Reviewerly/exHarmony huggingface dataset:
Implementation and Performance Analysis of Novel Support Vector Machine Classifier for Detecting Eye Cancer Image in comparison with Decision Tree

The focus of the research is to identify and detect eye cancer using novel Support Vector Machine (SVM) in contrast with Decision tree (DT). Materials and Methods: Samples are analyzed using two groups with 50 eye images. The SVM algorithm was considered as g1 and g2 as a decision tree algorithm for detection of cancerous cells in the eye image. Results: SVM has achieved a notable value of 95.0% when compared with a decision tree algorithm of 87.45% with significance (p<0.05). Conclusion: The SVM algorithm has better implication accuracy of 95% to the decision tree for the analysis and detection of eye cancer.
Faster Pattern Matching under Edit Distance : A Reduction to Dynamic Puzzle Matching and the Seaweed Monoid of Permutation Matrices

We consider the approximate pattern matching problem under the edit distance. Given a text T of length n, a pattern P of length m, and a threshold k, the task is to find the starting positions of all substrings of T that can be transformed to P with at most k edits. More than 20 years ago, Cole and Hariharan [SODA’98, J. Comput.’02] gave an $\mathcal{O}(n+k^{4}\cdot n/m)$ time algorithm for this classic problem, and this runtime has not been improved since.Here, we present an algorithm that runs in time $\mathcal{O}\left(n+ k^{3.5}\sqrt{\log m\log k}\cdot n/m\right)$, thus breaking through this longstanding barrier. In the case where $n^{1/4+\varepsilon}\leq k\leq n^{2/5-\varepsilon}$ for some arbitrarily small positive constant $\varepsilon$, our algorithm improves over the state-of-the-art by polynomial factors: it is polynomially faster than both the algorithm of Cole and Hariharan and the classic $\mathcal{O}(kn)$-time algorithm of Landau and Vishkin [STOC’86, J. Algorithms’89].We observe that the bottleneck case of the alternative $\mathcal{O}(n+k^4 \cdot n / m$-time algorithm of Charalampopoulos, Kociumaka, and Wellnitz [FOCS’20] is when the text and the pattern are (almost) periodic. Our new algorithm reduces this case to a new Dynamic Puzzle Matching problem, which we solve by building on tools developed by Tiskin [SODA’10, Algorithmica’15] for the so-called seaweed monoid of permutation matrices. Our algorithm relies only on a small set of primitive operations on strings and thus also applies to the fully-compressed setting (where text and pattern are given as straight-line programs) and to the dynamic setting (where we maintain a collection of strings under creation, splitting, and concatenation), improving over the state of the art.
Kualitas Pelayanan Electronic Library pada UPT Perpustakaan Universitas Pendidikan Ganesha

This research aim to determine the Electronic Library service quality at the UPT of the Ganesha Education University Library, seen from the dimensions of Affect of Service, Information Access, Library as Place, and Personal Control. The research type in this study is a descriptive research type. This research population is students who are members of the Ganesha University of Education library. The research sample was determined by accidental sampling technique as many as 387 orangts. Using the questionnaire method in data collection. Meanwhile, the data analysis technique used descriptive analysis. The results of this study indicate that the service quality of the Electronic Library at the UPT of the Ganesha University of Education Library generally has a good service assessment, however, there are still several aspects that need to be addressed related to the availability of reading material in the library or the Electronic Library services and inadequate infrastructure.
Research on the Demand for Reference Books in Colleges and the Feasibility of Digital Transformation

As an important part of China's educational publishing market, the publishing of reference books in colleges has gradually declined in market performance due to the procurement restrictions, channel expansion, digital reading and other factors. In the exploration of the path to restore the market and transform and upgrade, it is necessary to investigate the needs and purchase intentions of users in order to provide new ideas contrapuntally. Based on the theory of fragmented mass audience and use and gratification approach, this research uses literature analysis, case studies and questionnaires to build models and analyze data on variables such as user needs, preferences and satisfaction, and finally provides ideas and suggestions for the future compilation and development direction of reference books in colleges.
Financial Institutions and Market Information Dissemination Techniques for Small and Medium Enterprises: A Case of Selected SMEs in Dar es Salaam, Tanzania

Small and Medium Enterprises significantly contribute to the nation's economic development and employment creation.The study explored the market information dissemination methods financial institutions employ to reach target SMEs for decision-making.Specifically, it identified the dissemination techniques, the effectiveness of dissemination techniques and the language used in designing market information.Both primary and literature surveys were intensively performed.Quantitative data were subjected to the SPSS programme of which frequencies and counts were produced.On the other hand, qualitative data from the key informants were analysed through thematic content.The study revealed that financial institutions use different information dissemination techniques.These included media formats such as TV, newspapers, leaflets and brochures, door-to-door visits and the Internet.The effectiveness of dissemination techniques depends on the location, time and language.The study recommends applying the proper use of common language in designing market information, timely delivery of information and choosing appropriate dissemination techniques based on location in urban and rural areas.
Unsupervised domain adaptation through transferring both the source-knowledge and target-relatedness simultaneously

&lt;abstract&gt;&lt;p&gt;Unsupervised domain adaptation (UDA) is an emerging research topic in the field of machine learning and pattern recognition, which aims to help the learning of unlabeled target domain by transferring knowledge from the source domain. To perform UDA, a variety of methods have been proposed, most of which concentrate on the scenario of single source and the single target domain (1S1T). However, in real applications, usually single source domain with multiple target domains are involved (1SmT), which cannot be handled directly by those 1S1T models. Unfortunately, although a few related works on 1SmT UDA have been proposed, nearly none of them model the source domain knowledge and leverage the target-relatedness jointly. To overcome these shortcomings, we herein propose a more general 1SmT UDA model through transferring both the source-knowledge and target-relatedness, UDA-SKTR for short. In this way, not only the supervision knowledge from the source domain but also the potential relatedness among the target domains are simultaneously modeled for exploitation in the process of 1SmT UDA. In addition, we construct an alternating optimization algorithm to solve the variables of the proposed model with a convergence guarantee. Finally, through extensive experiments on both benchmark and real datasets, we validate the effectiveness and superiority of the proposed method.&lt;/p&gt;&lt;/abstract&gt;
Poison Neural Network-Based mmWave Beam Selection and Detoxification With Machine Unlearning

Deep neural network-based learning methods have been considered promising techniques used in beam selection problems. However, existing research ignores the peculiar vulnerabilities of neural networks. The adversaries can use data poisoning to embed predefined triggers into a model during training time such that the neural network-based beam model may make an incorrect output decision of a test example when patched with the trigger. Data poisoning offers attackers the possibility to build backdoors. The goal of backdoors is often unethical, such as giving users a poor experience by manipulating infected models to output inappropriate beams. In this paper, first, we introduce a simple backdoor attack method by using data poisoning in a mmWave beam selection system. By numerical simulations, we verify that this poisoning attack is effective for neural networks with different structures. In addition, we explore the effect of poisoned data volume on the effect of backdoor attacks. The results show that the backdoor can be successfully implanted into the beam selection neural network. Besides, we fine-tune the trained model for a new wireless communication environment, and the results show that backdoors still exist even when the model is tuned with data from new scenarios. Then, we propose a machine unlearning solution to mitigate the backdoor of the trained beam selection model. The problem of eliminating backdoors is modeled as a minimax optimization problem. We propose a novel adversarial unlearning method along with label smoothing to solve the backdoor removal problem. We compared the proposed backdoor elimination method with the classical fine-tuning elimination method and the neural network pruning method through numerical simulations. The results show that the fine-tuning and the pruning methods cannot effectively remove the backdoor. The proposed machine unlearning method can make the trained model forget about the backdoor under the condition that the performance of the benign task (beam selection tasks when the trigger does not appear) is guaranteed to be slightly degraded. In summary, our work illustrates that data poisoning-based backdoor attacks may exist in wireless networks, and we propose a scheme to eliminate backdoors.
On the Convergence of Hybrid Server-Clients Collaborative Training

Modern distributed machine learning (ML) paradigms, such as federated learning (FL), utilize data distributed at different clients to train a global model. In such paradigm, local datasets never leave the clients for better privacy protection, and the parameter server (PS) only performs simple aggregation. In practice, however, there is often some amount of data available at the PS, and its computation capability is strong enough to carry out more demanding tasks than simple model aggregation. The focus of this paper is to analyze the model convergence of a new hybrid learning architecture, which leverages the PS dataset and its computation power for collaborative model training with clients. Different from FL where stochastic gradient descent (SGD) is always computed in parallel across clients, the new architecture has both parallel SGD at clients and sequential SGD at PS. We analyze the convergence rate upper bounds of this <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aggregate-then-advance</i> design for both strongly convex and non-convex loss functions. We show that when the local SGD has an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(1/t)$ </tex-math></inline-formula> stepsize, the server SGD needs to scale its stepsize to no slower than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(1/t^{2})$ </tex-math></inline-formula> in order to strictly outperform local SGD with strongly convex loss functions. The theoretical findings are corroborated by numerical experiments, where advantages in terms of both accuracy and convergence speed over clients-only (local SGD and FED AVG) and server-only training are demonstrated.
A Hybrid Framework for Ranking Cloud Services Based on Markov Chain and the Best-Only Method

Cloud computing technology has undergone tremendous growth in recent years, and there are now many cloud service providers (CSPs).This makes CSP selection a challenging process for cloud users.Further complications arise when users modify the priorities of their requirements.Moreover, concerns such as complex computation, inconsistencies, and rank reversal have been raised in current approaches, resulting in less reliable results.This study presents a new hybrid multiple-criteria decision-making (MCDM) framework for ranking cloud services based on Markov chains combined with the best-only method (BOM).The Markov chain is used to record and track the changes in the priorities of user requirements and determine their final values.Then, the BOM method is utilized to determine the final weights of the QoS criteria based on pairwise comparisons made by the cloud user or decision maker and the final priorities of the user's requirements.Finally, the cloud services are ranked, and the best CSP is selected.The proposed framework was validated using a case study and a real dataset.Performance, consistency, rank conformance, and sensitivity analyses were performed to evaluate the proposed framework.The obtained results prove that the proposed framework is computationally efficient and fully consistent, considers the user requirements and its transition pattern, and is robust to rank reversal.INDEX TERMS Cloud computing (CC), cloud service selection, QoS criteria, cloud service providers (CSPs), multiple-criteria decision-making (MCDM), best-only method (BOM), Markov chains, rank reversal.
Channel-Correlation-Based Selective Knowledge Distillation

As a simple yet effective model compression method, knowledge distillation (or KD) is used to learn a small lightweight student network by transferring valuable knowledge from a pretrained cumbersome teacher network. However, existing KD methods usually consider the feature knowledge either in different layers or individual samples, failing to explore more detailed information in different channels from the perspective of sample relationships. Meanwhile, the negative influences contained in the teacher knowledge are also not well investigated, especially, when using the response-based knowledge. To address the above-mentioned issues, we devise a novel KD approach entitled channel correlation-based selective KD (or CCSKD). Specifically, to distill rich knowledge from feature representations, we not only consider the feature knowledge from different channels for individual samples but also take into account the relational knowledge based on per-channel features for different samples. Furthermore, to further distill positive response-based knowledge, a selective strategy is developed, i.e., selective KD, to progressively correct the negative influences from the teacher knowledge during the distillation process. We perform extensive experiments on three image classification data sets, CIFAR-100, Stanford Cars, and Tiny-ImageNet, to demonstrate the effectiveness of the proposed CCSKD, which outperforms recent state-of-the-art methods with a clear margin. Our codes are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/gjplab/CCSKD</uri> .
Gaussian Belief Space Path Planning for Minimum Sensing Navigation

In this article, we propose a path planning methodology for a mobile robot navigating through an obstacle-filled environment to generate a reference path that is traceable with moderate sensing efforts. The desired reference path is characterized as the shortest path in an obstacle-filled Gaussian belief manifold equipped with a novel information-geometric distance function. The distance function we introduce is shown to be an asymmetric quasi-pseudometric and can be interpreted as the minimum information gain required to steer the Gaussian belief. An RRT*-based numerical solution algorithm is presented to solve the formulated shortest-path problem. To gain insight into the asymptotic optimality of the proposed algorithm, we show that the considered path length function is continuous with respect to the topology of total variation. Simulation results demonstrate that the proposed method is effective in various robot navigation scenarios to reduce sensing costs, such as the required frequency of sensor measurements and the number of sensors that must be operated simultaneously.
Reflective Learning With Label Noise

Learning with noisy labels is one of the most challenging tasks in semi-supervised learning, and it poses significant problems in various practical applications. In the network learning process, the noisy labels concealed in the training dataset are easy to remember, resulting in poor generalization performance. To overcome this problem, inspired by the correction ability of humans – “think and learn from the past,” an end-to-end dynamic correction framework against label noise called Reflective Learning (RL) is proposed. This solution incorporates valuable knowledge from the past network training process to assist in correcting noisy labels. Specifically, during network training, a dynamic iterative function is implemented to adaptively correct noisy labels by employing the network’s predictive distribution information of all training epochs. This dynamic iterative function takes the form of a Standard Normal Distribution function to effectively match the changes of noisy label correction information contained in the network’s predictive probabilities. The proposed method is general and applicable to any backbone network and different types of noise without auxiliary information. Experiments are conducted on datasets with synthetic and real-world label noise datasets, including CIFAR-10, CIFAR-100, Tiny-ImageNet, and Clothing1M. They demonstrate that the proposed method is superior to the state-of-the-art results.
SYNERGETIC METHODOLOGY IN THE MANAGEMENT OF DUAL LEARNING

Summary. The article considers the requirements for education, which orient the teacher to the use of tools adequate to the level of development of modern technologies of dual education and which meet the needs of the beginning of the XXI century. The research is devoted to the philosophical understanding of the transition to the introduction of synergetic methodology for managing educational processes. Without substituting general pedagogical, psychological, cultural bases and educational concepts, philosophy, as a systemic vision of the problems and prospects of educational development, must first consider the fundamental worldview demands. In this context, the application of synergetics and synergetic approach is raised, which successfully reflect the different levels of consideration of the applicability and interaction of dual learning and the current state of education. It is proposed to use synergetics both in education itself, for educational purposes, and to use the synergy of the educational process itself. The publication, through the prism of philosophy, attempts to identify and rethink the results of research on the nature of the use of synergetic methodology in relation to educational processes in the management of dual learning. The article proves that educational synergetics applies universal haracteristics of self-organized systems for dual learning.
Joint Intra-Domain and Inter-Domain Information Modeling for Image-to-Image Translation

To solve the disability of modeling inter-domain semantic and intra-domain long-range information in current image style transferring algorithms this article proposes a novel image style transferring algorithm SSC-GAN. This method extracts the semantic features by constructing the semantic shortcut connections, thereby enhancing its capability of modeling semantic difference between image domains. Meanwhile, the self-attention mechanism is introduced for the modeling of long-range dependency in the image domain. The SSC-GAN can significantly improve the performance of image style transferring without extra computation. Through the extensive experiments on vangh2photo and selfie2anime datasets, the proposed method achieves excellent visual effects and reduces FID and KID by 1.3 and 1.1 on average respectively, which verifies the effectiveness of SSC-GAN.
Zero-Watermarking Algorithm for Remote Sensing Image via BEMD and DFT

In order to solve the problem of modifying the host image when the traditional image watermarking algorithm protects the copyright of remote sensing image, and to improve the robustness of remote sensing image watermarking algorithm, a remote sensing image zero watermarking algorithm based on bi-dimensional empirical mode decomposition (BEMD) and discrete Fourier transform (DFT) is proposed. Firstly, BEMD is used to decompose the host image to get the intrinsic mode function and margin, and the residual information is divided into 8×8 blocks DFT, and the high frequency coefficients are constructed into coefficient images. Then Arnold transform is used to scramble the watermark image to improve the security of the watermark image. Then the size of the watermark image is adjusted to solve the problem of image size matching between the host image and the watermark image in the process of watermark embedding. Finally, the coefficient image decomposed by DFT and the watermark image are constructed into feature image by XOR operation, and the construction of zero watermark in remote sensing image is realized. The experimental results of a large number of remote sensing images and watermark images show that the proposed algorithm can effectively resist all kinds of attacks such as filtering, sharpening, histogram equalization, Gamma transform, noise, scaling and so on. In the case of 12 attacks and 60 parameters, 95.0% of the extracted watermark images have a normalized correlation value of more than 0.990 with the original watermark image, compared with the traditional remote sensing image watermarking algorithm. The algorithm ensures the harmlessness of the host image, so that the feature image saved in the notary office for copyright protection verification will not cause any damage to the host image, and has better robustness.
Monocular Accumulated Depth Estimation with Recursive Feature Fusion

Existing depth estimation methods usually use an encoder-decoder structure, but different areas in the image vary in the difficulty and feature demand for depth estimation. This paper proposes a recursive feature fusion monocular depth accumulation estimation method. In the encoder, recursive feature fusion fuses multi-scale features by recursively using gated recurrent units. It extracts features that adapt to the needs of different image areas, replacing cross-layer connections. In the decoder, the depth accumulation estimation decomposes the depth reconstruction process into multiple layers. Different layers predict depth maps of specific granularity and accumulate depth estimation results. Compared with other methods, such as DPT, BTS, and DORN, the proposed method achieved competitive results on two benchmark datasets, KITTI and NYU Depth V2, with Abs Rel reaching 0.058 and 0.107, and RMSE reaching 2.411 and 0.386, respectively.