Reviewerly/exHarmony huggingface dataset:
Intelligent Onion Routing and UAV-based Electronic Health Record Sharing Framework for Healthcare 4.0

Electronic Health Records (EHRs) seem to be an efficient data storage platform for storing patient's sensitive medical data in healthcare 4.0. But, EHRs deployed on the cloud or web that can be easily exploited by malicious attackers, which raises the need for a secure data sharing framework in healthcare 4.0. Thus, this paper proposes a secure and protected unmanned aerial vehicle (UAV)-assisted EHR framework for healthcare 4.0 by adopting the onion routing (OR) network and Artificial Intelligence (AI)-based technique. The incorporated OR network secures the patient's confidential data against various security and anonymity attacks which is being stored in the EHR with the help of UAVs. Moreover, we have combined OR network with an AI-based technique using various machine learning models to perform the classification on Ehr's sensitive data as malicious and non-malicious. Further, the OR network is simulated inside the shadow simulator where different onion nodes are designed to relay the patient's medical data from the sender to the intended receiver, i.e., EHR. Finally, the performance evaluation of an AI and OR-based data sharing framework is evaluated in terms of accuracy, precision, recall, and training time where Boosted Tree (BT) surpasses other machine learning (ML) models with the improved accuracy of 95.6 % and OR network maintains the anonymity of the data communication in healthcare 4.0. Moreover, the OR network has lower computational complexity (processing time = 0.002742 s) due to the adoption of ML models.
METODE STRUKTURAL ANALITIK SINTETIK (SAS) UNTUK MENINGKATKAN KEMAMPUAN MEMBACA HURUF BRAILLE PADA ANAK TUNANETRA DI BHAKTI LUHUR MALANG

This research was motivated by the difficulty in reading braille in blind students shown by non-fluent behavior in reading both syllables, words and simple sentences in braille. In addition, subjects were slow to identify syllables, and hesitated in reading, especially when reading simple readings, consisting of 3-5 sentences. The purpose of this study was to determine whether the Structure Analysis Synthesis (SAS) method can improve braille reading skills in blind children in Bhakti Luhur Malang who are in grade II SDLB-A. The method used is an experimental method with a Single Subject Research design A-B-A design. The results showed that in the baseline phase (A1) which was carried out as many as 3 sessions, the average score was 44.3%. Meanwhile, in the Intervention phase (B) which was carried out as many as 6 sessions obtained an average score of 53.8%. And the baseline phase 2 (A2) carried out as many as 3 sessions obtained an average score of 86%. The results of visual analysis in conditions show that the directional tendency increases from session to session, which means that it shows a change in good (+). Meanwhile, in the analysis between conditions, the level change from the baseline phase to the intervention phase increased from the previous data, while the percentage of overlap after calculating the intervention conditions to the baseline obtained an overlap percentage of 50%, which means that the intervention has a good effect. While in the intervention condition with baseline condition 2, an overlap percentage of 0% was obtained, which means that the intervention has a good effect on braille reading ability, the conclusion that can be drawn is that changes in braille reading ability in blind children occur due to braille reading interventions using the SAS method.
Assessment of the environmental impacts of gold mining activities at Gankombol (Adamawa-Cameroon) using Leopold matrix, Fecteau grid and remote sensing approach

In the present study, the environmental impacts of gold mining activities at Gankombol (Adamawa Cameroon) are assessed over four-year period. The Leopold matrix and the Fecteau grid are used to identify the environmental impacts and to determine their absolute importance. The spatio-temporal variation of spectral indices and land degradation are assessed using remote sensing analysis. The approach adopted is based on the calculation of spectral indices and spatio-temporal analysis. For this purpose, from Sentinel-2 satellite images, spectral indices such as the normalized difference vegetation index (NDVI), the brightness index (BRI), the normalized difference water index (NDWI), the bare soil index (BSI) and the dry bare soil index (DBSI) are computed during four years (2016, 2019, 2020 and 2021). The environmental degradation maps are generated by using supervised Random Forests (RF) algorithm and field observations. Finally, the confusion matrix and the kappa coefficient are used to check the performance and the accuracy of the classification maps. The environmental impact assessment by the Leopold matrix and the Fecteau grid reveal that the environmental impacts of mining activities are mainly negative and manifested by deforestation, degradation of water and soil quality, and high risks to human health. These impacts have a major absolute importance. The analysis of the spectral indices suggest a clear difference between the values obtained for each year due to the evolution of the mining activity over time. The environmental degradation maps which made it possible to discriminate water bodies, exploited area, vegetation and barren soils show that the values of water bodies decrease from 16.33 to 11.69 km2 between 2016 and 2020, then increase to 13.46 km2 in 2021. The exploited area increases from 0.00 to 13.75 km2 between 2016 and 2021. Vegetation cover decreases from 24.49 to 15.25 km2 between 2016 and 2021, while barren soils increase from 19.65 to 21.53 km2 between 2019 and 2021. These results reveal significant environmental degradation in the study area due to mining activities.
A Novel Smartphone-Based Human Activity Recognition Approach using Convolutional Autoencoder Long Short-Term Memory Network

In smart and intelligent health care, smartphone sensor-based automatic recognition of human activities has evolved as an emerging field of research. In many application domains, deep learning (DL) strategies are more effective than conventional machine learning (ML) models, and human activity recognition (HAR) is no exception. In this paper, we propose a novel framework (CAEL-HAR), that combines CNN, Autoencoder and LSTM architectures for efficient smartphone-based HAR operation. There is a natural synergy between the modeling abilities of LSTMs, autoencoders, and CNNs. While AEs are used for dimensionality reduction and CNNs are the best at automating feature extraction, LSTMs excel at modeling time series. Taking advantage of their complementarity, the proposed methodology combines CNNs, AEs, and LSTMs into a single architecture. We evaluated the proposed architecture using the UCI, WISDM public benchmark datasets. The simulation and experimental results certify the merits of the proposed method and indicate that it outperforms computing time, F1-score, precision, accuracy, and recall in comparison to the current state-of-the-art methods.
Resilient Multi-Robot Multi-Target Tracking

We address the problem of ensuring resource availability in a networked multi-robot system performing distributed target tracking. Specifically, we consider a multi-target tracking scenario where the targets are driven by exogenous inputs that are unknown to the robots performing the tracking task. Robots track the positions of targets using a form of the Distributed Kalman Filter (DKF). We use the trace of each robot’s sensor measurement noise covariance matrix as a measure of its sensing quality. When a robot’s sensing quality deteriorates, the team’s communication graph is modified by adding edges such that the robot with deteriorating sensor quality may share information with other robots to improve the team’s target tracking ability. This computation is performed centrally and is designed to work without a large change in the number of active inter-robot communication links. Our method generates coordinates for the robots such the new communication graph can be realized in 3D. To achieve this, we propose two mixed integer semi-definite programming formulations, namely an ‘agent-centric’ strategy and a ‘team-centric’ strategy. We implement both formulations and a greedy, baseline strategy in simulation. Our simulation results show that the team-centric approach outperforms both agent-centric and greedy methods. Additionally, we show the effectiveness of our method in real-world settings through a multi-robot experiment performed in real-time <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the need to track multiple targets by means of a multi-robot team. When robots in the team experience degradation in their sensing quality our method reconfigures the team’s communication graph by repositioning robots. This allows the robot with deteriorated sensing quality to benefit from sensor measurements from its neighbors. In previous work, we solved this problem under the strong assumption the targets moved under inputs that were known to the tracking team. In this paper we remove this assumption. Our experiments show comparable tracking accuracy for both settings, suggesting that this strong assumption is not always necessary for good tracking results. Our method is straightforward to implement in Python using off-the-shelf optimization solvers. We assume that the team performs target tracking using a distributed algorithm, but has access to a powerful centralized base station to solve the computationally expensive underlying optimization problems. Developing a fully decentralized method and finding better ways to solve the underlying optimization problems are potential directions for future work.
Gradient Boosting

In this chapter, we explore gradient boosting, a powerful ensemble machine learning method, for both regression and classification tasks. With a focus on accessibility, we minimize abstract mathematical theories and instead emphasize two concrete numerical examples with small datasets related to predicting house sale prices and ease of selling houses in the property market. By providing a step-by-step walkthrough, we illuminate the inner workings of gradient boosting and offer sample codes and comparisons to the gradient boosting models available in the scikit-learn library. Upon completing this chapter, readers will possess a comprehensive understanding of gradient boosting's mechanics, its connection to the implementation and performance of the algorithm, and be well-prepared to apply it in real-world projects.
Comparison of Selected Neural Network Models Used for Automatic Liver Tumor Segmentation

Automatic and accurate segmentation of liver tumors is crucial for the diagnosis and treatment of hepatocellular carcinoma or metastases. However, the task remains challenging due to imprecise boundaries and significant variations in the shape, size, and location of tumors. The present study focuses on tumor segmentation as a more critical aspect from a medical perspective, compared to liver parenchyma segmentation, which is the focus of most authors in publications. In this paper, four state-of-the-art models were trained and used to compare with UNet in terms of accuracy. Two of them (namely, based on polar coordinates and Visual Image Transformer (ViT)) were adopted for the specified task. Dice similarity measure is used for the comparison. A unified baseline environment and preprocessing parameters were used. Experiments on the public LiTS dataset demonstrate that the proposed ViT based network can accurately segment liver tumors from CT images in an end-to-end manner, and it outperforms many existing methods (tumour segmentation accuracy 56%, liver parenchyma 94% Dice). The average Dice similarity measure for the considered images was found to be 75%. The obtained results seem to be clinically relevant.
An Empirical Study on the Robustness of Active Learning for Biomedical Image Classification Under Model Transfer Scenarios

Active learning (AL) is a popular training strategy that involves iteratively selecting training examples for annotation, typically those for which the current model is most uncertain. This allows to learn more effectively with fewer labeled examples beyond what could be achieved with random samples. However, AL research literature often over-simplifies the evaluation by assuming that the model to guide training data acquisition and the successor model used in the final deployment are identical. In real-world scenarios that is almost never the case since 1) due to performance reasons acquisition models often have less complexity compared to the successor models and 2) successor models are frequently replaced by potentially better performing models in productive environments. In this paper, we systematically study the effects of transferring an actively sampled training data set from an acquisition model to different successor models for biomedical image classification tasks. Our research shows that training a successor model with an actively-acquired data set is most promising, if acquisition and successor model are of similar architecture.
Prediction of Web Browsing Behavior based on Sequential Data Mining

Discovering time-related transaction behavior or patterns is helpful for businesses in suggesting appropriate products to their customers. For web systems, it is important to understand customers’ browsing behavior in order to design or recommend products or services that customers need. This study proposes an approach for predicating web browsing behavior that integrates the concepts of sequential data mining, Borda majority count, bit-string operation and PrefixSpan algorithm. By incorporating the concept of Borda majority count and sequential data mining, the proposed approach can discover majority-based priorities of items for recommendation and improve prediction accuracy. In addition, the proposed approach employs the concept of bit-string operation and the PrefixSpan algorithm to increase computational efficiency. This research employs the concept of ensemble methods that combine multiple models to derive improved results. Compared to previous methods, the proposed approach can yield higher prediction accuracy. Moreover, the proposed approach can provide flexibility for decision makers in adjusting a minimum support level and the number of items for recommendation. The proposed approach can also be applied to many fields.
Trust Your Good Friends: Source-Free Domain Adaptation by Reciprocal Neighborhood Clustering

Domain adaptation (DA) aims to alleviate the domain shift between source domain and target domain. Most DA methods require access to the source data, but often that is not possible (e.g., due to data privacy or intellectual property). In this paper, we address the challenging source-free domain adaptation (SFDA) problem, where the source pretrained model is adapted to the target domain in the absence of source data. Our method is based on the observation that target data, which might not align with the source domain classifier, still forms clear clusters. We capture this intrinsic structure by defining local affinity of the target data, and encourage label consistency among data with high local affinity. We observe that higher affinity should be assigned to reciprocal neighbors. To aggregate information with more context, we consider expanded neighborhoods with small affinity values. Furthermore, we consider the density around each target sample, which can alleviate the negative impact of potential outliers. In the experimental results we verify that the inherent structure of the target features is an important source of information for domain adaptation. We demonstrate that this local structure can be efficiently captured by considering the local neighbors, the reciprocal neighbors, and the expanded neighborhood. Finally, we achieve state-of-the-art performance on several 2D image and 3D point cloud recognition datasets.
A Holistic Study on India’s Currency Revolution: E-rupee

In the realm of digital revolution and advancement, the subject matter of currency had been evolved to many different scenarios. From coins to notes to plastic money and finally to digital currency, our traditional medium of exchange had gone through a big transition in the past decades. In the economic concept of going for paperless and cashless, man has developed several different modes like Debit card and credit cards (Plastic money) as meeting economic settlement of exchange. But unfortunately, due to environmental commitment of cutting the utility of plastics and centralization of chips manufacturing ability to some few nation’s only, the concept of plastic money can’t be a one fix permanent solution to this currency revolution initiatives. Therefore we have many economies today who are concentrating their efforts towards making their currency digital. The concept of Central Bank Digital Currency (CBDC) is the new concept in this story of currency Revolution. If we check out the global scenario, there are close to 10 countries around the world who have digital currency, as per the reports of India times. To a surprise of the world, we had littoral and islands nation’s too, who are really doing a good job to channelize their currency towards a digital shift. The Bahamas, Jamaica and Singapore like island nation’s too had taken a massive step towards currency digitalization. In the bid to make Indian Currency also adapt with this digital shift, we in India too had successfully running our currency digitalization for the E-Rupees. Although, E-Rupees success in India is yet in the pilot stage and its full-fledged implementation is yet to be put in place. But the point of the matter is that India very soon is going to be in e rupees market. E rupee will going to be in every pocket very soon. Thanks to the digitalization, and demonetization of 2016 that E-Rupees will be the new reality of India. E-Rupees will be in every hands very soon. Point of the matter is that we all must push our self for this currency revolution. India is hopeful That the pilot project of E-Rupees will chase the full-fledged success and very soon India will chase economic triumph. The introduction of E-Rupees will initiate a new dawn of economic growth for the Indian people. Indians will be going to be more empowered in the financial terms as Indian Central Bank i.e. will be making better initiatives for strengthening the businesses as well as Indian citizens. In this research paper, we will be analyzing all the aspects about E-Rupees and how it can be a game changer for Indian Economy in the coming years. Also we will be focusing on its potential merits and demerits of aggregate economy as a whole as well as for the ultimate users. This research paper is prepared from the secondary data sources, and after taking references from like-minded journals, magazines, articles, editorials and research papers. The aim of writing this research paper is to highlight the fact that How Indian Currency is stepping ahead towards digital revolution in this digitalized world.
Supervised machine learning

For example, an algorithm can sort a list of arbitrary strings—such as a list of words or names—in alphabetical order. If such an algorithm was presented with the tuple (David, Robert, Anna, Carl), the correct output would be the tuple (Anna, Carl, David, Robert).
An Investigation into the Effect of Control Tokens on Text Simplification

Recent work on text simplification has focused on the use of control tokens to further the state of the art. However, it is not easy to further improve without an in-depth comprehension of the mechanisms underlying control tokens. One unexplored factor is the tokenisation strategy, which we also explore. In this paper, we (1) reimplemented ACCESS, (2) explored the effects of varying control tokens, (3) tested the influences of different tokenisation strategies, and (4) demonstrated how separate control tokens affect performance. We show variations of performance in the four control tokens separately. We also uncover how the design of control tokens could influence the performance and propose some suggestions for designing control tokens, which also reaches into other controllable text generation tasks.
The Fewer Splits are Better: Deconstructing Readability in Sentence Splitting

In this work, we focus on sentence splitting, a subfield of text simplification, primarily motivated by an unproven idea that if you divide a sentence into pieces, it should become easier to understand. Our primary goal in this paper is to determine whether this is true. In particular, we ask, does it matter whether we break a sentence into two or three? We report on our findings based on Amazon Mechanical Turk. More specifically, we introduce a Bayesian modeling framework to further investigate to what degree a particular way of splitting the complex sentence affects readability, along with a number of other parameters adopted from diverse perspectives, including clinical linguistics, and cognitive linguistics. The Bayesian modeling experiment provides clear evidence that bisecting the sentence leads to enhanced readability to a degree greater than when we create simplification by trisection.
Target-Level Sentence Simplification as Controlled Paraphrasing

Automatic text simplification aims to reduce the linguistic complexity of a text in order to make it easier to understand and more accessible. However, simplified texts are consumed by a diverse array of target audiences and what might be appropriately simplified for one group of readers may differ considerably for another. In this work we investigate a novel formulation of sentence simplification as paraphrasing with controlled decoding. This approach aims to alleviate the major burden of relying on large amounts of in-domain parallel training data, while at the same time allowing for modular and adaptive simplification. According to automatic metrics, our approach performs competitively against baselines that prove more difficult to adapt to the needs of different target audiences or require significant amounts of complex-simple parallel aligned data.
Lexically Constrained Decoding with Edit Operation Prediction for Controllable Text Simplification

Controllable text simplification assists language learners by automatically rewriting complex sentences into simpler forms of a target level. However, existing methods tend to perform conservative edits that keep complex words intact. To address this problem, we employ lexically constrained decoding to encourage rewriting. Specifically, the proposed method predicts edit operations conditioned to a target level and creates positive/negative constraints for words that should/should not appear in an output sentence. The experimental results confirm that our method significantly outperforms previous methods and demonstrates a new state-of-the-art performance.