Reviewerly/exHarmony huggingface dataset:
An ANN based bidding strategy for resource allocation in cloud computing using IoT double auction algorithm

In the cloud computing, a double auction is extensively used for resource trading. Different clients and cloud service providers offer multiple bids for respective resources (Virtual Machines). Collecting multiple competitive equilibrium values from different cloud computing centers is difficult and partial information may arise. Therefore several learning models have been implemented to assist the bidding strategy in the cloud market. In a double auction, the existence of a problem is individual bounded rationality and fragmentary statistics. However we have implemented several traditional (Linear Regression, Random Forest, Decision Tree, Support Vector Regressor, Gradient Boosting Regressor and Artificial Neural Network) but the Artificial Neural Network model has given the best result for the learning mechanism of predicting the prices for both sides (client, service provider) according to their requirements. Our algorithm provided an accuracy of 97 % concerning state of the art techniques. It increased the profits of clients and service providers as well as reduced resource wastage. This learning model was constructive and effective. Different learning models have been analyzed based on efficiency and accuracy for predicting the final price of the users (buyers & sellers).
Analysis of Automatic Gender Prediction in Social Media by using Xgboost Algorithm

The motivation behind this article is to break down the profiles of informal organization clients to decide the gender of the profile proprietor. To this end, various AI apparatuses have been utilized and utilized in the field of portrayal from a site clarifying client choices. The review depends on online media and presumes that there are numerous things that can be utilized to decide the gender of the personality proprietor. Also, subsequent to inspecting online media data, studies have shown that this sexual orientation personality exercise can be performed proficiently utilizing AI procedures with 97.30%.
An Indispensable Contemplation on Natural Language Processing Using Ensemble Techniques for Text Classification

NLP is a computer-based software technology that used primarily to translate the natural human language into machine understandable format efficiently. The inputs for the machine could be text, audio, video, image, and speech. NLP is emerging globally and is being used in many applications. One popular application of NLP is Siri introduced in Apple Inc. This created a greater impact on iPhone community. Machines being trained with respect to voice search will facilitate the future generation in performing day-to-day activities. Machine learning (ML) algorithms have contributed extensively for building this popular framework. The paper aims to provide an NLP solution to a text classification problem and solve it using NLP and machine learning to get the best results and the best accuracy.
The Role of Blockchain in Commercial Revolution

The implementation of blockchain technology by various businesses has gained impetus at the same time as within the finance area it is achieving new heights progressively. To generate a distributed ledger a blockchain (data structure) is utilized. Basically, a blockchain is a collection of (a page of a ledger or record book) i.e. blocks in a sequential fashion. A record book holds all dealings, timestamps, the hash value of the preceding block, record book compensation, record book number, and others. Each single record book holds a hash value of the preceding record book or block, therefore making a series of record books accompanying one another. In this research work, while discovering these DApps, we will discover them at a stage that is sufficient to comprehend in what way they work and manage various concerns. In this research work, we will deliberate and recognize Blockchain arrangement, how Ethereum works, writing smart contracts, the introduction of web3.js, and wallet services. Finally, we put forward promising clarifications in detail for improving the safety of the distributed ledger for industrialized applications
Alpha-Beta Hybrid Quantum Associative Memory Using Hamming Distance

This work presents a quantum associative memory (Alpha-Beta HQAM) that uses the Hamming distance for pattern recovery. The proposal combines the Alpha-Beta associative memory, which reduces the dimensionality of patterns, with a quantum subroutine to calculate the Hamming distance in the recovery phase. Furthermore, patterns are initially stored in the memory as a quantum superposition in order to take advantage of its properties. Experiments testing the memory's viability and performance were implemented using IBM's Qiskit library.
Prioritizing Multi-Interwoven Factors in the Project Management Office Using Delphi and Fuzzy DEMATEL

This study aims to evaluate the cause-and-effect relationships of potential PMO functions at project-oriented organizations. In this research, the Delphi technique was first employed to identify the potential PMO functions, and DEMATEL technique was conducted to evaluate the cause-and-effect relationships. According to the research findings, structure determination and project organization were the most effective PMO functions, whereas counseling and training through PMO was identified as the most important function. Moreover, participation in project implementation through PMO was the most affected function. To improve the PMO performance, project-oriented organizations can focus on effective functions and causes in order to modify and improve them.
Artificial Intelligence-Based Sustainable Development of Smart Heritage Tourism

World heritage is a kind of affirmation and high honor given by the international community to the important civilization, historical relics, or natural landscape of a country and nation. This paper studies and analyzes the sustainable development of heritage tourism boosted by smart tourism based on big data artificial intelligence. This paper first analyzes big data and then introduces the concept of smart tourism. Smart tourism is a new future-oriented tourism form that serves the public, enterprises, and governments. It uses the Internet of Things, cloud computing, next-generation communication network, high-performance information processing, intelligent data mining, and other technologies in tourism experience, industrial development, administrative management, and other applications, so that tourism physical resources and information resources have been highly systematically integrated and deeply developed and activated. And then, we analyze and discuss artificial intelligence algorithms. Artificial intelligence is a new technological science that is researched and developed on the basis of computer science as a simulation and extension of human intelligence activities. Finally, a comprehensive analysis is made on the tourism ecological footprint of natural heritage sites and the carrying capacity of tourism ecology in natural heritage sites. The experimental results of this paper show that the tourism development of this natural heritage site is in a sustainable state. The reasons are as follows: First, the average tourist ecological footprint of the place is 0.009466 hm2, the average tourism ecological carrying capacity is 0.032861 hm2, and there is an average ecological surplus of 0.02339 hm2; secondly, the average tourist natural footprint is 18285.93 hm2, the average tourism environmental carrying capacity is 40421.97 hm2, and the average ecological surplus is 22136.04 hm2. To sum up, it shows that its tourism development is in a state of sustainable development.
Automatically Guided Selection of a Set of Underwater Calibration Images

The 3D reconstruction of underwater scenes from overlapping images requires modeling the sensor. While underwater self-calibration gives good results when coupled with multi-view algorithms, calibration or pre-calibration with a pattern is still necessary when scenes are weakly textured or if there are not enough points of view of the same points; however, detecting patterns on underwater images or obtaining a good distribution of these patterns on a dataset is not an easy task. Thus, we propose a methodology to guide the acquisition of a relevant underwater calibration dataset. This process is intended to provide feedback in near real-time to the operator to guide the acquisition and stop it when a sufficient number of relevant calibration images have been reached. To perform this, pattern detection must be optimized both in time and success rate. We propose three variations of optimized detection algorithms, each of which takes into account different hardware capabilities. We present the results obtained on a homemade database composed of 60,000 images taken both in pools and at sea.
RLBrowse: Generating Realistic Packet Traces with Reinforcement Learning

Automated Web Browsing tools, such as Selenium and headless browsers, are used to collect traffic traces from networked applications, with which statistical models describing the traffic are obtained. However, we show that traces from Selenium and headless browsers have markedly different traffic characteristics than human generated traces, with potential impact on the quality of the obtained models. To overcome this limitation, we propose RLBrowse, an automated web automation framework that imitates human browsing habits by separating web automation from the browser using reinforcement learning. By separating the browser and automation tool, RLBrowse improves on 9 out of the 13 traffic trace features tested. The distribution of packet sizes in a trace improves the most, with a nearly 400 % improvement. We test RLBrowse by collecting a corpus of network packet traces on a set of human-navigated website browsing sessions, and by RLBrowse and Selenium. In the subsequent analysis, we identify key differences in the resulting packet traces.
A Case Study of Online Learning Coaching on Late-Adolescents : Focusing on Changes of Self-directed Learning Ability, Time Management Behavior, and Self-Efficacy

The purpose of this study was to explore the quantitative and qualitative changes of self-directed learning ability, time management behavior, and self-efficacy of late adolescent who started their college in the Covid 19 era, while they were participating in the online learning coaching offered by their university. For this, 10 college students participated in the online learning coaching program designed with 7 sessions through zoom meeting based on GROW model. In order to explore quantitative changes, differences of pre- and post-test results of self-directed learning ability, time management behavior and self-efficacy were compared with paired t-test and they showed significant increases in all three factors. In order to find out qualitative changes, participant observation and interview data were collected and analyzed based on constant comparison method. Major results were as follows. First, participants’ career goals were changed from vague to more detailed ones. Second, participants’ study plans were from rudimentary to more specialized with individual situation. Third, participants’ time management behavior were from disordered to ordered based on priority with college class. Fourth, participants’ confidence were from low to high through successful experiences with implementing their own plans. Based on these results, several issues were discussed for extending and activating online learning coaching for the college youths.
Free2Shard

In this paper, we formulate and study a new, but basic, distributed resource allocation problem arising in scaling blockchain performance. While distributed resource allocation is a well-studied problem in networking, the blockchain setting additionally requires the solution to be resilient to adversarial behavior from a fraction of nodes. Scaling blockchain performance is a basic research topic; a plethora of solutions (under the umbrella of sharding) have been proposed in recent years. Although the various sharding solutions share a common thread (they cryptographically stitch together multiple parallel chains), architectural differences lead to differing resource allocation problems. In this paper we make three main contributions: (a) we categorize the different sharding proposals under a common architectural framework, allowing for the emergence of a new, uniformly improved, uni-consensus sharding architecture. (b) We formulate and exactly solve a core resource allocation problem in the uni-consensus sharding architecture -- our solution, Free2shard, is adversary-resistant and achieves optimal throughput. The key technical contribution is a mathematical connection to the classical work of Blackwell approachability in dynamic game theory. (c) We implement the sharding architecture atop a full-stack blockchain in 3000 lines of code in Rust -- we achieve a throughput of more than 250,000 transactions per second with 6 shards, a vast improvement over state-of-the-art.
Rail Lone Worker Safety Solution with Deep Learning

The European Framework Directive on Safety and Health at Work sets requirements that companies must provide adequate supervision and protection to protect workers’ health and safety. A watch-based solution has been designed and prototyped specifically for lone worker safety protection in the rail industry, characterized by a harsh working environment. The prototype integrated edge-based Artificial Intelligence accident detection, rule-based management functions for automatic & manual safety alert and for battery energy consumption, as well as worker support functions. An event-driven messaging function has been developed for transmission of detected incident data between the worker and the remote assistance team, in addition to voice communications. This event-driven message has been inspired by European Standards on Cooperative Intelligent Transport System. The developed prototype has been tested in a lab environment with promising results. Real on-site testing with lone worker is currently ongoing.
New models to aid the discovery of critical raw material deposits for the Green Stone Age

Abstract Critical raw materials (CRMs) will be crucial for delivery of the technologies society requires to meet its decarbonization goals and successfully address the current climate crisis. Although there are a number of well-established ore deposit models, few have been developed for CRMs. History demonstrates that ore deposit models are commonly developed to explain new discoveries. However, it is also possible to develop new ore deposit models based solely on basic geochemical and geological principles. We review the development of several ore deposit models, suggest some deposits for which models have not yet been developed and utilize the specific example of hydrothermal nickel deposits in the Central African Copperbelt to illustrate how new ore deposit models for CRM deposits may be formulated. The current impetus to discover new resources of what have been up to the present elemental curiosities should spur significant research, including work to determine the geochemical behaviour of CRMs across a range of physio-chemical conditions. There is no reason to believe that economically significant concentrations of any of the newly critical elements do not exist in nature. Imagination should make the future of CRM discovery in the Green Stone age bright.
Examining German and American teachers’ perceptions about students’ use of the internet to conduct research

There is a critical need to understand the factors that influence the development of students’ skills for using the Internet to search for and locate information. Teachers play an important role in this process and there is a related impetus to identify the dynamics of instruction in information-seeking activities. This study investigated German and American middle and secondary school teachers’ perceptions about preparing students to use the Internet as a tool for research. Utilizing theoretical thematic analysis, results indicated teachers from both countries felt specific student attributes contributed to and inhibited processes associated with online information searches. Importantly, there were also similarities and differences in the strategies teachers perceived as necessary for preparing students that were linked to pedagogical and contextually-specific factors. Implications for future investigations include incorporating observations of instruction and collecting teaching artefacts to further identify the pedagogical strategies implemented by teachers from each country.
Research Directions of Applied Cognitive Sciences

Cognitive science is an interdisciplinary field of investigation of the mind and intelligence [...]
Multilevel feature fusion and edge optimization network for self-supervised monocular depth estimation

Monocular depth estimation is an essential step in scene geometry understanding. However, the depth maps predicted by the existing methods have the problems of loss of small target details and blurred edges. To this end, we propose a monocular depth estimation method based on multilevel feature fusion and edge optimization to obtain depth maps with rich depth details and precise edges. First, we improved the encoder part to make it better adapted for the depth estimation task. In addition, we fill the U-shaped network with a dense feature fusion layer (Dense-FL) to capture global context information and then combine the proposed structure attention module to further enhance the obtained feature information, so the depth map contains more detailed information. Finally, we design an edge optimization module to incorporate edge features into the training process and combine the proposed reweighted loss and image edge detail loss to constrain the network, so as to further improve the learning ability of the model to the edge of the object. The experimental results on the KITTI dataset show that the depth prediction results obtained by our method perform better at small targets and the edges of objects in the scene, and the structure of the objects is more complete. Generalization experiments on the Cityscapes and Sceneflow datasets also further confirmed the effectiveness and superiority of our proposed method.
Hypermemetic Logic and Communication Mechanism of Internet Memes Modelling Considering Chinese Haze Duanzi as Sample

Hypermemetic logic and the communication mechanism of Internet memes modelling considering Chinese Haze Duanzi as sample is discussed in this study. nternet environment as an information ecosystem, material cycling is happening in the production and communication process of Internet memes, and the energy that driving the communication is from the rhetorical action. Firstly, This study found that there are four hypermemetic logic including economic, social, cultural, and political, which generate corresponding energy flows into the information system and reflect their political and non-political nature for Internet memes in the phenomenon of Haze Duanzi. Secondly, The communicaiton of strong memes through assimilation, retention, expression and transmission has the possibility of moving beyond the energy system to other systems (variation), which leads to the factor in one system being brought to other systems, thus triggering the resonance between systems. To some extent, meme theory which regards meme as a relatively independent replication factor, expands the traditional rhetoric theory which only regards people as rhetorical subjects, and reveals the autonomy in the communication of memes. Furthermore, through the communication modelling and the algorithm design, the optimization of the work has been conducted. Through the testing on the different scenarios, the performance of the proposed model is verified.
Dance Fitness Action Recognition Method Based on Contour Image Spatial Frequency Domain Features and Few-Shot Learning

In recent years, the research work of artificial intelligence technology has progressed rapidly, and various classic Few-Shot learning models have achieved unprecedented success in many artificial intelligence application fields. These include face recognition, object classification detection and tracking, speech recognition, and natural language processing, which greatly facilitate our lives. This paper aims to identify dance fitness movements based on contour image spatial frequency domain features and Few-Shot learning technology. This paper proposes a Few-Shot learning method based on contrastive average loss for Few-Shot learning. This method makes the learned model more representative by improving the loss function and performing a normalization process, and it proposes a feature extraction algorithm that combines improved LBP and HOG for action recognition technology. The experimental results show that the recognition accuracy of the algorithm in this paper is 93.10%, 90.30%, and 92.70% for walking, opening hands, and running, respectively. This illustrates the effectiveness of the fusion feature algorithm.
Adaptive framework towards radar‐based diversity gesture recognition with range‐Doppler signatures

Radar-based hand gesture recognition (HGR) has attracted growing interest in human–computer interaction. A rich diversity in how people perform gestures causes a large intra-class variance, and the sample quality varies from person to person. It makes HGR more challenging to identify dynamic, complicated, and deforming hand gestures. It is urgent for the real world to explore a robust method that better identifies the gestures from non-specified users. To address the above issues, an adaptive framework is proposed for gesture recognition, and it has two main contributions. First of all, a trajectory range Doppler map (t-RDM) is obtained by non-coherent accumulating for inter-frame dependencies, and then t-RDM is enhanced to highlight the trajectory information. Taking into account different movement patterns of the gestures, a two-pathway convolutional neural network targeted for raw and enhanced t-RDMs is proposed, which independently mines discriminative information from the two t-RDMs with different salient features. Second, an adaptive individual cost (AIC) loss is proposed, aiming to establish a powerful feature representation by adaptively extracting the commonalities in variant gestures according to the sample quality. Based on a public dataset using soli radar, the proposed method is evaluated on two tasks: cross-person recognition and cross-scenario recognition. These two recognition modes require that the training set and the test set are mutually exclusive not only at the sample level but also at the source level. Extensive experiments demonstrate that the proposed method is superior to the existing approaches for alleviating the low recognition performance caused by gesture diversity.
Manipulating Remittances: Strengthening Autocratic Regimes with Currency Overvaluation and Remittance Flows

This paper investigates the link between remittances and autocratic regime stability. It challenges the prevailing assumption that remittances cannot be directly captured as a source of hard capital by states. It proposes that remittances increase regime durability by incentivizing and enabling currency overvaluation and seigniorage revenue generation in autocratic states that produce non-freely convertible currencies. It uses cross-national time-series data from autocratic regimes in Sub-Saharan African countries from 1975-2015 to test this theory. The analysis shows that remittances increase autocratic regime durability in countries that have monopoly control over domestic currency production.