Reviewerly/exHarmony huggingface dataset:
Focusing on features with higher importance degree: A significance-wise mechanism for image restoration

With the aim of retrieving high-quality images from corrupted versions, image restoration meets the extensive demand of application scenarios. State-of-the-art methods solve this problem by means of designing convolution blocks in multistage architectures. However, the existing methods blindly extract and fuse features without considering which parts of the features are more effective for image restoration. In this paper, we propose a significance-wise mechanism with the goal of identifying the importance degree of each region in the feature representation and providing crucial feature information. Through the calculation of nonlinear function in the significance-wise mechanism, the region which is more conducive to image restoration will get higher importance values. Two important types of information are generated within the attention mechanism, including: (i) feature-sufficient maps with abundant feature representations, and (ii) significance-wise maps with the importance degree of patch information in the corrupted images. With the coordination of feature-sufficient maps and significance-wise maps, the network architecture can focus higher attention on crucial parts of feature information. Furthermore, we design a multistage feature fusion block with the significance-wise mechanism. Compared with existing attention mechanisms, our significance-wise mechanism has the ability to identify and generate crucial feature representation for multiple image restoration tasks. Due to the novel design of the network, we successfully implement multiple restoration tasks only by fine-tuning the number of channels once on a network. Abundant quantitative and qualitative experiments demonstrate that the effective image restoration network (EIRN) outperforms existing state-of-the-art algorithms on eleven datasets across a series of image restoration tasks, including image deblurring, denoising, super-resolution, and deraining. The source code and pretrained models will be available at https://github.com/XinyueZhangqdu/EIRN.
Pre-Trained Generative Architectures for Question-Asking Chatbots on Technical Text: A Case Study

In recent years, chatbots' usefulness has spread to an ever-expanding number of domains. Chatbots can be used in educational contexts to ask students questions about topics they are studying for self-testing. Designing models for dialogue systems includes the crucial step of generating appropriate responses. In this study, we propose using chatbots which ask questions specifically catered to a technical domain of interest. To that purpose, we've chosen the fundamental concepts of the Java programming language as our domain. We begin by creating a collection of Java-based dialogues with various sets of question-answer pairings of varying difficulty across various topics. Our Java dialogue dataset includes 10000 multi-turn dialogues generated by an algorithm that simulates a Java interview on basic concepts. Every conversation serves as a training example for our proposed framework. Then, for training on our dataset, we use versions of two different transformer-based pre-trained generative architectures. We compare and evaluate the architectures based on standard automatic metrics for dialogue response generation and other parameters specific to our use-case. We observe that the usage of generative architectures pre-trained as dialog models can generate newer questions, in most cases meaningful in the context of the interview. However, the model design could benefit from incorporating a further knowledge component in order to improve the production of questions both in terms of topic and suitable level of difficulty.
Adaptive Clustering Algorithm for Big Health Data Classification

This paper presents an analysis of tobacco and alcohol consumption among different age groups, using data collected by the World Health Organization (WHO). The analysis quantifies alcohol and tobacco consumption in grams per day, aggregating the total amount consumed by each age group. Consumption of alcohol is considered not only through wine, but also through other mediums such as medicine, inhalers, etc. while tobacco is consumed through smoking, dry tobacco, etc. Our analysis also takes into account whether the cases of consumption were controlled or uncontrolled. The majority of cases analyzed in our study were found to be controlled. Finally, we present graphical representations of the analysis results, highlighting the dependencies between different variables.
Real-Time Casualty Detection System Using CCTV Surveillance: A Deep Learning Approach

The use of CCTV cameras for surveillance has become widespread in contemporary years, and there is a demand for efficient and accurate real-time casualty detection system to ensure the safety of the public. Our paper proposes a deep learning-based approach for real-time casualty detection using CCTV surveillance. Our approach uses a Densenet121 model that is trained on a huge dataset of casualty images to detect 13 different types of casualties. The model achieved an AUC score of 0.88, demonstrating its high accuracy in detecting casualties. We evaluated our proposed method on a real-time CCTV surveillance dataset and demonstrated its effectiveness in detecting casualties in real-time. Our system can provide timely alerts to emergency responders, enabling them to quickly and effectively respond to incidents and save lives. The results of our study demonstrate the potential of deep learning-based methods for real-time casualty detection using CCTV surveillance, and the importance of such systems in ensuring public safety.
A survey of automatic speech recognition deep models performance for Polish medical terms

Among the numerous applications of speech-to-text technology is the support of documentation created by medical personnel. There are many available speech recognition systems for doctors. Their effectiveness in languages such as Polish should be verified. In connection with our project in this field, we decided to check how well the popular speech recognition systems work, employing models trained for the general Polish language. For this purpose, we selected 100 words from the International Classification of Diseases dictionary, the Polish-language version of the International Statistical Classification of Diseases and Health Problems. The words were read into a microphone by five women and five men and also generated with a speech synthesizer using a male and a female voice. This resulted in 1,200 recordings tested with the following systems: Whisper, Google speech-to-text, and Microsoft Azure speech-to-text. The achieved word recognition performance is reflected by the calculated metrics: WER, WIL, Levenshtein distance, Jaccard distance, MER, and CER. Results show that the highest efficiency for most cases was obtained by Azure speech-to-text. However, none of the tested models is ready for voice-filling medical records, describing cases, or prescribing treatment, because the number of errors made when converting speech to text is too high.
A binary particle swarm optimization algorithm based on cauchy mutation operator and inertia weight dynamic adjustment

The conventional binary particle swarm optimization algorithm frequently encounters issues of sluggish convergence velocity and a proclivity to become ensnared in local optima. Within this paper, we present a new binary particle swarm optimization algorithm based on the Cauchy mutation operator and inertia weight dynamic adjustment to address these issues. Firstly, the population is initialized using the Tent chaotic mapping, which preserves the diversity of the population. Secondly, the inertia weight is dynamically adjusted to expedite the rate of convergence and enhance the algorithm's local exploitation capability. Lastly, the Cauchy mutation operator is introduced to improve The formula for updating the velocity, enlarging the search range of particles, enhancing algorithm diversity, and effectively avoiding Being entrapped within local optima. Empirical findings from six benchmark test functions showcase the enhanced capability of the proposed algorithm to evade local optima while simultaneously refining the convergence performance.
Data Augmentation Method for Meter Instance Segmentation Under Data Balancing Strategy

With the development of smart grids, the methods of using cameras or robots for image acquisition and recognition of meters are gradually replacing traditional manual inspections. To enable more efficient meter recognition by deep learning models, the quality of training data is of paramount importance, with data imbalance being a major challenge in rough data. We propose a data augmentation method for meter instance segmentation under a data balancing strategy, called meter balanced object paste (MeterBOP), which pastes meter objects into images of different scenes according to predefined rules, thereby optimizing the data distribution. The approach selects the pasting meter types by class-balanced sampling and scales them to various sizes for scale balance. The pasting regions are generated according to the distribution of existing objects in the image, thus achieving spatial balance while avoiding severe occlusion caused by introducing new objects. Experiments demonstrate that our method improves the performance of Mask R-CNN by 3.1 Box mAP and 1.0 Mask mAP on power meter image-mask (PMI-M) dataset. Furthermore, the method can provide stable performance improvements under various detectors and conditions, exhibiting superior performance enhancement compared to other data augmentation methods.
Achievement of Good Energy Efficiency in WSN using Sleep/Awake Technology

Wireless Sensor Networks (WSN) consist of numerous small battery powered sensors designed to collect measurement data from specified locations within an area of interest. The management of these energy-constrained sensors is a great challenge for WSN-based applications designers, since these sensors have to collect and transmit data for long periods of time. Various strategies have been developed to reduce energy consumption and thus prolong network lifetime. Among them, Sleep/Awake schemes are of great importance due to their highly efficient mechanism. Sleep/Awake technologies in WSN provide an effective way of decreasing energy consumption while maintaining the efficiency of data collection and transmission. Sleep/Awake-based schemes typically consist of two components, namely the sleep scheduling algorithm and the wake scheduling algorithm. The former determines the duration and timing of the sleeping phase of a particular node and the latter determines the duration and timing of the wake phase. These algorithms take into account different parameters such as the node's energy level, the acceptable transmission rate, the current network load, and the network topology. The main goal of Sleep/Awake technology is to achieve good energy efficiency in WSN by reducing the energy consumption of the nodes. The sleep scheduling algorithm ensures that nodes go to sleep at the appropriate times so that the quota of the total energy used for transmission is minimized. The wake scheduling algorithm, on the other hand, wakes the nodes up periodically to collect and transmit data. Aside from energy efficiency, Sleep/Awake technology also helps in preserving the longevity of the nodes. Through the judicious use of Sleep/Awake algorithms, WSNs can achieve maximum performance and energy efficiency.
Joint prediction of discrete and continuous emotions from dance videos based on FFN

Dance performance provides an effective way to express human emotions. Compared with voice and facial expression, it is clear that the regularity of posture change is hard to capture. However, changes of posture can make emotional expressions more vivid in nature, which do well in the analysis of emotional states. In this paper, we propose a hybrid feature for emotion classification in dance performance by means of a well-designed feature fusion network. The novelty of this method is the joint prediction of discrete emotions and continuous emotions, which improves the accuracy and robustness of emotion recognition methods. Finally, we conduct experiments on the official dataset, and the effectiveness of our proposed method is clearly demonstrated through quantitative and qualitative experiments.
2D to 3D pedestrian detection algorithm based on deep learning

Based on the actual demand of the public for 3D content generation technology and targeting the problems existing in traditional 2D to 3D image conversion methods, we propose a 2D to 3D pedestrian detection algorithm based on deep learning. The algorithm uses object segmentation to first extract the human body's 2D outline from the input image. It then combines shallow features of the image to use a fully connected layer to map latent vectors to low-dimensional mesh vertex feature vectors, ultimately generating a triangle mesh for the 3D model. By alternately using SpiralConv and upsampling operations, the 3D mesh of the pedestrian can be generated more accurately. Experimental results show that the algorithm can accurately construct a 3D model of a pedestrian and has real-time feedback capability. The algorithm is suitable for real-time monitoring scenarios such as action recognition. Compared to traditional 2D to 3D image conversion methods, the algorithm uses deep learning to automatically learn features, which has higher accuracy and transferability. Additionally, the algorithm extracts 2D contour information of the human body using object segmentation, which improves the accuracy of generating 3D meshes. We believe that this algorithm has an important role to play in the development of 3D content generation technology.
Automated Calculation of CVSS v3.1 Temporal Score Based on Apache Log4j 2021 Vulnerabilities

Common Vulnerability Scoring System (CVSS) is a well-established standard for an evaluation of vulnerability criticality of Information and Communication Technology (ICT) infrastructure. An analysis was performed aimed at selected vulnerabilities, considering their base score, temporal score, reporting and exploit availability through time. Data obtained using publicly available sources, including National Vulnerability Database (NVD), ExploitDB and AttackerKB, reports and social media posts, were analyzed using a dedicated python application. Obtained results contribute to the basic research seeking to establish automated temporal score calculation that allows to assess the likelihood of exploitation for a given vulnerability.
Data-Centric AI in Healthcare

The predominant paradigm for AI development over the last few decades has been a model- or software-centric approach, in which building a machine learning system requires writing code to implement algorithms, models, as well as taking that code and training it on data. In the last few years, there has been tremendous progress in neural networks and other algorithms, and the code is essentially a good open source issue that you can download from GitHub today for numerous applications.
Data-Centric AI

Data-centric AI refers to an approach to artificial intelligence (AI) that places a strong emphasis on the use of data. This approach involves collecting, processing, and analyzing large amounts of data to extract insights, identify patterns, and make predictions. Data-centric AI is often used in machine learning, where algorithms are trained on large datasets to learn how to perform specific tasks.
Model-Centric AI

Machine learning is a way for computers to learn to perform tasks without explicit programming. It is like, if we want to teach a child some new thing, then we can show them how to do it and ask them to repeat it. Depending on how the child is performing that task, we can give them suggestions.
Introduction

Artificial Intelligence (AI) is a multidisciplinary field that aims to create intelligent systems which are able to perform tasks that typically need human intelligence. These systems rely on a variety of building blocks that work together to enable AI capabilities.
IoT-Based Reinforcement Learning Using Probabilistic Model for Determining Extensive Exploration through Computational Intelligence for Next-Generation Techniques

Computing intelligence is built on several learning and optimization techniques. Incorporating cutting-edge learning techniques to balance the interaction between exploitation and exploration is therefore an inspiring field, especially when it is combined with IoT. The reinforcement learning techniques created in recent years have largely focused on incorporating deep learning technology to improve the generalization skills of the algorithm while ignoring the issue of detecting and taking full advantage of the dilemma. To increase the effectiveness of exploration, a deep reinforcement algorithm based on computational intelligence is proposed in this study, using intelligent sensors and the Bayesian approach. In addition, the technique for computing the posterior distribution of parameters in Bayesian linear regression is expanded to nonlinear models such as artificial neural networks. The Bayesian Bootstrap Deep Q-Network (BBDQN) algorithm is created by combining the bootstrapped DQN with the recommended computing technique. Finally, tests in two scenarios demonstrate that, when faced with severe exploration problems, BBDQN outperforms DQN and bootstrapped DQN in terms of exploration efficiency.
About micro- and nanoscale gold in the veil of gold-bearing territories (on the example of a mineralization site in the basin of the river Adamikha, Amur region)

The article identifies the problem of the need to take into account micro- (1 mm to 0.12 microns) and nanoscale (&lt;0.12 microns) gold in placers and weathering crusts in order to increase the objectivity of their and eroded ore objects potential assesment. The results of technological studies of gold-bearing deluvial deposits in the valley of the Adamikha river basin (Amur region) are presented. A quantitative assessment of the content of micro- and nanogold by fractions of loose material was made. It was found that in the studied sample of such material (weighing 50 kg), the major part (78%) refers to native gold with a dimension of &lt;0.12 microns. Including 0.1% is in concentrator minerals, and 3.5% of the fraction (&lt;3 microns), probably colloidal, is in technical solutions of wet sieve. Moreover, 3/4 of the total amount (18% visible and 57% invisible) of gold is concentrated in the fraction (1–0.5 mm). It is established that, taking into account the free invisible gold, the resources of the precious metal of the object under study increase by 3.4 times. The possibility of a more objective quantitative assessment of the Au potential of promising areas, taking into account the content of fine gold, is shown. Possible ways of solving the problem of a more reliable assessment of geological reserves and estimation of forecast gold resources, taking into account the knowledge of the quantities of micro- and nanoscale fractions, are proposed. The authors believe that studies of the forms of allocation and distribution, including invisible (&lt;0.12 microns) gold in placers and weathering crusts will contribute to a more correct assessment of the studied gold-bearing objects and territories with the possibility of increasing the real resource potential of the precious metal in the region.
EMOTIONAL INTELLIGENCE OF A MUSICIAN-PERFORMER

The purpose of the research presented in the article is to reveal some features of the differentiation and integration of emotions in music and musical performing activities. The article reveals the multifaceted nature of the psychological concepts of “emotional intelligence” in relation to musical performing creativity. An attempt is made to search for the significance of the development of emotional intelligence in achieving the unity of technical and artistic-imaginative, logical-constructive and emotional-sensual, reflective and intuitive, reproductive and creative principles in musical and instrumental performance. It is shown that in the conditions of musical performance, emotional intelligence receives a specific refraction, determined by the characteristics of musical and artistic activity. This is manifested in the development of such qualities as managing own emotions, self-motivation, empathy, etc.
Heuristic dynamic path planning algorithm based on SALSTM-DDPG

Abstract In the field of path planning, there are many excellent traditional algorithms proposed by scholars, but usually these traditional methods based on static environment, these methods lack the ability to deal with dynamic variable environment. A heuristic reinforcement learning dynamic environment path planning algorithm (SALSTM-DDPG) combining self-attention and long short-term memory network(LSTM) is proposed in this paper. Firstly, an automatic encoder is constructed to reduce the feature dimension of the local cost map, which reduces the complexity of the overall model. Then the local cost map and global path after dimensionality reduction are taken as input, the original source of information is guaranteed to the maximum extent possible by this method, at the same time, the global optimal path is used to guide the local path planning. Finally, the deep reinforcement learning algorithm DDPG is used for specific motion control, the Actor part is built using a network based on the SALSTM algorithm. This approach allows previous sequence to be used as a reference when an Actor makes decisions, this enables dynamic obstacles to be predictably avoided by the robot. The feasibility and efficiency of the proposed algorithm are proved by experiments.
Monopoly-based augmented reality game design as a practice media in learning the Physics of magnetism concepts

Abstract The most common student problems are frequently forgetting concepts, lots of memorized formulas, difficulty imagining magnetic phenomena, and lack of motivation. The uses of Augmented Reality technology can help students to visualize magnetic phenomena, increase their mastery and motivate students. This study aims to design a monopoly-based augmented reality game as a practice media in learning physics of magnetism concept. This study uses the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). The results of this study are Augmented Reality-Based Monopoly Games as practice media in learning the physics of Magnetism Concepts. The game developed is very feasible according to the results of feasibility test with likert scale of 1 to 5 by media experts, software engineering experts, subject matter experts, and readability testing score by teacher and students. The results are 87.37% from media experts, 86.58% from the software engineering experts, 87.78% from subject matter experts, 87.92% from teacher, and 91.77% from students. So, it can be concluded that the Augmented Reality-Based Monopoly Games developed is very feasible as practice media in learning the physics of Magnetism Concepts.
Generalization Ability of Deep Reinforcement Learning-based Navigation Based on Curriculum Learning

Abstract In this paper, curriculum learning is studied as an approach to improve generalization ability in navigation tasks, that is, improve the agent’s ability of navigating in scenarios different from those used for training. The agent is trained based on the TD3 algorithm, and curriculum learning selects different stages (i.e. different curricula) of Empty/Sparse/Normal worlds for training. Via extensive numerical comparisons with agents trained under such curricula, it is shown that properly used curriculum learning improves the agent’s ability of generalization. Furthermore, an automatic curriculum learning (Auto-CL) approach is proposed. Auto-CL is shown to have even better generalization than the standard curriculum learning, since it makes the agent able to navigate in new environments with more than 6% shorter paths in more than 21% shorter time.