Reviewerly/exHarmony huggingface dataset:
RANCANG BANGUN SISTEM INFORMASI PENJUALAN MENGGUNAKAN METODE WATERFALL

Abstract - esign and Design of Computer Sales Information Systems using the waterfall method is a study that aims to design and build sales information systems at a computer retail store or "Artha Komputer Center". Artha Computer Center is one of the retails or stores in Pacitan that provides various types of computers and laptops. In the management of sales that are still conventional in nature, this research focuses more on the design and implementation of systems that hope to provide alternative solutions in the management of goods. So with this sales information system, it is expected that the management of goods is more organized, sales transactions are safer from mistakes. This waterfall method uses a sequential approach by following several phases, including system requirements analysis, system design, coding on the system, testing and the last one is maintenance..
PENGARUH KONDISI PENCAHAYAAN TERHADAP HASIL SURVEI KECEPATAN KENDARAAN DENGAN PORTABLE SPEED GUN DAN APLIKASI BERBASIS ANDROID

Technological advances developed rapidly with the use of the Android system in 2007. Android is an operating system or mobile-based operating system that is very widely used today. Mainly on smart phones (smartphones) or tablets. In addition, Android has a large community of application developers that extend the functionality of the device, generally written in a customized version of the programming language. One of the applications based on android is the "Speed ​​Gun" application. This application functions to measure the speed of an object moving by touch such as a velocimeter.&#x0D; The expected objective of the results of this study is to determine the results and the difference between the vehicle speed survey method using a portable speed gun and Android-based applications in each period.&#x0D; And the effect of lighting between the morning and evening periods on portable speed gun equipment and Android-based applications for vehicle speed surveys.The results of the speed survey using the speedgun android application equipment show that the speed data is close to the manual method, the difference in speed using the portable speedgun equipment for the afternoon period has an average difference of 9.2 km / hour which is the biggest difference compared to the speed difference between the speedgun android application equipment. and the manual method and the use of equipment for lighting conditions, shows that the portable speedgun equipment is highly influenced by lighting conditions, where from the analysis of the morning period, the average difference is 2.3 km / hour and the average difference is 9.2 km / evening.
Robot Pengiriman Obat Dengan Rute Tercepat Menggunakan Algoritma Djikstra

ABSTRACT&#x0D; The development of today's era has become a necessity where technology is one of the important factors in human life, humans always create innovations to make work easier and solve problems that always arise. In December 2019 the emergence of a pandemic caused by a virus called Covid -19 This virus can be transmitted from human to human through splashes of body fluids or contaminated surfaces. To solve this problem the author took the initiative to help nurses by designing drug distribution tools so that nurses do not need to make direct contact with patients and drug distribution will be sent more efficiently, this tool uses a TCRT line sensor which is used to identify the path then the data is sent to the Arduino microcontroller UNO to be processed to find out the fastest route using the djikstra algorithm then instructs the DC motor to move towards the cooled destination.&#x0D;
A Blockchain-Based Semi-Centralized Business System for Multi-Enterprise

Blockchain technology has been widely used in supply chain finance, e-commerce, logistics management, and in-tellectual property protection. It establishes trust relationships among participants and maintains a traceable distributed ledger. However, at its core, the blockchain's decentralized consensus mechanism makes it ineffective in terms of performance and privacy protection. To address these issues, this paper proposes a solution for multi-enterprise business environment based on the Hyperledger Fabric framework. Different from the decentralized feature of traditional blockchain, this solution introduces an authentication mechanism and selects participants as managers to establish a semi-centralized business system. The paper analyzes the needs of multiple enterprises when they participate in business activities and describes the elements and components of the system consisting of them. Finally, simulation experiments are given to demonstrate the effectiveness of the proposed system.
Improvement of Steering Performance for Autonomous Vehicles Based on Preview Model

This paper presents an approach to traveling along a reference path and evading obstacles with smooth steering angle for autonomous vehicles when trajectory planner and control run asynchronously. Trajectory in this paper is composed of position and time information, which is produced through decision maker, reference line calculator, trajectory smoother and speed planner. In order to restrain possible oscillation of steering angle, a vehicle dynamic model with preview is applied. It is transformed into a standard form of state space and can be directly used in control. Considering the control runs at a higher frequency than planning, a progression method coordinating these two parts is applied, which bases on time points on the trajectory and matches points with vehicle position after preview. The simulation results demonstrate improvement on the smoothness of steering angle. And vehicle's lateral error on tracking trajectory is kept small.
A Photoplethysmography Signal Compression Method of Extreme Learning Machine

As one of the important physiological signals for human health monitoring, there are massive photoplethysmography (PPG) signals and excessive demands on storage and network communication resources for long time monitoring. Therefore, it is desirable to compress the PPG signal where redundancy exists without losing important information. In this paper, a PPG signal compression method with extreme learning machine (ELM) is proposed. ELM does not require iterative adjustment of the hidden layer parameters and has the advantages of high generalization ability and fast learning speed. First, the PPG signal is segmented and processed as input and output data. Second, the parameters of the hidden layer based on the set training set is calculated. Then, the obtained parameters are used to compress and reconstruct the PPG signal, one PPG wave band at a time, until the end of the training. Comparing with the error Back Propagation Training (BP) neural network, for the 4 min sampled data, the average time to compress and reconstruct the PPG signal is 0.026±0.007, the MSE is 0.004±0.004 and the MAD is 0.031±0.026.
A Video Detection Method of Smoking Behavior Based on Key Point a Priori and DN DETR

With the development of artificial intelligence and computer vision technologies, while Transformer transitions from the field of natural language processing to the field of object detection, video detection of smoking behavior based on DETR and its variant models has received widespread attention. However, the computational complexity of Transformer is too high, which leads to serious hardware consumption and low detection efficiency. In this work, we propose a smoking behavior detection method that combines the human key point detection algorithm BlazePose and DN DETR, which improves the detection efficiency by clearing redundant frames in surveillance videos and arrived detection accuracy of 94% for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$AP_{50}$</tex> . BlazePose ensures that the video frames transmitted to DN DETR contain people. And DN DETR is used for the high accuracy detection of smoking behavior in the video frames. The experimental results have shown that the proposed method is of high detection efficiency for smoking behavior and good practicality in engineering applications.
Improving Cutoff Frequency Estimation Accuracy with Initial Probe-environment Correlations

We investigate the effect of the initial probe-environment correlations in estimating the cutoff frequency which is one of the crucial parameters characterizing the environment of a quantum system. The two-qubit probe, referred to as a probe constructed by two qubits, is prepared in the product state and allowed to interact with the environment. Under the initial probe-environment correlations, we derive the analytic quantum Fisher information (QFI) and the corresponding quantum signal-to-noise ratio (QSNR), which quantify the estimation accuracy of the cutoff frequency. By comparing the QFI and QSNR with the initial probe-environment correlations to those without correlations, it shows that the initial probe-environment correlations can be used to improve the accuracy of estimating the cutoff frequency in the estimation scheme of two-qubit probes.
A Transfer Learning-Based Adaptive Particle Swarm Optimization for Global Optimization Problem

Particle swarm optimization (PSO) is a popular evolutionary algorithm and widely used to solve practical engineering problems. However, most of the existing methods search for the optimal solution starting from zero initial information, and do not make use of the historical information obtained when solving similar problems previously. This will cause the method to waste lots of computing resources to a certain extent. Recently, the idea of transfer learning has received widespread attention. Transfer learning is a humanized machine learning method that aims to transfer knowledge from one domain (source domain) to another domain (target domain) so that the target domain can achieve better learning results. Therefore, in this study, the idea of transfer learning is extended to the field of evolutionary optimization, and a transfer learning-based adaptive particle swarm optimization framework is proposed (TAPSO). Firstly, the adaptive clustering model matching strategy (ACMS) is proposed to find the historical problems matching the target problem. In ACMS, density-based clustering strategy and maximum mean discrepancy work together to find historically similar problems for the current problem. Secondly, the adaptive knowledge transfer strategy (AKTS) is used to transfer knowledge from the original problem to the target problem. Finally, the comprehensive learning particle swarm optimization algorithm is embedded into the transfer learning framework proposed in this study. Extensive experiments have confirmed the effectiveness of the proposed transfer learning-based adaptive particle swarm optimization framework.
Multi-Missile Routes Co-Planning Method Based on Improved Ant Colony Algorithm

Aiming at the problem of multi-missile routes co-planning under the background of long-range precise attacking the sea targets, a multi-missile routes co-planning method based on improved ant colony algorithm is proposed. Firstly, the attack coordinated modes of anti-ship missile on the sea is introduced, and the multi-missile coordinated constraint model is constructed. Then, the multi-missile routes co-planning method based on ant colony and multi-subgroup algorithm is described. Finally, the feasibility of the algorithm is verified by simulation analysis.
Improved Generative Adversarial Network with Hybrid Attention Mechanism for Path Planning

Multi-agent path planning is one of the most challenging problems in the field of artificial intelligence. In this paper, an improved generative adversarial network model with hybrid attention mechanism for data-driven path planning is proposed. Firstly, by introducing spatial attention mechanism, positional attention mechanism and channel attention mechanism, a hybrid attention module is designed. This module not only establishes feature relationship between different locations, but also provides adaptive feature weights to highlight key information. Next, by embedding the hybrid attention module into the generative adversarial network, an improved generative adversarial network model is developed. The proposed model can capture effective feature information in the image and ensures that the generated path can evade obstacles accurately. Finally, some simulation results are provided to verify the superiority and accuracy of the model in path planning.
The invention and role of cryptocurrencies

This chapter recounts the creation of Bitcoins through blockchain technology and how Bitcoins were mimicked by thousands of other cryptocurrencies. It shows how cryptocurrencies became tradable assets with a market capitalization that reached $3 trillion in 2021, before melting down to under $1 trillion in 2022. It describes various cryptocurrency trading platforms and the trading abuses associated with this market. This chapter relates the debate over whether cryptocurrencies are simply a new form of gambling or whether they serve a valuable function by acting as a store of value that cannot be inflated like a government-issued fiat currency. This chapter then shows how widespread abuses in cryptocurrency trading led regulators to apply their existing regulations to this unique market. That effort soon led to competition by regulators to assert jurisdiction over this new asset class. That competition caused much confusion and inhibited further development of blockchain technology.
Constructing a regulatory structure for cryptocurrencies

This chapter examines the debate over cryptocurrency regulation. It analyzes whether this new asset class can be regulated in a way that will not inhibit its growth. It considers the viability of applying the historical “functional” regulatory model for financial services to cryptocurrencies. It also considers the application of specific jurisdictional assignments to the SEC and CFTC by Congress, as was done in the case for stock index contracts that were regulated on the basis of whether they were traded on stock or commodity exchanges. This chapter explains why SEC regulation of cryptocurrencies as securities will cripple, if it does not destroy, that market. This chapter further describes why the CFTC’s regulatory structure is more in keeping with crypto market trading platforms. It sets forth the reasons why the “actual delivery” exclusion in the CEA should be retained so that this asset class may develop and evolve without undue restrictions.
CFTC Regulation of cryptocurrencies as "commodities"

This chapter relates how Congress granted the CFTC exclusive jurisdiction over the regulation of commodity futures contracts. Excluded from the CFTC’s regulatory authority were “spot” market contracts that call for the “actual delivery” of a commodity. This chapter describes the historical background and application of the actual delivery exclusion. It addresses Supreme Court cases dating back to the nineteenth century that used the actual delivery concept to determine whether futures contracts were subject to state anti-gambling laws. This chapter then describes the CFTC’s decades long battles over whether it was excluded by the actual delivery exclusion from regulating leveraged contracts in foreign currency, energy products, swaps, precious metals and “hedge-to-arrive” contracts. It then addresses the application of the actual delivery exclusion to cryptocurrencies and analyzes the CFTC’s enforcement cases that are seeking to regulate the cryptocurrency market despite that exclusion.
Characteristics-Capturing Neural Dynamics for Solving Time-Dependent Nonlinear Equations With Periodic Noise

Nowadays, many dynamic systems show the characteristics of multiple-input-multiple-output and nonlinearity, which usually involve the solution of time-dependent nonlinear equations. This paper focuses on the accurate solution of time-dependent nonlinear equations with periodic noises considered. In the hardware or numerical implementations of an actual system, inevitable internal disturbances or external factors caused by the changeable scene may greatly affect the accuracy of the solution. Most of the existing time-dependent anti-noise neural dynamics (ND) models can effectively suppress some constant noise, but they are not satisfactory when facing some periodic noise with high frequency. To solve this problem, a characteristics-capturing ND (CCND) model is designed, which considers the periodic noise from the perspective of harmonic expansion so as to effectively capture harmonic characteristics and eliminate them. Theoretical analysis proves the robustness of the CCND model with periodic noise disturbance considered. Moreover, numerical simulations and robotic experiments further confirm the effectiveness of the CCND model.
Small Target Augmentation for Urban Remote Sensing Image Real-Time Segmentation

Urban remote sensing (URS) image segmentation is very important for many applications from automotive navigation to infrastructure monitoring, and urban management. There are numerous small targets in URS image due to a large shooting field of view. However, the existing learning-based real-time image segmentation methods are not strong enough to handle small target and edge segmentation problems well, resulting in small targets that are easy to be missed, and blurred target edges. To segment the small targets and edges more accurately in real time, we propose a fast URS image segmentation method based on a multi-layer pixel attention mechanism (MPAM). We improve the performance and efficiency of the URS image segmentation from two perspectives: model and data. Specifically, to enhance semantic detailed information such as small targets and edges, we design mask-guided edge and small target feature enhancement modules in the real-time segmentation network. In addition, we propose a small target data enhancement method which uses an interpolation algorithm to amplify small targets in URS images, in order to improve the efficiency of existing URS data. The experimental results on Vaihingen, Potsdam, and DLRSD datasets show that the segmentation accuracy of our method reaches 86.74 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> mIoU, which is better than the state-of-the-art algorithms STDC, CFNet, and UNetFormer.
High-Confidence Sample Augmentation Based on Label-Guided Denoising Diffusion Probabilistic Model for Active Deception Jamming Recognition

Accurate recognition of the types of mainlobe active deception jamming is essential for radar systems to take anti-jamming countermeasures. A bunch of deep learning (DL)-based recognition methods that require largescale datasets for training have shown promising results. However, capturing a sufficient number of diverse deception jamming samples is particularly intricate in actual dynamic and complex battlefields, the yielding limited or unbalanced datasets presents a significant challenge in training and generalizing DL models. This letter proposes a deep generative model, called label-guided denoising diffusion probabilistic model (LG-DDPM), to address the issue of limited or class-imbalanced active deception jamming samples through data augmentation. By embedding label information into the diffusion process, the proposed model can generate and expand the active deception jamming samples specific to a pre-defined class even under low jamming-to-noise ratios (JNR) scenarios. The proposed method demonstrates superior performance in terms of both the fidelity and diversity of the generated jamming samples as well as the recognition accuracy of the DL recognizer when compared to state-of-the-art methods. Experimental results demonstrate the effectiveness and robustness of the proposed method.
Investigating the Sample Weighting Mechanism Using an Interpretable Weighting Framework

Training deep learning models with unequal sample weights has been shown to enhance model performance in various typical learning scenarios, particularly for imbalanced and noisy-label learning scenarios. A deep understanding of the weighting mechanism facilitates the application of existing weighting strategies and illuminates the design of new weighting strategies for real learning tasks. Scholars have focused on exploring existing weighting methods. However, their studies mainly establish how the weights of samples influence the model training. Little headway is made on the weighting mechanism, i.e., which and how the characteristics of a sample influence its weight. In this study, we adopt a data-driven approach to investigate the weighting mechanism by utilizing an interpretable weighting framework. First, a wide range of sample characteristics is extracted from the classifier network during training. Second, the extracted characteristics are fed into a new neural regression tree (NRT), which is a tree model implemented by a neural network, and its output is the weight of the input sample. Third, the NRT is trained using meta-learning within the whole training process. Once the NRT is learned, the weighting mechanism, including the importance of weighting characteristics, prior modes, and specific weighting rules, can be obtained. We conduct extensive experiments on benchmark noisy and imbalanced data corpora. A package of weighting mechanisms is derived from the learned NRT. Furthermore, our proposed interpretable weighting framework exhibits superior performance in comparison to existing weighting strategies.
Combination of Information in Labeled and Unlabeled Data via Evidence Theory

For classification with few labeled and massive unlabeled patterns, co-training, which uses information in labeled and unlabeled data to classify query patterns, is often employed to train classifiers in two distinct views. The classifiers teach each other by adding high-confidence unlabeled patterns to training dataset of the other view. Whereas, the direct adding often leads to some negative influence when retraining classifiers because some patterns with wrong predictions are added into training dataset. The wrong predictions must be considered for performance improvement. To this end, we present a method called Combination of Information in Labeled and Unlabeled (CILU) data based on evidence theory to effectively extract and fuse complementary knowledge in labeled and unlabeled data. In CILU, patterns are characterized by two distinct views, and the unlabeled patterns with high-confidence predictions are first added into the other view. We can train two classifiers by few labeled training data and high-confidence unlabeled patterns in each view. The classifiers are fused by evidence theory, and their weights which aim to reduce the harmful influence of wrong predictions are learnt by constructing an objection function on labeled data. There exist some complementary information between two distinct views, so the fused classifiers in two views are also combined. In order to extract more useful information in unlabeled data, the semi-supervised Fuzzy C-mean clustering paradigm is then employed to yield clustering results. For a query pattern, the classification results and clustering results obtained by combined classifiers and clustering partition are integrated here to make the final class decision.