Reviewerly/exHarmony huggingface dataset:
What determines bitcoin liquidity? A penalized regression approach

We investigate which factors contribute most to the liquidity of Bitcoin, using a diverse universe of candidate factors reflecting key developments in the crypto market and the global economy. The empirical analysis relies on three regularized linear regression methods, viz. LASSO, adaptive LASSO, and elastic net. We also apply a cross-fit partialing-out LASSO instrumental-variables regression model, as a supplementary approach to handle endogeneity. Findings reveal that trading volume and realized volatility of Bitcoin, cryptocurrency hacks, Ethereum liquidity, and public attention are the most common drivers of liquidity, irrespective of the penalized regression approach and liquidity proxy adopted. Our evidence confirms the paramountcy of cryptocurrency-specific factors over global economic and financial ones in influencing Bitcoin liquidity.
Application of Internet of Things Based on Big Data Ecosystem in Factory Energy Consumption Analysis Model

The demand for energy in factories is huge. With abundant energy supply, factories can raise their production capacity to a new height. Energy is the material basis of domestic economic development. As the largest developing country in the world, energy shortage has become an urgent problem for China. In this paper, an Internet of Things based on big data ecosystem is proposed to analyze the energy consumption of the factory and build a model. The Internet of Things technology of big data ecosystem can be summarized as a technology that uses information sensing devices to complete the transaction and network connection according to the protocol content. A total of 853,000 power distribution operations were carried out in the power grid. In 2019, the average ratio of decision tree algorithm, machine learning algorithm, and machine learning algorithm was 36.8%, 37.4%, and 43.5%, respectively. Compared with the three methods, the method in this paper increased by 37.9% year-on-year and reduced the power outage by 2.63 million households, which is equivalent to a corresponding reduction of 35 users per operation. The functional requirements of the IoT energy consumption analysis system in factories based on big data ecosystem are reflected in three aspects: energy consumption monitoring and management, power control and management, and energy consumption supervision and analysis. Based on the management of energy consumption monitoring and power control through the software platform, the functional requirements of the system are analyzed.
The Synergy of Metadata and Metamodel through Algorithm Modeling—Case Study of the Roof Tiles in Yangxindian Palace (Beijing, China)

The investigation of cultural heritage is a continuous process, in which a large scale of metadata is gradually accumulated, i.e., condition data, intervention data, and the comprehension of building techniques. We take the roof tile of Yangxindian Palace as an example. The total number of roof tiles is 17,154. Their positions are various, as they are equally distributed on double-curved irregular surfaces. Their metadata are multisource and heterogeneous. Therefore, the integration between the model and the data can be complex. This paper aims to generate the M1 (metalevel) model with its graph database based on the M3 (metalevel) model through algorithm modeling. This synergy approach consists of three parts. First is the data collection. The unique identifier is used as the index while investigating onsite, which eventually forms the metadata repository. Second is the algorithm modeling. All the roof tiles are rebuilt in Grasshopper with identifiers. Their metadata are merged through Cypher syntax, which can generate the graph database in Neo4j. Third is the delivery. The IFC model and knowledge graph are connected in real time during the whole process. The significance of this paper is to provide a synergy approach and discuss its efficiency for the integration between a model and data of very large quantities. The results show that it is efficient to express the semantic relationships of both conditions and interventions. It is practicable to lightweight the IFC model and to associate metadata into the graph database, enabling online browsing and querying.
Circuit Implementation of Variable-Order Scaling Fractal-Ladder Fractor with High Resolution

Extensive research has been conducted on the scaling fractal fractor using various structures. The development of high-resolution emulator circuits to achieve a variable-order scaling fractal fractor with high resolution is a major area of interest. We present a scaling fractal-ladder circuit for achieving high-resolution variable-order fractor based on scaling expansion theory using a high-resolution multiplying digital-to-analog converter (HMDAC). Firstly, the circuit configuration of variable-order scaling fractal-ladder fractor (VSFF) is designed. A theoretical demonstration proves that VSFF exhibits the operational characteristics of variable-order fractional calculus. Secondly, a programmable resistor–capacitor series circuit and universal electronic component emulators are developed based on the HMDAC to adjust the resistance and capacitance in the circuit configuration. Lastly, the model, component parameters, approximation performance, and variable-order characteristics are analyzed, and the circuit is physically implemented. The experimental results demonstrate that the circuit exhibits variable-order characteristics, with an operational order ranging from −0.7 to −0.3 and an operational frequency ranging from 7.72Hz to 4.82kHz. The peak value of the input signal is 10V. This study also proposes a novel method for variable-order fractional calculus based on circuit theory. This study was the first attempt to implement feasible high-resolution continuous variable-order fractional calculus hardware based on VSFF.
Motion planning in complex urban environments: An industrial application on autonomous last‐mile delivery vehicles

Abstract In this article, a motion planning framework for autonomous driving is explored to achieve unmanned last‐mile delivery vehicle application in complicated urban scenario. This approach can dramatically improve the intelligence, driving safety, driving robustness, and scalability of autonomous vehicles. In this framework, a specific High‐Definition (HD) map representation was proposed for last‐mile delivery applications, and a Route Planning layer generates a kinematically feasible reference line with smoothness condition based on Routing and HD Map components. Then a Scenario Planning layer considers routing results and both static and dynamic obstacles into account to select a corresponding scenario to execute, such as cruise on‐road scenario, cross intersection scenario, parking scenario, and so forth. Finally, a Trajectory Planning layer which is classified with trajectory generation and optimization modules is described. In the trajectory generation part, a rough path–speed profile and corresponding decisions, such as nudge, stop, yield, and so forth, are generated. Then, the rough path–speed profile is postprocessed by optimization algorithms in the trajectory optimization part. On the basis of the real road test results from thousands of times for JD.com's real autonomous delivery vehicle operations and approximate 115,475 km of fully autonomous driving mode in an urban scenario, the proposed motion planning framework demonstrates the efficiency of autonomous driving, improves the driving quality and reduces the manual intervention.
Cell Recognition Using BP Neural Network Edge Computing

This exploration is to solve the efficiency and accuracy of cell recognition in biological experiments. Neural network technology is applied to the research of cell image recognition. The cell image recognition problem is solved by constructing an image recognition algorithm. First, with an in-depth understanding of computer functions, as a basic intelligent algorithm, the artificial neural network (ANN) is widely used to solve the problem of image recognition. Recently, the backpropagation neural network (BPNN) algorithm has developed into a powerful pattern recognition tool and has been widely used in image edge detection. Then, the structural model of BPNN is introduced in detail. Given the complexity of cell image recognition, an algorithm based on the ANN and BPNN is used to solve this problem. The BPNN algorithm has multiple advantages, such as simple structure, easy hardware implementation, and good learning effect. Next, an image recognition algorithm based on the BPNN is designed and the image recognition process is optimized in combination with edge computing technology to improve the efficiency of algorithm recognition. The experimental results show that compared with the traditional image pattern recognition algorithm, the recognition accuracy of the designed algorithm for cell images is higher than 93.12%, so it has more advantages for processing the cell image algorithm. The results show that the BPNN edge computing can improve the scientific accuracy of cell recognition results, suggesting that edge computing based on the BPNN has a significant practical value for the research and application of cell recognition.
Motion Gesture Delimiters for Smartwatch Interaction

Smartwatches are increasingly popular in our daily lives. Motion gestures are a common way of interacting with smartwatches, e.g., users can make a movement in the air with their arm wearing the watch to trigger a specific command of the smartwatch. Motion gesture interaction can compensate for the small screen size of the smartwatch to some extent and enrich smartwatch-based interactions. An important aspect of motion gesture interaction lies in how to determine the start and end of a motion gesture. This paper is aimed at selecting gestures as suitable delimiters for motion gesture interaction with the smartwatch. We designed six gestures (“shaking wrist left and right,” “shaking wrist up and down,” “holding fist and opening,” “turning wrist clockwise,” “turning wrist anticlockwise,” and “shaking wrist up”) and conducted two experiments to compare the performance of these six gestures. Firstly, we used dynamic time warping (DTW) and feature extraction with KNN ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math> -nearest neighbors) to recognize these six gestures. The average recognition rate of the latter algorithm for the six gestures was higher than that of the former. And with the latter algorithm, the recognition rate for the first three of the six gestures was greater than 98%. According to experiment one, gesture 1 (shaking wrist left and right), gesture 2 (shaking wrist up and down), and gesture 3 (holding fist and opening) were selected as the candidate delimiters. In addition, we conducted a questionnaire data analysis and obtained the same conclusion. Then, we conducted the second experiment to investigate the performance of these three candidate gestures in daily scenes to obtain their misoperation rates. The misoperation rates of two candidate gestures (“shaking wrist left and right” and “shaking wrist up and down”) were approximately 0, which were significantly lower than that of the third candidate gesture. Based on the above experimental results, gestures “shaking wrist left and right” and “shaking wrist up and down” are suitable as motion gesture delimiters for smartwatch interaction.
Construction of Cloud Library Intelligent Service Platform Relying on Artificial Neural Network

With the advanced development of data center and network information technology, digital library has become a user sharing platform with a variety of digital information sources, providing users with flexible and diverse personalized services. However, the existing digital library lacks data analysis information and only publishes book information and related borrowing frequency and other information. As an ideal architecture model for library applications, cloud library can integrate distributed digital information sources, optimize digital libraries, and provide users with flexible, diverse, and personalized services. Therefore, based on the related research on cloud library and neural network algorithm, this paper not only focuses on the artificial network algorithm, but also focuses on the concept, features, and method of building the digital library platform based on the artificial network algorithm. Under the support of related theories and technologies of cloud library service platform, it also builds a cloud library intelligent service platform model based on artificial neural network. The research results showed that the time spent by BPNN in processing borrowed books was generally about 6 minutes, while the time required by RNN was 10 minutes. However, the processing of BPNN is not stable. Therefore, when building an intelligent service platform, different algorithms need to be selected according to individual needs.
Supporting Library Users’ Research Workflows with EdTech Tools

In this column, we will share the benefits and challenges of adopting two EdTech tools (Scholarcy and Grammarly) to support library users’ research workflows. We share how we introduced Grammarly and Scholarcy to the college community through a marketing campaign via ConvertKit, an email marketing tool, and the benefits and challenges of Grammarly and Scholarcy’s vendor-supplied usage statistics to support student success.
A collaboration of multi-agent model using an interactive interface

Multi-agent reinforcement learning algorithms scarcely attend to noisy environments, in which agents are inhibited from achieving optimal policy training and making correct decisions. This work investigates the effect of noises in multi-agent environments and proposes a multi-agent actor-critic with collaboration (MACC) model. The model uses lightweight communication to overcome the interference from noises. There are two policies for each agent in MACC: collaboration policy and behavior policy. The behavior of an agent not only depends on its own state but also be influenced by each other agent through a scalar, collaboration value. The collaboration value is generated by the collaboration policy for each individual agent, and it ensures a succinct consensus about the environment. This paper elaborates on the training of the collaboration policy and specifies how it coordinates the behavior policy in a manner of temporal abstraction mechanism, while the observation sequence is considered for more accurate perception. Several experiments on multi-agent collaboration simulation platforms demonstrate that the MACC performs better than baselines in noisy environments, especially in partially observable environments.
Comparative Performance Evaluation of Key Exchange Protocols

The paper presents a comparative performance evaluation of typical constructions of authenticated key exchange protocols using asymmetric and/or symmetric cryptography. The protocols are implemented using JAKE, a lightweight Java frame-work for prototype implementation and experimental analysis of secure-channel protocols, including attacks and performance evaluation. JAKE uses the standard library of cryptographic classes included in OpenJDK. The experiments were carried out for different cryptographic algorithms and security levels. The comparative analysis demonstrates the important performance gains achieved by switching from Finite-Field Cryptography (FFC) to Elliptic-Curve Cryptography (ECC), for Diffie-Hellman (DH) key agreement, signatures, and public-key certificates. The algorithms based on ECC reduce substantially the performance gap between authentication using symmetric cryptography and pre-shared secret keys (faster, but with limited applications) and authentication using asymmetric cryptography and public-key certificates. They also show a substantial performance degradation from 128-bit to 192-bit or 256-bit security, so the security level should be adjusted according to application requirements.
Research and Development of an E-commerce with Sales Chatbot

E-commerce is the process of conducting commerce through computer networks. An individual sitting in front of a computer may use all of the internet's resources to purchase or sell things. Unlike conventional commerce, which is carried out physically with a person's effort to travel and obtain items, e- commerce has made it simpler for humans to eliminate physical labor and save time. This online business management system has also assisted retailers in saving money on upfront costs such as storefronts and staff. The objective is to design an advanced e- commerce system with a smart chatbot to provide a user-friendly experience for consumers. Consumers will save time with the quick accessibility feature of the chatbots in the system. With the help of natural language processing, we have developed a realtime chatbot with smart features. On this smart e-commerce website, we have tried to solve some of the problems with the existing e-commerce platform in our country. The system operates on the Django platform, which is built on Python. The system's architecture is created by using Django's Model-View- Template. HTML, CSS, JavaScript, and bootstrap are utilized for the front end and validation. For the backend, we have used an SQLite database. Clients, as it has been discovered, usually have a difficult time shopping on e-commerce because no support assistant can provide immediate feedback. The system consists of several sections, including the registration system, the login system, the search system, the order system, the add product system, the view product system, the order received system, and an interface to communicate with the bot. If users have any queries, they may contact the admin panel via the contact us page. Overall, the system is quick and reliable. A user-friendly interface boosts the purchasing experience for consumers across the country who have access to the internet. The system's accuracy is high enough for satisfying results and thus successfully supports people in making better judgments while being low-cost and user-friendly in design.
A Robust Information Hiding Scheme Using Third Decomposition Layer of Wavelet Against Universal Attacks

Watermarking is one of the most common data hiding techniques for multimedia elements. Broadcasting, copy control, copyright protection and authentication are the most frequently used application areas of the watermarking. Secret data can be embedded into the cover image with changing the values of the pixels in spatial domain watermarking. In addition to this method, cover image can be converted into one of the transformation such as Discrete Wavelet Transformation (DWT), Discrete Cousin Transformation (DCT) and Discrete Fourier Transformation (DFT). Later on watermark can be embedded high frequencies of transformation coefficients. In this work, cover image transformed one, two and three level DWT decompositions. Binary watermark is hided into the low and high frequencies in each decomposition. Experimental results show that watermarked image is robust, secure and resist against several geometric attacks especially JPEG compression, Gaussian noise and histogram equalization. Peak Signal-to-Noise Ratio (PSNR) and Similarity Ratio (SR) values show very optimal results when we compare the other frequency and spatial domain algorithms.
An Approach for Automatic Discovery of Rules Based on ECG Data Using Learning Classifier Systems

Personalized medicine aims to understand the underlying relationships between the multitudes of factors affecting a patient's health and provide physicians with an evidence-based approach to customize the treatment based on patient-specific characteristics. Machine-learning techniques can examine available data and discover relationships and patterns that may not be explicitly expressed within the data. In this case, physicians can use this knowledge for hypothesis testing and conduct investigations into the possible conditions that affect the patients' health. The benefits of personalized medicine include improved patient satisfaction, reduced length of hospitalization, enhanced treatment outcomes, and increased overall efficiency of the health care system. In this paper, we present an approach based on Learning Classifier Systems (LCS) to automatically discover rules that can support medical decision-making in evaluating the patient's heart condition. LCS are considered adaptive rule-based systems that can evolve a set of classifiers called rules based on a learning component that assigns credit to existing rules and an evolutionary component that helps discover new ones. The proposed approach is based on the implementation of an accuracy-based LCS that has been modified to support rules learning for personalized medical decision-making. The experimental results in the case study section provide a proof of concept for rules learning based on ECG data to discover rules that can support physicians in the medical decision-making process.
Comparative Study of Sha-256 Optimization Techniques

A hash function is a useful one-way trap cryptographic algorithm that converts an input of any size to an output of a fixed length of bits based on the choice of the hash function. In this paper, we compared various hash optimization techniques to reduce extra hashes while mining cryptocurrencies. Also, we introduce the concept of higher performance by splitting the hashing tasks over various servers. In most exceptionally reliable systems, subsystem or module failures that do not affect a system failure can still worsen system performance. The split system approach introduces a more effective way of offering reliability in a distributed system in general. To assess the system's reliability, this paper proposed a simple mathematical model that can capture the reliability of the system and higher throughput.
Adoption of Blockchain-based Artificial Intelligence in Healthcare

Data and technology have made it possible to find solutions to a wide variety of challenges in healthcare. Blockchain and Machine Learning gives the best solutions together in performing various tasks in the Smart Health care system. With these two new emerging technologies, that have materialized in the last decade. It has been demonstrated that machine learning may be useful in a variety of fields because of its ability to recognize patterns in data, perform analyses, and reach choices. To create appropriate choices, machine learning requires a sufficient amount of data. Data sharing and data reliability are critical components of machine learning in order to increase its accuracy. Blockchain Technology’s decentralized database places a premium on data exchange. Consensus in Blockchain technology ensures the legitimacy and security of data. Converging these two technologies can result in highly accurate machine learning results combined with the security and stability of Blockchain Technology. In this paper, we will have opportunity to know about Machine learning integration along with Blockchain in the field of Healthcare. We proposed secure, transparent and intelligent methods in the Smart Health care Industry using Machine learning models and blockchain technology to enhance security level and train our models to improve diagnostic, prevention, treatment of the patient, patient rights, patient autonomy and equality in the health care system.