Reviewerly/exHarmony huggingface dataset:
Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold

Single image pose estimation is a fundamental problem in many vision and robotics tasks, and existing deep learning approaches suffer by not completely modeling and handling: i) uncertainty about the predictions, and ii) symmetric objects with multiple (sometimes infinite) correct poses. To this end, we introduce a method to estimate arbitrary, non-parametric distributions on SO(3). Our key idea is to represent the distributions implicitly, with a neural network that estimates the probability given the input image and a candidate pose. Grid sampling or gradient ascent can be used to find the most likely pose, but it is also possible to evaluate the probability at any pose, enabling reasoning about symmetries and uncertainty. This is the most general way of representing distributions on manifolds, and to showcase the rich expressive power, we introduce a dataset of challenging symmetric and nearly-symmetric objects. We require no supervision on pose uncertainty -- the model trains only with a single pose per example. Nonetheless, our implicit model is highly expressive to handle complex distributions over 3D poses, while still obtaining accurate pose estimation on standard non-ambiguous environments, achieving state-of-the-art performance on Pascal3D+ and ModelNet10-SO(3) benchmarks.
Bagging and Single Decision Tree Approaches to Dispersed Data

The article is dedicated to the issue of classification based on independent data sources. A new approach proposed in the paper is a classification method for independent local decision tables that is based on the bagging method. For each local decision table, sub-tables are generated with the bagging method, based on which the decision trees are built. Such decision trees classify the test object, and a probability vector is defined over the decision classes for each local table. For each vector decision classes with the maximum value of the coordinates are selected and the final joint decision for all local tables is made by majority voting. The results were compared with the baseline method of generating one decision tree based on one local table. It cannot be clearly stated that more bootstrap replicates guarantee better classification quality. However, it was shown that the bagging classification trees produces more unambiguous results which are in many cases better than for the baseline method.
Adaptation of metaheuristic algorithms to improve training performance of an ESZSL model

Zero-shot learning (ZSL) is a recent promising learning approach that is similar to human vision systems. ZSL essentially allows machines to categorize objects without requiring labeled training data. In principle, ZSL proposes a novel recognition model by specifying merely the attributes of the category. Recently, several sophisticated approaches have been introduced to address the challenges regarding this problem. Embarrassingly simple approach to zeroshot learning (ESZSL) is one of the critical of those approaches that basically proposes a simple but efficient linear code solution. However, the performance of the ESZSL model mainly depends on parameter selection. Metaheuristic algorithms are considered as one the most sophisticated computational intelligence paradigms that allows to approximate optimization problems with high success. This paper addresses this problem by adapting leading metaheuristic algorithms to automatically train the parameters of a linear ESZSL model. The model is statistically validated by performing a series of experiments with benchmark datasets.
Proving Resistance Against Infinitely Long Subspace Trails: How to Choose the Linear Layer

Designing cryptographic permutations and block ciphers using a substitutionpermutation network (SPN) approach where the nonlinear part does not cover the entire state has recently gained attention due to favorable implementation characteristics in various scenarios.For word-oriented partial SPN (P-SPN) schemes with a fixed linear layer, our goal is to better understand how the details of the linear layer affect the security of the construction. In this paper, we derive conditions that allow us to either set up or prevent attacks based on infinitely long truncated differentials with probability 1. Our analysis is rather broad compared to earlier independent work on this problem since we consider (1) both invariant and non-invariant/iterative trails, and (2) trails with and without active S-boxes.For these cases, we provide rigorous sufficient and necessary conditions for the matrix that defines the linear layer to prevent the analyzed attacks. On the practical side, we present a tool that can determine whether a given linear layer is vulnerable based on these results. Furthermore, we propose a sufficient condition for the linear layer that, if satisfied, ensures that no infinitely long truncated differential exists. This condition is related to the degree and the irreducibility of the minimal polynomial of the matrix that defines the linear layer. Besides P-SPN schemes, our observations may also have a crucial impact on the Hades design strategy, which mixes rounds with full S-box layers and rounds with partial S-box layers.
Dialogue Based Disease Screening Through Domain Customized Reinforcement Learning

In this paper, we study the problem of leveraging dialogue agents learned from reinforcement learning (RL) that can interact with patients for automatic disease screening. This application requires efficient and effective inquiry of appropriate symptoms to make accurate diagnosis recommendations. Existing studies have tried to use RL to perform both symptom inquiry and diagnosis simultaneously, which needs to deal with a large, heterogeneous action space that affects the learning efficiency and effectiveness. To address the challenge, we propose to leverage the models learned from the dialogue data to customize the settings of the reinforcement learning for more efficient action space exploration. In particular, a supervised diagnosis model is built and involved in the definition of state and reward. We also develop the clustering method to form a hierarchy in the action space. These customizations can make the learning task focus on checking the most relevant symptoms, which effectively boost the confidence of diagnosis. Besides, a novel hierarchical reinforcement learning framework with the pretraining strategy is used to reduce the dimension of action space and help the model to converge. For empirical evaluations, we conduct extensive experiments on both synthetic and real-world datasets. The results have demonstrated the superiority of our approach in diagnostic accuracy and interaction efficiency compared with other baseline methods.
Improving machine learning fairness with sampling and adversarial learning

Machine learning approaches learn models based on the statistical properties of training data. Learned models may be unfair due to bias inherent in the training data or because of spurious correlat...
Survey of Image Processing Techniques in Medical Image Assessment Methodologies

Medical image assessment represents the procedure utilized in the formulation of pictures of different parts of the body to study or identify a particular disease. There are many medical image assessment procedures carried out every week in the entire globe which means that the sector is rapidly growing as a result of the constant advancement of image processing methodologies such as Image Enhancement, Analysis, and Recognition. This article, presents a critical survey of medical image assessment based on the application of image processing methodologies. It also provides a summary of how image interpretation issues can be exemplified using various image analysis algorithms such as RIO-centered segmentation, k-means and watershed methodologies.
Real Time Magnitude Classification of Earthquake Waveforms using Deep Learning

&amp;lt;p&amp;gt;This study attempts to use Deep Learning architectures to design an efficient real time magnitude classifier for seismic events. Various combinations of Convolutional Neural Networks (CNNs) and Bi- &amp;amp; Uni-directional Long-Short Term Memory (LSTMs) and Gated Recurrent Unit (GRUs) are tried and tested to obtain the best performing model with optimum hyperparameters. In order to extract maximum information from the seismic waveforms, this study uses not only the time series data but also its corresponding Fourier Transform (spectrogram) as input. Furthermore, the Deep Learning architecture is combined with other machine learning algorithms to generate the final magnitude classifications. This study is likely to help seismologists in improving the Earthquake Early Warning System to avoid issuing false warnings, which not only alarms people unnecessarily but can also result in huge financial losses due to stoppage of industrial machinery etc.&amp;lt;/p&amp;gt;
Secure Remote Storage of Logs with Search Capabilities

Along with the use of cloud-based services, infrastructure, and storage, the use of application logs in business critical applications is a standard practice. Application logs must be stored in an accessible manner in order to be used whenever needed. The debugging of these applications is a common situation where such access is required. Frequently, part of the information contained in logs records is sensitive. In this paper, we evaluate the possibility of storing critical logs in a remote storage while maintaining its confidentiality and server-side search capabilities. To the best of our knowledge, the designed search algorithm is the first to support full Boolean searches combined with field searching and nested queries. We demonstrate its feasibility and timely operation with a prototype implementation that never requires access, by the storage provider, to plain text information. Our solution was able to perform search and decryption operations at a rate of, approximately, 0.05 ms per line. A comparison with the related work allows us to demonstrate its feasibility and conclude that our solution is also the fastest one in indexing operations, the most frequent operations performed.
Selfish Sparse RNN Training

Sparse neural networks have been widely applied to reduce the necessary resource requirements to train and deploy over-parameterized deep neural networks. For inference acceleration, methods that induce sparsity from a pre-trained dense network (dense-to-sparse) work effectively. Recently, dynamic sparse training (DST) has been proposed to train sparse neural networks without pre-training a large and dense network (sparse-to-sparse), so that the training process can also be accelerated. However, previous sparse-to-sparse methods mainly focus on Multilayer Perceptron Networks (MLPs) and Convolutional Neural Networks (CNNs), failing to match the performance of dense-to-sparse methods in Recurrent Neural Networks (RNNs) setting. In this paper, we propose an approach to train sparse RNNs with a fixed parameter count in one single run, without compromising performance. During training, we allow RNN layers to have a non-uniform redistribution across cell weights for a better regularization. Further, we introduce SNT-ASGD, a variant of the averaged stochastic gradient optimizer, which significantly improves the performance of all sparse training methods for RNNs. Using these strategies, we achieve state-of-the-art sparse training results, even better than dense model results, with various types of RNNs on Penn TreeBank and Wikitext-2 datasets.
Visual Prosody Supports Reading Aloud Expressively

Type is not expressive enough. Even the youngest speakers are able to express a full range of emotions with their voice, while young readers read aloud monotonically as if to convey robotic boredom. We augmented type to convey expression similarly to our voices. Specifically, we wanted to convey in text words that are spoken louder, words that drawn out and spoken longer, and words that are spoken at a higher pitch. We then asked children to read sentences with these new kinds of type to see if children would read these with greater expression. We found that children would ignore the augmentation if they weren't explicitly told about it. But when children were told about the augmentation, they were able to read aloud with greater vocal inflection. This innovation holds great promise for helping both children and adults to read aloud with greater expression and fluency.
From the corporate culture of national university to the global university: the case of the RUDN University

The relevance of the research is associated with the increasing competition in the global educational market, where Russian universities enter and pretend to the status of a global university due to the Project 5-100. Today an academic institution needs to form a brand in the actual international infosphere. This process based on many factors, one of which is the corporate culture (CC) of the university, and the study is concerned with the exploration of its features. The main characteristics of the concept of global university and the description of the features of the CC of the university are given. The results of the study of CC of the RUDN University, that were received in 2019 through a survey of teachers, staff and students of the university, are presented. In conclusion, the recommendations on how to transform the CC of a university so that it allows to form its brand in the global educational space are formulated. These recommendations will be useful to any university that works in Russia or enters the global education market.
Encryption and Decryption of Images based on Steganography and Cryptography Algorithms: A New model

Steganography is the art of hidden writing, and it refers to techniques for concealing knowledge within seemingly innocuous items called Cover Objects. There are various types of covers for encrypting sensitive information, but photographs are ubiquitous in everyday applications and have a high level of redundancy of representation. As a result, they are appealing candidates for use as cover items. This paper evaluates the LSB algorithm. One of the most well-known steganographic techniques is Least Significant Bit. Embedding with another type of encryption can make it more secure. Many of the scholars suggest the combination of encryption with Steganography adds more obstacles to get cracked. The paper is meant to introduce the concept of LSB with affine cipher encryption technique with the limitations of steganography.
CASCARO: Cascade of classifiers for minimizing the cost of prediction

Although the prediction performance is crucial for a classifier, its cost of use is also an essential issue for practical application. The aim of this article is to propose a prediction method that controls not only the error rate but also the cost of the construction of the classifier. The main idea is that some examples are easier to predict than others and can be predicted using fewer variables i.e. with a lower prediction cost. Our method, called CASCARO, is based on a cascade of reject classifiers of increasing cost. The first classifier of the cascade required only one variable, if the prediction is not reliable the second classifier requiring one more variable is used. The principle is repeated until the last classifier using all variables. This type of cascade raises two scientific problems: the structure of the cascade (the order of the classifiers) and the simultaneous computation of the rejection regions of the classifiers. The experiments show that CASCARO produces significant improvements in the use cost without decreasing prediction performance.
Adjusting systematic bias in high dimensional principal component scores

Principal component analysis continues to be a powerful tool in dimension reduction of high dimensional data. We assume a variance-diverging model and use the high-dimension, low-sample-size asymptotics to show that even though the principal component directions are not consistent, the sample and prediction principal component scores can be useful in revealing the population structure. We further show that these scores are biased, and the bias is asymptotically decomposed into rotation and scaling parts. We propose methods of bias-adjustment that are shown to be consistent and work well in the high dimensional situations with small sample sizes. The potential advantage of bias-adjustment is demonstrated in a classification setting.
Behavior-Based Method for Real-Time Identification of Encrypted Proxy Traffic

Encrypted proxy is often used to hide malicious behavior or criminal activity on the Internet. Therefore, identifying encrypted proxy traffic is essential for network management and communication security. Existing researches usually use statistical features to profile network flows, which only have limited effects on encrypted proxy traffic, and are not suitable for real-time identification. In this paper, a novel behavior-based approach for encrypted proxy traffic detection is proposed. Two unique behavior features, IP proxy and data encryption behaviors, which are highly related to the activity of accessing network through encrypted proxies, are defined as learning features. Machine learning techniques are adopted for encrypted proxy traffic identification. The experiments on a real V2Ray traffic dataset demonstrate that the behavior-based method can identify encrypted proxy traffic with high accuracy, up to 99.86%. Besides, the method can timely seek out target flows, as all those behavior features can be obtained in the first packet.
Adapting Unsupervised Syntactic Parsing Methodology for Discourse Dependency Parsing

One of the main bottlenecks in developing discourse dependency parsers is the lack of annotated training data.A potential solution is to utilize abundant unlabeled data by using unsupervised techniques, but there is so far little research in unsupervised discourse dependency parsing.Fortunately, unsupervised syntactic dependency parsing has been studied for decades, which could potentially be adapted for discourse parsing.In this paper, we propose a simple yet effective method to adapt unsupervised syntactic dependency parsing methodology for unsupervised discourse dependency parsing.We apply the method to adapt two state-of-the-art unsupervised syntactic dependency parsing methods.Experimental results demonstrate that our adaptation is effective.Moreover, we extend the adapted methods to the semi-supervised and supervised setting and surprisingly, we find that they outperform previous methods specially designed for supervised discourse parsing.Further analysis shows our adaptations result in superiority not only in parsing accuracy but also in time and space efficiency.
Co-Exploration of Graph Neural Network and Network-on-Chip Design Using AutoML

Recently, Graph Neural Networks (GNNs) have exhibited high efficiency in several graph-based machine learning tasks. Compared with the neural networks for computer vision or speech tasks (e.g., Convolutional Neural Networks), GNNs have much higher requirements on communication due to the complicated graph structures; however, when applying GNNs for real-world applications, say in recommender systems (e.g. Uber Eats), it commonly has the real-time requirements. To deal with the tradeoff between the complicated architecture and the high-demand timing performance, both GNN architecture and hardware accelerator need to be optimized. Network-on-Chip (NoC), derived for efficiently managing the high-volume of communications, naturally becomes one of the top candidates to accelerate GNNs. However, there is a missing link between the optimize of GNN architecture and the NoC design.
Adversarial Machine Learning: On the Resilience of Third-party Library Recommender Systems

In recent years, we have witnessed a dramatic increase in the application of Machine Learning algorithms in several domains, including the development of recommender systems for software engineering (RSSE). While researchers focused on the underpinning ML techniques to improve recommendation accuracy, little attention has been paid to make such systems robust and resilient to malicious data. By manipulating the algorithms’ training set, i.e., large open-source software (OSS) repositories, it would be possible to make recommender systems vulnerable to adversarial attacks. This paper presents an initial investigation of adversarial machine learning and its possible implications on RSSE. As a proof-of-concept, we show the extent to which the presence of manipulated data can have a negative impact on the outcomes of two state-of-the-art recommender systems which suggest third-party libraries to developers. Our work aims at raising awareness of adversarial techniques and their effects on the Software Engineering community. We also propose equipping recommender systems with the capability to learn to dodge adversarial activities.
On the Operation ∆ over Intuitionistic Fuzzy Sets

Recently, the new operation ∆ was introduced over intuitionistic fuzzy sets and some of its properties were studied. Here, new additional properties of this operations are formulated and checked, providing an analogue to the De Morgan’s Law (Theorem 1), an analogue of the Fixed Point Theorem (Theorem 2), the connections between the operation ∆ on one hand and the classical modal operators over IFS Necessity and Possibility, on the other (Theorems 3 and 4). It is shown that it can be used for a de-i-fuzzification. A geometrical interpretation of the process of constructing the operator ∆ is given.
Detection and Tracking of a Moving Target for UAV Based on Machine Vision

With the continuously development of the technology related to unmanned air vehicle (UAV), the automation and intelligent levels become higher and higher. In view of the actual needs of precise control and the specific requirements of accurate tracking competition, this paper designs and realizes a complete process of detection and tracking. First, the images of moving targets are collected by machine vision. Then, an improved Yolo V3 algorithm is applied to finish the target detection work and an achieved proportional integral derivative (PID) controller to track the moving target is designed. To verify the accuracy of the detection and tracking algorithm and the effectiveness of the complete process, a verification system is also built. Real flight test results show that the UAV can complete the task with a high dynamic target detection rate and accurate tracking rate.
Comparing MultiLingual and Multiple MonoLingual Models for Intent Classification and Slot Filling

With the momentum of conversational AI for enhancing client-to-business interactions, chatbots are sought in various domains, including FinTech where they can automatically handle requests for opening/closing bank accounts or issuing/terminating credit cards. Since they are expected to replace emails and phone calls, chatbots must be capable to deal with diversities of client populations. In this work, we focus on the variety of languages, in particular in multilingual countries. Specifically, we investigate the strategies for training deep learning models of chatbots with multilingual data. We perform experiments for the specific tasks of Intent Classification and Slot Filling in financial domain chatbots and assess the performance of mBERT multilingual model vs multiple monolingual models.