Reviewerly/exHarmony huggingface dataset:
Analysis of big data job requirements based on K-means text clustering in China

This paper aims to understand the characteristics of domestic big data jobs requirements through k-means text clustering, help enterprises, and employees to identify big data talents, and promote the further development of big data-related research. Firstly, the crawler software is used to crawl the recruitment information about "big data" on the zhaopin.com recruitment website. Then, Jieba word segmentation and K-means text clustering are used to cluster big data recruitment positions, and the number of clustering was determined by the average sum of squares within the group. Finally, big data jobs are divided into 10 categories, and the urban distribution, salary level, education requirements, and experience requirements of big data jobs are discussed and analyzed from the perspectives of the overall data set and clustering results, to clarify the characteristics of big data job demands. The analysis results show that the job demands of big data are mainly distributed in first-tier cities and new first-tier cities. Enterprises are more inclined to job seekers with a college degree or bachelor's degree and more than one year's relevant experience. There are wage differences among different types of jobs. The higher the position, the higher the requirement for education and experience will be.
High Performance DeepFake Video Detection on CNN-Based with Attention Target-Specific Regions and Manual Distillation Extraction

The rapid development of deep learning models that can produce and synthesize hyper-realistic videos are known as DeepFakes. Moreover, the growth of forgery data has prompted concerns about malevolent intent usage. Detecting forgery videos are a crucial subject in the field of digital media. Nowadays, most models are based on deep learning neural networks and vision transformer, SOTA model with EfficientNetB7 backbone. However, due to the usage of excessively large backbones, these models have the intrinsic drawback of being too heavy. In our research, a high performance DeepFake detection model for manipulated video is proposed, ensuring accuracy of the model while keeping an appropriate weight. We inherited content from previous research projects related to distillation methodology but our proposal approached in a different way with manual distillation extraction, target-specific regions extraction, data augmentation, frame and multi-region ensemble, along with suggesting a CNN-based model as well as flexible classification with a dynamic threshold. Our proposal can reduce the overfitting problem, a common and particularly important problem affecting the quality of many models. So as to analyze the quality of our model, we performed tests on two datasets. DeepFake Detection Dataset (DFDC) with our model obtains 0.958 of AUC and 0.9243 of F1-score, compared with the SOTA model which obtains 0.972 of AUC and 0.906 of F1-score, and the smaller dataset Celeb-DF v2 with 0.978 of AUC and 0.9628 of F1-score.
Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts

Dialogue models trained on human conversations inadvertently learn to generate toxic responses.In addition to producing explicitly offensive utterances, these models can also implicitly insult a group or individual by aligning themselves with an offensive statement.To better understand the dynamics of contextually offensive language, we investigate the stance of dialogue model responses in offensive Reddit conversations.Specifically, we create TOXICHAT, a crowd-annotated dataset of 2,000 Reddit threads and model responses labeled with offensive language and stance.Our analysis reveals that 42% of human responses agree with toxic comments, whereas only 13% agree with safe comments.This undesirable behavior is learned by neural dialogue models, such as DialoGPT, which we show are two times more likely to agree with offensive comments.To enable automatic detection of offensive language, we fine-tuned transformerbased classifiers on TOXICHAT that achieve 0.71 F 1 for offensive labels and 0.53 Macro-F 1 for stance labels.Finally, we quantify the effectiveness of controllable text generation (CTG) methods to mitigate the tendency of neural dialogue models to agree with offensive comments.Compared to the baseline, our best CTG model achieves a 19% reduction in agreement with offensive comments and produces 29% fewer offensive replies.Our work highlights the need for further efforts to characterize and analyze inappropriate behavior in dialogue models, in order to help make them safer. 1
Minimax Approximation of Sign Function by Composite Polynomial for Homomorphic Comparison

The comparison operation for two numbers is one of the most frequently used operations in several applications, including deep learning. As such, lots of research has been conducted with the goal of efficiently evaluating the comparison operation in homomorphic encryption schemes. Recently, Cheon <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (Asiacrypt 2020) proposed new comparison methods that approximated the sign function on homomorphically encrypted data using composite polynomials and proved that these methods had optimal asymptotic complexity. In this article, we propose a practically optimal method that approximates the sign function using compositions of minimax approximation polynomials. We prove that this approximation method is optimal with respect to depth consumption and the number of non-scalar multiplications. In addition, we propose a polynomial-time algorithm that determines the optimal composition of minimax approximation polynomials for the proposed homomorphic comparison operation using dynamic programming. The numerical analysis demonstrates that when minimizing runtime, the proposed comparison operation reduces the runtime by approximately 45 percent on average when compared to the previous algorithm. Likewise, when minimizing depth consumption, the proposed algorithm reduces the runtime by approximately 41 percent on average. In addition, when high precision in the comparison operation is required, the previous algorithm does not achieve 128-bit security, while the proposed algorithm does due to its small depth consumption.
Enhancement of underwater optical images based on background light estimation and improved adaptive transmission fusion

Underwater optical imaging technology plays a vital role in humans’ underwater activities. However, the serious quality degradation of underwater optical images hinders further development of such technology. This phenomenon is mainly caused by the absorption and scattering of light in the underwater medium. The blurred image formation model is widely used in the field of optical images and depends on two optical parameters: background light (BL) and the transmission map (TM). Therefore, we propose an underwater optical image enhancement method in the context of underwater optical image restoration and color correction. First, BL estimation based on the gray close operation, which can avoid the influence of white objects while accurately calculating BL, is proposed. Then, an improved adaptive transmission fusion (IATF) method is proposed, and the adjusted reversed saturation map (ARSM) method is applied to compensate for and refine the estimated TMs to obtain the final TMs. This paper also proposes a new underwater light attenuation prior (NULAP) method. Finally, to enhance color saturation and edge details, a statistical colorless slant correction fusion smoothing filter method is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods for dehazing, color and detail enhancement, and (uneven) light intensity.
What to Do With All Those Open-Ended Responses? Data Visualization Techniques for Survey Researchers

Open-ended survey responses, where respondents provide responses in an unstructured, open-text format instead of defined response categories, are often a successful way to solicit authentic and unexpected feedback, highlight the diversity of responses or nuances in opinions, and capture the “why” that complements quantitative survey data. However, there are many challenges to analyzing and reporting open-ended data. This article draws on visual design best practices, such as Gestalt principles and the authors’ combined experience to demonstrate several visualization strategies that are relatively simple to implement with open-ended data. The application of visualization best practices to open-ended data can increase recall and effective decision-making and can transform findings into a dynamic data story.
Fast and Accurate Object Detection in Remote Sensing Images Based on Lightweight Deep Neural Network

Deep learning-based object detection in remote sensing images is an important yet challenging task due to a series of difficulties, such as complex geometry scene, dense target quantity, and large variant in object distributions and scales. Moreover, algorithm designers also have to make a trade-off between model’s complexity and accuracy to meet the real-world deployment requirements. To deal with these challenges, we proposed a lightweight YOLO-like object detector with the ability to detect objects in remote sensing images with high speed and high accuracy. The detector is constructed with efficient channel attention layers to improve the channel information sensitivity. Differential evolution was also developed to automatically find the optimal anchor configurations to address issue of large variant in object scales. Comprehensive experiment results show that the proposed network outperforms state-of-the-art lightweight models by 5.13% and 3.58% in accuracy on the RSOD and DIOR dataset, respectively. The deployed model on an NVIDIA Jetson Xavier NX embedded board can achieve a detection speed of 58 FPS with less than 10W power consumption, which makes the proposed detector very suitable for low-cost low-power remote sensing application scenarios.
Smart Home Gateway Based on Integration of Deep Reinforcement Learning and Blockchain Framework

The development of information and communication technology in terms of sensor technologies cause the Internet of Things (IoT) step toward smart homes for prevalent sensing and management of resources. The gateway connections contain various IoT devices in smart homes representing the security based on the centralized structure. To address the security purposes in this system, the blockchain framework is considered a smart home gateway to overcome the possible attacks and apply Deep Reinforcement Learning (DRL). The proposed blockchain-based smart home approach carefully evaluated the reliability and security in terms of accessibility, privacy, and integrity. To overcome traditional centralized architecture, blockchain is employed in the data store and exchange blocks. The data integrity inside and outside of the smart home cause the ability of network members to authenticate. The presented network implemented in the Ethereum blockchain, and the measurements are in terms of security, response time, and accuracy. The experimental results show that the proposed solution contains a better outperform than recent existing works. DRL is a learning-based algorithm which has the most effective aspects of the proposed approach to improve the performance of system based on the right values and combining with blockchain in terms of security of smart home based on the smart devices to overcome sharing and hacking the privacy. We have compared our proposed system with the other state-of-the-art and test this system in two types of datasets as NSL-KDD and KDD-CUP-99. DRL with an accuracy of 96.9% performs higher and has a stronger output compared with Artificial Neural Networks with an accuracy of 80.05% in the second stage, which contains 16% differences in terms of improving the accuracy of smart homes.
On the Classification of MR Images Using “ELM-SSA” Coated Hybrid Model

Computer-aided diagnosis permits biopsy specimen analysis by creating quantitative images of brain diseases which enable the pathologists to examine the data properly. It has been observed from other image classification algorithms that the Extreme Learning Machine (ELM) demonstrates superior performance in terms of computational efforts. In this study, to classify the brain Magnetic Resonance Images as either normal or diseased, a hybridized Salp Swarm Algorithm-based ELM (ELM-SSA) is proposed. The SSA is employed to optimize the parameters associated with ELM model, whereas the Discrete Wavelet Transformation and Principal Component Analysis have been used for the feature extraction and reduction, respectively. The performance of the proposed “ELM-SSA” is evaluated through simulation study and compared with the standard classifiers such as Back-Propagation Neural Network, Functional Link Artificial Neural Network, and Radial Basis Function Network. All experimental validations have been carried out using two different brain disease datasets: Alzheimer’s and Hemorrhage. The simulation results demonstrate that the “ELM-SSA” is potentially superior to other hybrid methods in terms of ROC, AUC, and accuracy. To achieve better performance, reduce randomness, and overfitting, each algorithm has been run multiple times and a k-fold stratified cross-validation strategy has been used.
Blockchain-Secured Recommender System for Special Need Patients Using Deep Learning

Recommender systems offer several advantages to hospital data management units and patients with special needs. These systems are more dependent on the extreme subtle hospital-patient data. Thus, disregarding the confidentiality of patients with special needs is not an option. In recent times, several proposed techniques failed to cryptographically guarantee the data privacy of the patients with special needs in the diet recommender systems (RSs) deployment. In order to tackle this pitfall, this paper incorporates a blockchain privacy system (BPS) into deep learning for a diet recommendation system for patients with special needs. Our proposed technique allows patients to get notifications about recommended treatments and medications based on their personalized data without revealing their confidential information. Additionally, the paper implemented machine and deep learning algorithms such as RNN, Logistic Regression, MLP, etc., on an Internet of Medical Things (IoMT) dataset acquired via the internet and hospitals that comprises the data of 50 patients with 13 features of various diseases and 1,000 products. The product section has a set of eight features. The IoMT data features were analyzed with BPS and further encoded prior to the application of deep and machine learning-based frameworks. The performance of the different machine and deep learning methods were carried out and the results verify that the long short-term memory (LSTM) technique is more effective than other schemes regarding prediction accuracy, precision, F1-measures, and recall in a secured blockchain privacy system. Results showed that 97.74% accuracy utilizing the LSTM deep learning model was attained. The precision of 98%, recall, and F1-measure of 99% each for the allowed class was also attained. For the disallowed class, the scores were 89, 73, and 80% for precision, recall, and F1-measure, respectively. The performance of our proposed BPS is subdivided into two categories: the secured communication channel of the recommendation system and an enhanced deep learning approach using health base medical dataset that spontaneously identifies what food a patient with special needs should have based on their disease and certain features including gender, weight, age, etc. The proposed system is outstanding as none of the earlier revised works of literature described a recommender system of this kind.
An Embeddable Algorithm for Automatic Garbage Detection Based on Complex Marine Environment

With the continuous development of artificial intelligence, embedding object detection algorithms into autonomous underwater detectors for marine garbage cleanup has become an emerging application area. Considering the complexity of the marine environment and the low resolution of the images taken by underwater detectors, this paper proposes an improved algorithm based on Mask R-CNN, with the aim of achieving high accuracy marine garbage detection and instance segmentation. First, the idea of dilated convolution is introduced in the Feature Pyramid Network to enhance feature extraction ability for small objects. Secondly, the spatial-channel attention mechanism is used to make features learn adaptively. It can effectively focus attention on detection objects. Third, the re-scoring branch is added to improve the accuracy of instance segmentation by scoring the predicted masks based on the method of Generalized Intersection over Union. Finally, we train the proposed algorithm in this paper on the Transcan dataset, evaluating its effectiveness by various metrics and comparing it with existing algorithms. The experimental results show that compared to the baseline provided by the Transcan dataset, the algorithm in this paper improves the mAP indexes on the two tasks of garbage detection and instance segmentation by 9.6 and 5.0, respectively, which significantly improves the algorithm performance. Thus, it can be better applied in the marine environment and achieve high precision object detection and instance segmentation.
An integrative view of foveated rendering

Foveated rendering adapts the image synthesis process to the user’s gaze. By exploiting the human visual system’s limitations, in particular in terms of reduced acuity in peripheral vision, it strives to deliver high-quality visual experiences at very reduced computational, storage, and transmission costs. Despite the very substantial progress made in the past decades, the solution landscape is still fragmented, and several research problems remain open. In this work, we present an up-to-date integrative view of the domain from the point of view of the rendering methods employed, discussing general characteristics, commonalities, differences, advantages, and limitations. We cover, in particular, techniques based on adaptive resolution, geometric simplification, shading simplification, chromatic degradation, as well spatio-temporal deterioration. Next, we review the main areas where foveated rendering is already in use today. We finally point out relevant research issues and analyze research trends.
Pose-invariant face recognition with multitask cascade networks

In this work, a face recognition method is proposed for face under pose variations using a multitask convolutional neural network (CNN). Furthermore, a pose estimation method followed by a face identification module is combined in a cascaded structure and used separately. In the presence of various facial expressions as well as low illuminations, datasets that include separated face poses can enhance the robustness of face recognition. The proposed method relies on a pose estimation module using a convolutional neural network model and trained on three categories of face image capture such as the left side, frontal, and right side. Second, three CNN models are used for face identification according to the estimated pose. The Left-CNN model, Front-CNN model, and Right-CNN model are used to identify the face for the left, frontal, and right pose of the face, respectively. Because face images may contain some useless information (e.g., background content), we propose a skin-based face segmentation method using structure–texture decomposition and the color-invariant descriptor. Experimental evaluation has been conducted using the proposed cascade-based face recognition system that consists of the aforementioned steps (i.e., pose estimation, face segmentation, and face identification) and is assessed on four different datasets and its superiority has been shown over related state-of-the-art techniques. Results reveal the contribution of the separate representation, skin segmentation, and pose estimation in the recognition robustness.
Accelerating Reinforcement Learning with Learned Skill Priors.

Intelligent agents rely heavily on prior experience when learning a new task, yet most modern reinforcement learning (RL) approaches learn every task from scratch. One approach for leveraging prior knowledge is to transfer skills learned on prior tasks to the new task. However, as the amount of prior experience increases, the number of transferable skills grows too, making it challenging to explore the full set of available skills during downstream learning. Yet, intuitively, not all skills should be explored with equal probability; for example information about the current state can hint which skills are promising to explore. In this work, we propose to implement this intuition by learning a prior over skills. We propose a deep latent variable model that jointly learns an embedding space of skills and the skill prior from offline agent experience. We then extend common maximum-entropy RL approaches to use skill priors to guide downstream learning. We validate our approach, SPiRL (Skill-Prior RL), on complex navigation and robotic manipulation tasks and show that learned skill priors are essential for effective skill transfer from rich datasets. Videos and code are available at this https URL.
Visualizing a New Sustainable World: Toward the Next Generation of Virtual Reality in the Built Environment

What is the future of virtual reality (VR) in the built environment? As work becomes increasingly distributed across remote and hybrid forms of organizing as a result of the COVID-19 pandemic, there is a need to rethink how we use the set of collaborative technologies to move toward a sustainable world. We propose a new vision of VR as a discipline-agnostic platform for an interdisciplinary integration of the allied design, social, and environmental disciplines to address emerging challenges across the building sectors. We build this contribution through the following steps. First, we contextualize VR technologies within the changing digital landscape and underlying tensions in the built environment practices. Second, we characterize the difficulties that have arisen in using them to address challenges, illustrating our argument with leading examples. Third, we conceptualize VR configurations and explore underlying assumptions for their use across disciplinary scenarios. Fourth, we propose a vision of VR as a discipline-agnostic platform that can support built environment users in visualizing preferred futures. We conclude by providing directions for research and practice.