Reviewerly/exHarmony huggingface dataset:
Revision of prioritized $$\mathcal {E}\mathcal {L}$$ ontologies

In this paper, we investigated the evolution of prioritized $$\varvec{\mathcal {E}}\varvec{\mathcal {L}}$$ ontologies in the presence of new information that can be certain or uncertain. We propose an extension of $$\varvec{\mathcal {E}}\varvec{\mathcal {L}}$$ description logic, named $$\varvec{\mathcal {E}}\varvec{\mathcal {L}}_{\bot }^{+}$$ , within possibility theory to encode such knowledge. This extension provides a natural way to deal with the ordinal scale and represent knowledge in a way that can handle incomplete information and conflicting data. We provided a polynomial algorithm for computing the possibilistic entailment. Then, we defined the evolution process at the semantic and syntactic levels. Interestingly enough, we show that the syntactical algorithm is done in polynomial time.
Modeling Financial Products and Their Supply Chains

The objective of this paper is to explore how novel financial datasets and machine learning methods can be applied to model and understand financial products. We focus on residential mortgage backed securities, resMBS, which were at the heart of the 2008 US financial crisis. These securities are contained within a prospectus and have a complex waterfall payoff structure. Multiple financial institutions form a supply chain to create the prospectuses. To model this supply chain, we use unsupervised probabilistic methods, particularly dynamic topics models (DTM), to extract a set of features reflecting community (topic) formation and temporal evolution along the chain. We then provide insight into the performance of the resMBS securities and the impact of the supply chain communities through a series of increasingly comprehensive models. First, models at the security level directly identify salient features of resMBS securities that impact their performance. We then extend the model to include prospectus level features and demonstrate that the composition of the prospectus is significant. Our model also shows that communities along the supply chain that are associated with the generation of the prospectuses and securities have an impact on performance. We are the first to show that toxic communities that are closely linked to financial institutions that played a key role in the subprime crisis can increase the risk of failure of resMBS securities. History: Olivia Sheng served as the senior editor for this article. Funding: This research was partially supported by National Science Foundation [Grant CNS1305368] and National Institute of Standards and Technology [Grant 70NANB15H194]. Data Ethics &amp; Reproducibility Note: No data ethics considerations are foreseen related to this article. The code capsule is available on Code Ocean at https://doi.org/10.24433/CO.8845455.v1 and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2020.0006 ).
Evaluating automatic sentence alignment approaches on English-Slovak sentences

Parallel texts represent a very valuable resource in many applications of natural language processing. The fundamental step in creating parallel corpus is the alignment. Sentence alignment is the issue of finding correspondence between source sentences and their equivalent translations in the target text. A number of automatic sentence alignment approaches were proposed including neural networks, which can be divided into length-based, lexicon-based, and translation-based. In our study, we used five different aligners, namely Bilingual sentence aligner (BSA), Hunalign, Bleualign, Vecalign, and Bertalign. We evaluated both, the performance of the Bertalign in terms of accuracy against the up to now employed aligners as well as among each other in the language pair English-Sovak. We created our custom corpus consisting of texts collected in 2021 and 2022. Vecalign and Bertalign performed statistically significantly best and BSA the worst. Hunalign and Bleualign achieved the same performance in terms of F1 score. However, Bleualign achieved the most diverse results in terms of performance.
MEGF-Net: multi-exposure generation and fusion network for vehicle detection under dim light conditions

Abstract Vehicle detection in dim light has always been a challenging task. In addition to the unavoidable noise, the uneven spatial distribution of light and dark due to vehicle lights and street lamps can further make the problem more difficult. Conventional image enhancement methods may produce over smoothing or over exposure problems, causing irreversible information loss to the vehicle targets to be subsequently detected. Therefore, we propose a multi-exposure generation and fusion network. In the multi-exposure generation network, we employ a single gated convolutional recurrent network with two-stream progressive exposure input to generate intermediate images with gradually increasing exposure, which are provided to the multi-exposure fusion network after a spatial attention mechanism. Then, a pre-trained vehicle detection model in normal light is used as the basis of the fusion network, and the two models are connected using the convolutional kernel channel dimension expansion technique. This allows the fusion module to provide vehicle detection information, which can be used to guide the generation network to fine-tune the parameters and thus complete end-to-end enhancement and training. By coupling the two parts, we can achieve detail interaction and feature fusion under different lighting conditions. Our experimental results demonstrate that our proposed method is better than the state-of-the-art detection methods after image luminance enhancement on the ODDS dataset.
Security of the Decoy-State BB84 Protocol with Imperfect State Preparation

The quantum key distribution (QKD) allows two remote users to share a common information-theoretic secure secret key. In order to guarantee the security of a practical QKD implementation, the physical system has to be fully characterized and all deviations from the ideal protocol due to various imperfections of realistic devices have to be taken into account in the security proof. In this work, we study the security of the efficient decoy-state BB84 QKD protocol in the presence of the source flaws, caused by imperfect intensity and polarization modulation. We investigate the non-Poissonian photon-number statistics due to coherent-state intensity fluctuations and the basis-dependence of the source due to non-ideal polarization state preparation. The analysis is supported by the experimental characterization of intensity and phase distributions.
Hybrid cloud storage system with enhanced multilayer cryptosystem for secure deduplication in cloud

Data deduplication is a crucial technique in the field of data compression that aims to eliminate redundant copies of recurring data. This technique has gained significant popularity in the realm of cloud storage due to its ability to effectively reduce storage requirements and optimize bandwidth utilization. To ensure the safeguarding of sensitive data while simultaneously facilitating deduplication, researchers have put forth the concept of convergent encryption as a potential solution. This technique involves encrypting the data prior to its outsourcing, thereby enhancing the confidentiality of the information. In this work, an earnest endeavor is undertaken to formally tackle the issue of authorized data deduplication, with the aim of enhancing data security. Our approach combines the Diffie-Hellman algorithm and symmetrical external decision to protect and popularize information, ensuring end-to-end encryption to encourage user adoption of cloud storage. The proposed model employs block-level deduplication and guarantees the randomness of ciphertexts by generating encryption keys using the Diffie-Hellman algorithm. This method effectively counters both internal and external brute-force attacks, enhancing data security while reducing computational costs. An extensive experimentation is carried out to demonstrate that our approach is particularly beneficial in scenarios with multiple privilege sets. Overall, the proposed model offers an elaborate framework that maintains data privacy and strengthens security measures, contributing to a more efficient and secure cloud-based document search.
Ultra-high density and nonvolatile nanophotonic convolution processing unit

Photonic convolution processing units are the core components of photonic neural networks (PNNs). Traditionally, for an N × N photonic convolution processing unit, it typically requires O(N2) or O(Nlog2N) cascaded photonic devices, which leads to larger sizes and lower density. Therefore, nanophotonic neural networks (N-PNN) with high density are highly attractive. In this work, we propose an ultra-high density and nonvolatile N-PNN scheme, requiring only a compact photonic device to represent all weight values in the N × N convolution kernel, which results in a computational density of more than 5 petaflop operations per second per square millimeter (POPS/mm2) under ideal conditions. The footprints of the proposed 2 × 2 and 3 × 3 nanophotonic convolution processing units are only 4 × 4 μm2 and 6 × 6 μm2, respectively. Moreover, based on these two types of units, the N-PNN achieves image classification and recognition capabilities comparable to traditional computers. In addition, Owing to its lower insertion loss, our approach holds great significance for the scalability and on-chip integration of large-scale N-PNNs. This facilitated the integration of N-PNNs with existing electronic systems.
Few-shot learning via weighted prototypes from graph structure

Few-shot learning is attracting extensive research because of its ability to classify only a few co-trainable samples. Current few-shot learning approaches focus on learning class prototypes representation to solve problems by a simple averaging approach, but this approach ignores intra-class differences. In this paper, we propose a new weighted prototype network for few-shot learning. Our model consists of two modules, feature extraction and prototype modification. We first construct graphs from the embeddings obtained from the feature extraction module. Then we fed these graphs into graph neural networks in order to explore the contribution of each sample to its class prototype from the graph structure. The experimental results on three benchmark datasets show that our proposed model is comparable to the state-of-the-art few-shot learning approach.
Scene context‐aware graph convolutional network for skeleton‐based action recognition

Abstract Skeleton‐based action recognition methods commonly employ graph neural networks to learn different aspects of skeleton topology information However, these methods often struggle to capture contextual information beyond the skeleton topology. To address this issue, a Scene Context‐aware Graph Convolutional Network (SCA‐GCN) that leverages potential contextual information in the scene is proposed. Specifically, SCA‐GCN learns the co‐occurrence probabilities of actions in specific scenarios from a common knowledge base and fuses these probabilities into the original skeleton topology decoder, producing more robust results. To demonstrate the effectiveness of SCA‐GCN, extensive experiments on four widely used datasets, that is, SBU, N‐UCLA, NTU RGB + D, and NTU RGB + D 120 are conducted. The experimental results show that SCA‐GCN surpasses existing methods, and its core idea can be extended to other methods with only some concatenation operations that consume less computational complexity.
A novel twin branch network based on mutual training strategy for ship detection in SAR images

Abstract There are inconsistent tasks and insufficient training in the SAR ship detection model, which severely limit the detection performance of the model. Therefore, we propose a twin branch network and design two loss functions: regression reverse convergence loss and classification mutual learning loss. The twin branch network is a simple but effective method containing two components: twin regression network and twin classification network. Aiming at the inconsistencies between training and testing in regression branches, we propose a regression reverse convergence loss (RRC Loss) based on twin regression networks. This loss can make multiple training samples in the twin regression branch converge to the label from the opposite direction. In this way, the test distribution can be closer to the training distribution after processing. For inadequate training in classification branch, Inspired by knowledge distillation, we construct self-knowledge distillation using a twin classification network. Meanwhile, our proposed classification mutual learning loss (CML Loss) enables the twin classification network not only to conduct supervised learning based on the label but also to learn from each other. Experiments on SSDD and HRSID datasets prove that, compared with the original method, the proposed method can improve the AP by 2.7–4.9% based on different backbone networks, and the detection performance is better than other advanced algorithms.
Early Detection Information System for Delayed Sputum Conversion Risk Factors

Key indicators of tuberculosis control programs, two of them are success and cure rate. Moreover, to measure response to TB treatment, sputum conversion in the first two months is widely used. Low social determinants, presence of co-morbidities, medication nonadherence are risk factors that usually late identified and caused sputum conversion delay. This research aims to design an innovation early-detection system for sputum conversion risk factors in TB patients supported by an expert system. This research is operational research, involving all head of TB programs and head of primary health centres in Bandar Lampung, Indonesia. Research variables consist of social determinants and risk factors of sputum conversion delay. Method and data collection refer to first and second stage of system development life cycle. Results of focus group discussion obtain input and output system design. Input system design consists of risk factors list and yes or no answers choices based on TB patient’s examination. Meanwhile, output system design are expert recommendations based on input system. This information system provides both immediate detection as well as immediate expert recommendations, which will finally increase the chances of sputum conversion in the first two months of TB patient’s treatment.
The Relationship Between Geological and Environmental Aspects with the Anomalies of Track Elements and Heavy Metal Elements in the Volcanic Area of the Muria Peninsula

The Muria Peninsula is a complex of volcanic activity located in the back arc of Sunda, which includes Kudus, Jepara, and surrounding districts. Volcanic activity on Mount Muria, can be used to study geological conditions, which were closely related to chemical elements. The presence of chemical elements in the river sediments of the Muria Peninsula was also influenced by anthropogenic heavy metal pollutants present in river deposits along the Muria Peninsula. The purpose of this study was to determine the relationship between the geological conditions and the environmental conditions of the Muria Peninsula with the anomaly of track elements and heavy metal elements. The methods used in this research were the pick test method, XRF geochemistry and the results were correlated into a geochemical anomaly map. The results showed that there were trace elements anomalies of Ce and Nd in Jepara area which were influenced by Muria shoshonitic rocks. Anomaly of Cu element was found in Jepara area, anomaly of Mn element was found in Jepara and Demak, and anomaly of Zn element was found in Jepara and Demak. Anomaly of Cu, M,n and Zn elements were high in upstream area and caused by land use of dry agriculture, paddy fields and settlement areas.
Forecasting Agricultural Seeds Sales System Using The Additive Holt-Winters Method

Buana Seed is an agricultural business sector that sells various kinds of agricultural seeds, such as: chilli seeds, Servo tomato seeds, and Ranti tomato seeds. The problem found was the sale of seeds that was not optimal, because the owner used the previous period's sales as a reference. Due to an error in determining the number of seeds to be sown, there is often an overstock or understock. An information system is needed to assist seeders in determining the number of seeds to be sown. The forecasting method used is the "Additive Holt-Winters" method where the data used is sales data for each seed from January 2019 to August 2021. This method is based on three elements, namely linear, trend and seasonal data elements by giving three weights respectively in their predictions, namely α, β, and γ. There were 10 combinations of alpha (α), beta (β), and gamma (γ) made in the calculation. The accuracy value will be calculated using MAPE. MAPE results from each combination are different. The results of this forecasting calculation will be a reference in determining the seeds to be sown so that sales become optimal.
Privacy-aware document retrieval with two-level inverted indexing

Abstract Previous work on privacy-aware ranking has addressed the minimization of information leakage when scoring top k documents, and has not studied on how to retrieve these top documents and their features for ranking. This paper proposes a privacy-aware document retrieval scheme with a two-level inverted index structure. In this scheme, posting records are grouped with bucket tags and runtime query processing produces query-specific tags in order to gather encoded features of matched documents with a privacy protection during index traversal. To thwart leakage-abuse attacks, our design minimizes the chance that a server processes unauthorized queries or identifies document sharing across posting lists through index inspection or across-query association. This paper presents the evaluation and analytic results of the proposed scheme to demonstrate the tradeoffs in its design considerations for privacy, efficiency, and relevance.
Identification of the relationship between rainfall intensity and the probability of urban flooding in Nganjuk District

Floods are hydrometeorological disasters that often occur in various regions, including in Nganjuk Regency. From 2019 to 2022, there were urban floods that inundated highways, markets and hospitals and disrupted community and government activities. Heavy rain is one of the causes of flooding. Rain intensity, duration and frequency (IDF) curves were constructed using rainfall data from six rain gauge stations. This curve is used to determine the 10-year and 25-year probabilities of PUH intensity and the probability of causing flooding in Nganjuk District. Rain intensity in Nganjuk Regency is PUH 10 and 25 with an average rain duration of 15-60 minutes which is included in the moderate rain category, namely 20-50 mm/day. The results of this analysis will become the basis for consideration in formulating policies and strategies for urban flood disaster prevention and management in Nganjuk Regency. Therefore, coordination between the city government and the government is needed to reduce the risk of urban flooding in Nganjuk Regency.
Information System for Orphan Data Management in Karangsari Village, Kalimanah District, Purbalingga Regency

An orphan is a term used to refer to someone who has been left by their parents while still underage. Providing support for orphans is a commendable activity. Supporting orphans has many virtues, as stated in the following Hadith: Narrated by Abu Ya'la and Thobrani, Shahih At Targhib Al Albani: "Whoever includes an orphan among two Muslim parents in their meals and drinks, providing for their needs, will surely enter Paradise.". The first step in supporting orphans is to find them. Searching for orphan data is difficult to accomplish if we rely solely on manual data collection. Sometimes the obtained data does not match the desired requirements. To help facilitate this step, an information system for managing orphan data is created. The aim is to simplify the management of orphan data and assist the community in searching for orphan data. The development of this orphan data management information system utilizes the waterfall method, which includes the stages of analysis, design, implementation, testing, and maintenance using the Laravel framework. The result of this research is a web-based information system for managing orphan data, with a case study in Karangsari Village, Kalimanah, Purbalingga Regency.
Geographic Information System Mapping of Public Works Infrastructure of Kebumen Regency

Geographic Information System (GIS) is an information system that combines graphical (visual) data with text data (attributes) of geographically referenced objects on the Earth's surface. GIS is widely used today in various fields, including analysis, industries, and individual activities such as finding locations or routes to specific destinations. In this research, GIS is used to manage the digitization of infrastructure mapping in Kebumen Regency. The development objective of the GIS is to process and analyze the spatial data of infrastructure locations to generate accurate information. The waterfall method is employed in the development of this system, encompassing stages of analysis, design, implementation, testing, and maintenance using the CodeIgniter framework. The outcome of this research is the Geographic Information System for Mapping Public Works Infrastructure in Kebumen Regency based on Leaflet. The GIS infrastructure of Kebumen Regency facilitates the public in accessing information about the existing infrastructure in the area. The testing results using the black box testing method demonstrate that the system aligns with the defined requirements. The objective of this research is to develop a Geographic Information System (GIS) that can manage and analyze the spatial data of infrastructure in Kebumen Regency, resulting in accurate information. The waterfall method is used in the development of this system, with stages including analysis, design, implementation, testing, and maintenance using the CodeIgniter framework. The outcome of this research is the Geographic Information System for Mapping Public Works Infrastructure in Kebumen Regency based on Leaflet. With the GIS infrastructure of Kebumen Regency, the community can easily access information regarding the infrastructure in the area. This system enables users to visualize the locations of infrastructure and obtain attribute or text data related to those infrastructure objects. Through testing using the black box testing method, the system has been proven to meet the predefined requirements. This indicates that the developed GIS is reliable and provides accurate information about the infrastructure in Kebumen Regency. Thus, the development of this GIS provides significant benefits in managing spatial data of infrastructure and delivering accurate information to the public. This research has successfully achieved its objectives and produced a Geographic Information System that can be utilized as a tool for infrastructure mapping and spatial data analysis in Kebumen Regency.