Reviewerly/exHarmony huggingface dataset:
A Novel Replication-Less Image Retrieval Method from Cloud Platforms using Divergence Features

Real-time multimedia applications provide visual and audible services collaborated with the cloud platform for heterogeneous user requirements. Multi-source information storage and fusion satisfy the user demands through the applications. Contrarily, replication is a common issue demanding high space and time, increasing computation and retrieval time. For addressing the retrieval issues in replicated image storage, this article introduces a Divergent Feature-induced Extension (DFIE) method for large cloud platforms. The proposed method identifies the divergences alone in the input features correlated with the stored ones, in different feature extracted instances. In the feature divergent analysis, the deep recurrent learning paradigm is utilized. The iterations are used for identifying non-correlating features and their deviation for verifying non-replicable images. The process pursues region-based segmentation for a feature and edge-based divergence and similarity identification. The proposed method’s performance is analyzed using the metrics detection accuracy, complexity, and computing time.
I Want This, Not That: Personalized Summarization of Scientific Scholarly Texts

In this paper, we present a proposal for an unsupervised algorithm, P-Summ, that generates an extractive summary of scientific scholarly text to meet the personal knowledge needs of the user. The method delves into the latent semantic space of the document exposed by Weighted Non-negative Matrix Factorization, and scores sentences in consonance with the knowledge needs of the user. The novelty of the algorithm lies in its ability to include desired knowledge and eliminate unwanted knowledge in the personal summary. We also propose a multi-granular evaluation framework, which assesses the quality of generated personal summaries at three levels of granularity - sentence, terms and semantic. The framework uses system generated generic summary instead of human generated summary as gold standard for evaluating the quality of personal summary generated by the algorithm. The effectiveness of the algorithm at the semantic level is evaluated by taking into account the reference summary and the knowledge signals. We evaluate the performance of P-Summ algorithm over four data-sets consisting of scientific articles. Our empirical investigations reveal that the proposed method has the capability to meet negative (or positive) knowledge preferences of the user.
BUILDING RESILIENCE AGAINST HOAX: IMPROVING INTERNET MEDIA LITERACY IN THE ERA OF THE PANDEMIC COVID-19

In facing the Covid-19 pandemic, the importance of enhancing internet media literacy has become crucial in building resilience against misinformation. The dissemination of false information is increasingly worrisome, despite intensive social campaigns addressing hoaxes. This research aims to enhance internet media literacy in the era of the Covid-19 pandemic. This study adopts a literature research or library research methodology. The data collection method involves two sources: primary data and secondary data. Primary data is obtained from reliable journals, articles, and readings that support this research. Meanwhile, secondary data is obtained from relevant books that pertain to the main issues addressed in the research. The findings of this study include the high prevalence of misinformation, the increase in internet media literacy, and the model for combating hoaxes.
On Permutation Trinomials of the type $X^{q^2-q+1}+AX^{q^2}+BX$ over $\mathbb{F}_{q^3}$

Necessary and sufficient conditions on $A,B\in \mathbb{F}_{q^3}^*$ for $f(X)=X^{q^2-q+1}+AX^{q^2}+BX$ being a permutation polynomial of $\mathbb{F}_{q^3}$ are investigated via a connection with algebraic varieties over finite fields.
Federated Inverse Reinforcement Learning for Smart ICUs With Differential Privacy

Clinical decision-making models have been developed to support therapeutic interventions based on medical data from either a single hospital or multiple hospitals. However, models based on multihospital data require collaboration among hospitals to integrate local data, which can result in information leakage and violate patient privacy. To address this challenge, we propose a novel approach that combines federated learning (FL) with inverse reinforcement learning (IRL) to create an efficient medical decision-making support tool while preserving patient privacy. Our approach uses an IRL algorithm with differential privacy to train a neural network-based agent on local data containing clinician trajectories, which learns a private treatment policy by observing patients’ conditions. Additionally, we integrate FL into the proposed algorithm to learn a global optimal action policy collaboratively among various smart intensive care units, overcoming data limitations at each hospital. We evaluate our approach using real-world medical data and demonstrate that it achieves superior performance in a distributed manner.
Research on Non-IID Cervical Cell Image Classification Algorithm Based on Federated Learning

Aiming at the performance degradation of existing federated learning algorithms in non-IID image classification tasks, a method of improving SRGAN and dynamically balancing the distribution of client data sets is proposed to enhance the data of non-IID images, and for depth The learning method ignores the privacy and security issues of cervical cell classification, and proposes a method combining federated learning, attention mechanism and convolutional neural network to improve the performance of non-IID cervical cell classification. The experimental results show that the proposed algorithm is 1.77% better than the original convolutional neural network on the liquid-based Pap smear cervical image dataset that is artificially divided into non-independent and identical distribution, and the correct rate can reach 91.75 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ,which better improves the performance of the non IID distributed images on the federal learning algorithm.
Helicopter flying obstacle detection based on the fusion of infrared and optical images

Helicopters often encounter obstacles such as towers and high-voltage lines when flying at low altitude, and the safety problem is increasingly prominent. Optical images have high resolution, which provide rich color, texture, edge and other details of the detection object. Infrared images can still maintain the advantage of high detection rate at night or in the environment with poor visibility. Combining the characteristics and advantages of infrared and optical images, this paper designs a dual branch convolution neural network to detect helicopter flying obstacles. For infrared images, a single branch infrared image feature extraction network SBI-Net (Single Branch Infrared image Network) is designed to automatically extract the features of infrared images; For optical images, a single branch optical image feature extraction network SBO-Net (Single Branch Optical image Network) is designed to extract the features of optical images; Finally, the two networks are fused, and a dual branch feature fusion network IODBFF-Net (Dual Branch Feature Fusion Network model based on Infrared and Optical image) is proposed. The experimental results show that compared with infrared single branch network and optical single branch network, the detection accuracy of dual branch convolution neural network is improved by 2.06% and 40.25% respectively.
The Influence of Anti-violence Program of the “CSR” Carried Out by Media Institutions on Family Empowerment

Discussions about domestic violence are very often done in various activities, such as seminars, workshops, and research.Various efforts of the government and non-governmental organizations have also been carried out.With a number of these actions, the phenomenon of domestic violence should no longer exist or at least decrease, but this phenomenon still exists.The aims of this article entitle The Influence of Anti-Violence Program of the "CSR" carried out by Media Institutions on Family Empowerment are to describe a research to find what variables can reduce domestic violenc.Through Empowerment Theory, Structural Functional Theory, and Learning Process Theory, it is estimated that the main cause is because the anti-violence movement was not properly socialized, so that people do not have the provision to have the power to reduce domestic violence.There are two media institutions in Yogyakarta, namely Rakosa Female Radio, and Jogja TV which carry out corporate social responsibility programs in the form of anti-violence socialization through radio broadcasts, television broadcasts, antiviolence consultations services, and out bonding.Through the distribution of questionnaires and in-depth interviews, it was found that the intensity of socialization of the anti-violence CSR program by the media institutions had an influence on family empowerment to reduce domestic violence.
Creating a onepager website with TYPO3

I wanted to switch a small website into a one-pager website lately, and as it so happend - the site was managed with TYPO3. Turns out, with TYPO3 it’s a piece of cake. I already had a sitepackage and a template, so the only thing was to glue the pages in a string, make them distingusable from each other, and adjust the menu. Taking care of the glue The pages can be glued together with two viewhelpers from the vhs-extension. With we get all the subpages of our root page, and with we can get...
Preliminary study on Strait of Malacca Tide Wave Characteristic from AIS Data Mining

In this study, advanced data mining techniques and Automatic Identification System (AIS) data were used to examine the tidal and wave dynamics of the Strait of Malacca. This strait has been an important commercial route for centuries, connecting the Indian Ocean to the South China Sea and the Pacific Ocean. It is located between Sumatra Island's east coast and Peninsular Malaysia's west coast, with the Strait of Singapore at its southeast end. The study's initial findings indicate a strong correlation between the distance of ships' coordinates and the timing of the tide, which is mainly influenced by the harmonic motion of the sun and the moon. Through AIS data mining and analysis, this study demonstrates the potential for predicting sea heights during tidal events. These results could be further explored in future research. Overall, this study offers valuable insights into the intricate tidal and wave dynamics of the world's longest strait and highlights the promise of AIS data mining for advancing our knowledge of oceanic phenomena. The research outcomes could potentially facilitate the development of more precise and effective methods for forecasting sea heights and predicting other marine processes.
Improved artificial fish swarm based optimize rapidly-exploring random trees multi-robot exploration algorithm

To solve the problems of high storage resource consumption and low efficiency of the RRT exploration algorithm in the late stage of exploration, this paper proposes an Improved Artificial Fish Swarm based Optimize Rapidly-exploring Random Trees multi-robot Exploration Algorithm. Firstly, the efficiency of a single robot’s exploration of nearby unknown regions is improved by dynamically adjusting the step size of the RRT tree.Secondly, the improved artificial fish swarm algorithm is used to delete the redundant nodes in the RRT tree and optimize the node state in the RRT tree, which reduces the occupation of memory resources and improves the exploration efficiency of the RRT tree in the narrow environment.Results from comparative experiments in simulation environments with different degrees of openness show that the optimized exploration algorithm can save significant storage resources and show better exploration performance in narrow environments compared to the original RRT exploration algorithm.
HC-Net: Hierarchical Context integration Network for medical image segmentation

Accurate medical image segmentation is essential to achieve precise medical image analysis, e.g., blood vessel detection or lung segmentation, which has attracted researchers’ attention. So far, state-of-the-art techniques improving segmentation results have extracted the multi-scale contextual features with dilated convolution. However, these techniques cannot capture sufficient scale information due to only using several parallel independent branches, which result in leaving a gap between existed and ideal segmentation results.To address this problem, we design a hierarchical context integration network (HC-Net) which includes encoder module, hierarchical context integration module (HCM), and decoder module. Our HCM can not only extract the scale features in a single branch, but also learn the scale correlation among different branches. Meanwhile, self-attention mechanism therein is utilized to integrate context features and global dependencies adaptively for decoder Module. The proposed HC-Net has been applied two popular datasets, DRIVE and LUNA, and the experimental results show that our method outperforms state-of-the-art ones.
Lessons from debiasing data for fair and accurate predictive modeling in education

The past few years have witnessed an explosion of attention given to the bias displayed by Machine Learning (ML) techniques towards different groups of people (e.g., female vs. male). Although ML techniques have been widely adopted in education, it remains largely unexplored that to what extent such ML bias manifests itself in this specific setting and how it can be reduced and eliminated. Given the increasing importance of ML techniques in empowering educators to teach effectively, this study aimed to quantify the characteristics of the original datasets that might be correlated with the subsequent predictive unfairness displayed by ML models. To this end, we empirically investigated two types of data biases (i.e., distribution bias and hardness bias) towards students of different sexes and first-language backgrounds across a total of five frequently-performed predictive tasks in education. Then, to improve ML fairness, we drew inspiration from the well-established research in Class Balancing Techniques (CBTs), where samples are generated/removed to alleviate the predictive disparity between different prediction classes. We proposed two simple but effective strategies to empower class balancing techniques for alleviating data biases and improving prediction fairness. Through extensive analyses and evaluations, we demonstrated that ML models may greatly improve prediction fairness (improvement up to 66%) with only a small sacrifice (less than 1%) in prediction accuracy by balancing the training data with the use of students’ demographic information and the overall hardness bias measure. All data and code used in this study are publicly accessible via https://github.com/lsha49/FairEdu.
Academic Writing of EFL Students’ Undergraduate Theses: A Discourse Marker Analysis

Writing the background is obligatory in undergraduate theses, and discourse markers are essential to creating cohesion and coherence in academic writing. Thus, this paper aimed to investigate discourse markers used by the undergraduate students in writing their thesis backgrounds and the extent of accuracy of the use of DMs in the students’ thesis backgrounds. Data were collected from 28 undergraduate thesis backgrounds written by the students of the English Language Education Study Program of Sanata Dharma University, Yogyakarta batch 2016, who graduated between May 2020 and January 2021. Descriptive research was employed, and the data were examined using document analysis. Results showed that elaborative markers were used 763 times (74.58%) and followed by reason markers 95 times (9.29%), inferential markers 85 times (8.31%), and contrastive markers 80 times (7.82%). In general, the EFL students used DMs accurately in their thesis backgrounds. Some DMs were, however, used inappropriately. It is essential that the EFL students better understand how to use DMs appropriately when writing their thesis backgrounds.
Information

This article presents a brief history of the term “information” and its different meanings, which are both important and difficult because the different meanings of the term imply whole theories of knowledge. The article further considers the relation between “information” and the concepts “matter and energy”, “data”, “sign and meaning”, “knowledge” and “communication”. It presents and analyses the influence of information in information studies and knowledge organization and contains a presentation and critical analysis of some compound terms such as “information need”, “information overload” and “information retrieval”, which illuminate the use of the term information in information studies. An appendix provides a chronological list of definitions of information.
Categories of Response-Based, Feature-Based, and Relation-Based Knowledge Distillation

Deep neural networks have achieved remarkable performance for artificial intelligence tasks. The success behind intelligent systems often relies on large-scale models with high computational complexity and storage costs. The over-parameterized networks are often easy to optimize and can achieve better performance. However, it is challenging to deploy them over resource-limited edge-devices. Knowledge Distillation (KD) aims to optimize a lightweight network from the perspective of over-parameterized training. The traditional offline KD transfers knowledge from a cumbersome teacher to a small and fast student network. When a sizeable pre-trained teacher network is unavailable, online KD can improve a group of models by collaborative or mutual learning. Without needing extra models, Self-KD boosts the network itself using attached auxiliary architectures. KD mainly involves knowledge extraction and distillation strategies these two aspects. Beyond KD schemes, various KD algorithms are widely used in practical applications, such as multi-teacher KD, cross-modal KD, attention-based KD, data-free KD and adversarial KD. This paper provides a comprehensive KD survey, including knowledge categories, distillation schemes and algorithms, as well as some empirical studies on performance comparison. Finally, we discuss the open challenges of existing KD works and prospect the future directions.
B-cos Alignment for Inherently Interpretable CNNs and Vision Transformers

We present a new direction for increasing the interpretability of deep neural networks (DNNs) by promoting weight-input alignment during training. For this, we propose to replace the linear transformations in DNNs by our novel B-cos transformation. As we show, a sequence (network) of such transformations induces a single linear transformation that faithfully summarises the full model computations. Moreover, the B-cos transformation is designed such that the weights align with relevant signals during optimisation. As a result, those induced linear transformations become highly interpretable and highlight task-relevant features. Importantly, the B-cos transformation is designed to be compatible with existing architectures and we show that it can easily be integrated into virtually all of the latest state of the art models for computer vision - e.g. ResNets, DenseNets, ConvNext models, as well as Vision Transformers - by combining the B-cos-based explanations with normalisation and attention layers, all whilst maintaining similar accuracy on ImageNet. Finally, we show that the resulting explanations are of high visual quality and perform well under quantitative interpretability metrics.
Implementation of Face Recognition for Patient Identification Using the Transfer Learning Method

The hospital's status as a health center requires it to ensure patient safety, decrease incidents and treat patients. Identification of the patient is the primary source of patient safety difficulties. In addition to the patient's name and number, further patient-identifying components are needed to reduce this neglect. This work provides a solution in the form of biometric authentication, namely, face recognition. The convolutional neural network (CNN) approach can enable machine facial recognition. CNN is one of the deep learning techniques used to detect and identify picture objects. In this study, facial recognition was carried out using the transfer learning technique, VGGFace2 model pretraining, and SENet 50 model architecture. The dataset was collected via one-shot learning or a single sample per individual sampling. Applying the CNN model to the patient identification system yields two distinct outcomes: patient registration and verification. Registration utilizes a minimum distance of 0.35 and matches data with the complete database, whereas patient verification has a minimum distance of 0.28 and matches only the face in question. At the time of patient registration, the accuracy was between 90% and 100%. However, at the time of patient verification, the accuracy was 100%.
A remark on continued fractions for permutations and D-permutations with a weight $-1$ per cycle

We show that very simple continued fractions can be obtained for the ordinary generating functions enumerating permutations or D-permutations with a large number of independent statistics, when each cycle is given a weight $-1$. The proof is based on a simple lemma relating the number of cycles modulo 2 to the numbers of fixed points, cycle peaks (or cycle valleys), and crossings.
HomeRobot: Open-Vocabulary Mobile Manipulation

HomeRobot (noun): An affordable compliant robot that navigates homes and manipulates a wide range of objects in order to complete everyday tasks. Open-Vocabulary Mobile Manipulation (OVMM) is the problem of picking any object in any unseen environment, and placing it in a commanded location. This is a foundational challenge for robots to be useful assistants in human environments, because it involves tackling sub-problems from across robotics: perception, language understanding, navigation, and manipulation are all essential to OVMM. In addition, integration of the solutions to these sub-problems poses its own substantial challenges. To drive research in this area, we introduce the HomeRobot OVMM benchmark, where an agent navigates household environments to grasp novel objects and place them on target receptacles. HomeRobot has two components: a simulation component, which uses a large and diverse curated object set in new, high-quality multi-room home environments; and a real-world component, providing a software stack for the low-cost Hello Robot Stretch to encourage replication of real-world experiments across labs. We implement both reinforcement learning and heuristic (model-based) baselines and show evidence of sim-to-real transfer. Our baselines achieve a 20% success rate in the real world; our experiments identify ways future research work improve performance. See videos on our website: https://ovmm.github.io/.