Reviewerly/exHarmony huggingface dataset:
Action Classification for Partially Occluded Silhouettes by Means of Shape and Action Descriptors

This paper presents an action recognition approach based on shape and action descriptors that is aimed at the classification of physical exercises under partial occlusion. Regular physical activity in adults can be seen as a form of non-communicable diseases prevention, and may be aided by digital solutions that encourages individuals to increase their activity level. The application scenario includes workouts in front of the camera, where either the lower or upper part of the camera’s field of view is occluded. The proposed approach uses various features extracted from sequences of binary silhouettes, namely centroid trajectory, shape descriptors based on the Minimum Bounding Rectangle, action representation based on the Fourier transform and leave-one-out cross-validation for classification. Several experiments combining various parameters and shape features are performed. Despite the presence of occlusion, it was possible to obtain about 90% accuracy for several action classes, with the use of elongation values observed over time and centroid trajectory.
Steganography Analysis of Chaotic Carrier Signal Transmission with Non-linear Parametric Modulation

We explore the problem of evaluating the practical effectiveness of hidden signal transmission under chaotic carrier. As a signal carrier we use the Shimizu-Morioka chaotic oscillation generator. The useful signal is embedded into chaotic carrier by nonlinear modulation on chaotic generator parameters. The problem is to detect a steganography property for chaotic system nonlinear parametric modulation. The pseudo-random signal in the communication channel is analyzed for the presence of a mixed useful signal in it by Fourier series and by method of autocorrelation. The conclusion about the steganography of the method of hidden information transfer is given. The presented study is a part of chaotic carrier data transmission system design approach with useful signal reconstruction at receiver side by using nonlinear synergetic state observer developed in our previous publications.
Computation of Aortic Geometry Using MR and CT 3D Images

The proper computation of geometric parameters of the aorta and coronary arteries are very important for surgery planning, disease diagnoses, and age-related changes observation in the vessels. The accurate knowledge about the geometry of aorta and coronary arteries is required for the proper investigation of heart related diseases. The geometry of aorta and coronary arteries includes the diameter of the ascending and descending aorta and coronary arteries, length of the coronary arteries, branching angles of the coronary arteries and branching points. These geometric parameters from arteries can be computed from the 3D image data. In this paper, we propose an approach for calculating geometric parameters such as length, diameter of the aorta and angles of the coronary arteries. The proposed method automatically computes the geometry of aorta and left and right coronary arteries. The geometry is computed by logically dividing the aorta, calculating the centerline and extracting the features of aorta and coronary arteries. The method has been tested on different 3D CT/MR image data. The results of the proposed method are tested on different data sets to check its accuracy. The results show more accuracy and less computation time on noisy image data as compared to the already developed method. The obtained results are visualized and compared using visualization toolkit (VTK).
Hidden service publishing flow homology comparison using profile‐hidden markov model

In recent years, web servers pay attention to privacy and anonymity protection and choose to rely on hidden service to avoid exposure of the real geographic locations. Several studies have confirmed that hidden service is vulnerable to flow correlation attacks, specifically, the attacker has the ability to synchronize the behavior of both sides of the communication after observing the flow for an extended period of time. However, since hidden service publish descriptor flow is transient behavioral traffic, automatically capturing and analyzing publish flow becomes a challenge. In this paper, our focus is the intelligent identification of the descriptor publishing flow. We propose a model for the descriptor publishing flow correlation attack (DPFCA). The model resolves the complex relationship between the circuit establishment flow and the publishing flow, and is able to intelligently process the sequence identification and content classification of the descriptor correlation flow of the existing version and tags. It is worth mentioning that the DPFCA is based on the automated homology comparison of the profile-hidden Markov model (PHMM). The descriptor publishing flow is converted to an amino symbol sequence and then compare with the known homologous sequence group in the library of Profile. The experimental results show that our model can achieve higher performance in terms of accuracy and reliability of transient flow identification compared with the traditional flow correlation attack model.
EIS - Efficient and Trainable Activation Functions for Better Accuracy and Performance

Activation functions play a pivotal role in function learning using neural networks. The non-linearity in a neural network is achieved by repeated use of the activation function. Over the years, numerous activation functions have been proposed to improve neural network performance in several deep learning tasks. Basic functions like ReLU, Sigmoid, Tanh, or Softplus have been favorites among the deep learning community because of their simplicity. In recent years, several novel activation functions arising from these basic functions have been proposed, which have improved accuracy in some challenging datasets. We propose three activation functions with trainable parameters, namely EIS-1, EIS-2, and EIS-3. We show these three activation functions outperform widely used activation functions on some well-known datasets and models. For example, EIS-1, EIS-2, and EIS-3 beats ReLU by 5.55%, 5.32%, and 5.60% on ResNet V2 34, 5.27%, 5.24%, and 5.76% on VGG 16, 2.02%, 1.93%, and 2.01% on Wide-Res-Net 28-10, 2.30%, 2.11%, and 2.50% on Shufflenet V2 in CIFAR100 dataset while 1.40%, 1.27%, and 1.45% on ResNet V2 34, 1.21%, 1.09%, and 1.17% on VGG 16, 1.10%, 1.04%, and 1.16% on Wide-Res-Net 28-10, 1.85%, 1.60%, and 1.67% on Shufflenet V2 in CIFAR10 dataset respectively. The proposed functions also perform better than traditional activation functions like ReLU, Leaky ReLU, Swish, etc. in Object detection, Semantic segmentation, and Machine Translation problems.
Investigating interactions between types of order in categorization

This study simultaneously manipulates within-category (rule-based vs. similarity-based), between-category (blocked vs. interleaved), and across-blocks (constant vs. variable) orders to investigate how different types of presentation order interact with one another. With regard to within-category orders, stimuli were presented either in a "rule plus exceptions" fashion (in the rule-based order) or by maximizing the similarity between contiguous examples (in the similarity-based order). As for the between-category manipulation, categories were either blocked (in the blocked order) or alternated (in the interleaved order). Finally, the sequence of stimuli was either repeated (in the constant order) or varied (in the variable order) across blocks. This research offers a novel approach through both an individual and concurrent analysis of the studied factors, with the investigation of across-blocks manipulations being unprecedented. We found a significant interaction between within-category and across-blocks orders, as well as between between-category and across-blocks orders. In particular, the combination similarity-based + variable orders was the most detrimental, whereas the combination blocked + constant was the most beneficial. We also found a main effect of across-blocks manipulation, with faster learning in the constant order as compared to the variable one. With regard to the classification of novel stimuli, learners in the rule-based and interleaved orders showed generalization patterns that were more consistent with a specific rule-based strategy, as compared to learners in the similarity-based and blocked orders, respectively. This study shows that different types of order can interact in a subtle fashion and thus should not be considered in isolation.
Detail Matters: High-Frequency Content for Realistic Synthetic MRI Generation

Deep Learning (DL)-based segmentation methods have been quite successful in various medical imaging applications. The main bottleneck of these methods is the scarcity of quality-labelled samples needed for their training. The lack of labelled training data is often addressed by augmentation methods, which aim to synthesise realistic samples with corresponding labels. While the synthesis of realistic samples remains a challenging task, little is known about the impact of fine detail in synthetic data on the performance of DL-based segmentation models. In this work, we investigate whether, and to what extent, the high-frequency (HF) detail in synthetic brain MR images (MRIs) impacts the performance of DL-based segmentation methods. To assess the impact of HF detail, we generate two synthetic datasets, with and without HF detail and train corresponding segmentation models to evaluate the impact on their performance. The results obtained demonstrate that the presence of HF detail in synthetic brain MRIs, used during training, significantly improve the Dice score up to 1.73% for Gray Matter (GM), 1.34% for White Matter (WM) and 4.41% for Cerebrospinal Fluid (CSF); and therefore justify the need for synthesising realistic-looking MRIs.
A Deep Learning Technique for Classification of Breast Cancer Disease

In recent years researchers are intensely using machine learning and employing AI techniques in the medical field particularly in the domain of cancer. Breast cancer is one such example and many studies have proposed CAD systems and algorithms to efficiently detect cancer cells and tumors. Breast cancer is one of the dreadful cancers accounting for a large portion of deaths caused due to cancer worldwide mostly affecting women, needs early detection for proper diagnosis, and subsequent decrease in death rate. Thus, for efficient classification, we implemented different ML techniques on Wisconsin dataset [1] namely SVM, KNN, Decision Tree, Random Forest, Naive Bayes using accuracy as a performance metric, and as per observance, SVM has shown better results when compared to other algorithms. Also, we worked on Breast Histopathology Images [2] scanned at 40x which had images of IDC which is one of the most common types of breast cancers. And to work with the image dataset along with EDA we used high-end techniques like a mobile net where smote a resampling was used to handle imbalanced class distribution, CNN, SVC, InceptionResNetV2 where frameworks like Tensor Flow, Keras were loaded for supporting the environment and smoothly implement the algorithms.
Cost-effective crowdsourced join queries for entity resolution without prior knowledge

The join query, which finds matching pairs from two object sets, is a fundamental operation in computer systems and helps to solve many real problems, e.g., entity resolution. In this paper, we address the problem of join queries by leveraging crowdsourcing to obtain matching relationships. The goal is to minimize the monetary cost while maintaining high quality of query results. However, existing approaches focused on finding matching pairs from a single object set and assumed the existence of prior knowledge, which is not applicable in real applications. We propose a cost-effective crowdsourced join query framework that minimizes the overall monetary cost by reducing the monetary cost of labeling single pairs and the amount of comparison pairs. Specifically, we first propose a novel two-level confidence-based labeling model that minimizes the cost for labeling a single pair with confidence guarantee. This model crowdsources easy-judging pairs to ordinary workers, and asks for skilled workers who may charge more than ordinary workers to compare only hard-judging pairs. Statistical estimations are used to aggregate crowdsourcing results with 1−α confidence. Then, we propose a transitivity-based query scheme that minimizes the number of comparison pairs on the basis of transitive relations. Guided by the principle of eagerly identifying matching pairs, especially matching pairs from a single set, our scheme carefully designs the processing order of pairs in order to make full use of transitivities to infer new labels. The results of our extensive experiments demonstrate that the proposed framework can save much more monetary cost while assuring the accuracy of results.
Solar Image Hashing by Intermediate Descriptor and Autoencoder

The Solar Dynamics Observatory delivers data concerning various aspects of the Sun activity. Its Atmospheric Imaging Assembly, performs continuous full-disk observations of the solar chromosphere and corona in seven extreme ultraviolet channels with the 12-second cadence of high-resolution, over 16-megapixel images. In the paper, we create a fast, concise hash to retrieve similar solar images in this vast collection. We use a fully convolutional autoencoder along with a hand-crafted intermediate descriptor.
A Neural Ranker for Open-Domain Question Answering via Sentence-Level Jump Encoding

Open-domain question answering extract the answer for a question from a large scale corpus, which typically employs ranker to filter irrelevant paragraphs. As traditional retrieval approaches based on statistical characteristics, neural ranking model has gradually become a popular choice for filtering as its capacity of semantic features extraction. But massive corpus request high performance for filtering efficiency. Thus, acceleration of textual encoding by neural model is worthy of attention and there are many achievements on various of perspectives. Inspired by reading habit of human, this paper presents a novel neural ranker on sentence-level jump encoding which can better skip those irrelevant sentences than existing neural models. When people browsing text with questions, they tend to find the key sentence based on heuristic information of question and current reading. And our sentence-level jump encoder works in a similar way which can effectively and accurately locate key sentences for question. We evaluate our neural ranker on long paragraph setting of Quasar-T dataset, our method achieves significant improvements on both ranking acceleration and ranking accuracy, as well as surpass other question answering baselines on the whole question answering results.
Are you one of us?

When we are designing IT education and degree programs, knowledge about what graduates need to succeed is important. One source of such knowledge are employers. This paper presents a survey of Norwegian employers asking what they are looking for in a recently graduated IT candidate. We confirm that the so called soft skills are highly prized. Among these, we identify a less reported concept of fitting into company or team culture as the most important. Another somewhat unexpected result is that employers do not expect to see a portfolio from graduates. Among technologies mentioned, Java is at the top followed closely by C# and .NET. Python and JavaScript are also mentioned often. Our study suggests that soft skills should be a focus throughout IT education programs.
Dynamically Selected Ensemble for Data Stream Classification

Mining data streams is a hot topic in the machine learning (ML) community. In addition to learning and updating accurate models over time, these techniques must respect constraints that are not necessarily as strong in batch mode, such as time processing and memory consumption efficiency. A successful family of techniques in batch ML is dynamic classifier selection (DCS). However, these are roughly overlooked in data stream mining. In this paper, we propose a novel dynamic classifier selection framework for data streams called Double Dynamic Classifier Selection (DDCS). We compare DDCS against state-of-art methods for mining data streams in both synthetic and realworld datasets. Results depict that DDCS not only outperforms the state-of-art ensemble methods for data stream classification in terms of accuracy but is also significantly more efficient in terms of processing time and memory consumption.
TRAINING OF FUTURE TEACHERS IN THE CONDITIONS OF INTEGRATION OF HIGHER PEDAGOGICAL, GENERAL AND ADDITIONAL EDUCATION SYSTEMS

The relevance of this research is determined by the state order for the training of specialists who are able to organize the educational process taking into account the needs and interests of students who show creative and creative thinking in solving professional problems. Education, especially higher education, is considered as the main, leading factor of social and economic progress. The reason for such attention is the understanding that the most important value and the main capital of modern society is a person who is capable of searching for and mastering new knowledge and making non-standard decisions. The modern development of society requires a new system of education – «innovative training», which would form the ability of students to determine the future projectively, responsibility for it, faith in themselves and their professional abilities to influence this future. The main legislative documents «The Strategy of innovative development of the Russian Federation until 2024», «The Concept of development of additional education», «The Law on Education in the Russian Federation», as well as local acts of the subjects of the federation in the field of higher, general and additional education raise the problem of integration of these education systems into the category of important state problems and at the same time determine its relevance. The system of additional education with the active involvement of the future teacher in the implementation of work with children allows you to develop readiness for professional activity, both at school and in additional education. Providing professional training on the basis of the university equips the student with scientific knowledge and modern methods. This article attempts to theoretically substantiate the problem of integration of educational systems and present the conditions for its practical implementation.
Fast CNN Inference by Adaptive Sparse Matrix Decomposition

Truncated singular value decomposition (TSVD) method can accelerate convolution neural network (CNN) inference and reduce the number of model parameters because the convolution layer and full connection layer are represented by tensor and matrix. However, the hard threshold selection of TSVD algorithm is not suitable for the ever-changing neural network structure, and it brings about irreversible accuracy loss. To solve this problem, we propose a novel objective function, which can adaptively make CNNs sparse without hard threshold and further reduce the computation of CNNs. Specifically, different from SVD, we think the orthogonality of left and right singular matrices is unreasonable in the sparse decomposition problem. Orthogonal matrices mean that the singular vectors are unit vectors which are contrary to our goal of sparsification. Therefore, we add a L21 norm on singular vectors in order to obtain group sparsity. Besides, we use an alternative iterative method to solve the decomposed matrices automatically and the optimization is easy to implement. More importantly, the more iterations, the more sparse the model becomes. As a result, we can adaptively obtain a sparse and small CNN without specifying the sparsity rate of the big model. Finally, we test the classic CNN structures such as VGG, ResNet, WRN, DenseNet on CIFAR-10 and CIFAR-100. Experimental results verify the effectiveness of our algorithm. Our code is avaliable at: https://github.com//tnn2018/ASMD.
Fully probabilistic design for knowledge fusion between Bayesian filters under uniform disturbances

This paper considers the problem of Bayesian transfer learning-based knowledge fusion between linear state–space processes driven by uniform state and observation noise processes. The target task conditions on probabilistic state predictor(s) supplied by the source filtering task(s) to improve its own state estimate. A joint model of the target and source(s) is not required and is not elicited. The resulting decision-making problem for choosing the optimal conditional target filtering distribution under incomplete modelling is solved via fully probabilistic design (FPD), i.e. via appropriate minimization of Kullback–Leibler divergence (KLD). The resulting FPD-optimal target learner is robust, in the sense that it can reject poor-quality source knowledge. In addition, the fact that this Bayesian transfer learning (BTL) scheme does not depend on a model of interaction between the source and target tasks ensures robustness to the misspecification of such a model. The latter is a problem that affects conventional transfer learning methods. The properties of the proposed BTL scheme are demonstrated via extensive simulations, and in comparison with two contemporary alternatives.
NP-Hardness of 1-Mean and 1-Medoid 2-Clustering Problem with Arbitrary Clusters Sizes

We consider the following 2-clustering problem. Given n points in Euclidean space, partition it into two subsets (clusters) so that the sum of squared distances between the elements of the clusters and their centers would be minimum. The center of the first cluster coincides with its centroid (mean) while the center of the second cluster should be chosen from the set of the initial points (medoid). It is known that this problem is NP-hard if the cardinalities of the clusters are given as a part of the input. In this paper we prove that the peoblem remains NP-hard in the case of arbitrary clusters sizes.
A Novel Bi-Branch Graph Convolutional Neural Network for Aspect Level Sentiment Classification

Aspect-level sentiment classification is a fine-grained task in sentiment analysis whose main purpose is to identify the sentiment polarity of a specific aspect. Current Graph Convolutional Network (GCN) has its distinctive superiority in tackling sentiment classification both semantically and syntactically. However, GCN still has deficiencies in introducing the noise during processing and dealing with sentences of complex structure. To address these issues, we propose a novel Bi-branch GCN (Bi-B GCN). In our model, an attention weight graph, by employing the attention mechanism, is constructed to substitute the basic syntax dependency tree and thus to remove the irrelevant information. Furthermore, a semantic dependency graph is devised to supplement the semantic information to the syntax dependency tree, based on which the connection between different words can be captured. In addition, on the task of sentiment classification, the integration of semantic information and the syntactic information is conducted by using a combinational gated mechanism. Substantial experiments to validate the working performance of Bi-B GCN are performed on a variety of datasets. The encouraging results establish a strong evidence of the high accuracy of the proposed model.