Reviewerly/exHarmony huggingface dataset:
Friendly Training: Neural Networks Can Adapt Data To Make Learning Easier

In the last decade, motivated by the success of Deep Learning, the scientific community proposed several approaches to make the learning procedure of Neural Networks more effective. When focussing on the way in which the training data are provided to the learning machine, we can distinguish between the classic random selection of stochastic gradient-based optimization and more involved techniques that devise curricula to organize data, and progressively increase the complexity of the training set. In this paper, we propose a novel training procedure named Friendly Training that, differently from the aforementioned approaches, involves altering the training examples in order to help the model to better fulfil its learning criterion. The model is allowed to “simplify” those examples that are too hard to be classified at a certain stage of the training procedure. The data transformation is controlled by a developmental plan that progressively reduces its impact during training, until it completely vanishes. In a sense, this is the opposite of what is commonly done in order to increase robustness against adversarial examples, i.e., Adversarial Training. Experiments on multiple datasets are provided, showing that Friendly Training yields improvements with respect to informed data sub-selection routines and random selection, especially in deep convolutional architectures. Results suggest that adapting the input data is a feasible way to stabilize learning and improve the generalization skills of the network.
Complexity and Performance of Secure Floating-Point Polynomial Evaluation Protocols

Secure computation provides cryptographic protocols for collaborative applications with private inputs and outputs. In this paper, we examine a collection of protocols for secure evaluation of polynomials using secure floating-point arithmetic. The main goal is to provide a comparative analysis of their construction, complexity, performance, and tradeoffs in different application settings. The analysis demonstrates the performance gains that can be obtained by evaluating the polynomials using optimized secure multi-operand arithmetic instead of relying on generic constructions based on two-operand arithmetic. It also examines the relations between performance and complexity metrics for different execution environments (LAN, Internet), floating-point precision, and problem sizes. These protocols are part of a framework for secure multiparty computation with fixed-point and floating-point numbers based on Shamir secret sharing and related techniques.
Activity Knowledge Graph Recognition by Eye Gaze: Identification of Distant Object in Eye Sight for Watch Activity

From the side of upper-level applications which require planning the actions in robot or those which need to search the whole log of activities in smart home, the action predicate expressions in the form of knowledge graphs may play an important role. The sequence of activities alone, which can be supplied by the conventional activity recognition systems, may not be sufficient for those applications. The subject of the particular activity is crucial information in most of the cases, and the object of the particular activity is often necessary to identify the characteristics. From this perspective, we have investigated the activities recognized by activity recognition systems, trying to identify their hidden elements which play the role of the subject and the object of the activities, i.e. activity knowledge graph. If we focus on these hidden elements, they are categorized in two: (1) person (subject) - person (object) interaction, and (2) person (subject) - object (object) interactions. Depending on the class of activities, these two are sometimes faced great difficulties: the hidden elements for walk, pick-up, open, and drink are quite easy but those for look-at, see, watch, and throw are difficult. The source of difficulties arises from the fact that the object (object) is not contacted from the person (subject). In this paper we have developed a method which identifies non-contacted object by the direction of the eye gaze of the person (subject) in the category of watch (activity). Using ”Watching TV” data by Stair lab, the proposed system achieved 85% in accuracy.
Specializing generic Java data structures

The Collections framework is an essential utility in virtually every Java application. It offers a set of fundamental data structures that exploit Java Generics and the Object type in order to enable a high degree of reusability. Upon instantiation, Collections are parametrized by the type they are meant to store. However, at compile-time, due to type erasure, this type gets replaced by Object, forcing the data structures to manipulate references of type Object (the root of the Java type system). In the bytecode, the compiler transparently adds type checking instructions to ensure type safety, and generates bridge methods to enable the polymorphic behavior of parametrized classes. This approach can introduce non-trivial runtime overheads when applications extensively manipulate Collections.
Group’s Influence Value in Logistic Regression Model and Gradient Boosting Model

Measuring the influence of particular data point is an important task in designing a data hub. In recent years, several research works have addressed the problem. In this paper we analyze these approaches and show that they are too complicated to apply in practice. We propose a new lightweight approach to approximate the influence of data points. We evaluate our proposal on the popular Home Credit dataset to show the effective.
KDM Security for the Fujisaki-Okamoto Transformations in the QROM

Key dependent message (KDM) security is a security notion that guarantees confidentiality of communication even if secret keys are encrypted. KDM security has found a number of applications in practical situations such as hard-disk encryption systems, anonymous credentials, and bootstrapping of fully homomorphic encryption. Recently, it also found an application in quantum delegation protocols as shown by Zhang (TCC 2019). In this work, we investigate the KDM security of existing practical public-key encryption (PKE) schemes proposed in the quantum random oracle model (QROM). Concretely, we study a PKE scheme whose KEM is constructed by using Fujisaki-Okamoto (FO) transformations in the QROM. FO transformations are applied to IND-CPA secure PKE schemes and yield IND-CCA secure key encapsulation mechanisms (KEM). Then, we show the following results. In order to avoid square root security loss, we use a double-sided one-way to hiding (O2H) lemma proposed by Kuchta et al. (EUROCRYPT 2020). In the context of KDM security, there is a technical hurdle for using double-sided O2H lemma due to the circularity issue. Our main technical contribution is to overcome the hurdle.
Impact of AVX-512 Instructions on Graph Partitioning Problems.

Graph analysis now percolates society with applications ranging from advertising and transportation to medical research. The structure of graphs is becoming more complex every day while they are getting larger. The increasing size of graph networks has made many of the classical algorithms reasonably slow. Fortunately, CPU architectures have evolved to adjust to new and more complex problems in terms of core-level parallelism and vector-level parallelism (SIMD-level).
Cycle types of complete mappings of finite fields

We derive several existence results concerning cycle types and, more generally, the “mapping behavior” of complete mappings. Our focus is on so-called first-order cyclotomic mappings, which are functions on a finite field Fq that fix 0 and restrict to the multiplication x↦aix by a fixed element ai∈Fq on each coset Ci of a given subgroup C of Fq⁎. The gist of two of our main results is that as long as q is large enough relative to the index |Fq⁎:C|, all cycle types of first-order cyclotomic permutations with only long cycles on Fq⁎ can be achieved through a complete mapping, as can all permutations of the cosets of C. Our third main result provides new examples of complete mappings f such that both f and its associated orthomorphism f+id permute the nonzero field elements in one cycle.
Distilling the Long-Range Knowledge for Real-Time Semantic Segmentation

Deep convolutional networks have achieved great success in semantic segmentation. However, existing semantic segmentation networks are difficult to achieve an ideal balance between accuracy and speed. To tackle this dilemma, the key idea of this paper is to design a two-part knowledge distillation framework to improve the accuracy of real-time networks, including separated distillation and long-range distillation. Separated distillation treats semantic segmentation as an independent pixel classification task, aiming to align logits between a cumbersome teacher network and a lightweight student network. To extract the long-range knowledge from the scene, the similarity attention map is defined as knowledge, which is transfered from teacher to student through the long-range distillation. The framework proposed in this paper improves the accuracy of real-time networks on Cityscapes and Fisheye datasets by 2.7% and 1.7%, respectively. The distilled network is tested on NVIDIA Xavier embedded platform with speed of 15FPS. Code will be available here: https://github.com/WangYunnan/Real-Time-Semantic-Segmentation.
Prototypical Models for Classifying High-Risk Atypical Breast Lesions

High-risk atypical breast lesions are a notoriously difficult dilemma for pathologists who diagnose breast biopsies in breast cancer screening programs. We reframe the computational diagnosis of atypical breast lesions as a problem of prototype recognition on the basis that pathologists mentally relate current histological patterns to previously encountered patterns during their routine diagnostic work. In an unsupervised manner, we investigate the relative importance of ductal (global) and intraductal patterns (local) in a set of pre-selected prototypical ducts in classifying atypical breast lesions. We conducted experiments to test this strategy on subgroups of breast lesions that are a major source of inter-observer variability; these are benign, columnar cell changes, epithelial atypia, and atypical ductal hyperplasia in order of increasing cancer risk. Our model is capable of providing clinically relevant explanations to its recommendations, thus it is intrinsically explainable, which is a major contribution of this work. Our experiments also show state-of-the-art performance in recall compared to the latest deep-learning based graph neural networks (GNNs).
High-Quality Sonar Image Generation Algorithm Based on Generative Adversarial Networks

In this paper, we propose a high-quality sonar image generation model based on generative adversarial networks, which is used to generate user-controlled high-resolution and high-quality sonar images, so that the targets in these images have more obvious features and can be further used in engineering applications such as target detection and image classification. As a result of the development of underwater detection technology, sonar target detection technology is widely used in various engineering fields. High resolution and high-quality underwater sonar images have become an indispensable part of underwater detection. However, due to the high cost of sonar and the high cost of marine experiments, sonar datasets are scarce. Even then, few sonar data can be obtained, and most images have no target and have more noise so that they cannot be used for underwater detection. In recent years, using generative adversarial networks to generate images has gradually become an important method of solving the problem of data scarcity. But there are still some shortcomings — the images produced are poor and the resolution is low. To overcome the issue of poor image quality and low resolution caused by gradient disappearing and sudden change in the training process, we, therefore, use a controllable multi-layer transposed convolutional layer and gradient correction term to improve image resolution and image quality. A simple network structure does not make the model difficult to train, and the user may change the size of the generated image by simply changing the convolution parameter i to achieve the objective of improving the resolution. Moreover, the gradient correction term added in the training phase will always restrict the gradient to a certain value. To a certain degree, the problems of gradient disappearing and gradient sudden change in model training can be overcome, thereby providing a guarantee for the production of high-quality images. Experiments demonstrate that this method will successfully increase the resolution and image quality of the GAN-based image and make the image target more apparent.
Speech Recognition Method Based on Deep Learning and Its Application

To generate better evaluation and feedback in speech recognition for language learning, this paper proposes a computer-aided training scheme. We first analyze the speech processing flow of the foreign language training and learning system based on speech recognition technology. Then HMM based speech recognition technology for feature parameter extraction is adopted for codebook generation and template training and the improved Viterbi model is used to reduce the amount of Gauss computation. Finally, the expert database is used to correct phonemes, illustrated by oral English training in real environment. With the real scene data of large-scale oral English tests, the proposed method improves the recognition accuracy by 15%, which can provide learners timely, accurate and objective evaluation and feedback guidance.
Lanczos Algorithm for 2DPCA

Abstract The traditional PCA algorithm (Principle Component Analysis) can obtain the feature space of face image and realize face recognition by expanding the face image matrix into vectors in face recognition. 2DPCA (Two-dimensional Principle Component Analysis) doesn’t need to spread the image matrix into one-dimensional vectors. The covariance matrix is constructed directly by using two-dimensional image matrix, so that the calculation of eigenvalue and eigenvector is simplified. We apply the Lanczos algorithm to 2 DPCA in this paper for Indian faces. The numerical experiments show that our method runs much faster and gets better recognition rates than the traditional 2DPCA algorithm.
Optimizing Greedy Algorithm to Balance the Server Load in Cloud Simulated Environment

The performance of cloud services depends on the scheduling algorithms that distribute the incoming network traffic among their servers to achieve effectiveness in execution of tasks. These algorithms are assigning the tasks to various computing resources, and these resources are virtual in nature. In cloud, assigning tasks to corresponding resources are NP-hard in nature. The traditional scheduling algorithms like FCFS, SJF, Round Robin etc. will not be suitable to solve NP-hard scheduling problems. Cloud scheduling considers various criteria like resource utilization, cost, makespan and throughput. This paper has implemented the cloud scheduling algorithms such as Max-Min Algorithm, Min-Min Algorithm, Enhanced Max-Min Algorithm and Greedy Algorithm to balance the server load in cloud environment and have analyzed the results of these algorithms to identify the best scheduling algorithm. Results discussed in this paper have shown that, when the numbers of tasks are more, greedy algorithm outperform other scheduling algorithms while for less number of tasks, Enhanced Max-Min algorithm performs extremely well as compared to another task scheduling algorithm.
Graphical Transformation of OWL Ontologies to Event-B Formal Models

Formal methods use mathematical models to develop systems. Ontologies are formal specifications that provide reusable domain knowledge representations. Ontologies have been successfully used in several data-driven applications, including data analysis. However, the creation of formal models from informal requirements demands skill and effort. Ambiguity, inconsistency, imprecision, and incompleteness are major problems in informal requirements. To solve these problems, it is necessary to have methods and approaches for supporting the mapping of requirements to formal specifications. The purpose of this paper is to present an approach that addresses this challenge by using the Web Ontology Language (OWL) to construct Event-B formal models and support data analysis. Our approach reduces the burden of working with the formal notations of OWL ontologies and Event-B models and aims to analyze domain knowledge and construct Event-B models from OWL ontologies using visual diagrams. The idea is based on the transformation of OntoGraf diagrams of OWL ontologies to UML-B diagrams for the purpose of bridging the gap between OWL ontologies and Event-B models. Visual data exploration assists with both data analysis and the development of Event-B formal models. To manage complexity, Event-B supports stepwise refinement to allow each requirement to be introduced at the most appropriate stage in the development process. UML-B supports refinement, so we also introduce an approach that allows us to divide and layer OntoGraf diagrams.
A new approach to online training for the Fuzzy ARTMAP artificial neural network

The evolution of internet resources has led to an increase in the flow of data and, consequently, the need for classification or forecasting models that support online learning. The Fuzzy ARTMAP neural network has been used in the most areas of knowledge; however, few have explored real-time applications that require continuous training. In this work, a Fuzzy ARTMAP neural network with continuous training is proposed. This new network can acquire knowledge via classification or prediction. Modifications made to the architecture and learning algorithm enable online learning from the first sample of data and perform the classification or forecasting at any time during training. To validate the proposed model, three experiments were performed, one for forecasting and two for classification. Each experiment used benchmark databases and compared its final results with the results of the original Fuzzy ARTMAP neural network. The results demonstrate the ability of the proposed model to acquire knowledge from the first data samples in a stable and efficient way. Thus, this study contributes to the evolution of the Fuzzy ARTMAP neural network and introduces the continuous training method as an effective alternative to real-time applications. • A new approach is proposed to online training from the first samples. • The prediction or classification can be performed at any time during training. • The method selects the most relevant data to incorporate as permanent knowledge. • Significant improvements in accuracy and category proliferation have been achieved.
Scalable Transformation of Big Geospatial Data into Linked Data

In the era of big data, a vast amount of geospatial data has become available originating from a large diversity of sources. In most cases, this data does not follow the linked data paradigm and the existing transformation tools have been proved ineffective due to the large volume and velocity of geospatial data. This is because none of the existing tools can utilize effectively the processing power of clusters of computers. We present the system GeoTriples-Spark which is able to massively transform big geospatial data into RDF graphs using Apache Spark. We evaluate GeoTriple-Spark’s performance and scalability in standalone and distributed environments and show that it exhibits superior performance and scalability when compared to all of its competitors.
Secure, Decentralized, Privacy Preserving Machine Learning System Implementation over Blockchain

The traditional approach to centralized machine learning has transparency concerns. The future of machine learning is decentralized machine learning. Thus, many technological advance companies including Microsoft are also investing in researching approaches to decentralization in machine learning. With the upliftment of big data technology, designing optimized artificial intelligence algorithms is a must need. At the base of every machine learning algorithm we need data. Data is something that can be any unprocessed fact, value, text, sound or picture that is not being interpreted and analyzed. This data is not generated by just one party, multiple parties generate such data. The data will be geographically distributed amongst organizations. This pushes the need and research of distributed machine learning algorithms. In the current scenario, there is a central server which will run the machine learning algorithm and produce results, in this system obviously we need to collect all the data at that server itself. If the server is attacked then there is a problem of security of data. Also many organizations would not like to just lend their data to some third party. To address all such issues, we study all the possible ways for implementing a distributed machine learning system and propose a blockchain based distributed conservative system. Mainly, we design a Stochastic Gradient Descent (SGD) algorithm to learn a general predictive model over the trending blockchain technology, also taking care of Byzantine attack, using the within-N algorithm. Also analysis will be made on different machine learning algorithms and datasets as a part of testing, demonstrating the effectiveness of the model.
Research on the Evolution Path of Sentiment Analysis Technology Based on Bibliometrics

Sentiment analysis is a natural language processing technology with functions such as mining sentiment tendency classification and extracting sentiment features in texts. It is widely used in government and enterprise public opinion monitoring or product market research tasks. In order to understand the evolution process of the field of sentiment analysis at home and abroad, it is convenient for scholars to locate the knowledge structure and the development trend of the prediction field as a whole. This article uses social network tools such as CiteSpace and Pajek to collect 2260 document data in the core of Web of Science, and uses statistical analysis, co-occurrence and cluster analysis to reveal the phased characteristics of the evolution of the field and promote the object and the topic structure, through the co-citation analysis of the literature, the detection analysis of inflated words, the calculation of network characteristics, and the literature review method, it explains the phased evolutionary power, knowledge base and frontier hot spots from both qualitative and quantitative aspects. The main results show that the field of sentiment analysis can be divided into three stages according to the law of evolution, and the realization method dimension in the domain dimension is the main line of evolution throughout all stages.
Joint Face Detection and Landmark Localization Based on an Extremely Lightweight Network

Face detection and landmark localization are necessary steps in most face applications. At present, the method based on deep learning has shown obvious advantages in effect. However, most neural networks are computationally expensive and require special hardware for acceleration. To widely applied in real-world tasks, it is necessary to design a tiny model with fewer parameters, less computation cost, and fine performance. Therefore, we propose an extremely lightweight backbone for building a YOLOv3-style joint face detection and landmark localization model while compressing the parameters to the 0.15M level. We compare the proposed face detector with representative methods on the public benchmark. The results show that our proposed method can achieve performance much close to the representative face detector while a two-thirds reduction in the numbers of parameters and the computing costs. Moreover, our model has a lower failure rate (10%) of landmark localization and more robust.