Reviewerly/exHarmony huggingface dataset:
The mathematical characteristic of the fifth order Laplace contour filters used in digital image processing

The Laplace operator is a differential operator which is used to detect edges of objects in digital images.This paper presents the properties of the most commonly used fifth-order pixels Laplace filters including the difference schemes used to derive them (finite difference method -FDM and finite element method -FEM).The results of the research concerning third-order pixels matrices of the convolution Laplace filters used for digital processing of images were presented in our previous paper: The mathematical characteristic of the Laplace contour filters used in digital image processing.The third order filters is presented by Winnicki et al. (2022).As previously, the authors focused on the mathematical properties of the Laplace filters: their transfer functions and modified differential equations (MDE).The relations between the transfer function for the differential Laplace operator and its difference operators are described and presented here in graphical form.The impact of the corner elements of the masks on the results is also discussed.A transfer function, is a function characterizing properties of the difference schemes applied to approximate differential operators.Since they are relations derived in both types of spaces (continuous and discrete), comparing them facilitates the assessment of the applied approximation method.
Towards Food Image Retrieval via Generalization-Oriented Sampling and Loss Function Design

Food computing has increasingly received widespread attention in the multimedia field. As a basic task of food computing, food image retrieval has wide applications, that is, food image retrieval can help users to find the desired food from a large number of food images. Besides, the retrieved information can be applied to establish a richer database for the subsequent food content-related recommendation. Food image retrieval aims to achieve better performance on novel categories. Thus, it is worth studying to transfer the embedding ability from the training set to the unseen test set, that is, the generalization of the model. Food is influenced by various factors, such as culture and geography, leading to great differences between domains, such as Asian food and western food. Therefore, it is challenging to study the generalization of the model in food image retrieval. In this article, we improve the classical metric learning framework and propose a generalization-oriented sampling strategy, which boosts the generalization of the model by maximizing the intra-class distance from a proportion of positive pairs to avoid the excessive distance compression in the embedding space. Considering that the existing optimization process is in an opposite direction to our proposed sampling strategy, we further propose an adaptive gradient assignment policy named gradient-adaptive optimization , which can alleviate the intra-class distance compression during optimization by assigning different gradients to different samples. Extensive evaluation on three popular food image datasets demonstrates the effectiveness of the proposed method. We also experiment on three popular general datasets to prove that solving the problem from the generalization can also improve the performance of general image retrieval. Code is available at https://github.com/Jiajun-ISIA/Generalization-oriented-Sampling-and-Loss .
Clustering analysis of the seismicity of Van Province and its surroundings via spatial autocorrelation techniques filters

Destructive aftershocks such as the 7.2 Van earthquake on October 23, 2011, and the Hoy (Iran) earthquake with 5.9 on February 23, 2020, occurred in the province of Van and its surroundings.In earthquake studies, the issue of examining the distribution and homogeneity of earthquake incidences with Geographic Information Systems (GIS) based via spatial autocorrelation techniques is frequently investigated.Van province and its surroundings are among the areas with high earthquake risk due to its location on the East Anatolian Compressive Tectonic Block.The aim of this study is to analyze the spatial patterns of earthquakes with magnitude 4 and above that occurred in the province of Van and its surroundings during the instrumental period and to determine to cluster.Spatial cluster analyses play an important role in examining the distribution of seismicity.The data used in the study have been taken from the database system of the Earthquake Department of the Republic of Turkey Ministry of Interior Disaster and Emergency Management Presidency.Moran's I and Getis-Ord Gi methods from spatial autocorrelation techniques were preferred on the earthquake data set to be used in this research.It has aimed to determine the dangerous areas by testing the earthquake distributions in clustered regions via spatial autocorrelation techniques.
Absolute M split estimation as an alternative for robust M-estimation

The problem of outlying observations is very well-known in the surveying data processing.Outliers might have several sources, different magnitudes, and shares within the whole observation set.It means that it is not possible to propose one universal method to deal with such observations.There are two general approaches in such a context: data cleaning or robust estimation.For example, the robust M-estimation has found many practical applications.However, there are other options, such as R-estimation or the absolute M split estimation.The latter method was created to be less sensitive to outliers than the squared M split estimation (the basic variant of M split estimation).From the theoretical point of view, the absolute M split estimation cannot be classified as a robust method; however, it was proved that it could be used in such a context under certain conditions.The paper presents the primary comparison between that method and a conventional robust M-estimation.The results show that the absolute M split estimation predominates over the classical methods, especially when the percentage of outliers is high.Thus, that method might be used to process LiDAR data, including mismeasured points.Processing synthetic data from terrestrial laser scanning or airborne laser scanning confirms that the absolute M split estimation can deal with outliers sufficiently.
Human Interaction Understanding With Consistency-Aware Learning

Compared with the progress made on human activity classification, much less success has been achieved on human interaction understanding (HIU). Apart from the latter task is much more challenging, the main causation is that recent approaches learn human interactive relations via shallow graphical representations, which are inadequate to model complicated human interactive-relations. This paper proposes a deep consistency-aware framework aiming at tackling the grouping and labelling inconsistencies in HIU. This framework consists of three components, including a backbone CNN to extract image features, a factor graph network to implicitly learn higher-order consistencies among labelling and grouping variables, and a consistency-aware reasoning module to explicitly enforcing consistencies. The last module is inspired by our key observation that the consistency-aware reasoning bias can be embedded into an energy function or a particular loss function, minimizing which delivers consistent predictions. An efficient mean-field inference algorithm is proposed, such that all modules of our network could be trained in an end-to-end fashion. Experimental results demonstrate that the two proposed consistency-learning modules complement each other, and both make considerable contributions in achieving leading performance on three benchmarks of HIU. The effectiveness of the proposed approach is further validated by experiments on detecting human-object interactions.
FACIAL EMOTION RECOGNITION SYSTEM (FERS) USING MACHINE LEARNING

Face identification has been around for a while.Moving forward, it will be possible to assess employee emotion based on facial expressions, internal experiences, and recordings in the form of video, electric signals, or images.In order for cutting-edge artificial intelligence systems to replicate and forecast face reactions, human emotion recognition is critically required.This might help people make informed decisions about things like purpose determination, offering advertising, and security-related dangers.The human eye can easily identify emotions in images or videos, but computers find it more challenging to do so, necessitating the employment of different image processing techniques for feature extraction.Several machine learning approaches are suitable for this task.Before being evaluated against an adequate dataset for any detection or recognition, machine learning algorithms must first be trained.This study looks into a few feature extraction methods and machine learning algorithms that could help identify human mood precisely.In this research, the Facial Emotion Recognition System (FERS) has been presented to automatically detect employee emotions in real time using machine learning.The dataset was used to train the machine learning model.The FERS system can identify six different emotions, including happiness, sadness, surprise, fear, disgust, and fury, by processing the collected images.Results indicate that the intended objectives were achieved.The FER-2013 dataset was used to train the machine learning model.The facial expression image is categorised using neural networks and support vector machines (SVM), which also aid in predicting the right image based on the target image.
Secure and Low-Complexity Medical Image Exchange Based on Compressive Sensing and LSB Audio Steganography

Patients' data constitutes the vast majority of information exchanged over the Internet.The magnitude of maintaining its security and privacy directly correlates to the importance and privacy of the patients themselves.The manipulation of this data negatively impacts the patient's life and treatment, thus the researchers were inclined to find solutions for this confidentiality challenge by concealing the data to prevent unauthorized access.The process of data concealment is more complex when dealing with large amounts of data.Often, the proportions of the medical image are sizable, which creates challenges when compressing data while maintaining high accuracy.In this paper, a system for securing and compressing medical images based on the Compressive Sensing (CS) principle is presented.The medical image is divided into 8×4 sub-matrices that are multiplied by a 3×8 sensor matrix consisting of Gaussian random numbers.The proposed solution reduces the image's original size by about 30% and conceals it as a random distribution inside an audio (wav) file for more security, using the LSB technique for low complexity.For reconstruction, the sensed image is multiplied by the pseudo-inverse of Moore-Penrose.The statistical analysis proved the efficacy of the system in compression and recovery with reduced cost and time consumption, combined with reduced distortion of the cover file; it was also judged to be increasingly efficient compared to previous research.
Deep stacked denoising autoencoder for unsupervised anomaly detection in video surveillance

Due to the increase of crime and terror, security concerns are rising rapidly every day. The use of surveillance cameras for abnormal behavior detection has become an indispensable part of human beings. But the performance of most of the developed systems is not up to the mark because of the low performance and accuracy in detecting the abnormality in the videos due to mainly the presence of noise. The videos captured by the surveillance camera are generally born with no or more noise due to various reasons. To resolve such issues, we provide a snapshot regarding different categories of noise and handcraft techniques to resolve them. Non-local means, block matching, and 3D filtering filters perform astonishingly well while denoising the images. We also present a robust unsupervised deep learning model called deep stacked denoising autoencoder (DSDAE) for denoising the images and further use it for abnormal activity detection and localization in the videos. Our approach has achieved a noteworthy result in image denoising compared to other handcraft-based techniques. DSDAE uses a separate encoder for the extraction of appearance features using clean and noisy images and motion features through the optical flow images. Early fusion is done in the extracted features and passed to the decoder. Only those pixels whose reconstruction error is greater than the threshold will be considered abnormal pixels. Experiment results are compared quantitatively/qualitatively with the recent competitive state-of-the-art methods in the publicly available benchmark datasets Ped1, Ped2, CUHK Avenue, and ShanghaiTech that demonstrate the superior accuracy and performance of our DSDAE. The obtained area under the curve of DSDAE in Ped1, Ped2, CUHK Avenue, and ShanghaiTech is 98.14%, 97.92%, 95.89%, and 96.7%, respectively, whereas equal error rate for the same datasets is 5.4%, 4.5%, 12.03%, and 7.8%, respectively.
Hyperspectral Image Denoising via Nonlocal Spectral Sparse Subspace Representation

Hyperspectral image (HSI) denoising based on nonlocal subspace representation has attracted a lot of attention recently. However, most of the existing works mainly focus on refining the representation coefficient images (RCIs) using certain nonlocal denoiser but ignore the understanding why these pseudoimages have a similar spatial structure as the original HSI. In this work, we revisit such vein from the respective of principal component analysis (PCA). Inspired by an alternative sparse PCA, we propose a spectral sparse subspace representation strategy to simultaneously learn low-dimensional spectral subspace and novel RCIs with sparse loadings. It turns out that the resulting RCIs possess a more significant spatial structure due to the adaptive sparse combination of spectral bands. A simple nonlocal low-rank approximation is then employed to further remove the residual noise of the RCIs. Finally, the entire denoised HSI is obtained by inverse spectral sparse PCA. Extensive experiments on the simulated and real HSI datasets show that the proposed nonlocal spectral sparse subspace representation method, dubbed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NS3R</i> , has excellent performance both in denoising effect and running time compared with many other state-of-the-art methods.
Multipartite entanglement detection based on the generalized state-dependent entropic uncertainty relation for multiple measurements

We present the generalized state-dependent entropic uncertainty relations in a multiple measurements setting and the optimal lower bound is obtained by considering different measurement sequences. We then apply this uncertainty relation to witness entanglement and give the experimentally accessible lower bounds on both bipartite and tripartite entanglements. This method of detecting entanglement is applied to a physical system of two particles on a one-dimensional lattice and Greenberger-Horne-Zeilinger (GHZ)-Werner state. It is shown that, for measurements that are not in mutually unbiased bases, this generalized entropic uncertainty relation is superior to the previous state-independent one in entanglement detection. Furthermore, we conduct a demonstration of multipartite entanglement detection of GHZ states up to ten qubits on the Quafu cloud quantum computation platform. Our results might play important roles in detecting multipartite entanglement experimentally.
AMatFormer: Efficient Feature Matching via Anchor Matching Transformer

Learning based feature matching methods have been commonly studied in recent years. The core issue for learning feature matching is to how to learn (1) discriminative representations for feature points (or regions) within each intra-image and (2) consensus representations for feature points across inter-images. Recently, self- and cross-attention models have been exploited to address this issue. However, in many scenes, features are coming with large-scale, redundant and outliers contaminated. Previous self-/cross-attention models generally conduct message passing on all primal features which thus lead to redundant learning and high computational cost. To mitigate limitations, inspired by recent seed matching methods, in this paper, we propose a novel efficient Anchor Matching Transformer (AMatFormer) for the feature matching problem. AMatFormer has two main aspects: First, it mainly conducts self-/cross-attention on some anchor features and leverages these anchor features as message bottleneck to learn the representations for all primal features. Thus, it can be implemented efficiently and compactly. Second, AMatFormer adopts a shared FFN module to further embed the features of two images into the common domain and thus learn the consensus feature representations for the matching problem. Experiments on several benchmarks demonstrate the effectiveness and efficiency of the proposed AMatFormer matching approach.
A Survey on Controllable Abstractive Text Summarization

The main objective of automatic text summarization is to create a clear and precise summary while maintaining the essential information content and overall meaning. For the purpose of automatically summarising material, multiple techniques have recently been created and are now widely used across numerous fields. The two main strategies for automatic summarization are extraction and abstraction. While abstractive summarization approaches seek to produce relevant information in a novel way, extractive summarization techniques function by recognising important chunks of the text from documents. Abstractive text summarization (ATS) is one of the popular areas of research. Numerous models have been created for ATS. However, the generic summaries produced by the current summarising models do not take into account the preferences or expectations of the consumers. These systems ignore the preferences of the user, including the preferred length, style, entities, or how much of the information has already been read by the user, in which they may be interested. The ability to limit the summary length in abstractive summarization is a significant problem because generated summaries are employed in various scenarios that may have space or length constraints. Document summarization models, like people, have a variety of methods to understand a document's contents. We look into controllable text summarization that gives users some degree of control over a certain aspect of the output. The work that has been done in the field of controlled abstractive summarization is reviewed in this study. Additionally, we have provided a summary of the many controlled abstractive summarization approaches that have been utilised or developed by researchers as well as the assessment methodologies that are currently being used to these summaries.
Potential Analysis of Bitcoin Cryptocurrency as a Future Investment Asset: A Systematic Literature Review

Despite this promising potential, it is important to remember that investing in Bitcoin also involves risk. The price of Bitcoin is very volatile, and its value can change dramatically in a short amount of time. In addition, regulations regarding cryptocurrencies are still unclear in many countries, and regulatory changes could affect the price and adoption of Bitcoin. Security is also an important factor, as Bitcoin can be vulnerable to cyberattacks and theft. This study aimed to conduct studies related to the potential of bitcoin cryptocurrency as an asset for future investment as well as the potential risks that exist in this asset. Literature search efforts are carried out by searching on search engines using certain keywords. Articles that match the inclusion criteria are reviewed in depth. Bitcoin's potential as a future investment alternative is highly dependent on factors such as value growth, institutional adoption, and developments in blockchain technology. Nonetheless, it is important to be aware of the risks associated with investing in cryptocurrencies and to carry out in-depth research and consulting before making any investment decisions.
Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift

There has been a lot of excitement around using machine learning to improve the performance and usability of database systems. However, few of these techniques have actually been used in the critical path of customer-facing database services. In this paper, we describe Auto-WLM, a machine learning based automatic workload manager currently used in production in Amazon Redshift. Auto-WLM is an example of how machine learning can improve the performance of large data-warehouses in practice and at scale. Auto-WLM intelligently schedules workloads to maximize throughput and horizontally scales clusters in response to workload spikes. While traditional heuristic-based workload management requires a lot of manual tuning (e.g. of the concurrency level, memory allocated to queries etc.) for each specific workload, Auto-WLM does this tuning automatically and as a result is able to quickly adapt and react to workload changes and demand spikes. At its core, Auto-WLM uses locally-trained query performance models to predict the query execution time and memory needs for each query, and uses this to make intelligent scheduling decisions. Currently, Auto-WLM makes millions of decisions every day, and constantly optimizes the performance for each individual Amazon Redshift cluster. In this paper, we will describe the advantages and challenges of implementing and deploying Auto-WLM, as well as outline areas of research that may be of interest to those in the "ML for systems'' community with an eye for practicality.
Collection of tasks for the development of Natural Science competence of students in the requirements of PISA

Анотація (англійською мовою) The collection of tasks for the development of Natural Science competence of students in the requirements of PISA includes authors’ competence-oriented tasks, test questions on Natural Science literacy based on trial test questions of OECD PISA, designed to develop Natural Science competence of students. The collection can be used by applicants for Primary and High School Education, teachers of General Secondary Education Institutions, teachers of Specialized Secondary Education Institutions, and students of Pedagogical Higher Educational Institutions. Authors’ tasks and their levels of complexity are developed within the framework of mastering concepts, scientific methods and the ability to identify cause-and-effect relationships, graphic and visual data; the definition of Natural Science and Mathematical content, their structuring according to the classification features of PISA tasks (contexts and situations, competencies that are being worked out and developed, knowledge and attitude to knowledge, situations, values), meet the basic requirements of textology, are correctly formulated and provide an opportunity to test not only the knowledge of facts, but also the ability of Educational Applicants to critically analyse and skilfully operate with various types of information provided. The publication was carried out within the Jean Monnet Module (620287-EPP-1-2020-1-UA-EPPJMO-MODULE) in 2020-2023 at the Institute of Pedagogy of the National Academy of Educational Sciences of Ukraine. The European Commission’s support for this publication does not imply support for its content, which reflects only the opinion of participants. The commission cannot be held responsible for any use of the information contained in the collection.