Reviewerly/exHarmony huggingface dataset:
Small Objects Detection for Incomplete Annotated Cervical Cancer Cells Dataset

Traditional object detectors usually require fully annotated instances for training to achieve satisfactory detection results. However, for incompletely annotated image instances, especially in medical imaging, their detection results are still far from satisfactory due to the difficulty for sample annotation. In this regard, Co-mining self-supervised learning for incompletely annotated object detection performs excellently. However, its effectiveness in detecting smaller objects is not yet satisfactory. Therefore, this paper proposes an improved model called CO-FCOS to design and implement a better incompletely annotated detection algorithm for small objects. In order to fully utilize the position and category information of existing objects in the image, more effective data augmentation strategies are applied, and the predictions are generated by detectors in two branches respectively. To further improve the model’s adaptability and generalization performance under different scenarios and data distributions, an OTA dynamic label assignment strategy is introduced. In addition, the label consistency strategy breaks the limitations of the original model, generates more pseudo labels for the model, which enriches the training data, and improves the model performance. Experimental results show that, compared to the Co-mining model, our proposed CO-FCOS model improves the accuracy by 4.3 mAP on the cervical cancer TCT dataset and by 1.7 mAP on the COCO-miss50 incomplete annotation dataset.
Improved K-means Template Matching Target Recognition

Target recognition mainly includes feature extraction and target classification based on features. This paper mainly improves the traditional target classification algorithm based on K-means template matching to improve the target recognition rate. In view of the problems that the matching template established by the traditional target classification algorithm based on K-means template matching cannot accurately represent the distribution of recognition targets, the clustering center is fuzzy, and the recognition rate of some targets is low, this paper proposes an improved target classification algorithm based on K-means template matching. In the process of template establishment, more subclasses are added, and the mapping between subclasses and recognition targets is established. Thus, the problem of fuzzy clustering center is solved, and the target recognition rate is improved. Compared with the target classification algorithm based on SVM, it can achieve better target recognition rate with lower computation.
A Low-Complexity Convolution Network for Upscale Super-Resolution Image

We proposed a modified architecture to reduce the computation of the ESPCN network for super-resolution generation. Based on the deep separable convolution concept, the ESPCN-based network was embedded in the 1x1 convolution layer to reduce the computations. Experiment results showed that the proposed method greatly reduces the number of parameters and computational operators, compared with the original network. The numbers were reduced by about 65% while maintaining the image quality as upscale x16. Using the DIV2K dataset for training and verification, the super-resolution image became better than that by the conventional spatial algorithms with improved PSNR values and reduced blurring.
An Improved Unsupervised Semantic Segmentation Algorithm Based on CycleGAN

Due to advancements in computer vision, semantic segmentation models are applied for facial recognition on the road. Conventional models require a substantial data set. We introduced an enhanced algorithm based on a cyclic generative adversarial neural network. For facial recognition. In contrast to traditional models, this algorithm eliminated the need for extensive paired datasets and avoided labor-intensive pixel-level annotation, thereby significantly saving time and costs and allowing rapid semantic segmentation application. Initially, deep separable convolutions were downsampled to reduce model parameters. Subsequently, an attention mechanism was used in the generator based on comparative experiments, enhancing information selection. To address edge blurriness in the cyclic generative adversarial network, a third set of artificially blurred datasets was introduced into the discriminator to optimize the generator's output images. Finally, the Smooth L1 loss computing model was used with reduced gradients at low errors to reduce outliers and enhance network robustness. Experimental results showed algorithmic improvements on the original network, maintaining a stable generation of high-quality semantic segmentation images.
Adversarial Machine Learning in Recommender Systems

Recommender systems are ubiquitous. Our digital lives are influenced by their use when, for instance, we select the news to read, the product to buy, the friend to connect with, and the movie to watch. While enormous academic research efforts have been mainly focused on getting high-quality recommendations to reach maximum user satisfaction, little effort has been devoted to studying the integrity and security of these systems. Is there an underlying relationship between the characteristics of the historical user-item interactions and the efficacy of injection of false users/feedback strategies against collaborative models? Can public semantic data be used to perform attacks more potent in raising the recommendability of victim items? Can a malicious user poison or evade the image data of visual recommenders with adversarial perturbed product images? Is the family of model-based recommenders more vulnerable to multi-step gradient-based adversarial perturbations? Furthermore, is the adversarial training robustification still effective in the last scenario? Is this training defense influencing the beyond-accuracy and bias performance? This dissertation intends to pave the way towards more robust recommender systems, beginning with understanding how to robustify a model, what is the cost of robustness in terms of reduction of recommendation accuracy, and which are the novel adversarial risks of modern recommenders. This thesis, getting inspiration from the literature on the security of collaborative models against the insertion of hand-engineered fake profiles and the recent advances of adversarial machine learning methods in other research areas like computer vision, contributes to several directions: (i) the proposal of a practical framework to interpret the impact of data characteristics on the robustness of collaborative recommenders [Deldjoo et al., 2020], (ii) the design of powerful attack strategies using publicly available semantic data [Anelli et al., 2020], (iii) the identification of severe adversarial vulnerabilities of visual-based recommender models where adversaries can break the recommendation integrity by pushing products to the highest recommendation positions with a simple and human-imperceptible perturbation of products' images [Anelli et al., 2021b], (iv) the proposal of robust adversarial perturbation methods capable of completely breaking the accuracy of matrix factorization recommenders [Anelli et al., 2021a], and (v) a formal study that examines the effects of adversarial training in reducing the recommendation quality of state-of-the-art model-based recommenders Anelli et al. [2021c]. Awarded by: Politecnico di Bari, Bari, Italy on 24 January 2022. Supervised by: Tommaso DI Noia. Available at: https://iris.poliba.it/retrieve/dd89f8a6-faa4-ccdd-e053-6605fe0a1b87/Adversarial_Machine_Learning_in_Recommender_Systems.pdf.
Anomaly Detection From Low-Dimensional Latent Manifolds With Home Environmental Sensors

Human Activity Recognition poses a significant challenge within Active and Assisted Living (AAL) systems, relying extensively on ubiquitous environmental sensor-based acquisition devices to detect user situations in their daily living. Environmental measurement systems deployed indoors yield multiparametric data in heterogeneous formats, which presents a challenge for developing Machine Learning-based AAL models. We hypothesized that anomaly detection algorithms could be effectively employed to create data-driven models for monitoring home environments and that the complex multiparametric indoor measurements can often be represented by a relatively small number of latent variables generated through Manifold Learning (MnL) techniques. We examined both linear (Principal Component Analysis) and non-linear (AutoEncoders) techniques for generating these latent spaces and the utility of core domain detection techniques for identifying anomalies within the resulting low-dimensional manifolds. We benchmarked this approach using three publicly available datasets (hh105, Aruba, and Tulum) and one proprietary dataset (Elioth) for home environmental monitoring. Our results demonstrated the following key findings: (a) Nonlinear manifold estimation techniques offer significant advantages in retrieving latent variables when compared to linear techniques; (b) The quality of the reconstruction of the original multidimensional recordings serves as an acceptable indicator of the quality of the generated latent spaces; (c) Domain detection identifies regions of normality consistent with typical individual activities in these spaces; And (d) the system effectively detects deviations from typical activity patterns and labels anomalies. This study lays the groundwork for further exploration of enhanced methods for extracting information from MnL data models and their application within the AAL and possibly other sectors.
Multiple Views to Free Graph Augmentations

Self-supervised graph representation learning (GRL) has shown great success in scientific research and real-world applications. Nevertheless, one obstacle in GRL is the demand for graph augmentation (GA), which deeply impacts the representation qualities. On the one hand, GA supplements the data amount and enhances the robustness and quality of the representations. On the other hand, collocating appropriate augmentations claims nontrivial attempts. In this article, a new method to free GA is provided building a novel fuzzy view and two crisp views of the original graph. As all the views are transformed from the original graph, they are semantically similar and naturally considered to possess high-quality positive samples. In this way, the data amount is compensated to a degree without changing the raw node attributes or graph topology. Additionally, to ensure the diversity of the positives, asymmetric renormalization and noise perturbation are adopted. Experiments toward node-level tasks on several real-world datasets demonstrate the competition against several state-of-the-art models.
Adolescent’s Interpersonal Communication Patterns on the Development of Learning Motivation

Humans are social creatures who need each other in carrying out daily activities and meeting the needs of life. Humans often carry out interpersonal communication as a process of socializing with other individuals. Adolescent interpersonal communication greatly affects the development of individual
Guessing Cost: Bounds and Applications to Data Repair in Distributed Storage

The guesswork refers to the distribution of the minimum number of trials needed to guess a realization of a random variable accurately. In this study, a non-trivial generalization of the guesswork called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">guessing cost</i> (also referred to as cost of guessing) is introduced, and an optimal strategy for finding the ρ-th moment of guessing cost is provided for a random variable defined on a finite set whereby each choice is associated with a positive finite cost value (unit cost corresponds to the original guesswork). Moreover, we drive asymptotically tight upper and lower bounds on the logarithm of guessing cost moments. Similar to previous studies on the guesswork, established bounds on the moments of guessing cost quantify the accumulated cost of guesses required for correctly identifying the unknown choice and are expressed in terms of Rényi’s entropy. Moreover, new random variables are introduced to establish connections between the guessing cost and the guesswork, leading to induced strategies. Establishing this implicit connection helped us obtain improved bounds for the non-asymptotic region. As a consequence, we establish the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">guessing cost exponent</i> in terms of Rényi <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">entropy rate</i> on the moments of the guessing cost using the optimal strategy by considering a sequence of independent random variables with different cost distributions. Finally, with slight modifications to the original problem, these results are shown to be applicable for bounding the overall repair bandwidth for distributed data storage systems backed up by base stations and protected by bipartite graph codes.
Multi-View Graph Embedding Learning for Image Co-Segmentation and Co-Localization

Image co-segmentation and co-localization exploit inter-image information to identify and extract foreground objects with a batch mode. However, they remain challenging when confronted with large object variations or complex backgrounds. This paper proposes a multi-view graph embedding (MV-Gem) learning scheme which integrates diversity, robustness and discernibility of object features to alleviate this phenomenon. To encourage the diversity, the deep co-information containing both low-layer general representations and high-layer semantic information is generated to form a multi-view feature pool for comprehensive co-object description. To enhance the robustness, a multi-view adaptive weighted learning is formulated to fuse the deep co-information for feature complementation. To ensure the discernibility, the graph embedding and sparse constraint are embedded into the fusion formulation for feature selection. The former aims to inherit important structures from multiple views, and the latter further selects important features to restrain irrelevant backgrounds. With these techniques, MV-Gem gradually recovers all co-objects through optimization iterations. Extensive experimental results on real-world datasets demonstrate that MV-Gem is capable of locating and delineating co-objects in an image group.
Orchestrating serverless applications in the Cloud-to-Edge continuum

The orchestration of serverless applications presents different challenges due to the increasing request load they need to manage and the time-sensitive requirements. The combination of Cloud and Edge computing paradigms attempts to avoid their pitfalls while taking the best of both worlds: Cloud scalability and computing closer to the Edge where data is typically generated. However, placing microservices in such heterogeneous environments while meeting QoS constraints is a challenging task due to the geo-distribution of nodes and varying computational resources. In this paper we propose to extend Knative, an example of a serverless computing framework based on the Kubernetes platform, to enable dynamic serverless application orchestration, taking into account both infrastructure and application states. Our approach aims to reduce QoS violations and improve application response time in Cloud-to-Edge continuum scenarios.
An Optimized Blockchain-based Data Transmission System for Telemedicine

In the last year, the advent of new solutions and services in Information and Communication Technologies (ICT), has changed our society. The healthcare sector, with the introduction of remote patient monitoring systems, is one of the fields in which ICT is more pervasive. These systems play a significant role in enhancing medical care by providing useful and efficient solutions if the integrity of transmitted data is verified. In this paper, considering a tele-rehabilitation scenario, we propose an innovative Blockchain-based system to securely transmit medical data between patients and doctors. In our experiments, we made a scalability analysis considering two functions accessing the data on the Blockchain and an increasing number of parameters (1 to 20). Results showed that, by making an optimization on the data structure, the time required is constant.
The Effects of Title I Programs on Students Proficiency in the U.S. and Implications for Korea

[Purpose] This paper aims to empirically investigate the impact of the federally funded Title I program on the performance of students from low-income families, as assessed through the state's own assessments. Additionally, the study seeks to discern the relative effectiveness of two specific program, namely the school-wide program and the targeted assistant program, to see which program is more effective.&#x0D; [Methods] To accomplish these objectives, the research leverages two national data sets from 2008-09 to 2013-14, sourced from the U.S. Department of Education's EDFacts and the National Center for Education Statistics' Common Core of Data. Employing fixed-effects and mixed-effects models with the school-level data, the study conducts a rigorous examination of the program's impact across various grade levels.&#x0D; [Results] The findings of this study reveal that the Title I program exerts substantially positive effects on academic outcomes of economically disadvantaged students in lower grades, with moderate positive effects observed in middle grades. However, no discernible impact is observed at the high school level. In terms of the specific program effects, lower grade levels exhibited higher achievement when implementing the “school-wide program”, whereas high schools showed lower achievement. Conversely, transitioning from the “targeted assistant program” to the “school-wide program” demonstrated positive effects only at the high school level.&#x0D; [Conclusion] The results of this study demonstrate the importance of prioritizing investments in early grades when providing educational services or support to students in need. As students progress to higher grades, there is a need to design and implement programs that are tailored to their levels and circumstances. Furthermore, in allocating education finances for low-income groups, prioritize elementary schools for 'whole-school programs,' and support high schools in developing the capacity to run comprehensive programs.
An Efficient Method for Finding Improvements in Japanese Management Training Programs Using Text Mining

While changes in the business environment are accelerating, working life is lengthening as we enter an era of 100-year lifespans, making it necessary for working adults to continually improve their skills. Training for working professionals is becoming increasingly important, and educational institutions continuously strive to improve their training programs. However, it is difficult to efficiently extract important information from the vast number of participant satisfaction survey comments for the purpose of program improvement. In this study, we used text mining to examine how to efficiently retrieve important information from comment data of satisfaction surveys on management training programs in Japan. In general, a method for extracting dissatisfaction from the polarity of words in comments has been considered. Nonetheless, the analysis results confirmed that even when describing dissatisfaction or demands expressions, Japanese managers do not use straightforward expressions, but rather characteristic expressions. Based on an understanding of their politeness and considerate expressions, we learned that extracting and utilizing keywords from the comments of a small group was effective in efficiently detecting the overall dissatisfaction and demand expressions. It was also discovered that the same working adults used different words to express dissatisfaction and demands, depending on their job positions. Using these novel study findings, essential improvements can be efficiently explored from post-training satisfaction questionnaires, contributing to improving the quality of education for working adults in the future.
Cost-Effectiveness Analysis Of Ceftriaxone-Gentamicin And Ampicillin-Gentamicin In Pediatric Pneumonia At Hospital X Madiun

Pneumonia is an infectious disease caused by bacteria characterized by fever, chills, coughing up phlegm, and breathing difficulty. Generally pneumonia is caused by an infection of Streptococcus pneumoniae bacteria. Antibiotics is a therapy used in pediatric pneumonia patients at hospitals X. in Madiun. This study aims to determine a more cost-effective antibiotics therapy combination between ceftriaxone-Gentamicin and Ampicillin-Gentamicin in pediatric pneumonia patients at Hospital X in Madiun in 2019-2021. This study used a cross sectional design and data collection was carried out retrospectively. There were 39 patients as samples who were pediatric patients aged ≤ 12 years who were adjusted to the inclusion criteria. The datawere identified using decision trees and analyzed cost-effectiveness with ACER and ICER calculations. The ACER value of Ceftriaxone-Gentamicin antibiotic therapy is IDR 6,047,334.23, which is lower than Ampicillin-Gentamicin therapy whch is of IDR 9,730,746.21, thus showing that Seftriaxone-Gentamicin is more cost-effective than Ampicillin-Gentamicin. The ICER calculation has negative result of Idr 500,953.74 so it shows that the Ceftriaxone-Gentamicin combination antibiotic is more cost-effective.
Competition in the Process of Studying in Educational Institutions of Higher Education

Rivalry is present in various spheres of human activity, for example, rivalry (competition) for a workplace, for a position, for the necessary resources, for a free place in transport and for points/grades in the educational process. The article is devoted to the issues of competition among students in educational activities for grades, including in the implementation of the point-rating system of assessment in educational institutions of higher education. According to various studies and based on the experience of the author’s professional activity, the use of a point-rating evaluation system in educational organizations has both its advantages and disadvantages. So, one of the indisputable advantages is considered to be an increase in students’ motivation for learning activities (to complete the task better than classmates), and one of the disadvantages is considered to be competition (sometimes unfair) for the assessment necessary for the student (often, this is a tough comparison with the scores received by classmates, and dissatisfaction (not corresponding to the real state of affairs) earned assessment, etc.). The significance of these points is enhanced due to the subjectivity of the teacher’s assessment of students, as well as the peculiarities of the distribution of points (individually for each teacher) and the evaluation of various creative educational works. Based on the study of the interaction of competing parties in educational activities for points, some methods are proposed, the use of which contributes to conscientious rivalry among students (joint gain, gain due to advantages), which indicates the practical significance of the article. The article shows that even with a detailed description and justification of the assessment (drawing up a scale and its criteria), various controversial issues will still arise in the assessment of students, leading to a certain form of rivalry in educational activities.