Reviewerly/exHarmony huggingface dataset:
Localization of Pallets in Narrow Aisles for Unmanned Forklifts

As the application of unmanned forklifts becomes more and more widespread, logistics scenarios are constantly evolving. This paper focuses on a scenario where the pick-up of pallets perpendicular to forklift in narrow aisles is required. The narrow aisles only allow the forklift to complete the turning, performing pallet localization in front of it will be difficult. We proposes a method for localizing pallets in narrow aisles for unmanned forklifts. By installing the camera at an oblique angle above the pallet, the collected images are uploaded to the image processing system. The YOLOv5 is used to detect the pallet and extract the bounding boxes of the pallet class. Based on the bounding box coordinates, the original image is cropped. Convert the cropped image to grayscale for Canny edge detection, and then perform Hough line detection to extract the right edge of the pallet. This right edge will be compared with the reference pallet edge to obtain the relative localization between the pallet and the unmanned forklift.
ORCHESTRA AFTER V.V. ANDREEV IN RUSSIAN AND WORLD FOLK MUSICAL ART

The importance of the sound of the Great Russian Orchestra after V.V. Andreev for the evolution of mankind is considered. The Great Russian Orchestra is a work that has grown on Russian soil, created by Russian labor and based on the musical instruments of the Russian people. The role of the contrabass balalaika in creating the musical harmony of the orchestra, its maximum proximity to the harmony of the sounds of nature is noted. Andrew's art is the highest potential and cultural reserve for raising the spiritual and moral principles of modern society in the context of social transformation and digitalization.
‘A shifting precipice of unsettled law’?

This paper provides an up-to-date summary of how the US legal system treats evidence that uses the forensic stylistics method of authorship attribution analysis. Many scholars in both law and linguistics have written about some of the more notable cases on this issue, but none have attempted to trace the entire line of relevant case law since the advent of modern forensic stylistics, and relatively few summaries of any case law have appeared in the last ten years. It is hoped that a fresh look with updated legal research can add new insights for litigants, lawyers and linguists alike.
On Content-Aware Post-Processing: Adapting Statistically Learned Models to Dynamic Content

Learning-based post-processing methods generally produce neural models that are statistically optimal on their training datasets. These models, however, neglect intrinsic variations of local video content and may fail to process unseen content. To address this issue, this article proposes a content-aware approach for the post-processing of compressed videos. We develop a backbone network, called BackboneFormer , where a Fast Transformer using Separable Self-Attention, Spatial Attention, and Channel Attention is devised to support underlying feature embedding and aggregation. Furthermore, we introduce Meta-learning to strengthen BackboneFormer for better performance. Specifically, we propose Meta Post-Processing (Meta-PP) which leverages the Meta-learning framework to drive BackboneFormer to capture and analyze input video variations for spontaneous updating. Since the original frame is unavailable to the decoder, we devise a Compression Degradation Estimation model where a low-complexity neural model and classic operators are used collaboratively to estimate the compression distortion. The estimated distortion is then utilized to guide the BackboneFormer model for dynamic updating of weighting parameters. Experimental results demonstrate that the proposed BackboneFormer itself gains about 3.61% Bjøntegaard delta bit-rate reduction over Versatile Video Coding in the post-processing task and “BackboneFormer + Meta-PP” attains 4.32%, costing only 50K and 61K parameters, respectively. The computational complexity of MACs is 49k/pixel and 50k/pixel, which represents only about 16% of state-of-the-art methods having similar coding gains.
A high efficient software implementation of IIR filtering

when the software implements the design of infinite impulse response filter (IIR filter), the larger the amount of filtering data and the higher the filtering order, the more time will be spent because of the data shift of each calculation.<sup>[1]</sup> By means of parameter replication, the IIR filtering software replicates the parameters once when initializing the software. In the calculation of IIR filtering, the array subscript changes in a cycle <sup>[2]</sup>, replacing the way of data shift for filtering calculation, thus greatly improving the execution efficiency of the software and saving the operation time. The higher the order of IIR filter, the higher the efficiency of software promotion and the more time saving.
Quantum error correction scheme for fully-correlated noise

This paper investigates quantum error correction schemes for fully-correlated noise channels on an n-qubit system, where error operators take the form $$W^{\otimes n}$$ , with W being an arbitrary $$2\times 2$$ unitary operator. In previous literature, a recursive quantum error correction scheme can be used to protect k qubits using $$(k+1)$$ -qubit ancilla. We implement this scheme on 3-qubit and 5-qubit channels using the IBM quantum computers, where we uncover an error in the previous paper related to the decomposition of the encoding/decoding operator into elementary quantum gates. Here, we present a modified encoding/decoding operator that can be efficiently decomposed into (a) standard gates available in the qiskit library and (b) basic gates comprised of single-qubit gates and CNOT gates. Since IBM quantum computers perform relatively better with fewer basic gates, a more efficient decomposition gives more accurate results. Our experiments highlight the importance of an efficient decomposition for the encoding/decoding operators and demonstrate the effectiveness of our proposed schemes in correcting quantum errors. Furthermore, we explore a special type of channel with error operators of the form $$\sigma _x^{\otimes n}, \sigma _y^{\otimes n}$$ and $$\sigma _z^{\otimes n}$$ , where $$\sigma _x, \sigma _y, \sigma _z$$ are the Pauli matrices. For these channels, we implement a hybrid quantum error correction scheme that protects both quantum and classical information using IBM’s quantum computers. We conduct experiments for $$n = 3, 4, 5$$ and show significant improvements compared to recent work.
Investigation On Carbon Emission from Energy Usage at Universiti Tun Hussein Onn Malaysia

Carbon emissions are the main contributor to global warming, and it is well known that emissions must be significantly reduced to avoid the worst effects of climate change. The objective of this study is to investigate the carbon emissions from energy use in Universiti Tun Hussein Onn Malaysia campus. The main data collection approach for this study is the analysis of technical reports on energy consumption from 2015 to 2021 collected from the Development and Maintenance Center (PPP) and Sustainable Campus Office (SCO). The data collected was analyzed and compared over seven years of data presentation and analysis. The results show that the highest energy consumption has a significant impact on increasing carbon emissions on campus, with the highest emissions in 2015 at 1955.38 tCO2e and the lowest in 2021 at 965.70 tCO2e. On the other hand, based on the collected data, depending on the building, from 2018 to 2020, the highest carbon emission was demonstrated by the Faculty of Technical and Vocational Education (FPTV) with 2,875 tCO2e. As a result, lower energy consumption will lead to less carbon emissions that could create a better environment with less pollution and significantly lower costs.
Vehicle Appearance Damage Detection Based on Mask R-CNN

research-article Share on Vehicle Appearance Damage Detection Based on Mask R-CNN Authors: Fei Meng Automotive Data of China Co.,Ltd., China and China Automotive Technology and Research Center Co.,Ltd., China Automotive Data of China Co.,Ltd., China and China Automotive Technology and Research Center Co.,Ltd., China 0009-0001-8176-4525View Profile , Qianqian Zhu Automotive Data of China Co.,Ltd., China and China Automotive Technology and Research Center Co.,Ltd., China Automotive Data of China Co.,Ltd., China and China Automotive Technology and Research Center Co.,Ltd., China 0009-0008-6963-3355View Profile , Xuening Wu Automotive Data of China Co.,Ltd., China and China Automotive Technology and Research Center Co.,Ltd., China Automotive Data of China Co.,Ltd., China and China Automotive Technology and Research Center Co.,Ltd., China 0009-0001-8609-5850View Profile Authors Info & Claims PRIS '23: Proceedings of the 2023 5th International Conference on Pattern Recognition and Intelligent SystemsJuly 2023Pages 31–35https://doi.org/10.1145/3609703.3609709Published:16 August 2023Publication History 0citation15DownloadsMetricsTotal Citations0Total Downloads15Last 12 Months15Last 6 weeks10 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Penerapan Strategi Index Card Match Untuk Meningkatkan Motivasi Belajar Siswa Pada Mata Pelajaran SKI (Penelitian Eksperimen di Kelas X MAN 2 Garut)

This research is motivated by the learning conditions of students in classes, especially in SKI subjects, which tend to be boring because the method that is always used is lectures, so that students sometimes do not focus on learning, are easily distracted from concentration, and also easily decrease their learning motivation.Teachers rarely use other methods of learning and choose to hold quizzes and use video media to keep students focused on learning.The purpose of this study is to determine the implementation of the strategyIndex Card Match to increase student motivation in SKI subjects.The method used in this research is quantitative experimental type.This research was conducted in class X MAN 2 Garut in the 2022/2023 academic year.With a quasi-experimental research method.The variable in this study is Strategy Implementation Index Card Match (X) and Student Learning Motivation (Y).The population in this study were all students of class X MAN 2 Garut.The sample in this study was taken by technique purposive sampling, namely class X IIS 1 as the experimental class and class X IIS 3 as the control class.Data collection techniques in this study were observation, documentation, tests, questionnaires and interviews.Data analysis techniques in this study, namely testing the hypothesis using the average similarity test formula (test).The results of this study show the average value posttest experimental class 89 and posttest control class 85.After conducting the t test, the results obtained were tcount (thitung) = 4.39 > ttable (ttabel) = 2.71, meaning that it is located in the H₁ reception area.So it can be concluded that there is a significant difference in final ability after being given different treatments.The results of the questionnaire correlation test were 0.610 with tcount (thitung) 4.80 > ttable (ttabel) 2.02, then H₁ is accepted and H₀ is rejected.So there is an increase between Strategies Index Card Match with Student Learning Motivation in the SKI subject for class X students at MAN 2 Garut.
Blockchain based data management technology for future intelligent network architecture

Artificial Intelligence (AI) and Blockchain (BC) are two of the most popular and disruptive technologies for future wireless communication network. AI can provide management and strategy for distributed nodes of network through its powerful learning and automatic adaptation capabilities, which helps the network realizing intelligent endogenous. Blockchain can provide the strict security requirements of future communication systems, as well as the desired transparency and trustiness for the decentralized network, due to its built-in security features. There are tangible signs that the future research will focus on how to explore and utilize the potentials of AI and blockchain more thoroughly in future network. This paper first introduces the AI-based network intelligent system and blockchain system respectively, and focuses on the analysis of blockchain data management technology for the future network. Finally, it looks forward to the further development of AI and blockchain on 6G networks.
Making Process Trace Classification More Explainable: Approaches and Experiences in the Medical Field

Medical process traces store the sequence of activities performed on a patient, during the implementation of a diagnostic or treatment procedure. Medical process trace classification can be used to verify whether single traces meet some expected criteria, or to make predictions about the future of a running trace, thus supporting resource planning and quality assessment. State-of-the-art process trace classification resorts to deep learning, which is proving powerful in several application domains; however, deep learning classification results are typically not explainable, an issue which is particularly relevant in medicine. In our recent work we are tackling this problem, by proposing different approaches. On the one hand, we have defined trace saliency maps, a novel tool able to visually highlight what trace activities are particularly significant for the classification task, resorting to artificial perturbations of the trace at hand that are classified in the same class as the original one. Trace saliency maps can also be paired to the corresponding countermaps, built from artificial trace perturbations classified in a different class, able to refine the map information. On the other hand, we are adopting a string alignment strategy to verify what activities are conserved, in like-nearest-neighbours (i.e., referring to the nearest traces classified in the same class as the trace under examination). Specific activities, identified in similar trace positions, can in fact justify the classification outcome, which was based on non-explainable latent features, extracted by the deep learning technique. In the chapter, we will describe the two approaches, and provide some experimental results in the field of stroke patient management.
Deep Learning Based for Cryptocurrency Assistive System

Cryptocurrency is branded as a digital currency, an alternative exchange currency system with significant ramifications for the economies of rising nations and the global economy. In recent years, cryptocurrency has infiltrated almost all financial operations; hence, cryptocurrency trading is frequently recognized as one of the most popular and promising means of profitable investment. Lately, with the exponential growth of cryptocurrency investments, many Alternative Coins (Altcoins) resurfaced to mimic the fiat currency. There are several methods to forecast cryptocurrency prices that have been widely used in forecasting fiat and stock prices. Artificial Intelligence (AI),Machine Learning(ML) and Deep Learning(DL) provide a different perspective on how investors can estimate crypto price trend and movement. In this paper, as cryptocurrency price is time-dependent, Recurrent Neural Network (RNN) is presented due to RNN’s nature, which is well suited for Time Series Analysis (TSA). The topology of the proposed RNN model consists of three stages which are model groundwork, model development, and testing and optimization. The RNN architecture is extended to three different models specifically Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bi-Directional Long Short-Term Memory (LSTM). There are a few hyperparameters that affect the accuracy of the deep learning model in predicting cryptocurrency prices. Hyperparameter tuning set the basis for optimizing the model to improve the accuracy of cryptocurrency prediction. Next, the models were tested with data from different coins listed in the cryptocurrency market. Then, the model was experimented with different input features to figure out how accurate and robust these models in predicting the cryptocurrency price. GRU has the best accuracy in forecasting the cryptocurrency prices based on the values of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Executional Time, scoring 2.2201, 0.8076, and 200s using the intraday trading strategy as input features.
GreenKGC: A Lightweight Knowledge Graph Completion Method

Knowledge graph completion (KGC) aims to discover missing relationships between entities in knowledge graphs (KGs).Most prior KGC work focuses on learning embeddings for entities and relations through a simple scoring function.Yet, a higher-dimensional embedding space is usually required for a better reasoning capability, which leads to a larger model size and hinders applicability to real-world problems (e.g., large-scale KGs or mobile/edge computing).A lightweight modularized KGC solution, called GreenKGC, is proposed in this work to address this issue.GreenKGC consists of three modules: representation learning, feature pruning, and decision learning, to extract discriminant KG features and make accurate predictions on missing relationships using classifiers and negative sampling.Experimental results demonstrate that, in low dimensions, GreenKGC can outperform SOTA methods in most datasets.In addition, low-dimensional GreenKGC can achieve competitive or even better performance against high-dimensional models with a much smaller model size.We make our code publicly available.
Reform Research about High School Mathematics Curriculum Standards and Analysis on Teaching Materials

Under the background of Normal Professional Certification, We will start a series of teaching revolutions about a curriculum named high school mathematics curriculum standards and analysis on teaching materials based on concepts “students-centered, output oriented, consistent improvement”. The passages will clarify the new concept of curriculum teaching revolution from various aspects like the background of teaching revolution, the mode of teaching, the cultivation of teacher’s morality, the education of ideological and political work, and the methods of assessment. Improving teaching effectiveness through teaching reform and promoting the comprehensive cultivation of teacher training students' abilities can achieve the goal of effectively.
Blockchain-Enabled Smart Healthcare Systems Using IoT

A healthcare system is the medical platform through which patients get better treatment. Several hospitals and doctors are linked together through this platform in a hierarchy and treat the patient easily. In view of the COVID-19 pandemic, it is a very challenging task to treat the patients physically; therefore, there is a need to redevelop the healthcare system to fulfill the modern demand. The state of the art of this scheme is to provide remote treatment facilities and build a secure online system. Blockchain technology combined with IoT provides the solution through its vital features of transparency, immutability, decentralization, and trust-less environment. IoT devices capture the patient health status and process it and send to blockchain; therefore, information sharing between devices, hospitals, and doctors are at risk and need to provide security. Using key management scheme in smart contract to ensure integrity of data, this scheme provides better treatment at cheap cost as well as efficiency and security compared to traditional healthcare systems.
Health Care Management: Cancer Prediction and Diagnosis Using Artificial Intelligence (AI)

Cancer is the most communal leading cause of death globally due to cell growth abnormality which leads to destroy of body tissue. Due to excessive intake of tobacco, sun exposure, radiation exposure and chemical substances are the major cause of cancer risk. The Cancer is a global phenomenon and especially breast cancer is most common among the women. Breast contain various tissues ranging from fatty to tense, network of lobes are formed by these tissues. The normal cells in the breast changes and grow out of control forming a mass cells called tumor. The tumor can be classified in stage I, II, III and IV based on cancerous and benign spread through lymph vessels. The spreading in different area is not under control resulting in loss of life. The early prediction of breast cancer can save women’s life. This research work focuses on strong image detection process for the breast cancer mammography images by considering the whole slide image. Here, Machine Learning (ML)-based studies are carried out for cancer diagnosis, and classification of patients’ based on diagnostic results. Deep Learning approaches, a branch of ML, have recently showed a great help to physicians in medical oncology with the development of medical-imaging diagnostic systems trying to improve disease diagnosis and the early detection of tumors. The input processing and feature extraction are performed using Gaussian filter then segmentation done the specialized features isolated using Entropy PCA test are done for the three groups namely (i) the deep neural networks (DNNs) (ii) the convolutional neural networks (CNNs) and (iii) the recurrent neural networks (RNNs).The breast cancer identification is performed with the support of MATLAB and the experiments showed an exhaustive pattern change in tumor cells and the estimation results clearly depicts the risk factors for further treatment and to propose the early symptoms of breast cancer.
From Real to Virtual Worlds

In this paper I explore pathways of pedagogical enquiry that are apparently well-trodden; however, in the time of Covid-19, there has been a general revaluation of traditional ways of doing things at all levels of society. This also applies to the context of education, which has seen widespread reflection on the adaptation of existing pedagogical solutions, as well as the invention of new ones. The quasi-universal implementation of anti-viral lockdown measures has led to a drive towards the adoption of digital platforms for teaching: in many cases these have been introduced suddenly, without sufficient deliberation or the due preparation of staff and pupils (Teräs et al 2020). While digital solutions are clearly fundamental in this moment of crisis, it is important not to use their apparent efficiency to smooth over existing faults in education systems, or naturalise (Barthes 1957) these temporary solutions as resolving issues on a long-term basis. Technology, though popular with corporations and politicians, does notnecessarily offer solutions to traditional pedagogical problems (Cuban 2004: 20-21). While some studies appear to suggest that student performance has actually risen due to​ the replacement of physical with virtual instruction (Gonzalez et al 2020), it is imperative at this moment to highlight traditional values and ensure that they will not be forgotten in the emerging paradigms of future learning. The notion of education as ‘flourishing’ is central to what is felt to be the classical model, or ideal (Luntley 2016); in this conception, education concerns the growth of the person as a whole. This paper aims to contribute to this debate, in the specific context of language education, asking whether a kind of Aristotelian ‘flourishing’ is possible through Computer Mediated Communication (CMC), and problematising the adaptation of communicative language teaching methodology in an online context.