Miracle-dz/UniFAR-FTUs huggingface dataset:
Analytical Method for Social Network User Profile Textual Content Monitoring Based on the Key Performance Indicators of the Web Page and Posts Analysis

The article considers the development of methods and software for processing web pages and user messages in social networks, blogs, or forums. A method of textual content support based on analysing information about the comments from web pages of social network users is proposed. This method is built on the principles of web analytics and uses analytical data to evaluate web resources. The analytical method of text content support uses the analysis of key performance indicators to form many keywords to increase the potential audience of the blog or e-commerce sites. Software tools for technical support of textual content have been developed. A method of designing and implementing systems for monitoring textual content of Internet blogs and Internet forums, which reflect theoretical research results was proposed. From the standpoint of a systemic approach, propose the application of the principles of web resources processing for the implementation of the life cycle of textual content, which allowed to develop of a method of content support. The main problems of functional services for managing a web page or profile of users of social networks, blogs, and forums for their further promotion in search engines and attracting a potential/permanent audience are analyzed.
An Approach and a Software Tool for Automatic Source Code Generation driven by Business Rules

This paper proposes an approach to automatic source code generation driven by business rules. This approach is inspired by low-code and automatic programming to improve the software development process and accelerate product delivery through the source code generation from natural language statements. The proposed approach considers business rules as input, uses the triplestore model for knowledge representation based on business rules, utilizes association rules to suggest attribute data types, and produces an abstract data model. This abstract data model is a framework for software components generation of various purposes and syntax, such as SQL scripts for database tables creation and Java Beans for server-side implementation. A software solution based on the proposed approach translates the data model into the source code of software components: MySQL database and Java classes. But it can be extended to generate various software components based on different syntax rules. Performed experiments demonstrate that generated software components are verified and valid since they were checked using static code analysis and dynamic testing. Conclusion formulates research outcomes, obtained results, and limitations. Future work outlines the next research steps in this field.
Optimised planning of distribution network with photovoltaic system, battery storage, and DSTATCOM

In this circumstance of global warming, energy market deregulation, and enormous load growth, distribution network entails a proficient strategy to maintain the reliability and efficiency of the power service. Incorporation of solar photovoltaic (PV) system and battery storage (BS) in coordination with distributed static compensator (DSTATCOM) is a competent and practical approach to alleviate the power quality and reliability concern. In this study, a comprehensive strategic model is presented to optimally deploy PV, BS, and DSTATCOM to maximise voltage profile improvement, reliability, economic, and ecological benefit of the network. An accurate and precise novel voltage profile improvement indicator namely network voltage profile improvement index is proposed. Benefit-cost ratio and environment benefit index are proposed to quantify economic and environmental benefits, respectively. Similarly, reliability indices such as expected energy not served are used to appraise the system reliability. A fuzzy based extended version of NSGA II is utilised for the optimal deployment of the devices considering security limits. The proposed method is tested on 33-bus and 69-bus distribution networks considering time variant practical load models and the obtained results validate the efficacy and efficiency of the proposed method when compared with other multi-objective algorithms.
Analysis of the Application of Stochastic Gradient Descent to Detect Network Violations

Over the past decade, data sizes have grown faster than processor speeds. In this context, the capabilities of statistical machine learning methods are limited by computational time, not sample size. A more accurate analysis reveals qualitatively different trade-offs in the case of small and large learning problems. This work considers the occurrence of a large-scale case involving the basic algorithm's computational complexity and the implementation of optimization in non-trivial ways. Optimization algorithms such as stochastic gradient descent, which demonstrate exceptional performance for large-scale problems, are considered. The work compares the algorithms of gradient descent, stochastic gradient, and standard deterministic gradient descent. Here are given ways to apply them in machine learning and considered a formal description of the operation of gradient descent, stochastic gradient, and standard deterministic gradient descent algorithms. Here is analyzed the difference in the process of algorithms. The advantages and disadvantages of each of them are given. A comparison of both approaches is considered: deterministic and Stochastic algorithms. This work has analyzed the effectiveness of these two approaches. Convolutional (convolutional) neural networks are considered, which, unlike conventional deep neural networks, use convolutions to find patterns in images, thereby improving prediction in image recognition problems. A model of a convolutional neural network is presented. Here are analyzed the parameters of training the input sample and its testing. As a result of using a simulation of the usual gradient descent, when the weight is updated only after viewing all the elements, the value of the loss function is considered. The analysis of the selected problem and model also justifies the results of its use.
A new approach to the push–push oscillator based on a differential SIW bandpass filter

ABSTRACT A novel push–push oscillator based on a differential bandpass filter (BPF) using substrate integrated waveguide (SIW) technique is presented in this paper. For the first time, the differential SIW BPF is utilized as a frequency stabilization element to provide differential outputs in designing the push–push oscillator. The complex quality factor (Qsc)-peak frequency of the BPF is chosen for the design at approximately half the final output frequency to achieve low phase noise performance. Moreover, two transmission zeros (TZs) below the passband and two TZs above the passband which highly improve the out-of-band rejection are obtained by using both electromagnetic coupling and cross coupling. An SIW power combiner is also designed to obtain the desired signal. A push–push oscillator has been proposed and experimentally demonstrated at 27.8 GHz to verify the above design concept. The measured results show that the output power is −17.3 dBm with a phase noise of 128.6 dBc/Hz at 1-MHz offset.
A case study on the use of data mining for detecting and classifying abnormal power system modal behaviors

Abstract This article presents a case study involving data mining (DM) and the knowledge discovery (KD) process for the Western US power grid. With the installation of phasor measurement units (PMUs) in the US power systems, there is potential to improve wide-area situational awareness using data analytics. The KD process is challenging due to the complex nature of the power grid, data propriety issues, the file size of the time-series data, and questions of how to extract meaningful features. This case study demonstrates how the KD process can be implemented in light of these challenges to detect the anomalies of interest.
Research Progress on Conducting Polymer-Based Biomedical Applications

Conducting polymers (CPs) have attracted significant attention in a variety of research fields, particularly in biomedical engineering, because of the ease in controlling their morphology, their high chemical and environmental stability, and their biocompatibility, as well as their unique optical and electrical properties. In particular, the electrical properties of CPs can be simply tuned over the full range from insulator to metal via a doping process, such as chemical, electrochemical, charge injection, and photo-doping. Over the past few decades, remarkable progress has been made in biomedical research including biosensors, tissue engineering, artificial muscles, and drug delivery, as CPs have been utilized as a key component in these fields. In this article, we review CPs from the perspective of biomedical engineering. Specifically, representative biomedical applications of CPs are briefly summarized: biosensors, tissue engineering, artificial muscles, and drug delivery. The motivation for use of and the main function of CPs in these fields above are discussed. Finally, we highlight the technical and scientific challenges regarding electrical conductivity, biodegradability, hydrophilicity, and the loading capacity of biomolecules that are faced by CPs for future work. This is followed by several strategies to overcome these drawbacks.
Rehabilitation and return to sports after anterior shoulder stabilization

Anterior shoulder stabilization has become the treatment of choice for athletes after anterior glenohumeral dislocation. Rehabilitation after surgical stabilization plays a vital role in returning the athlete to their desired sports participation level. Historically, rehabilitation, progression, and return to sport clearance after anterior stabilization surgery has been based on tissue healing and elapsed time from surgery. In general, good to excellent functional results have been achieved with this approach and a large proportion of athletes are able to return to sports, with low recurrence rates. However, substantially fewer are able to return to preinjury levels of sports participation; inadequate assessment and thus management of risk factors may be a potential cause. Rehabilitation guidelines and evidence provide only scant information for the clinician when assessing an athlete’s preparedness to return to sports. Additionally, the unique demands of each sport and non-modifiable factors create an additional layer of complexity in return to sport decision making. In this review article, rehabilitation guidelines after anterior shoulder stabilization, examination of factors utilized to determine return to sports preparedness, and clinical tests and measures that may be used during decision making are discussed. The importance of matching rehabilitation interventions based on the goals of each stage and criterion based progressions are emphasized. Establishment of the fundamental motion, strength, and stabilization is paramount before entering into return to sport preparation and testing. Once the athlete is nearing return to sport, appropriate tests and measures must should be performed to replicate the demands commonly placed on the shoulder during sports. Based on the available evidence and the unique functional requirements of each sport, it is recommended that a battery of tests and measures are utilized to assess multiple aspects of sports participation.
Simulation‐based optimization for rigorous assessment of ground plane modifications in compact UWB antenna design

Introducing ground plane modifications is a popular approach in the design of compact UWB antennas. Yet, specific topological alterations are normally reported on case to case basis without thorough investigations concerning their general suitability for antenna miniaturization. In particular, detailed performance comparison of different ground plane modifications is lacking in the literature. In this article, the effect of selected ground plane modifications on achievable miniaturization rate is considered based on a set of four UWB antennas. EM‐driven optimization is carried out to minimize the antenna footprints while maintaining acceptable matching within the UWB frequency range. In each case, all geometry parameters of the respective structures are utilized in the design process. For the sake of fair comparison, all antennas are implemented on the same dielectric substrate. Our results indicate a clear performance pattern, here, an advantage of the elliptical ground plane slit below the feed line over the rectangular one (average size reduction ratio of 26% versus 19% across the benchmark set). Our conjectures are confirmed by physical measurements of the fabricated antenna prototypes.
Tunable Spin and Orbital Edelstein Effect at (111) LaAlO3/SrTiO3 Interface

Converting charge current into spin current is one of the main mechanisms exploited in spintronics. One prominent example is the Edelstein effect, namely, the generation of a magnetization in response to an external electric field, which can be realized in systems with lack of inversion symmetry. If a system has electrons with an orbital angular momentum character, an orbital magnetization can be generated by the applied electric field, giving rise to the so-called orbital Edelstein effect. Oxide heterostructures are the ideal platform for these effects due to the strong spin–orbit coupling and the lack of inversion symmetries. Beyond a gate-tunable spin Edelstein effect, we predict an orbital Edelstein effect an order of magnitude larger then the spin one at the (111) LaAlO3/SrTiO3 interface for very low and high fillings. We model the material as a bilayer of t2g orbitals using a tight-binding approach, whereas transport properties are obtained in the Boltzmann approach. We give an effective model at low filling, which explains the non-trivial behaviour of the Edelstein response, showing that the hybridization between the electronic bands crucially impacts the Edelstein susceptibility.
GLIN: A (G)eneric (L)earned (In)dexing Mechanism for Complex Geometries

Although spatial indexes shorten the query response time, they rely on complex tree structures to narrow down the search space. Such structures in turn yield additional storage overhead and take a toll on index maintenance. Recently, there have been a flurry of efforts attempting to leverage Machine-Learning (ML) models to simplify the index structures. However, existing geospatial indexes can only index point data rather than complex geometries such as polygons and trajectories that are widely available in geospatial data. As a result, they cannot efficiently and correctly answer geometry relationship queries. This paper introduces GLIN, an indexing mechanism for spatial relationship queries on complex geometries. To achieve that, GLIN transforms geometries to Z-address intervals, and then harnesses an existing order-preserving learned index to model the cumulative distribution function between these intervals and the record positions. The lightweight learned index greatly reduces indexing overhead and provides faster or comparable query latency. Most importantly, GLIN augments spatial query windows to support queries exactly for common spatial relationships. Our experiments on real-world and synthetic datasets show that GLIN has 80%-90% lower storage overhead than Quad-Tree and 60% -80% than R-tree and 30% - 70% faster query on medium selectivity. Moreover, GLIN's maintenance throughput is 1.5 times higher on insertion and 3 - 5 times higher on deletion.
PlaneSDF-Based Change Detection for Long-Term Dense Mapping

The ability to process environment maps across multiple sessions is critical for robots operating over extended periods of time. Specifically, it is desirable for autonomous agents to detect changes amongst maps of different sessions so as to gain a conflict-free understanding of the current environment. In this letter, we look into the problem of change detection based on a novel map representation, dubbed Plane Signed Distance Fields (PlaneSDF), where dense maps are represented as a collection of planes and their associated geometric components in SDF volumes. Given point clouds of the source and target scenes, we propose a three-step PlaneSDF-based change detection approach: (1) PlaneSDF volumes are instantiated within each scene and registered across scenes using plane poses; 2D height maps and object maps are extracted per volume via height projection and connected component analysis. (2) Height maps are compared and intersected with the object map to produce a 2D change location mask for changed object candidates in the source scene. (3) 3D geometric validation is performed using SDF-derived features per object candidate for change mask refinement. We evaluate our approach on both synthetic and real-world datasets and demonstrate its effectiveness via the task of changed object detection.
Depression Detection Using Social Networking Sites Via Data Mining

— Social networking craze is increasing day by day in today’s world. Social media is popular among youngsters, senior citizens and most the people. But with the excessive use of social network communication, it affects human life very badly. Recently, a growing number of mental disorders are observed in social networks, like the dependence on cybernetic relationships, information overload and network restriction, etc. Currently, the symptoms of those mental disorders are observed, which causes several dangerous issues. Human emotions like depression are inner sentiments of citizenry which expose actual behaviours of a private. Analysing and determining this sort of emotions from people’s social activities in virtual world is often very helpful to know their behaviour. Detecting words that express negativity during a social media message is one step towards detecting depressive moods. Depression doesn’t just occur for a personal in vacuum; it can affect your friends, family, co-workers, and everybody around you. Additionally, depression may impact how you perform at work or your levels of concentration, so, it can negatively affect productivity. To know if a Facebook type system user could exhibit depression over a period of your time, then system will ready to identify the depressed user supported his activities like posts and chats, which incorporates emotional type keywords.
Identifying multichannel coherent couplings and causal relationships in gravitational wave detectors

The gravitational-wave detector is a complex and sensitive collection of advanced instruments that are impacted not only by mechanical/electronics systems but also by the surrounding environment. Hence, it is of great importance to classify and mitigate noises to detect gravitational-wave signals by using information from many auxiliary channels related to such devices and surroundings. This improves the signal-to-noise ratio and reduces false alarms from coincident loud events. For this reason, it is essential for identifying coherent relationships between complex channels. This study presents a way of identifying (non-) linear couplings between associated channels by using the method of correlation coefﬁcients. And we show that the method can be applied to practical problems in the gravitational-wave detector, such as noises by lightning strokes, air compressors vibrations, and noises caused by wind effects.
How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition

A major goal of artificial intelligence (AI) is to create an agent capable of acquiring a general understanding of the world. Such an agent would require the ability to continually accumulate and build upon its knowledge as it encounters new experiences. Lifelong or continual learning addresses this setting, whereby an agent faces a continual stream of problems and must strive to capture the knowledge necessary for solving each new task it encounters. If the agent is capable of accumulating knowledge in some form of compositional representation, it could then selectively reuse and combine relevant pieces of knowledge to construct novel solutions. Despite the intuitive appeal of this simple idea, the literatures on lifelong learning and compositional learning have proceeded largely separately. In an effort to promote developments that bridge between the two fields, this article surveys their respective research landscapes and discusses existing and future connections between them.
Temporal Lift Pooling for Continuous Sign Language Recognition

Pooling methods are necessities for modern neural networks for increasing receptive fields and lowering down computational costs. However, commonly used hand-crafted pooling approaches, e.g., max pooling and average pooling, may not well preserve discriminative features. While many researchers have elaborately designed various pooling variants in spatial domain to handle these limitations with much progress, the temporal aspect is rarely visited where directly applying hand-crafted methods or these specialized spatial variants may not be optimal. In this paper, we derive temporal lift pooling (TLP) from the Lifting Scheme in signal processing to intelligently downsample features of different temporal hierarchies. The Lifting Scheme factorizes input signals into various sub-bands with different frequency, which can be viewed as different temporal movement patterns. Our TLP is a three-stage procedure, which performs signal decomposition, component weighting and information fusion to generate a refined downsized feature map. We select a typical temporal task with long sequences, i.e. continuous sign language recognition (CSLR), as our testbed to verify the effectiveness of TLP. Experiments on two large-scale datasets show TLP outperforms hand-crafted methods and specialized spatial variants by a large margin (1.5%) with similar computational overhead. As a robust feature extractor, TLP exhibits great generalizability upon multiple backbones on various datasets and achieves new state-of-the-art results on two large-scale CSLR datasets. Visualizations further demonstrate the mechanism of TLP in correcting gloss borders. Code is released 1 . exhibits excellent generalizability upon multiple backbones upon two large-scale CSLR datasets with significant performance boost. Visualizations verify the effects of TLP for correcting gloss borders.
Few-shot Fine-grained Image Classification via Multi-Frequency Neighborhood and Double-cross Modulation

Traditional fine-grained image classification typically relies on large-scale training samples with annotated ground-truth. However, some sub-categories have few available samples in real-world applications, and current few-shot models still have difficulty in distinguishing subtle differences among fine-grained categories. To solve this challenge, we propose a novel few-shot fine-grained image classification network (FicNet) using multi-frequency neighborhood (MFN) and double-cross modulation (DCM). MFN focuses on both spatial domain and frequency domain to capture multi-frequency structural representations, which reduces the influence of appearance and background changes to the intra-class distance. DCM consists of bi-crisscross component and double 3D cross-attention component. It modulates the representations by considering global context information and inter-class relationship respectively, which enables the support and query samples respond to the same parts and accurately identify the subtle inter-class differences. The comprehensive experiments on three fine-grained benchmark datasets for two few-shot tasks verify that FicNet has excellent performance compared to the state-of-the-art methods. Especially, the experiments on two datasets,"Caltech-UCSD Birds"and"Stanford Cars", can obtain classification accuracy 93.17\% and 95.36\%, respectively. They are even higher than that the general fine-grained image classification methods can achieve.