Reviewerly/exHarmony huggingface dataset:
K-Means++ Clustering Algorithm in Categorization of Glass Cultural Relics

We used statistical methods to study the classification of high-potassium glass and lead–barium glass and analyzed the correlation between the chemical composition of different types of glass samples. We investigated the categorization methodology of glass cultural relics, conducted a principal component analysis on the chemical composition data of the glass, and developed a case-specific clustering algorithm (K-Means++) to further categorize the glass cultural relics. K-Means++ was developed to reduce the sensitivity of a traditional K-Means clustering algorithm, by choosing the next clustering center with probability inversely proportional to the distance from the current clustering center. Then we verified the validity of the six subcategories we defined by inertia and silhouette score and evaluated the sensitivity of the clustering algorithm. We obtained a robustness ratio that maintained over 0.9 in the random noise test and a silhouette score of 0.525 in the clustering, which illustrated significant divergence among different clusters and showed the result is reasonable. With our proposed algorithm and classification result, a more comprehensive understanding of glass relics can be gained.
A Hierarchical Parallel Graph Summarization Approach Based on Ranking Nodes

Graph summarization techniques are vital in simplifying and extracting enormous quantities of graph data. Traditional static graph structure-based summarization algorithms generally follow a minimum description length (MDL) style, and concentrate on minimizing the graph storage overhead. However, these methods also suffer from incomprehensive summary dimensions and inefficiency problems. In addition, the need for graph summarization techniques often varies among different graph applications, but an ideal summary method should generally retain the important characteristics of the key nodes in the final summary graph. This paper proposes a novel method based on ranking nodes, called HRNS, that follows a hierarchical parallel graph summarization approach. The HRNS first preprocesses the node ranking using a hybrid weighted importance strategy, and introduces the node importance factor into traditional MDL-based summarization algorithms; it then leverages a hierarchical parallel process to accelerate the summary computation. The experimental results obtained using both real and simulated datasets show that HRNS can efficiently extract nodes with high importance, and that the average importance over six datasets ranges from 0.107 to 0.167; thus, HRNS can achieve a significant performance gain on speedups, as the sum error ratios are also lower than the methods traditionally used.
Strong Interference UAV Motion Target Tracking Based on Target Consistency Algorithm

In recent years, unmanned aerial vehicle (UAV) image target tracking technology, which obtains motion parameters of moving targets and achieves a behavioral understanding of moving targets by identifying, detecting and tracking moving targets in UAV images, has been widely used in urban safety fields such as accident rescue, traffic monitoring and personnel detection. Due to the problems of complex backgrounds, small scale and a high density of targets, as well as mutual occlusion among targets in UAV images, this leads to inaccurate results of single object tracking (SOT). To solve the problem of tracking target loss caused by inaccurate tracking results, this paper proposes a strong interference motion target tracking method based on the target consistency algorithm for SOT based on an interframe fusion and trajectory confidence mechanism, fusing previous frames for the tracking trajectory correction of current frames, learning again from previous frames to update the model and adjusting the tracking trajectory according to the tracking duration. The experimental results can show that the accuracy of the proposed method in this paper is improved by 6.3% and the accuracy is improved by 2.6% compared with the benchmark method, which is more suitable for applications in the case of background clutter, camera motion and viewpoint change.
Agile software development and software practitioners’ productivity amidst the COVID-19 pandemic: a narrative review

Purpose As the novel coronavirus 2019 (COVID-19) impacts the world, software practitioners are collaboratively working remotely from home. The pandemic has disrupted software practitioners’ productivity forcing changes to agile methodology adopted by software practitioners in software organizations. Therefore, this study aims to provide implication on the issues and recommendations for improving software practitioners’ productivity and also examine the impact of the COVID-19 pandemic on agile software development. Design/methodology/approach This paper adopts a narrative literature review to provide early assessment based on secondary data from the literature and available document reports from studies published from 2019 to 2022 to explore software practitioners’ productivity and agile software development during the working from home directive amidst the COVID-19 pandemic. A total of 60 sources which met the inclusion criteria were used to provide preliminary evidence grounded on secondary data from the literature. Descriptive analysis was used to provide qualitative findings from the literature. Findings Findings from this study present the significance of working from home directive on agile software development and software practitioners’ productivity. More importantly, findings from the secondary data shed light on software practitioners’ productivity adopting agile software development amidst the COVID-19 pandemic. Additionally, the findings present virtual collaborative platforms used by software practitioners, technical and social barriers of agile software development during the pandemic and recommendations for remote agile software development. Originality/value This study explores the significance of working from home directive on software practitioners’ productivity during COVID-19 pandemic and further investigates how are software practitioners’ productivity adopting agile software development practices amidst the COVID-19 pandemic. Besides, this study discusses the challenges software practitioners currently face and offers some strategies to bridge the gaps in agile software development to help software practitioners, system developers, software managers and software organizations adapt to the changes caused by the pandemic.
Construction and analysis of multi-relationship bipartite network model

Abstract Bipartite networks that characterize complex relationships among data arise in various domains. The existing bipartite network models are mainly based on a type of relationship between objects, and cannot effectively describe multiple relationships in the real world. In this paper, we propose a multi-relationship bipartite network (MBN) model, which can describe multiple relationships between two types of objects, and realizes simple weighted bipartite network reconstruction. Our model contains three major modules, namely multi-relationship bipartite network modeling (MBNM), multi-relationship aggregation module (MAM) and network reconstruction module (NRM). In MBNM, a multi-relationship bipartite network is proposed to describe multiple relationships between two types of objects. In the MAM, considering that different relationships have different information for the model, we introduce a novel relationship-level attention mechanism, and the aggregation of multiple relationships is carried out through the importance of each relationship. Based on the learning framework, the NRM can learn the potential representations of nodes after multi-relationship aggregation, and design a nonlinear fusion mechanism to reconstruct weighted bipartite network. We conducted extensive experiments on three real-world datasets and the results show that multi-relationship aggregation can effectively improve the performance of the model. In addition, experiments also show that our model can outperform existing competitive baseline method.
StartCards — A method for early-stage software startups

Software startups are important drivers of economy on a global scale, and have become associated with innovation and high growth. However, the overwhelming majority of startups ends in failure. Many of these startup failures ultimately stem from software engineering issues, and requirements engineering (RE) ones in particular. Despite the emphasis placed on the importance of RE activities in the startup context, many startups continue to develop software without a clear market or customer, having never had meaningful contact with their would-be customer. We develop a method aimed at early-stage startups that is intended to help startups through the initial stages of the startup process: StartCards. The method emphasizes the importance of idea and product validation activities in particular in order to tackle anti-patterns related to (a lack of) RE in startups. This method is based on existing literature, both grey and academic literature. StartCards was developed using the Canonical Action Research (CAR) approach, over the course of 4 AR cycles. During the AR process, the method was used by 44 student startup teams in a practical course setting. Data from the use of the method was collected through self-reporting in the form of modified learning diaries, mentoring meetings with the startup teams, and a qualitative survey. We consider the current version of StartCards useful for early-stage startups based on the data we have collected. The method can also be used as a pedagogical tool in startup education. The paper presents the first published version of the method. While work on the method continues, the method is deemed ready for use.
Improving medical device usability by reducing complexity using a novel predictive models-based user interface assessment tool

This project seeks to improve medical device usability through the development of a novel assessment tool generated in Microsoft Excel to predict and analyze the time and mental effort required for various operators when using medical device hardware and smartphone mobile application dyads. This assessment tool uses operators previously validated in FLM and MTM-1 predictive models for other applications to analyze the perceptual, cognitive, and action-based steps associated with use of such device-application dyads (Karger & Bayha, 1987; Lee et al., 2015). By using this tool, we can identify opportunities to focus user interface design and usability research efforts to minimize complexity before users take it upon themselves to do so, ultimately creating safer and more effective products for patients. The objective of the analysis conducted with the user interface (UI) assessment tool outlined below was to demonstrate a proof of concept that can be validated with future research.
Multi‐modal knowledge graph inference via media convergence and logic rule

Abstract Media convergence works by processing information from different modalities and applying them to different domains. It is difficult for the conventional knowledge graph to utilise multi‐media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective. To address the issue, an inference method based on Media Convergence and Rule‐guided Joint Inference model (MCRJI) has been proposed. The authors not only converge multi‐media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction. First, a multi‐headed self‐attention approach is used to obtain the attention of different media features of entities during semantic synthesis. Second, logic rules of different lengths are mined from knowledge graph to learn new entity representations. Finally, knowledge graph inference is performed based on representing entities that converge multi‐media features. Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi‐media features and knowledge graph inference, demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi‐media features.
Strengthened teaching–learning-based optimization algorithm for numerical optimization tasks

The teaching–learning-based optimization algorithm (TLBO) is an efficient optimizer. However, it has several shortcomings such as premature convergence and stagnation at local optima. In this paper, the strengthened teaching–learning-based optimization algorithm (STLBO) is proposed to enhance the basic TLBO’s exploration and exploitation properties by introducing three strengthening mechanisms: the linear increasing teaching factor, the elite system composed of new teacher and class leader, and the Cauchy mutation. Subsequently, seven variants of STLBO are designed based on the combined deployment of the three improved mechanisms. Performance of the novel STLBOs is evaluated by implementing them on thirteen numerical optimization tasks, including the seven unimodal tasks (f1–f7) and six multimodal tasks (f8–f13). The results show that STLBO7 is at the top of the list, significantly better than the original TLBO. Moreover, the remaining six variants of STLBO also outperform TLBO. Finally, a set of comparisons are implemented between STLBO7 and other advanced optimization techniques, such as HS, PSO, MFO, GA and HHO. The numerical results and convergence curves prove that STLBO7 clearly outperforms other competitors, has stronger local optimal avoidance, faster convergence speed and higher solution accuracy. All the above manifests that STLBOs has improved the search performance of TLBO. Data Availability Statements: All data generated or analyzed during this study are included in this published article (and its supplementary information files).
Hardware implementation of Ring-LWE lattice cryptography with BCH and Gray coding based error correction

Lattice structure based cryptographic algorithms are well-known in Post-Quantum-Cryptography (PQC) due to their strong security against quantum computers, better performance and practicality in hardware implementation. Among popular Ring learning with errors (RLWE) lattice problem based cryptosystems, NewHope Simple is a promising key encapsulation mechanism (KEM). The major challenges of the algorithm are timeXarea complexity, bandwidth utilization, and a trade-off between security level and decryption failure rate (DFR). In this paper, we propose a timeXspace optimized hardware implementation of the NewHope Simple KEM along with Bose-Chaudhuri-Hocquenghem (BCH) and weighted Gray coding based error correction technique. The design is validated on Zynq7000 Artix-7 FPGA. The proposed NewHope Simple design achieves 5.03X improvement in areaXtime product than other hardware implementations, and at least 12% enhancement in execution time compared to various Graphics Processing Unit (GPU) based implementations. The result shows that our BCH decoder requires 5.8X lesser computation time than its counterparts. Our error correction technique achieves 10.4% better error rate than BCH for 4 bits per symbol. This improvement in bit error rate (BER) can be exploited to enhance bandwidth utilization and security. The proposed architecture can complete a successful key exchange in 634μs.
Mammogram Screening for Breast Density Classification using a soft voting ensemble of Swin Transformers and ConvNext models

Breast cancer risk is increased by dense breast tissue. The existing mammographic density classification methods cannot provide sufficient classification accuracy. The classification of breast density is still a challenging issue. In this paper, we propose an ensemble method for improving the accuracy of mammographic breast density classification. The ensemble method uses two views of screening mammography and employs state-of-the-art deep learning-based methods such as Swin Transformer and ConvNeXts model as base learners for local decisions and soft voting to predict the final decision. The method has been thoroughly evaluated on the benchmark DDSM dataset; it achieved an accuracy of 97.61%, a sensitivity of 95.11%, and a specificity of 98.32%. The comparison shows that the method significantly outperforms the state-of-the-art methods for breast density classification. This study provides insight into the application of deep learning to classify breast density.
Policy Poisoning in Batch Learning for Linear Quadratic Control Systems via State Manipulation

In this work, we study policy poisoning through state manipulation, also known as sensor spoofing, and focus specifically on the case of an agent forming a control policy through batch learning in a linear-quadratic (LQ) system. In this scenario, an attacker aims to trick the learner into implementing a targeted malicious policy by manipulating the batch data before the agent begins its learning process. An attack model is crafted to carry out the poisoning strategically, with the goal of modifying the batch data as little as possible to avoid detection by the learner. We establish an optimization framework to guide the design of such policy poisoning attacks. The presence of bi-linear constraints in the optimization problem requires the design of a computationally efficient algorithm to obtain a solution. Therefore, we develop an iterative scheme based on the Alternating Direction Method of Multipliers (ADMM) which is able to return solutions that are approximately optimal. Several case studies are used to demonstrate the effectiveness of the algorithm in carrying out the sensor-based attack on the batch-learning agent in LQ control systems.
A Text Classification Model Based on Gaussian Multi-Head Self Attention Mechanism for Chinese Medical Data

In order to improve the matching level of the Q&A system, text classification methods are used to classify user questions. Therefore, this paper proposes a text classification based on a hybrid model of CNN and Transformer in chinese medical dataset. Unlike the existing traditional CNN-based methods, we use a Gaussian multi-head self attention mechanism to obtain the text’s spatial features. On the chinese medical dataset ,experimental results show that compared with the baseline model the proposed model performs better in accuracy,recall,precision and F1, in which the accuracy achieves 93.20% and the F1 value achieves 94.66%.
Research on real-time path planning and obstacle avoiding for mobile robot swarms based on an advanced artificial potential field method

Unmanned mobile robots have broad application prospects in space exploration. In order to meet the increasing technical requirements, it is of great practical significance to study robot cluster work, path planning, and obstacle avoidance. A method is proposed in this paper to achieve the functions mentioned above built on an improved artificial potential field (APF). By introducing a dynamic attractive factor based on the number of obstacles and setting different goal point information for leader and follower robots, obstacle avoidance navigation and cooperative following function can be achieved under the force of the potential field. Finally, using webots to build an environment and robots for simulation. The results and planned path map testify to the feasibility and effectiveness of this method.
A Chinese NER Method Based on Chinese Characters' Multiple Information

A Chinese named entity recognition method that integrates multiple information is proposed for the inadequate prior knowledge of word vectors in the pre-trained model-based Chinese named entity recognition method. Based on the existing BERT model, the feature representation of Chinese character vectors is enhanced by constructing a table of bias and word frequency vectors of all Chinese characters in the dataset, embedding them into an improved word fusion model for encoding, and finally, feeding them into CRF for decoding. The experimental results show that the proposed method has improved F1 values over the public datasets MSRA, Weibo and Resume while maintaining fast decoding speed and low computer resource consumption, and performs better in terms of robustness and generalization ability than models such as BiLSTM-CRF and Lattice-LSTM.
CAPNet: Context and Attribute Perception for Pedestrian Detection

With a focus on practical applications in the real world, a number of challenges impede the progress of pedestrian detection. Scale variance, cluttered backgrounds and ambiguous pedestrian features are the main culprits of detection failures. According to existing studies, consistent feature fusion, semantic context mining and inherent pedestrian attributes seem to be feasible solutions. In this paper, to tackle the prevalent problems of pedestrian detection, we propose an anchor-free pedestrian detector, named context and attribute perception (CAPNet). In particular, we first generate features with consistent well-defined semantics and local details by introducing a feature extraction module with a multi-stage and parallel-stream structure. Then, a global feature mining and aggregation (GFMA) network is proposed to implicitly reconfigure, reassign and aggregate features so as to suppress irrelevant features in the background. At last, in order to bring more heuristic rules to the network, we improve the detection head with an attribute-guided multiple receptive field (AMRF) module, leveraging the pedestrian shape as an attribute to guide learning. Experimental results demonstrate that introducing the context and attribute perception greatly facilitates detection. As a result, CAPNet achieves new state-of-the-art performance on Caltech and CityPersons datasets.
Implementing GDPR-Compliant Surveys Using Blockchain

The immutability of data stored in a blockchain is a crucial pillar of trust in this technology, which has led to its increasing adoption in various use cases. However, there are situations where the inability to change or delete data may be illegal. European Union’s General Data Protection Regulation (GDPR)—applying to any company processing personal data from European citizens—explicitly entitles individuals to the right to rectification and the right to be forgotten. In this paper, we describe the design of a system to deploy and process survey data in a GDPR-compliant manner. It combines an Hyperledger Fabric blockchain to ensure that data cannot be tampered with and InterPlanetary File Systems (IPFS) for storage. With the proposed arrangement, we reap several security benefits enabled by blockchain’s immutability without running afoul of the regulations. Furthermore, the proof-of-concept is generic and can easily be adapted to various domains.
Decentralized Differentially Private Without-Replacement Stochastic Gradient Descent

While machine learning has achieved remarkable results in a wide variety of domains, the training of models often requires large datasets that may need to be collected from different individuals. As sensitive information may be contained in the individual's dataset, sharing training data may lead to severe privacy concerns. Therefore, there is a compelling need to develop privacy-aware machine learning methods, for which one effective approach is to leverage the generic framework of differential privacy. Considering that stochastic gradient descent (SGD) is one of the most commonly adopted methods for large-scale machine learning problems, a decentralized differentially private SGD algorithm is proposed in this work. Particularly, we focus on SGD without replacement due to its favorable structure for practical implementation. Both privacy and convergence analysis are provided for the proposed algorithm. Finally, extensive experiments are performed to demonstrate the effectiveness of the proposed method.