Reviewerly/exHarmony huggingface dataset:
Auto-Fit: A Human-Machine Collaboration Feature for Fitting Bounding Box Annotations

Large high-quality annotated datasets are essential in training deep learning models, but are expensive and time-consuming to create. A large chunk of time in the annotation process goes into adjusting bounding boxes to fit the desired object. In this paper, we propose the facilitation of human machine collaboration through the creation of an Auto-Fit feature which automatically tightens an initial bounding box around an object being annotated. The challenge lies in making this feature class agnostic in order to allow its usage regardless of the type of object being annotated. This is achieved through the use of various computer vision algorithms to extract the desired object as a foreground mask, determine the coordinates of its extremities, and redraw the bounding box based on these new coordinates. The best results were achieved with the Grabcut algorithm, which attained an accuracy of 84.69% on small boxes. The Pytorch implementation of ResNet-101 pre-trained on the COCO train2017 dataset is also used as a foreground extractor in one iteration of the implementation, in order to provide a baseline comparison between the performance of a computer vision-based solution versus one based on a standalone object detection model. This garnered an accuracy of 83.04% on small boxes, showing that the computer vision-based solution is able to surpass the accuracy of a standalone object detection model.
Subspace Guided Collaborative Clustering Based on Optimal Transport

Collaborative clustering is a promising approach in the learning from other learners research area. Although extensive research have been done to improve the collaborative approaches, they still suffer from several issues, including the mechanism of exchanging the information and how to measure the quality of this information. In this paper we introduce a new model of collaboration guided by feature selection, where the main idea is to choose the features that give the best representation for each collaborator and guarantee the communication between them, while preserving the privacy of each collaborator. Collaborative clustering will be developed within the framework of the theory of optimal transport. Indeed, this theory offers a formalism that is highly adapted to collaboration between members of a set of collaborators. Extensive experiments were conducted on multiple data-sets to evaluate the proposed approach and demonstrate its utility.
Identification of Security Requirements from the Selected Set of Requirements under Fuzzy Environment

The elicitation of software requirements is an essential activity of requirement engineering whose task is to collect, gather, or identity the stakeholders requirements. The requirements elicited are of various types like performance requirements, testing requirements, system requirements, functional requirements, etc. according to the need of the stakeholders. Among all the requirements, security requirements play a crucial role to safeguard valuable information from unauthorized users in an organization. In our review process, we found that security requirements elicitation methods lack in answering that how to gather the security requirements from the selected set of functional requirements when stakeholders take part in decision making process and preferences of the software requirements are specified by the use of linguistic variables. Therefore, in order to tackle this problem, in this paper, for the selection of security requirements we propose a fuzzy-based method, so that identification, authentication, and authorization of security requirements can be elicited only for those requirements which are important according to the security point of view. The applicability of the proposed method is discussed by means of an example.
Reversible Privacy Protection with the Capability of Antiforensics

In this paper, we propose a privacy protection scheme using image dual-inpainting and data hiding. In the proposed scheme, the privacy contents in the original image are concealed, which are reversible that the privacy content can be perfectly recovered. We use an interactive approach to select the areas to be protected, that is, the protection data. To address the disadvantage that single image inpainting is susceptible to forensic localization, we propose a dual-inpainting algorithm to implement the object removal task. The protection data is embedded into the image with object removed using a popular data hiding method. We further use the pattern noise forensic detection and the objective metrics to assess the proposed method. The results on different scenarios show that the proposed scheme can achieve better visual quality and antiforensic capability than the state-of-the-art works.
Blockchain technology in the banking sector

The newly invented blockchain technology is beginning to show its capabilities and occupy many sectors of the economy. One of these is the banking sector. Considering the opportunities, development prospects, and challenges of introducing blockchain technology into the economy, will blockchain technology be able to meet its expectations? And how blockchain technology finds its application in the Russian economy.
Metagraph Based Approach for Neural Text Question Generation

The paper considers the task of generating questions by given text. The generation of high-quality questions allows us to solve many problems, for example, in the field of teaching – for the automatic creation of tests for training materials or the enrichment of interaction techniques for question-answering systems. Leading research in this area shows that models based on the Seq2Seq architecture achieve the best quality. However, such models do not use the hidden structure of the text, which is essential for generating semantically correct questions. New works on this topic use additional data in the form of the graphs, representing the dependencies of the words in a sentence. In this article, an approach that uses a metagraph model of text as the initial structure for storing and enriching data with additional information and semantic relationships is considered. After generating a metagraph model of the text, the metagraph is decomposed into a multipartite graph, which allows its usage in existing models for generating text questions without losing information about the additional hierarchical and semantical dependencies of the text.
Co-creating Value with the Cooperative Turn: Exploring Human-Machinic Agencies Through a Collective Intelligence Design Canvas

This essay introduces a "collective intelligence design canvas" (v0.1) developed by members of anticipate, a non-disciplinary research network initiated in 2019 in response to shared interests in exploring the cooperative turn in comprehensions of agency, value, and intelligence. The network has focused its analysis on the vocabularies policy makers and researchers use to explore the transformation of agency in distributed intelligent systems. A structured overview of these vocabularies is included. To facilitate further engagement with the network's collective intelligence design conversation, authors have synthesized these vocabularies into a design canvas highlighting the main conceptual layers of this process.
A Guided Learning Approach for Item Recommendation via Surrogate Loss Learning

Normalized discounted cumulative gain (NDCG) is one of the popular evaluation metrics for recommender systems and learning-to-rank problems. As it is non-differentiable, it cannot be optimized by gradient-based optimization procedures. In the last twenty years, a plethora of surrogate losses have been engineered that aim to make learning recommendation and ranking models that optimize NDCG possible. However, binary relevance implicit feedback settings still pose a significant challenge for such surrogate losses as they are usually designed and evaluated only for multi-level relevance feedback. In this paper, we address the limitations of directly optimizing the NDCG measure by proposing a guided learning approach (GuidedRec) that adopts recent advances in parameterized surrogate losses for NDCG. Starting from the observation that jointly learning a surrogate loss for NDCG and the recommendation model is very unstable, we design a stepwise approach that can be seamlessly applied to any recommender system model that uses a point-wise logistic loss function. The proposed approach guides the models towards optimizing the NDCG using an independent surrogate-loss model trained to approximate the true NDCG measure while maintaining the original logistic loss function as a stabilizer for the guiding procedure. In experiments on three recommendation datasets, we show that our guided surrogate learning approach yields models better optimized for NDCG than recent state-of-the-art approaches using engineered surrogate losses.
Digital Tools Entering the Scene in STEM Activities for Physics Teaching

This paper focuses on the problematic role of digital tools in STEM activities for Physics teaching. A group of 47 teachers of Physics and Chemistry, from middle and secondary education, attended a continuous professional development training course during the academic year 2019/2020 that aimed to promote the development, adaptation, and implementation of STEM activities, centered on various topics in Physics (sound, electricity, kinetic and potential energy, mass and weight, gravitational pendulum and free fall) in teachers’ practices. Most of the activities already had an integration proposal for digital tools. Results revealed that teachers’ options range from adding specific and unspecific tools to implement STEM activities. In most cases, they only replace other non-digital tools. This work contributes to foster the action and reflection related to teachers’ knowledge and experience to introduce digital tools productively, amplify their options, and explore other related areas.
Application of Data Mining in English Online Learning Platform

Abstract English is becoming more and more important in our life and English learning is also conducted anytime and anywhere. With high-tech products more and more popular, learning English through mobile phones and other products is very convenient. There are numerous platforms for English online learning, but they provide a very single learning content. All learners, no matter what their learning purposes are, have the same learning content, thus problems will follow. Based on the analysis of current situation, this paper puts forward solutions, case analysis and conclusion process. The application of data mining technology to English online learning platform provides 80% ideas for the construction of online learning platform. Statistics show that nearly 70 million people study online every year.
Robust Object Tracking with Continuous Data Association based on Artificial Potential Moving Horizon Estimation

In this paper, a novel object tracking method based on moving horizon estimation (MHE) is introduced that integrates data association into numerical optimization on Bayesian state estimation. Object tracking is a classical problem that often appears in radar and vision systems for which either deterministic or probabilistic approach has been applied. While the former often encounter a failure of association, the latter avoids it by computing the expectation concerning the observations, but it requires the prior knowledge of the probabilistic distribution and may suffer from outliers. In this paper, a partially deterministic approach is built on MHE to resolve these concerns. Data association is realized by a potential function comprising the superposition of Gaussian distributions centered at each observed feature. The potential function is embedded into the stage cost of MHE; the optimal data association is conducted using the sequence of observations within the horizon. Thus robust object tracking is achieved utilizing multi-sampling data and integration of both dynamics and explicit constraints reflecting physical limitations. The advantage of the proposed method is verified through object tracking experiments on the crowded environment, where occlusion and misrecognition frequently occur.
The Trinity Teaching Mode of Wisdom Tree, Tencent Classroom and QQ Group—Online Teaching Practice

At the beginning of 2020, a sudden outbreak of COVID-19 forced us to take online teaching methods in engineering mechanics courses. The design details of the course and the trinity teaching mode of Wisdom Tree, Tecent Classroom, and QQ group were introduced in this paper. Also, the assessment method and score analysis was given. Finally, according to the course report provided by the Wisdom Tree, the online open course of engineering mechanics was explained.
Optimization and practice of requirement analysis based on prototype portrait in software development process

Aiming at the problems of incomplete description, ambiguity and inconsistency in the traditional requirements analysis method based on prototype system in software development, this paper proposes a requirement analysis method based on prototype portrait, introduces the process of obtaining the software requirement specification and designing the software by using the prototype portrait method according to the user’s requirements, and formalizes the process of the method, and develops the software with the prototype portrait requirement document approved by the customer, so as to ensure the correctness of the designed functional requirements. The practical results show that the requirement analysis method based on prototype portrait overcomes the problems of long development cycle and high failure rate existing in the traditional requirements analysis method of prototype system, and improves the quality and cycle of software development.
Research on text detection method based on improved yolov3

Text detection in natural scenes is one of the hot issues in the field of computer vision. Aiming at the problems of complex text background, object occlusion and illumination changes in natural scenes, this paper proposes a method of text detection in natural scenes based on improved YOLOv3 (UDSP- YOLO), this method uses the CLAHE image enhancement preprocessing method to eliminate the impact of lighting changes in the natural scene on the target recognition effect, and uses random spatial sampling pooling (S3Pool) as the downsampling method of the feature extraction network to preserve the space of the feature map Information solves the problem of background interference in complex environments. The 8 times down-sampling feature map output by the feature extraction network is up-sampled by 2 times, and the 2 times up-sampling feature map is spliced with the feature map output by the second residual block to establish The output is a 4 times downsampling feature fusion target detection layer, and the improved network model is used to compare experiments with the original network on the ICDAR2015 data set. The results show that the improved network model effectively improves the detection accuracy.
Sarcasm Detection in Online Social Network: Myths, Realities, and Issues

Sarcasm is a statement used by sophisticated people on social media and blog-oriented websites. It is used to express the indirect information on the statements which is already making a buzz on social media. It is a platform where the various forms of judgmental or normal statements are shared by millions of people. And it becomes hugely difficult to identify, whether the statements made are a complement or mockery statements, sometimes too hard for humans to identify. This increases the scope and need for recognizing the sarcastic statements for improving automatic sentiment analysis. Sentiment analysis is referred to as the method of identification and aggregation of the sarcasm statements by Internet users for specific attitudes or opinions. Sarcasm detection systems use different types of techniques such as Rule-Based Approach, Pattern-Based Approach, Machine Learning Approach, and Context-Based Approach. The system analyzes sarcasm detection on the Twitter dataset by using Techniques such as Support Vector Machine, Random Forest, Naïve Bayes, and Maximum Entropy.
A New Code-Based Blind Signature Scheme

Abstract Blind signature is an important cryptographic primitive with widespread applications in secure e-commerce, for example to guarantee participants’ anonymity. Existing blind signature schemes are mostly based on number-theoretic hard problems, which have been shown to be solvable with quantum computers. The National Institute of Standards and Technology (NIST) began in 2017 to specify a new standard for digital signatures by selecting one or more additional signature algorithms, designed to be secure against attacks carried out using quantum computers. However, none of the third-round candidate algorithms are code-based, despite the potential of code-based signature algorithms in resisting quantum computing attacks. In this paper, we construct a new code-based blind signature (CBBS) scheme as an alternative to traditional number-theoretic based schemes. Specifically, we first extend Santoso and Yamaguchi’s three pass identification scheme to a concatenated version (abbreviated as the CSY scheme). Then, we construct our CBBS scheme from the CSY scheme. The security of our CBBS scheme relies on hardness of the syndrome decoding problem in coding theory, which has been shown to be NP-complete and secure against quantum attacks. Unlike Blazy et al.’s CBBS scheme which is based on a zero-knowledge protocol with cheating probability $2/3$, our CBBS scheme is based on a zero-knowledge protocol with cheating probability $1/2$. The lower cheating probability would reduce the interaction rounds under the same security level and thus leads to a higher efficiency. For example, to achieve security level $2^{-82}$, the signature size in our CBBS scheme is $1.63$ MB compared to $3.1$ MB in Blazy et al.’s scheme.
3D object recognition with a linear time‐varying system of overlay layers

Object recognition is a challenging task in computer vision with numerous applications. The challenge is in selecting appropriate robust features with tolerable computing costs. Feature learning attempts to solve the feature extraction problem through a learning process using various samples of the objects. This research proposes a two-stage optimization framework to identify the structure of a first-order linear non-homogeneous difference equation which is a linear time-varying system of overlay layers (LtvoL) that construct an image. The first stage consists of the determination of a finite set of impulses, called overlay layers, by the application of a genetic algorithm. The second stage defines the coefficients of the corresponding difference equation derived from L2 regularization. Classification of the test images is possible by a novel process exclusively designed for this model. Experiments on the Washington RGB-D dataset and ETH-80 show promising results which are comparable to those of state-of-the-art methods for RGB-D-based object recognition.
Generation of nonclassical states by superposition of number-conserving operations on squeezed thermal state

Abstract The nonclassicality is the prerequisite for quantum states to be applied into quantum information, especially for quantum metrology. Here we theoretically investigate the non-classical properties of the non-Gaussian state generated by repeatedly operating a number-conserving generalized superposition of products (GSP), i.e., ( s 1 aa † + t 1 a † a ) m with <?CDATA ${s}_{1}^{2}+{t}_{1}^{2}=1,$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msubsup> <mml:mrow> <mml:mi>s</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>1</mml:mn> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msubsup> <mml:mo>+</mml:mo> <mml:msubsup> <mml:mrow> <mml:mi>t</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>1</mml:mn> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msubsup> <mml:mo>=</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> </mml:math> on the squeezed thermal state (STS), in terms of second-order correlation function, Mandel’s Q parameter, quadrature squeezing and Wigner function (WF). It is shown that, compared to the cases of the STS, the GSP-STS with the high-order GSP operations ( m &gt; 1) at the small-squeezing levels can be beneficial to the existence of the photon-antibunching effect, the sub-Poissonian distribution and the partial negativity of the WF, apart from the quadrature squeezing. In addition, for the case of m = 1, we also compare with the non-classical properties of several different non-Gaussian resources, involving the photon-subtracted-then-added (PSTA) STS, the GSP-STS and the photon-added-then-subtracted (PATS) STS. It is found that the PSTA-STS with respect to the sub-Poissonian distribution and the partial negativity of the WF has a better performance than others. Significantly, the generated GSP-STS has an obvious advantage of showing the photon-antibunching effects, compared to the PSTA-STS and the PATS-STS, which means that our scheme may have an excellent guidance for the practical implementations in quantum information.
Brief Paper: Augmentation of Hidden Markov Chain for Complex Sequential Data in Context

The classical HMM is defined by a parameter triple  = (, A, B), where each parameter represents a collection of probability distributions: initial state, state transition and output distributions in order.This paper proposes a new stationary parameter = ( 1 , 2 , … , ) where N is the number of states and = (| = , ) for describing how an input pattern y ends in state = at time t followed by nothing.It is often said that all is well that ends well.We argue here that all should end well.The paper sets the framework for the theory and presents an efficient inference and training algorithms based on dynamic programming and expectation-maximization.The proposed model is applicable to analyzing any sequential data with two or more finite segmental patterns are concatenated, each forming a context to its neighbors.Experiments on online Hangul handwriting characters have proven the effect of the proposed augmentation in terms of highly intuitive segmentation as well as recognition performance and 13.2% error rate reduction.
Reward Space Noise for Exploration in Deep Reinforcement Learning

A fundamental challenge for reinforcement learning (RL) is how to achieve efficient exploration in initially unknown environments. Most state-of-the-art RL algorithms leverage action space noise to drive exploration. The classical strategies are computationally efficient and straightforward to implement. However, these methods may fail to perform effectively in complex environments. To address this issue, we propose a novel strategy named reward space noise (RSN) for farsighted and consistent exploration in RL. By introducing the stochasticity from reward space, we are able to change agent’s understanding about environment and perturb its behaviors. We find that the simple RSN can achieve consistent exploration and scale to complex domains without intensive computational cost. To demonstrate the effectiveness and scalability of the proposed method, we implement a deep Q-learning agent with reward noise and evaluate its exploratory performance on a set of Atari games which are challenging for the naive [Formula: see text]-greedy strategy. The results show that reward noise outperforms action noise in most games and performs comparably in others. Concretely, we found that in the early training, the best exploratory performance of reward noise is obviously better than action noise, which demonstrates that the reward noise can quickly explore the valuable states and aid in finding the optimal policy. Moreover, the average scores and learning efficiency of reward noise are also higher than action noise through the whole training, which indicates that the reward noise can generate more stable and consistent performance.