Reviewerly/exHarmony huggingface dataset:
MathNET: Using CNN Bangla Handwritten Digit, Mathematical Symbols, and Trigonometric Function Recognition

Scientific methods are mostly based on mathematical solutions. Optical character recognition (OCR) is one of the most required solution for digitalizing and/or processing the handwritten documents into any digital form. Therefore, the movement toward the use of digital documents in the scientific community has significantly increased. Scientific documents and more precise mathematical documents are sometimes initially drafted in handwritten form. In this scope, to let understand the machine about those mathematical equations, so in this paper, a research is done on scripted mathematical symbol recognition model, which was built of 32,400 images dataset. So many research in English handwritten OCR has already achieved very good accuracy, but for Bengali language handwritten OCR, there are a minimum number of research that is done till now. This dataset contains samples of handwritten mathematical symbols and especially Bengali numerical digits. In detail, this dataset captures—Bangla numerical digits, operators of algebra, set-symbols, limit, calculus, symbols of comparison, delimiters, etc. Thus, MathNET is presented as a model that helps to recognize 10 handwritten Bangla digits and 44 other handwritten mathematical symbols. The planned model training accuracy is 96.01% and validation accuracy with test data is 96.50%, which is very good precision for identification of mathematical symbols.
Uncertainty-Aware Weakly Supervised Action Detection from Untrimmed Videos

Despite the recent advances in video classification, progress in spatio-temporal action recognition has lagged behind. A major contributing factor has been the prohibitive cost of annotating videos frame-by-frame. In this paper, we present a spatio-temporal action recognition model that is trained with only video-level labels, which are significantly easier to annotate. Our method leverages per-frame person detectors which have been trained on large image datasets within a Multiple Instance Learning framework. We show how we can apply our method in cases where the standard Multiple Instance Learning assumption, that each bag contains at least one instance with the specified label, is invalid using a novel probabilistic variant of MIL where we estimate the uncertainty of each prediction. Furthermore, we report the first weakly-supervised results on the AVA dataset and state-of-the-art results among weakly-supervised methods on UCF101-24.
Deep Learning Based Vehicle Detection with Images Taken from Unmanned Air Vehicle

Especially in recent years, unmanned aerial vehicles (UAV) have been used in many platforms for military, commercial and entertainment purposes. Thanks to high computational graphics cards, many computer vision applications based on deep learning can work in real time on unmanned aerial vehicles. In this study, fast and high-performance object detection algorithms Faster R-CNN, YOLOv3-Tiny and SSD algorithms are used to detect vehicles in image taken from UAVs. Thus, the positions of vehicles driving in traffic are found automatically.
Using Hadoop Technology to Overcome Big Data Problems by Choosing Proposed Cost-efficient Scheduler Algorithm for Heterogeneous Hadoop System (BD3)

Day by day advanced web technologies have led to tremendous growth amount of daily data generated volumes. This mountain of huge and spread data sets leads to phenomenon that called big data which is a collection of massive, heterogeneous, unstructured, enormous and complex data sets. Big Data life cycle could be represented as, Collecting (capture), storing, distribute, manipulating, interpreting, analyzing, investigate and visualizing big data. Traditional techniques as Relational Database Management System (RDBMS) couldn’t handle big data because it has its own limitations, so Advancement in computing architecture is required to handle both the data storage requisites and the weighty processing needed to analyze huge volumes and variety of data economically. There are many technologies manipulating a big data, one of them is hadoop. Hadoop could be understand as an open source spread data processing that is one of the prominent and well known solutions to overcome handling big data problem. Apache Hadoop was based on Google File System and Map Reduce programming paradigm. Through this paper we dived to search for all big data characteristics starting from first three V's that have been extended during time through researches to be more than fifty six V's and making comparisons between researchers to reach to best representation and the precise clarification of all big data V’s characteristics. We highlight the challenges that face big data processing and how to overcome these challenges using Hadoop and its use in processing big data sets as a solution for resolving various problems in a distributed cloud based environment. This paper mainly focuses on different components of hadoop like Hive, Pig, and Hbase, etc. Also we institutes absolute description of Hadoop Pros and cons and improvements to face hadoop problems by choosing proposed Cost-efficient Scheduler Algorithm for heterogeneous Hadoop system.
Best Practices for Scientific Research on Neural Architecture Search

Finding a well-performing architecture is often tedious for both DL practitioners and researchers, leading to tremendous interest in the automation of this task by means of neural architecture search (NAS). Although the community has made major strides in developing better NAS methods, the quality of scientific empirical evaluations in the young field of NAS is still lacking behind that of other areas of machine learning. To address this issue, we describe a set of possible issues and ways to avoid them, leading to the NAS best practices checklist available at this http URL.
Application of Homomorphic Encryption in Machine Learning

The linear regression is a machine learning algorithm used for prediction. But if the input data is in plaintext form then there is a high probability that the sensitive information will get leaked. To overcome this, here we are proposing a method where the input data is encrypted using Homomorphic encryption. The machine learning algorithm can be used on this encrypted data for prediction while maintaining the privacy and secrecy of the sensitive data. The output from this model will be an encrypted result. This encrypted result will be decrypted using a Homomorphic decryption technique to get the plain text. To determine the accuracy of our result, we will compare it with the result obtained after applying the linear regression algorithm on the plain text.
Hyperbolic Spider Monkey Optimization Algorithm

Background: Spider monkey optimization algorithm is recently developed natureinspired algorithm. It is based on fission-fusion social structure of spider monkeys. Perturbation rate is one of the important parameter of spider monkey optimization algorithm, which affects the convergence behavior of spider monkey optimization algorithm. Generally, perturbation rate is a linearly increasing function. However, due to the availability of non-linearity in different applications, a non-linear function may affect the performance of spider monkey optimization algorithm. Objective: This paper provides a detailed study on various perturbation techniques used in spider monkey optimization algorithm and recommends a novel alternative of hyperbolic spider monkey optimization algorithm. The new approach is named as hyperbolic Spider Monkey Optimization algorithm as the perturbation strategy inspired by hyperbolic growth function. Methods: The proposed algorithm is tested over a set of 23 CEC 2005 benchmark problems. Results: The experimental outcomes illustrate that the hyperbolic spider monkey optimization algorithm effectively increase the reliability of spider monkey optimization algorithm in comparison to the considered approaches. Conclusion: The hyperbolic spider monkey optimization algorithm provides improved perturbation rate, desirable convergence precision, rapid convergence rate, and improved global search capability.
Massive-mode polarization entangled biphoton frequency comb

A frequency-multiplexed entangled photon pair and a high-dimensional hyperentangled photon pair are useful to realize a high-capacity quantum communication. A biphoton frequency comb (BFC) with entanglement can be used to prepare both states. We demonstrate polarization entangled BFCs with over 1400 frequency modes, which is approximately two orders of magnitude larger than those of earlier entangled BFCs, by placing a singly resonant periodically poled LiNbO3 waveguide resonator within a Sagnac loop. The BFCs are demonstrated by measuring the joint spectral intensity, cross-correlation, and autocorrelation. Moreover, the polarization entanglement at representative groups of frequency modes is verified by quantum state tomography, where each fidelity is over 0.7. The efficient generation of a massive-mode entangled BFC is expected to accelerate the increase of capacity in quantum communication.
Commands for Autonomous Vehicles by Progressively Stacking Visual-Linguistic Representations

In this work, we focus on the object referral problem in the autonomous driving setting. We use a stacked visual-linguistic BERT model to learn a generic visual-linguistic representation. Each element of the input is either a word or a region of interest from the input image. To train the deep model efficiently, we use a stacking algorithm to transfer knowledge from a shallow BERT model to a deep BERT model.
Machine-checked semantic session typing

Session types—a family of type systems for message-passing concurrency—have been subject to many extensions, where each extension comes with a separate proof of type safety. These extensions cannot be readily combined, and their proofs of type safety are generally not machine checked, making their correctness less trustworthy. We overcome these shortcomings with a semantic approach to binary asynchronous affine session types, by developing a logical relations model in Coq using the Iris program logic. We demonstrate the power of our approach by combining various forms of polymorphism and recursion, asynchronous subtyping, references, and locks/mutexes. As an additional benefit of the semantic approach, we demonstrate how to manually prove typing judgements of racy, but safe, programs that cannot be type checked using only the rules of the type system.
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime

We analyze the convergence of the averaged stochastic gradient descent for overparameterized two-layer neural networks for regression problems. It was recently found that a neural tangent kernel (NTK) plays an important role in showing the global convergence of gradient-based methods under the NTK regime, where the learning dynamics for overparameterized neural networks can be almost characterized by that for the associated reproducing kernel Hilbert space (RKHS). However, there is still room for a convergence rate analysis in the NTK regime. In this study, we show that the averaged stochastic gradient descent can achieve the minimax optimal convergence rate, with the global convergence guarantee, by exploiting the complexities of the target function and the RKHS associated with the NTK. Moreover, we show that the target function specified by the NTK of a ReLU network can be learned at the optimal convergence rate through a smooth approximation of a ReLU network under certain conditions.
The Changes in Team Cognition and Cognitive Artifact Use During Agile Software Development Project Management

This study examines changing team cognition and cognitive artifact use as agile software development iterations progress to better understand team member interactions. The four case studies conducted observed the distributed cognition on the team changing from planning, managing, developing, and concluding tasks in iterations to deliver working functionality. Cognitive artifacts used throughout the iteration also changed. This study provides a clearer understanding of how and when team cognition and artifact use change as agile software development teams use artifacts to manage projects. Interactions between team members and artifacts move from individual to social interactions as the iteration progresses with frequent, short, continuous communication and interactions.
ResNet-Based Model for Cancer Detection

Cancer is a horrible disease and a major reason to cause death in the world. Early detection and diagnosis can help doctor save life. Many computer-aided diagnosis techniques use image processing to help doctor do cancer detection and obtain considerable achievements. In this paper, we propose a novel ResNet-based deep learning network to identify metastatic cancer from cancer scan images. Furthermore, we apply Test Time Augmentation to make our model more robust and improve detection accuracy. The results of experiments on a slightly modified version of the PatchCamelyon (PCam) benchmark dataset (the original PCam dataset contains duplicate images due to its probabilistic sampling, however, the version we use does not contain duplicates), which packs the clinically-relevant task of metastasis detection into a straightforward binary image classification task, indicates that the proposed ResNet-based model has achieved the state-of-the-art performance, which goes beyond performance of previous VGG16, VGG19 models.
Chapter 31. Quantified Boolean Formulas

Solvers for quantified Boolean formulas (QBF) have become powerful tools for tackling hard computational problems from various application domains, even beyond the scope of SAT. This chapter gives a description of the main algorithmic paradigms for QBF solving, including quantified conflict driven clause learning (QCDCL), expansion-based solving, dependency schemes, and QBF preprocessing. Particular emphasis is laid on the connections of these solving approaches to QBF proof systems: Q-Resolution and its variants in the case of QCDCL, expansion QBF resolution calculi for expansion-based solving, and QRAT for preprocessing. The chapter also surveys the relations between the various QBF proof systems and results on their proof complexity, thereby shedding light on the diverse performance characteristics of different solving approaches that are observed in practice.
Local Adaptive U-net for Medical Image Segmentation

Medical image segmentation is the primary measure of medical image analysis. With the development of deep learning, U-net based approaches have presented for different medical image segmentation tasks. However, the pooling and the simple convolution operation for deep feature maps in the U-shaped network would lead to the coarse segmentation result. In this paper, we design a local adaptive U-net (LA U-net) for medical image segmentation. There are two major modules: the Local Adaptive Module (LAM) and Multi-scale Convolution Module (MCM) in the network. The LAM get more feature maps from each down-sampling process. The MCM capture more global information for the encoding path. To validate the proposed network's performance, we verify it on two datasets: DRIVE dataset, and ISIC 2018 dataset; the results show that LA U-net achieves superior performance on two datasets.
A constant rate non-malleable code in the split-state model

Non-malleable codes, introduced by Dziembowski, Pietrzak and Wichs in ICS 2010, have emerged in the last few years as a fundamental object at the intersection of cryptography and coding theory. Non-malleable codes provide a useful message integrity guarantee in situations where traditional error-correction (and even error-detection) is impossible; for example, when the attacker can completely overwrite the encoded message. Informally, a code is non-malleable if the message contained in a modified codeword is either the original message, or a completely “unrelated value”. The family which received the most attention is the family of tampering functions in the so called (2-part) split-state model: here the message x is encoded into two shares L and R, and the attacker is allowed to arbitrarily tamper with each L and R individually. In this work, we give a constant rate non-malleable code from the tampering family containing so called 2-lookahead functions and forgetful functions, and combined with the work of Dodis, Kazana and the authors from STOC 2015, this gives the first constant rate non-malleable code in the split-state model with negligible error. The full version of this paper can be found here: https://eprint.iacr.org/2019/1299.
Chapter 7. Proof Complexity and SAT Solving

This chapter gives an overview of proof complexity and connections to SAT solving, focusing on proof systems such as resolution, Nullstellensatz, polynomial calculus, and cutting planes (corresponding to conflict-driven clause learning, algebraic approaches using linear algebra or Gröbner bases, and pseudo-Boolean solving, respectively). There is also a discussion of extended resolution (which is closely related to DRAT proof logging) and Frege and extended Frege systems more generally. An ample supply of references for further reading is provided, including for some topics omitted in this chapter.
Cyber-crime during the COVID-19 pandemic

The pandemic of COVID-19 and the imposed lockdown, has led to more people to be confined at home with many more hours to spend online each day and increasingly relying on the Internet to access services, they normally obtain offline.
Fault Diagnosis and Location based on Graph Neural Network in Telecom Networks

As the scale of telecom networks continues to expand and the structure becomes more complex, telecom networks generate a large number of continuous warning messages every day. It may lead to massive network paralysis. To solve this problem, this paper proposes a fault diagnosis scheme of telecom networks based on Graph Neural Network (GNN). The scheme can be applied to locate the fault-root-devices. Firstly, Long Short-Term Memory (LSTM) is used to cluster the state level of partial devices, and the results provide prior information for the construction of the GNN. Then, the embedded representation of graph network is introduced to aggregate the information of the whole graph. Finally, the device network is set up and the data information is embedded into the whole graph network. GNN model is built to diagnose the fault of all devices and locate the fault- root-devices. Moreover, the performance of the model constructed respectively by Graph Convolutional Network (GCN) and Graph Attention Network (GAT) are compared and analyzed. Based on public fault data, the experimental results show that the accuracy of GAT is higher than that of GCN; the correct rate of fault diagnosis can exceed 93 %; and the fault- root-devices can be accurately located. In addition, the complexity is only O(n) in our scheme.
Autonomous Agriculture Marketing Information System Through Blockchain: A Case Study of e-NAM Adoption in India

The National Agricultural Market, through networking of the APMCs, provides electronic trading for different kinds of agricultural produce. Under e-NAM, all the trading is to be done through a digital platform as prescribed to ensure transparency in the transactions and provide a fair price to the farmers. The study was conducted in three APMCs of Uttar Pradesh to analyze the ground-level practices occurring in these APMCs and to assess the level of adoption of e-NAM. A considerable variation in the arrival and bidding prices obtained from the APMCs was found when compared with the data available on the agriculture market information system (Agmarknet).In this chapter, we thus propose blockchain-based infrastructure to facilitate a highly transparent and autonomous system to further strengthen the information system and enable the efficient application of government rules and regulations pertaining to agricultural transactions in the APMCs through the utilization of smart contracts.
Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clustering

Few-shot classification tends to struggle when it needs to adapt to diverse domains. Due to the non-overlapping label space between domains, the performance of conventional domain adaptation is limited. Previous work tackles the problem in a transductive manner, by assuming access to the full set of test data, which is too restrictive for many real-world applications. In this paper, we set out to tackle this issue by introducing a inductive framework, DaFeC, to improve Domain adaptation performance for Few-shot classification via Clustering. We first build a representation extractor to derive features for unlabeled data from the target domain (no test data is necessary) and then group them with a cluster miner. The generated pseudo-labeled data and the labeled source-domain data are used as supervision to update the parameters of the few-shot classifier. In order to derive high-quality pseudo labels, we propose a Clustering Promotion Mechanism, to learn better features for the target domain via Similarity Entropy Minimization and Adversarial Distribution Alignment, which are combined with a Cosine Annealing Strategy. Experiments are performed on the FewRel 2.0 dataset. Our approach outperforms previous work with absolute gains (in classification accuracy) of 4.95%, 9.55%, 3.99% and 11.62%, respectively, under four few-shot settings.
Quantum routings for single photons with different frequencies

Controllable single-photon routing takes the central roles in optical quantum networks for quantum information processing. Given that most of the schemes previously proposed are specifically designed for the photons with selected frequencies, here we investigate how to implement the routings of single photons with different frequencies. We show that the routing capabilities of the photons with different frequencies are manipulatable by properly designing the configuration of the scatters such as the cavity with embedded atoms and the channel boundaries. This is particularly important to implement the bandwidth routings of photons in future.
Constructing quantum circuits with global gates

Abstract There are various gate sets that can be used to describe a quantum computation. A particularly popular gate set in the literature on quantum computing consists of arbitrary single-qubit gates and two-qubit CNOT gates. A CNOT gate is however not always the natural multi-qubit interaction that can be implemented on a given physical quantum computer, necessitating a compilation step that transforms these CNOT gates to the native gate set. An especially interesting case where compilation is necessary is for ion trap quantum computers, where the natural entangling operation can act on more than two qubits and can even act globally on all qubits at once. This calls for an entirely different approach to constructing efficient circuits. In this paper we study the problem of converting a given circuit that uses two-qubit gates to one that uses global gates. Our three main contributions are as follows. First, we find an efficient algorithm for transforming an arbitrary circuit consisting of Clifford gates and arbitrary phase gates into a circuit consisting of single-qubit gates and a number of global interactions proportional to the number of non-Clifford phases present in the original circuit. Second, we find a general strategy to transform a global gate that targets all qubits into one that targets only a subset of the qubits. This approach scales linearly with the number of qubits that are not targeted, in contrast to the exponential scaling reported in (Maslov and Nam 2018 New J. Phys. 20 033018). Third, we improve on the number of global gates required to synthesise an arbitrary n -qubit Clifford circuit from the 12 n − 18 reported in (Maslov and Nam 2018 New J. Phys. 20 033018) to 6 n − 8.