Reviewerly/exHarmony huggingface dataset:
Storage and Verification of Medical Records Using Blockchain, Decentralized Storage, and NFTs

With the world moving toward digitization, there is a dire need to have the medical records digitized, i.e., to develop the capability and ability to store and verify medical records in a secure, fast, easy, and private way. Blockchain is an immutable and trustless ledger, and using these two fundamental properties, we have proposed a novel and unique method where both patient and the hospitals can confirm to the identities, claims, and files of each other; this trustless and confirmable method is in the benefit of both the transacting parties. We have also compared the previous methodologies and their approaches based on some fundamental qualitative and quantitative measures which are important for the acceptability, adoptability, security, and scalability of the said approaches. And then finally, we have gone ahead and discussed how our proposed two-stage verification is much more secure on both ends, i.e., the hospitals and the patients and is far more decentralized and has no technical central point of failure as compared to some cited approaches and thus making it by far the best approach.
Logical Formalization for a HMDCS-UV

Natural approaches to multifaceted planning solve each problem using specific representations and techniques appropriate to the problem. Predictive models are developed for the types of actions to be planned for the emergent states of the system. The computational tools used to run these models to predict and assess the impact of alternative actions and plans under different circumstances draw on the details of the field. For example, geometry, kinematics, and dynamics are the tools required for motion or manipulation planning. This paper describes the logical formalization of Hybrid Monitoring, Detection and Cleaning System for Unmanned Vehicles (HMDCS-UV) (Bella et al. [1]). This formalization defines a conceptual model and representation of classical planning to simulate the behavior of these unmanned vehicles in monitoring and cleaning up polluted ocean regions.
In-Depth Analysis of Artificial Intelligence in Mammography for Breast Cancer Detection

In this paper, we’ll take a look at how AI has been used, and where it is now, in the interpretation of breast-mammography images. Some other prospective applications of AI in diagnostic imaging are also addressed. In women, breast cancer is the most frequent form of cancer. While it may be challenging, finding breast irregularities greatly reduces the risk of getting breast cancer and dying from it. This is true even in cases when anomalies in the breast are obscured by clothing or other factors. Using a combination of imaging methods and the possibilities offered by AI, computers aid radiologists in their quest for an accurate diagnosis of breast diseases. This means that computers are now far more efficient than humans in carrying out imaging tasks. Because of recent improvements in breast cancer detection methods, including mammography, ultrasonography, and magnetic resonance imaging, the number of confirmed cases has grown. The development of AI has the potential to impact almost every facet of contemporary life.
Assessment of Variable Threshold for Anomaly Detection in ECG Time Signals with Deep Learning

Anomaly detection in electrocardiogram (ECG) signals is a crucial task in the medical domain. But determining a threshold for detecting the abnormal signal is challenging, and it significantly impacts the performance of a Deep Learning (DL) model. In this paper, we propose a methodology to assess the impact of variable thresholds on anomaly detection in ECG signals generated using the reconstruction error of an autoencoder model. First, we consider a set of random thresholds and generate the associated confusion matrix along with a ROC curve to analyze the varying performance. We then propose a method to systematically generate a set of thresholds and find the optimal threshold using the associated ROC curve. Our experiment suggests that the proposed method of using a ROC curve for a set of systematically generated thresholds to find the (near-) optimal threshold can potentially improve the detection of abnormalities in the ECG signal.
Software Reuse Approach Based on Review and Analysis of Reuse Risks from Projects Uploaded to GitHub

Modern and large software systems usually are not developed from scratch. Reuse operations for small modules are not complicated activities. When reuse is organized on level of algorithms or software features, practices of many companies show that reuse procedures are performed on low maturity levels (analysis of software bugs and reuse risks are often performed approximately). According to researches of IBM and many other companies the later you will define any kind of error the more expensive and large scale will be cost of improving your software. Paper proposes an approach based of reverse engineering activities aimed to estimate reuse risks of existing projects on GitHub before their further reuse. Proposed approach is designed by analysis of typical activities performed in research laboratories of software companies and considers specific of working with GitHub. Model for estimating of reuse risks is proposed. Model covers reuse risks for multilayer applications, but can be extended for other types of projects. Recommendations for developers for extension of the model are outlined.
TeamUp: Form Best Project Teams

Team formation is a critical task in many contexts such as business, sports, healthcare, research, education, and more. In the academic settings, students working in teams on group projects is proven to be a very effective learning methodology. Our software engineering course features a semester-long team project as the key component of this course. However, team assignments are complex and nontrivial tasks, necessitating a careful assessment of many different factors to ensure optimal performance of the team as well as individual participants’ satisfaction. Usually a predefined set of questions are used to understand participant’s capabilities and preferences, and teams are formed either manually or automatically based on the results of those questions. In this paper, we propose a generalized question definition by associating each question with three factors: multiple choice/multiple answer, similarity or diversity, and option valuation, in order to consider various types of factors, provide flexibility and capture each individual’s characteristics. We then propose two team performance score functions that differentiate similarity questions from diversity questions and capture each of their team formation objectives. A heuristic team formation algorithm, TeamUp, is proposed, attempting to maximize the team performance as well as participants’ preferences. Through the initial evaluation we show our proposed algorithm can perform well for different team size and type of questions.
Holistic (Software) Quality Theory. An Improved Definition and Meta-Model

The current article introduces a short historical review of the roots of the holistic (software) quality theory. This review is used both as a presentation of the ground on which the holistic quality has been built upon and correspondingly to introduce a classification of the quality models known today. Since its introduction in 2017, the holistic (software) quality was several times enriched; one such advancement is part of the current work concerning the meta-model used to describe quality itself; thus, better control, management and etc., are achieved.
Analysis of Code Mixing in Unila Zoom Photography Student Speech

: This research was conducted with the intention of describing the utterances used by Unila photography zoom ukm students. Speeches using different languages will produce various contexts. This research was reviewed using a descriptive qualitative method. By obtaining data in the form of dialogue between Javanese-speaking students and Indonesian-speaking students, the results of this study will be carried out by describing whether the existing code mixing can make communication between speakers and interlocutors run straight or not according to the goals of the speakers and interlocutors. Keywords: speech, code mixing, university students. DOI: http://dx.doi.org/10.61436/rietm/v1i2.pp38-41
Chapter 2. Life at the intersection

In colloquial Norwegian, han ‘he’ and hun ‘she’ can occur as a determiner with common nouns with human reference (e.g., han mannen, lit. ‘he man-the’). This study investigates the lateral relations of this construction (henceforth: han mannen) and two other definite-referring constructions with human reference: noun phrases that contain the adnominal determiner den (i.e., den mannen) and those that realize definiteness by a suffixed article (i.e., mannen). The study argues that han mannen lives at the intersection of these two constructions, i.e., that it is motivated by two constructions to which it is laterally related. The lateral relations between the constructions are evaluated by random forests, variable importance measures, and partial dependence plots.
An adaptive modeling for bifacial solar module levelized cost and performance analysis for mining application

Abstract Power density and efficiency typically dominate design approaches for power electronics. However, cost optimality is in no way guaranteed by these strategies. A design framework that minimizes the (i) levelized cost of electricity (LCOE), (ii) collection of light, and (iii) irradiance of the generation system is proposed as a solution to this flaw. From an improvement of the swarm behavior optimization model to get a minimum LCOE of solar panel, we design to optimize height, tilt angle, azimuth angle, and some parameters to solve the objective function and LCOE improvement problem to obtain the optimal design problem. In adaptive salp swarm optimization (ASSO), this change's proposed model producer swarm behavior is regarded as an adaptive process that keeps the algorithm from prematurely converging during exploration. The proposed algorithm's performance was confirmed using benchmark test functions, and the results were compared with those of the salp swarm optimization (SSO) and other efficient optimization algorithms. LCOE condition as far as “land‐related cost” and “module‐related cost” demonstrates that the optimal design of bifacial farms is determined by the interaction of these parameters. This proposed model can be used to evaluate visibility on building surfaces that are suitable for mining applications like crushing. Experimentation results show Minimum LCOE AS 0.05 (€/Kw)minimum irradiance and collection light as 336.23(w/m 2 ) and 83.02%n proposed framework model. The swarm optimization method is contrasted with the optimal parameters derived from a conventional solver.
A hybrid attention and dilated convolution framework for entity and relation extraction and mining

Mining entity and relation from unstructured text is important for knowledge graph construction and expansion. Recent approaches have achieved promising performance while still suffering from inherent limitations, such as the computation efficiency and redundancy of relation prediction. In this paper, we propose a novel hybrid attention and dilated convolution network (HADNet), an end-to-end solution for entity and relation extraction and mining. HADNet designs a novel encoder architecture integrated with an attention mechanism, dilated convolutions, and gated unit to further improve computation efficiency, which achieves an effective global receptive field while considering local context. For the decoder, we decompose the task into three phases, relation prediction, entity recognition and relation determination. We evaluate our proposed model using two public real-world datasets that the experimental results demonstrate the effectiveness of the proposed model.
Thermally-robust spatiotemporal parallel reservoir computing by frequency filtering in frustrated magnets

Physical reservoir computing is a framework for brain-inspired information processing that utilizes nonlinear and high-dimensional dynamics in non-von-Neumann systems. In recent years, spintronic devices have been proposed for use as physical reservoirs, but their practical application remains a major challenge, mainly because thermal noise prevents them from retaining short-term memory, the essence of neuromorphic computing. Here, we propose a framework for spintronic physical reservoirs that exploits frequency domain dynamics in interacting spins. Through the effective use of frequency filters, we demonstrate, for a model of frustrated magnets, both robustness to thermal fluctuations and feasibility of frequency division multiplexing. This scheme can be coupled with parallelization in spatial domain even down to the level of a single spin, yielding a vast number of spatiotemporal computational units. Furthermore, the nonlinearity via the exchange interaction allows information processing among different frequency threads. Our findings establish a design principle for high-performance spintronic reservoirs with the potential for highly integrated devices.
Participatory Design for Cognitive Science: Examples From the Learning Sciences and Human−Computer Interaction

Abstract Given the recent call to strengthen collaboration between researchers and relevant practitioners, we consider participatory design as a way to advance Cognitive Science. Building on examples from the Learning Sciences and Human−Computer Interaction, we (a) explore what , why , who , when , and where researchers can collaborate with community members in Cognitive Science research; (b) examine the ways in which participatory‐design research can benefit the field; and (c) share ideas to incorporate participatory design into existing basic and applied research programs. Through this article, we hope to spark deeper discussions on how cognitive scientists can collaborate with community members to benefit both research and practice.
Optimization Strategies for BERT-Based Named Entity Recognition

Transfer learning through language modeling achieved state-of-the-art results for several natural language processing tasks such as named entity recognition, question answering, and sentiment analysis. However, despite these advancements, some tasks still need more specific solutions. This paper explores different approaches to enhance the performance of Named Entity Recognition (NER) in transformer-based models that have been pre-trained for language modeling. We investigate model soups and domain adaptation methods for Portuguese language entity recognition, providing valuable insights into the effectiveness of these methods in NER performance and contributing to the development of more accurate models. We also evaluate NER performance in few/zero-shot learning settings with a causal language model. In particular, we evaluate diverse BERT-based models trained on different datasets considering general and specific domains. Our results show significant improvements when considering model soup techniques and in-domain pretraining compared to within-task pretraining.
Time series online forecasting based on sequence decomposition learning networks

Research on time series online modeling has received much attention in recent years. The existing advanced methods are often accompanied by the problem of low model established efficiency, so it is difficult to realize online modeling in practical applications. This paper proposed a sequence decomposition learning networks (SDLN) to solve time series online forecasting problems. In contrast to most time series forecasting methods, SDLN is a novel time series online forecasting method without capturing sliding time window features. The SDLN consists of five layers of neurons, namely, the input layer, decomposition layer, sparse layer, hidden layer, and output layer, where the additions of the decomposition layer and sparse layer enable a fast model calculation speed and do not require the capture of sliding time window features. In addition, it can establish time series forecasting models with better model accuracy and model stability in a very short model calculation time. To verify the effectiveness of SDLN, eight publicly available time series datasets are applied. Experimental results show that SDLN can obtain better model performance than other state-of-the-art methods, especially in terms of model computation speed. It can achieve a model accuracy of 10-4 to 10-2 and a model computation time of 10-2 seconds for short- and medium-term forecasting on all eight datasets. Therefore, SDLN is an effective time series online modeling method.
Improvement of Chinese seal detection based on polar coordinate transformation

At present, most deep networks in text detection tasks have a prominent effect on regular text detection, but a poor effect on curved text detection. This is because most of the curved text has irregular lengths. When the text is curved, the length is often irregular, some paragraphs are elongated, some paragraphs are shortened, and the inclined Angle of the curved text is large, so the boundary box cannot fit the text area well in text detection. However, for Chinese seals, the Angle of text bending is difficult to be fitted with the commonly used text detection box, so this paper uses OpenCV to carry out polar coordinate transformation, and converts the original curved text into regular text so that the detection box is easier to fit for the next step of recognition. However, after polar coordinate transformation, some characters will be divided and thus cannot be recognized. This paper further proposes to use text detection to process the transformed boundary box, so that the characters can be accurately recognized without being divided after polar coordinate transformation. Through experiments, the recall rate of character recognition inside the official seal can reach 92.23%. However, only 27.32% of official seal characters without processing are recognized, which indicates that the recognition effect after transformation by polar coordinate method is good.
Bayesian minimum mean square error for transmissivity sensing

We address the problem of estimating the transmissivity of the pure-loss single-mode bosonic channel from the Bayesian point of view, i.e., when a prior probability distribution function (PDF) on the transmissivity is available. We compute the quantum limit of the Bayesian minimum mean square error of estimating transmissivity. Specifically, we consider two prior PDFs: the two-point distribution (relevant for reading from an optical drive) and the beta distribution (relevant for imaging a reflective pixelated scene). If the probe's mean photon number is an integer, for the two-point PDF we prove that the optimal probe is a Fock state and the optimal measurement is photon counting, while for the beta PDF our numerical results provide evidence on the optimality of the Fock state probe and photon counting. When the probe's mean photon number is any (nonnegative) real number, we conjecture the form of the optimal probe state and we study the performance of photon counting, which is a suboptimal yet practical measurement. Our methods can be applied for any prior PDF. We discuss how different precision metrics and priors on the parameter influence the quantum-optimal probe and measurement. To our knowledge, these results on transmissivity estimation with quantum-limited precision represent the first Bayesian analysis of the problem.
Fault detection of air quality measurements using artificial intelligence

In this work we use Artificial Intelligence (AI) for the detection of faults in air quality measurements. This is crucial in large air quality monitoring networks in particular were fault detection can be a complex and time consuming process. The proposed methodology encompasses several essential steps in anomaly detection. Data preprocessing ensures the quality and relevance of the data by applying techniques like data cleaning, outlier removal, and feature selection. The Isolation Forest model is trained using the pre-processed data, and appropriate hyperparameters are determined through cross-validation. Anomaly detection is performed using the trained model, allowing the identification of abnormal events or instances. The visualization of anomalies provides a clear representation of abnormal patterns, facilitating the interpretation and understanding of air quality data. The proposed methodology can help environmental agencies, researchers, and policymakers in identifying abnormal air quality events, enhancing the accuracy of monitoring systems, and facilitating timely interventions. This methodology can be applied to other industries also, to improve operations and reduce risk.
It’s a joke, not a dick. So don’t take it too hard”: online sexual harassment in Indian universities

Recently, while there has been some attention to the issues online harassment in higher education, the impacts of online sexual harassment have been lost within the broader focus. There is negligible research looking at these specific experiences within Indian universities. To address this gap, this paper explores three different but interconnected forms of online sexual harassment—image-based sexual abuse, online chat rooms, and trolling in the context of Indian universities. Following the works of Liz Kelly (1987) and Clare McGlynn, Erika Rackley and Ruth Houghton (2017), this paper establishes the importance of understanding online sexual harassment as a continuum of other forms of offline sexual violence having physical, mental, and financial impacts on survivors, deeply affecting their sense of safety. In doing so, this paper attempts to develop a materialist understanding of online sexual harassment in Indian universities in turn demonstrating the confluence of India’s patriarchal and casteist society and an authoritarian state who use technology as a powerful disciplining tool to push women and queer people out of digital public spaces. This research attempts to establish that this disciplining and silencing of women and queer people are essential for the spread of both techno-capitalism and Brahmanical Hindutva nationalism.
Credit Card Fraud Identification using Logistic Regression and Random Forest

Fraud is an ancient yet ever-changing profession. Because of the digitization of money, financial transactions, banks, fraudsters now have a limitless number of possibilities to perpetrate crime from behind a screen, anywhere around the world. Fraud has a broad influence, with direct ramifications for business and the economy. It is of great worry to cybercrime organizations as recent studies have proven that ML algorithms may successfully be utilized to identify fraudulent transactions in massive amounts of payment data. Such techniques may identify fraudulent transactions in real time, which human auditors may miss. In this research, we apply supervised ML algorithms to the issue of fraud identification by analyzing simulated financial transaction data that is available to the public. Our aim is to show how supervised ML methods may be utilized to successfully identify data with extreme class disproportion. By way of example, we show how exploratory analysis may be utilized to identify fraudulent from real purchases. We also show that Random Forest outperform Logistic Regression when applied to a clearly distinguished dataset.