Reviewerly/exHarmony huggingface dataset:
Agile project management challenge in handling scope and change: A systematic literature review

Uncertainty has become a challenge in project management during the age of the Covid-19 pandemic. Project management required a more flexible and dynamic approach; therefore, it is believed that Agile project management could provide a solution to this problem. Agile has been widely used in multiple industry areas as a practical and flexible approach in project management practices. In the project, the scope is considered one aspect that can directly affect the budget and timing. Unfortunately, it seems to be one of the most neglected domains in Agile and conventional. Agile is recognized for its rapid improvement as well as its willingness to embrace change. However, uncontrolled change can potentially delay and make the project overspend. This paper aims to examine the challenges and discover the best practices in handling scope and change in Agile. A systematic literature review (SLR) was carried out through the literature sources, which entailed IEEE Explore, ScienceDirect, Emerald Insight, ProQuest, and Wiley Online. The finding was seven categories, and eighteen issues were found. Then twenty-four best practices were mapped to the challenges. We found the most significant challenge came from People & Organization, User Requirement Prioritization, Over-scope Requirement, and Communication & Coordination. Those areas are considered the most significant challenge because they can create delays and overspend in the project.
COVID-19, policy change, and post-pandemic data governance: a case analysis of contact tracing applications in East Asia

Abstract In an era of digitalization, governments often turn to digital solutions for pressing policy issues, and the use of digital contact tracing and quarantine enforcement for COVID-19 is no exception. The long-term impacts of the digital solutions, however, cannot be taken for granted. The development and use of data tools for pandemic control, for example, may have potentially detrimental and irreversible impacts on data governance and, more broadly, society, in the long run. In this paper, we aim to explore the extent to which COVID-19 and digital contact tracing have led to policy change in data governance, if at all, and what the implications of such change would be for a post-COVID world. We compare the use of contact tracing and monitoring applications across mainland China, Hong Kong, and Singapore to illustrate both the enormous benefits and potential risks arising from the design of contact tracing applications and the involvement of stakeholders in the various stages of the policy cycle to combat the COVID-19 pandemic. We argue that, while COVID-19 has not changed the nature of issues, such as public trust in data governance, the increasing involvement of big tech in data policies, and data privacy risks, it has exacerbated those issues through the accelerated adoption of data technologies.
Sort and Deep-SORT Based Multi-Object Tracking for Mobile Robotics: Evaluation with New Data Association Metrics

Multi-Object Tracking (MOT) techniques have been under continuous research and increasingly applied in a diverse range of tasks. One area in particular concerns its application in navigation tasks of assistive mobile robots, with the aim to increase the mobility and autonomy of people suffering from mobility decay, or severe motor impairments, due to muscular, neurological, or osteoarticular decay. Therefore, in this work, having in view navigation tasks for assistive mobile robots, an evaluation study of two MOTs by detection algorithms, SORT and Deep-SORT, is presented. To improve the data association of both methods, which are solved as a linear assignment problem with a generated cost matrix, a set of new object tracking data association cost matrices based on intersection over union, Euclidean distances, and bounding box metrics is proposed. For the evaluation of the MOT by detection in a real-time pipeline, the YOLOv3 is used to detect and classify the objects available on images. In addition, to perform the proposed evaluation aiming at assistive platforms, the ISR Tracking dataset, which represents the object conditions under which real robotic platforms may navigate, is presented. Experimental evaluations were also carried out on the MOT17 dataset. Promising results were achieved by the proposed object tracking data association cost matrices, showing an improvement in the majority of the MOT evaluation metrics compared to the default data association cost matrix. In addition, promising frame rate values were attained by the pipeline composed of the detector and the tracking module.
Federated Random Forests can improve local performance of predictive models for various healthcare applications

Limited data access has hindered the field of precision medicine from exploring its full potential, e.g. concerning machine learning and privacy and data protection rules.Our study evaluates the efficacy of federated Random Forests (FRF) models, focusing particularly on the heterogeneity within and between datasets. We addressed three common challenges: (i) number of parties, (ii) sizes of datasets and (iii) imbalanced phenotypes, evaluated on five biomedical datasets.The FRF outperformed the average local models and performed comparably to the data-centralized models trained on the entire data. With an increasing number of models and decreasing dataset size, the performance of local models decreases drastically. The FRF, however, do not decrease significantly. When combining datasets of different sizes, the FRF vastly improve compared to the average local models. We demonstrate that the FRF remain more robust and outperform the local models by analyzing different class-imbalances.Our results support that FRF overcome boundaries of clinical research and enables collaborations across institutes without violating privacy or legal regulations. Clinicians benefit from a vast collection of unbiased data aggregated from different geographic locations, demographics and other varying factors. They can build more generalizable models to make better clinical decisions, which will have relevance, especially for patients in rural areas and rare or geographically uncommon diseases, enabling personalized treatment. In combination with secure multi-party computation, federated learning has the power to revolutionize clinical practice by increasing the accuracy and robustness of healthcare AI and thus paving the way for precision medicine.The implementation of the federated random forests can be found at https://featurecloud.ai/.Supplementary data are available at Bioinformatics online.
Tensor Completion-Based Incomplete Multiview Clustering

Incomplete multiview clustering is a challenging problem in the domain of unsupervised learning. However, the existing incomplete multiview clustering methods only consider the similarity structure of intraview while neglecting the similarity structure of interview. Thus, they cannot take advantage of both the complementary information and spatial structure embedded in similarity matrices of different views. To this end, we complete the incomplete graph with missing data referring to tensor complete and present a novel and effective model to handel the incomplete multiview clustering task. To be specific, we consider the similarity of the interview graphs via the tensor Schatten p -norm-based completion technique to make use of both the complementary information and spatial structure. Meanwhile, we employ the connectivity constraint for similarity matrices of different views such that the connected components approximately represent clusters. Thus, the learned entire graph not only has the low-rank structure but also well characterizes the relationship between unmissing data. Extensive experiments show the promising performance of the proposed method comparing with several incomplete multiview approaches in the clustering tasks.
A Surrogate-Assisted Evolutionary Feature Selection Algorithm With Parallel Random Grouping for High-Dimensional Classification

Various evolutionary algorithms (EAs) have been proposed to address feature selection (FS) problems, in which a large number of fitness evaluations are needed. With the rapid growth of data scales, the fitness evaluation becomes time consuming, which makes FS problems expensive optimization problems. Surrogate-assisted EAs (SAEAs) have been widely used to solve expensive optimization problems. However, the SAEAs still face difficulties in solving expensive FS problems due to their high-dimensional discrete decision variables. To address this issue, we propose an SAEA with parallel random grouping for expensive FS problems, in which three main components consist. First, a constraint-based sampling strategy is proposed, which considers the influence of the constraint boundary and the number of selected features. Second, a high-dimensional FS problem is randomly divided into several low-dimensional subproblems. Surrogate models are then constructed in these low-dimensional decision spaces. After that, all the subproblems are optimized in parallel. The process of random grouping and parallel optimization continues until the termination condition is met. Finally, a final solution is chosen from the best solution in the historical search and the best solution in the last population using a random, distance-, or voting-based method. Experimental results show that the proposed algorithm generally outperforms traditional, ensemble, and evolutionary FS methods on 14 datasets with up to 10 000 features, especially when the required number of real fitness evaluations is limited.
Introduction to Graph Neural Networks

Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social
Provably Secure and Fast Color Image Encryption Algorithm Based on S-Boxes and Hyperchaotic Map

The World Wide Web is experiencing a daily increase in data transmission because of developments in multimedia technologies. Consequently, each user should prioritize preventing illegal access of this data by encrypting it before moving it over the Internet. Numerous color image encryption schemes have been developed to protect data security and privacy, indifferent to the computation cost. However, most of these schemes have high computational complexities. This research proposes a fast color image scrambling and encryption algorithm depending on different chaotic map types and an S-box that relies on a hyperchaotic map principle. The first step involves converting color image values from decimal representation to binary representation in the scrambling stage by changing the location of the bits according to a proposed swapping algorithm. Next, in the second scrambling stage, the same process occurs after returning color image values from binary representation to decimal representation and generating an S-box with the assistance of two types of chaotic map, namely, a 2D Zaslavsky map and a 3D Hénon map. Thus, this S-box is relied upon to swap the locations of the pixels in the color image. The encryption procedure begins with the production of three key matrices using a hybrid technique that employs two low-complexity types of chaotic map, namely, a 1D Logistic map and a 3D Hénon map, followed by an XORed as a lightweight process between each key generated for the three matrices and the corresponding red, green, and blue image channels. According to the findings, the proposed scheme demonstrates the most efficiency in terms of lowering the computational cost and shows its effectiveness against a wide range of cryptographic attacks.
Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions

Phishing has become an increasing concern and captured the attention of end-users as well as security experts. Existing phishing detection techniques still suffer from the deficiency in performance accuracy and inability to detect unknown attacks despite decades of development and improvement. Motivated to solve these problems, many researchers in the cybersecurity domain have shifted their attention to phishing detection that capitalizes on machine learning techniques. Deep learning has emerged as a branch of machine learning that becomes a promising solution for phishing detection in recent years. As a result, this study proposes a taxonomy of deep learning algorithm for phishing detection by examining 81 selected papers using a systematic literature review approach. The paper first introduces the concept of phishing and deep learning in the context of cybersecurity. Then, taxonomies of phishing detection and deep learning algorithm are provided to classify the existing literature into various categories. Next, taking the proposed taxonomy as a baseline, this study comprehensively reviews the state-of-the-art deep learning techniques and analyzes their advantages as well as disadvantages. Subsequently, the paper discusses various issues that deep learning faces in phishing detection and proposes future research directions to overcome these challenges. Finally, an empirical analysis is conducted to evaluate the performance of various deep learning techniques in a practical context, and to highlight the related issues that motivate researchers in their future works. The results obtained from the empirical experiment showed that the common issues among most of the state-of-the-art deep learning algorithms are manual parameter-tuning, long training time, and deficient detection accuracy.
Robust image matching via local graph structure consensus

Image matching plays a vital role in many computer vision tasks, and this paper focuses on the mismatch removal problem of feature-based matching. We formulate the problem into a general yet effective optimization framework based on graph matching by combining integer quadratic programming with a compensation term for discouraging matches, termed as Local Graph Structure Consensus (LGSC). Considering the local area similarity of those potential true matches, we design a local graph structure for preserving geometric topology, which contains a local indicator vector and a local affinity vector for each correspondence. The local indicator vector is utilized for edge construction, while the local affinity vector represents the match correctness of the nodes and edges between two graphs. In particular, the ranking shift with scale and rotation invariance is exploited to represent the node affinity. Ultimately, we derive a closed-form solution with linearithmic time and linear space complexity. Moreover, a multi-scale and iterative graph construction strategy is proposed to promote the performance of our method in terms of robustness and effectiveness. Extensive experiments on various real image datasets demonstrate that our LGSC can achieve superior performance over current state-of-the-art approaches.
BA-RMKABSE: Blockchain-aided Ranked Multi-keyword Attribute-based Searchable Encryption with Hiding Policy for Smart Health System

Due to the popularity of electronic medical technology and the rapid rise of cloud storage technology, how to securely share medical data has become a research hotspot at present. Fast and efficient keywords search in medical cloud is the key first step to achieve it. However, untrusted servicer may return the false search results or the results related to some special commercial purpose. Additionally, privacy leakage in medical data will also damage the benefits of the users and primary data owners. Ensuring fair and accurate searching of shared data and protecting the privacy of medical data have become important in the medical sharing system. This paper aims at solving these problems and a search framework for smart health system is proposed. In the framework, two smart contracts, named search smart contract(SSC) and verify smart contract(VSC), are constructed based on Ethereum blockchain, which are used to match the user’s trapdoor with index and check the correctness of search results. To improve the search accuracy, we realizes a ranking multi-keyword search where it only returns the top-k documents which best meet the user’s needs. Additionally, a new attribute-based encryption(ABE) is introduced to keep confidentiality and access authority of the sharing data. And the proposed ABE supports the hiding policy which solves the privacy protection during the data share. For reducing the computing burden of the data user, the decrypted-cloud service assistant system is proposed. Security proof and performance comparisons prove the security and efficiency of the scheme.
Enhanced Flow Direction Arithmetic Optimization Algorithm for mathematical optimization problems with applications of data clustering

Data clustering is one of the most common and challenging problems in the machine learning domain. It requires an efficient method to be addressed. This paper proposed a new version of the Flow Direction Algorithm (FDA) to solve various optimization problems. The proposed method is called FDAOA, which enhanced the performance of the original Flow Direction Algorithm by the arithmetic operators that have been used in the Arithmetic Optimization Algorithm (AOA). The main aim of the proposed FDAOA is to avoid the recognized weaknesses in the original methods; stuck in the local area, premature convergence, and weak equilibrium between the exploration and exploitation search mechanisms. The proposed method is tested on two sets of various problems to validate its performance. In the first set, twenty-three benchmark functions are used, which belong to three categories; seven unimodal functions, six multimodal functions, and ten fixed dimension functions. In the second set, eight common data clustering problems are used to prove the ability of the proposed FDAOA to deal with real-world optimization problems. The results of the proposed method are compared with other well-established methods, and the proposed FDAOA achieved promising output compared to the other methods on various tested problems. The proposed method got the optimal clustering solutions almost in all the tested data clustering problems with clear significant improvements against the other comparative methods.
Distilling Holistic Knowledge with Graph Neural Networks

Knowledge Distillation (KD) aims at transferring knowledge from a larger well-optimized teacher network to a smaller learnable student network. Existing KD methods have mainly considered two types of knowledge, namely the individual knowledge and the relational knowledge. However, these two types of knowledge are usually modeled independently while the inherent correlations between them are largely ignored. It is critical for sufficient student network learning to integrate both individual knowledge and relational knowledge while reserving their inherent correlation. In this paper, we propose to distill the novel holistic knowledge based on an attributed graph constructed among instances. The holistic knowledge is represented as a unified graph-based embedding by aggregating individual knowledge from relational neighborhood samples with graph neural networks, the student network is learned by distilling the holistic knowledge in a contrastive manner. Extensive experiments and ablation studies are conducted on benchmark datasets, the results demonstrate the effectiveness of the proposed method. The code has been published in https://github.com/wyc-ruiker/HKD
Conditional Diffusion for Interactive Segmentation

In click-based interactive segmentation, the mask extraction process is dictated by positive/negative user clicks; however, most existing methods do not fully exploit the user cues, requiring excessive numbers of clicks for satisfactory results. We propose Conditional Diffusion Network (CDNet), which propagates labeled representations from clicks to conditioned destinations with two levels of affinities: Feature Diffusion Module (FDM) spreads features from clicks to potential target regions with global similarity; Pixel Diffusion Module (PDM) diffuses the predicted logits of clicks within locally connected regions. Thus, the information inferred by user clicks could be generalized to proper destinations. In addition, we put forward Diversified Training (DT), which reduces the optimization ambiguity caused by click simulation. With FDM,PDM and DT, CDNet could better understand user’s intentions and make better predictions with limited interactions. CDNet achieves state-of-the-art performance on several benchmarks.
VENet: Voting Enhancement Network for 3D Object Detection

Hough voting, as has been demonstrated in VoteNet, is effective for 3D object detection, where voting is a key step. In this paper, we propose a novel VoteNet-based 3D detector with vote enhancement to improve the detection accuracy in cluttered indoor scenes. It addresses the limitations of current voting schemes, i.e., votes from neighboring objects and background have significant negative impacts. Before voting, we replace the classic MLP with the proposed Attentive MLP (AMLP) in the backbone network to get better feature description of seed points. During voting, we design a new vote attraction loss (VALoss) to enforce vote centers to locate closely and compactly to the corresponding object centers. After voting, we then devise a vote weighting module to integrate the foreground/background prediction into the vote aggregation process to enhance the capability of the original VoteNet to handle noise from background voting. The three proposed strategies all contribute to more effective voting and improved performance, resulting in a novel 3D object detector, termed VENet. Experiments show that our method outperforms state-of-the-art methods on benchmark datasets. Ablation studies demonstrate the effectiveness of the proposed components.
Deadline-constrained energy-aware workflow scheduling in geographically distributed cloud data centers

The energy cost of cloud data centers is increasingly concerned worldwide; the minimization of energy cost is becoming an urgent problem. Considering data centers are geographically distributed, electricity prices are different in each data center. Consequently, it is also critical to assign workflow tasks to the geographically distributed data centers because data required by tasks is usually conserved in the given data center. So, as electricity prices and data transmission times change, it becomes a big challenge to minimize energy costs when scheduling workflow tasks to heterogeneous servers in cloud data centers. A DEWS (Deadline-constrained Energy-aware Workflow Scheduling) algorithm is proposed in this paper, which consists of task sequencing, VND-based data center searches, task sequence adjustment, and VM searching with Dynamic Voltage Frequency Scaling (DVFS). The DVFS method is included in the optimization procedure to cut down the additional energy cost of service providers. The experimental results show that the proposed algorithm outperforms the compared algorithms and reduces energy cost by 5%–20%.