Reviewerly/exHarmony huggingface dataset:
Balanced Sample Assignment and Objective for Single-Model Multi-Class 3D Object Detection

Accurately detecting multi-class objects in a single pass is critical but challenging for real-world autonomous driving scenarios. Several single-class anchor-based methods have recently achieved the state-of-the-art performance in the car category, but when extending to multi-class detection tasks, their performance on small objects (i.e., pedestrians and cyclists) is limited. We find that the core problem that causes this phenomenon lies in the unbalanced sample quality and the classification objective. To address this problem, we proposed a single-model multi-class 3D object detector with balanced sample assignment and objective, named BSAODet. Specifically, the quality-balanced sample assignment (QBSA) is introduced to dynamically collect stable high-quality samples for each class according to the predicted sample performance and geometric constraints. In conjunction with the QBSA, the class-balanced classification objective (CBCO) performs instance-wise label normalization and weighting on positive samples, preventing the model from biasing toward objects with more samples. Extensive experiments on the popular KITTI dataset, the latest large-scale ONCE dataset, and the challenging Waymo Open Dataset show that our method steadily improves the performance of current state-of-the-art detectors by 2–7 mAP in pedestrians and cyclists while maintaining competitiveness in cars. Moreover, our best model achieves 66.31 mAP on three classes, outperforming all published LiDAR-only detectors on the KITTI benchmark.
Emotion Detection From Micro-Blogs Using Novel Input Representation

Emotion is a natural intrinsic state of mind that drives human behavior, social interaction, and decision-making. Due to the rapid expansion in the current era of the Internet, online social media (OSM) platforms have become popular means of expressing opinions and communicating emotions. With the emergence of natural language processing (NLP) techniques powered by artificial intelligence (AI) algorithms, emotion detection (ED) from user-generated OSM data has become a prolific research domain. However, it is challenging to extract meaningful features for identifying discernible patterns from the short, informal, and unstructured texts that are common on micro-blogging platforms like Twitter. In this paper, we introduce a novel representation of features extracted from user-generated Twitter data that can capture users’ emotional states. An advanced approach based on Genetic Algorithm (GA) is used to construct the input representation which is composed of stylistic, sentiment, and linguistic features extracted from tweets. A voting ensemble classifier with weights optimized by a GA is introduced to increase the accuracy of emotion detection using the novel feature representation. The experimental results demonstrate that the proposed approach outperforms the state-of-the-art classical machine learning-based emotion detection techniques, achieving the highest level of precision (96.49%), recall (96.49%), F1-score (96.49%), and accuracy (96.49%).
Scene Recognition for Visually-Impaired People’s Navigation Assistance Based on Vision Transformer with Dual Multiscale Attention

Notable progress was achieved by recent technologies. As the main goal of technology is to make daily life easier, we will investigate the development of an intelligent system for the assistance of impaired people in their navigation. For visually impaired people, navigating is a very complex task that requires assistance. To reduce the complexity of this task, it is preferred to provide information that allows the understanding of surrounding spaces. Particularly, recognizing indoor scenes such as a room, supermarket, or office provides a valuable guide to the visually impaired person to understand the surrounding environment. In this paper, we proposed an indoor scene recognition system based on recent deep learning techniques. Vision transformer (ViT) is a recent deep learning technique that has achieved high performance on image classification. So, it was deployed for indoor scene recognition. To achieve better performance and to reduce the computation complexity, we proposed dual multiscale attention to collect features at different scales for better representation. The main idea was to process small patches and large patches separately and a fusion technique was proposed to combine the features. The proposed fusion technique requires linear time for memory and computation compared to existing techniques that require quadratic time. To prove the efficiency of the proposed technique, extensive experiments were performed on a public dataset which is the MIT67 dataset. The achieved results demonstrated the superiority of the proposed technique compared to the state-of-the-art. Further, the proposed indoor scene recognition system is suitable for implementation on mobile devices with fewer parameters and FLOPs.
Would You Like to Listen to My Music, My Friend? An Experiment on AI Musicians

The rapid development of AI-related technologies and the upsurge of Metaverse are driving AI musicians into people’s lives. To clarify the link between anthropomorphism, users’ perceptions, and attitudes, and to explore the impact of listening type (listening to AI-generated music or not) on the relationship between anthropomorphism and users’ perceptions, we conducted a 2 × 2 experiment based on the two basic dimensions of social cognition. A total of 211 valid samples were collected and the partial least squares (PLS) method was used for data analysis to test the research model and hypotheses. The results show that more human-like AI musicians elicit higher users’ competence perceptions, which are crucial facilitators of users’ attitudes towards AI musicians; higher warmth perceptions help users form better attitudes towards AI musicians; listening to AI-generated music or not moderates the impact of anthropomorphism degree on users’ competence perceptions. This study promotes the understanding of the impact of AI musicians’ anthropomorphism and guides the design of AI musicians’ images to some extent.
Research on Two-Round Self-Balancing Robot SLAM Based on the Gmapping Algorithm

Aiming at the inconvenience of inspection and monitoring of coal mine pump room equipment in a narrow and complex environment, this paper proposes and designs a two-wheel self-balancing inspection robot based on laser SLAM. Using SolidWorks, the three-dimensional mechanical structure of the robot is designed, and the overall structure of the robot is analyzed by finite element statics. The kinematics model of the two-wheel self-balancing robot was established, and the multi-closed-loop PID controller was used to design the two-wheel self-balancing control algorithm of the robot. The 2D LiDAR-based Gmapping algorithm was used to locate the robot and construct the map. Through the self-balancing test and anti-jamming test, it is verified that the self-balancing algorithm designed in this paper has a certain anti-jamming ability and good robustness. By using Gazebo to build a simulation comparison experiment, it is verified that the selection of the particle number is of great significance for improving the map accuracy. The actual test results show that the constructed map has high accuracy.
Hybrid Recommendation Network Model with a Synthesis of Social Matrix Factorization and Link Probability Functions

Recommender systems are becoming an integral part of routine life, as they are extensively used in daily decision-making processes such as online shopping for products or services, job references, matchmaking for marriage purposes, and many others. However, these recommender systems are lacking in producing quality recommendations owing to sparsity issues. Keeping this in mind, the present study introduces a hybrid recommendation model for recommending music artists to users which is hierarchical Bayesian in nature, known as Relational Collaborative Topic Regression with Social Matrix Factorization (RCTR-SMF). This model makes use of a lot of auxiliary domain knowledge and provides seamless integration of Social Matrix Factorization and Link Probability Functions into Collaborative Topic Regression-based recommender systems to attain better prediction accuracy. Here, the main emphasis is on examining the effectiveness of unified information related to social networking and an item-relational network structure in addition to item content and user-item interactions to make predictions for user ratings. RCTR-SMF addresses the sparsity problem by utilizing additional domain knowledge, and it can address the cold-start problem in the case that there is hardly any rating information available. Furthermore, this article exhibits the proposed model performance on a large real-world social media dataset. The proposed model provides a recall of 57% and demonstrates its superiority over other state-of-the-art recommendation algorithms.
Tone Mapping Operator for High Dynamic Range Images Based on Modified iCAM06

This study attempted to solve the problem of conventional standard display devices encountering difficulties in displaying high dynamic range (HDR) images by proposing a modified tone-mapping operator (TMO) based on the image color appearance model (iCAM06). The proposed model, called iCAM06-m, combined iCAM06 and a multi-scale enhancement algorithm to correct the chroma of images by compensating for saturation and hue drift. Subsequently, a subjective evaluation experiment was conducted to assess iCAM06-m considering other three TMOs by rating the tone mapped images. Finally, the objective and subjective evaluation results were compared and analyzed. The results confirmed the better performance of the proposed iCAM06-m. Furthermore, the chroma compensation effectively alleviated the problem of saturation reduction and hue drift in iCAM06 for HDR image tone-mapping. In addition, the introduction of multi-scale decomposition enhanced the image details and sharpness. Thus, the proposed algorithm can overcome the shortcomings of other algorithms and is a good candidate for a general purpose TMO.
Cat-state encoding of a quantum information processor module with cavity–magnon system

We propose a scheme to realize the strong coupling between two distant magnons by using coupled cavities as a data bus. This coupling strength can be adjusted by the parameters of the system. One magnon is considered as an information processor, while the other one acts as an information memorizer. This hybrid system is applied to construct a quantum information processor module. Based on the continuous-variable encoding (cat-state encoding) of logical qubits, the information can be transferred from the processor to the memorizer via one step of the operation. This model provides a promising hardware platform for robust quantum information processing.
Effects of social skills on lexical alignment in human-human interaction and human-computer interaction

Over the past decade, people's language behaviors towards computer partners have been capturing growing interests with the prevalence of interaction with dialogue systems. However, it remains controversial whether people perceive and respond to computer and human partners in the same way. To address this issue, the current study investigated whether speakers converge with their conversational partner on lexical choices (i.e., lexical alignment) to the same extent when they believed the partner was a human (i.e., human-human interaction, HHI) and when they believed the partner was a computer (i.e., human-computer interaction, HCI), and whether the strength of lexical alignment is moderated by individuals' social skills in the same fashion in HHI and HCI. A speech-based picture naming and matching task was adopted to measure participants' lexical alignment towards their conversational partner while participants' social skills were assessed by using the Chinese University-students Social Skill Inventory (ChUSSI). Results indicated that lexical alignment in HCI was stronger than that in HHI (79.5% vs. 58.6%). In addition, participants' social skills score, in particular the score on protecting Partner's Mianzi (i.e., dignity and prestige) in the ChUSSI significantly predicted participants' propensity of lexical alignment in HHI but didn't in HCI. More specifically, participants who were evaluated to be more concerned with others' social standing were significantly more likely to align with their partner in the HHI context (β = 0.896, Z = 2.847, p < 0.001), but this correlation did not hold in HCI (β = −0.333, Z = −1.241, p = 0.214). These findings shed light on the potential boundary between speakers' representations of human and computer interlocutors.
Automated event extraction of CVE descriptions

The dramatically increasing number of vulnerabilities makes manual vulnerability analysis increasingly more difficult. Automatic extraction of vulnerability information can help improve vulnerability analysis. However, the existing vulnerability information extraction methods do not extract from the perspective of events, and the existing event extraction methods do not consider the unique sentence structure characteristics of vulnerability descriptions, which makes it difficult to extract vulnerability information effectively. To extract vulnerability information, we treat each vulnerability as an event, and propose an approach, VE-Extractor, to automatically perform vulnerability event extraction from textual descriptions in vulnerability reports for vulnerability analysis, including extraction of vulnerability event trigger (cause) and event arguments (e.g., consequence, operation). First, we propose a new labeling method BIOFR (Begin, Inside, Outside, Front, Rear) to construct an event-perspective vulnerability data benchmark. Then, we design a question template based on event trigger, to automatically extract vulnerability event arguments through the BERT Q&A model. Experiments show the effectiveness of VE-Extractor for automatically extracting events from vulnerability description, with significant performance improvement over state-of-the-art techniques, e.g., F1-score is increased by 45.12% and 21.02% in vulnerability consequence and operation extraction, respectively. The proposed VE-Extractor achieves a higher precision and accuracy than the state-of-the-art methods. Experiments results show that our approach is effective in extracting vulnerability event information and can be used to assist vulnerability analysis, such as vulnerability classification.
Deep Learning Model to Identify Hide Images using CNN Algorithm

In this paper, we are obtaining and solving the problem of face identification and verification including with mask and without mask face images. In this algorithm they model allows users to use the webcam, digital cameras and multimedia cameras for identify and detect several face related features in the faces. In this paper, we are conducting detailed and systematic result to verify the effectiveness of these classic feature learning systems on linear and nonlinear class imbalanced outcomes. We also demonstrate more discriminatory deep representation features can be learned through the implementation of a deep network model. This model is maintaining the margin of the both classes including clusters. With using Convolutional Neural Network (CNN), they are providing efficient result in with mask and without mask face image. They are providing good result in both offline and real time performance with predictable value of accuracy. They are done research in evaluations of being made for publicly available datasets like DEEPFace and with mask and without mask dataset. The proposed model is working best result in different-different face related datasets to identify with face mask and without face mask images.
A Comprehensive Review on Deep Learning based Image Dehazing Techniques

The principle goal of image treating is to visualise, refine, repair, recovery, and identify a picture. Haze, fog, smog, and so on. vitiate the picture situation of outside scene. Haze found in a picture is an abrasive problem because it controls the comparison and changes the coloration. Numerous programs do community steadfastly due to it. The inclusion of haze also reduce the clearity of image in water. It should not include in awful climate situations is essential and unignoring area of laptop pix. A few problems had been framed by way of the haze found in ecosystem inside the subject of earthly pictures, as for image remote topics, the condense of giant amount of thick atmosphere is important. Due to the condition of scattering of light that is using the haze debris, the assessment and hence the vision get decreased. The phenomenon of image de-hazing corrupts image first-class, it can badly affects more programs that uses image handling. The suppression and air light causes scattering. Haze decrease the mild meditated from, the gadgets which can be mixed with greater mild found in surroundings. The main aim of this process is to enhance the contemplated light from the combined light. A image's perseverance, strength can be enhanced by means of usage of the de-hazing approach.
Enhancing Security of PGP with Steganography

In recent years, cryptography has gained a lot of scientific traction. It is the technique for creating and utilising hidden writing as cyphers or codes in text. Steganography is a scientific art form for concealing information inside of transformed protected information. The word “hidden writing” (steganography) has Greek origins. The word “steganography” is divided into two parts: “graphic” or “writing” text, and “Steganos,” which meaning “secret or covered” in the context of hiding hidden communications. Pretty Good Privacy (PGP) is using many home users, organizations, businesses, private and government agencies. The whole world has evolved into transferring the message from user to receiver using standard encrypting E-mail. Only those who peoples are intended to read message and communicate files or message in encrypted and decrypted form using public and private key cryptography techniques. Because asymmetric encryption is utilised in the current PGP system, there is a potential that data transfer may be attacked. To counteract such assaults, we presented a new steganography method for PGP with increased security, and we also introduced the idea of two-stage secure steganography.
Research Pattern of Internet of Things and its Impact on Cyber Security

The world is experiencing a great advancement in technology which gives us the relief of accessing any kind of service in any corner of the world. Human beings being social animals are interconnected through the Internet of Things to achieve a higher quality of life. The IoT helps billions of people to connect through the world through wireless computing. The exceeding use of IoT makes it prone to cyber threats too. The security of information is becoming very crucial, and it is a challenge to minimize the threats due to the lack of security and awareness. Also, when the information is of national and international use and importance, it becomes even more significant and authoritative. Also, cyber-attacks and cyber warfare have increased massively. Therefore, the creation of some new rules and laws has become important to reduce the malicious threats of reduced security of cyber information. This paper is concerned about different issues and challenges faced and what steps are taken to help curb this hitch.
Comparative Analysis of BERT-base Transformers and Deep Learning Sentiment Prediction Models

The state-of-the-art Bidirectional Encoder Representations from Transformers (BERT) and Deep Learning (DL) models are used for Natural Language Processing (NLP) applications. Social media marketing and customers positive sentiments play major role for many online businesses.It is a crucial task for companies to predict customers sentiment based on context from online reviews. Predicting accurate sentiment is a time-consuming and challenging task due to high volume of unstructured customers review dataset. There are many previous experimental results reveals the performance and inaccuracy issues on large scale customer reviews datasets. This paper presents the comparative analysis of experimental research work on BERT, Hybrid fastText-BILSTM, and fastText Trigram models overcome more accurate sentiment prediction challenges. We propose fine-tuned BERT and Hybrid fastText-BILSTM models for large customer review datasets. This comparative analysis results show that the proposed fine-tuned BERT model performs better compare to other DL models in terms of accuracy and other performance measures.
“Generic and unusable”1: Understanding employee perceptions of cybersecurity training and measuring advice fatigue

Security Education Training and Awareness (SETA) programs often fail to reduce organisational cyber risk, and this is linked to the way that employees perceive and appraise such programs. Rather than improving employee awareness, a poorly implemented SETA program may cause fatigue and result in risky cyber behaviours. This paper describes two studies aimed respectively at examining how SETA programs can lead to fatigue and development of a measure of SETA Advice-Related Cybersecurity Fatigue. In Study 1, a repertory grid technique was used to examine employee responses to a series of SETA videos. A total of 24 in-depth semi-structured interviews were conducted with individuals from a variety of industries. Key themes related to the content, style, and design, of cybersecurity training videos, but also employees’ perceived characteristics of the intended audience and broader preconceptions of cybersecurity principles. In Study 2, we developed the Cybersecurity Advice Fatigue Scale (CAFS) Scale, a self-report measure of the fatigue which results from poor cybersecurity advice. A principal component analysis of CAFS scores for 457 working adults revealed a five-factor structure that broadly aligns with the themes identified by the qualitative analyses of Study 1. The results of both studies highlight that employees make inferences about the corporate motivation behind the SETA program, and this influences their receptivity to the content. From an applied perspective, cybersecurity practitioners can use the CAFS to identify features of their cybersecurity training programs which should be improved to enhance the program's efficacy.