Reviewerly/exHarmony huggingface dataset:
Management Research of Big Data Technology in Financial Decision-Making of Enterprise Cloud Accounting

There are certain risks in the network data of cloud accounting platform, and the accuracy of audit data directly affects the subsequent financial decisions of enterprises. In order to ensure the accuracy of enterprise decision management, this paper constructs a cloud accounting IT audit model based on big data technology. First, the enterprise’s solvency, profitability and operation capacity are taken as the indicators of financial status, and the evaluation index system is constructed. Then the data are preprocessed, after which the analytic hierarchy process is used to analyse the data and determine the audit focus. Finally, [Formula: see text]-means algorithm, rule association algorithm, SQL statement and audit intermediate table are used to conduct audit analysis, and audit doubts are found, and the effectiveness of the method is verified with specific cases. Through the experimental analysis, we can see that the average accuracy rate of the model built by the research institute is 98.37%, and the average recall rate is 97.67%. This model can accurately identify and judge the abnormal situation of the data in the cloud accounting platform, provide more accurate and effective data analysis results for the financial decision-making of enterprises, and have positive significance for the sustainable development of enterprises.
PERANCANGAN SISTEM INFORMASI OPTIK PADA SALSA FORTUNA BERBASIS WEB

Optik Salsa Fortuna is a business engaged in the sale of optics that provides a variety of glasses, frames, contact lenses, eyeglass cleaning fluid and other optical accessories, which is located on Jl.Raya K H Abdul Halim No.396, Majalengka Wetan.The problems faced by the Salsa Fortuna Optical Shop include problems in processing goods data that are still not computerized, there is no proof of transactions such as proof of sale. Another problem that exists at the Salsa Fortuna Optic Shop is that they often experience difficulties in checking stock, there is no sales report prepared and their profits are only using a recap of notes for reports.Based on the problems above, we need an information system that can help process goods data, sales reports and profits by combining components that have not been computerized into one system so that later they can be used in business processes to make them more effective and faster, in making the system using the waterfall method which is very suitable in solving this problem. The purpose of this research is to find out the system that is running at Optik Salsa Fortuna, then evaluate and find problems that hinder business processes at Optik Salsa Fortuna, to make it easy for shops to record transactions which of course will generate sales reports and profit reports. precise and accurate, and to implement a sales information system at Optics Salsa Fortuna, so that it can solve existing problems
PERANCANGAN SISTEM INFORMASI PEMESANAN JASA WEDDING ORGANIZER BERBASIS WEB

The development of technology can affect many aspects, including the economy, politics, government, and many others. These influences have brought about many changes in lifestyle and the way people perceive things, particularly for those accustomed to and living within a technological environment. One of the most well-known information technologies in society is the internet, and websites are not only used to display information. The development of websites has greatly helped companies to support their activities, such as online ordering. In addition to the ordering process, data processing activities must also be computerized, starting from the processing of product data that can later be used to select pre-wedding photo packages according to customer preferences while maximizing the budget they have. This includes processing customer data from registration to ordering on the website. With the website, it is hoped that company information can spread widely and directly maximize performance.&#x0D; Generally, pre-wedding photo orders are still manually processed. Some common weaknesses found in companies that offer pre-wedding photo services include having a website, but the ordering process is still done manually. It would be better if the company processes orders directly through their website, making it more effective and efficient. In addition to the ordering process, data processing activities must also be computerized, starting from the processing of product data that can later be used to select pre-wedding photo packages according to customer preferences while maximizing their budget. This includes processing customer data from registration to ordering on the website. With a website, it is expected that company information can spread widely and directly maximize performance.
SISTEM INFORMASI ANALISIS PLAYER TOURNAMENT MOBILE LEGENDS BERBASIS WEB PBESI MAJALENGKA

This research is motivated by the increasing number of E-Sports users or enthusiasts worldwide, as E-Sports has become an integral part of official sports competitions. Therefore, by creating an information system to process the data from Mobile Legends tournaments, the aim is to make informed decisions in identifying potential athletes who can bring honor to their region or country. In conclusion, this system is developed to provide support for decision-making, provide information related to the E-Sports organization that oversees the E-Sports ecosystem, namely the Indonesian Esports Association (PBESI), and offer prediction features to estimate the likelihood of victory for each player in the competition
Wheel Behavior Measurement Based on Ultra-High-Speed Zoom-Tracking Video Shooting

Analysis of the behavior between the wheel and the road surface is important for understanding a vehicle's safe traveling performance. In contrast, sensors attached to the wheels have many limitations, and it is difficult to capture slipping phenomena such as spinning and sliding with rotation sensors alone. In addition, it is difficult for a fixed-point camera to capture phenomena between the wheel and the road surface that cannot be predicted when and where they will occur because the angle of view is limited. Here, high-speed zoom tracking was performed remotely using ultra-high-speed active vision. Image analysis of the rotating wheel, which is always magnified at the center of the high frame rate image, enabled non-contact measurement of wheel behavior while the vehicle moved. Experiments were conducted using actual N-gauge model vehicles and Segways to clarify the slipping phenomena of running vehicles and analysis of wheel deformation behavior. By analyzing tracking zoomed images taken at 500 fps, we quantitatively verified the measurement of slip during sudden stop and sudden acceleration and wheel deformation behavior during running over hump.
Research on the path of integrating the excellent traditional culture into the Ideological and political precision teaching in colleges and universities driven by artificial intelligence

At present, the precision teaching supported by intelligent technology has some shortcomings in practice, such as single technology bias, lack of thinking of educational cognitive logic, and weakening of teachers' role status. One of the reasons lies in the lack of theoretical perspective on the academic logic of technology-enabled precision teaching. From the perspective of educational cognition, on the basis of clarifying the concept connotation of precision teaching, the research analyzes the existing practical difficulties in the process of its development, such as goal setting, diagnostic evaluation and activity development. Based on the function analysis of intelligent technology, the realization logic of intelligent technology-enabled precision teaching goal, evaluation and activity accurate development is discussed: With the help of group analysis, individual needs diagnosis, mapping relationship analysis to achieve accurate target setting; With the help of data acquisition, analysis mining and visualization technologies, the automatic implementation and intelligent analysis of evaluation are realized to facilitate the hierarchical and precise intervention of the application of evaluation results. With the help of resource recommendation and tool assistance, it can help the accurate design and practice of multiple learning activities, and provide support for the process intervention and real-time feedback of learning activities.
Latent Transformer-based Non-Parallel Voice Conversion with Cycle Consistency and Variance Compensator

Our proposed method presents a novel approach for training voice conversion models in a non-parallel setting, eliminating the need for a parallel corpus, called latent transformer-based non-parallel voice conversion (VC). Although GAN-based voice conversion methods are widely recognized as benchmark approaches, their capability to capture time-frequency patterns is limited, this limitation restricts their application to converting only the mel-cepstrum domain, rather than the mel-spectrogram domain. To overcome this limitation, previous approaches have incorporated additional modules such as time-frequency adaptive normalization (TFAN), but this results in an increase in the number of learned parameters. Our proposed method consists of a cycle consistency module (CCM), a latent transformer, and a variance compensator that enhances naturalness and expressiveness in generated speech. Specifically, the variance compensator module (VCM) incorporates an additional transformer-based latent improving module that provides more acoustic variance information, including pitch, duration, and energy. Our proposed method eliminates the need for an additional module like TFAN and achieves better results compared to CycleGAN-based voice conversion methods in terms of subjective evaluation of naturalness and speaker similarity.
An AI Edge Computing-Based Smart Guide Dog Harness for Assisting the Visually Impaired

This paper proposes a smart guide dog harness of a visual assistance system based on AI edge computing technology and uses technologies such as image recognition, positioning, and navigation. The operation process is to wear the auxiliary harness after the guide dog. Classify different objects, obstacles, and environmental features. For example, it can identify pedestrians, vehicles, traffic signs, etc., ahead and communicate relevant information to the user in real-time through the voice system of the wearable headset. This enables the visually impaired to perceive the surrounding environment more comprehensively, avoid collisions with obstacles, and navigate more accurately, providing spiritual support to visually impaired people and improving their willingness to integrate into society.
An Optimization Method for Non-IID Federated Learning Based on Deep Reinforcement Learning

Federated learning (FL) is a distributed machine learning paradigm that enables a large number of clients to collaboratively train models without sharing data. However, when the private dataset between clients is not independent and identically distributed (non-IID), the local training objective is inconsistent with the global training objective, which possibly causes the convergence speed of FL to slow down, or even not converge. In this paper, we design a novel FL framework based on deep reinforcement learning (DRL), named FedRLCS. In FedRLCS, we primarily improved the greedy strategy and action space of the double DQN (DDQN) algorithm, enabling the server to select the optimal subset of clients from a non-IID dataset to participate in training, thereby accelerating model convergence and reaching the target accuracy in fewer communication epochs. In simulation experiments, we partition multiple datasets with different strategies to simulate non-IID on local clients. We adopt four models (LeNet-5, MobileNetV2, ResNet-18, ResNet-34) on the four datasets (CIFAR-10, CIFAR-100, NICO, Tiny ImageNet), respectively, and conduct comparative experiments with five state-of-the-art non-IID FL methods. Experimental results show that FedRLCS reduces the number of communication rounds required by 10–70% with the same target accuracy without increasing the computation and storage costs for all clients.
Rethinking Pedagogical Use of Eye Trackers for Visual Problems with Eye Gaze Interpretation Tasks

Eye tracking technology enables the visualisation of a problem solver's eye movement while working on a problem. The eye movement of experts has been used to draw attention to expert problem solving processes in a bid to teach procedural skills to learners. Such affordances appear as eye movement modelling examples (EMME) in the literature. This work intends to further this line of work by suggesting how eye gaze data can not only guide attention but also scaffold learning through constructive engagement with the problem solving process of another human. Inferring the models’ problem solving process, be it that of an expert or novice, from their eye gaze display would require a learner to make interpretations that are rooted in the knowledge elements relevant to such problem solving. Such tasks, if designed properly, are expected to probe or foster a deeper understanding of a topic as their solutions would require not only following the expert gaze to learn a particular skill, but also interpreting the solution process as evident from the gaze pattern of an expert or even of a novice. This position paper presents a case for such tasks, which we call eye gaze interpretation (EGI) tasks. We start with the theoretical background of these tasks, followed by a conceptual example and representation to elucidate the concept of EGI tasks. Thereafter, we discuss design considerations and pedagogical affordances, using a domain-specific (chemistry) spectral graph problem. Finally, we explore the possibilities and constraints of EGI tasks in various fields that require visual representations for problem solving.
Progressive Physics-Driven Deep Conversion Of sRGB to Raw Images

Raw images are commonly processed by image signal processing (ISP) algorithms to standard RGB (sRGB) images in order to save the storage space and provide a suitable format for human visual system. Distortion and information loss are introduced in this procedure which degrades the performance of following tasks with these sRGB images as the input. To leverage the benefits from raw images, reversing sRGB images to raw images is expected. In this paper, a progressive physics-driven deep learning algorithm is proposed for the conversion of sRGB images to higher quality raw images by fusing physical-driven and data-driven deep learning approaches. Based on an observation that deep convolution neural networks (CNNs) are biased towards learning low-frequency functions, the proposed framework includes a high-frequency aware guidance branch to provide progressive guidance for the reconstruction branch. Experimental results indicate that the proposed algorithm improves reconstruction quality of existing data-driven approaches. It also reduces the sensitivity of existing data-driven approaches to test data in the sense that those images that are hard to be restored by the existing approaches are improved much more.
An Energy-Efficient, Resource-Efficient and High Frame-Rate End-to-End Pedestrian Detector Using HOG-SVM for Intelligent Edge Devices

This paper proposes a Histogram of Oriented Gradients-Support Vector Machine (HOG-SVM) based pedestrian detector with an end-to-end fully-pipelined architecture to achieve a high frame rate by improving the throughput, and reduce the power consumption by minimizing the data movement. To further improve the energy efficiency under the high frame rate, a bit-width pruning method is used to remove the gray-scale converter's redundant data bit width, and a block-score normalization is employed to significantly reduce the normalizer's required divisions. The reduced computation amount also saves the hardware overhead while maintaining the same calculation accuracy. Besides, a modeling and analysis method of the SVM-classifier-Multiply-ACcumulate (MAC) array is proposed to further improve the energy efficiency and save the logic resources, by optimizing the array size with a hardware utilization of 98.4% while maintaining the same throughput. The FPGA implementation results of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$640\times 480$</tex> video show a high frame rate of up to 439 fps @143 MHz and a high energy efficiency of 0.76 nJ/pixel with 46.7% fewer LUTs, 22.4% fewer registers, 88.3% fewer DSPs, compared to the state-of-the-art design. The ASIC implementation in 55 nm also confirms a high energy efficiency of 0.35 nJ/pixels at 613 fps and 200 MHz as well as a hardware overhead of 177 k gates and 108 Kbits SRAM.
Dynamic Gesture Recognition Method Based on Self-Learning Sparse Representation

Using wireless body area networks to monitor and recognize human motion is one of the research hotspots in wireless body area networks, and gesture is an important component of human motion. Gesture, as a natural, intuitive, and easy to learn human-computer interaction method, can provide operational convenience for health monitoring in wireless body area networks. Gestures are divided into static and dynamic gestures. Static gestures rely solely on the external shape and contour information of the hand to convey information. Dynamic gestures also include gesture timing information for gesture movements, which can express richer and more accurate information. Data acquisition methods for gesture movements include data glove based, vision based, EMG based, and inertial sensor based methods. This paper studies a dynamic gesture recognition method based on acceleration sensors.
The Prediction and Evaluation of Amazon Product’s Reputation Based on the Improved SSA-LSTM Model

The rise of the Internet has promoted the vigorous development of online shopping. Evaluating and predicting the reputation of products is of great significance for sellers to identify the reputation trend of products and make sales plans in time. In this paper, based on the customer reviews data for hair dryer provided by Sunshine Company for Amazon Markets from August 2005 to August 2015, we propose five indexes: number of reviews, high confidence review rate, weighted average rating star, weighted average review score, and review score skewness. Then we use SSA-LSTM (Singular Spectrum Analysis-Long Short-Term Memory networks) time series method to predict the trend of product reputation. We find that the time series data processed by SSA has higher prediction accuracy than the unprocessed data, which proves that SSA processing can improve the accuracy of product reputation prediction. Moreover, we propose a product reputation evaluation method combining star rating and review data and use improved SSA-LSTM time series prediction method to predict product reputation. Overall, our results indicate that the above methods have achieved high prediction accuracy and have certain reference value for sellers' sales strategy formulation.
Virtual Scenarios of Earthquake Early Warning to Disaster Management in Smart Cities Based on Auxiliary Classifier Generative Adversarial Networks

Effective response strategies to earthquake disasters are crucial for disaster management in smart cities. However, in regions where earthquakes do not occur frequently, model construction may be difficult due to a lack of training data. To address this issue, there is a need for technology that can generate earthquake scenarios for response training at any location. We proposed a model for generating earthquake scenarios using an auxiliary classifier Generative Adversarial Network (AC-GAN)-based data synthesis. The proposed ACGAN model generates various earthquake scenarios by incorporating an auxiliary classifier learning process into the discriminator of GAN. Our results at borehole sensors showed that the seismic data generated by the proposed model had similar characteristics to actual data. To further validate our results, we compared the generated IM (such as PGA, PGV, and SA) with Ground Motion Prediction Equations (GMPE). Furthermore, we evaluated the potential of using the generated scenarios for earthquake early warning training. The proposed model and algorithm have significant potential in advancing seismic analysis and detection management systems, and also contribute to disaster management.
A Lightweight Algorithm for Detecting Fake Multimedia Contents on Social Media

The significant growth of the fourth industrial revolution (Industry 4.0) coupled with the widespread adoption of social media across the world has initiated new challenges that deserve the attention of researchers and industry leaders especially in detecting and preventing fake multimedia contents on social media. The forging of multimedia contents like videos and images for malicious activities is gradually becoming very rampant and this has serious psychological, health, political and economic consequences on the targeted individuals or close associates of the victims. The application of deepfake algorithms to make manipulated videos and images has contributed in making it very difficult to identify fake videos and images from the real multimedia contents. The availability of the internet and social media has made the spread of deepfake videos and images very fast and at an alarming rate. This research work understanding the dire need to detect deepfake videos and images (multimedia contents) proposes a lightweight algorithm to detect deepfake videos and images on social media platforms. The need for a lightweight algorithm is essential to enable low computational devices to be able to apply the algorithm without computational challenges and overheads. The proposed model has demonstrated a significant reduction in the computational and time complexities. The research work also presented a comparative analysis of some selected deep learning models with emphasis on the datasets used, their features and challenges identified.
Incorporating Environmental Issues in Teaching and Learning English in Ampenan (Jangkok River)

English becomes one of the most fundamental subjects should be taught in a tourist destination including in the seashore area in Ampenan (Kota Tua). Collaboration with the youth community namely Ibra and Yayasan Saling Jaga Indonesia (previously the name is Yayasan Saling Jaga Indonesia). Community has a positive impact on the area involving economic and environmental aspects. Teaching English with implementing Rubbish Payment (one of RELASI’s program) encourage children and young people to be more aware to their environment and maintain the seashore place to become a good tourism place in Mataram. Some certain materials concern on environmental matter therefore by imbedding environmental issue in the materials pursue and open up the learners’ mind to be more aware to their surroundings, particularly to the plenty of rubbish in the seashore. Communicative teaching methods and designed authentic materials used in the teaching and learning process. It is expected that this program could promote and assist the government of West Nusa Tenggara to reduce, reuse, and recycle plastics that becomes one of the most current problems in the worlds, especially in West Nusa Tenggara.