Reviewerly/exHarmony huggingface dataset:
FIXME: synchronize with database! An empirical study of data access self-admitted technical debt

Developers sometimes choose design and implementation shortcuts due to the pressure from tight release schedules. However, shortcuts introduce technical debt that increases as the software evolves. The debt needs to be repaid as fast as possible to minimize its impact on software development and software quality. Sometimes, technical debt is admitted by developers in comments and commit messages. Such debt is known as self-admitted technical debt (SATD). In data-intensive systems, where data manipulation is a critical functionality, the presence of SATD in the data access logic could seriously harm performance and maintainability. Understanding the composition and distribution of the SATDs across software systems and their evolution could provide insights into managing technical debt efficiently. We present a large-scale empirical study on the prevalence, composition, and evolution of SATD in data-intensive systems. We analyzed 83 open-source systems relying on relational databases as well as 19 systems relying on NoSQL databases. We detected SATD in source code comments obtained from different snapshots of the subject systems. To understand the evolution dynamics of SATDs, we conducted a survival analysis. Next, we performed a manual analysis of 361 sample data-access SATDs, investigating the composition of data-access SATDs and the reasons behind their introduction and removal. We identified 15 new SATD categories, out of which 11 are specific to database access operations. We found that most of the data-access SATDs are introduced in the later stages of change history rather than at the beginning. We also observed that bug fixing and refactoring are the main reasons behind the introduction of data-access SATDs.
Sensurfaces

Ubiquitous touch sensing surfaces are largely influenced by touchscreens' look and feel and fail to express the physical richness of existing surrounding materials. We introduce Sensurfaces, a plug-and-play electronic module that allows to rapidly experiment with touch-sensitive surfaces while preserving the original appearance of materials. Sensurfaces is composed of plug-and-play modules that can be connected together to expand the size and number of materials composing a sensitive surface. The combination of Sensurfaces modules allows the creation of small or large multi-material sensitive surfaces that can detect multi-touch but also body proximity, pose, pass, or even human steps. In this paper, we present the design and implementation of Sensurfaces. We propose a design space describing the factors of Sensurfaces interfaces. Then, through a series of technical evaluations, we demonstrate the capabilities of our system. Finally, we report on two workshops validating the usability of our system.
Space–ground QKD network based on a compact payload and medium-inclination orbit

Significant progress has been made in satellite-based quantum key distribution (QKD), and urgent follow-up work is to explore the optimal solution for building practical quantum constellations. Here, we demonstrate successful QKD based on the compact terminal on the Tiangong-2 Space Lab and construct a space–ground quantum network among four ground stations. The medium-inclination orbit of Tiangong-2 Space Lab can obtain multiple available passes for the same ground station in one night, increasing the key generation amount directly. Further analysis results show that the medium-inclination orbit and Sun-synchronous orbit can form good complementarity in future quantum constellations. As a comprehensive demonstration, this work takes a step toward cost-effective quantum satellites and provides a perspective for satellite constellation construction with different orbit types.
Recognizing Medical Search Query Intent by Few-shot Learning

Online healthcare services can provide unlimited and in-time medical information to users, which promotes social goods and breaks the barriers of locations. However, understanding the user intents behind the medical related queries is a challenging problem. Medical search queries are usually short and noisy, lack strict syntactic structure, and also require professional background to understand the medical terms. The medical intents are fine-grained, making them hard to recognize. In addition, many intents only have a few labeled data. To handle these problems, we propose a few-shot learning method for medical search query intent recognition called MEDIC. We extract co-click queries from user search logs as weak supervision to compensate for the lack of labeled data. We also design a new query encoder which learns to represent queries as a combination of semantic knowledge recorded in an external medical knowledge graph, syntactic knowledge which marks the grammatical role of each word in the query, and generic knowledge which is captured by language models pretrained from large-scale text corpus. Experimental results on a real medical search query intent recognition dataset validate the effectiveness of MEDIC.
Blockchain technology-based sustainable management research: the status quo and a general framework for future application

The problems of data leakage and unreliable information transfer in the management process make sustainability management an inevitable need for future development. Globally, there is increasing attention paid to blockchain technology and particularly its application in addressing sustainable management issues, both from academia and industry. Aiming to deepen the understanding of how blockchain technology could deal with sustainable management issues across different disciplines, this paper investigates the latest research on the application of blockchain technology in sustainable management published from 2017 to 2021. It is found that there is a drastic surge of publications in the recent 2 years. The analysis focuses on authors' origins, the collaboration network of the keywords, countries, and research topics covered. The application of blockchain technology in five key sectors of sustainable management, encompassing energy management, construction management, supply chain management, environmental management, and e-government management, is selected for further analysis detail. Also, a general framework for applying blockchain technology is proposed for broadening its use and dealing with sustainable management issues. The findings show that the identified 108 publications are distributed in 75 different journals, and scholars from China, the UK, and the USA have been working closely in BT-based sustainable management research. Blockchain technology is just emerging in sustainable management, and there is a great potential for applying blockchain technology to improve sustainable management performance and, more importantly, to provide solutions to quite a few long-lasting problems in these sectors. Opportunities for future research are also presented and discussed.
BERT Against Social Engineering Attack: Phishing Text Detection

Social engineering attack uses a wide range of human interaction tricks with the goal of achieving sensitive information. Certain tricks involve sending malicious SMS to the victim where the victim is convinced by the SMS, making a security mistake by clicking a malicious link or giving away confidential information. Machine learning algorithms for spam filtering are effective measures against such SMS spam. This paper demonstrated a novel universal spam detection model using pre-trained Google bidirectional encoder representations from Transformers (BERT) for classifying spam SMS in real-time scenarios. Subsequently, different classification techniques on the datasets are evaluated, based on their accuracy, precision, and recall. An overall accuracy reached 99%, with an F1 score of 0.97. The results and implications confirmed that the distilled BERT is an effective approach for spam detection.
The Use of Machine Learning Model in the Evaluation of College Students’ Employment and Entrepreneurship Level

This exploration aims to investigate the employment and entrepreneurship of college students. College students majoring in information and computing sciences in Xinxiang City are taken as the research object. A machine learning model for college students’ employment and entrepreneurship is established. Experiments are conducted using Python language and a machine learning framework. First, based on the employment and entrepreneurship indexes of college students in machine learning, the initial data of college students’ training quality evaluation are obtained from the educational administration system, library management system, college evaluation materials, and questionnaire survey. Then, the combination weighting method is adopted to determine the index weight, quantify the data, and modify the parameters of the data provided by the framework. The Gaussian kernel is selected as the kernel function, and the sample data used by the machine learning model are labeled. Finally, the sample data are employed to train and test the model. After the consistency test, the model reaches the optimal value after 7 iterations, with an error rate of 0.01 and an accuracy rate of 99%. The final error rate of entrepreneurship and innovation model based on machine learning is less than 0.1, which is consistent with the actual situation. The model can meet the requirements of college students’ entrepreneurship and employment evaluation. It proves that it can be applied to the research of college students’ employment and entrepreneurship and has certain theoretical guidance and practical significance for the evaluation of college students’ employment and entrepreneurship level.
Additional considerations and response to ‘graphical representation of overlap for <scp>OVErviews</scp> (<scp>GROOVE</scp> tool)’

N/A
Multi-Level Credit Assignment for Cooperative Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) has become more and more popular over recent decades, and the need for high-level cooperation is increasing every day because of the complexity of the real-world environment. However, the multi-agent credit assignment problem that serves as the main obstacle to high-level coordination is still not addressed properly. Though lots of methods have been proposed, none of them have thought to perform credit assignments across multi-levels. In this paper, we aim to propose an approach to learning a better credit assignment scheme by credit assignment across multi-levels. First, we propose a hierarchical model that consists of the manager level and the worker level. The manager level incorporates the dilated Gated Recurrent Unit (GRU) to focus on high-level plans and the worker level uses GRU to execute primitive actions conditioned on high-level plans. Then, one centralized critic is designed for each level to learn each level’s credit assignment scheme. To this end, we construct a novel hierarchical MARL algorithm, named MLCA, which can achieve multi-level credit assignment. We also conduct experiments on three classical and challenging tasks to demonstrate the performance of the proposed algorithm against three baseline methods. The results show that our method gains great performance improvement across all maps that require high-level cooperation.
Preventing proof-of-work mining attacks

Bitcoin mining is the process of generating new blocks in the Bitcoin blockchain. This process is vulnerable to different types of attacks. One of the most famous attacks in this category is selfish mining. This attack is essentially a strategy that a sufficiently powerful mining pool can follow to obtain more revenue than its fair share. The reason that selfish mining is effective is the difficulty adjustment algorithm used in the Bitcoin network. In this paper, we analyze the profitability of selfish mining with respect to time and propose an alternative difficulty adjustment algorithm that discourages selfish mining while allowing the Bitcoin network to remain scalable. We analyze our proposed solution, present the results, and discuss its effectiveness. Based on our analysis, our proposed algorithm effectively increases the profitability waiting time for the attackers to almost double its original value. For example, for a miner with 40% of the network's hash power, the algorithm extends the waiting time from 4 weeks to more than 11 weeks. This will discourage attackers from performing their malicious activities. We also show that our proposed algorithm allows the network to scale while it increases the waiting time.
Self-Supervised Learning for Time-Series Anomaly Detection in Industrial Internet of Things

Industrial sensors have presently emerged as a very important device for monitoring environmental conditions in the manufacturing system. However, abnormal behavior of these smart sensors may cause some failure or potential risk during system operation, thereby increasing the high availability of the entire manufacturing process. An anomaly detection tool in industrial monitoring system must detect any abnormal behavior in advance. Recently, self-supervised learning demonstrated comparable performance with other methods while eliminating manually labeled processes in training. Moreover, this technique decreases the complexity of the training model in lightweight devices to increase the processing time and detect accurately the health of equipment assets. Therefore, this paper proposes an anomaly detection method using a self-supervised learning framework in a time-series dataset to improve the model performance in terms of high accuracy and lightweight method. With the consideration of time-series data augmentation for generating pseudo-label, a classifier using one-dimension convolutional neural network (1DCNN) is applied to learn the characteristics of normal data. This classification model output will effectively measure the degree of abnormality. The experimental results indicate that our proposed method outperforms classic anomaly detection methods. Furthermore, the model deployment in a real testbed is performed to illustrate the efficiency of the self-supervised learning method for time-series anomaly detection.
A hybrid engineering algorithm of the seeker algorithm and particle swarm optimization

Abstract A newly hybrid algorithm is proposed based on the combination of seeker optimization algorithm and particle swarm optimization. The hybrid algorithm is based on a double population evolution strategy, and the populations of individuals are evolved from the seeker optimization algorithm and the particle swarm optimization separately. The populations of individuals employ an information sharing mechanism to implement coevolution. The hybrid algorithm enhances the individuals’ diversity and averts fall into the local optimum. The hybrid algorithm is compared with particle swarm optimization, the simulated annealing and genetic algorithm, the dragonfly algorithm, the brain storming algorithm, the gravitational search algorithm, the sine cosine algorithm, the salp swarm algorithm, the multi-verse optimizer, and the seeker optimization algorithm, then 15 benchmark functions, five proportional integral differential control parameters models, and six constrained engineering optimization problems are selected for optimization experiment. According to the experimental results, the hybrid algorithm can be used in the benchmark functions, the proportional integral differential control parameters optimization, and in the optimization constrained engineering problems. The optimization ability and robustness of the hybrid algorithm are better.
Solar thermal generation forecast via deep learning and application to buildings cooling system control

Reliable prediction of solar thermal power is essential for optimal operation and control of renewable energy driven distributed power systems. This paper presents a Convolutional Neural Networks (CNNs) based multivariate approach for forecasting power generation from solar thermal collectors over multiple horizons simultaneously. It also demonstrates an application of solar thermal power generation forecasting in a building cooling system as part of a predictive central controller. Historical data from an evacuated collector field and a single axis tracking collector field have been used to develop the prediction models and assess the performance of the proposed approach. Experimental results show that the proposed approach provides accurate prediction for multiple forecast horizons: MAPE is 2.99%–4.18% for 30 min to 24 h ahead prediction. The proposed approach utilising both historical and predicted future weather data achieves 25%–37% improvements of accuracy compared to its univariate counterpart that uses only lagged power data as input. It also outperforms existing data driven approaches based on NNs, LSTM, and RF, and achieves 5.46%–21.28% statistically significant improvements compared to them.
Insulator Umbrella Disc Shedding Detection in Foggy Weather

The detection of insulator umbrella disc shedding is very important to the stable operation of a transmission line. In order to accomplish the accurate detection of the insulator umbrella disc shedding in foggy weather, a two-stage detection model combined with a defogging algorithm is proposed. In the dehazing stage of insulator images, solving the problem of real hazy image data is difficult; the foggy images are dehazed by the method of synthetic foggy images training and real foggy images fine-tuning. In the detection stage of umbrella disc shedding, a small object detection algorithm named FA-SSD is proposed to solve the problem of the umbrella disc shedding occupying only a small proportion of an aerial image. On the one hand, the shallow feature information and deep feature information are fused to improve the feature extraction ability of small targets; on the other hand, the attention mechanism is introduced to strengthen the feature extraction network's attention to the details of small targets and improve the model's ability to detect the umbrella disc shedding. The experimental results show that our model can accurately detect the insulator umbrella disc shedding defect in the foggy image; the accuracy of the defect detection is 0.925, and the recall is 0.841. Compared with the original model, it improved by 5.9% and 8.6%, respectively.
A new fast technique for pattern matching in biological sequences

Abstract At numerous phases of the computational process, pattern matching is essential. It enables users to search for specific DNA subsequences or DNA sequences in a database. In addition, some of these rapidly expanding biological databases are updated on a regular basis. Pattern searches can be improved by using high-speed pattern matching algorithms. Researchers are striving to improve solutions in numerous areas of computational bioinformatics as biological data grows exponentially. Faster algorithms with a low error rate are needed in real-world applications. As a result, this study offers two pattern matching algorithms that were created to help speed up DNA sequence pattern searches. The strategies recommended improve performance by utilizing word-level processing rather than character-level processing, which has been used in previous research studies. In terms of time cost, the proposed algorithms (EFLPM and EPAPM) increased performance by leveraging word-level processing with large pattern size. The experimental results show that the proposed methods are faster than other algorithms for short and long patterns. As a result, the EFLPM algorithm is 54% faster than the FLPM method, while the EPAPM algorithm is 39% faster than the PAPM method.
PSO Algorithm-Based Design of Intelligent Education Personalization System

The application of artificial intelligence in the field of education is becoming more and more extensive and in-depth. The intelligent education system can not only solve the limitations of location, time, and resources in the traditional learning field but it can also provide learners with a convenient, real-time, and interactive learning environment and has become one of the important applications in the Internet field. Particle swarm optimization (PSO) is a swarm intelligence-enabled stochastic optimization scheme. It is derived from a model of bird population foraging behavior. Because of its benefits of ease of implementation, high accuracy, and quick convergence, this algorithm has gained the attention of academics, and it has demonstrated its supremacy in addressing real issues. This paper aims to study the optimization of PSO in the field of computational intelligence, improve the matching degree of learning resource recommendation and learning path optimization, and improve the learning efficiency of online learners. This paper suggests intelligent education as the center, takes the PSO algorithm as the main research object, and expounds the related concepts of intelligent education and PSO algorithm. It uses swarm intelligence algorithms for intelligent education personalized services. He focuses on PSO algorithm and its work in intelligent education recommendation and learning path planning. Experiments show that the average value of the difference between the two obtained by the NBPSO algorithm is 1.13E + 02 and the variance 1.88E + 02 is the smallest. Therefore, PSO aids in improving the quality and consistency of online course resource development. In conclusion, the research results of this paper further demonstrate the advantages of PSO algorithm in solving the problem of personalized service in intelligent education. It can promote the in-depth application of swarm intelligence optimization algorithms in intelligent online learning systems. This effectively enhances the intelligent service level of the online learning system and increases the efficiency of online learning.
Multi-person 3D pose estimation from a single image captured by a fisheye camera

Multi-person 3D pose estimation with absolute depths for a fisheye camera is a challenging task but with valuable applications in daily life, especially for video surveillance. However, to the best of our knowledge, such problem has not been explored so far, leaving a gap in practical applications. In this work, we first propose a method for multi-person 3D pose estimation from a single image taken by a fisheye camera. Our method consists of two branches to estimate absolute 3D human poses: (1) a 2D-to-3D lifting module to predict root-relative 3D human poses (HPoseNet); (2) a root regression module to estimate absolute root locations in the camera coordinate (HRootNet). Finally, we propose a fisheye re-projection module without using ground-truth camera parameters to connect two branches, alleviating the impact of image distortions on 3D pose estimation and further regularizing prediction absolute 3D poses. Experimental results demonstrate that our method achieves the state-of-the-art performance on two public multi-person 3D pose datasets with synthetic fisheye images and our newly collected dataset with real fisheye images. The code and new dataset will be made publicly available.