Reviewerly/exHarmony huggingface dataset:
Efficient spam filtering through intelligent text modification detection using machine learning

Spam emails have long been a source of concern in the field of computer security. They are both monetarily and technologically costly, as well as extremely harmful to computers and networks. Despite the rise of social networks and other Internet-based information exchange venues, email communication has become increasingly important over time, necessitating the urgent improvement of spam filters. Although various spam filters have been developed to help prevent spam emails from reaching a user's mailbox, there has been little research into text modifications. Because of its simplicity and efficiency, Naive Bayes is currently one of the most used methods of spam classification. However, when emails contain leetspeak or diacritics, Naive Bayes is unable to correctly categorize them. As a result, we created a novel method to improve the accuracy of the Naive Bayes Spam Filter to detect text alterations and correctly classify emails as Spam or ham in this proposal. When compared to Spamassassin, our Python approach uses a combination of semantic, keyword, and machine learning algorithms to improve Naive Bayes accuracy. Furthermore, we identified a link between email length and spam score, indicating that Bayesian Poisoning, a contentious concept, is an actual occurrence used by spammers.
Improved Feature Generating Networks for Zero-Shot Learning

The purpose of zero-shot learning (ZSL) is to identify pictures of unseen classes, and there is no intersection between the training set and the test set. While the purpose of generalized zero-shot learning (GZSL) is to identify images from not only unseen classes but also seen classes, which is more challenging. Plenty of current ZSL and GZSL recognition methods are founded on feature generation methods like Variational Autoencoder (VAE), Generative Adversarial Networks (GAN) and so on to extenuate the data disproportion problem. While the previous works rarely focus on whether the features extracted from raw images have an impact on these generative models. In our work, we propose a novel architecture so as to ameliorate the original feature to enhance the performance of generating Networks not only for zero-shot Learning (ZSL) but also for generalized zero-shot learning (GZSL). Our approach utilizes a specific large-scale pre-trained model to gather the features from three distinct granular datasets namely AWA2, CUB, and SUN. Then, we concatenate the feature generated by this large-scale pre-trained model and the new feature of classification for training purposes generated by a dimensionality reduction method. Based on the state-of-the-art models, our means can raise the precision rates by about 15% for ZSL, and about 9.5% for GZSL, in all the conducted experiments on the three datasets. We further visualize the original feature and the feature processed by our method through t-SNE, and the result shows the feature data of the same class is more compact.
Alessandro Capone, Pragmatics and philosophy: Connections and ramifications (Perspectives in Pragmatics, Philosophy &amp; Psychology <b>22</b>). Cham: Springer, 2019. Pp. iv + 311.

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An Application-Oriented Survey on the Adaptability of Artificial Intelligence for Natural Language Processing

The application domains for artificial intelligence and machine learning are expanding exponentially in recent times. The domains such as automation in software and business processes, healthcare, agriculture, robotics, and mostly natural language processing have seen the most adaptations. The domain of natural language processing seeks the maximum adaptation due to various sub domain applications such as textual classification, detection of emotions, design and delivery of virtual assistant tools, extraction of knowledge, content summarization, content recommendations, user profiling for content consumption habits, grammar and dialect verification, and text summation. These subdomains cater to a wide range of purposes such as user profiling or emotion extraction for surveying or feedback analysis for data-driven applications.
Affective Interaction in UrbanIxD: Designing for the Lived Urban Experience

Individuals and communities develop affective connection to places. These experiences form an intrinsic part of how they relate to and interact in the built environment, but have received little attention in the smart city agenda. Understanding affect is critical to the design of successful places, and, moreover, to motivating a wide range of changes in cities, such as increased participation or pro-environmental behaviors. This research answers calls in HCI and urban interaction design (UIxD) for a better understanding of affective, place-based experiences. I develop rich, situated understanding of how places function and the types of emotions individuals and communities develop with respect to them, in order to design augmented urban experiences. By probing, speculating and prototyping interactions, I show how affective connection to places can be captured for digital placemaking, which instances of affective interactions can be imagined in future cities, and how such processes can be appropriated by designers, researchers and other disciplines in the built environment. This research ultimately contributes to a vision of a more responsive built environment to human emotion.
Students’ Opinions on Online Learning Management during the COVID-19 Situation

This study was to explore students’ opinions on online learning management during the COVID-19 situation and additional suggestions in order to improve students’ learning outcomes for the next semester. The population of this study was 555 second year students who enrolled in the TOEIC Preparation course in the second semester of 2021 academic year. The samples in this study were 150 students derived through Simple Random Sampling Technique. Research instrument used in this study was a questionnaire. Data analysis was percentage, mean, standard deviation, and content analysis. The research findings were: 1) students had the highest opinion at a high rank in materials, followed by accessibility and assessment; and 2) the additional suggestions were listed as quizzes and games applications should be used as the classroom activities; more listening activities and practices should be concerned; vocabulary exercises are important for students to review the vocabulary in the TOEIC test; and online applications for practice TOEIC reading should be used in the classroom learning.
AI/ML, Optimization and EDA in the TILOS AI Research Institute

Optimization means finding the best possible solution to a given problem -- but challenges of scale and complexity keep many real-world optimization needs beyond reach. The Institute for Learning-enabled Optimization at Scale (TILOS) is a new National AI Research Institute led by UC San Diego in partnership with MIT, National University, Penn, UT Austin and Yale. TILOS is sponsored by the U.S. National Science Foundation, with partial support from Intel Corporation. The TILOS mission: make impossible optimizations possible, at scale and in practice. This talk introduces TILOS, its research agenda, and how it aims to establish a "national nexus" of AI and machine learning, optimization, and use domains that include integrated-circuit design and design automation. The talk will point out directions along which the interplay of learning and optimization can boost the scaling and quality of EDA outcomes.
Saliency Methods Analysis for Paintings

The topic of visual saliency is well-known and spreads across numerous disciplines. In this paper, we examine how saliency models perform on images with specific characteristics. We explore a saliency of 14 different digitized paintings, all representing a biblical scene of The Last Supper. We evaluate the performance of different saliency models. The models are using the traditional approach as well as the deep learning approach. For the evaluation, we use three different metrics, AUC, NSS and CC. As ground truth, we use eye-tracking data from 35 participants. Our analysis shows that deep-learning methods predict the most salient parts of the paintings very similar to real eye fixations. We also checked the consistency of gaze patterns among the participants.
A Novel, RRT* Based Approach to the Coordination of Multiple Planetary Rovers

The use of multiple autonomous rovers could significantly extend the scientific capabilities of future planetary exploration missions. Central to this is the ability to safely coordinate the planned paths of such robots. Previous work has shown that RRT* is a method well suited to single-agent path planning. The aim of this work is to evaluate the use of a RRT* path planner first for a single planetary rover, then applied to different coordination strategies for a small team of rovers as a multi-agent method of path planning. Two centralised coordination methodologies are applied: fixed-path coordination and prioritised planning. An evaluation is carried out, based on a comparison of the number of collisions and the time taken to plan paths, where the prioritised planning coordination algorithm is selected as the most appropriate for this application. The results drawn from this work suggest that the combined use of prioritised planning and RRT* could be appropriate for use in the guidance, navigation and control systems of planetary rovers.
Traffic Classification of SDN Network using Machine Learning Algorithms

Traffic classification is becoming an important research area especially with the day-to-day machine learning software defined networking advancements. It is utilized for networking executives, administration estimation, network plan, security observing, and publishing. The proposed model used two machine learning models logistic regression and kmeans are used to classify ping, telnet, dns, and voice flows(VoIP) with the utilization of an Internet-based motor (DITG) that utilizes two techniques for Artificial Intelligence (AI). The host on these organizations is associated with the open vswitch (Ovs) over the two organizations. The Ovs are joined by a reviewer named Ryu (signifying "streaming" in Japanese. Ryu is designated "ree-yooh") and gathers street figures between the beneficiaries. These figures are examined by Python's robotized archive arranging framework and learned street signs are carried out. The proposed work will classify the type of network traffic flowing between two hosts.
Robustness Assurance Quotient: Demonstrating Context Matters for AI Performance and ML Security

We present a novel approach to developing robust AI in light of context-varying situations. This methodology harnesses a suite of indicators to establish a Robustness Assurance Quotient (RAQ) tailored to address environmentally noisy data while maintaining parity with current standards, namely the Fréchet Inception Distance (FID) metric. While the FID metric successfully indicates the closeness of highly structured data, it is less successful measuring closeness of inherently noisy data. A score for ascertaining the closeness between Generative Adversarial Network (GAN)-simulated, inherently noisy radiofrequnecy data and original GAN signals are proposed by focusing on the subject and context of the image. The RAQ demonstrably improved our GAN’s generated content similarity to original waterfall images of radiofrequency signal. We believe our Robustness Assurance Quotient can have a profound impact on improving the robustness of various AI models, in diverse application domains ultimately reducing the negative impact of noisy data.
Salient Region Detection via Multiple Hierarchy and Competition Mechanism

The detection of salient regions has attracted an increasing attention in machine vision. In this study, a novel and effective framework for saliency region detection is proposed to solve the problem of the low detection accuracy of traditional methods. Firstly, we divide the image into three levels. Second, each level uses three different feature methods to generate different feature saliency maps. Subsequently, a novel integration mechanism, termed competition mechanism, is introduced into the coarse saliency maps at the same level, and the two coarse saliency maps with the highest similarity are selected for fusion to ensure the effectiveness of the salient region map. Accordingly, after adjusting the scales of the saliency map after the fusion of different levels, among three coarse saliency maps of the different levels, the two feature maps with the most significant difference are selected to fuse to further obtain the final refined saliency map. Finally, using the proposed method, experiments on three benchmark datasets were conducted. As demonstrated by the experimental results, the proposed algorithm is superior to other state-of-the-art methods.
Design and Implement of University Teachers’ Workload Management System

Abstract In order to achieve the information management of the workload of university teachers and effectively raise the work efficiency of the staff, in this paper, we used platforms like Microsoft Visual Studio 2015 and Microsoft SQL Sever 2012, developed a professional management system based on C/S architecture, which aims to efficiently and informationally manage the scientific research and teaching work information of the university teachers. The system is easily to operate and it enables the functional improvement. Facilitate the statistics and rewards of the teachers’ workload, and also helps teachers to fully understand their own work and pave the way for the next work. It is of great significance to stimulate teachers’ work enthusiasm and improve work efficiency.
Monitoring soil mercury content based on hyperspectral data and machine learning methods

Mercury (Hg) is a heavy metal pollutant characterized by global migration, high bioaccumulation, and high biological toxicity. Soil Hg pollution seriously harms soil environmental quality and human health. The higher the content of Hg in the soil, the more serious the harm. Therefore, the acquisition of soil Hg content information is crucial for soil monitoring and restoration, and human health. The purpose of this study is to establish the best hyperspectral prediction model of soil Hg content in the study area. A total of 90 soil samples were collected from the mining areas and suburbs, and the Hg content was analyzed. Several preprocessing methods were applied to the hyperspectral data. Partial least-squares regression (PLSR), orthogonalized PLSR, random forest, and back propagation neural network (BPNN) were used to establish the relationship between soil hyperspectral data and soil Hg content to predict the soil Hg content. Specifically, the transferability of the best prediction model in yellow-brown and cinnamon soil data was discussed. The results showed that BPNN + standard normal variable + full-band (4 nm) is the optimum method to predict soil Hg content (Rtrain2=0.94, Rtest2=0.85). The transferability of the model is the best in yellow-brown soil. This research provides technical support for the rapid monitoring of soil Hg pollution information in central and eastern Shaanxi-Province, and the results enrich prediction models of soil Hg content in different soil types.
A workload evaluation method for electric power design documents

Under the background of the generation of big data, the number of power design documents has increased sharply. It is inefficient to use traditional manual methods to count the workload, and there is a lack of effect of automatic and accurate evaluation. Traditional data processing methods fail to mine the structural spatial features in design documents, which is difficult to adapt to the spatial transformation of design elements, resulting in inaccurate feature information. For the calculation method, the traditional method uses simple weighted calculation, which is difficult to describe the complex nonlinear relationship between eigenvalues and workload. In this paper, a calculation method of power design document workload based on neural network is proposed. After obtaining the common graphic information in the power design document with known workload, the graphic data is simplified and normalized to the first eigenvector; Taking the largest B feature coefficients of each type of element as the feature vector of this type of element, and combining the feature vectors of all types of elements into the second feature vector; The third feature vector is obtained by the offset angle of any two entities relative to the main direction of any one of the two entities. The trained deep neural network model is used to obtain the workload of power design documents to be evaluated. According to the characteristics of power design documents, the evaluation method can effectively represent the structural spatial relationship and related invariance features of graphic elements in design documents. It relies on extracting the number of graphic elements in design documents, the spatial structure tightness of graphic elements and the direction statistics of graphic elements as the local and global feature representation information, the deep neural network structure model is introduced as the learning and training model to mine the nonlinear structural relationship between features. Through modeling, the workload of power design documents is automatically processed and analyzed to improve the accuracy of workload evaluation and calculation. The establishment of the model is suitable for all kinds of graphic design documents, which is universal and practical.
Metadata definitions for a set of notional use cases in the IEEE P4001 standard for hyperspectral imaging

Hyperspectral images are ultimately analyzed by users for specific applications. Metadata supplied with the images are critical to enable the user to extract full information from the data. An important part of IEEE P4001 Hyperspectral Standard is therefore to define metadata, which will be physics-informed and related to the camera characteristics. With metadata such defined, it will for example be possible for a user to estimate signaldependent noise in the images. Different applications will require different levels of metadata. P4001 therefore defines notional use cases for machine vision, laboratory, and Geospatial Imaging, in order of increasing need for metadata. P4001 will define required and optional metadata for these use cases.
Construction of Correlation Analysis Model of College Students’ Sports Performance Based on Convolutional Neural Network

This paper proposes a network model recurrent fully connected network (RFC-Net) based on recurrent full convolution and polarization change. RFC-Net enriches the network by reconstructing and fine-tuning the fully convolutional network and adding recurrent convolutions to it. By studying the data mining technology of multidimensional association rules, based on the existing algorithms, this paper improves the shortcomings of the algorithms and realizes an efficient and practical method for data mining based on interdimensional multidimensional association rules. On the basis of mastering the actual student information, the effectiveness of the method is tested, and an employment analysis system based on association rules is established. Aiming at the fact that traditional grade prediction methods ignore the different influences of different behavioral characteristics on grades, and considering that behavioral data in different periods have different influences on student grades, the grade prediction problem is abstracted into a time series classification problem. The mechanism is combined with long short-term memory neural network to construct a performance prediction model based on Attention-BiLSTM. Experiments show that the prediction model proposed in this paper improves the accuracy and effectively improves the prediction quality compared with the logistic regression model with a better prediction effect in the traditional benchmark model and the long short-term memory neural network model without the introduction of the attention mechanism. Research shows that physical performance and academic performance are not contradictory. We must face up to the status of physical exercise in schools; as long as physical exercise is properly arranged, it can inspire students to form a spirit of unity, interaction, positivity, and perseverance in cultural studies.
Everything you control is not everything: Achieving intention-concealed visit on social networks

With the flourishment of Artificial Intelligence (AI), the quality of services on online social networks (OSNs) has improved tremendously. Through the introduction of AI into OSNs, providers enhance the users experience. However, the risk of data misuse has also been magnified. And there has been a trend towards users’ mistrust when it comes to sharing their true intention on OSN platforms, primarily because of privacy concerns. In this paper, we propose an effective intention-concealing visitation framework named Aviv, which acts as a credible and private third-party where it generates a rtificial i ntention- c oncealed b rowsing (AICB trajectories) for users to conceal their true intentions. The browsing trajectory is a sequence composed of blogs, bloggers, news, or a mixture containing visitable content. Specifically, Aviv first extracts accessible graphs composed of visitable contents from applied OSN platforms. Then an elaborate and personalized divert scoring process is conducted to measure optional decoy visits’ effectiveness. Finally, using the divert scoring values, elaborate and personalized decoy visits are picked out, Aviv composes AICB trajectories with true intentions and picked decoy visits. Aviv is implemented as a credible private third-party and tested on a real OSN, the performance evaluation results show that Aviv is effective and efficient.
Emotion Analysis Model Based on Multi-head Attention and Tree_LSTM

Most of the emotion analysis models based on attention mechanism and neural network adopt single-layer attention mechanism, and the feature expression is relatively single, which can not be applied to various types of sentences. Based on the existing attention mechanism and neural network model, this paper proposes a method to fuse emotional information and combine multi head attention mechanism and Tree&#x005F;LSTM&#x0027;s emotion analysis model. aims to enable the model to obtain more levels of information about sentences in different representation spaces, dynamically adjust the feature weight through the multi head self attention mechanism, and introduce emotional information on the basis of existing network input to further obtain more text syntax information and improve the feature expression ability of the model. Finally, the emotion category is obtained by softmax classifier. The experimental results of coae2014 microblog data set show that compared with Bi&#x005F;LSTM, CNN&#x005F;LSTM, Self-attention&#x005F;BILSTM, Self&#x005F;attention&#x005F;Tree&#x005F;BiLSTM model, which further improves the performance of emotion classification.