Reviewerly/exHarmony huggingface dataset:
Another Approach of DeFi: P2P Smart Contracts

The relevance of the chosen topic lies in the fact that in the modern world the role of technologies and alternative methods of providing banking services is constantly growing. This work is aimed at revealing the main indicators of the functioning of P2P lending, highlighting their advantages over classical centralized banks, as well as using smart contracts for implementation in the blockchain environment. The leading approach to the study of this problem is the analysis of the econometric and financial parameters of banks providing the P2P lending service (on the example of the Lending club company), the study of the smart contract functioning algorithm in P2P. The methodology for calculating credit risk was adapted for P2P lending, the main parameters affecting credit risk were disclosed, significant and insignificant indicators of the probability of default were calculated, an improved methodology was proposed for assessing the quality of a borrower based on a scoring model and applied in the blockchain environment.
Social Image–Text Sentiment Classification With Cross-Modal Consistency and Knowledge Distillation

Social media sentiment analysis, which aims to evaluate the attitudes of online users based on their posts, has attracted significant research attention due to its successful application in the field of social media monitoring. It is a beneficial way to utilize multimodal information uploaded by users in order to improve sentiment classification ability. However, existing multimodal fusion-based approaches continue to face difficulties due to the issues of between-modality semantic inconsistency and missing modality. To address these issues, we propose a cross-modal consistency modeling-based knowledge distillation framework for image–text sentiment classification of social media data. Specifically, we design a hybrid curriculum learning strategy to measure the semantic consistency of multimodal data, then gradually train all image–text pairs from easy to hard, which can effectively handle the massive amounts of noise caused by inconsistencies between image and text data on social media. Moreover, in order to alleviate the problem of missing images in unimodal posts, we propose a privileged feature distillation method, in which the teacher model additionally considers images as privileged features, to transfer the visual knowledge to the student model, thereby enhancing the accuracy for text sentiment classification. Extensive experiments conducted over three real-world social media datasets demonstrate the effectiveness and superiority of the proposed multimodal sentiment analysis model.
Transcript Alignment for Historical Handwritten Documents: The MiM Algorithm

Libraries contain a large number of digital images of handwritten documents of historical and cultural interest, and digital transcriptions are also available for some of them. The ability to trace back to the portion of the image that contains the handwritten text starting from the transcription can be essential for the study of the document by scholars in humanities, as well as for the development of modern technologies that greatly facilitate the search, indexing, and transcription of ancient documents. We propose a method to perform the transcription alignment automatically. The method analyzes images of handwritten text lines together with the corresponding transcription and performs alignment by analyzing the line alternatively from left to right and from right to left, making the method language-independent. Experiments on the Bentham Collection dataset have shown that the method can correctly align more than 75% of the text. We also show that, by using a GUI we have designed for the purpose, our method reduces the time for error-free alignment by more than 47% compared to the time required for manual alignment.
SA-YOLO: The Saliency Adjusted Deep Network for Optical Satellite Image Ship Detection

Ship detection from remote sensing images plays an important role in military and civilian fields. However, since the small size of ship targets and the interference of cloud cover, this task still suffers from great missed detection and false-alarm. To tackle these problems, a Saliency Adjusted YOLO (SA-YOLO) for optical satellite image ship detection is developed. First, due to the fact that the ship in low resolution imagery can be regarded as a salient object, we designed a saliency guided dense sampling layer (SDSL) to improve the spatial sampling of small ship targets. Secondly, the saliency region-aware convolution (SAConv) strategy is designed to improve the representation capability of salient regions and increase the attention of network to these regions. We validated the proposed method using more than 2000 remote sensing images from GF-1 satellite. The experimental results demonstrated that the proposed method obtained a better detection performance than the state-of-the-art methods.
Fair decision making via automated repair of decision trees

Data-driven decision-making allows more resource allocation tasks to be done by programs. Unfortunately, real-life training datasets may capture human biases, and the learned models can be unfair. To resolve this, one could either train a new, fair model from scratch or repair an existing unfair model. The former approach is liable for unbounded semantic difference, hence is unsuitable for social or legislative decisions. Meanwhile, the scalability of state-of-the-art model repair techniques is unsatisfactory.
Verification of the Impact of Central Bank Digital Currency (CBDC) Issuance on Net Interest Income of Vietnamese Commercial Banks

This study aims to predict the impact of Central Bank Digital Currency (CBDC) issuance on the net interest income (NII) of commercial banks in Vietnam – the main and most important source of revenue for financial institutions with the assumption that CBDCs issued by the State Bank of Vietnam would be widely accessible, coexist as a supplement to cash, and be viewed by consumers as an alternative to bank deposits, thereby the net interest income of commercial banks would be adversely affected. The predictive analysis with the panel data model is applied to this study to specifically assess the impact of the CBDC issuance on the NII of 13 typical commercial banks in Vietnam. The result shows that even in the backdrop of favorable growth in macroeconomic data, issuing CBDC will have a direct impact on percent cash and will slow commercial banks' NII growth in which large banks may be more vulnerable than smaller banks with higher return savings deposits. This opens up a quest for the future to continue to study and evaluate the impact on other performance indicators of the bank in comprehensively to identify adaptive solutions in the inevitable context of the digital economy.
Cuneiform Reading Using Computer Vision Algorithms

This paper presents a new method for computer-assisted recognition of horizontal strokes in photographs of cuneiform tablets with 90,52 % accuracy. The cuneiform script is the oldest attested writing system in the world, used for over three thousand years throughout the ancient Near East, primarily by the cultures of Mesopotamia (modern Iraq). It was impressed on clay tablets and engraved on stone slabs by writing strokes. Researchers have been trying to speed up the process of reading the tablets using different methods, as manual copying of the tablets and their transliteration is time consuming. This research, therefore, aims to recognize the elementary components, i.e., the strokes, of cuneiform signs from photographs of ancient cuneiform tablets, in order to enable effective OCR using the latest computer vision algorithms. The main difference between other approaches and ours is that we work directly with the two-dimensional photographs, instead of three-dimensional models, as there are many more 2D images available in public online repositories. The goal is to partly automate the process of identifying and reading cuneiform signs, thus speeding up the process of rediscovering these ancient texts and civilizations.
Using GANs to Generate Lyric Videos

Artificial intelligence (AI) technologies have become increasingly common in creative practices in recent years. The rising number of research initiatives that emerge at the intersection of AI and art prompts researchers and artists to analyze the creative and explorative applications of AI in the context of art. First, this paper describes a specific AI art piece, an AI-generated music video for a song called Initiation, illustrating how AI can be used for creative purposes. This art piece was created in the context of the opening of Esch2022, the European Capital of Culture. Then, the paper provides an overview of the potential implications of AI technologies on the general understanding and creation of art.
Predictive Models Using Supervised Neural Network for Pollutant Removal Efficiency in Petrochemical Wastewater Treatment

The important process in wastewater treatment is the removal of pollutants, and the dataset having so many features may cause difficulty training the data and predicting key variables. This work aims to propose set parameters through normalization techniques, feature selection techniques, and AI techniques. The datasets have 36 features and a key parameter, and experimental datasets contain 628. Constant factor, Z-score, and Min-max normalization are the normalization techniques used to normalize the petrochemical wastewater dataset. SelectKBest, ExtraTreeClassifier, PCA, and RFE are the feature selection techniques for data mining. Then finally done with AI implementation with the help of a supervised neural network technique called backpropagation neural network (BPNN).
Quantformer: Learning Extremely Low-precision Vision Transformers

In this article, we propose extremely low-precision vision transformers called Quantformer for efficient inference. Conventional network quantization methods directly quantize weights and activations of fully-connected layers without considering properties of transformer architectures. Quantization sizably deviates the self-attention compared with full-precision counterparts, and the shared quantization strategy for diversely distributed patch features causes severe quantization errors. To address these issues, we enforce the self-attention rank in quantized transformers to mimic that in full-precision counterparts with capacity-aware distribution for information retention, and quantize patch features with group-wise discretization strategy for quantization error minimization. Specifically, we efficiently preserve the self-attention rank consistency by minimizing the distance between the self-attention in quantized and real-valued transformers with adaptive concentration degree, where the optimal concentration degree is selected according to the self-attention entropy for model capacity adaptation. Moreover, we partition patch features in different dimensions with differentiable group assignment, so that features in different groups leverage various discretization strategies with minimal rounding and clipping errors. Experimental results show that our Quantformer outperforms the state-of-the-art network quantization methods by a sizable margin across various vision transformer architectures in image classification and object detection. We also integrate our Quantformer with mixed-precision quantization to further enhance the performance of the vanilla models.
Satellite MEC with Federated Learning: Architectures, Technologies and Challenges

Satellite communication has made great progress in recent years since it is characterized by wide information coverage and can support diverse types of users, which beneficially fulfills the demand of beyond 5G communications. Besides, mobile edge computing (MEC) technologies energize the edge devices with computational abilities to deal with the majority of training tasks without having to upload to the cloud server, which substantially enhances a system's efficiency. In satellite MEC, the raw data of edge users vested in different owners cannot be allowed to be shared, considering data privacy requirements. To address this, federated learning (FL) architecture can be applied to satellite MEC where only parameters and model updates can be transmitted, which avoids the interaction of raw data from diverse sources. In this article, we construct a FL-based satellite MEC architecture, followed by introducing its key techniques in the aspects of resource management and multi-modal data fusion. Furthermore, we study the data privacy and security protection on the FL-aided satellite MEC relying on a blockchain framework. Finally, we portray the challenges of FL-aided satellite MEC systems.
To Follow or Not to Follow: Understanding <i>Issue/Pull-Request Templates</i> on GitHub

For most Open Source Software (OSS) projects, issues and Pull-requests (PR) are the primary means by which stakeholders of a project report and discuss software problems and code changes, and their descriptions are important for people to understand them. To help ensure the informational quality of issue/PR descriptions, GitHub introduced the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">issue/PR template</i> feature, which pre-populates the description for anyone trying to open a new issue/PR. To better understand this feature, we report on a large-scale, mixed-methods empirical study of templates that explores contents, impacts, and perceptions. Our results show that templates typically contain elements to greet contributors, explain project guidelines, and collect relevant information. After template adoption, the monthly volume of incoming issues and PRs decreases, and issues have fewer monthly discussion comments and longer resolution duration. Although both contributors and maintainers positively rated the usefulness of templates from various aspects, they also reported challenges in using templates (e.g., excessive and irrelevant information request) and suggested potential improvements of the template feature (e.g., better user interaction and advanced automation). This work contributes to the informed use and targeted improvement of templates to enhance OSS practitioners’ collaboration and interaction.
Deep Learning Based Super-Resolution for Medical Volume Visualization with Direct Volume Rendering

Modern-day display systems demand high-quality rendering. However, rendering at higher resolution requires a large number of data samples and is computationally expensive. Recent advances in deep learning-based image and video super-resolution techniques motivate us to investigate such networks for high fidelity upscaling of frames rendered at a lower resolution to a higher resolution. While our work focuses on super-resolution of medical volume visualization performed with direct volume rendering, it is also applicable for volume visualization with other rendering techniques. We propose a learning-based technique where our proposed system uses color information along with other supplementary features gathered from our volume renderer to learn efficient upscaling of a low resolution rendering to a higher resolution space. Furthermore, to improve temporal stability, we also implement the temporal reprojection technique for accumulating history samples in volumetric rendering. Our method allows high-quality reconstruction of images from highly aliased input as shown in Fig. 1.
Improving Neural Machine Translation for Low Resource Languages Using Mixed Training: The Case of Ethiopian Languages

Neural Machine Translation (NMT) has shown improvement for high-resource languages, but there is still a problem with low-resource languages as NMT performs well on huge parallel data available for high-resource languages. In spite of many proposals to solve the problem of low-resource languages, it continues to be a difficult challenge. The issue becomes even more complicated when few resources cover only one domain. In our attempt to combat this issue, we propose a new approach to improve NMT for low-resource languages. The proposed approach using the transformer model shows 5.3, 5.0, and 3.7 BLEU score improvement for Gamo-English, Gofa-English, and Dawuro-English language pairs, respectively, where Gamo, Gofa, and Dawuro are related low-resource Ethiopian languages. We discuss our contributions and envisage future steps in this challenging research area.
Emergency Clinical Procedure Detection via Wearable Sensors

Communicating a patient’s state accurately during transfer from emergency medical technicians to hospital personnel is crucial for optimal care. Prior work demonstrated automated algorithms that combined wearable sensors with video data from cameras to detect clinical procedures and improve this information transfer. However, incorporating video requires task-or environment-specific installation mechanisms, raises privacy concerns, and is susceptible to occlusion and image noise. The presented approach detects clinical procedures using wearable sensors (i.e., inertial and electrophysiological) only and the procedures’ subtasks to mitigate the sensors’ signal variability to provide clinical procedure detection.
On the Subfield Codes of a Subclass of Optimal Cyclic Codes and Their Covering Structures

A class of optimal three-weight $$[q^k-1,k+1,q^{k-1}(q-1)-1]$$ cyclic codes over , with $$k\ge 2$$ , achieving the Griesmer lower bound, was presented by Heng and Yue [IEEE Trans. Inf. Theory, 62(8) (2016) 4501–4513]. In this paper we study some of the subfield codes of this class of optimal cyclic codes when $$k=2$$ . The weight distributions of the subfield codes are settled. It turns out that some of these codes are optimal and others have the best known parameters. The duals of the subfield codes are also investigated and found to be almost optimal with respect to the sphere-packing bound. In addition, the covering structure for the studied subfield codes is determined. Some of these codes are found to have the important property that any nonzero codeword is minimal. This is a desirable property which is useful in the design of a secret sharing scheme based on a linear code. Moreover, we present a specific example of a secret sharing scheme based on one of these subfield codes.
Neural Image Parts Group Search for Person Re-Identification

Employing partition strategy to explore fine-grained features has been verified to be beneficial for person re-identification in recent literature. However, existing methods primarily rely on expert experience to manually design various partition strategies, which may lead to a sub-optimal solution for fine-grained features exploration. In this paper, we propose a Neural Parts Group Search (NPGS) strategy that auto-searches the optimal parts group via evolutionary algorithm (EA) to facilitate the network to exploit the local details. And during search process, designing a high-quality search space is especially crucial for an efficient optimization. Considering the human top-down structure and the semantic coherence of parts, we design a coarse-to-fine parts search space (C2F-PSP) in NPGS, which effectively reduce the search complexity without the loss of parts expressivity. Additionally, since only employing the high-level semantic features is insufficient for the NPGS to search effective parts, we further develop an efficient feature aggregation strategy named hierarchical low-rank bilinear pooling that progressively integrates the high-level semantic property and the low-level fine-grained details to facilitate the NPGS to explore the fine-grained features. Furthermore, to relieve the interference of background during parts search process, we propose a novel Relational Attention Module (RAM) by exploiting the channel and spatial structural interdependence of pixels to strengthen the discriminative regions. Extensive experiments on the mainstream evaluation datasets demonstrate that our method outperforms the recent state-of-the-art Re-ID models.
Evaluation Metrics for Ontology Modules

A modular approach to ontology development, enables reuse and enhance maintainability. Ontology modules can either be extracted from a large ontology or built from scratch and then aligned to construct a large ontology. The evaluation of these modules is very important. However, upto now only one tool has been developed for this purpose and it does not consider the evaluation of the ontology modules developed from scratch. In this paper the list of evaluation metrics identified for the evaluation of both types of ontology modules is presented. The metrics have been grouped into six categories and also needful has been done to identify metrics which can be used to evaluate ontology modules developed from scratch.
Lung image segmentation based on Involution UNet model

The existing lung segmentation algorithms have problems such as insufficient feature extraction ability and low segmentation efficiency. To address this problem, we propose a new UNet-like model called In-UNet. In order to expand the perceptual field and reduce feature loss, we integrated the involution and Inception modules into U-Net. In addition, the segmentation efficiency of the network was improved by using the Mish activation function. The experimental results on the lung CT dataset show that the method can segment the lung parenchyma region effectively. It also has better segmentation effect than other segmentation networks.
Evaluating Machine Translation of Latin Interjections in the Digital Library of Polish and Poland-related News Pamphlets

In The Digital Library of Polish and Poland-related Ephemeral Prints from the 16th, 17th and 18th Centuries a small fraction of items contains manually created Latin–Polish dictionaries explaining Latin fragments injected into Polish content. At the same time, rapid development of machine translation creates new opportunities for creating such dictionaries automatically. In this paper, we verify whether existing translation solutions are already capable of generating useful results in this Latin-Polish setting. We investigate two systems available for this language pair: the familiar Google neural engine and the GPT-3 model, then we test the translation of isolated and context-embedded phrases and evaluate its results with both automatic and human metrics: BLEU and White’s 5-point scale of adequacy and fluency.
APS-Net: An Adaptive Point Set Network for Optical Remote-Sensing Object Detection

Oriented object detection in optical remote-sensing images has been a challenging task due to arbitrary orientations and densely packed distribution of objects. Specifically, most existing methods lack adaptivity when regressing objects with different shapes and orientations. Although the point set representation is relatively flexible, the initial distribution of the point set is fixed in advance. In addition, some models based on the point set cannot get high location precision of points, affecting the bounding box generation. In this letter, we propose an Adaptive Point Set Network (APS-Net) for optical remote-sensing object detection, including three improvements. First, we propose the initial distribution learner (IDL) to learn the optimal initial aspect ratio, which helps the point set fit the object’s shape well. Second, we design the uncertainty measurement module (UMM), which considers the uncertainty of point location to improve location precision. Third, we introduce the local outlier factor (LOF) in the loss to punish outlier points more reasonably. Extensive experiments demonstrate that our proposed model achieves state-of-the-art performance on three commonly used datasets (i.e., DOTA-v1.0, UCAS-AOD, and HRSC2016) in the remote-sensing field.