Reviewerly/exHarmony huggingface dataset:
Data Association Rules Mining Method Based on Improved Apriori Algorithm

In the existing data mining technology, there are some shortcomings in association rule mining methods. In this paper, aiming at the problem that the mining efficiency of Apriori algorithm is not high when dealing with large database, the genetic algorithm is introduced to improve the Apriori algorithm. This paper first introduces the basic concept and principle of association rules, describes the basic idea of Apriori algorithm in detail, and an improved algorithm based on Partition algorithm is proposed for its shortcomings. Then introducing the principle and operation flow of genetic algorithm in detail, and puts forward the corresponding improvement solutions for the coding scheme and fitness function. Finally, the association rule mining is established by combining genetic algorithm with Apriori algorithm. Compared with other methods, the experimental results show that the proposed method has better performance, higher mining efficiency and better data mining.
Long-Term Human Video Generation of Multiple Futures Using Poses

Generating future video from an input video is a useful task for applications such as content creation and autonomous agents. Especially, prediction of human video is highly important. While most previous works predict a single future, multiple futures with different behavior can potentially occur. Moreover, if the predicted future is too short (e.g., less than one second), it may not be fully usable by a human or other systems. In this paper, we propose a novel method for future human pose prediction capable of predicting multiple long-term futures. This makes the predictions more suitable for real applications. After predicting future human motion, we generate future videos based on predicted poses. First, from an input human video, we generate sequences of future human poses (i.e., the image coordinates of their body-joints) via adversarial learning. Adversarial learning suffers from mode collapse, which makes it difficult to generate a variety of multiple poses. We solve this problem by utilizing two additional inputs to the generator to make the outputs diverse, namely, a latent code (to reflect various behaviors) and an attraction point (to reflect various trajectories). In addition, we generate long-term future human poses using a novel approach based on unidimensional convolutional neural networks. Last, we generate an output video based on the generated poses for visualization. We evaluate the generated future poses and videos using three criteria (i.e., realism, diversity and accuracy), and show that our proposed method outperforms other state-of-the-art works.
MDRSteg: large-capacity image steganography based on multi-scale dilated ResNet and combined chi-square distance loss

Image steganography has emerged as a method of hiding secret data within an image file to ensure the security of the transmitted data. In this study, we propose an architecture named MDRSteg to unobtrusively hide a large-size image in another image based on a residual neural network with dilated convolution and multi-scale fusion. The architecture consists of an embedding network to hide the secret image in the cover-image and a revealing network to reveal the secret image from the stego-image, both networks are made up of fully convolutional residual modules. The networks are jointly trained with a loss function which is the combination of chi-square distance (CSD) and mean-square error. The proposed MDRSteg are trained and tested on three datasets, Labeled Faces in the Wild, Pascal visual object classes, and ImageNet. Extensive experiments have been done and the experimental results suggest that the proposed model can not only hide a large size image in another image with good invisibility and large hiding capacity (24 bits-per-pixel), but also exhibits good generalization ability. The experimental results also show that dilated convolution, multi-scale fusion, and combined CSD loss function have positive effects on the delicate image steganography results and proves that the model is practically useful for many applications.
WITHDRAWN: Essentials of blockchain technology for modern world applications

Blockchain is a buzzword these days which has found its significance in areas ranging from the financial sector, banking sector, and various other ones to attain societal benefits. Blockchain adopts a decentralized or distributed property to provide services to its customers. A blockchain is viewed as a collection of blocks just as a linked list, where each node is said to have a data field and a pointer field that points to the next node in the list. Hashing is a preferable technique followed by blockchain where a set of input values are hashed to generate an output. There are enormous components in blockchain architecture, each displays a primary role to make it efficient for communication among multiple parties. It also favours to automate essential activities pertaining to various industries that otherwise is carried out manually and huge costs are paid to the intermediate group of people who does the work. This work presents an overview of blockchain and its associated technologies to make it convenient for the researchers to utilize the benefits. Based on the inputs shared in this work, people can identify the need to switch towards the blockchain technology, which is trending technology in the modern days. Also, the current works prominently focus on throughput and response time metrics which can greatly have an impact on the overall performance of the network with huge number of blocks and transaction inflow, outflow rate. These metrics are upgraded to key based retrieval to ensure secure access by multiple users and robustness to withstand failure of a single block turned to be an intruder. The achievable outcome can be brought down to seconds when using a decentralized cloud-based access.
Design of AI-based self-learning platform for college English listening

Due to the current ineffectiveness of College English listening class, this paper puts forward a design of AI-based self-learning platform aimed at improving students' listening ability with AI assistance. The design concept and operation flow of this AI-based learning platform derive from the current application problems of AI-aided listening platforms for English learning. A survey is conducted to analyze the application characteristics of present AI-aided listening platforms for college English listening. The data analysis of the survey shows that the intelligent listening products have the merits such as rich listening resources, big exercise database and various learning channels. However, there are also some demerits like lack of specific and personalized exercise guidance, too wide material accumulation without logical classification, poor supervision and evaluation system. Based on the application problems, this paper introduces an AI-based self-learning platform for college English listening, which adopts three layer design concept e.g. service layer, technical layer and data layer. The three layers help form the integrated learning circle to improve students' daily self-learning efficiency. This AI-based self-learning platform can provide individual tailored teaching services, such as spontaneous learning help, learning suggestions and evaluations based on core technologies of artificial intelligence. It will create an effective self-learning environment and improve students' English listening proficiency in a better and more effective way.
A Process for Inference of Columnar NoSQL Database Schemas

Although NoSQL Databases do not require a schema a priori, to be aware of the database schema is essential for activities like data integration, data validation or data interoperability. This paper presents a process for inference of columnar NoSQL DB schemas. We validate the proposed process through a prototype tool that is able to extract schemas from the HBase columnar NoSQL database system. HBase was chosen as a case study because it is one of the most popular columnar NoSQL solutions. When compared to related work, we novel by proposing a simple solution for the inference of column data types for columnar NoSQL databases that store only byte arrays as column values, as well as a generated schema that follows the JSON Schema format.
Efficient Hybrid Cryptography Algorithm

Cryptography is the operation of transferring a specific message to prevent an attacker from accessing the contents of a message. To increase the level of security in any communication, both parties must have a copy of the encryption key. The Data Encryption Standard algorithm is insufficient due to its weak key generation, so that the key must be reconfigured to make this algorithm more secure, effective, and stronger. The key for encryption enhances securities of the Data Encryption Standard algorithm. This research assumed a combination of two efficient encryption algorithms to achieve the goal of information security by adding a new level of security to the Data Encryption Standard algorithm using the elliptic curve cryptography algorithm. This aim was met by adding two new key functions; the first one is EncK(), and the second one is DecK() for an encryption and decryption key of the Data Encryption Standard algorithm to make this algorithm more secure against attackers. The results obtained from this research also demonstrate good resistance against a brute-force attack, which makes the system more effective by applying the elliptic curve cryptography algorithm to encrypt and decrypt keys using the Data Encryption Standard. In addition, these modifications enhance the degree of complexity, increase key search space, and make the ciphered message more difficult for an attacker to crack.
Preference Elicitation in Assumption-Based Argumentation

Various structured argumentation frameworks utilize preferences as part of their inference procedure. In this paper, we consider an inverse of the standard reasoning problem, seeking to identify what preferences could lead to a given set of conclusions being drawn. We ground our work in the Assumption-Based Argumentation (ABA) framework, and present an algorithm which computes and enumerates all possible sets of preferences (restricted to three identified cases) over the assumptions in the system from which a desired conflict-free set of conclusions can be obtained under a given semantics. After describing our algorithm, we establish its soundness, completeness and complexity.
Effects of image capture and correction approaches on quantifying results of lateral flow assays with mobile phones

There have been numerous attempts to use mobile phone images to interpret results of lateral flow assays (LFA’s). Many initial efforts created attachments to position test strips by the camera or added external light sources. To see widespread use, especially in low-resource settings, for aiding test interpretation or performing some level of quantification, a mobile phone LFA reader should not require materials beyond the test and would be phone-agnostic. To assess the feasibility of this approach, twelve CareStart malaria LFA cassettes were run using spiked whole blood. About 880 images of these cassettes were acquired using three brands of phones and under various lighting conditions, imaging distances, and viewing angles. Test strip regions were converted to 1-dimensional (1D) intensity profiles along the direction of flow. Corrections for color accuracy, gamma, and white balance were implemented, and features such as peak height and area under the curve of control and test lines were used in linear regression. Both a fully connected and a 1D convolutional neural network were trained on the 1D profiles of test strips without feature extraction as well. The best regression models achieved R<sup>2</sup> of 0.77 and prediction error of 102 ng/ml. A multi-class support vector machine provided 84% accuracy for a semi-quantitative approach of negative or weak, medium, and high positives. For all analyses, corrections to color, white balance, etc. did not provide meaningful improvements, and limiting analysis to a single phone was not substantially better. Thus, there is promise for a device-agnostic mobile phone LFA reader.
Development and Implementation of Anti Phishing Wi-Fi and Information Security Protection APP based on Android

Abstract In the era of information revolution, China’s Internet construction has achieved unprecedented development. However, in the current public WiFi, the proportion of phishing WiFi with high risks is very high. This paper designs and implements a tool to detect wireless network security. It starts with the following: attack, defense, and detection. Firstly, based on the key technology of wireless attack, five functional modules of wireless attack system are designed. Then, by analyzing the shortcomings of traditional detection methods, a lightweight phishing attack detection scheme for the public is explored. The scheme is based on TMM algorithm and HDT algorithm. Finally, a phishing attack detection tool for operator network is designed and implemented based on the scheme, which can provide consumers with basic user information to connect WLAN, and detect the network effectively.The results show that all functions of the detection tool designed in this paper can run normally. It can meet the detection requirements of wireless phishing AP in the current environment and achieve the expected purpose, which has extensive market value and good scientific significance.
Providing interactive and field laboratories while teaching university marine biology classes in an era of COVID‐19

An entirely online upper-division university marine invertebrates course modeled after a field experience-intensive course that also provided interaction with live animals and research experience was offered at Rosario Beach Marine Laboratory in the summer of 2020. We describe online methods we used for providing field experiences to students participating online, as well as a workstation and interactive method for identification and detailed anatomical examination of live macroinvertebrates with students. Students were also involved as active participants in a field research project. Nearly all of the equipment involved is inexpensive or readily available in most university biology laboratories or classrooms.
Research advances in adaptive interferometry for optical freeform surfaces

The predecessor of China Optics was China Optics and Applied Optics Digest, which was founded in 1985. At that time, it was the only retrieval journal in the field of optics in China. At the end of 2008, China Optics and Applied Optics Digest was renamed
Questionnaire on class management readiness for future teachers

Introduction. The relevance of the research is determined by the special role of the class teacher at the present stage of the development of Russian education. The preparation of future teachers at the university for implementing the social and pedagogical function of class management requires the development of new approaches to the educational process, in particular, the provision of informative diagnostic methods. Materials and methods. The study used a method of experimental modeling of the questionnaire on class management readiness. The basis for creating the questionnaire was the idea of the structural organization of readiness for class management: motivational and required, cognitive, activity, and emotional-reflexive components. Statistical methods: Spearman's rank correlation coefficient; cluster analysis. Results. The stimulating materials of the questionnaire were developed, consisting of four subscales, corresponding to the component composition of readiness for class management. The subscales of the questionnaire do not duplicate each other and have independent diagnostic value. Thus, all the rank correlation coefficients are significant at the level of 0.01; at the same time, there are no indicators close in modulus to 1. Significant correlations between the diagnostic results of each subscale and the final result of readiness for class management indicate the internal consistency of the entire questionnaire. Conclusion. The initial psychometric check of the questionnaire of future teachers' readiness for class management showed the admissibility of its use to assess this construct. The questionnaire makes up for the shortage of diagnostic tools for examining the readiness of university students to implement the educational function at school and can also be used to assess the effectiveness of some aspects of the educational process at university.
Energy-Based Learning for Scene Graph Generation

Traditional scene graph generation methods are trained using cross-entropy losses that treat objects and relationships as independent entities. Such a formulation, however, ignores the structure in the output space, in an inherently structured prediction problem. In this work, we introduce a novel energy-based learning framework for generating scene graphs. The proposed formulation allows for efficiently incorporating the structure of scene graphs in the output space. This additional constraint in the learning framework acts as an inductive bias and allows models to learn efficiently from a small number of labels. We use the proposed energy-based framework to train existing state-of-the-art models and obtain a significant performance improvement, of up to 21% and 27%, on the Visual Genome and GQA benchmark datasets, respectively. Furthermore, we showcase the learning efficiency of the proposed framework by demonstrating superior performance in the zero- and few-shot settings where data is scarce.
Peningkatan Hasil Klasifikasi pada Algoritma Random Forest untuk Deteksi Pasien Penderita Diabetes Menggunakan Metode Normalisasi

Diabetes is a disease caused by high blood sugar in the body or beyond normal limits. Diabetics in Indonesia have experienced a significant increase, Basic Health Research states that diabetics in Indonesia were 6.9% to 8.5% increased from 2013 to 2018 with an estimated number of sufferers more than 16 million people. Therefore, it is necessary to have a technology that can detect diabetes with good performance, accurate level of analysis, so that diabetes can be treated early to reduce the number of sufferers, disabilities, and deaths. The different scale values for each attribute in Gula Karya Medika’s data can complicate the classification process, for this reason the researcher uses two data normalization methods, namely min-max normalization, z-score normalization, and a method without data normalization with Random Forest (RF) as a classification method. Random Forest (RF) as a classification method has been tested in several previous studies. Moreover, this method is able to produce good performance with high accuracy. Based on the research results, the best accuracy is model 1 (Min-max normalization-RF) of 95.45%, followed by model 2 (Z-score normalization-RF) of 95%, and model 3 (without data normalization-RF) of 92%. From these results, it can be concluded that model 1 (Min-max normalization-RF) is better than the other two data normalization models and is able to increase the performance of classification Random Forest by 95.45%.&#x0D;
An RRT-Based Path Planning Strategy in a Dynamic Environment

The real-time RRT-based path planning strategy is designed for the non-holonomic robot in a dynamic environment. The sampling-based strategy, which consists of a pre-processing RRT path planner and a real-time planner, navigate the robot to avoid the unknown moving obstacle, which is time-varying or move randomly. Additionally, the algorithm contains a simple temporary target determination function, and ensures its feasibility in the target-unknown situation. It decreases the realtime computational complexity because of the omission of moving obstacle segmentation, velocity computation, or original path replanning. The feasibility of the navigation strategy is verified by using computation simulation.
QFAST: Conflating Search and Numerical Optimization for Scalable Quantum Circuit Synthesis.

We present a quantum synthesis algorithm designed to produce short circuits and to scale well in practice. The main contribution is a novel representation of circuits able to encode placement and topology using generic gates, which allows the QFAST algorithm to replace expensive searches over circuit structures with few steps of numerical optimization. When compared against optimal depth, search based state-of-the-art techniques, QFAST produces comparable results: 1.19x longer circuits up to four qubits, with an increase in compilation speed of 3.6x. In addition, QFAST scales up to seven qubits. When compared with the state-of-the-art rule based decomposition techniques in Qiskit, QFAST produces circuits shorter by up to two orders of magnitude (331x), albeit 5.6x slower. We also demonstrate the composability with other techniques and the tunability of our formulation in terms of circuit depth and running time.
Deep Learning Application in Broadcast Tennis Video Annotation

We are in the era that sport is increasingly defined by data. Rich data is a powerful enabling foundation for novel insights on the game and on player actions, and consequently used for fan engagement and better decision making. Detailed data that has specifications of each shot is currently missing in tennis, as the game dynamics is fast and it is beyond human ability to manually record all specifications of each and every shot, even using scoring software. In this paper, we present an intelligent system to automatically recognize player actions, ball and player movements, and important game events. The system annotates the video with a suitable set of keywords for fast retrieval in broadcast production. Various techniques of computer vision, alignment, filtering, and pre-trained deep learning models are utilized by our system. The evaluation of our results on multiple broadcast videos show great accuracy and timeliness. The implications of the work presented in this paper are profound in the current workflow in broadcast coverage of a tennis match where normally multiple video operators and judges are needed to identify events and retrieve the related clips from multiple cameras.
FLAML: A Fast and Lightweight AutoML Library

We study the problem of using low computational cost to automate the choices of learners and hyperparameters for an ad-hoc training dataset and error metric, by conducting trials of different configurations on the given training data. We investigate the joint impact of multiple factors on both trial cost and model error, and propose several design guidelines. Following them, we build a fast and lightweight library FLAML which optimizes for low computational resource in finding accurate models. FLAML integrates several simple but effective search strategies into an adaptive system. It significantly outperforms top-ranked AutoML libraries on a large open source AutoML benchmark under equal, or sometimes orders of magnitude smaller budget constraints.