Reviewerly/exHarmony huggingface dataset:
CAFC-Net: A Critical and Align Feature Constructing Network for Oriented Ship Detection in Aerial Images

Ship detection is one of the fundamental tasks in computer vision. In recent years, the methods based on convolutional neural networks have made great progress. However, improvement of ship detection in aerial images is limited by large-scale variation, aspect ratio, and dense distribution. In this paper, a Critical and Align Feature Constructing Network (CAFC-Net) which is an end-to-end single-stage rotation detector is proposed to improve ship detection accuracy. The framework is formed by three modules: a Biased Attention Module (BAM), a Feature Alignment Module (FAM), and a Distinctive Detection Module (DDM). Specifically, the BAM extracts biased critical features for classification and regression. With the extracted biased regression features, the FAM generates high-quality anchor boxes. Through a novel Alignment Convolution, convolutional features can be aligned according to anchor boxes. The DDM produces orientation-sensitive feature and reconstructs orientation-invariant features to alleviate inconsistency between classification and localization accuracy. Extensive experiments on two remote sensing datasets HRS2016 and self-built ship datasets show the state-of-the-art performance of our detector.
Good Learning, Bad Performance: A Novel Attack Against RL-Based Congestion Control Systems

Reinforcement Learning (RL) has been applied to solve decision-making problems in computer network designs, especially in TCP congestion control. As RL-based congestion control methods enable powerful learning abilities, it achieves competitive performance and adaptiveness advantages over the traditional methods. However, RL-based systems suffer from adversarial attacks that generate perturbations to significantly degrade the performance. In this paper, we conduct a comprehensive study of adversarial attacks against RL-based congestion control systems. Unlike the state-of-the-art adversarial attacks on images where an attacker can easily obtain the input states to introduce perturbations, the attacker cannot directly obtain the input states in congestion control settings that are only available to the agents. It is challenging to add effective perturbations without knowing the input states for RL-based congestion control models. To solve the challenge, we develop an adversarial attack to estimate states of the target agent, craft adversarial perturbations, and apply the generated perturbations in an automated fashion. We evaluate how our adversarial attack affects the target agent&#x2019;s decision-making process. Our experiments illustrate that our attack can effectively reduce about 50&#x0025; average throughput while increasing more than 36x latency and 45&#x0025; packet loss rate.
Machine learning techniques with ANOVA for the prediction of breast cancer

Breast cancer is the most common disease all around the globe [1].Humanity grappled with recognizing and handling breast cancer in the last 3500 years [2].Alongside the past civilizations and between the 18th and 19th centuries [2], it was acknowledged that breast cancer is difficult to control in the worst stage.Breast cancer continued to be a significant female health matter more than 3500 years ago [2].In 2020 India's new breast cancer cases were 2261419, death number was 684996 as per Globocan report [3].Advanced identification of breast cancer will enlarge the possibility of retrieval and longevity of life.Breast Cancer is a complex disease that is activated due to unchecked break-up of cells inside the terminal vessel and lobular of the breast [4].Breast cancer mainly occurs due to mutation in genes [5].Due to day-to-day habits of urban population, more breast cancer cases are found in cities than villages [6].Genes are small pieces of deoxyribonucleic acid (DNA) that develop in chromosomes.DNA carries the specification for structuring proteins.DNA orders contain four nucleotides: adenosine, cytidine, guanosine, and thymine (A, C, G, and T).The precise order of nucleotides, when combined they form a DNA sequence [7].
Performance Analysis of Building Integrated Photovoltaic of High-rise Buildings in Urban Areas

Building Integrated Photovoltaic (BIPV) system per-formance is analyzed with a view to occupying the majority of the unused space of vertical walls and harnessing more incident energy than the conventional solar panel installation system. The BIPV system incorporated in high-rise buildings harnesses irradiance not only from the horizontal surface but also from vertical walls which cannot be harnessed by other installation techniques. For analysis purposes, the environmental impact, like cloud shadow impact, has taken into consideration that shows the vertical walls consisting of four sides (North - East - South - West) can harness almost 49&#x0025; more energy than the conventional horizontal-based system. Moreover, the analysis also shows the cloud shadow impact is more significant for the horizontally-mounted photovoltaic panels than vertically-mounted photovoltaic panels.
Hunion Traceability: A New Type of Blockchain Traceability System Based on SM2, SM3 and SM4

Data traceability with the great serviceability has been widely used in various scenarios. However, the existing traceability mechanism often applies internationally accepted cryptographic algorithms such as MD5 and RSA in the data transmission process. These algorithms have been confirmed to have certain loopholes in security. This paper proposes a traceability system based on SM2, SM3, and SM4 algorithms. We establish a certificate issuance verification mechanism to improve the data transmission security performance of the traceability system based on SM2 and SM3. At the same time, the system uses the SM4 algorithm to design a blockchain interaction scheme using data digest, and uses homomorphic encryption to verify the correctness of the data. In addition, we also replaced the SM2 in the system with the RSA, and the SM3 with the MD5 to test the pros and cons of the two different algorithm systems in terms of security performance and execution efficiency. The test results show that the traceability mechanism based on SM2, SM3, and SM4 performs 1-2 seconds faster than the traceability mechanism based on RSA and MD5 in terms of average transaction response time. At the same time, SM2, SM3, and SM4 also have better security performance.
Safe Encryption Algorithm for Secured Message Communication Using Dcombo: A New Sorting Technique

The DES and AES algorithms are often utilized to transfer messages. All those specifications can not appear to be sufficiently safe and rapid to deal with the communication issues. A modern technology for encrypting the sort message has already been postulated. This paper focuses on the safe platform used by encrypting and decrypting the text to address the security of data. In this document, we suggest a new sorting technique with an accurate encryption. The fundamental assumption of this model is to upgrade safety by changing the Bubble sorting method for encryption and decryption of a text message, which is much easier to perform and to enhance quality. DCombo sort is called the new sorting approach. In digital data communication it performs at least 1.5 times comparatively higher than previous technique.
A study on bilateral matching of team-science talents in new R&amp;D institutions based on grey correlation-cloud model

The problem of bilateral matching of teams and scientific and technical talents is studied in new R&amp;D institutions with different forms of uncertain assessment information. A decision method is proposed based on a combination of grey correlation and cloud model. The method firstly applies interval grey numbers to characterize uncertain assessment score information and cloud models to characterize uncertain linguistic assessment information; secondly, the two different pieces of information are converted into grey correlation coefficients by applying grey correlation analysis methods to the assessment values, so as to solve indicator weights, and assemble assessment data based on indicator weights and cloud models; finally, the bilateral matching model is constructed and the matching results are solved based on the cloud model data features and the dual objectives of maximum satisfaction and minimum uncertainty. The case analysis and method comparison show that the method is feasible and effective.
Application of Internet of Things and Traceability Technology in the Whole Industrial Chain in Mountainous Areas by Blockchain

This paper through investigation and research on the whole process of the supply chain of bayberry in mountainous areas. Based on the advanced technology of information science such as blockchain and big data, we have created a complete set of traceability and supervision mechanisms such as the bayberry quality and safety of risk assessment and warning, all round supervision during the entire process, and crisis handling after the incident. In the process, by maintaining public health, ensuring the quality of agricultural products, and promoting industrial development, we have achieved an overall improvement in the living standards of farmers.
Correlation Metric Selection based Correlation Alignment for Cross-project Defect Prediction

Cross-project defect prediction (CPDP) aims to identify defective software modules in a target project by using historical defect data from other source projects. Recently, CPDP has attracted much more research interest. However, existing CPDP models are parametric methods, which usually require intensive parameter selection and tuning to achieve better prediction performance. Moreover, software metrics (features) usually have strong correlation among themselves and this is detrimental to build prediction models. However, most CPDP methods don't consider to reduce correlated metrics, which may bring negative effect on the performance of CPDP. In the article, we proposed a new Correlation Metric Selection based Correlation Alignment (CMSCA) approach for CPDP to address the above concerns. Specifically, CMSCA is a non-parametric algorithm, which can perform knowledge transfer across projects without the need for parameter selection and tuning. It is simple but effective. Through an empirical investigation of 5 publicly-available defect datasets, experimental results demonstrate that the proposed CMSCA model outperforms or has comparable to the related CPDP models.
Data Security in Cloud Computing Using a Hybrid Algorithm Approach

Cloud computing is a technology that provides users with computing resources and storage. It removes the need for businesses and institutes to maintain expensive computing facilities and improves organizations by its services. This paper aims to use cryptography techniques to enhance data security in the cloud by implementing the provided algorithms. The work in this paper is majors on implementing a hybrid algorithm (symmetric and asymmetric algorithms). We proposed a hybrid cryptosystem that comprises symmetric and asymmetric algorithms using 3DES, RSA, and SHA-3 algorithms to enable tight security in the cloud. 3DES is for encryption RSA for authentication and SHA-3 for integrity.
Educational Mobile Games as a Tool for Increasing Vocabulary When Learning a Foreign Language

The study considers the features of the algorithms of educational mobile games. The research analyzes the main functions and characteristics of 55 educational mobile games. The system of each mobile application for learning a foreign language consists of 13 key algorithms. An experiment involving three Russian (342 participants) higher educational institutions and two Kazakh (158 participants) universities was conducted. The experiment was based on the analysis and comparison of the effectiveness of Quizlet and Memrise (the first stage of the study), Tandem and ELSASpeak (the second stage of the study) with traditional learning methods (textbooks, notes, classroom lessons with teachers). The results indicate that students memorize 80-90% of vocabulary with the help of mobile applications. The synthesis of traditional learning methods and mobile pedagogy is the most effective. The results of the study can be used as an aid when using mobile learning methods in teaching foreign languages.
Optimizing constraint driven container orchestration policies in a cloud environment

The exponential surge in the usage of container based applications hosted on datacenters has resulted in a significant increase in energy consumption. This highlights the necessity for optimized container orchestration systems, thus making it important for infrastructures that provide Container-as-a-Service (CaaS) to have efficient scheduling, rescheduling, resource provisioning and container networking. Our work proposes various methods for optimizing constraints associated with container orchestration which include resource utilization, network data footprint, and energy efficiency. The aforementioned constraints are optimized by two new approaches. The first one is a hybrid algorithm that utilizes the concepts of genetic and bin-packing algorithms for better resource utilization. The second approach is based on a dynamic programming paradigm to reduce network data footprint. Furthermore, for improving energy efficiency, we have employed Dynamic Voltage and Frequency Scaling (DVFS). By putting together these approaches to optimize container orchestration, we noticed an overall improvement which has led to a decrease in energy consumption by 22.88&#x0025; and the reduction in network usage by 16.04&#x0025
Introduction

The book is arbitrarily structured around the parameters of melody, rhythm, harmony, texture, and pre-compositional approaches. All chapters start with a brief note on terminology and general recommendations for the instructor and the student. The first five chapters offer a variety of exercises that range from analysis and style imitation, to the use of probabilities. The chapter about pre-compositional approaches offers original techniques that a student composer can implement in order to start a new work. This last section of the book fosters creative connections with other disciplines such as math, visual arts, and architectural acoustics. Each of the 100 exercises contained in the book proposes a unique set of guidelines and constraints intended to place the student in a specific compositional framework. Through those compositional boundaries the student is encouraged to produce creative work within a given structure. Using the methodologies in this book, students will be able to create their own outlines for their compositions, making intelligent and educated compositional choices that balance reasoning with intuition.
A Real-Time Sentimental Analysis on E-Commerce Sites in Nigeria Using Machine Learning

E-commerce has become very popular because of the service provided and the comfort it provides to users but with this comes different advantages and disadvantages which makes users consider other opinions before making a decision to purchase an item. These days sentiment analysis has become one of the important tasks which helps people to express their opinion on products and services being rendered, sentimental analysis is applied in different aspect of the human world which provides polarity in users opinion to others when making a decision. Sentimental analysis provides aid the analysis of reviews and comments to give or provide a summarized polarity percentage on an event or product. In E-commerce sentimental analysis is important because it assists users to make a decision on products on products. This project will be focused on utilizing Naïve bayes algorithm, SVM and Logistic regression to create a recommendation system that recommends the best e-commerce site to buy from.
Bayesian Nonparametric Modeling and Transfer Learning for Tracking Under Measurement Noise Uncertainty

We propose a method for object tracking under unknown and time-varying environmental conditions that incorporates transfer learning with Bayesian filtering and Bayesian nonparametric modeling. The main tracking task assumes that the sensor measurement noise characteristics are unknown and change with time. The characteristics are learned by incorporating knowledge that was previously acquired and stored by multiple sources tracking under similar varying conditions. We assume that each learning source models their own time-varying noise distribution using Dirichlet process mixtures whose parameters are learned using Bayesian nonparametric priors. The multiple models are transferred and combined to model variation in the main tracking task. Simulations demonstrate the improved tracking performance when compared to tracking without transferred knowledge.
GDP vs. LDP: A Survey from the Perspective of Information-Theoretic Channel

The existing work has conducted in-depth research and analysis on global differential privacy (GDP) and local differential privacy (LDP) based on information theory. However, the data privacy preserving community does not systematically review and analyze GDP and LDP based on the information-theoretic channel model. To this end, we systematically reviewed GDP and LDP from the perspective of the information-theoretic channel in this survey. First, we presented the privacy threat model under information-theoretic channel. Second, we described and compared the information-theoretic channel models of GDP and LDP. Third, we summarized and analyzed definitions, privacy-utility metrics, properties, and mechanisms of GDP and LDP under their channel models. Finally, we discussed the open problems of GDP and LDP based on different types of information-theoretic channel models according to the above systematic review. Our main contribution provides a systematic survey of channel models, definitions, privacy-utility metrics, properties, and mechanisms for GDP and LDP from the perspective of information-theoretic channel and surveys the differential privacy synthetic data generation application using generative adversarial network and federated learning, respectively. Our work is helpful for systematically understanding the privacy threat model, definitions, privacy-utility metrics, properties, and mechanisms of GDP and LDP from the perspective of information-theoretic channel and promotes in-depth research and analysis of GDP and LDP based on different types of information-theoretic channel models.
Machine learning approach to identify adverse events in scientific biomedical literature

Monitoring the occurrence of adverse events in the scientific literature is a mandatory process in drug marketing surveillance. This is a very time-consuming and complex task to fulfill the compliance and, most importantly, to ensure patient safety. Therefore, a machine learning (ML) algorithm has been trained to support this manual intellectual review process, by automatically providing a classification of the literature articles into two types. An algorithm has been designed to automatically classify "relevant articles" which are reporting any kind of drug safety relevant information, and those which are not reporting an adverse drug reaction as "not relevant." The review process is consisted of many rules and aspects which needed to be taken into consideration. Therefore, for the training of the algorithm, thousands of documents from previous screenings have been used. After several iterations of adjustments and fine tuning, the ML approach is definitively a great achievement in pre-sorting the articles into "relevant" and "non-relevant" and supporting the intellectual review process.
Comparison of Two Learning Networks for Time Series Prediction

Hierarchical mixtures of experts (HME) [JJ94] and radial basis function (RBF) networks [PG89] are two architectures that learn much faster than multilayer perceptrons. Their faster learning is due not to higherorder search mechanisms, but to restricting the hypothesis space of the learner by constraining some of the layers of the network to use linear processing units. It can be conjectured that since their hypothesis space is restricted in the same manner, the approximation abilities of the two networks should be similar, even though their computational mechanisms are different. An empirical verification of this conjecture is presented, based on the task of predicting a nonlinear chaotic time series generated by an infrared laser.
Chinese Language and Literature Intelligent Teaching System Based on Data Mining Algorithm

In recent years, due to the development of “Chinese boom” and the proposal of the “One Belt One Road” strategy, people’s enthusiasm for learning Chinese has become more and more high, and Chinese literature majors have developed vigorously. Since the reform and opening up, international exchanges have become increasingly close, and society’s requirements for the talents of Chinese language and literature (CLL) majors have been increasing. Not only academic research talents and teachers with rooted education are needed, but also comprehensive talents based on the comprehensive ability of CLL are needed. At the same time, with the rapid development of Internet technology and the advent of the era of big data, various data processing technologies have also emerged, among which data mining has played an important role. Therefore, the use of data mining algorithms to study the design of CLL intelligent teaching system has a certain effect. This article uses questionnaire survey method and data analysis method, intends to use data mining algorithm to break through the traditional teaching mode of CLL, and provide new ideas for designing a set of intelligent teaching system. According to the survey results, most of the interviewees believe that intelligent teaching systems should be used in the teaching of CLL, and this will inject more vitality into the development of teaching work.
Designerly Tele-Experiences: A New Approach to Remote Yet Still Situated Co-Design

The COVID-19 pandemic disrupted processes interaction designers took for granted, challenging some of our most commonplace design practices. Participatory and situated approaches have been impacted the most: where we engaged stakeholders in-person and in-context, during this time we must co-design remotely and in virtual environments. Such a dramatic change calls for new co-design methods. In this article, we present a novel remote strategy for involving stakeholders to co-design interactive technology: Designerly Tele-Experiences (DTE). Our methodological proposal enables participants to experience early design concepts in-the-wild as a provocation to contribute new ideas that resonate with their experiential preferences. Here we describe the rationale for DTE, unpack how it builds on and extends existing methods, and provide actionable guidelines from our experience of using it in our work. Our contribution will empower interaction designers to embrace participatory and situated approaches even when engaging stakeholders in person is not possible or desirable.