Reviewerly/exHarmony huggingface dataset:
Analysis of Social and Environmental Vulnerability Against Landslides Using Scoring and Weighting Methods at Probolinggo District

Abstract Landslides phenomena majority occur in area which is adjacent with mountain or hill where the soil and rock structure move down the slopes. It is caused by land instability such as loss of plants or trees in highland which have function of binding soil grains while maintaining pores of soil beneath. Based on data from the Regional Disaster Management Agency (BPBD), one of districts/cities in East Java is categorized as prone against landslides is at Probolinggo District where 108 disaster occurred over 2021. Landslide disasters certainly have an impact on the environment and social aspects in vicinity. Therefore, this research aim to assess areas which has prone level against landslides in terms of social and environmental vulnerabilities through analysis of potential for landslide disasters in Probolinggo District. The research using weighting parameter methods in order to obtain the level of social and environmental vulnerability. It is divided into four categories namely low, medium, high, and very high. Maron sub-district become the largest area with very high level of social vulnerability about 33.7% of the area of one sub-district. Meanwhile, in area of environmental vulnerability which is dominated by very high levels of vulnerability is at Krucil sub-district about 88.5% of the area of one sub-district.
From Campus to Cloud

Arriving on a new campus in a new position, I found myself immediately tasked with migrating a digital institutional repository. My experiences revealed how, through careful planning, academic libraries with limited resources can make effective use of relatively inexpensive cloud services supporting their technology infrastructure projects.
Accessing inaccessible information via quantum indistinguishability

Abstract In this paper we present and analyze an information-theoretic task that consists in learning a bit of information by spatially moving the ‘target’ particle that encodes it. We show that, on one hand, the task can be solved with the use of additional independently prepared quantum particles, only if these are indistinguishable from the target particle. On the other hand, the task can be solved with the use of distinguishable quantum particles, only if they are entangled with the target particle. Our task thus provides a new example in which the entanglement apparently inherent to independently prepared indistinguishable quantum particles is put into use for information processing. Importantly, a novelty of our protocol lies in that it does not require any spatial overlap between the involved particles. Besides analyzing the class of quantum-mechanical protocols that solve our task, we gesture towards possible ways of generalizing our results and of applying them in cryptography.
Model Concept Learning Sebagai Penunjang Keaktifan Pada Aktivitas Pembelajaran Siswa

This study aims to analyze the application of the concept learning model in increasing student learning activeness in PAI learning. This research is quantitative, with a Time-Series type experimental design (time-series). The results of the analysis of this study indicate that the application of the concept learning model in PAI learning can increase the activeness of student learning, as seen from the more students who experience an increase in learning activeness after the application of the concept learning model as many as 29 students out of 30 students. The advantages of the application process are: 1) Students are more motivated to learn; 2) Students find it easier to understand the material; (3) The level of students' understanding of teaching materials is getting better. The constraints and drawbacks are; 1) requires quite a lot of time; 2) The teacher must be more intensive because the teacher is no longer the one providing the information but working together with the students to manage the class to find the basic concepts of the material to be studied; 3) In learning activities will tend to be dominated by active students only.
Influence of Blockchain on the Auditing Profession

Blockchain technology has become indispensable in finance and IT, profoundly transforming transactional data access, storage, and reporting. It also unlocks groundbreaking applications beyond its original scope. However, regulation remains crucial for organizations. Audits are vital to ensure transparent communication of business strategies and effective governance of information technology and financial resources. This chapter explores auditing's goals, significance in organizations, blockchain's features, diverse use cases, and the regulatory and ethical challenges it poses. Furthermore, it analyzes blockchain governance and its disruptive impact on traditional auditing practices and procedures.
Auditing Agile Release Management

The management of software releases is a significant element for most businesses. However, many organizations suffer from poor release management due to rapid demand for the back-to-back release of services or products. An efficient release management procedure can support frequent releases with minimal risks. Therefore, auditing a release management process is critical to ensure the changes are delivered consistently every time and with the same level of quality. The method introduced in this chapter identifies different risks companies encounter due to poor release management. The solution to this issue is to present a control activity lifecycle that involves auditors to detect vulnerabilities at each level of the agile development cycle and offer solutions to the risks discovered during the assessment. Adopting this control activity lifecycle for agile release management will significantly decrease exposure to risks that can harm agile projects, protecting the firm's business applications, guaranteeing quality, and enhancing its reputation.
Cataloging Library Resources in a South African Public Library: A Case Study of Professional Knowledge

AbstractThe paper examines the knowledge and practices of catalogers at the City of Cape Town Metropolitan Public Libraries (CCTML). The study aims to understand the extent to which catalogers understand current and emerging cataloging practices and to make recommendations for improving practice. The extent of access and the use of library resources depends greatly upon the quality of the library catalog. The following research questions were formulated: what cataloging knowledge do catalogers of CCTML have? How is cataloging carried out in CCTML? What challenges do catalogers of CCTML experience in cataloging library resources? Qualitative approach and case study design were employed. The population of the study was made of 6 catalogers. Data was collected through the focus group interview. Findings indicated that catalogers had knowledge of cataloging standards. They downloaded the catalog records from the OCLC although, they also created original catalog records in case they did not find the matching manifestations on OCLC. Moreover, they experienced challenges of assigning subject headings for non-roman scripts, allocation of the correct classification notation and the advanced use of the cataloging standards. The study recommends the exchange programmes in cataloguing, use of technological tools, artificial intelligence in cataloging.Keywords: Catalogcatalogingcataloging knowledgeartificial intelligence Disclosure statementNo potential conflict of interest was reported by the author(s).
Three-Layer Denoiser: Denoising Parallel Corpora for NMT Systems

In recent years, the field of Machine Translation has witnessed the emergence and growing popularity of Neural Machine Translation (NMT) systems, especially those constructed using transformer architectures. A critical factor in developing an effective NMT model is not just the volume, but also the quality of data. However, removing noise from parallel corpora, which involves the intricacies of two distinct languages, presents a significant challenge. In this paper, we introduce and assess a method for eliminating such noise, known as the Three-layer Denoiser. The first layer of this process, termed textual normalization, involves data cleaning using predetermined rules. The second layer incorporates a text feature extractor and a binary classifier, while the third layer evaluates the quality of sentence pairs using a pre-trained transformer model. Experimental results, obtained from training various NMT models with both clean and raw data, indicate a rise of up to 2.64 BLEU points in the models trained with sentence pairs that were filtered by the Denoiser.
Improving performance in small datasets via pre-trained architectures based on VGGFace and VGGFace2 datasets

Face recognition algorithms have achieved outstanding results under controlled conditions, mainly through deep learning computational techniques. However, the performance under uncontrolled conditions still needs improvement. Facial recognition systems in real-world problems often deal with uncontrolled conditions such as occlusion and pose and lighting variations, which degrade recognition performance. Despite such limitations, with enough training samples, it is still possible to reach high performance via existing deep-learning architectures. Nevertheless, the lack of training samples often results in low recognition accuracy in this domain. In this study, it has been shown that utilizing pre-trained models for the facial recognition task can enhance performance significantly in scenarios with a low number of training images available.
A strategy for interpreting and visualizing the results of matrix-trifactorization-based coclustering algorithms

Information yielded by unsupervised learning is often hard to interpret due to the lack of defined labels. To overcome this, we propose and illustrate a strategy for interpreting and visualizing the results of coclustering algorithms based on trifactorization. Our method consists of three steps: (1) vector space visualization; (2) cluster characterization by top documents/words; and (3) cocluster characterization by comparing top words between different clusters. The latter allows exploring the resulting clusters in a way which considers the relationship between attribute cluster and data cluster for every data cluster, instead of just the data cluster with the highest association with this attribute cluster. We illustrate the use of our method for the Non-negative Block Value Decomposition on a dataset of scientific abstracts.
Distributional Safety Critic for Stochastic Latent Actor-Critic

When employing reinforcement learning techniques in real-world applications, one may desire to constrain the agent by limiting actions that lead to potential damage, harm, or unwanted scenarios. Particularly, recent approaches focus on developing safe behavior under partial observability conditions. In this vein, we develop a method that combines distributional reinforcement learning techniques with methods used to facilitate learning in partially observable environments, called distributional safe stochastic latent actor-critic (DS-SLAC). We evaluate the DS-SLAC performance on four Safety-Gym tasks and DS-SLAC obtained results better than those reached by state-of-the-art algorithms in two of the evaluated environments while being able to develop a safe policy in three of them. Lastly, we also identify the main challenges of performing distributional reinforcement learning in the safety-constrained partially observable setting.
Correlation of multiplicative functions over Fq[x]$\mathbb {F}_q[x]$: A pretentious approach

Abstract Let denote the set of monic polynomials of degree n over a finite field of q elements. For multiplicative functions , using the recently developed “pretentious method,” we establish a “local‐global” principle for correlation functions of the form where are fixed polynomials. These results were then applied to find limiting distributions of sums of additive functions. We also study correlations with Dirichlet characters and Hayes characters. As a consequence, we give a new proof of a function field analog of Kátai's conjecture that states that if the average of the first divided difference of a completely multiplicative function whose values lie on the unit circle is zero, then it must be a Hayes character. We further extend this result to pairs and triplets of completely multiplicative functions.
Developing a Machine Learning Algorithm for Improved Management of Congestive Heart Failure Patients in the Emergency Department

Background and aim: Congestive heart failure is a prevalent and serious condition that poses significant challenges in the emergency department setting. Prompt and accurate management of congestive heart failure patients is crucial for improving outcomes and optimizing resource utilization. This study aims to address these challenges by developing a machine learning algorithm and comparing it to a traditional logistic regression model that can assist in the triage, resource allocation, and long-term prognostication of congestive heart failure patients. Methods: In this investigation, we used the MIMIC-III database, a publicly accessible resource containing patient data from ICU settings. Traditional logistic regression, along with the robust XGBoost and random forest algorithms, was harnessed to construct predictive models. These models were built using a range of pretreatment clinical variables. To pinpoint the most pertinent features, we carried out a univariate analysis. Ensuring robust performance and broad applicability, we adopted a nested cross-validation approach. This method enhances the precision and validation of our models by implementing multiple cross-validation iterations. Results: The performance of machine learning algorithms was assessed using the area under the receiver operating characteristic curve (AUC). Notably, the random forest algorithm, despite having lower performance among the machine learning models still demonstrated significantly higher AUC than traditional logistic regression. The AUC for the XGBoost was 0.99, random forest 0.98, while traditional logistic regression was 0.57. The most important pretreatment variables associated with congestive heart failure include total bilirubin, creatine kinase, international normalized ratio (INR), sodium, age, creatinine, potassium, gender, alkaline phosphatase, and platelets. Conclusion: Machine learning techniques utilizing multiple pretreatment clinical variables outperform traditional logistic regression in aiding the triage, resource allocation, and long-term prognostication of congestive heart failure patients in the intensive care unit setting using MIMIC III data.
Statistical Modeling of Shadows in SAR Imagery

Shadows are a special distortion in synthetic aperture radar (SAR) imaging. They often hamper proper image understanding and target recognition but also offer useful information, and therefore, the statistical modeling of SAR image shadows is imperative. In this endeavor, we systematically deduced the statistical models of shadows in multimodal SAR images, including single-look intensity and amplitude images and multilook intensity and amplitude images in a real domain and complex domain, respectively. In particular, for the filtered SAR image shadow, we introduced the generalized extreme value (GEV) distribution to characterize its statistics. We carried out an experiment on shadows in a real SAR image and conducted chi-square goodness-of-fit tests on the deduced models. Furthermore, we compared the deduced statistical models of shadows with state-of-the-art statistical models of SAR imagery. Finally, suggestions are given for selecting the optimal statistical model of shadows in multimodal SAR images.
A dual-path instance segmentation network based on nuclei contour in histology image

Abstract Accurate segmentation of nuclei in histology images is essential for digital pathology. However, previous work should have considered integrating nuclei contour information into network learning. The main problems are (1) nuclei contour information needs to be fully considered, resulting in inaccurate spatial location of nuclei. (2) Small nuclei in dense regions or irregularly shaped nuclei cannot be segmented. (3) Mainstream networks consider more long-distance semantic information and larger receptive fields and do not consider the fusion of feature maps with different semantics. To solve the above problems, we have proposed a contour-based dual-path instance segmentation network. Firstly we reconstructed the nuclei contour information using algorithms and morphological manipulations. Provide input for subsequent contouring networks. Then we designed a dual-path network. It can extract nuclei features and contour features independently in the encoding stage and fuse the feature maps at different scales in the decoding stage. In the decoding stage, we use the attention module with a newly designed fusion mechanism, which allows us to fuse different semantics of feature maps for simple and efficient fusion. Finally, using the watershed algorithm, we use the network segmentation results to get the instance segmentation results of the nuclei. We used four publicly available datasets, including Kumar, CPM-15, CPM-17, and TNBC, which contain the nuclei of many organs and different forms of nuclei in the human body. Compared with the mainstream methods, we obtained the best AJI metrics, which are 0.656,0.717, and 0.602. In conclocess can effectively improve the performance of the nuclei instance segmentation network. It can accurately locate the scattered nuclei in space with small segmented nuclei in dense regions.
Collaborative Filtering Based on Rough C-Means Clustering with Missing Value Processing

A significant problem in collaborative filtering (CF) tasks is that the data often contains many missing values. Conventional rough clustering-based CF employs single imputation methods, but this can introduce distortion into the data by substituting equable values that differ from the original ones, leading to potential biases in the results. Moreover, using the whole data strategy, in which rows containing missing values in the data matrix are deleted, to handle missing values leads to a problem where, because most of the data in CF tasks consists of missing values, nearly all rows are deleted, making the analysis challenging. Therefore, we propose a method, referred to as RCM-PDS, which introduces the partial distance strategy (PDS) into RCM. The goal of this approach is to perform clustering using the original data matrix with missing values without prior imputation. Additionally, we propose its application to CF as RCM-PDS-CF. Furthermore, we validate its recommendation performance through numerical experiments using a real-world dataset.
TouriER: Temporal Knowledge Graph Completion by Leveraging Fourier Transforms

In recent times, numerous studies on static knowledge graphs have achieved significant advancements. However, when extending knowledge graphs with temporal information, it poses a complex problem with larger data size, increased complexity in interactions between objects, and a potential for information overlap across time intervals. In this research, we introduce a novel model called TouriER, based on the MetaFormer architecture, to learn temporal features. We also apply a data preprocessing method to integrate temporal information in a reasonable manner. Additionally, the utilization of Fourier Transforms has proven effective in feature extraction. Through experiments on benchmark datasets, the TouriER model has demonstrated better performance compared to well-known models based on standard metrics.
Digital learning in Saudi University: evaluating digital transformation post Covid-19

Digital transformation is the use of electronic technology in teaching-learning activities during post-COVID-19 in Saudi Arabia. As a result, this paper looks at the percentage of development of digital transformation platforms acceptance in Saudi Arabia's. This research offers a novel contribution to the field of digital learning by providing efficient and detailed analyses of digital transformation, broadening the comprehension of the development of digital technologies in the platforms of education sectors, focusing on Saudi Arabia as a unique context. This research method is to describe the personal data of the study sample, frequencies and percentage statistical methods used. To test the reliability of the questionnaire, the study adopts Cronbach’s Alpha factor. To measure the rating scale of each statement in the questionnaire in the Five Likert Scale, a relative importance index is used. The main motive of the study is to prove statements such as the conventional learning issue has been reduced by digital learning. The conclusions of this article reveal Saudi Arabia's educational sector's attitude towards emerging technology. This study result will investigate the universities’ efforts to establish a rationalisation to enhance operating efficiencies through research by incorporating data analysis from a digital platform.
Why Inverse Layers in Pavement? Why Zipper Fracking? Why Interleaving in Education? a General Explanation

In many practical situations, if we split our efforts into two disconnected chunks, we get better results: a pavement is stronger if instead of a single strengthening layer, we place two parts of this layer separated by no-so-strong layers; teaching is more effective if instead of concentrating a topic in a single time interval, we split it into two parts separated in time, etc. In this paper, we provide a general explanation for all these phenomena.
Time to Absorption in Markov Chains as a Mixture Distribution of Hypo-Exponential Distributions

Abstract We given an elementary proof that in a Markov chain with absorbing states, and positive probability of absorption at some time <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mi>t</m:mi> <m:mo>&gt;</m:mo> <m:mn>0</m:mn> </m:mrow> </m:math> {t&gt;0} , time to absorption follows a mixture distribution of hypo-exponential random variables. We use this fact to show that early approximations of such a distribution yield the length of the shortest path from an initial state to an absorbing state. Thus different Markov chains with the same distance of shortest paths can yield identical first order approximations. Our work is motivated by the classical Armitage and Doll model of carcinogenesis.
Inference Problem in Probabilistic Multi-label Classification

In multi-label classification, each instance can belong to multiple labels simultaneously. Different evaluation criteria have been proposed for comparing ground-truth label sets and predictions. Probabilistic multi-label classifiers offer a unique advantage by allowing optimization of different criteria at prediction time, but they have been relatively underexplored due to a shortage of insights into inference complexity and evaluation criteria. To shrink this gap, we present a generic approach for developing polynomial-time inference algorithms for a family of criteria and discuss the potential (dis)advantages of some commonly used criteria. Finally, we envision future work aimed at providing a comprehensive understanding of inference complexity and criteria selection.