Reviewerly/exHarmony huggingface dataset:
Multifaceted Analysis of Fine-Tuning in Deep Model for Visual Recognition

In recent years, convolutional neural networks (CNNs) have achieved impressive performance for various visual recognition scenarios. CNNs trained on large labeled datasets can not only obtain significant performance on most challenging benchmarks but also provide powerful representations, which can be used to a wide range of other tasks. However, the requirement of massive amounts of data to train deep neural networks is a major drawback of these models, as the data available is usually limited or imbalanced. Fine-tuning (FT) is an effective way to transfer knowledge learned in a source dataset to a target task. In this paper, we introduce and systematically investigate several factors that influence the performance of fine-tuning for visual recognition. These factors include parameters for the retraining procedure (e.g., the initial learning rate of fine-tuning), the distribution of the source and target data (e.g., the number of categories in the source dataset, the distance between the source and target datasets) and so on. We quantitatively and qualitatively analyze these factors, evaluate their influence, and present many empirical observations. The results reveal insights into what fine-tuning changes CNN parameters and provide useful and evidence-backed intuitions about how to implement fine-tuning for computer vision tasks.
Improving the Performance of the LSTM and HMM Models via Hybridization.

Language models based on deep neural networks and traditional stochastic modelling have become both highly functional and effective in recent times. In this work, a general survey into the two types of language modelling is conducted. We investigate the effectiveness of the Hidden Markov Model (HMM), and the Long Short-Term Memory Model (LSTM). We analyze the hidden state structures common to both models, and present an analysis on structural similarity of the hidden states, common to both HMM's and LSTM's. We compare the LSTM's predictive accuracy and hidden state output with respect to the HMM for a varying number of hidden states. In this work, we justify that the less complex HMM can serve as an appropriate approximation of the LSTM model.
PEDAGOGICAL PRESS AS A SOURCE OF INFORMATION ABOUT THE DEVELOPMENT OF TEACHERS’ PROFESSIONAL SKILLS BY MEANS OF THEATRICAL ART

The article analyzes publications in the pedagogical press of the Soviet period (Radianska pedahohika, Radianska shkola) and traces the development process of professional mastery of teachers by means of theatrical art; the professional deficiencies of teachers in the field of theatrical creativity are revealed; the importance of using the forms, methods, means of theatrical art in pedagogical practice of teachers is revealed; the problem of artistic and aesthetic education of students in the educational process of the school is highlighted; the focus is on the creative collaboration of theatrical groups with teachers of general education institutions.It was identified that despite of ideological features of almost every analyzed published work it provided the following issues: artistic and creative experience of school work, outstanding pedagogue-practitioners, heads, and experience of curricular, extra-curricular and campus activities of teacher using methods, forms, approaches, and means of performing arts in educational process; collaboration of schools and theatres (TYS (Theatre of young spectators), MAAT (M. Gorky Moscow Artistic Academia Theatre)) including school teachers with theatre pedagogues; improvement of teacher’s professional skills in the context of artistic (theatre) amateur activities (school teacher theatre, drama club). Performing arts in development of teacher’s professional skills was determined by the authorities’ careful attention to performing arts in the aspect of ways of ideological impact on individual consciousness. It was indicated that in the educational process teacher’s use of means of performing arts depended on enactments, decisions, tasks managed by the authorities. In the process of investigation it was indicated that within the similarity of subject matter of journal published works the last ones highlight the necessity to improve teacher’s professional skills in the context of artistic activities in particular teachers’ theatres and drama clubs.
Dynamic Consolidation of Virtual Machine: A Survey of Challenges for Resource Optimization in Cloud Computing

Background:: Virtualization is an efficient technology that accelerates available data center to support efficient workload for the application. It completely based on guest operating system which keeps track of infrastructure that keeps track of real time usage of hardware and utilization of software. Objective:: To address the issues with Virtualization this paper analyzed various virtualization terminology for treating best effective way to reduce IT expenses while boosting efficiency and deployment for all levels of businesses. Methods: This paper discusses about the scenarios where various challenges met by Dynamic VM consolidation. Dynamic conclusion of virtual machines has the ability to increase the consumption of physical setup and focus on reducing power utilization with VM movement for stipulated period. Gathering the needs of all VM working in the application, adjusting the Virtual machine and suitably fit the virtual resource on a physical machine. Profiling and scheduling the virtual CPU to another Physical resource. This can be increased by making live migration with regards to planned schedule of virtual machine allotment. Results:: The recent trends followed in comprehending dynamic VM consolidation is applicable either in heuristic-based techniques which has further approaches based on static as well as adaptive utilization threshold. SLA with unit of time with variant HOST adoption (SLATAH) which is dependent on CPU utilization threshold with 100% for active host. Conclusion:: The cloud provider decision upon choosing the virtual machine for their application also varies with their decision support system that considers data storage and other parameters. It is being compared for the continuous workload distribution as well as eventually compared with changing demands of computation and in various optimization VM placement strategies.
Adversarial Security Attacks and Perturbations on Machine Learning and Deep Learning Methods

The ever-growing big data and emerging artificial intelligence (AI) demand the use of machine learning (ML) and deep learning (DL) methods. Cybersecurity also benefits from ML and DL methods for various types of applications. These methods however are susceptible to security attacks. The adversaries can exploit the training and testing data of the learning models or can explore the workings of those models for launching advanced future attacks. The topic of adversarial security attacks and perturbations within the ML and DL domains is a recent exploration and a great interest is expressed by the security researchers and practitioners. The literature covers different adversarial security attacks and perturbations on ML and DL methods and those have their own presentation styles and merits. A need to review and consolidate knowledge that is comprehending of this increasingly focused and growing topic of research; however, is the current demand of the research communities. In this review paper, we specifically aim to target new researchers in the cybersecurity domain who may seek to acquire some basic knowledge on the machine learning and deep learning models and algorithms, as well as some of the relevant adversarial security attacks and perturbations.
Your coflow has many flows: sampling them for fun and speed

Coflow scheduling improves data-intensive application performance by improving their networking performance. State-of-the-art online coflow schedulers in essence approximate the classic Shortest-Job-First (SJF) scheduling by learning the coflow size online. In particular, they use multiple priority queues to simultaneously accomplish two goals: to sieve long coflows from short coflows, and to schedule short coflows with high priorities. Such a mechanism pays high overhead in learning the coflow size: moving a large coflow across the queues delays small and other large coflows, and moving similar-sized coflows across the queues results in inadvertent round-robin scheduling.We propose Philae, a new online coflow scheduler that exploits the spatial dimension of coflows, i.e., a coflow has many flows, to drastically reduce the overhead of coflow size learning. Philae pre-schedules sampled flows of each coflow and uses their sizes to estimate the average flow size of the coflow. It then resorts to Shortest Coflow First, where the notion of shortest is determined using the learned coflow sizes and coflow contention. We show that the sampling-based learning is robust to flow size skew and has the added benefit of much improved scalability from reduced coordinator-local agent interactions. Our evaluation using an Azure testbed, a publicly available production cluster trace from Facebook shows that compared to the prior art Aalo, Philae reduces the coflow completion time (CCT) in average (P90) cases by 1.50× (8.00×) on a 150-node testbed and 2.72× (9.78×) on a 900-node testbed. Evaluation using additional traces further demonstrates Philae's robustness to flow size skew.
Learning Neural Networks with Adaptive Regularization

Feed-forward neural networks can be understood as a combination of an intermediate representation and a linear hypothesis. While most previous works aim to diversify the representations, we explore the complementary direction by performing an adaptive and data-dependent regularization motivated by the empirical Bayes method. Specifically, we propose to construct a matrix-variate normal prior (on weights) whose covariance matrix has a Kronecker product structure. This structure is designed to capture the correlations in neurons through backpropagation. Under the assumption of this Kronecker factorization, the prior encourages neurons to borrow statistical strength from one another. Hence, it leads to an adaptive and data-dependent regularization when training networks on small datasets. To optimize the model, we present an efficient block coordinate descent algorithm with analytical solutions. Empirically, we demonstrate that the proposed method helps networks converge to local optima with smaller stable ranks and spectral norms. These properties suggest better generalizations and we present empirical results to support this expectation. We also verify the effectiveness of the approach on multiclass classification and multitask regression problems with various network structures.
Textual Realities: An Aristotelian Realist Ontology of Textual Entities

This paper is a study conducted within the domain of textual scholarship, which develops a cartographic ontology of the four primary entity types dealt with by scholars working within this domain: (1) the text, (2) the document, (3) the version, (4) and the work. The specific ontology advanced is an Aristotelian realist treatment of the four entity types, which synthesizes (a) implicit Aristotelian ontologies present in the theoretical textual scholarly works of Greg, Tanselle, and Shillingsburg, with (b) explicit Aristotelian ontologies of textual entities put forward by Gracia and Bruin, to (c) develop a novel position that best describes the realities of these four entity types within two specific Aristotelian ontological frameworks: (1) the fourfold model of causation, and (2) the substance/accidence theory.
Automatic Recognition of Legal Amount Words of Bank Cheques in Devanagari Script: An Approach Based on Information Fusion at Feature and Decision Level

Legal amount word recognition is an essential and challenging task in the domain of automatic Indian bank cheque processing. Further intricacies get accumulated by inherent complexities in Devanagari script besides cursiveness present in handwriting. Due to segmentation ambiguity and variability of constituent parts present in handwritten word analytical approach is inadequate, in contrast, to the holistic paradigm, where the word is taken indivisible entity. Despite the proliferation of various feature representations, it still remains a challenge to get effective representation/description for holistic Devanagari words. In this paper, we made an attempt to exploit robust, most discriminative and computationally inexpensive Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) for effective characterization of Devanagari legal amount words taking into account different writing styles and cursiveness. Two models are proposed based on fusion strategies for word recognition. In the first model, LBP and HOG features are fused at feature level and in second, fused at decision level. In both models, recognition is performed by the nearest neighbour (NN) and support vector machine (SVM) classifiers. For corroboration of the results, extensive experiments have been carried out on ICDAR 2011 Devanagari Legal amount word dataset. Experimental results reveal that fusion based approaches are more robust than conventional approaches.
Introduction to genetic programming

tutorial Open Access Share on Introduction to genetic programming Authors: Una-May O'Reilly MIT CSAIL MIT CSAILView Profile , Erik Hemberg MIT CSAIL MIT CSAILView Profile Authors Info & Claims GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2019 Pages 710–725https://doi.org/10.1145/3319619.3323398Published:13 July 2019Publication History 0citation306DownloadsMetricsTotal Citations0Total Downloads306Last 12 Months72Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Spontaneous visual search during the first two years: Improvement with age but no evidence of efficient search

Efficient visual search, wherein reaction times to acquire targets are largely independent of array size, is commonly observed in adults. Evidence for efficient search in infants may imply that selective attention to visual features is similar across development. In the current cross-sectional eye-tracking study, we examined spontaneous visual search at 6, 12, 18, and 24 months. Infants were presented with Random arrays (one target among 7, 13, or 26 pseudorandomly distributed elements) and Circle arrays (one target among 4, 7, or 13 elements arranged in a circle). Contrary to predictions, we did not find evidence of efficient search among infants. With increasing array size, time-to-target increased, the proportion of targets fixated (analogous to accuracy) decreased, and the proportion of first looks to the target decreased for both types of array (ps < .001). For Random arrays, the proportion of first looks to the target was similar to chance for all ages and array sizes; for Circle arrays, it exceeded chance for some ages and array sizes. The proportion of targets fixated and first looks to target increased with age across display types (ps < .05). We also tested adults with the same stimuli under similar conditions; the adults showed evidence of efficient visual search. Possible explanations and implications are discussed.
Fake Bitrate Detection of HEVC Videos Based on Prediction Process

In order to defraud click-through rate, some merchants recompress the low-bitrate video to a high-bitrate one without improving the video quality. This behavior deceives viewers and wastes network resources. Therefore, a stable algorithm that detects fake bitrate videos is urgently needed. High-Efficiency Video Coding (HEVC) is a worldwide popular video coding standard. Hence, in this paper, a robust algorithm is proposed to detect HEVC fake bitrate videos. Firstly, five effective feature sets are extracted from the prediction process of HEVC, including Coding Unit of I-picture/P-picture partitioning modes, Prediction Unit of I-picture/P-picture partitioning modes, Intra Prediction Modes of I-picture. Secondly, feature concatenation is adopted to enhance the expressiveness and improve the effectiveness of the features. Finally, five single feature sets and three concatenate feature sets are separately sent to the support vector machine for modeling and testing. The performance of the proposed algorithm is compared with state-of-the-art algorithms on HEVC videos of various resolutions and fake bitrates. The results show that the proposed algorithm can not only can better detect HEVC fake bitrate videos, but also has strong robustness against frame deletion, copy-paste, and shifted Group of Picture structure attacks.
Latent Adversarial Defence with Boundary-guided Generation

Deep Neural Networks (DNNs) have recently achieved great success in many tasks, which encourages DNNs to be widely used as a machine learning service in model sharing scenarios. However, attackers can easily generate adversarial examples with a small perturbation to fool the DNN models to predict wrong labels. To improve the robustness of shared DNN models against adversarial attacks, we propose a novel method called Latent Adversarial Defence (LAD). The proposed LAD method improves the robustness of a DNN model through adversarial training on generated adversarial examples. Different from popular attack methods which are carried in the input space and only generate adversarial examples of repeating patterns, LAD generates myriad of adversarial examples through adding perturbations to latent features along the normal of the decision boundary which is constructed by an SVM with an attention mechanism. Once adversarial examples are generated, we adversarially train the model through augmenting the training data with generated adversarial examples. Extensive experiments on the MNIST, SVHN, and CelebA dataset demonstrate the effectiveness of our model in defending against different types of adversarial attacks.
Construction of arithmetic secret sharing schemes by using torsion limits

Cascudo, Cramer, and Xing [Torsion limits and Riemann-Roch systems for function fields and applications, IEEE Trans. Inf. Theory, 2014] gave a construction of arithmetic secret sharing schemes by using the torsion limits of algebraic function fields and Riemann-Roch systems. In this work, we give some new conditions for the construction of arithmetic secret sharing schemes. Furthermore, we give new bounds on the torsion limits of certain towers of function fields over finite fields.
cs-means: Determining optimal number of clusters based on a level-of-similarity

This paper proposes a centroid-based clustering algorithm which is capable of clustering data-points with n-features, without having to specify the number of clusters to be formed. The core logic behind the algorithm is a similarity measure, which collectively decides whether to assign an incoming data-point to a pre-existing cluster, or create a new cluster and assign the data-point to it. The proposed clustering algorithm is application-specific and is applicable when the need is to perform clustering analysis of a stream of data-points, where the similarity measure between an incoming data-point and the cluster to which the data-point is to be associated with, is greater than the predefined Level-of-Similarity.
Explicit Conditionals in the Framework of Classical Conditional Logic

The paper proposes a first approach to systems whose language includes two primitives (&gt;+ and &gt;-) as symbols for factual and counterfactual conditionals which are explicit, i.e. that are stated jointly with the truth or falsity of the antecedent clause. In systems based on this language, here called 2-conditional, the standard corner operator may be defined by (Def&gt;) A &gt; B := (A &gt;+ B)∨(A &gt;- B), while in classical conditional systems one could introduce the two symbols for explicit conditionals by the definitions (Def&gt;+) A &gt;+ B := A∧(A&gt;B) and (Def&gt;-) A&gt;- B := ¬A∧(A&gt;B). Two 2-conditional systems, V² and VW², are axiomatized and proved to be definitionally equivalent to the monoconditional systems V and VW. A third system VWTr² is characterized by an axiom stating the transitivity of factual conditionals and is shown to be distinct from V², from VW² and from the 2-conditional version of Lewis’ well-known system VC, here named VC². The same may be said for a fourth system VW²◊ ±, based on an axiom inderivable in VC²: ◊(A &gt;+ B) ⊃ ◊( ¬A &gt;- ¬B). VC² contains what is here called a “semi-collapse” of the operator &gt;+ and it is argued that it is inadequate as a logic for both factual and counterfactual conditionals. The last section shows that several different definitions of the corner operators in terms of &gt;+ and &gt;- may be introduced as an alternative to (Def&gt;).
Graph Neural Lasso for Dynamic Network Regression

The regression of multiple inter-connected sequence data is a problem in various disciplines. Formally, we name the regression problem of multiple inter-connected data entities as the "dynamic network regression" in this paper. Within the problem of stock forecasting or traffic speed prediction, we need to consider both the trends of the entities and the relationships among the entities. A majority of existing approaches can't capture that information together. Some of the approaches are proposed to deal with the sequence data, like LSTM. The others use the prior knowledge in a network to get a fixed graph structure and do prediction on some unknown entities, like GCN. To overcome the limitations in those methods, we propose a novel graph neural network, namely Graph Neural Lasso (GNL), to deal with the dynamic network problem. GNL extends the GDU (gated diffusive unit) as the base neuron to capture the information behind the sequence. Rather than using a fixed graph structure, GNL can learn the dynamic graph structure automatically. By adding the attention mechanism in GNL, we can learn the dynamic relations among entities within each network snapshot. Combining these two parts, GNL is able to model the dynamic network problem well. Experimental results provided on two networked sequence datasets, i.e., Nasdaq-100 and METR-LA, show that GNL can address the network regression problem very well and is also very competitive among the existing approaches.
How Satellite Data Improve Earthquake Monitoring

Case studies from around the world illustrate the power of geodetic data in earthquake monitoring.
A New computation reduction based nonlinear Kalman filter

This article introduces a new algorithm for nonlinear state estimation based on deterministic sigma point and EKF linearized framework for priori mean and covariance respectively. This method reduces the computation cost of UKF about 50% and has better accuracy compared to EKF due to propagating mean and Covariance of state to 3rd order Taylor series. Several types of Kalman filter have been presented before to reduce the computation cost of UKF, however, this new KF is a better choice because of its simplicity, numerical stability, and accuracy for real-time implementation. Examples verify the effectiveness of the proposed method.