Reviewerly/exHarmony huggingface dataset:
A Great Learning Stratagem through Artificial Bee Colony Technique for Grouping

There have been many attempts to create training algorithms that can successfully train neural networks in a reasonable amount of time while also achieving acceptable generalisation performance since the inception of neural networks itself. The training of feed-forward neural networks using outmoded gradient grounded learning procedures, such as BP (Back Propagation), often gets trapped in local minima and requires a great deal of time to converge. All of the network weights and biases must be tweaked iteratively for these algorithms to perform properly. Recently, a new training method for randomised feed-forward neural networks known as the ELM (Extreme Learning Machine) has gained prominence. With ELM, the output weights are computed analytically, while the hidden layer parameters are first seeded at random and held constant throughout the learning process. Over the past decade, the ELM framework's popularity has skyrocketed due to its quick training speed and wide applicability. This study delves further into the subject of ELM and the research questions that have arisen around it. Methods based on swarm intelligence are articulated clearly. ELM's recent controversy is also discussed at length. Then, a fresh ELM K-means approach is created. The K-means approach is used to accomplish clustering in the high- dimensional feature space that has been projected from the data using ELM. Then, a hybrid strategy, ELM-ABC, is described, which employs the ABC algorithm to carry out clustering in the ELM topographic space. The method is an improvement over the ELM K-means approach.
LARVIS - Linux AIRelied Virtual Intelligent System

The purpose of this paper is to propose a solution to a long-standing challenge in the Linux desktop operating system, which is the absence of a built-in Intelligent Virtual Assistant (IVA) across popular distributions like Ubuntu, Mint, and Fedora. The proposed IVA framework offers a range of functionalities, including file management, fact retrieval, system volume control, internet speed testing, and time and date reporting, among others. This framework, called LARVIS, has been developed using the Speech Recognition, gTTS, and Keras libraries, and it can be deployed on any Linux distribution. One of the unique features of LARVIS is its exceptional ability to navigate the file system and execute file manipulation tasks, setting it apart from existing IVAs such as Cortana. With the implementation of LARVIS, Linux desktop systems can become more user-friendly, streamlined, and efficient, providing a valuable solution to the challenge of a lack of IVA on this platform.
Blockchain-enabled device command operation security for Industrial Internet of Things

For the convenience and automation of industrial manufacturing and production management, users usually issues commands to control and manage all devices based on the Industrial Internet of Thing(IIoT) cloud platform. This distributed management mode has two disadvantages: (1) The gateway control is adopted by the IIoT cloud platform for the device command operation. Once having the command rights on the IIoT cloud platform, users can operate the devices without restrictions; (2) The IIoT cloud platform records the log of the user's device command operation behavior through a database or file, which is vulnerable to tampering. Therefore, a blockchain-enabled device command operation security scheme for the IIoT cloud platform is proposed, which adds a flexible control factor – command asset quota – to control the number of command operations, and added the allocation and recycling of the command asset quotas based on blockchain transactions, as well as the storage and recording of the command operation behavior logs. In the paper, the classification, value calculation, asset model and quota allocation and recycling mechanism of device commands are descripted in detail, and the reference implementation project is carried out. Finally, this scheme prototype is implemented based on the IIoT platform Jetlinks, OceanConnect and the alliance chain Fisco Bcos. Experiments show that this scheme not only solves the above two problems, but also is feasible, security, efficient and resilient.
Role of Internet of Things (IOT) in Smart Finance and Banking

In this manuscript, the authors have discussed the benefits and challenges faced by the banks for the adoption of IOT based system. The Reserve Bank of India (RBI) has continuously guided and aware to the people. Banks are also awaring to the customers about the benefits of IOT. IOT played a vital role in grooming the country's economy through banking and finance. With IOT, Internet banking, fund transfers, and online shopping also became easy and comfortable. Technology also makes it possible to work in finance and banking in a safer and more secure manner than in the past. The suggestions given in this manuscript will also be helpful in the sector of banking and finance.
Multisensor fusion sensor and improved Relief-F algorithms Based violence detection in schools

A widespread social problem is bullying in primary schools. Compared to school violence globally, primary school violence is seen as more harmful. Fusion and improved Relief-F algorithms are recommended as a multi-sensor-based method for early school violence detection. Data is collected by two motion sensors that play a major role in detecting violence in elementary school and in our daily life activities. A total of 7 types of violence related activities are checked. Time and frequency province characteristics, features are recovered and filtered by using the enhanced relief-F method. The authors then create a classifier with two levels. Random Forest is first level classifier which splits the jump function from others. In the previous paper the author used decision tree classifier but in this proposed work random forest classifier is used and the following level identifies the remaining 8 categories of functions using a radial basis function neural network. The recognition outcomes of the two sensors are then combined using a decision layer fusion method. Our research has shown that, on average, primary school violence can be recognized with an accuracy of 84.4%, whereas daily life can be recognized with an accuracy of 97.3%.
Analysis on Implementation of Artificial Intelligence in the sports Activity

Since artificial intelligencetechnology has been developing recently, it is clear that many industries are benefiting from it. In this occasion, artificial intelligence has demonstrated pivotal to the sports area. It has been urgent in guaranteeing that the business changes from obsolete practices to additional contemporary ones. Artificial intelligence (computer-based intelligence) can be seen as a supporting innovation that explicitly upholds competitors' actual instruction preparing through strategies like information examination and recreation of preparing situations. Albeit however research on artificial intelligence is still in its beginning phases, it is critical to examine how it very well might be utilized in sports preparing on the grounds that this state-of-the-art innovation might here and there at any point make it more straightforward for people to truly prepare. This study starts by inspecting the earlier work on computer-based intelligence applications. This paper explores three explicit circumstances of man-made intelligence application in sports preparing and depicts the key ideas in view of the fundamental thought and related research discoveries of computer-based intelligence. This study centers around the nearby association between artificial intelligence (simulated intelligence) and actual schooling guidance and underlines the advantages of simulated intelligence, like its utilization, straightforwardness, and development.
Human activity recognition system

Smartphones offer a great platform for ongoing behavioral research. However, it might be difficult to observe people's behavior in the wild because users' actions and behaviors can vary depending on the situations and environment's they are in. The user's behavioral environment must be taken into account when modelling and analyzing human activity in the wild because it is just as important as knowing the variety of physical activities. This study integrates human behavioral circumstances with physical activities to create a unique framework for context-aware human activity recognition. The goal of the current active research area called "Human Activity Recognition" is to comprehend human behavior through the interpretation of sensory data. The recordings of 30 study participants engaging in activities of daily living (ADL) while wearing a smartphone strapped on their waist with inertial sensors were used to create the Human Activity Recognition database. Each individual carried out the fundamental tasks of (WALKING, STAIRS CLIMBING, SITTING, STANDING, SLEEPING). Different classification techniques, including CNN and Random Forest, were used to construct the system. The outcome of the experiment demonstrates the precision, ability to distinguish between activities, and ability to graphically compare data.
Research on Learning Resource Recommendation Algorithm Based on User Demand Evolution

With the continuous development of online education platforms, a vast number of learning resources have brought challenges to users’ choices. It is feasible to introduce the recommendation algorithms in e-commerce directly into the online learning platforms to recommend courses for learners. However, they often ignore the dynamic evolution of learning needs, resulting in partially ineffective recommendations. To solve this problem, we propose a method called LR-DE, which takes the change rule of user needs into account. It first exploits gated recurrent units to extract the users’ learning interest states at each time step. Then, to address the phenomenon of interest incoherence in behavior sequences, we introduce the attention mechanism to upgrade the update gate in GRUs structure and construct a new network structure called AUGRU. At the same time, the bilinear feature interaction is used to calculate the correlation scores between the click histories and the candidates so as to capture the co-occurrence relation between the two courses. Experimental results show that our method is superior to the existing methods in learning resource recommendation tasks and can effectively improve recommendation accuracy.
Nonclassicality versus quantum non-Gaussianity of photon-subtracted displaced Fock state

In this paper, a quantitative investigation of the non-classical and quantum non-Gaussian characters of the photon-subtracted displaced Fock state $|{\psi}\rangle=a^kD(\alpha)|{n}\rangle$, where $k$ is number of photons subtracted, $n$ is Fock parameter, is performed by using a collection of measures like Wigner logarithmic negativity, linear entropy potential, skew information based measure, and relative entropy of quantum non-Gaussianity. It is noticed that the number of photons subtracted ($k$) changes the nonclassicality and quantum non-Gaussianity in a significant amount in the regime of small values of the displacement parameter whereas Fock parameter ($n$) presents a notable change in the large regime of the displacement parameter. In this respect, the role of the Fock parameter is found to be stronger as compared to the photon subtraction number. Finally, the Wigner function dynamics considering the effects of photon loss channel is used to show that the Wigner negativity can only be exposed by highly efficient detectors.
Leveraging Blockchain Technology to Alleviate Problems of Music Industry

The transition from sales to streaming began with the debut of Spotify, and the context of income-generated streams in the music industry has been sporadically examined. The music that artists have produced has greatly enriched people's lives. Despite the industry's record-breaking earnings, musicians are concerned about the reduced royalties they receive. When artists release their works, the majority of people download and reproduce them, raising copyright concerns as well as issues with music sharing. Furthermore, it is getting harder and harder to comprehend how royalties are calculated in the music business. This will have a hugely detrimental effect on the passion and motivation of artists. This article analyses fundamental problems in the music industry and suggests a solution that tries to address these problems. The solution uses a smart contract to quickly and effectively pay musicians' royalties by keeping music-relevant information on the blockchain network. Our solution makes it easier for copyright income to be awarded to artists, which will aid in preventing the illicit uploading of music to other platforms.
Predicting Earthquake Measurements using Deep Learning

In order to prevent loss of life during an earthquake that the 3S-AE-CNN model can provide an innovative approach to predicting earthquake parameters quickly and accurately, which can be very useful for early warning systems to prevent earthquake-related deaths and damages. It is impressive that the model can determine earthquake size and location just three seconds after the P-wave begins, using wave features that can be easily identified. The model's small errors in predicting magnitude, latitude, and longitude for the most probable locations show its potential for accurate earthquake parameter prediction. Moreover, the ability produce in 3S-AE-CNN model to deliver the estimated earthquake parameters to a centralized IoT system for timely reactions can be very valuable for disaster management and earthquake risk reduction. The fact that this model outperforms the benchmark in size and position determination makes it a promising tool for EEWS. Overall, the 3S-AE-CNN model described in this study can offer an effective and efficient approach to earthquake parameter prediction and help reduce earthquake-related risks and losses.
Online and real-time mask-guided multi-person tracking and segmentation

Applications of multi-object tracking and segmentation (MOTS) include autonomous driving and video surveillance systems. In these applications, the tracking system must be able to track in real-time and online. Recent studies to improve the tracking performance of MOTS have been actively conducted; however, most studies fail to consider online and real-time performance. This paper proposes a MOTS system that operates in real time using a deep learning–based model with a light backbone, and can be tracked online. Additionally, state-of-the-art trackers use an object’s bounding box to extract re-identification (ReID) features, but include unnecessary background. Additionally, this causes confusion in accurately expressing the appearance features of an object, resulting in difficulty in matching. To solve this difficulty, instead of providing the features of the bounding box as the input of the ReID branch, we focus on expressing the object by providing the mask features of the object as an input. As a result of the mask-based ReID experiment, the association accuracy performance was higher than that of the existing bounding box–based ReID model, and among the KITTI MOTS benchmarks, it ranked second among models that can operate online. Our experiments show that background information causes ambiguous ReID matching in MOTS systems, and that object mask information is important for avoiding ambiguous matching.
The Autonomous Policy Decision Maker Simulator (APDM-Simulator) (Basic Manual)

This basic manual proposes a new autonomous simulator to evaluate and solve socio-economic, business, and financial problems with using AI, big data, data sciences, neural networks, and multidimensional graphical models to help policy makers and academics to understand and solve dynamic and complex socio-economic problems as a whole. In our case, this basic manual presents a new technology under the construction of an electronic application (theoretical framework) that is mixing AI, big data, data sciences, neural networks, and Econographicology simultaneously. The new electronic application is called “The Autonomous Policy Decision Maker Simulator (APDM-Simulator). Finally, we introduce the APDM-Simulator theoretical framework and few examples about how to use the APDM-Simulator to solve any socio-economic issue(s). The APDM-Simulator is using as a main database based on the uses of all volumes (45) and issues (285) from the Journal of Policy Modelling (JPM) published by Elsevier in the last past forty-five years (1978-2023).
Perspective Chapter: Blockchain Technology in the Field of Energetics – Organization of Effective Energy Market

The article is devoted to the topic of blockchain applications in the field of energetics. A role of the technology in the digital economy development has been discussed; corresponding examples from Russian, Chinese, Estonian experience have been presented. A synergy between usage of blockchain and other information technology such as big data, intelligent avatars and the Internet of things has been demonstrated. Implementation of the blockchain technology into the functional scheme of the energy market has been proposed as a ways to ensure its decentralization and openness. Energy supply contracts can be negotiated directly between producers and consumers without intermediaries, so all the market actors have a high level of autonomy in purchasing and selling. The technology provides services for preparation and issuance of necessary invoices, as well as for making settlements for the entire transaction. Blockchain technology ensures economic and information security of transaction participants and includes convenient tools for realization of their market activities. Difficulties and risks, connected with the technology implementation, have been discussed, as well as main ways of its further development.
An Efficient Algorithm for Secure Key Management in Cloud Environment

Cloud infrastructure is efficient and scalable in all aspects; hence an enhanced method of safety measures is needed for secured data transfer. Security solution for cloud centric PC’s was reliable in providing the on-demand cloud services. The common way to protect file before sending the file is encrypting the file. So safe key generation and allocation to the client for file decryption is a big task. The significant method we proposed is to receive the secret key secretly by the allowed user. We proposed wellorganized algorithm to share out the secret key in cloud computing environment. In this paper the safety method used depends on two things. One is improving the security by using secure socket shell channel and the second one is sharing the secret key to the allocated user using well organized key distribution algorithm.
GINT: A Generative Interpretability method via perturbation in the latent space

As the neural networks get deeper and deeper, model interpretation becomes more necessary and important, especially in high-risk fields such as medicine and finance. From the perspective of feature attribution, most existing attempts aim to identify relevant features contributing the most to the prediction. Among them, perturbation-based methods mainly explore the corresponding model’s output by randomly perturbing the given features. When the data is high-dimensional and sparse, perturbing the feature space may be inefficient and meaningless since it ignores the feature correlations in the data distribution. In this paper, we introduce a novel Generative INTerpretability method, named GINT, which generates perturbations in the latent space. We propose a unified framework for perturbation-based methods, which describes the characteristics of a suitable perturbation for interpretation. Under the framework, we adopt a generative model to generate perturbation instead of randomly perturbing. Subsequently, we sample perturbations from the generative model for a given instance and its prediction and calculate the feature importance by analyzing those perturbations. We conduct extensive experiments to validate the effectiveness and efficiency of the proposed method.
3D pedestrian localization fusing via monocular camera

In the field of intelligent transportation, autonomous driving technologies, especially visual sensing solutions have attracted increasing attention in recent years. There are still some challenges in pedestrian location based on the monocular camera, as the pedestrian is a non-rigid object and its depth information cannot be obtained from the monocular camera easily and accurately. In this paper, a pedestrian location framework based on monocular cameras is proposed. The framework consists of three parts: coarse positioning, auxiliary information generation and information fusion. In the part of coarse positioning, the human skeleton information is obtained from the monocular images and a light-weight feed-forward neural network is used to predict the pedestrian position based on the skeleton information. In the part of auxiliary information generation, pseudo-LiDAR points with pedestrian depth information are generated from the monocular images through an auxiliary network. Finally, the outputs of the above two parts are fused to achieve the pedestrian location. The experimental results on KITTI dataset show that our method has achieved better performance than other methods.
Cooperative Mining System to Improve Bitcoin Scalability

Blockchain is a Distributed Ledger Technology (DLT) that allows users to exchange values directly without a need for trusted third parties. Bitcoin is one of the most popular digital cryptocurrencies that is based on blockchain technology. However, it faces scalability problems including transaction throughput, latency, and starvation. Bitcoin transaction throughput is very low compared to traditional payment methods. Additionally, many Bitcoin transactions suffer from delays and starvation as miners prefer transactions with higher fees. More of the current research focuses on how to enhance Bitcoin scalability by improving the performance of consensus techniques, dividing the network into smaller ones with different parts of the blockchain, or completely changing the blockchain data structure. Unfortunately, engaging in this problem usually affects either decentralization or security; this is called the blockchain trilemma. This paper proposes the Cooperative Mining System (CMS), which depends on enhanced proof of work consensus algorithm. This proposed system increases transaction throughput and eliminates transaction latency and starvation without affecting decentralization and security. In CMS for each epoch, miners cooperated to create one super-block that contains more transactions than the traditional Bitcoin block. Whereas miners create their traditional blocks simultaneously, broadcast them, wait to receive other miners’ blocks, and lastly create a super-block that contains all the transactions of the gathered blocks. The Simulation results of the CMS and Bitcoin system using different case scenarios show a significant improvement in CMS compared to the current Bitcoin system. The CMS greatly increases the transaction throughput and eliminates transaction latency and starvation.