Reviewerly/exHarmony huggingface dataset:
What Is the Cost of Privacy?

Grade research has to be replicable, thus the used data need to be publicly available. Speaking, e.g., about object detection task, where image data for autonomous driving also contain privacy information such as faces and license plates, the publication of data may be harmful to captured people. The solution to the moral dilemma is to anonymize the data. In this study, our aim is to investigate the effect of various anonymization techniques on the performance of algorithms that use such data. We discuss anonymization methods that remove and replace privacy data and select three methods to replace the privacy data: blurring, permutation, and replacing the area with a constant value. We adopted the Cityscapes dataset from which we extracted areas containing privacy information and are the manner of the anonymization methods. Our benchmark involves three famous object detectors: YOLOv3, Mask R-CNN with ResNet-50 backbone, and Mask R-CNN with Swin-T backbone. The results show that the impact of anonymization methods on the performance is negligible and the impact is similar for both convolutional-based and transformer-based backbones. Since the anonymization did not impact the model’s performance, we would recommend to anonymize datasets of this kind to prevent potential GDPR issues.
Scene-Adaptive Instance Modification for Semisupervised Pedestrian Detection

Limited supervision makes the task of pedestrian detection very challenging. Increasing the diversity of pedestrian samples is critical for semisupervised pedestrian detection (SPD). We propose to improve SPD from a new perspective by separating instance-specific content from scene style, thus performing content-based modification on the pedestrian samples collected from unannotated images of both target and external scenes. Toward this end, we propose a scene-adaptive and content-based instance modification model (SACIM), in which a pedestrian sample is represented by a scene-style code and a content code. One benefit of SACIM is that pedestrian samples from an external scene can be utilized by changing the corresponding scene-style codes. For the samples extracted from unannotated images, however, there is typically an issue of poor alignment. Another benefit is to address this issue by modifying their contents. The synthesized data will be used to improve pedestrian-background classification, which leads to more accurate pseudoannotations for training scene-specific pedestrian detectors. Extensive experiments with comparisons to the state-of-the-art SPD methods demonstrate the advantage of SACIM.
Reinforced Two-Stream Fuzzy Neural Networks Architecture Realized With the Aid of One-Dimensional/Two-Dimensional Data Features

A novel structure of reinforced two-stream fuzzy neural networks (TSFNNs) realized with the aid of fuzzy logic and transfer learning method is presented. This architecture consists of a TSFNN and a fusion strategy. TSFNN architecture consists of two combined networks of both fuzzy rules-based radial basis function neural networks (FRBFNN) and convolutional neural networks (CNNs). In the TSFNN architecture, one stream employs the deep CNN to extract the spatial information of images and effectively learn the high-level features and another stream uses the FRBFNN to analyze the distribution of data points over the input space and learn to capture complex relationships in data. In the fusion strategy, the outputs of two streams are concatenated by a softmax function, which normalizes the output to a probability distribution. A transfer learning method is considered to reconstruct new data representation as the inputs of CNN to mine potential spatial features of data. Moreover, L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm regularization is used to alleviate the possible overfitting and enhance the generalization ability. The proposed method not only inherits the advantages of FRBFNN and CNN such as global feature extraction ability, good local approximating performance, ability of handling uncertainty by fuzzy logic but also improves the classification performance under the synergy between two-stream architecture and the fusion strategy. Experimental results obtained for a diversity of datasets as well as partial discharge datasets be using in the real life of fault diagnosis and black plastic wastes datasets for recycling confirm the effectiveness of the proposed TSFNN. A comprehensive comparative analysis is covered. This design can simultaneously capture different level information of inputs and easing the insufficient problem of extracting features from a single steam. Especially, we show that the synergistic effect of FRBFNN, CNN, enabling deep learning for generic classification tasks and multipoint crossover, and L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm regularization can effectively improve the performance of the TSFNNs.
A Survey on Image Emotion Analysis for Online Reviews

Emotions are sentiments, opinions, and feelings which are expressed by the public through text, images, and videos. Opinion analysis for the Internet data is now attracting a growing research people provide feedback on the Internet through the reviews and images in different platforms like Instagram, Facebook, Twitter, and other online Websites on the products. Major work was implemented for processing the sentences. Finite amount of the research that focuses on analyzing opinions of image information. Image emotion topics will be ANPs, i.e., adjective noun pairs manually concealed tags for Internet imagery those helpful of predicting opinions, or else emotions convey by the people in terms of pictures. The main aim is to predict emotions of the images which are not label. To raise this issue, deep learning methods are utilized for opinion analysis of images, since the deep learning techniques have the capabilities for successfully understanding the behavior of images.
Development of a Blockchain Network for DR Service Traceability and Certification

This chapter aims at describing the integration of a blockchain platform for the execution of DR events into an existing power network without the need of additional devices and using the SMs already installed. For this purpose, the following key aspects are described: the current protocol used by many States for the execution of DR events, the blockchain platform used, the developed architecture and the logic implemented for the development of a new system for the execution and certification of DR events.
Dynamic Selection of Optional Feature for Object Detection

To obtain the most intuitive pedestrian target detection results and avoid the impact of motion pose uncertainty on real-time detection, a pedestrian target detection system based on a convolutional neural network was designed.Dynamic Selection of Optional Feature (DSOF) module and a center branch were proposed in this paper, and the target was detected by an anchor-free method.Although almost all the most advanced target detectors use pre-defined anchor boxes to run through the possible positions, scales, and aspect ratios of search targets, their effectualness, and generalization ability are also limited by the anchor boxes.Most anchor-based detectors use heuristically guided anchor frames.Such a design is difficult to detect objects of different types and sizes, especially objects with highly overlapping boundaries.To solve this problem, the DSOF module is proposed in this paper, which selects for each instance the most appropriate feature layer through automatic feature selection.After using multi-level prediction, stacks of low-grade prediction bounding boxes will be generated far away from the target center.To eliminate these low-grade detections, we introduce a new center branch to predict the deviation of a pixel from its corresponding bounding box.This score is used to reduce the weight of the low-grade detection bounding box and merge the detection sequences into the Non-Maximum Suppression (NMS).
Spoofing Tweet Generator Considering Points of Interest of Target User

Deep-learning-based spoofing message generators are generally trained to mimic the writing style of the target user with a large number of his/her messages on social media. This makes users with a small number of messages seem to be safe from identity spoofing. However, people tend to post messages about their experience when they visit their points of interest (PoIs), where their personality traits are reflected on. Based on the assumptions that the interest of the target user can be estimated from a small number of his/her past messages and messages about semantically similar PoIs have similar patterns regardless of users, we propose a method for generating spoofing messages that appear to be the experiences at PoIs selected according to the estimated interest of the target user. We experimentally verified that approximately 20 tweets are sufficient for selecting appropriate PoIs and generating the spoofing tweets.
Temporal Density-Aware Sequential Recommendation Networks with Contrastive Learning

Sequential recommendation (SR) provides personalized contents based on the user's historical interactions. Previous SR methods focus on introducing temporal signals of interaction sequence into their sequence encoders without exploring the effect of temporal density information on user preferences. To bridge this gap, we propose the Temporal Density-aware Sequential Recommendation Networks with Contrastive Learning (TDSRec). The specifics of our research mainly consist of two parts. First, we integrate temporal density information into sequential recommendation when capturing user preferences. In detail, through our proposed Temporal KDE Module, we map timestamps into temporal density vectors aiming at improving the recommendation performance. Second, we introduce contrastive learning into TDSRec to alleviate the lack of supervised learning signals as a result of sparse user-item interactions. In detail, instead of directly editing the raw sequence, we leverage two base sequence encoders to derive self-supervision signals. In this way, our approach can naturally eliminate the interference of hand-crafted data augmentation strategies on the raw sequence data. Through extensive experiments on five test datasets, we find that our proposed TDSRec outperforms state-of-the-art baselines. NDCG@10 has been improved by 6.2%, 2.9%, 2.1%, 1.9%, and 5.2% on MovieLens, Beauty, Video Games, CDs\&Vinyl, and Movies\&TV, respectively.
Application of Real-Time Multimodal Data Analysis for Marketing

All kinds of data and information are related to the development of marketing in our country, and affect the development speed, growth scale and development plan of marketing in our country. To optimize the marketing development strategy in time through market feedback is conducive to the adjustment of marketing development structure and the sustainable development of marketing in China. This paper briefly analyzes the impact of marketing from the perspective of big data and big data analysis cycle, starting with saving time and changing the nature of marketing, Fine market differentiation, Big data analysis and many changes in marketing Finally, big data analysis proposes the application of marketing strategies to help Chinese marketing better formulate marketing strategies according to market changes, to take a leading position in Chinese marketing competition.
Visualizing Analog and Digital Diagnostic Provenance in Pathology

To support digital pathology equivalence studies, we digitized the microscope workflows of pathologists using a microscope camera and video mosaicking, creating spatiotemporal visualizations of glass and digital slide diagnostic workflows.
Biomedical Semantic Textual Similarity: Evaluation of Sentence Representations Enhanced with Principal Component Reduction and Word Frequency Weighting

Biomedical texts encode semantics in domain vocabulary, extensive use of acronyms, proper nouns, named entities, and numerical values with implied meaning. This information is absent from the surface text form, making semantic textual similarity challenging for models trained on the general English language. This paper evaluates different techniques of sentence embedding in semantic textual similarity search in the biomedical domain. We compare static embeddings, transformer-based representations (focusing on models fine-tuned to the biomedical domain), and sentence transformers. We also introduce two auxiliary techniques: principal component reduction and word frequency embedding weighting. To gain better insights into the latent properties of sentence embeddings, we perform directional expectation tests. We conduct our experiments on two benchmark datasets: the BIOSSES and the Clinical Outcomes. We find that sentence transformers are surprisingly effective, outperforming fine-tuned transformer-based models. Initial experiments also suggest the efficacy of principal component reduction and embedding weighting by word frequency.
Spectral-Invariant Matching Network

As the need for sensor fusion systems has grown, developing methods to find correspondences between images with different spectral ranges has become increasingly important. Since most images do not share low-level information, such as textures and edges, existing matching approaches fail even with convolutional neural networks (CNNs). In this paper, we propose an end-to-end metric learning method, called SPIMNet (SPectral-Invariant Matching Network) for robust cross- and multi-spectral image patch matching. While existing methods based on CNNs learn matching features directly from cross- and multi-spectral image patches, SPIMNet transforms across spectral bands and discriminates for similarity in three steps. First, (1) SPIMNet is adjusted for a feature domain by introducing a domain translation network; then (2) two Siamese networks learn to match the adjusted features with the same spectral domain; and (3) the matching features are fed to fully-connected layers to determine the identity of the patches as a classification task. By effectively incorporating each step, SPIMNet achieved state-of-the-art results on a variety of challenging datasets, including both RGB-NIR and RGB-Thermal image pairs. To demonstrate its robustness, we submit the source codes of SPIMNet as supplementary material.
Machine Learning for Classification of Cancer Dataset for Gene Mutation Based Treatment

The objective of this paper is to develop a Machine learning model that can classify cancer patients. Gene mutation-based treatment has a very good success ratio, but only a few cancer institutes follow it. This research uses natural language processing techniques to remove unwanted text and convert the categorical data into numerical data using response coded and one-hot encoded. Then we apply various classification algorithms to classify the training data. The proposed system has the advantage of reducing the time to analyze and classify clinical data of patients, which translates into less wait time for patients in order to get results from pathologists. The results of our experiment will demonstrate that the Stacking Classifier algorithm with One-Hot encoding and Term Frequency – Inverse Document Frequency (TF-IDF) techniques perform better than other Machine Learning methods with around 67% accuracy on the test data.
Secure Mobile Agent Using Identity Combined Key Signature-Based Schnorr Protocol

Mobile agent technology is a software paradigm which can migrate from one station to another, in order to execute a code or to collect information via network, and only recently that it has seen a significant degree of use in different domain applications as e-commerce. Because of its intelligence combined with its distributed architecture, including its capability to delegate, communicate, and negotiate tasks among other agents, it has gained widespread recognition. Yet because of its strong mobility the mobile agent faces some security issues as verifying the integrity and authenticity of information carried by the agent or its code while its migration over the network. In this chapter, we propose a protocol for transaction security by adopting a new identity combined key-based Schnorr signature inspired from the secure ID-based signature scheme and the Schnorr signature, applied to the agent technology, thus to validate its authentication as a digital transaction as an alternative for certificate-based proxy signatures to grant its data integrity, authentication, and non-repudiation in digital communications.
Application Research of YOLOv3 Incorporating Self-attention Mechanism

Improving the accuracy of target recognition is always the focus of machine learning research. YOLOv3 multi-scale detection accelerates the speed while ensuring accuracy, and the self-attention mechanism takes into account the attention weight of each pixel feature to enhance the ability of information extraction. In view of the large size difference between different targets in the target detection task, which makes it difficult to effectively detect multi-size targets, and the publication of the latest self-attention mechanism COT.NET, a combination of the YOLOv3 network Darknet-53 and the self-attention network COT.NET is proposed. The idea of YOLOv3 is improved by adding a self-attention mechanism to the residual structure of YOLOv3.Through verification on the VOC image set, the improved YOLOv3 is 1.34% higher than the original YOLOv3 map. Experimental results show that YOLOv3 integrated into the self-attention mechanism can improve the accuracy of image recognition.
Fiscal Health of Dhaka and Kolkata

Fiscal health refers to the capacity of a government organization to strategize, implement, and bear the expenses of vital public services and investments in a sustainable way without jeopardizing the financial stability of the organization.
Gcha-Net: Global Context and Hybrid Attention Network for Automatic Liver Segmentation

Purpose: Live segmentation is a key technology and prerequisite in liver cancer diagnosis and surgical planning. The accuracy and robustness of automatic liver segmentation play an important role in the treatment effect. However, the manual delineating process is not only time-consuming but also prone to errors. To solve these problems, we present an automatic end-to-end network with global context and hybrid attention mechanism for liver CT segmentation.Methods: In this work we proposed a global context and hybrid attention network with the encoder-decoder architecture, called GCHA-Net, which combines global context and hybrid attentions to encourage adaptive learning of structural and detailed features. We first designed a global attention module (GAM) to model the global interdependencies in the channel and positional dimensions. Meanwhile, the global context features from GAM is directly connected to each layer of decoder to capture global context information. Then, we proposed a feature aggregation module to selectively aggregate the global context features, the low-level and high-level of features at each position by constructing a local attention module (LAM). Different from the self-attention mechanism, LAM computes the weighted sum of each position at all channels considering the low-level features and the global context features as input, which can focus on the liver regions and suppress irrelevant information.Results: The experimental results on the dataset LiTS2017 shows that the DPC value and DG value of liver were 96.5% and 96.9%, respectively. Compared with the state-of-the-art models, our model has superior performance in liver segmentation. In addition, we test the experiment results on the 3Dircadb dataset, and it shows our model can obtain the highest accuracy compared with the closely related models.Conclusions: A global context network combining channel and positional attention mechanism is developed, which can effectively capture the global context information and build the correlation between different convolutional layers.
Improved Rnn Model for Real-Time Human Activity Recognition

This study focuses on the automatic detection of human actions in video streams. The requirement to detect what human activities occur in videos is known as human action recognition due to significant differences in people's visual and motion appearance and actions, camera perspective shifts, moving background, occlusions, noise, and a massive amount of video data. Many methodologies involved in recognizing and detecting labor activity in industrial solutions based on deep learning. There are several beneficial applications in this area, including smart homes, valuable artificial intelligence, human-computer interactions, and improvements in security in many areas, including security, transportation, education, visual surveillance, autonomous driving vehicles, entertainment, and medication through the management of falls or assisting in medication consumption for the elderly. Action recognition from videos has not been addressed extensively and effectively, primarily due to the tremendous variations resulting from background and scale. The main goal is to achieve better accuracy for the improved Recurrent Neural Networks (RNN) model using Long Short-Term Memory (LSTM) method. In addition, the performance evaluation of Deep Learning-based approaches has been compared to other related works. Therefore, an improved approach to RNN is proposed to recognize human actions. To classify the videos, a multilayer RNN with a specific type of LSTM and Gated Recurrent Units (GRU) is used to extract features from video sequences. In this work, human action recognition datasets UCF (UCF sport, UCF-101) are used for both training and testing. Test results show that the proposed model obtained better performance in terms of accuracy. Finally, the overall model accuracy in the UCF-HAR dataset is 93.78 for the improved RNN model.
Planting a Poison SEAD: Using Social Engineering Active Defense (SEAD) to Counter Cybercriminals

By nearly every metric, the status quo of information security is not working. The interaction matrix of attacker-defender dynamics strongly favors the attacker who only needs to be lucky once. We argue that employing social engineering active defense (SEAD) will be more effective to countering malicious actors than maintaining the traditional passive defensive strategy. The Offensive Countermeasures (OCM) approach to defense advocates for three categories of countermeasures: annoyance, attribution, and attack. Annoyance aims to waste the attacker’s time and resources with the objective of not only deterrence but also to increase the probability of detection and attribution. Attribution attempts to identify who is launching the attack. Gathering as much threat intelligence on who the attacker is, provides the best possible defense against future attacks. Finally, attack involves running code on the attacker’s system for the purpose of deterrence and attribution. In this work, we advocate for utilizing similar approaches to deny, degrade, and de-anonymize malicious actors by using social engineering tools, tactics, and procedures against the attackers. Rather than fearing the threats posed by synthetic media, cyber defenders should embrace these capabilities by turning these against criminals. Future research should explore ways to implement synthetic media and automated SEAD methods to degrade the capabilities of online malicious actors.