Reviewerly/exHarmony huggingface dataset:
Data-Driven Wireless Anomaly Detection Using Spectral Features

In this work, we present an anomaly detection algorithm for wireless spectrum data and evaluate its ability to accurately detect interfering transmissions. The algorithm considers three types of interfering signals: a co-channel transmission, an interfering continuous-wave transmission, or an adversarial replay attack. At the core of each anomaly detector is a feature space transformation, which learns a compressed representation of the data, followed by various metrics on this representation. Four feature space transformations and four detection metrics are investigated, each of which represents different characterizations of the distribution of the data, totaling 16 configurations of the anomaly detector. Our algorithm is evaluated using Wi-Fi spectrum data from real-world radio frequency (RF) captures in both mild and harsh channel conditions. We present an analysis of the performance of the various configurations under each of the interference types at varying signal-to-interference ratios. Lastly, we discuss the implications of this performance on the the distribution of the data and the recommended model to detect each type of anomaly.
Evolving Interpretable Classification Models via Readability-Enhanced Genetic Programming

As the impact of Machine Learning (ML) on busi-ness and society grows, there is a need for making opaque ML models transparent and interpretable, especially in the light of fairness, bias, and discrimination. Nevertheless, interpreting complex opaque models is not trivial. Current interpretability approaches rely on local explanations or produce long explanations that tend to overload the user's cognitive abilities. In this paper, we address this problem by extracting interpretable, transparent models from opaque ones via a new readability-enhanced multi-objective Genetic Programming approach called REMO-GP. To achieve that, we adapt text readability metrics into model complexity proxies that support evaluating ML interpretability. We demonstrate that our approach can generate global interpretable models that mimic the decisions of complex opaque models over several datasets, while keeping model complexity low.
Knowledge-Driven Program Synthesis via Adaptive Replacement Mutation and Auto-constructed Subprogram Archives

We introduce Knowledge-Driven Program Synthesis (KDPS) as a variant of the program synthesis task that requires the agent to solve a sequence of program synthesis problems. In KDPS, the agent should use knowledge from the earlier problems to solve the later ones. We propose a novel method based on PushGP to solve the KDPS problem, which takes subprograms as knowledge. The proposed method extracts subprograms from the solution of previously solved problems by the Even Partitioning (EP) method and uses these subprograms to solve the upcoming programming task using Adaptive Replacement Mutation (ARM). We call this method <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{PushGP}+\text{EP}+\text{ARM}$</tex> . With <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{PushGP}+\text{EP}+\text{ARM}$</tex> , no human effort is required in the knowledge extraction and utilization processes. We compare the proposed method with PushGP, as well as a method using subprograms manually extracted by a human. Our <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{PushGP}+\text{EP}+\text{ARM}$</tex> achieves better train error, success count, and faster convergence than PushGP. Additionally, we demonstrate the superiority of PushGP+EP+ARM when consecutively solving a sequence of six program synthesis problems.
Emotion and Sentiment Recognition using Natural Language Processing and Machine Learning Techniques during Disaster Situation

Detecting emotions of the residents during disaster scenario is important for the government agencies to properly take care of its constituents. COVID-19 is a global disaster scenario that has caused unprecedented shutdowns, unemployment, death, and isolation. The behavioral and emotional health impact of COVID-19 is investigated in this study through the use of sentiment analysis and emotion recognition. The dataset is formed by collecting tweets from the seven months before COVID-19 became prevalent in March 2020 and the following seven months after. VADER sentiment analysis method was used to determine if a tweet was positive, negative, or neutral. For emotion recognition, several machine learning algorithms were evaluated and Convolutional Neural Network (CNN) Long-Short Term Memory (LSTM) performed better than the other models. Hence, CNN-LSTM was used to classify the emotion of each tweet as either anger, fear, joy, or sadness. Each tweet has a longitude and latitude stored with it that was geocoded to give the exact location, which was used to compare the states within the USA, and finally compare the USA as a whole with Canada, and Mexico. Sentiment analysis shows that all countries have experienced an increase in negative tweets. Emotion recognition shows that compared to Canada and Mexico, USA has experienced a steep drop in emotional health.
Construction of English Translation System Based on Computer Software Technology

In the era of industrialization in pursuit of efficiency, the role of computer software technology in modern translation practice is becoming increasingly prominent. With the continuous growth of people’s knowledge, computer software technology is not a new term for people. In this paper, the English translation system based on computer software technology is roughly designed, and the Bleu algorithm is used to analyze and compare the passing rate of computer translation and the success rate of word scanning and translation of different English translation software. The results show that the success rate of word scanning is the highest 100%, the lowest 96%, the success rate of translation is the highest 99.7%, and the lowest 99.2%. In addition, the evaluation of teachers and students on the accuracy of computer translation is investigated and analyzed by means of questionnaire. The results show that 44.2% of teachers think the translation accuracy is low, and 21.5% of students think the translation accuracy is low.
Security Assurance through Penetration Testing

Penetration testing is a type of security testing used to determine whether an application is securely built by exploiting the vulnerabilities in a system. The vulnerabilities can be discovered and found in the phases of implementation or operation of a system or application. Most importantly, penetration testing proceeds in a white hat state in which the permission of simulations and attacks are first granted by the system owner. In this paper, a penetration testing demonstration will be carried out along with the tools and technique. Kali Linux will be the system for penetration testing as it fulfilled the requirements of professional penetration testing and security auditing. The penetration test will be performed in a pre-configured private network, virtualized system, and devices with various tools. The aim of the paper will be on a detailed concept of implementation of a penetration test including the area of its impact, general function, and critical analysis. The methodologies of the attacks that performed in this paper included reverse shell exploitation, advance port scanning, and password brute-forcing. Besides, an overview of a penetration test including the phase and its process will be introduced and brought into this demonstration and a detailed description of the attacks was included in the paper.
Intelligent Tourism System Based on Android Platform

The tourism market maintains a rapid growth momentum. More people can learn about the destination, arrange accommodation and travel through the mobile network. The main purpose of this paper is the design and implementation of the tourism system based on the Android platform, combined with the traditional tourism products and intelligent terminal technology, to provide a design scheme and implementation method of the intelligent tourism system based on the Android platform. The system hardware adopts the ARM platform, and the Android operating system is transplanted to this platform. In terms of software system construction, MyEclipse is used as the development system and Simple Net is used as the implementation framework; the Android client uses Java and Mysql languages to create a back-end database system. Finally, Apache HTTP Server Performance Tester can be used to test the system on a company site with 2000Mbps network bandwidth. The average server usage does not change much during the test, less than 5%. The system can still maintain good performance to provide services for users.
Evaluation of Core point Descriptors Enforced in the Pedestrian Identification in Lower Quality Images

Pedestrian identification is an inexhaustible and sufficiently large task in any hyper intelligent videotape monitoring, as it supports abecedarian information for a more in-depth understanding of the videotape footage. Because of the possibility of perfecting safety systems, it contains an egregious extension to automotive operations. This is available as an ADAS option from a number of automakers. The pedestrian identification network was trained using images of pedestrians and non-pedestrians. The train PedNet.m coadjutor script is used to train this network in Computer Vision. The sliding window method reduced the size of the image patches to (64 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> 32). The patch dimensions are derived from a heat map that depicts the dispersion of pedestrians in the images in the data set. The patch dimensions are derived from a heat map that depicts the dispersion of pedestrians in the images in the data set. It denotes the absence of pedestrians at various scales and locations in the images. Patches of pedestrians closer to the camera are cut and processed in this illustration. Non-Maximal Suppression (NMS) refers to the patches that have been obtained in order to combine them and display entire pedestrians. The composition focuses on the achievement of the linear binary pattern (LBP) abstract background fluctuations capturing and Histogram of Oriented Gradients (HOG) alternatives with altered parameters to categorizing is attained by Support vector machine Classifier in this evaluation.
The Classification and Classification of Big Data Based on the Internet of Things

With the rapid development of the information Internet, data resources have become the new oil and new driving force to promote the development of national economy and social life. The impact of data on the development of media, technology, business and other fields is becoming more and more profound. People are constantly creating data in technological life, and they are also affected by the constantly generated massive data. This article studies the theory of big data classification and classification based on the Internet of Things, and describes the important application of big data classification and classification for social and economic development. The data test shows that the big data classification and classification research based on the Internet of Things has high efficiency and accuracy in the classification and classification of big data.
Forensic sketch-to-photo transformation with improved Generative Adversarial Network (GAN)

Many of the limitations of Convolutional Neural Networks started to become apparent with the development of "Capsule neural networks", and more research is now being done on Capsule Networks since it is believed that it will be the state-of-the-art for image classification tasks in the future. In this paper, the discriminator inside a GAN for face sketch to photo translation employs capsule neural network layers rather than the traditional convolutional layers. Incorporating the Capsule design into the Discriminator will improve the classification loss and speed up the convergence compared to using traditional convolutions. With 188 hand-drawn sketch-photo pairings available in the CUHK dataset and constructed 1000 sketch-photo pairs of CelebA HQ dataset, our proposed method is evaluated. The results of the experiments demonstrate that the proposed approach can generate images that are visually extremely near to the real photos and have rich texture information and features. The translation of face sketch to photo can greatly help the law enforcement agencies to track and arrest the criminals.
SOM Neural Network Based Gaussian Mixture PHD Algorithm for Multi-Sensor Multi-Target Tracking

In the multi-sensor multi-target tracking (MSMTT) problem, the matching of the measurements and the targets will generate a huge computational burden, resulting in an unsatisfactory real-time performance of the maneuvering targets tracking. In order to reduce the computational burden, this paper proposes a self-organizing feature map (SOM) neural network based Gaussian mixture probability hypothesis density algorithm (SOM-GMPHD). Firstly, a distributed filtering MSMTT algorithm based on SOM neural network is proposed. The distributed SOM-GMPHD algorithm (DSOM-GMPHD) has two fusion steps. Secondly, to further reduce the computational complexity, a centralized SOM-GMPHD algorithm (CSOM-GMPHD) with only one-step fusion is proposed. The computational complexity analysis of the existing MSMTT algorithms (DGMPHD and CGMPHD) and the proposed SOM-GMPHD algorithms are carried out in this paper. Finally, the effect of the proposed algorithms is evaluated in the simulation experiment.
Professional Practices and Classroom Engagement in Online and Blended Teacher Training Environments

Blended learning, a combination of the conventional physical learning in classrooms with online instructions, has become a significant approach in online teaching and learning, especially after the COVID-19 pandemic. While various schools have succeeded in this pedagogical practice, challenges have been inevitable. This chapter intends to address the significance of online and blended learning in teacher professional development and its desirable attributes. The content presented in this chapter is helpful to both pre-service and in-service teachers in the relevant professional development, as well as overcoming the myths and challenges of blended learning.
Using Bayesian Networks for Cognitive Assessment of Student Understanding of Buoyancy: A Granular Hierarchy Model

ABSTRACTCognitive diagnostic assessment based on Bayesian networks (BN) is developed in this paper to evaluate student understanding of the physical concept of buoyancy. we propose a three-order granular-hierarchy BN model which accounts for both fine-grained attributes and high-level proficiencies. Conditional independence in the BN structure is tested and utilized to validate the proposed model. The proficiency relationships are verified and the initial Q-matrix is refined. Then, an optimized granular hierarchy model is constructed based on the updated Q-matrix. All variants of the constructed models are evaluated on the basis of the prediction accuracy and the goodness-of-fit test. The experimental results demonstrate that the optimized granular-hierarchy model has the best prediction and model-fitting performance. In general, the BN method not only can provide more flexible modeling approach, but also can help validate or refine the proficiency model and the Q-matrix and this method has its unique advantage in cognitive diagnosis. AcknowledgmentsThis research was supported by Humanities and social science Foundation of Ministry of Education of China (No. 22YJCZH160), and partially supported by Humanities and social science Foundation of Ministry of Education of China (No. 22YJC880065) and National Nature Science Foundation of China (No. 31900794, No. U1911201, and No. 32071093)Disclosure statementNo potential conflict of interest was reported by the authors.Correction StatementThis article has been republished with minor changes. These changes do not impact the academic content of the article.Additional informationFundingThe work was supported by Humanities and Social Science Foundation of the Ministry of Education of China [22YJCZH160], and partially supported by Humanities and Social Science Foundation of the Ministry of Education of China [22YJC880065] and National Nature Science Foundation of China [31900794, U1911201, 32071093].
Reconstruction of the Logic and Process of Building an Artistic Image (Based on the Image of Sarah in Meir Shalev's Novel "Esav")

The article describes the experience of reconstructing the logic and process of building an artistic image. Reconstruction includes two stages: analysis of the creation of a specific artistic image and theoretical generalization in order to reach constructions that can be extended to other cases of the construction of artistic images. At the same time, the author in the reconstruction relies on his research of art and his works. In the latter, he examines, on the one hand, the problems that the artist solves, on the other hand, the answer to these problems, which is the construction of events of artistic reality. It is in this context that artistic images are created. Their function is twofold: it is an expressive means, one of the tools for building artistic reality, and at the same time the "bricks" from which the world of artistic reality is created. The characteristics of the image of Sarah from Meir Shalev's novel "Esav", the schemes and expressive means used in its construction are analyzed. The mystery of the impact of an artistic image on the reader is discussed. In the last part of the article, the author compares the artistic image with ideal objects in philosophy and science. He argues that an ideal object does not need to be perceived as an event, it is used in reasoning and cognition, where the main thing is not the vision and experience of a phenomenon, but the constancy, consistency of its properties; and the problems that a philosopher or scientist solves are different.
Advances in Automatic Meeting Minute Generation: A Survey

We faced the largest crisis of the twenty-first century at the start of 2020: the COVID-19 pandemic. In the midst of the turmoil, the generation ultimately found a method to get the job done by using automation in many aspects of life. Following the epidemic, we saw an 87% increase in video conferencing technologies for daily communications. Almost everything, from online gatherings to college lectures to business meetings, was housed on the internet, which, because it was virtual, increased the odds of ineffective interactions. In reality, statistics collected from employees across all domains reveal that people frequently miss essential points since taking minutes of meetings is a time-consuming, distracting, and extremely dull chore, and that over 37 billion dollars is squandered on ineffective meetings. Keeping track of significant decisions and agreements that were reached at a meeting requires the use of meeting minutes. The issues addressed and the choices made must be recorded in order to be reviewed at the start of the following meeting and for future reference. Many businesses retain salaried personnel to take minutes of meetings, using up valuable time and resources. We provide a method to enable staff members to have productive conversations that will increase a company's productivity by making greater use of the tools and technical improvements that are now accessible. Our approach extracts crucial information from significant debates using Deep Learning methods. The suggestion is for an automated method to record minutes and transcripts of a meeting with the benefit of speaker identification. The model we suggest will be able to recognise the speaker using Mel Frequency Cepstral Coefficient (MFCC)[12], convert an audio file into plain text using Deep Neural Networks (DNN), and summarise the meeting transcript into condensed minutes with the aid of Transformers.
A readiness assessment of an Indonesian pilot project: Case study Kedungputri, a premium irrigation area, Central Java

Abstract Modernization in irrigation has finally touched down in Indonesia with better efforts in technological advancement, institutional strengthening and human resource enhancement. The current project in Kedungputri has proven to hold the potential for modernization. This paper answers how ready the irrigation area status is for the modernization ideal. The ambitious project is performed on a premium irrigation area, an irrigation area with a reservoir as its water source, or in this case potential reservoir as the dam is still under construction. It is a strategic project, with its upstream Bener Dam constructed by the Government of Indonesia with financing of the World Bank's SIMURP (Strategic Irrigation Modernization and Urgent Rehabilitation Project). The analysis uses purposive sampling and qualitative methodology based on existing documents and evidence of the ongoing modernization project since 2019. This readiness status is based on five pillars of the Indonesian version of irrigation modernization. The findings show that the conditions of institutional arrangements have unique and significant impacts on the readiness status, which is covered under the soft components. We urge that modernization efforts should start with the soft components.
Domain-Conditioned Normalization for Test-Time Domain Generalization

Domain generalization aims to train a robust model on multiple source domains that generalizes well to unseen target domains. While considerable attention has focused on training domain generalizable models, a few recent studies have shifted the attention to test time, i.e., leveraging test samples for better target generalization. To this end, this paper proposes a novel test-time domain generalization method, Domain Conditioned Normalization (DCN), to infer the normalization statistics of the target domain from only a single test sample. In order to learn to predict the normalization statistics, DCN adopts a meta-learning framework and simulates the inference process of the normalization statistics at training. Extensive experimental results have shown that DCN brings consistent improvements to many state-of-the-art domain generalization methods on three widely adopted benchmarks.
Searching for N:M Fine-grained Sparsity of Weights and Activations in Neural Networks

Sparsity in deep neural networks has been extensively studied to compress and accelerate models for environments with limited resources. The general approach of pruning aims at enforcing sparsity on the obtained model, with minimal accuracy loss, but with a sparsity structure that enables acceleration on hardware. The sparsity can be enforced on either the weights or activations of the network, and existing works tend to focus on either one for the entire network. In this paper, we suggest a strategy based on Neural Architecture Search (NAS) to sparsify both activations and weights throughout the network, while utilizing the recent approach of N:M fine-grained structured sparsity that enables practical acceleration on dedicated GPUs. We show that a combination of weight and activation pruning is superior to each option separately. Furthermore, during the training, the choice between pruning the weights of activations can be motivated by practical inference costs (e.g., memory bandwidth). We demonstrate the efficiency of the approach on several image classification datasets.