Reviewerly/exHarmony huggingface dataset:
The Development of “Internet +” Smart Environmental Protection Multi-Environment Perception System

With the development of the economy and people’s longing for a better living environment, the smart and environmentally friendly ecological environment has entered a period of intensive research. However, the current ecological environment resources cannot be used reasonably, and word of mouth and tourism economy are showing negative growth. In order to be able to conduct an in-depth study of the ecological environment, based on “Internet +” and perceived value, this paper builds a comprehensive evaluation model of a smart and environmentally friendly ecological environment based on a multiple perception system. This article establishes an evaluation index system that combines two systems of multiple perception systems and ecological environment, six secondary indicators, and 22 three-level indicators. In this paper, the comprehensive evaluation value of the smart environmental protection ecological environment is 0.78595, and the level of the multi-perception system is good. At the same time, the corresponding evaluation values of visual perception, functional service perception, and cultural and activity perception are 0.75431, 0.70196, and 0.78846, respectively. The comprehensive evaluation value of the ecological environment is 0.65734, and the level of ecological environment development is average. The corresponding evaluation values of the subsystem environmental quality level, environmental pressure level, and environmental improvement level are 0.69943, 0.63491, and 0.60279, respectively. The coordination value of the multi-perception system and the ecological environment is 0.9978, which is an ultra-high-level coordination stage. The comprehensive evaluation value of the two is 0.8659. At this stage, the degree of interaction between the two is strong and affects their development.
Fuzzy Inference System based Non fungible token FORUM APPLICATION

Forums are online discussion sites where users can share ideas and post text messages to help. In other words, it's an area for people to talk via posted messages. NFTs (Non-Fungible Tokens) are data that are added to files that create unique signatures. It can be in image files, songs, tweets, text published on websites, physical objects, and various other digital formats. Essentially, this means that someone can own a digital file "and have it marked with a code to distinguish it from any digital copy". This application converts certain content in the forum which can be considered valuable and unique information into an NFT, with the consent of the user. In existing NFT trading platforms, an NFT creator can't be anyone. The creator has to be verified by the app and has to create NFTs according to the app specifications. Also, it becomes difficult to know whether the user is trustable or whether the content provided by the user is authentic or not. Therefore, an app is made where people of different interests come together and share their opinions/knowledge on a certain subject of a certain category where any valuable information is to be highlighted and the creator should be properly credited for the ownership of the information as an NFT.
Attention-enhanced and trusted multimodal learning for micro-video venue recognition

Unified venue representation is typically generated by integrating multi-modal information of micro-videos. It is also important to achieve reliable multi-modal fusion. In prior works, the reliability of decisions from multiple modalities is limited without the uncertainty estimation, which indicates whether or to what extent such decisions can be trusted. To this end, in this paper an attention-enhanced and trusted multimodal learning (AETML) model is proposed to achieve reasonable multimodal decision fusion for micro-video venue recognition. Specifically, a domain-adaptive visual transformer (VT) is used as the robust visual feature extractor. Denoise autoencoder and sentence2vector methods are applied to extract acoustic and textural features. Then, attention nets are devised to enhance the extracted features, and output class probabilities of each modality. An uncertainty estimation network is constructed to dynamically assess the reliability of such decisions, which are transformed into Dirichlet distribution for modeling the uncertainty. Lastly, trusted prediction results are yielded by integrating the adjusted distributions with uncertainty estimations according to the Dempster–Shafer evidence theory. The experimental results on a public micro-video dataset show that the proposed model outperforms the state-of-the-art methods. Further studies validate the effectiveness and robustness brought by the uncertainty estimation.
Industrial Robot Vibration Anomaly Detection Based on Sliding Window One-Dimensional Convolution Autoencoder

Model-based methods can be used to detect anomalies in industrial robots, but they require a high level of expertise and are therefore difficult to implement. The lack of sufficient data on the anomalous operation of industrial robots limits data-driven anomaly detection methods. This study proposes Sliding Window One-Dimensional Convolutional Autoencoder (SW1DCAE), an unsupervised vibration anomaly detection algorithm for industrial robots, that can directly act on the original vibration signal and effectively improve detection accuracy. First, the convolutional neural network and the autoencoder model are effectively integrated to construct a one-dimensional convolutional autoencoder model. Secondly, the sliding window algorithm is used for data enhancement, and the dropout technique is introduced to improve the generalization ability of the model. Finally, the reconstruction error of the input sample is calculated and compared with the error threshold to determine whether the operation state of the industrial robot is normal or not. This study discusses the effect of different convolution kernel widths, sliding window sizes, dropout ratios, and other parameters on model performance. Validation with vibration signals collected from an industrial robot test bench shows that this unsupervised anomaly detection algorithm has good accuracy and F1 score.
PARAMETERS OF CREATIVITY IN MODERN PSYCHOTHERAPEUTIC PRACTICE

The principal purpose of the study is to determine and compile the main parameters of creativity and creative thinking that affect psychotherapeutic practice. To achieve this goal, the methods of sequencing influence were applied, which allowed offering our methodological approach to the issue of determining the parameters of creativity and creative thinking that are all suitable for the practice of introducing psychotherapeutic activities. Also, the method of expert analysis was used, and by conducting online interviews and polling leading experts in the field of psychotherapy, a list of the most important parameters of creativity that take place in psychotherapeutic practice was determined. As a result, the set goal was achieved, and it was determined which parameter of creativity has the greatest value and influence on psychotherapeutic activity. The study is of practical value since this methodological approach is flexible and can be applied in the practice of a psychotherapist. The study has limitations and they only relate to the psychotherapeutic activity in Ukraine.
A Dynamic System to Assist Vision for Visually Challenged

People are always in need of someone to inform them of what is going on in their environment. The advancement of technology has resulted in the widespread adoption of Android applications. This paper discusses an Android application that aids visually impaired people in better understanding their surroundings and overcoming social, environmental, and technological difficulties. Mobile phones and cameras have become commonplace in our daily lives. Our method seeks to understand the context of an image or a group of images (video) and explain it in natural language such as English before converting it to speech. As a result, these technologies can be utilised to help visually impaired persons become more aware of their surroundings and enjoy life as normal people do.
A Siamese network framework for bank intelligent Q&amp;A prediction

Abstract With the development of financial technologies and the explosive growth of data, the intelligent customer services have attracted a considerable attention of academic and industrial experts. The calculation of question similarity has become a key to the question and answer (Q&amp;A) of financial intelligent customer service. In this work, we propose a Siamese network called W2V‐Siamese‐BiLSTM for bank Q&amp;A prediction. Based on the Word2vec text information vectorization, the pre‐training model of embedding layer is established, and two bidirectional long short‐term memory (Bi‐LSTM) networks are introduced to encode the upper layer input. The weights are shared in the encoding layer, and finally the Manhattan distance is used in the similarity calculation layer. The experimental results show that the supervised similarity calculation framework proposed in this work has good applicability in real financial Q&amp;A. The accuracy of the proposed method is 81.2%. The analysis further highlights the effectiveness and superiority of supervised learning models in the financial field.
Extended Object Tracking with IMM Filter for Automotive Pre-Crash Safety Applications

While automotive radar is widely used for comfort applications such as ACC (Adaptive Cruise Control) and safety applications such as EBA (Emergency Brake Assist) since more than 20 years, it's recently also gaining interest in applications such as 360° near-range sensing for pre-crash applications. Pre-crash scenarios exhibit multiple motion mode changes within a fraction of seconds and as such they demand for faster data acquisition and more efficient tracking mechanisms compared to the traditional applications mentioned above. In this work, fast mode pre-crash radar detections are tracked using a combination of Interacting Multiple Model (IMM) and Extended Object Tracking (EOT). The IMM-EOT approach outperforms tracking using either IMM or EOT alone in terms of accuracy, adaptability to motion mode changes and ability to provide additional details of the track such as extent information. The method is verified on radar measurements from both indoor and outdoor test facilities. The measurement set up resembles accident scenarios with both full and partial overlap of the target with respect to ego vehicle. The effect of reduced chirp count and critical maneuvering on tracker performance is studied.
Do Developers Modify Dead Methods during the Maintenance of Java Desktop Applications?

Background: Dead code is a code smell. It can refer to code blocks, variables, parameters, fields, methods, classes, etc. that are unused and/or unreachable. Aim: Results from past empirical studies indicate that dead code is widespread in both desktop and web-based software applications. Also, researchers have shown that both comprehensibility and maintainability of source code are negatively affected when dead code is present. Nevertheless, we still know little about maintenance operations involving dead code. Method: We conducted an exploratory empirical study on 13 open-source Java desktop applications, whose software projects were hosted on GitHub, to provide preliminary evidence on whether, and to what extent, developers modify dead code—more specifically, dead methods—when they deal with the maintenance of open-source Java desktop applications. Results: The most important results of our study can be summarized as follows: (i) developers modify dead methods; (ii) dead methods are modified to a different extent as compared to alive methods; (iii) developers spend time modifying dead methods that are removed in subsequent commits; and (iv) developers modify dead methods that are later revived to a different extent as compared to dead methods that are later removed. Conclusions: One of the conclusions of our study is: developers should remove dead methods, whose presence and purpose are not properly documented, to avoid unnecessary modifications to dead methods during the maintenance of software applications.
Hadamard matrices related to a certain series of ternary self-dual codes

In 2013, Nebe and Villar gave a series of ternary self-dual codes of length $2(p+1)$ for a prime $p$ congruent to $5$ modulo $8$. As a consequence, the third ternary extremal self-dual code of length $60$ was found. We show that the ternary self-dual code contains codewords which form a Hadamard matrix of order $2(p+1)$ when $p$ is congruent to $5$ modulo $24$. In addition, it is shown that the ternary self-dual code is generated by the rows of the Hadamard matrix. We also demonstrate that the third ternary extremal self-dual code of length $60$ contains at least two inequivalent Hadamard matrices.
Joint Modeling of Image and Label Statistics for Enhancing Model Generalizability of Medical Image Segmentation

Although supervised deep-learning has achieved promising performance in medical image segmentation, many methods cannot generalize well on unseen data, limiting their real-world applicability. To address this problem, we propose a deep learning-based Bayesian framework, which jointly models image and label statistics, utilizing the domain-irrelevant contour of a medical image for segmentation. Specifically, we first decompose an image into components of contour and basis. Then, we model the expected label as a variable only related to the contour. Finally, we develop a variational Bayesian framework to infer the posterior distributions of these variables, including the contour, the basis, and the label. The framework is implemented with neural networks, thus is referred to as deep Bayesian segmentation. Results on the task of cross-sequence cardiac MRI segmentation show that our method set a new state of the art for model generalizability. Particularly, the BayeSeg model trained with LGE MRI generalized well on T2 images and outperformed other models with great margins, i.e., over 0.47 in terms of average Dice. Our code is available at https://zmiclab.github.io/projects.html .
Estimation of Vertical Jump Height Based on IMU Sensor

To validate inertial measurement unit for the estimation of jump height and flight time of vertical jump, the IMU sensor was positioned on the back of the participant at L5 vertebrae. The initial rotation angle between sensor plane and ground plane was estimated by using the average value of the three-axis acceleration in the static process before jumping. The human posture in the process of vertical jump is compensated based on the IMU data. The take-off time and jump height of vertical jumps were calculated then. The linear programming method is used for the flight time and jump height measured by IMU and force plate. The correlation coefficients can reach 0.9998 and 0.9985 respectively. The mean absolute error and relative error of jump height calculated by IMU are 0.89cm and 0.0914 respectively. The analysis results show that IMU can be used to evaluate the vertical jump height effectively and reliably.
Detection of Multiple Emotions in Texts Using Long Short-Term Memory Recurrent Neural Networks

Recognizing emotions from text applies to every part of our lives, like enhancing human-computer interaction, mental health monitoring, recognizing public sentiment about any national, international, or political event. Given the importance of emotion analysis, especially the classification of multi-labeled emotions, this paper proposes a deep learningbased system to address the issue of classifying multi-labeled emotions in texts. Toward this aim, by combining several datasets, a dataset first created which all samples are multilabeled, and then, using the long short-term memory recurrent neural network (LSTM), a new network is designed to detect multiple emotions from the texts. The GloVe and FastText have been used to find semantic, syntactic, and related words. Moreover, the attention property is utilized to improve the accuracy of the network. The comparative results indicated that the proposed model performs better compared to the existing methods in terms of accuracy.
Knowledge Representation and The Semantic Web: An Historical Overview of Influences on Emerging Tools

Abstract: suite of standards known as the Semantic Web is transforming the Internet into a semantic graph rather than a graph of hypertext links. This paper will describe how various ideas and initiatives in artificial intelligence knowledge representation influenced its design. We begin with the seminal work by Alan Turing and Alonzo Church that led to the definition of Turing Machines, enabled digital computing, and provided the mathematical theory of computation, which has been one of the determining factors for Artificial Intelligence knowledge representation. We then provide a brief history of artificial intelligence knowledge representation, starting with groundbreaking researchers, such as Newell and Simon, then to the first "AI boom" driven primarily by rule-based expert systems, followed by major initiatives such as Cyc and the DARPA Knowledge Sharing Initiative. We will discuss how innovations from these initiatives affected standards that, in turn, led to the suite of standards known as the Semantic Web. We conclude with a brief overview of the most important issues currently facing those who wish to see widespread adoption of Semantic Web technology in the industry.
Empowerment of LMS "Prometheus" for MPEI Educational Process

The paper presents the operation of the learning management system "Prometheus" with additional capabilities, which are not provided by the standard specification of the system. The empowerment of the learning management system was made to organize high-quality training within educational environment of National Research University "Moscow Power Engineering Institute". The technique of automatic generation of test tasks in the format of LMS "Prometheus" is described. The export of students’ grades from the LMS "Prometheus" to MPEI educational process information system "BARS" is considered. The integration of objects into the testing system of LMS "Prometheus" is considered, scenarios for using PDF multimedia objects in the testing system is shown.
Growing Up in a Complex World

The interaction design community has a long history of design and research for and with children, including designing installations for public spaces. This paper explores children's engagement in socio-cultural issues through speculative installations exhibited in a cultural institution. Over the past ten years, we explored the boundaries between technology, play, learning, and mastery through more than forty interaction design student projects collaborating with various cultural institutions. Although only a portion of projects focused on time-relevant socio-cultural issues such as pollution, refugee crises, or climate change, they opened for reflections on possible ways of including children in dialogues concerning contemporary challenges. The paper contributes a framework for designing and analyzing speculative installations for children based on the 'darkness scale' (reflecting the seriousness and complexity of the context), scaffolding engagement, age-appropriate speculative pointers, and linking the present with a desirable future. We showcase two installations and use the framework to discuss them.
Clustering based detection of small target pedestrians for smart cities

Detecting small targets in large images is challenging, especially for pedestrian detection in the field of self-driving cars for smart cities, for at least two reasons: (1) the pixels of small targets are blurry and are affected by the background; (2) the distribution of small targets is generally sparse and uneven, leading to the reduction of the detection efficiency. In this manuscript, we propose a clustering based scheme for the detection of small target pedestrians to address above difficulties. Clustering detection yields object cluster regions and gives an estimation of the object scales for these regions, so the clustered region is consistent with the input size of the fine detection model. Then, the detection results of small target pedestrians in the clustered region are obtained and restored to the input image to achieve the final detection results. Our detection strategy has several advantages over previous solutions. It saves a lot of computational power and running time which is vital for self-driving cars. Furthermore, the detection model is replaceable, making our scheme an easy solution to more complex tasks. We demonstrate the effectiveness of our scheme on both YOLOv4 and Faster R-CNN as their detection performance can be greatly improved.
Cryptographic–Biometric Self-Sovereign Personal Identities

This article describes a hybrid of self-sovereign identities (SSIs), cryptographic authentication, and biometric authentication, which allows remote yet secure proof of identity, all while addressing privacy concerns. The proposed technique addresses cooperative attacks, which are unique to the SSI embodiment of blockchain technology.
Extracting Environments from Function Closures

In this paper, we study the mechanism for extracting the environment from a function closure in the framework of the lambda calculus. We propose the lambda calculus with environment extraction, which extends environment calculus. We define the untyped calculus: the syntax and the operational semantics. We show that the operational semantics are consistent using translation of the lambda calculus with environment extraction into the lambda calculus with records and pairs. Next, we give a simple type theory to the lambda calculus with environment extraction and prove that the type system holds the subject reduction theorem.
Comparative Study on Different Color Spaces for Segmentation of Acute Leukemia using Automatic Otsu Clustering

Leukemia is common cancer that occurs due to the abnormality in the production of white blood cells in the human blood. This disease which is likely to affect youngsters below the age of 15 years old can be detected by analysing bone marrow samples. However, the process of extracting bone marrow samples is complicated and this could cause patients to experience discomfort, hence blood samples could serve as alternatives since it also contains white blood cell (WBC) information that was needed in determining acute lymphocytic leukemia (ALL). As the development of ALL is quite fast, the early detection of the diseases is vital; hence this segmentation method was developed. The blood images of ALL patients will first be converted from RGB to HSV and Lab color space before the grayscale images produced used as the input to Otsu by clustering technique resulting in black background images while the cells will be staying in white color. Next, the watershed ridge technique has been used to remove the overlapping cells before retrieving the colored version of the WBC images. The results from testing the image segmentation method using a different component from different color spaces showed that the images applied with component b from the Lab color space proved to produce the clearest image of ALL subtypes compared to the other color components applied as the accuracy produced for B cell, T cell and normal cell were at 99.17%, 99.88%, and 99.92% respectively.