Reviewerly/exHarmony huggingface dataset:
Enhanced face age progression and regression model using hyper-parameter tuning-large scale GAN by hybrid heuristic improvement

ABSTRACTThe main challenge is to automate the model for aged or de-aged face generation. However, there are certain limitations on accuracy for age estimation and identity preservation. To achieve this, a new face age progression and regression is proposed by Hyper-parameter Tuning-Large Scale Generative Adversarial Network (HT-Large Scale GAN) with Pollination Rate-based Sunflower Dolphin Swarm Optimization (PR-SDSO). The input images are collected and fed into the object detection model, where the viola Jones algorithm is utilized. Here, the pre-processing is done by median filtering and contrast enhancement. The face age progression and regression are accomplished by novel HT-Large Scale GAN, where the hyperparameters are optimized by a new algorithm of PR-SDSO. Throughout the result analysis, the proposed model ensures that it provides the appropriate synthesized images for both the progression and regression phases and acquires less error to improve the quality of the image.KEYWORDS: Face age progression and regressionobject detection modelviola-jones algorithmmedian filtering and contrast enhancement‌deep learningdolphin swarm algorithm‌pollination rate-based sunflower dolphin swarm optimizationhyper-parameter tuning-large scale generative adversarial networks Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsTejaswini YadavTejaswini Yadav received master degree from Pune University. She is currently research scholar in MIT-ADT University and her research area includes machine learning and artificial intelligence.Rajneeshkaur SachdeoRajneeshkaur Sachdeo received Ph.D. degree from SGBAU State University, Amravati. She is currently Dean of Engineering and Head of Computer Science and Engineering at MIT-ADT University, Pune. Her research area includes Data Security and privacy, natural language processing and linguistics, machine learning, data mining, and Wireless network. She is a member of ISTE, IACSIT and CSI.
Region-Aware Deep Feature-Fused Network for Robust Facial Landmark Localization

In facial landmark localization, facial region initialization usually plays an important role in guiding the model to learn critical face features. Most facial landmark detectors assume a well-cropped face as input and may underperform in real applications if the input is unexpected. To alleviate this problem, we present a region-aware deep feature-fused network (RDFN). The RDFN consists of a region detection subnetwork and a region-wise landmark localization subnetwork to explicitly solve the input initialization problem and derive the landmark score maps, respectively. To exploit the association between tasks, we develop a cross-task feature fusion scheme to extract multi-semantic region features while trading off their importance in different dimensions via global channel attention and global spatial attention. Furthermore, we design a within-task feature fusion scheme to capture the multi-scale context and improve the gradient flow for the landmark localization subnetwork. At the inference stage, a location reweighting strategy is employed to transform the score maps into 2D landmark coordinates. Extensive experimental results demonstrate that our method has competitive performance compared to recent state-of-the-art methods, achieving NMEs of 3.28%, 1.48%, and 3.43% on the 300W, AFLW, and COFW datasets, respectively.
T5 for Hate Speech, Augmented Data, and Ensemble

We conduct relatively extensive investigations of automatic hate speech (HS) detection using different State-of-The-Art (SoTA) baselines across 11 subtasks spanning six different datasets. Our motivation is to determine which of the recent SoTA models is best for automatic hate speech detection and what advantage methods, such as data augmentation and ensemble, may have on the best model, if any. We carry out six cross-task investigations. We achieve new SoTA results on two subtasks—macro F1 scores of 91.73% and 53.21% for subtasks A and B of the HASOC 2020 dataset, surpassing previous SoTA scores of 51.52% and 26.52%, respectively. We achieve near-SoTA results on two others—macro F1 scores of 81.66% for subtask A of the OLID 2019 and 82.54% for subtask A of the HASOC 2021, in comparison to SoTA results of 82.9% and 83.05%, respectively. We perform error analysis and use two eXplainable Artificial Intelligence (XAI) algorithms (Integrated Gradient (IG) and SHapley Additive exPlanations (SHAP)) to reveal how two of the models (Bi-Directional Long Short-Term Memory Network (Bi-LSTM) and Text-to-Text-Transfer Transformer (T5)) make the predictions they do by using examples. Other contributions of this work are: (1) the introduction of a simple, novel mechanism for correcting Out-of-Class (OoC) predictions in T5, (2) a detailed description of the data augmentation methods, and (3) the revelation of the poor data annotations in the HASOC 2021 dataset by using several examples and XAI (buttressing the need for better quality control). We publicly release our model checkpoints and codes to foster transparency.
Comparative Analysis of Machine Learning Algorithms for Predicting Mobile Price

While buying new mobile, deciding whether a mobile with given features is worth its price often takes time. The main theme of this paper is presenting a comparative analysis of the Machine Learning algorithms, Decision Tree, Random Forest, and Regression tree models for predicting the price range of a mobile, using ML algorithms. The cost of the mobile depends on a set of features such as battery capacity (maH), clock speed (GHz), memory size (GB), random access memory (RAM), no. of cores (dual, quad, octal) etc. Our experimental result demonstrates that the random forest model outperformed other models; the Decision tree and regression model were used for the study. In our paper we have implemented machine learning techniques to train the model for predicting the mobile price range and we have further extended this work to find out which technique among the used ones give us the prediction with highest accuracy.
Optimizing Port Multi-AGV Trajectory Planning through Priority Coordination: Enhancing Efficiency and Safety

Efficient logistics and transport at the port heavily relies on efficient AGV scheduling and planning for container transshipment. This paper presents a comprehensive approach to address the challenges in AGV path planning and coordination within the domain of intelligent transportation systems. We propose an enhanced graph search method for constructing the global path of a single AGV that mitigates the issues associated with paths closely aligned with obstacle corner points. Moreover, a centralized global planning module is developed to facilitate planning and scheduling. Each individual AGV establishes real-time communication with the upper layers to accurately determine its position at complex intersections. By computing its priority sequence within a coordination circle, the AGV effectively treats the high-priority trajectories of other vehicles as dynamic obstacles for its local trajectory planning. The feasibility of trajectory information is ensured by solving the online real-time Optimal Control Problem (OCP). In the trajectory planning process for a single AGV, we incorporate a linear programming-based obstacle avoidance strategy. This strategy transforms the obstacle avoidance optimization problem into trajectory planning constraints using Karush-Kuhn-Tucker (KKT) conditions. Consequently, seamless and secure AGV movement within the port environment is guaranteed. The global planning module encompasses a global regulatory mechanism that provides each AGV with an initial feasible path. This approach not only facilitates complexity decomposition for large-scale problems, but also maintains path feasibility through continuous real-time communication with the upper layers during AGV travel. A key advantage of our progressive solution lies in its flexibility and scalability. This approach readily accommodates extensions based on the original problem and allows adjustments in the overall problem size in response to varying port cargo throughput, all without requiring a complete system overhaul.
Curbing Anomalous Transactions Using Cost-Sensitive Learning

Credit card fraud is one of the most common crimes in online transactions, posing a significant threat to banks, credit card companies, and consumers. There are several strategies to prevent and identify these anomalous activities, but they all struggle with the class imbalance problem. Usually, traditional algorithms tend to ignore this unequal distribution of data, thereby neglecting the misclassification of costs. This leads to poor classification, which affects the fraud prediction. Generic data level algorithms are typically used to balance such unequal data, but cost misclassification (Singh and Jain in J Inf Optim Sci 41:1–14, 2020 [1]) persists, so this paper attempts to solve both the class imbalance problem (Singh and Jain in J Inf Optim Sci 41:1–14, 2020 [1]; Singh et al. in J Exper Theor Artif Intell 34:571–598, 2021 [2]) and the cost misclassification problem by using a correlation-based feature selection method, optimizing these features with the help of an optimization algorithm, the Genetic algorithm (GA) (Roseline et al. in Comput Electr Eng 102:1–11, 2022 [3]), and finally, a cost-sensitive (Ling et al. in Cost sensitive learning. Encyclopedia of machine learning. Springer, Boston, pp 231–235, 2011 [4]) classifier to deal with cost misclassification.
Exponential Visual Stabilization of Wheeled Mobile Robots Based on Active Disturbance Rejection Control

In this paper, an active disturbance rejection control (ADRC) based visual servoing strategy is designed to maneuver the wheeled mobile robot from different initial poses to the desired pose at an exponential rate. In addition, the problems of monocular vision that lacking depth information and unknown camera-to-robot translational parameters are taken into consideration. In this strategy, an input state scaling technique is used to obtain two decoupled subsystems, then two control laws are designed separately. The un-modeled information and disturbances of the system are uniformly estimated by an extended state observer (ESO) and compensated for by a switching controller. Simulation results validate the effectiveness of the proposed scheme.
A Research on the Impact of Big Data Analytics on the Telecommunications Sector

The telecommunications industry is making significant strides in technological advancements, and Big Data Analytics is playing a crucial role. Unlike in the past, the industry is no longer confined to providing phone and internet services. Big Data Analytics and AI have been successful in replacing obsolete and time-consuming techniques with modern algorithms that simplify the analysis and handling of large amounts of data from diverse consumer bases. Computer vision techniques have proven particularly useful in this regard. This article explores how Big Data Analytics is disrupting the telecoms sector by breaking down boundaries using technologies like Self-optimizing Networks (SON), Robotics Process Automation (RPA), and Chatbots.
Online Hate Speech Identification Using Fine-tuned ALBERT

The increased use of social media platforms has escalated the outspread of online hate speech. Hate speech can take numerous forms, such as political, racial, religious, LGBTQ+, gender-based, nationality-based and disability-based; It can overlap and intersect with various forms of oppression and discrimination and can lead to severe and harmful impacts on society. It is crucial to combat online hate speech and create a more inclusive and secure online environment. Several approaches have already been investigated, such as n-grams, Convolutional Neural Networks (CNN), Recurrent units, Gated Recurrent Units (GRU), and even their combinations to recognize text-based online hate speech. Even though they were able to give satisfactory results, their contextual understanding of the text needed to be stronger as it is a bit of a complex concept. The main reasons these approaches fail are the non-availability of larger datasets to take full benefit of the model’s architecture and their lack of context understanding capability. In this work, we have explored the usage of one of the transformer-based architectures named A Lite Bidirectional Encoder Representations from Transformers (ALBERT) to address the problem of contextual understanding and recognize online textual hate speech efficiently with limited resources and a smaller dataset. Even in the untrained form, ALBERT provides a better understanding of context than any recurrent unit. Thus the ALBERT, pre-trained on a large corpus, gave the flexibility of using a smaller dataset to fine-tune them to a particular downstream task of hate speech identification as it already has a notion of language. ALBERT has already proved its contextual understanding capability with various benchmarked datasets, viz., SQuAD and GLUE. Thus, fine-tuning them to our downstream task allows utilizing this to our benefit. With this approach, we achieved better results than the state-of-art in terms of all four metrics: Precision (6.16%), Recall (6.59%), F1-Score (6.41%), and Accuracy (10.9%).
Review of Human Target Detection and Tracking Based on Multi-view Information Fusion

With the increasing availability and affordability of cameras, pedestrian detection and tracking technology have become widely used in various applications such as intelligent monitoring, behavioral analysis, and traffic control. However, traditional single-view detection methods have limitations, including susceptibility to occlusion and a limited field of view. Multi-view detection and tracking methods have gained significant attention from both academia and industry, as they can overcome the limitations of single-view methods by leveraging complementary information from multiple views, resulting in improved algorithm performance. This paper presents a systematic review of multi-view information fusion for human target detection and tracking, focusing on three dimensions: dataset, multi-view detection, and multi-view tracking. Furthermore, it discusses potential applications of these methods in the power field and suggests future directions for development.
Behavior Recognition Method Based on Object Detection for Power Operation Scenes

Currently, power substations and distribution networks rely heavily on on-site operations, but accidents caused by operating personnel’s violations are common. Manual supervision of operating personnel behavior faces significant challenges in the current situation. However, thanks to advancements in object detection and recognition technology, the precision and efficiency of detection and recognition have now reached practical application standards. This paper proposes a behavior recognition method based on object detection that is specifically adapted to the power scene using the Faster R-CNN object detection network. Firstly, a deep neural network is utilized to obtain object detection results. The network has been specifically tailored for the electric power scene during the training process, with enhancements made to small object detection such as small detection box generation, data augmentation, and threshold adjustment. Subsequently, a pre-designed logical judgment module is employed for behavior recognition in order to obtain the final behavior recognition results. This paper includes multiple personnel behavior datasets recorded in the electric power scene, and the proposed behavior recognition method demonstrated high precision when tested on practical electric power scene datasets, thus meeting the requirements for practical applications.
Federated Topic Model and Model Pruning Based on Variational Autoencoder

Topic modeling can uncover themes and patterns in large documents. However, when cross-analysis involves multiple parties, data privacy becomes a key issue. Federated learning allows multiple parties to jointly train models while protecting privacy. But there are gains and losses, and in the case of federation, there are communication and performance challenges. In order to solve the above problems, this paper proposes a method to establish a federated topic model while ensuring the privacy of each node, and use neural network model pruning to accelerate the model. In addition, to handle the tradeoff between model training time and inference accuracy, two different methods are proposed to determine the model pruning rate. The first method involves slow pruning throughout the entire model training process, which has limited acceleration effect on the model training process, but can ensure that the pruned model achieves higher accuracy. The second strategy is to quickly reach the target pruning rate in the early stage of model training, and then continue to train the model with a smaller model size. This approach may lose more useful information but can complete the model training faster. Experimental results show that the federated topic model pruning based on the variational autoencoder proposed in this paper can greatly accelerate the model training speed and inference speed while ensuring the model’s performance.
Privacy Challenges and Solutions in Implementing Searchable Encryption for Cloud Storage

With the instantaneous growth of cloud usage, IoT devices, smart cities, 4G communication, augmented reality, and virtual reality the requirement for digital storage has also risen to accelerate. By delivering data storage and continuing to manage data, cloud storage has now become a crucial part of the new eon. The usage of cloud computing has expanded dramatically. Shifting data and information to cloud servers has reduced a lot of anxiety related to storing it on physical storage devices. The frugality of data submitted has been a vital duty to safeguard the security of sensitive data. Generally, cloud servers are prone to cyberattacks that will lead to the exploitation of specific sensitive data of enterprises or institutions. Hence, there is a need to give some efficient strategies that not only preserve the information maintained on the server but also do not hamper the facility of using services. Searchable Encryption (SE) refers to a technique that enables users to run search operations over encrypted data. Encryption of data is a need to ensure the privacy of their data. Searchable Symmetric Encryption (SSE) allows us to secretly send our data to another user while keeping the possibility to choose to search through the encrypted data. In this paper, we discussed searchable encryption in-depth, such as the efficiency of contemporary SE schemes, privacy challenges, different single and multiple read/write schemes, and some probable future improvements and ideas provided.
Efficient Partitioning Method of Large-Scale Public Safety Spatio-Temporal Data Based on Information Loss Constraints

The storage, management, and application of massive spatio-temporal data are widely applied in various practical scenarios, including public safety. However, due to the unique spatio-temporal distribution characteristics of real-world data, most existing methods have limitations in terms of the spatio-temporal proximity of data and load balancing in distributed storage. Therefore, this paper proposes an efficient partitioning method of large-scale public safety spatio-temporal data based on information loss constraints (IFL-LSTP). The IFL-LSTP model specifically targets large-scale spatio-temporal point data by combining the spatio-temporal partitioning module (STPM) with the graph partitioning module (GPM). This approach can significantly reduce the scale of data while maintaining the model’s accuracy, in order to improve the partitioning efficiency. It can also ensure the load balancing of distributed storage while maintaining spatio-temporal proximity of the data partitioning results. This method provides a new solution for distributed storage of massive spatio-temporal data. The experimental results on multiple real-world datasets demonstrate the effectiveness and superiority of IFL-LSTP.
An Improved Multi-robot Coverage Method in 3D Unknown Environment Based on GBNN

The research on multi-robot collaborative search based on bio-inspired neural networks in 3D unknown environments is of great value and significance, but robots tend to fall into local optimum in the late search period. This article proposes a multi-robot collaborative search algorithm based on the GBNN model for the problem of multi-robot collaborative coverage search in a 3D unknown environment. When the robot is in a local optimal state, we use a guiding target point to help the robot jump out of the local optimal state and reach the unsearched area. Finally, simulation experiments were conducted to verify the effectiveness of the proposed algorithm. Based on random starting points, complete coverage can be achieved.
The Key to Autonomous Intelligence is the Effective Synergy of Human-Machine-Environment Systems

Autonomous systems are the key to not only intelligentization but also smartification. Human intelligence works prior to the provision of data, whereas machine intelligence works thereafter. An autonomous system transcends not only formalized arithmetic computations but also traditional logic calculations—it is a new game-type computation-calculation system that incorporates the strengths of humans, machines, and the environment. It relies chiefly on the effective synergy between them to form a game mode in a smartified system. During this process, artificial intelligence (AI) plays an important role in the gaming of the future, but there are three unsurmountable obstacles: explainability, learning, and common sense [1]. This article concludes with an introduction to potential issues of future warfare, including deep situation awareness, uncertainty, and human issues in integrated human-machine intelligence. Therefore, the key to winning the future of gaming lies in thorough comprehension of the mechanisms for integrated human-machine intelligence and the approaches to effective synergy.
Improving Dialogue Summarization with Mixup Label Smoothing

The abstractive dialogue summarization models trained with Maximum Likelihood Estimation suffer from the overconfident issue because the training objective encourages the model to assign all probability to the hard target. Although Label Smoothing is widely adopted to prevent the models from being overconfident, it assumes a pre-defined uniform distribution that is not adaptive and is not an ideal soft target. Therefore, we propose a Mixup Label Smoothing method in this paper, which exploits the general knowledge from the language model to construct a flexible soft target to present diverse candidates. We conceptualize the hypothesis distribution obtained from a pretrained language model as the context-smoothing target, which encodes much knowledge through the massive pretraining corpus and implies more possible candidate summaries. Extensive experiments on three popular dialogue summarization datasets demonstrate that our method effectively outperforms various strong baselines, as well as in low-resource settings.
Trajectory Planning of Launch Vehicle Orbital Injection Segment Under Engine Failure Based on DDPG Algorithm

Traditional methods for rocket orbiting have serious deficiencies in orbital accuracy and computational efficiency, and cannot handle engine failure which leads to thrust drop. Accordingly, this manuscript designs a trajectory planning algorithm based on DDPG for the launch vehicle into orbit, transforms the rocket orbiting into an MDP problem, and constructs reward functions that satisfy the flight process and terminal constraints. The simulation results show that this method has significantly improved the accuracy of orbiting and calculation efficiency, and has the ability to adjust to suitable orbit adaptively under failure.