Reviewerly/exHarmony huggingface dataset:
A Meta Classifier Model for SMS Spam Detection using MultinomialNB - LinearSVC Algorithms

There has been a rise in the use of mobile devices during the past few decades, networks and short message services (SMS) has emerged as a form of communication. SMS spam is a problem for SMS users as well. SMS spam, also referred to as bulk texts, is any irrelevant communications sent via mobile networks [2]. Several factors contribute to the abundance of spam communications, The fact that so many individuals use mobile phones increases the probability that they will be the object to send bulk messages which is known as spam [1]. Furthermore, sending spam is inexpensive, which may be great news to the attackers. Particularly, spam detection is a highly advanced research area with a variety of established algorithms [15]. In order to correctly identify spam data or communications, this approach looks into a Multinomial Naive Bayes-Linear SVC methodology [4]. Pre-processing is applied to the input dataset to get rid of characters and content that aren’t appropriate or relevant [5]. To train the model a Multinomial Naive Bayes-Linear SVC methodology is used for the prediction of spam messages [10]. The final test outcomes have demonstrated that Multinomial Naive Bayes-Linear SVC model works better than earlier models in spam detection such as LSTM, SVM and naive bayes, with an accuracy of 9S.3S%.
Understanding the Effectiveness of Modern Technologies on Training Retention and Satisfaction

E-learning has become a very common and useful tool for skill development. After the COVID-19 pandemic, the necessity and adaptability of this method increased drastically. Hence, various studies focused on understanding the effectiveness of this method. The academic literature highlights the need to explore alternative solutions that can increase a learner's content retention and satisfaction when learning online. Modern technologies can be a great solution for this need with the correct application and development. This paper focuses on analyzing the effects of content retention and satisfaction through three technologies: online platform, virtual reality (VR), and hologram display. A quantitative and qualitative pilot study was developed to analyze the impact of the three different methods. Based on the results of the study, the online platform had the highest average score on content retention, and the hologram display had the highest satisfaction rate among all the groups.
Mixed Reality Based Teleoperation of Surgical Robotics

Many surgical robotic systems are controlled by mechanical based devices that require the operator to remain at a fixed location away from the robot. This restriction in mobility and physical barrier between the surgeon and the robot may reduce procedural efficiency. Thus, we propose an alternative teleoperation approach and mixed reality based system that uses the surgeon's tracked hand poses to control the robot through the use of an untethered head mounted display. We conducted a controlled user study to assess the efficacy of our system. Our experimental results indicate that, for the ring-wire task we tested, there is not a considerable difference in the performance of users compared to existing mechanical based teleoperation devices.
Retail &amp;amp; Wholesale Central Bank Digital Currencies: Comparison of Select Models, Use Case Analysis &amp;amp; Exploration of Potential Technical Features

This article provides comprehensive insight into the current status of development of CBDCs. We describe the objectives and the limitations of central bank-issued digital money and characterize CBDCs in relation to the existing forms of money. Focusing on the implications of major models proposed and trials conducted, we examine the feasibility of both retail and wholesale CBDCs. We describe the use case of CBDCs in the context of the private banking system, cross-border transactions, and payroll processing. Finally, we describe the desirable technical features associated with system security, privacy as well as advanced functionality.
Proof of Useful Work Based on Matrix Computation

Due to the inherently wasteful nature of traditional proof of work mechanisms in blockchain networks, researchers are motivated to find alternatives that can make use of the computational power expended in the process. There are a number of such applications, however most of them target very specific problems, and few are proven to work in practice. This paper presents a proof of work mechanism based on matrix computation, serving a much wider range of applications while serving as the consensus mechanism of a blockchain network. I will cover the current research in the proof of useful work area, and focus on Proof of eXercise (PoX), another mechanism based on matrix computation. Proof of Computation (PoC), the consensus mechanism described in this paper, is a redesigned version of PoX. The mechanism is backed by a series of experiments that confirm the functionality of the protocol.
Endowing Runtime Self-adaptation to an Autonomous Pallet Truck

The need for greater flexibility, as well as new developments in navigation and cognition, have motivated the introduction of autonomous mobile robots (AMRs) in retail intralogistics and also in retail outlets. The autonomy of AMRs means that they can make continuous decisions on how to manage the mission taking into account the dynamics of a scenario where people and robots will share the same environment. To successfully accomplish this task, the AMR must autonomously monitor its own state, detect potential problems and react appropriately, modifying the course of action in real time when necessary. In this paper we describe our approach to provide an autonomous low-lift pallet AMR with run-time adaptability to internal and external context changes. The paper proposes the use of a two-steps fuzzy inference engine. The first one encodes a set of relevant non-functional properties using QoS metrics. The second one uses these metrics, and fuzzified context items, for evaluating the alternatives to the nominal course of action. Decision making is encoded into Behaviour Trees (BTs). The approach has been successfully endowed in a low-lift pallet robot from MetraLabs GmbH and tested in real-world environments.
Lifeline Messenger Real Time Chat Application: Using MERN Stack

Chat application have gained immense popularity among Internet users and smartphone owners. Millions of smartphones users utilize chat application on monthly basis. The advent of the Internet has revolutionized our world, bringing everything within our reach. From sharing information to making purchases, the Internet has made the world feel like a small community. This project revolves around the Internet as well, focusing on the significance of chat applications in our daily lives and their impact on the technological landscape. The objective is to develop a chat system using MERN stack. This application allows individual to exchange messages privately or publicly and facilitates the sharing of various resources such as files, images, and videos. The proposed project aims to address the growing need for a robust and feature-rich chat application in todayâ€™s technological world. By leveraging the MERN stack, the system will offer a user-friendly interface, seamless message transfer, and the ability to share diverse resources. Moreover, it will prioritize the security and privacy of user data, setting itself apart from existing chat applications that often overlook these critical aspects.
Deep Convolutional Neural Networks applied to Hand Keypoints Estimation

Accurate estimation of hand shape and position is an important task in various applications, such as human-computer interaction, human-robot interaction, and virtual and augmented reality. In this paper, it is proposed a method to estimate the hand keypoints from single and colored images utilizing the pre-trained deep convolutional neural networks VGG-16 and VGG-19. The method is evaluated on the FreiHAND dataset, and the performance of the two neural networks is compared. The best results were achieved by the VGG-19, with average estimation errors of 7.40 pixels and 11.36 millimeters for the best cases of two-dimensional and three-dimensional hand keypoints estimation, respectively.
Support provision in a digitalized world: The consequences of social sharing across different communication channels

People tend to share their emotional experiences with others, with sharing increasingly done online. This raises questions about the quality of computer-mediated vs. face-to-face sharing. The present review (1) outlines conditions for sharing to improve emotional and relational well-being, (2) discusses when computer-mediated sharing with other people may (not) be conducive to these conditions, and (3) reviews recent research on the effectiveness of computer-mediated sharing with humans and virtual agents. It is concluded that the emotional and relational consequences of sharing are dependent on the responsiveness of the listener, regardless of the communication channel. Differences exist, however, in the extent to which channels are conducive to various forms of responding, with implications for speakers' emotional and relational well-being.
A Proposed Keyword-Based Feature Extraction Approach for Labeling and Classifying Egyptian Mobile Apps Arabic Slang User Requirements Reviews

Mobile applications (apps) review feature is supplied by most mobile apps platforms, which authorize users to evaluate, comment, and rate apps after utilizing it. User reviews are identified as an oriental source to enhance mobile applications (apps) and raise the importance for users. With the acute rise in the quantity of reviews, how to functionally and efficiently analyze and mining the user reviews and recognize serious user requirements from them to enhance the mobile apps. In this paper, we suggest an automatic approach for identifying and classifying requirements into Functional Requirements (FR), Non-Functional Requirements (NFR) and Sentimental Requirements (SR) from Egyptian Mobile Apps Arabic Slang Reviews (MASR), utilizing a group of techniques Term Frequency – Inverse Document Frequency (TF-IDF), Bag of Words (BOW) and Natural Language Processing (NLP) techniques with keywords selection. We suggest applying Classifier Chains (CC) approach to convert classifying multi-labeled data problem into one or more problems of single labeling, and utilizing the hybrid stack classification model, which combines Machine Learning (ML) and Deep Learning (DL) approaches consist of Logistic Regression (LR), Random Forest (RF), and Multi-Layer Perceptron Neural Network (MLP-NN). The hybrid stack classification model accomplishes high accuracy results for classifying Egyptian MASR user requirements as follow: (99.7%) for classifying Performance, (99.5%) for classifying Dissatisfied Users, (98.8%) for classifying Others, and (98.1%) for classifying Security, (97.9%) for classifying Usability, and (97.4%) for classifying Feature Requests.
Efficient Neuromorphic Reservoir Computing Using Optoelectronic Memristors for Multivariate Time Series Classification

Reservoir computing (RC) has attracted much attention as a brain-like neuromorphic computing algorithm for time series processing. In addition, the hardware implementation of the RC system can significantly reduce the computing time and effectively apply it to edge computing, showing a wide range of applications. However, many hardware implementations of RC use different hardware to implement standard RC without further expanding the RC architecture, which makes it challenging to deal with relatively complex time series tasks. Therefore, we propose a bidirectional hierarchical light reservoir computing method using optoelectronic memristors as the basis for the hardware implementation. The approach improves the performance of hardware-implemented RC by allowing the memristor to capture multilevel temporal information and generate a variety of reservoir states. Ag[Formula: see text]GQDs[Formula: see text]TiOx[Formula: see text]FTO memristors with negative photoconductivity effects can map temporal inputs nonlinearly to reservoir states and are used to build physical reservoirs to accomplish higher-speed operations. The method’s effectiveness is demonstrated in multivariate time series classification tasks: a predicted accuracy of 98.44[Formula: see text] is achieved in voiceprint recognition and 99.70[Formula: see text] in the mobile state recognition task. Our study offers a strategy for dealing with multivariate time series classification issues and paves the way to developing efficient neuromorphic computing.
Mobile Microlearning: Enhancing Listening, Speaking, Writing, and Reading Skills

This chapter demonstrates how microlearning lessons can improve the four primary language skills—listening, speaking, writing, and reading—and provides examples of each.
Overview of Technology in Teaching and Learning

This chapter provides an overview of the use of technology in teaching and learning from the 1970s to the present to help the reader understand the theory and practice of mobile microlearning. The chapter has three themes: an overview of Computer-assisted language learning based pedagogy, an introduction to mobile-assisted language learning, and an introduction to theories of learning.
IMPLEMENTASI SISTEM PEMBERIAN PAKAN AYAM OTOMATIS BERBASIS IOT PADA AYAM KAMPUNG MENGGUNAKAN APLIKASI PONSEL PINTAR

Feeding native chickens is generally done manually so it is less effective. This automatic free range chicken feeding system is the right solution to make it easier for farmers to set the feeding schedule and the amount of free range chicken feed. Feeding was carried out according to the age data and the number of native chickens entered. The age and number of free range chickens will determine the time schedule for feeding and the amount of free range chicken feed given. The system uses Real Time Clock (RTC) as a time comparison to set the time schedule for feeding native chickens. The amount of free range chickens feed is regulated based on delay in opening the feed reservoir valve. The sytem is equipped with the concept of Internet Of Things (IOT). The data on the system will be sent to the cloud data server which is then forwarded to the smart phone application so that it can be monitored for feed availability and remote feeding status through the application on the farm manager’s smart phone as long as it is connected to the internet network. Feeding native chickens can also be done directly via the button available on the smartphone application. The results obtained are the system can provide chicken feed according to the schedule that has been set.
PENYUSUNAN SOP MANAJEMEN INSIDEN KEHILANGAN ATAU PENCURIAN DATA PADA DSDABM KOTA BANDUNG MENGGUNAKAN FRAMEWORK ITIL V3

With the development of Information Technology at the Bandung City DSDABM, it has an impact on the high user needs, both the use of devices, IT services and the need for data and information security. With the existence of vulnerabilities and threats to both IT equipment and data and information, there is a need for incident management in dealing with any IT problems or as a function of IT services to users so that business processes can continue to run. This study will describe the incident management process flow, incident category, service strategy and incident escalation flow based on the proposed ITIL V3 Framework. The result of this research is the preparation of incident management SOP documents that are clearer and more structured both to users and to IT managers in carrying out IT equipment maintenance, service and handling of each incident. Keywords: Information Technology, Security, Data and Information, Incident Management, ITIL V3
A Pb isotope model for the Proterozoic of northern Australia with a focus on the McArthur Basin

The Pb isotopic composition of mineral exploration samples can be used to help determine their metallogenic association and hence to assess prospectivity in geological provinces where there is an established Pb isotope database. We have developed such a database, available online, and models that relate Pb isotopic signatures to known metallogenic events in Proterozoic terrains in northern Australia. Two terrain-specific Pb isotope model curves, the Western Fold Belt curve and the Eastern Fold Belt curve, were developed based on major ore systems in the Mount Isa and McArthur terrains. The Western Fold Belt curve was optimised to give an age of 1640 Ma for the McArthur Formation HYC deposit. This is the well-constrained age of the Barney Creek Formation, host of the mineralisation. To validate the accuracy of the models, we attempted to determine the initial Pb isotope ratios of granitic rocks from across the Proterozoic terrains with ages varying from 1870 to 1550 Ma. The curves give model ages for major ore systems including Mount Isa, George Fisher Zn–Pb, Century and Cannington that are consistent with their stratigraphic locations and accepted metallogenesis. Focusing on the McArthur Basin, a database of 59 deposits and prospects was developed. In addition to the 1640 Ma HYC event, an older event at ca 1700 Ma has been fingerprinted, a younger Upper McArthur event at ca 1570 Ma, an event coeval with the Mount Isa Orogeny at ca 1550–1500 Ma and a late Roper event younger than 1500 Ma. There is also evidence of hydrothermal activity of Cambrian age and recent weathering-related metal concentration.
A particle swarm optimization algorithm based on diversity-driven fusion of opposing phase selection strategies

Abstract Opposition-based learning (OBL) is often embedded in intelligent optimization algorithms to solve practical engineering and mathematical problems, but the combinatorial problems among different OBL variants are rarely studied. To this end, we propose a novel OBL variant based on the principle of optical imaging, which combines two novel types of quasi-opposite learning and extended opposite learning, called diversity-driven fused opposition learning (SQOBL). First, a density center based on a neighborhood model is proposed. Based on the rapid convergence of the centroid, combined the advantages of density and centroid to construct a double mean center (DMC) to replace the original center point in quasi-opposite learning based on the principle of refraction. Secondly, an extended opposite learning method based on optical refraction imaging is proposed. Diversity is then exploited to drive different opposing learning strategies at different stages of evolution, thus controlling the exploration and utilization of the algorithm. Finally, SQOBL was embedded in the PSO with eight others representative OBL variants to find the most optimal solution for a test suite. In addition, 8 novel intelligent optimization algorithms and the first three algorithms were selected to evaluate the performance of the latest CEC2022 benchmark test set and realistic constrained optimization problems. Experiments with 56 test functions and 3 real-world constraint optimization problems show that the proposed SQOBL has good integrative properties in CEC2015, CEC2017, CEC2020, and CEC2022 test suites.
A Multi-scale Fusion Obstacle Detection Algorithm for Autonomous Driving Based on Camera and Radar

&lt;div&gt;Effective circumstance perception technology is the prerequisite for the successful application of autonomous driving, especially the detection technology of traffic objects that affects other tasks such as driving decisions and motion execution in autonomous vehicles. However, recent studies show that a single sensor cannot perceive the surrounding environment stably and effectively in complex circumstances. In the article, we propose a multi-scale feature fusion framework that exploits a dual backbone network to extract camera and radar feature maps and performs feature fusion on three different feature scales using a new fusion module. In addition, we introduce a new generation mechanism of radar projection images and relabel the nuScenes dataset since there is no other suitable autonomous driving dataset for model training and testing. The experimental results show that the fusion models achieve superior accuracy over visual image-based models on the evaluation criteria of PASCAL visual object classes (VOC) and Common Objects in Context (COCO), about 2% over the baseline model (YOLOX).&lt;/div&gt;
Distributional and hierarchical reinforcement learning for physical systems with noisy state observations and exogenous perturbations

Reinforcement learning has shown remarkable success in various applications, and in some cases, even outperforms human performance. However, despite the potential of reinforcement learning, numerous challenges still exist. In this paper, we introduce a novel approach that exploits the synergies between hierarchical reinforcement learning and distributional reinforcement learning to address complex sparse-reward tasks, where noisy state observations or non-stationary exogenous perturbations are present. Our proposed method has a hierarchical policy structure, where random rewards are modeled as random variables that follow a value distribution. This approach enables the handling of complex tasks and increases robustness to uncertainties arising from measurement noise or exogenous perturbations, such as wind. To achieve this, we extend the distributional soft Bellman operator and temporal difference error to include the hierarchical structure, and we use quantile regression to approximate the reward distribution. We evaluate our method using a bipedal robot in the OpenAI Gym environment and an electric autonomous vehicle in the SUMO traffic simulator. The results demonstrate the effectiveness of our approach in solving complex tasks with the aforementioned uncertainties when compared to state-of-the-art methods. Our approach demonstrates promising results in handling uncertainties caused by noise and perturbations for challenging sparse-reward tasks, and could potentially pave the way for the development of more robust and effective reinforcement learning algorithms in real physical systems.
Dynamic feature weakening for cross-modality person re-identification

Cross-modality person re-identification (Re-ID) can be used to perform all-weather pedestrian monitoring tasks by matching the visual features captured in both visible and infrared images. However, the existing mainstream methods mainly rely on a few high response modality-invariant features, leading to problems such as easy loss of person cues and poor algorithm robustness in the recognition process. In this paper, we propose a cross-modality recognition method with dynamic feature weakening (DFW) and flexible segmentation learning as the core mechanisms, specifically: (1) DFW is introduced into the modality-shared network to weaken the model’s over-reliance on high response regions of the image, thereby globally collecting and enriching diverse modality-invariant features. (2) A segmented learning network (SLN) with overlap penalty constraint is used for fine-grained mining of each modality-invariant feature, while avoiding redundant focus on a region and maintaining complete learning of diversity information. The experimental results show that the method in this paper greatly enriches the feature representation, and significantly improves the recognition accuracy of the model. On the SYSU-MM01 dataset, compared with the traditional modality feature learning method, the Rank1 and mAP of this method are increased by 2.56% and 4.53%, respectively.