Reviewerly/exHarmony huggingface dataset:
Differences in how leaders and employees view organizational changes: Lessons from an international multicultural context

The research aims to provide actionable insights to a Dubai-based digital payment company following a leadership-driven organizational transformation. Semi-structured interviews with executives and staff were supplemented by observations and field notes. The study found that foreign employees experienced confusion due to a lack of clear communication or information. Hierarchical work environments and legacy problems hindered employee change. Fear and distrust prevented employees from voicing their concerns. Employees and executives shared similar opinions on the work environment and organizational culture. Leaders did not place enough importance on early worker involvement, communication, and information flow, which hindered their vision and evaluation of employees' conduct. Earlier discussion and resolution of these issues may narrow the gap between the CEO and employees.
Trigonometric phase net: a robust method for extracting wrapped phase from fringe patterns under non-ideal conditions

Obtaining the wrapped phase is a crucial step in fringe projection profilometry. However, reliably and efficiently extracting the wrapped phase from fringe patterns under non-ideal conditions remains a challenging problem. Neural networks have demonstrated higher robustness under non-ideal conditions; however, they struggle to handle abrupt data at the edge of the phase cycle when directly predicting the wrapped phase. To address this issue, we propose "trigonometric phase net (TPNet)," an approach that leverages the distribution characteristics of wrapped phase data. TPNet uses a neural network to predict the wrapped phase in the form of sine and cosine values; the wrapped phase is then calculated using the a tan function. This approach not only avoids the direct processing of abrupt data by the neural network but also facilitates network convergence due to its similar distribution law as fringe patterns. We also introduce a new loss function, Losssincos, which is designed to align with the sine and cosine function distribution of the network's output. This loss function improves the accuracy of the neural network in indirectly predicting the wrapped phase. Our experiments demonstrate that TPNet can accurately extract the wrapped phase from single frame fringe patterns under non-ideal conditions.
Tasks on the recurrence relation "All sums"

In the article, using the example of solving few problems, the methodology for studying the theme "Problems on the recurrence relation "All sums" is illustrated in preparing schoolchildren for Olympiads in informatics. The study is based on the sequential solution of increasingly complex tasks. For each problem, the following materials are given: the formulation of the problem, the idea of a solution with a proposal to come up with an implementation on their own, the solution in the Pascal programming language. Distance learning system (http://dl.gsu.by) is the effective technical base for teaching. The system allows to offer for a student a formulation of the problem; to submit the solution for review; to get a verdict from the system — a correct or incorrect solution; for incorrect solution, the number of the test on which the solution did not pass is indicated. A student can take a test (input and output data), on which his solution did not pass, figure out what the error is in his program, correct and send the solution again. In addition, for each task there is a link on it to the topic in the forum at site, where you can ask a question on solving this problem and / or read the answer if the questions have already been asked before.
Detail Preserving Non-rigid Shape Correspondences

Understanding shapes is an organic process for us (humans) as this is fundamental to our interaction with the surrounding world. However, it is daunting for the machines. Any shape analysis task, particularly non-rigid shape correspondence is challenging due to the ever-increasing resolution of datasets available. Shape Correspondence refers to finding a mapping among various shape elements. The functional map framework deals with this problem efficiently by not processing the shapes directly but rather specifying an additional structure on each shape and then performing analysis in the spectral domain of the shapes. To determine the domain, the Laplace-Beltrami operator has been utilized generally due to its capability of capturing the global geometry of the shape. However, it tends to smoothen out high-frequency features of shape, which results in failure to capture fine details and sharp features of shape for the analysis. To capture such high-frequency sharp features of the shape, this work proposes to utilize a Hamiltonian operator with gaussian curvature as an intrinsic potential function to identify the domain. Computationally it is defined at no additional cost, keeps global structural information of the shape intact and preserves sharp details of the shape in order to compute a better point-to-point correspondence map between shapes.
DeMAC: Towards detecting model poisoning attacks in federated learning system

Federated learning (FL) is an efficient distributed machine learning paradigm for the collaborative training of neural network models by many clients with the assistance of a central server. Currently, the main challenging issue is that malicious clients can send poisoned model updates to the central server, making FL vulnerable to model poisoning attacks. In this paper, we propose a new system named DeMAC to improve the detection and defence against model poisoning attacks by malicious clients. The main idea behind the new system is based on an observation that, as malicious clients need to reduce the poisoning task learning loss, there will be obvious increases in the norm of gradients. We define a metric called GradScore to measure this norm of clients. It is shown through experiments that the GradScores of malicious and benign clients are distinguishable in all training stages. Therefore, DeMAC can detect malicious clients by measuring the GradScore of clients. Furthermore, a historical record of the contributed global model updates is utilized to enhance the DeMAC system, which can spontaneously detect malicious behaviours without requiring manual settings. Experiment results over two benchmark datasets show that DeMAC can reduce the attack success rate under various attack strategies. In addition, DeMAC can eliminate model poisoning attacks under heterogeneous environments.
On k-means iterations and Gaussian clusters

Nowadays, k-means remains arguably the most popular clustering algorithm [1], [2]. Two of its main properties are simplicity and speed in practice. Here, our main claim is that the average number of iterations k-means takes to converge (τ¯) is in fact very informative. We find this to be particularly interesting because τ¯ is always known when applying k-means but has never been, to our knowledge, used in the data analysis process. By experimenting with Gaussian clusters, we show that τ¯ is related to the structure of a data set under study. Data sets containing Gaussian clusters have a much lower τ¯ than those containing uniformly random data. In fact, we go considerably further and demonstrate a pattern of inverse correlation between τ¯ and the clustering quality. We illustrate the importance of our findings through two practical applications. First, we describe the cases in which τ¯ can be effectively used to identify irrelevant features present in a given data set or be used to improve the results of existing feature selection algorithms. Second, we show that there is a strong relationship between τ¯ and the number of clusters in a data set, and that this relationship can be used to find the true number of clusters it contains.
The Foundations of Computational Imaging: A signal processing perspective

Twenty-five years ago, the field of computational imaging arguably did not exist, at least not as a standalone arena of research activity and technical development. Of course, the idea of using computation to form images had been around for several decades, largely thanks to the development of medical imaging—such as magnetic resonance imaging (MRI) and X-ray tomography—in the 1970s and synthetic-aperture radar (SAR) even earlier. Yet, a quarter of a century ago, such technologies would have been considered to be a subfocus of the wider field of image processing. This view started to change, however, in the late 1990s with a series of innovations that established computational imaging as a scientific and technical pursuit in its own right.
Enhancing soft computing techniques to actively address imbalanced regression problems

While research in the area of imbalance, which is understood as classes that are not equally represented, is mainly addressed in classification, it has hardly been studied in regression, where data maldistribution, or imbalance, can be defined as the existence of some specific subdomains of the output variable misrepresented in the training data set, resulting in low accuracy for new instances within these subdomains. The small amount of state-of-the-art techniques are “passive”, meaning they are only applied in preprocessing. In this contribution, we propose two new specific evolutionary algorithms based on fuzzy rules to “actively” address imbalanced regression problems and improve the overall performance of the algorithms instead of just addressing the imbalance problem. The results obtained after applying statistical tests to 32 regression datasets that handle more than 3000 partitions show the effectiveness of the proposed methods when compared to the best previous proposal, a passive method called SMOGN. We can conclude: (1) we cannot affirm, since the equality hypotheses have not been rejected, that there are significant performance differences between using stratified and non-stratified data, thus we will use stratification to preserve a minimum representation of the minority set, (2) both fuzzy rule-based methods obtain better results in terms of the imbalance metric when using SMOGN, but in both methods this incurs a cost in accuracy (with confidence scores of over 99.0%), and (3) the proposed methods outperform those using SMOGN as they get slightly better results in the imbalance metric, with better average ranks in both proposals, and obtain significantly better results in global accuracy, that is, all the performance metrics studied improve statistically with a confidence score of over 99.0%, with the exception of one metric, which scores above 90.0%.
How to cope with malicious federated learning clients: An unsupervised learning-based approach

With the advent of data-driven and Artificial Intelligence solutions, data providers such as Internet of Things (IoT)-enabled devices and sensors have become essential for intelligent services. In conventional machine learning, data collected from sensors and edge devices are transmitted to a centralized server for model training. Although it achieves state-of-the-art results, this process has drawbacks, such as privacy concerns, data leakage, etc. Specifically, some companies or services like hospitals want to keep their data themselves. Since they do not share data on a server, training on the centralized server cannot take the overall benefit of all data. Therefore a new training method is proposed, namely federated learning. In this learning, users keep their data to themselves and train their local model with their data. After training, users send their model weights to the server instead of data. When the centralized server takes all weights of users, it starts federated aggregations and generates a new global model. This new model is distributed among clients. This method provides privacy and security over clients’ data. However, In the malicious environment, where malicious clients trained with corrupted data send their weights to a centralized server, global model aggregation degrades because of harmful weights. Also, in a collaboration environment, where clients can send and receive data from other clients, malicious clients can send their corrupted data to benign clients, negatively affecting their local training. In the end, those clients might turn into partially malicious clients. In this work, we improve our previous research, score-based aggregation (SBA), and develop a malicious-client elimination algorithm before aggregation. With this algorithm, global model training can be converged higher scores according to unseen validation data scores even if there are 50% malicious rate among clients.
Digital topological groups

In this article, we develop the basic theory of digital topological groups. The basic definitions directly lead to two separate types, based on the details of the continuity required of the group multiplication. We define NP1- and NP2-digital topological groups, and investigate their properties and algebraic structure. The NP2-type is very restrictive, and we give a complete classification of NP2-digital topological groups. We also give many examples of NP1-digital topological groups. We define digital topological group homomorphisms, and describe the digital counterpart of the first isomorphism theorem.
Hic-KGQA: Improving multi-hop question answering over knowledge graph via hypergraph and inference chain

Question answering over knowledge graph (KGQA) aims at answering natural language questions posed over knowledge graphs (KGs). Moreover, multi-hop KGQA requires reasoning across multiple triplets in KGs to get to the answer. Unfortunately, KGs often lack complete information and contain many missing links, which poses huge challenges for multi-hop KGQA. To address this, recent several approaches have introduced KG embedding techniques, which have shown good performance on the multi-hop KGQA task. However, these methods ignore the semantic correlations between paths and questions, and the reasoning process is not easily explained. Furthermore, traditional KG embedding methods consider only low-order pairwise relations and ignore the higher-order relations among entities, leading to a sub-optimal embedding result. To address these problems, we propose Hic-KGQA, a novel hypergraph and inference chain-based model for multi-hop KGQA. Specifically, Hic-KGQA first generates pre-trained entity embeddings with multiple semantics via a hypergraph-based KGC module (HKM). Then, an inference chain modeling module (ICMM) is designed to learn the importance of different inference chains for the question and encode the highest-ranked inference chain into an embedding representation. Finally, two scoring networks are used to evaluate the correlation between the candidate answers and the questions from the perspective of both the triplet facts and the reasoning process to obtain more accurate answers. Furthermore, the mutual information maximization (MIM) is innovatively implemented to capture richer common path features from similar cases to alleviate the missing path problem caused by KG incompleteness. Experiments show that Hic-KGQA significantly outperforms existing state-of-the-art methods and is explainable.
Robust Teacher: Self-correcting pseudo-label-guided semi-supervised learning for object detection

Semi-supervised object detection methods have become increasingly popular in computer vision owing to their success in reducing data labeling costs. However, low-quality pseudo-labels exhibit difficulties in correction and severely limit the performance of existing state-of-the-art models. To address these problems, based on the contemporary teacher–student dual framework, we develop a novel self-correcting pseudo-label module to generate more reliable predictions for unlabeled data. Simultaneously, to mitigate the inherent class bias in pseudo-labels, we integrate multi-label classification results for measuring the re-balanced focal loss to enable class-agnostic, robust semi-supervised learning. Furthermore, pseudo-label-guided copy-paste is proposed as a simple yet efficient data augmentation technique to enhance instance representation learning within diverse complex scenes. The above key designs constitute our proposed approach, which we call Robust Teacher. Extensive experiments on PASCAL VOC and MS COCO demonstrate that Robust Teacher achieves competitive results and outperforms state-of-the-art models by a large margin. Our comprehensive ablation studies further verify the effectiveness of the key components. The code will be publicly available at https://github.com/Complicateddd/RobustT.
A diversity-driven migration strategy for distributed evolutionary algorithms

The increasing complexity of real-world problems raises new challenges to evolutionary computation. Distributed models have been successfully employed by many evolutionary algorithms (EAs) to deal with these challenges. In particular, distributed models provide a means to enable collaboration between multiple subpopulations, thus allowing the design of strategies to deal with premature convergence and loss of diversity, which are common problems in traditional evolutionary algorithms. Through introducing periodic migrations, many Distributed Evolutionary Algorithms (DEAs) have been proposed to improve the balance between exploration and exploitation. However, most of them focus on performing migrations at fixed or probabilistic intervals. In this work, we present a mechanism to estimate the moment of executing the migrations by assessing the loss of diversity of the subpopulations. Another relevant issue is that most studies choose to migrate the best or a random individual. We report a strategy that identifies a migrant individual capable of generating diversity that helps a given subpopulation explore non-visited regions without harming its health. The proposed approach uses an online clustering algorithm to create clouds of good-fitness individuals that have been previously migrated. The solution to be migrated must be extracted from a cloud whose population distribution is sufficiently different from the population distribution of the original subpopulation. We called this approach a Diversity-driven Migration Strategy (DDMS). The efficiency of DDMS is experimentally compared against traditional migration strategies (fixed and probabilistic) on the CEC’2014 test suite. Considering the average error values for the objective function, the proposed approach is specially better in 50D and 100D (dimensional) instances. Regarding the diversity, the proposed strategy is better in 100% and about 96% of the test functions in 50D and 100D scenarios, respectively. In general, we have found the DDMS mechanisms to be of considerable benefit, especially in more complex problems such as the ones based on hybrid and composition objective functions.
Blockchain Technology

Blockchain is one of the revolutionary tools that has proven to be effective in resolving various problems in the banking industry. Blockchain technology has diversified applications over varied sectors as it facilitates the systematic recording of transactions in an effective, cheap, and safe manner. Blockchain technology offers various services to the banking industry which have improved the scalability and security of the banks. Thus in order to captivate the interest of researchers, academicians and bankers, the chapter presents a comprehensive review of the impact of blockchain on the banking industry. Moreover there is an urgent need to conduct extensive research into several aspects of banking with blockchain so as to overcome hindrances in the adoption of blockchain. The study provides a holistic framework highlighting the present status and future prospects of the adoption of blockchain technology in banks. Further, it describes how the adoption of blockchain can make the banking industry more secure and facilitate faster transaction recording.
Challenges for freedom of expression on the internet in social crisis contexts: The case of Chile (2019–2022)

This article describes the status of freedom of expression in Chile in the crisis context that began with the October 2019 social outburst based on an analysis of primary and secondary data that includes complaints from platform users, legislative work and court of justice sentences. The main detected issues include situations of algorithmic censorship, bills that contribute more problems than regulatory solutions and courts of justice that operate as online content moderators in defamation and slander cases. We conclude by identifying problems of different scopes and levels, but which equally refer to a dissociation between the freedom of expression violations suffered by platforms users in this context of unrest and matters of concern regarding freedom of expression on the internet, both in parliamentary initiatives and court of justice sentences, in which internet content moderation is addressed based on pre-digital legal criteria.
Artificial Intelligence and the Medicine of the Future

The convergence of AI and machine learning (ML), electronic health records (EHRs), the Internet of Things (IoT), and enhanced data transfer and accessibility has within the last decade delivered a new paradigm in healthcare known as Healthcare 4.0. This rapid transformation to a new digital age in medicine promises new approaches to clinical diagnosis, prediction, decision-making, personalised healthcare and remote patient monitoring. In this chapter, we describe some of the history behind ML in healthcare and review the ML-Big Data nexus, the taxonomy of ML (supervised, unsupervised and reinforcement Learning), the fundamental differences between the fields of statistics and ML, the usual workflow used to develop and validate ML algorithms and how the predictive accuracy of ML algorithms is evaluated. We also discuss the barriers towards the implementation of ML algorithms for clinical decision support within healthcare including ethical considerations, data governance and security, clinician and patient confidence, transparency, data bias, as well as the issues preventing a more rapid integration of AI into healthcare. Finally, we describe some of the more recent developments of ML in healthcare, including quantum ML, federated learning, automated ML, natural language processing and the new progress made in precision medicine via a renewed focus on using reinforcement learning. Throughout the text, we try as far as possible to map the various algorithms and architectures of ML to research questions and healthcare applications with a focus on the older patient population.