Reviewerly/exHarmony huggingface dataset:
Multirotor Long-Reach Aerial Pruning with Wire-Suspended Saber Saw

Pruning work at high altitude is a dangerous work with a high risk of accidents for human workers. In this research, we propose a multirotor flying robot that is equipped with a wire-suspended device and performs pruning task. We use a saber saw as a cutting tool. If the cutting tool is installed on the body of the multirotor platform, it is difficult for the flying robot to approach the desired work point if there are obstacles such as other branches around the target branch to be pruned. Therefore, in this study, we propose a saber saw suspended from the body of the multirotor platform with two wires. The wire-suspended device is equipped with a saber saw and four ducted fans that produces thrust in any direction on the horizontal plane. This ducted fan system can be used to suppress the swing of the wire-suspended device to make positioning of the saber saw blade to the target point easier, and to improve the efficiency of the cutting and reduce the cutting time by providing a pushing force to the saber saw. As a result, the pruning work could be performed efficiently. Experiments have demonstrated that aerial pruning is possible using the long-reach wire-suspended saber saw.
Identifying Requirements and Quality Attributes from the Point of View of Users of Mobile Digital Libraries

Due to the COVID-19 pandemic there has been a significant growth in the adoption of online educational resources, including mobile digital libraries. Thus, several quality attributes have been indicated by both industry and academy for designing useful and usable digital libraries. Nonetheless, we are not aware of a complete list of requirements that could be met to develop high quality digital libraries in the mobile context. In this paper, we try to meet this gap by identifying features that digital libraries should provide. To do so, we carried out an analysis of 20 Brazilian mobile digital libraries in the market, identifying requirements that provide functionalities for users. Also, we analyzed app comments from the top rated and least rated mobile digital libraries apps, to extract quality attributes. In all, we identified 14 requirements through the analysis of the applications and 49 quality attributes considering the opinions on users’ comments. The list of requirements and quality attributes is useful to understand the users’ expectations and understand what to maintain or remove when designing and developing a mobile digital library.
Updatable ElGamal Encryption Scheme with Forward and Backward Security for Cloud Storage

Updatable encryption plays an important role in cloud storage scenario due to providing update functionality for ciphertext data and resisting key compromise attacks. However, most of updatable encryption schemes adopted a type of partitioning strategy: leakage sets or firewalls technique, which prohibited the adversary from querying the key at the challenge-equal epochs and hardly captured forward and backward security. In this paper, we enhance the existing security model by considering a corruption oracle that allows any secret key query and overcomes the dependence on leakage sets, and then present the first updatable public-key encryption scheme with no-directional key updates and uni-directional ciphertext updates on the basis of the classical ElGamal encryption scheme. The proposed scheme can minimize the information leakage of update tokens by using the indistinguishable obfuscation technique of the punctured program. Moreover, under the framework of the enhanced security model, the proposed scheme is proven to be IND-CPA. Finally, compared with the existing updatable encryption schemes, the proposed scheme eliminates leakage sets and firewalls to capture forward and backward security.
Society of Knowledge and ICT skills in Universities

Today's new realities and social demands involve a technological apparatus that societies that seek to generate sustainable development through the generation and dissemination of knowledge cannot be ignored. This research addresses identifying key areas to be developed to promote a timely knowledge society for the digital age. Hermeneutics and documentary content analysis facilitated the method, concluding that generating more extraordinary digital skills in those responsible for transmitting knowledge is essential to achieving durability and efficient transmission of information.
Factors Influencing Residents’ Intention toward Source Separation of Plastic Noodle Wrapper Waste: A Case Study of Jabodetabek, Indonesia

Economic growth and urban development have induced environmental damages. It because of plastic consumption never stops increasing, and make it into environmental pollution. The contribution of noodle wrapper into environment has become significant since Indonesia is the second biggest instant noodles’ consumer in the world. As the most rapidly urban development and populous area in Indonesia, Jabodetabek (Jakarta-Bogor-Depok-Tangerang-Bekasi) contributes to the high plastic waste generation. Separation waste at source is important for the successful of any waste management. However, the waste separation behaviors are not common in Indonesia and the study about residents’ separate intentions for noodle wrapper is scarce. For filling this gap, we have tried to identify the critical factors that affect the residents’ intention towards noodle wrapper separation. Data drawn from 435 questionnaires were used to assess demographic factors and the TPB constructs (i.e. attitude, subjective norms, perceived behavioral control, knowledge, past behavior, willingness to accept, intention, and behavior). Results of this study showed that subjective norms had the most significant influence on the intention of noodle wrapper separation followed by past behavior, attitude, and knowledge. While perceived behavioral control and willingness to accept do not have a significant effect on the intention of noodle wrapper separation.
Research into Oil Painting Recognition and Analysis System Based on Image Intelligent Algorithm

In the information society, the art market is quickly expanding in volume, and the development of oil painting art, as an important component of the art industry, is increasingly going global and becoming popularized. In order to improve the analysis and recognition ability of oil paintings with increasingly large volume, this study designs an oil painting recognition and analysis system based on image intelligent algorithm, which integrates the recognition of oil painting color and literary and scientific features and the intelligent detection function of oil painting. Through intelligent recognition of oil painting features, this study calculate the difference threshold between oil painting color and shape features, so as to realize the accurate recognition of oil painting.
Study of Operational Capability Attributes of Complex Information Systems Based on ANP Sensitivity Theory

As modernization accelerates, various new technologies and new ways of operation proliferate, and the complexity of human-computer interaction systems continues to increase. The gradual complication of functions and tasks of complex information systems places special training demands on the operator’s operational capabilities. How to develop individualized training programs for different types of operators on the basis of identifying the differences in each ability attribute has become an important issue to improve training efficiency and train specialized and high-fit operator teams. In this study, firstly, on the basis of analyzing the operational tasks of complex information systems, a model of the competence indicator system for operators of different seats in complex information systems is established. The weights of the indicator system are evaluated by using the ANP model, and the weights of the three operator alternatives are obtained, on the basis of which the characteristics of the relative magnitude of change in the proportion of the three alternative options are identified using the method of sensitivity analysis. The results of the subjective clustering of the various characteristics show that the characteristics of the magnitude of variation of these competencies can be finally classified into four categories, namely, “experienced, knowledgeable, fixed, and cumulative competencies.” Finally, the proposed competency training program for operators of different levels and individual characteristics can be used as a reference for efficient training of operating competencies of complex information systems.
Automatic Mining of Code Fix Patterns from Code Repositories

The majority of conventional automated approaches to examination and correction of source code (such as static and dynamic analysis methods) often prove to be not fully adequate for modern software engineers. They generally demand the existence of preprescribed patterns of unwanted code and/or proposed patches. Thus, such solutions are limited in implementation to a relatively narrow set of widely known cases.The approach proposed and implemented in this work allows for the rapid adaptation of the developed tool to yet unknown and domain-specific cases, while offering more fine-granularity, by focusing on vertex-to-vertex matching of code modifications represented by abstract syntax trees and comparison of vertex data using word-embedding tools. The evaluation is based on real C, C++, and C# code repositories from open source OS Tizen, as well as synthetic tests.
Exploitability Assessment with Genetically Tuned Interconnected Neural Networks

With the ever-increasing digitalization of data, processes, and services, people rely more than ever on computerized systems. These systems are practical and efficient, so people and institutions have come to depend on them substantially. However, computerized systems are not perfect, they can have cybersecurity vulnerabilities that allow hackers to attack these systems. Since those systems have so many responsibilities, they can also cause serious consequences when invaded by malicious people. Considering the huge volume of newly discovered vulnerabilities, it is impossible to create and apply security patches for all of them in a timely manner. Hence we need to prioritize security patches. The state of the art for vulnerability prioritization is the CVSS, an incomplete system, which has no scientific evidence and considers only the severity of a vulnerability, without taking into consideration its probability of exploitation, greatly hampering its efficiency. In this paper we propose V-REx, an open-source software that uses multiple neural networks tuned by a modified genetic algorithm to predict the exploitation probability of a vulnerability and therefore helping to prioritize them. Our experiments unveil that V-REx was more efficient than CVSS in prioritizing the vulnerabilities published in year of 2019.
Multi-Aspect Diversification of Samples and Features for Advanced Creation of Rule Ensemble

Rule learning has been a popular machine learning branch owing to the advantage of such learning approaches in model interpretability. Since rule learning approaches are generally sensitive to the changes of training data, i.e., a slight modification of the training data may lead to a large variation on the performance of prediction, it results in the development of rule ensemble approaches for improving the stability of the learning performance. In this research, a new approach of rule ensemble is proposed, which is designed to involve multi-level creation of diversity and leads to production of an ensemble of ensembles. In particular, the proposed approach involves the joint adoption of the Bagging and Boosting approaches for achieving diversification of instances, and on the basis of this adoption, a combination of multiple ways of handling continuous attributes is undertaken for achieving diversification of features. The empirical results indicate that the proposed rule ensemble approach illustrates considerably better performance than those of popular approaches of standard rule learning as well as other more recent methods of decision tree ensemble construction.
Weakly Supervised Object Localization with Noisy-Label Learning

A novel perspective for Weakly Supervised object localization is proposed in this paper. Most recent pseudo-label-based methods only consider how to get better pseudo-labels and do not consider how to apply these imperfect labels properly. We propose the Noisy-Label Learning on Weakly Supervised object localization (NL-WSOL) to improve localization performance by cleaning defective labels. First, we generate labels which more focused categories for images in the label generation stage. Then, we judge the quality of pseudo labels and enhance the labels with poor quality. Moreover, we introduce a composite loss function to guide the network training in the pseudo-label-based training phase. Our method achieves 97.39% localization performance on the CUB-200–2011 test set.
Content based image retrieval using feature fusion technique

This paper contributes a feature fusion technique for efficient content-based image retrieval. It aims to implement an accurate feature extraction method through the proposed system model. It can be a promising candidate for image retrieval applications as it concatenates the features from deep learning architecture and local binary patterns. Experimentation is done on the benchmark Core11000 Database that has been divided into 10 classes of beaches, food, buses, flowers, dinosaurs, elephants, horses, monuments, mountains and snow, and people and villages in Africa. The proposed method achieves an average precision of 95.14% and an average recall of 9.514% while retrieving 10 images corresponding to a given query image. These results are superior to the existing methods based on deep learning models and local patterns. Also, for variations in the number of retrieved images, respective average precision, and average recall are calculated.
Click is Not Equal to Purchase: Multi-task Reinforcement Learning for Multi-behavior Recommendation

Reinforcement learning (RL) has achieved ideal performance in recommendation systems (RS) by taking care of both immediate and future rewards from users. However, the existing RL-based recommendation methods assume that only a single type of interaction behavior (e.g., clicking) exists between user and item, whereas practical recommendation scenarios involve multiple types of user interaction behaviors (e.g., adding to cart, purchasing). In this paper, we propose a Multi-Task Reinforcement Learning model for multi-behavior Recommendation (MTRL4Rec), which gives different actions for users’ different behaviors with a single agent. Specifically, we first introduce a modular network in which modules can be reused or isolated from each other to model the commonalities and differences between users’ behaviors. Then a task routing network is used to generate routes in the modular network for each behavior task. Finally, we adopt a hierarchical reinforcement learning architecture to improve the efficiency of MTRL4Rec. Experiments on two public datasets validated the effectiveness of MTRL4Rec.
Pedestrian re-identification based on Swin Transformer

At present, most mainstream pedestrian re-identification(ReID) methods are based on convolution neural network(CNN). However, CNN has the problems of information loss caused by down-sampling algorithm and remote dependence modeling. Transformer architecture has achieved good performance in various visual tasks recently. In order to explore its ability in ReID task, we designed a ReID model based on Swin Transformer architecture, and improve it by combining multi-scale feature fusion. Specifically, Swin Transformer is used as a backbone network to extract image features, and design two modules to enhance feature perception. (1) Shallow feature extraction module (SFE) is embedded in the initial position of the model to generate robust shallow feature expression. (2) Multi-scale feature fusion module (MFM) extracts signs from different stages of the model, and further enhances the feature extraction ability of the model by fusing different levels of features. Based on experimental results, the proposed model based on Swin Transformer achieves a higher recognition accuracy than the mainstream CNN-based pedestrian re-identification method.
API Description-Based Conformance Assessment of Architectural Design Decision

Microservice APIs are often designed based on Domain-Driven Design. It can be challenging to judge the quality of the relation between such a design and the implemented API, especially when facing frequent releases and changes in an API and its design models. Manual conformance assessment of this relation is time-intensive and error prone. This paper proposes a novel approach for automated conformance assessment of API designs in relation to API design decisions. Our approach aims to provide the first fully automated conformance assessment approach based solely on the code of API Descriptions such as OpenAPI. We applied and exemplified our approach for checking conformance to the patterns and practices in an API design decision for mapping links in a system’s domain model to API representations. The total accuracy score (F1-measure) for decision option identification in our multi-case study for this decision is 97.22%.
Research of Cryptocurrencies Function of Instant Payments in the Tourism Sector: Risks, Options, and Solutions

This research aims to provide an overview of the technological solutions of instant payment with cryptocurrencies in the tourism sector. The analysis has been done on various cryptocurrencies and technologies related to them, their transaction processing speed, alternatives on how to optimize it, and what impact it has on cryptocurrency payments adoption to the tourism sector. It was noticed that various places of services in the tourism sector could take a lot of benefits from accepting the cryptocurrency transactions, such as an additional group of clients, eased payment mobility and fast settlement of the received funds, but they are also avoiding accepting the most popular cryptocurrencies due to the volatility of their market, high transactional costs and low transaction confirmation speed, which would cause an issue to manage the customers’ queue while comparing to traditional currency payments. This research revealed that there are solutions to avoid the mentioned drawbacks by offering less popular cryptocurrency acceptance or taking the possible risks of accepting 0-confirmation transactions. The prototype of instant payments solution is suggested where cryptocurrency payment adoption in the tourism sector could be reached by accepting 0-confirmation transactions of the most popular cryptocurrencies with an instant exchange to fiat currencies at the merchant side.
A Management Specification for Big Data Sharing in Smart Mine

With the rapid advancement of the informatization process of the coal industry, applications based on the Internet of Things and intelligent processing technologies for smart mines have sprung up. The sharing and aggregation of various colliery automation system data is the core foundation for the realization of intelligent mine applications. The resolution of integrity and real-time issues in data sharing determines the availability of upper-level intelligent applications. This paper proposes a set of smart mine specifications. It regulates coal mine data sharing behavior from three perspectives: data collection, data transmission, and data storage. The data source specification specifies the protocol for data collection and transmission. The evaluation indicators and governance methods are described in the data quality specification. It sets data reliability sharing standards based on the characteristics of each business system. The labels defined in the data storage specification model realize the efficient management of data indexing which can be used by professional and non-professional users in the coal industry. We constructed an experimental system based on real measured data from a certain colliery. The system can collect, manage and store the data from various automation systems following the specifications.
Iterative Bregman Algorithm for Monte Carlo Enhancement

Sampled distributions are used to price, hedge and produce scenarios for derivative products which is essential for risk management. This approach accumulates many types of discretisation errors. These can be linked to data quality input, to calibration algorithms, discretisation in time, interpolation in space or even dependent on the sample’s size N. All these errors contribute in errors that are detrimental to the quality of the final result. We propose to work on the trajectories and smoothly transport them into a new version which is more appropriate to fit the target.The proposed method acts directly on the generated sample and is therefore model free.
Implementation of the C4.5 Decision Tree Algorithm Method for Selection of Facial Mask Skin Care Products

Facial treatment is a method for maintaining skin health and treating various facial skin problems. Facial masks for skincare are currently one of the most popular facial treatments. Many local and international cosmetic brands offer facial mask products in various variants, where each variant has its functions and benefits. Therefore, consumers should get advice from experts before selecting facial mask skincare suitable for their skin type. Hence, in a modern environment, it is necessary to create an expert system that can act as an expert and allow customers to conduct online consultations for the proper facial treatment. Various methods, such as the decision tree algorithm C4.5, can be applied to build an expert system for selecting facial masks for skin care. The C4.5 algorithm's decision tree method transforms existing data into a decision tree, then converted into a decision rule. Customer data was used in this study, with 220 customers used to build decision tree rules and 44 customer data used for accuracy testing. The result of testing data obtained an accuracy rate of 84.09%.
Polarization in Personalized Recommendations: Balancing Safety and Accuracy

Recommender systems are beneficial to both service providers and users, but may also have unintended side effects. In this paper, we address the societal impact of recommender systemss on user polarization resulting from over-personalized recommendations. First, we model the user preference gap (uPG) as the distance between two timeseries, representing the user's consumption per content categories. Second, we map the uPG score onto the not-yet-rated items, and consider two approaches to minimize uPG per user, when they go beyond a certain threshold. In particular, we propose post and pre-constrained versions of the alternating least square algorithm that reduce the user's uPG score, hence avoiding to reinforce her polarization over time. Interestingly, these newly constrained algorithms still maintain a high level of recommendation accuracy. Our simulation results, derived from real datasets, show that our solutions enable personalization with a reasonable level of polarization.