Reviewerly/exHarmony huggingface dataset:
From Asset Flow to Status, Action, and Intention Discovery: Early Malice Detection in Cryptocurrency

Cryptocurrency has been subject to illicit activities probably more often than traditional financial assets due to the pseudo-anonymous nature of its transacting entities. An ideal detection model is expected to achieve all three critical properties of early detection, good interpretability, and versatility for various illicit activities. However, existing solutions cannot meet all these requirements, as most of them heavily rely on deep learning without interpretability and are only available for retrospective analysis of a specific illicit type. To tackle all these challenges, we propose Intention Monitor for early malice detection in Bitcoin, where the on-chain record data for a certain address are much scarcer than other cryptocurrency platforms. We first define asset transfer paths with the Decision Tree based feature Selection and Complement to build different feature sets for different malice types. Then, the Status/Action Proposal module and the Intention-VAE module generate the status, action, intent-snippet, and hidden intent-snippet embedding. With all these modules, our model is highly interpretable and can detect various illegal activities. Moreover, well-designed loss functions further enhance the prediction speed and the model’s interpretability. Extensive experiments on three real-world datasets demonstrate that our proposed algorithm outperforms the state-of-the-art methods. Furthermore, additional case studies justify that our model not only explains existing illicit patterns but also can find new suspicious characters.
GuaRD: Guaranteed robustness of image retrieval system under data distortion turbulence

Image search systems could be endangered by adversarial attacks and data perturbations. The image retrieval system can be compromised either by distorting the query or hacking the ranking system. However, existing literature primarily discusses attack methods, whereas the research on countermeasures to defend against such adversarial attacks is rare. As a defense mechanism against the intrusions, quality assessment can complement existing image retrieval systems. "GuaRD" is proposed as an end-to-end framework that uses the quality metric as a weighted-regularization term. Proper utilization and balance of the two features could lead to reliable and robust ranking; the original image is assigned a higher rank while the distorted image is assigned a relatively lower rank. Meanwhile, the primary goal of the image retrieval system is to prioritize searching the relevant images. Therefore, the use of leveraged features should not compromise the accuracy of the system. To evaluate the generality of the framework, we conducted three experiments on two image quality assessment(IQA) benchmarks (Waterloo and PieAPP). For the first two tests, GuaRD achieved enhanced performance than the existing model: the mean reciprocal rank(mRR) value of the original image predictions increased by 61%, and the predictions for the distorted input query decreased by 18%. The third experiment was conducted to analyze the mean average precision (mAP) score of the system to verify the accuracy of the retrieval system. The results indicated little deviation in performance between the tested methods, and the score was not effected or slightly decreased by 0.9% after the GuaRD was applied. Therefore, GuaRD is a novel and robust framework that can act as a defense mechanism for data distortions.
Identification of a spatial distribution threshold for the development of a solar radiation model using deep neural networks

Abstract We propose an approach to develop a solar radiation model with spatial portability based on deep neural networks (DNNs). Weather station networks in South Korea between 33.5–37.9° N latitude were used to collect data for development and internal testing of the DNNs, respectively. Multiple sets of weather station data were selected for cross-validation of the DNNs by standard distance deviation (SDD) among training sites. The DNNs tended to have greater spatial portability when a threshold of spatial dispersion among training sites, e.g. 190 km of SDD, was met. The final formulation of the deep solar radiation (DSR) model was obtained from training sites associated with the threshold of SDD. The DSR model had RMSE values &lt;4 MJ m −2 d −1 at external test sites in Japan that were within ±6° of the latitude boundary of the training sites. The relative difference between the outputs of crop yield simulations using observed versus estimated solar radiation inputs from the DSR model was about 4% at the test sites within the given boundary. These results indicate that the identification of the spatial dispersion threshold among training sites would aid the development of DNN models with reasonable spatial portability for estimation of solar radiation.
Provably minimum data complexity integral distinguisher based on conventional division property

Division property is an effective method for finding integral distinguishers for block ciphers, performing cube attacks on stream ciphers, and studying the algebraic degree of boolean functions. One of the main problems in this field is how to provably find the smallest input multiset leading to a balanced output. In this paper, we propose a new method, using the division property, to find integral distinguishers for permutation functions and block ciphers, with provably-minimum data complexity, in the conventional division property model. The new method is based on a precise and efficient analysis of the target output bit’s algebraic normal form. We examine the proposed method on LBlock, TWINE, SIMON, Present, Gift, and Clyde-128 block ciphers. Although in most cases, the results are consistent with the distinguishers reported in previous work, their optimality is proved, in the conventional division property model. Moreover, the proposed method can find distinguishers for 8-round Clyde-128 with less data complexity than previously reported. Based on the proposed method, we also develop an algorithm capable of determining the maximum number of balanced output bits for integral distinguishers with a certain number of active bits. Accordingly, for the ciphers under study, we determine the maximum number of balanced bits for integral distinguishers with data complexities set to minimum and slightly higher, resulting in improved distinguishers for Gift-64, Present, and SIMON64, in the conventional model.
Quantum and classical field scattered on a single two-level system

In many problems, the scattering amplitudes of a weak coherent pulse are almost equivalent to the ones of a single propagating photon. We thoroughly compare the scattering of (i) a short microwave coherent pulse from a rf generator and (ii) a vacuum-photon coherent superposition from the two-level emitter, both directed to a single two-level system---the probe. To do that, we use two superconducting qubits to implement emitter and probe, both strongly coupled to the same waveguide. However, with the use of a magnetic circulator we couple the field from the emitter to the probe without reverse backaction, thereby working with a cascaded atomic system implemented in a waveguide-QED setup. By measuring the dynamics of the scattered field, we find a certain discrepancy between two cases, confirmed by analytical and numerical study. Particularly, we find the optimal amplitude ${\mathrm{\ensuremath{\Omega}}}_{*}$ of classical pulse mimicking the superposition from the emitter, for which the difference becomes very small (but nonvanishing) and is almost unavailable to measure in practice.
Relationship Between Linguistic Complexity and MT Errors in the Context of Inflectional Languages

The quality produced by the MT tool varies, from very high to very low, depending on the intrinsic linguistic features of the source and target languages. The more lexical words the text content, the more information contains. Lexical richness is a multidimensional construct for assessing language development. In our study, we apply this concept for assessing the quality of machine translation. We attempt to link lexical richness with types of errors occurring in machine translation. We focus on the identification of the relationship between five types of MT errors and examined measures of language complexity. We used Goodman and Kruskal’s gamma to determine which measures of language complexity are associated with the MT error types. The results revealed that not all measures of lexical richness are associated with the MT errors in each examined error category. We showed that readability does not associate with an error rate, namely with error categories used in the present study.
The Task of Generating Text Based on a Semantic Approach for a Low-Resource Kazakh Language

In this article, the authors consider the problem of text generation for low- resource languages, using the Kazakh language as an example, based on semantic analysis. Machine learning method is used in the generation of text documents and sources in the Kazakh language. First, semantic analysisis performed, the number of words in the given text, the number of stop words, the number of symbols, etc. Then the TF-IDF algorithm is used to find the semantically important words of the text. Annotation of the given text by means of semantic analysis. And at the end, generation of text with advanced semantic analysis. A corpus for the Kazakh language was prepared for experiments and research. GPT-3 and NLG are used in the process of generation. Generation by means of semantic analysis of the text gives us some great opportunities. The Recurrent Neural Network (RNN) method is used during generation.Generation gives us a lot of opportunities, including not spending time on unnecessary information. It will provide an article or short text related to the keywords you searched for. The description of the developed approach and practical results of experiments are presented.
Automatic Speech Recognition Improvement for Kazakh Language with Enhanced Language Model

Last time there are unbelievable results in Natural Language Processing(NLP) and Automatic Speech Recognition(ASR). As a result, everybody can use smart search engines such as ChatGPT, smart voice assistants such as Siri, Alexa and more. But these opportunities are available only to the people who can use English or other common languages. For people who use low-resource languages these products are not available. As collection of transcribed data is time consuming and expensive process, scientists search ways of implementing reliable ASR models for low-resource languages. One of ASR improving methods in the case of lack of data is the use of external language model built on text larger than text in the entire dataset. And use this language model in the decoding process. As Kazakh language is also one of low-resource languages it is was decided to test this approach for kazakh language with different language models like Sequential RNNLM and Transformer LM. Inclusion of language model trained on bigger dataset allowed to decrease error values especially for Word Error Rate (WER). The best result was obtained with Transformer LM, WER was decreased to 7.2%.
Generation of Arabic Commonsense Explanations

Generating natural language explanations is a challenging task in natural language processing (NLP). With recent advancements in deep learning and language modeling techniques increased attention toward explanation generation. However, generating explanations based on commonsense reasoning remains a distinct and challenging problem. While many researchers have explored automatic explanation generation using deep learning approaches, no work has been done on generating Arabic explanations. This paper addresses this issue by presenting the Arabic machine translation of the Explanations for CommonsenseQA (Arabic-ECQA) and Open Mind Common Sense (Arabic-OMCS) datasets and fine-tuning the pre-trained AraGPT-2 model to automatically generate Arabic explanations. The performance of the fine-tunes AraGPT-2 models is evaluated using STS, METEOR, and ROUGE scores. To the best of the authors knowledge, this work is the first of its kind to generate Arabic explanations. To accelerate research on Arabic NLP, we make the datasets code publicly available ( https://github.com/MohamedELGhaly/Arabic-ECQA ).
An Effectiveness of Repeating a Spoken Digit for Speaker Verification

In recent years, there has been significant research in speaker verification, and the use of deep neural networks has dramatically enhanced the performance of speaker verification systems. This paper aims to investigate the effect of a predefined passphrase on the system’s performance, using the state-of-the-art ECAPA-TDNN model as a speaker modeling technique. Our study focuses on discovering dominant passphrases from spoken digits through text-dependent speaker verification trial types. By comparing the performance of spoken digits and considering their pronunciations, we can analyze the influence of passphrases in the human voice on the speaker verification system. Furthermore, we identify that repeating incrementally up to a certain number of times leads to improvements in the accuracy of the system. Overall, our study has significant implications for advancing speaker authentication systems, especially systems deployed on embedded devices, which require low computation resources and need to be optimized in many aspects.
Nested Semisupervised Learning for Cross-Note Abbreviation Detection in Vietnamese Clinical Texts

Abbreviation detection in clinical texts is popular and significant due to its contribution to enhancing readability and shareability of electronic medical records (EMRs). Nonetheless, it is limited to low-resource languages like Vietnamese because there is no available labeled dataset for the task. More development is thus needed to handle this task on Vietnamese clinical texts. On the other hand, there are many different note types where abbreviations are generated and used by many various groups of physicians, nurses, and other stakeholders. This fact leads to the necessity of processing a wide diversity of clinical texts for abbreviation detection. At this moment, none of the existing works takes into account the context where abbreviation detection is asked for the clinical texts that belong to one note type, unfortunately with the availability of the labeled clinical texts of another note type. This challenge results in a so-called cross-note abbreviation detection task in our work. In such a context, we address this task on Vietnamese clinical texts by proposing nested semisupervised learning. Our resulting Nested-SSL method is capable of detecting abbreviations in real Vietnamese clinical texts effectively. It is based on an existing semisupervised learning method and then boosts the core semisupervised learning process by a fold-based enhancement scheme in favor of F-measure of the minority class. In the empirical evaluation with real EMRs, Nested-SSL always outperforms its base semisupervised learning method and some existing ones. Its better performance lays the foundations for effectively preprocessing Vietnamese clinical texts in other tasks on EMRs.
Differential-Privacy Preserving Trajectory Data Publishing for Road Networks

In the sharing of user trajectory data of road networks, privacy leakage emerges to be a major concern because attackers may make aggressive reasoning and analysis based on the published trajectory data with certain background knowledge to obtain the privacy information (e.g. location) associated with individuals. Most existing trajectory privacy-preserving methods require special assumptions about the types of attacks and their associated background knowledge, are therefore unable to achieve the required strength for privacy protection. This paper proposes a novel algorithm of differential privacy preserving trajectory data publishing for road networks by spatial coupling of ambiguity using a noisy R-tree (Cons-XRT), which can resist attacks with arbitrary background knowledge even in the case of sparse trajectories. Our algorithm first blurs the spatial trajectory locations using an R-tree index of the trajectory data to form a noisy R-tree of the trajectory that satisfies the differential privacy preserving condition. It then generates trajectory count values to hide the relative changes of the statistical data of adjacent sections in adjacent periods, and eliminate the fluctuations of statistical data. Finally, it deploys a fast query algorithm for spatial range count query which uses the noise counts in the noisy R-tree index nodes to quickly return the number of moving objects satisfying differential privacy. Extensive experiments on real public transport vehicle trajectory datasets of Guangdong Province show that our Cons-XRT method achieves differential privacy trajectory protection which can resist the attacks with maximum background knowledge.
Automatic DNN architecture design using CPSOTJUTT for power system inspection

Abstract To quickly and accurately automatically design more high-precision deep neural network models (DNNs), this paper proposes an automatic DNN architecture design ensemble model based on consensus particle swarm optimization-assisted trajectory unified and TRUST-TECH (CPSOTJUTT), called CPSOTJUTT-EM. The proposed model is a three-layer model, and its core is a three-stage method for addressing the sensitivity of the local solver to the initial point and enabling fast and robust training DNN, effectively avoiding missing high-quality DNN models in the process of automatic DNN architecture design. CPSOTJUTT has the following advantages: (1) high-quality local optimal solutions (LOSs) and (2) robust convergence against random initialization. CPSOTJUTT-EM consists of the bottom layer: stable and fast design high-quality DNN architectures, the middle layer: exploration for a diverse set of optimal DNN classification engines, and the top layer: ensemble model for higher performance. This paper tests the performance of CPSOTJUTT-EM on public datasets and three self-made power system inspection datasets. Experimental results show that the CPSOTJUTT-EM has excellent performance in automatic DNN architecture design, DNN model optimization. And the CPSOTJUTT-EM can automatically design high-quality DNN ensemble models, laying a solid foundation for the application of DNN in other fields.
Use of Digital Elevation Models in Metallogenic Investigations on the Example of the Central Part of the Lower Amur Province

Abstract —The digital elevation model (SRTM03) of the central part of the Lower Amur province is analyzed. The Limuri–Amgun ring structure is identified on the basis of a set of features, and it is also the location of the Pilda–Limuri and Kherpuchin–V’yunsky gold-ore regions, as well as the Albazino deposit, known for large industrial gold deposits and many small deposits and ore occurrences. The distribution of linear and ring elements of the relief is analyzed and geologically interpreted, and its spatial relationship with gold deposits and ore occurrences is shown. The relationship between the morphological severity of the ring structure and the deep structure of the region is shown. It is revealed that the structure is magmatic and characterized by a high concentration of intrusive bodies. According to the reference data on the age and composition of the igneous complexes of the region, the formation of the ring structure is associated with the processes of formation and transformation of the Earth’s crust and upper mantle during the end of the Late Cretaceous and the onset of the Paleocene in a suprasubduction geodynamic setting.
Deep Learning Techniques for Computer Vision

Object detection or shape reconstruction of an object from images plays a vital role in computer vision, computer graphics, optics, optimization theory, statistics, and various fields. The goal of object detection is to empower the machines to locate and identify the items or things in the given image or video. An object forms the image in human eyes or on a camera sensor is defined by its shape, reflectance, and illumination. This overview covers the estimation of the object shape, reflectance, illumination, recent object detection algorithms and data sets used in recent research works. The classic photometric stereo is aimed to reconstruct the surface orientation from the known parameters of reflectance and illumination in multiple images. Object detection approaches are used to find various pertinent objects in a given single image and location of the objects. There are various datasets for objection detection, some of them are addressed here.
A Comparative Analysis of Signature Recognition Methods

Signature recognition is the process of automatically identifying or verifying an individual's signature to determine its authenticity. The basic motivation of developing signature recognition systems is to check whether a signature has been done by an authorized user /genuine user or an unauthorized user/a forger. The objective of this chapter is to study different algorithms that are used to authenticate and authorize the signatures of the individual. For personal identification and verification, Signatures are the most acceptable and economical way that is used for this purpose. Signature verification is used for documents like bank transactions and in offices as well. It is a huge time-consuming task for verifying a large number of documents. Hence the verification systems led to huge dramatic changes based on the physical characteristics and the behavioural characteristics of the individual. The verification methods used in the past suffer from flaws. This chapter provides a comparative analysis of various techniques used for recognizing signatures.
A Study on Evaluation and Analysis of Edge Detection Operators

One of the key stages in both image processing and computer vision is edge detection. For analysis and measurement of several fundamental attributes of an object or set of objects in an image, such as area, perimeter, and form, correct identification of the edges of the objects in the image is crucial. The edge detection operators employed in image processing must therefore be thoroughly understood. In this chapter, fundamental theories and comparative assessments of several edge detection operators are discussed along with a proposed improved contour detection scheme for better performance measurement. The technique has been used to process a number of digital photos, and improved performance in terms of contour detection has been attained.
Low Light Face Detection System

Detection of faces from low light image is challenging. Such images often suffer from poor contrast, poor intensity, and high noise make it challenging to gather information and identify people or objects. The proposed method uses a deep learning-based approach to enhance the images by capturing multiple exposures and combining them to generate a high-quality image. Max-margin object detection (MMOD) human face detector, a face detection algorithm is used to identify and enhance the faces in the images. MMOD algorithm is a deep learning-based object detection approach which accurately detects human faces in images. By identifying and enhancing the faces in the images, they are made more visible and recognizable, even in low light conditions. The experimental results of this proposed approach demonstrate its effectiveness in enhancing face visibility and raising the quality of low-light images. This method has practical applications in areas such as security surveillance, where capturing high-quality images under low light conditions is critical.