

Reversible Boolean function (RBF) is a one-to-one function which maps n-bit input to n-bit output. Reversible logic synthesis has been widely studied due to its connection with low-energy computation as well as quantum computation. In this paper, we give a structured decomposition for even RBFs. Specifically, for n ≥ 6, any even n-bit RBF can be decomposed to 7 blocks of (n-1)-bit RBF, where 7 is a constant independent of n and the positions of these blocks have a large degree of freedom. Moreover, if the (n-1)-bit RBFs are required to be even as well, we show for n ≥ 10, even n-bit RBF can be decomposed to 10 even (n - 1)-bit RBFs. In short, our decomposition has block depth 7 and even block depth 10. Our result improves Selinger's work in block depth model, by reducing the constant from 9 to 7 and from 13 to 10, when the blocks are limited to be even. We emphasize that our setting is a bit different from Selinger's work. In Selinger's constructive proof, each block is placed in one of two specific positions and thus the decomposition has an alternating structure. We relax this restriction and allow each block to act on arbitrary (n - 1) bits. This relaxation keeps the block structure and provides more candidates when choosing the positions of blocks.
Constructive Galois Connections

Galois connections are a foundational tool for structuring abstraction in semantics and their use lies at the heart of the theory of abstract interpretation. Yet, mechanization of Galois connections using proof assistants remains limited to restricted modes of use, preventing their general application in mechanized metatheory and certified programming. This paper presents constructive Galois connections, a variant of Galois connections that is effective both on paper and in proof assistants; is complete with respect to a large subset of classical Galois connections; and enables more general reasoning principles, including the "calculational" style advocated by Cousot. To design constructive Galois connections we identify a restricted mode of use of classical ones which is both general and amenable to mechanization in dependently-typed functional programming languages. Crucial to our metatheory is the addition of monadic structure to Galois connections to control a "specification effect." Effectful calculations may reason classically, while pure calculations have extractable computational content. Explicitly moving between the worlds of specification and implementation is enabled by our metatheory. To validate our approach, we provide two case studies in mechanizing existing proofs from the literature: the first uses calculational abstract interpretation to design a static analyzer; the second forms a semantic basis for gradual typing. Both mechanized proofs closely follow their original paper-and-pencil counterparts, employ reasoning principles not captured by previous mechanization approaches, support the extraction of verified algorithms, and are novel.
Simulating leaky integrate-and-fire neuron with integers

The leaky integrate-and-fire (LIF) neuron represents standard neuronal model used for numerical simulations. The leakage is implemented in the model as exponential decay of trans-membrane voltage towards its resting value. This makes inevitable the usage of machine floating point numbers in the course of simulation. It is known that machine floating point arithmetic is subjected to small inaccuracies, which prevent from exact comparison of floating point quantities. In particular, it is incorrect to decide whether two separate in time states of a simulated system composed of LIF neurons are exactly identical. However, decision of this type is necessary, e.g. to figure periodic dynamical regimes in a reverberating network. Here we offer a simulation paradigm of a LIF neuron, in which neuronal states are described by whole numbers. Within this paradigm, the LIF neuron behaves exactly the same way as does the standard floating point simulated LIF, although exact comparison of states becomes correctly defined.
Machine learning of phonetic transcription rules for Greek

The purpose of this work is to define an automatic machine learning approach that learns rules of how to transcribe Greek words into the International Phonetics Association’s (IPA’s) phonetic alphabet. It is an algorithmic approach that mines rules of phonologic transcription from training data (phonologically transcribed Greek words) that are available in the internet and in other resources.
An Application of ASP Theories of Intentions to Understanding Restaurant Scenarios: Insights and Narrative Corpus

Abstract This paper presents a practical application of Answer Set Programming to the understanding of narratives about restaurants. While this task was investigated in depth by Erik Mueller, exceptional scenarios remained a serious challenge for his script-based story comprehension system. We present a methodology that remedies this issue by modeling characters in a restaurant episode as intentional agents . We focus especially on the refinement of certain components of this methodology in order to increase coverage and performance. We present a restaurant story corpus that we created to design and evaluate our methodology.
Comparison Training for Computer Chinese Chess

This paper describes the application of modified comparison training for automatic feature weight tuning. The final objective is to improve the evaluation functions used in Chinese chess programs. First, we apply n-tuple networks to extract features. N-tuple networks require very little expert knowledge through its large numbers of features, while simultaneously allowing easy access. Second, we propose a modified comparison training into which tapered eval is incorporated. Experiments show that with the same features and the same Chinese chess program, the automatically tuned feature weights achieved a win rate of 86.58% against the hand-tuned features. The above trained version was then improved by adding additional features, most importantly n-tuple features. This improved version achieved a win rate of 81.65% against the trained version without additional features.
One-Mode Wigner Quasi-Probability Distribution Function for Entangled Coherent States Generated by Beam Splitter and Cavity QED

In this paper, we use the displacement operator together with parity operation to construct the superposition of two coherent states. By transmitting this superposition from 50–50 beam splitter the two-mode qubit-like entangled coherent state (ECS) is generated. Moreover, we introduce a controllable method for producing qutrit-like ECS using atom–field interaction in cavity QED and beam splitter. We will show that the distances of peaks of Wigner functions for reduced density matrices of two-mode ECS's are entanglement sensitive and can be witnesses of entanglement. To confirm the results we use concurrence measure to compare bipartite entanglement of ECS's with the behaviour of peaks of Wigner functions. Moreover, we investigate decoherence effects on Wigner function, arising from transmitting ECS's through noisy channels.
Deep Policy Hashing Network with Listwise Supervision

Deep-networks-based hashing has become a leading approach for large-scale image retrieval, which learns a similarity-preserving network to map similar images to nearby hash codes. The pairwise and triplet losses are two widely used similarity preserving manners for deep hashing. These manners ignore the fact that hashing is a prediction task on the list of binary codes. However, learning deep hashing with listwise supervision is challenging in 1) how to obtain the rank list of whole training set when the batch size of the deep network is always small and 2) how to utilize the listwise supervision. In this paper, we present a novel deep policy hashing architecture with two systems are learned in parallel: aquery network and a shared and slowly changingdatabase network. The following three steps are repeated until convergence: 1) the database network encodes all training samples into binary codes to obtain whole rank list, 2) the query network is trained based on policy learning to maximize a reward that indicates the performance of the whole ranking list of binary codes, e.g., mean average precision (MAP), and 3) the database network is updated as the query network. Extensive evaluations on several benchmark datasets show that the proposed method brings substantial improvements over state-of-the-art hashing methods.
Millimeter-wave Phased Antenna Array for Automotive Radar

In modern conditions of ever-growing traffic, various systems of driver assistance are increasingly used. They have many assignment functions that greatly simplify the process of driving a vehicle. The most frequently designated systems are based on radar. The aim of the article is to consider of the features of building a phased antenna array for a automotive radar. The important role in it is assigned to the task of reducing the level of interference lobe in directivity pattern of the antenna. The results of modeling the phased antenna array are also presented.
An Ensemble Approach for Classification of Breast Histopathology Images

This paper presents an approach to classify breast histopathology images based on the mitotic count. An ensemble of handcrafted features, a bag of features (BoF), and convolutional neural network (CNN) are done for mitosis detection. Initially, mitotic regions are segmented followed by extraction of handcrafted features, BoF, and CNN-learned features from the candidate region. A non-linear support vector machine with Gaussian radial basis function kernel is used for the classification of each feature set independently. The proposed work utilizes the main tissue components of the images in an ensemble style to correctly classify the input histopathology image into benign or malignant. It also enables to maximize the performance by leveraging the disconnected feature set. Publicly available ICPR 2012 and 2014 mitosis dataset of is used for evaluation. The proposed ensemble method yielded a much improved F1 score. Better accuracy and fewer computing resources of our approach make it feasible for clinical use.
Collusion Risk and Responsibility in Public Cryptocurrency Protocol Development

Cryptocurrencies are based on cryptography-based asset disposals broadcasted peer-to-peer to be validated decentrally according to consensus mechanisms in compliance with consented protocols. Protocol development is associated with risks, and there are conflicts of interests. Collusion involving protocol developers may increase such risks. This paper explores liability for collusion in cryptocurrency protocol development. It is found that liability gives some, but not sufficient, protection against harmful collusion.
BLP - Boundary Likelihood Pinpointing Networks for Accurate Temporal Action Localization

Despite tremendous progress achieved in temporal action detection, state-of-the-art methods still suffer from the sharp performance deterioration when localizing the starting and ending temporal action boundaries. Although most methods apply boundary regression paradigm to tackle this problem, we argue that the direct regression lacks detailed enough information to yield accurate temporal boundaries. In this paper, we propose a novel Boundary Likelihood Pinpointing (BLP) network to alleviate this deficiency of boundary regression and improve the localization accuracy. Given a loosely localized search interval that contains an action instance, BLP casts the problem of localizing temporal boundaries as that of assigning probabilities on each equally divided unit of this interval. These generated probabilities provide useful information regarding the boundary location of the action inside this search interval. Based on these probabilities, we introduce a boundary pinpointing paradigm to pinpoint the accurate boundaries under a simple probabilistic framework. Compared with other C3D feature based detectors, extensive experiments demonstrate that BLP significantly improves the localization performance of recent state-of-the-art detectors, and achieves competitive detection mAP on both THUMOS' 14 and ActivityNet datasets, particularly when the evaluation tIoU is high.
Fireworks: Reproducible Machine Learning and Preprocessing with PyTorch

Here, we present a batch-processing library for constructing machine learning pipelines using PyTorch and dataframes.It is meant to provide an easy method to stream data from a dataset into a machine learning model while performing reprocessing steps such as randomization, train/test split, batch normalization, etc. along the way.Fireworks offers more flexibility and structure for constructing input pipelines than the built-in dataset modules in PyTorch (Paszke et al., 2017), but is also meant to be easier to use than frameworks such as Apache Spark (Zaharia et al., 2016).
Multi-Scale Collaborative Network for Human Pose Estimation

Human pose estimation is a fundamental but challenging task in computer vision. The estimation of human pose mainly depends on the global information of the keypoint type and the local information of the keypoint location. However, the consistency of the cascading process makes it difficult for each stacking network to form a differentiation and collaboration mechanism. In order to solve these problems, this paper introduces a new human pose estimation framework called Multi-Scale Collaborative (MSC) network. The pre-processing network forms feature maps of different sizes, and dispatches them to various locations of the stack network, with small-scale features reaching the front-end stacking network and large-scale features reaching the back-end stacking network. A new loss function is proposed for MSC network. Different keypoints have different weight coefficients of loss function at different scales, and the keypoint weight coefficients are dynamically adjusted from the top hourglass network to the bottom hourglass network. Experimental results show that the proposed method is competitive in MPII and LSP challenge leaderboard among the state-of-the-art methods.
PERSONALIZED SCHOLARLY ARTICLE RECOMMENDATIONS BASED ON THE RECURRENT NEURAL NETWORK AND PROBABILISTIC MODELS

Searching scholarly articles, i.e., research papers, on the Web is a challenge even for researchers who are familiar with the searched topics and articles of a particular domain of interest, needless to say for users who are not familiar with the