bluuebunny/crossref_metadata_2025 huggingface dataset:
PERKEMBANGAN MADRASAH SEBAGAI INSTITUSI PENDIDIKAN ISLAM PALING MODERN PADA MASA DINASTI ‘ABBASIYAH (750-1250 M)
Authors: Wasito
Published: July 2024

Penelitian ini hendak membahas terkait sejarah madrasah sebagai Lembaga Pendidikan yang paling maju di zaman keemasan Islam, dinasti Abbasiyah. Sebelum Dinasti Abbasiyah, pusat kegiatan Dunia Islam selalu bermuara pada masjid. Masjid dijadikan centre of education. Pada Dinasti Abbasiyah inilah mulai adanya pengembangan keilmuan dan teknologi diarahkan ke dalam ma'had. Lembaga ini kita kenal ada dua tingkatan yaitu maktab/kuttab dan masjid serta tingkat pedalaman. Pada perkembangan selanjutnya mulailah dibuka madrasah-madrasah yang dipelopori Nizhamul Muluk yang memerintah pada tahun 456-485 H. Lembaga inilah yang kemudian berkembang pada masa Dinasti Abbasiyah. Penelitian ini menggunakan metode penelitian kajian Pustaka. Yaitu sebuah penelitian yang mengkaji suatu objek pembahasan berdasarkan literatur pustakayang didapatkan. Penelitian ini menghasilkan temuan: 1) Motivasi yang mendasari kelahiran madrasah, yaitu selain motivasi agama dan motivasi ekonomi karena berkaitan dengan ketenagakerjaan, juga motivasi politik. 2) Meskipun madrasah sebagai lembaga pendidikan dan pengajaran di Dunia Islam baru timbul sekitar abad ke-4 H. Ini bukan berarti bahwa sejak awal perkembangannya Islam tidak mempunyai lembaga pendidikan dan pengajaran. Pada periode awal telah berdiri beberapa madrasah yang menjadi cikal bakal munculnya Madrasah Nizhamiyah. 3) Perhatian kaum Muslimin terhadap pendirian sekolah di segala penjuru negeri, menunjukkan betapa tinggi peradaban mereka. Umat Islam memandang bahwa ilmu merupakan asas setiap kemajuan.
Makna Tradisi Nyebuh dalam Budaya Madura Di Desa Sungai Jawi Kota Pontianak
Authors: Andriyani Fitri
Published: July 2024

Tulisan ini menganalisis tradisi Nyebuh dengan menjelaskan deskripsi dan makna pelaksanaan tradisi Nyebuh di wilayah Madura dengan mengambil area penelitian di Desa Sungai Jawi Dalam, Kecamatan Pontianak Barat. Dalam penelitian ini penulis menggunakan pendekatan kualitatif. Data diekstraksi dalam pemahaman yang mendalam dan komprehensif. Data penelitian ini diperoleh melalui observasi partisipan dan pengumpulan sumber bacaan, literatur, jurnal, buku dan media cetak online yang berkaitan dengan penelitian ini. Peneliti juga melakukan wawancara dengan masyarakat Madura yang terlibat langsung dalam pelaksanaan tradisi Nyebuh di desa Sungai Jawi Dalam. Hasil penelitian ini menunjukkan bahwa pelaksanaan tradisi Nyebuh adalah upacara atau memperingati seribu hari kematian. Kegiatan ini dipenuhi dengan keselamatan dan merupakan yang terakhir atau penutup dari beberapa keselamatan sebelumnya. Namun, bukan berarti keluarga yang berduka lupa dan tidak mendoakan mereka, namun keselamatan akan tetap dilakukan melalui kegiatan lain. Tradisi ini masih diturunkan dari generasi ke generasi di masyarakat Madura dan dilestarikan dalam setiap perayaan kematian yang diadakan, karena mengandung kesucian yang tidak dapat digantikan oleh yang lain, serta bentuk cinta dan ketulusan dari keluarga kepada roh yang akan mengucapkan selamat tinggal dan pergi selamanya.
Integritas Banser Kabupaten Nganjuk Dalam Prinsip NKRI Harga Mati: Sebuah Perspektif Cultural Theory
Authors: Arif Muhammad , Darwati Yuli
Published: July 2024

Abstrak
Banser is a semi-autonomous body of the Ansor Youth Movement of the largest mass organization in Indonesia, namely Nahdlatul Ulama'. Banser has a field of assignments related to the field of social religion which is more practical and strategic. Regeneration in Ansor and Banser bodies is held every week, and continues to be ongoing in order to prepare new cadres. Regeneration must be carried out actively and programmed in accordance with the vision and mission that has been set. Distribution and independence continues to be carried out in stages. This vision and mission will continue to be realized over the next few years in order to form massive integrity and make a major contribution to all Indonesian people. So that a principle can be formed that is very strongly internalized within Banser's body, namely "NKRI Harga Mati". In accordance with the social conditions in Nganjuk Regency, the research carries the theme of integrity in the principle of the Unitary State of the Republic of Indonesia. The research was carried out using qualitative methods in a case study approach. This approach was chosen because the object of study is a system, so case studies were chosen as an alternative to describe the phenomena that arise. The data purification process was carried out using observation, interview, and recording methods by checking the validity of the data using triangulation. Cultural Theory was chosen as the analytical knife in positioning the Banser problem in response to the era of globalization and the challenges of the times with the principle of the Unitary Republic of Indonesia at Dead Price.
TAUHID SEBAGAI LANDASAN KEBUDAYAAN MASYARAKAT
Authors: Amir Reza Kusuma Amir
Published: July 2024

Abstrak
Tauhid sebagai landasan dan pijakan pengembangan budaya keilmuan dapat dilacak pada terbentuknya geneologinya konsepsi tentang Tuhan dalam pengertian yang spesifik. Pengetahuan tentang hubungan antara Tuhan dan dunia, antara pencipta dan ciptaan, atau antara prinsip Ilahi dengan manifestasi kosmik, merupakan basis paling fundamental dari kesatuan antara sains dan pengetahuan spiritual. Itulah sebabnya Al-Qur’an kemudian berperan sebagai sumber inteleketualitas dan spiritualitas Islam. selain itu juga berfungsi sebagai basis bukan hanya bagi agama dan pengetahuan spiritual, tetapi bagi semua jenis pengetahuan, sumber utama inspirasi pandangan Muslim tentang keterpaduan sains dan pengetahuan spiritual. Gagasan keterpaduan ini bahkan merupakan konsekuensi dari gagasan keterpaduan semua jenis pengetahuan, namun problem umat saat ini banyak yang berpendapat bahwa dualism menjadi jalan untuk meraih kemajuan seperti yang ada di Barat. Seperti yang dipraktikan di dalam kegiatan masyarakat dan pendidikan sekolah
DZIKIR DAN WIRID DI PESANTREN DARUR RAHMAN KIEMAS SENDANGAGUNG
Authors: Alfarisi M. Alan
Published: July 2024

Dzikir merupakan inti dan ruh dari segala amal ibadah yang dilakukan umat Islam. Namun dewasa ini dzikir dan wirid yang merupakan bentuk istikamah dari dzikir seakan kurang diperhatikan dan cenderung diabaikan. Banyak umat Islam zaman ini yang tidak memperhatikan dzikir dan wirid lagi, dan mereka cenderung mengabaikan. Sehingga seluruh ibadah mereka hanya bernilai formalitas dan tidak bernilai spiritual. Karena itu, umat Islam zaman ini banyak yang mengalami kekosongan spiritual, akibatnya mereka tidak merasakan bahagia walaupun bergelimang harta dan serba mudah. Lebih parahnya, agama Islam tidak lagi menjadi suatu pegangan yang dapat mengatur dan mengendalikan perilaku mereka. Sehingga perilaku mereka sering kali melanggar syariat Islam. Meskipun mereka tetap melakukan sholat dan ibadah ritual mereka. Dengan realita itu, maka penulis tertarik untuk meneliti Ponok Pesantren Darur Rahman Kiemas yang banyak dari santrinya adalah orang-orang nakal. Namun dengan metode pendidikan yang ada menjadikan mereka sadar, kemudian mereka berubah menjadi lebih baik perilakunya. Penelitian ini menggunakan metode penelitian lapangan dengan mewawancarai beberapa orang yang terkait. Setelah penelitian diketahui dzikir dan wirid apa saja yang diamalkan para santri pesantren Darur Rahman Kiemas, yang dengan wirid dan dzikir yang dilakukan dapat menerapi perilaku para santri.
Paradigma Filosofis Toleransi Dalam Moderasi Beragama
Authors: Budiman Akli  , Dwi Noviani
Published: July 2024

Tulisan ini membahas tentang bagaimana paradigama filosiosfis toleransi dalam moderasi beragama, dimana yang menjadi pokok pembahasannya adalah mengenai bagaimana pandangan filosofis terhadap makna dan implementasi toleransi dikaitkan dengan wacana moderasi dalam beragama. Metode yang digunakan penulis dalam melakukan penelitian ini adalah melalui metode kualitatif deskriptif dengan kajian dokumen kepustakaan. Pengumpulan data diambil dari sumber berupa, literatur, artikel, buku dan dari website pendukung lainnya yakni kajian-kajian serta video di youtube.  Kemudian dari data yang dikumpulkan lalu dituangkan dalam tulisan selanjutnya dianalisis dengan metode deskriptif. Konsep toleransi sudah digaungkan oleh para pendahulu pendiri bangsa ini dan bahkan jauh sebelum berdirinya Indonesia. Di dalam bertoleransi semakin banyak perbedaan maka semakin banyak pula diperlukan sikap tenggang rasa pengendalian diri dan pengertian. Jika ada pertentangan seperti apapun itu harus memiliki kesadaran untuk selalu menjaga kesetiakawanan, toleransi dan rasa persaudaraan.
Community service: Creation of village concrete pathways
Authors: Jaya I Made , Pugra I Wayan , Suasira I Wayan , Sutapa I Ketut
Published: July 2024

Roads are important infrastructure for human movement and are connecting access between regions. Good road infrastructure will make it more comfortable and easier for road users to move from one place to another. Good road conditions will have a beneficial impact from an economic perspective on the progress of an area. The condition of the roads in the Jagaraga village area is currently still worrying. There is a section of road which is currently still a rocky dirt road which is the access that must be passed by the people of Jagaraga Village. This condition gets worse when the rainy season enters and the dirt road will become muddy and even slippery when traversed. This is the reason for carrying out Community Service in Jagaraga Village. This community service activity was carried out by the Community Service Team together with the community to work together and work together to repair roads in the village area with concrete materials.
Community service: Artificial intelligence in employment and the reconfiguration of the workforce in the digital age
Authors: Molina-Menéndez Erick Eduardo , Cedeño-Acosta Nathaly Juleidy , Cedeño-Cuzme Cristhian Leonel , Rodríguez-Gámez María , Saltos-Arauz Wilber Manuel
Published: July 2024

The impact of artificial intelligence on employment and the configuration of the workforce in the digital age is a topic of great relevance today. The objective of the research is to analyze how the adoption of artificial intelligence is transforming work roles, generating both opportunities and challenges for workers. Changes in skill demand, the emergence of new occupations, and potential automation of tasks are examined, as well as implications for workforce training and adaptation. A bibliographic review and a qualitative analysis of the aspects involved in the introduction of new technologies in the work process were carried out. The result was that artificial intelligence could be implemented to improve employment and take advantage of the opportunities offered in all work spheres, mainly science and education.
'Walking to Work' Extract from The Backyard Adventurer (2021)
Authors: Miles Beau
Published: July 2024

This paper is an extract from Beau Mile’s The Backyard Adventurer (2021) published by Brio Books. It is the text of the voiceover for the corresponding film ‘Walking 90 Kms to work’ available on Youtube  - https://www.youtube.com/watch?v=R-Zyud8xh2c – Miles redefines adventure through his attempts to transform a mundane part of his life into an adventure. The first part of the text details missing travel and adventure when increasingly locked into a regular lifestyle, before deconstructing the necessity of travelling great distances or pushing oneself to extremes in order to experience adventure. In an attempt to demonstrate his argument, Miles decides to walk to work instead of driving. He documents his mindset and experiences when completing his daily 90km commute from a semi-rural lifestyle block to the inner city campus of the university where he works. The simple of idea of “walking to work” reveals how distanced we are from the spaces that surround our transport routes; how insulated we have become from the world around us. The text and film provides an intriguing performance of the philosophy that it articulates.
Book Review: The Social Imperative: Architecture and the City in China, Edited by Koon Wee Actar Publishers
Authors: Exacoustou Myrto (Petunia)
Published: July 2024

This review looks at The Social Imperative: Architecture and the City in China, a collection of critical essays and manifestos aimed at enhancing the discourse on contemporary Chinese architecture and urbanism. In particular, it introduces the voices of Chinese architects, urbanists and intellectuals who are themselves central to this discourse.
Análise multitemporal da vegetação da Reserva Extrativista Chico Mendes por meio da plataforma Mapbiomas
Authors: Souza Vanessa Sidrim da Silva de , Vasconcelos Marcelo Augusto Machado , Bittencourt Paulo Celso Santiago , Bittencourt Paulo Celso Santiago , Santos Artur Vinícius Ferreira dos , Melo Paulo Alves de , Melo Kellem Cristina Prestes , Silva Romulo Luiz Oliveira da , Silva Brenda Karina Rodrigues da
Published: July 2024

A Reserva Extrativista Chico Mendes, localizada na região sul do Acre próximo à fronteira com o Peru e a Bolívia, se tornou precursora na conciliação da biodiversidade e o seu uso sustentável pela população que abriga nesta região, porém a sua conservação tem sido ameaçada constantemente pelo aumento do desmatamento e incêndios na floresta, substituindo-as por pastagem para a criação de gado. A metodologia desta pesquisa abordamos o tema “conservação da vegetação na reserva extrativista Chico Mendes” que foi estruturada em etapas principais. Na primeira etapa, foi realizada uma revisão bibliográfica extensa para entender a história e o contexto da criação da Reserva Extrativista Chico Mendes, as espécies vegetais presentes e quaisquer dados ou estudos anteriores relacionados à conservação da vegetação na área. O objetivo deste artigo visa analisar por meio de estudos anteriores sobre o tema e utilizando uma abordagem multitemporal, com o auxílio das ferramentas Mapbiomas e Qgis, o estado de conservação da vegetação na Resex, que abrange uma área de aproximadamente 970 mil hectares, mostrando as mudanças ocorridas desde a sua criação até os dias atuais. O estudo constatar que a Reserva Extrativista Chico Mendes, embora seja uma área de proteção ambiental, vem sofrendo com diversos problemas relacionados à conservação de sua vegetação. A análise dos dados coletados evidenciou uma redução significativa na cobertura vegetal ao longo dos anos, devido principalmente à ação humana, como desmatamento para fins agrícolas e pecuária. Também podemos observar a importância da vegetação para a manutenção do equilíbrio ecológico e como o uso das geotecnologias pode auxiliar na proteção e monitoramento das reservas extrativistas.
Características fermentativas e valor nutritivo de híbridos de sorgo em quatro estádios de maturação
Authors: Moura Marielly Maria Almeida , Mizobutsi Edson Hiydu , Pires Daniel Ananias de Assis , Almeida Luciele Barboza de , Pires Neto Otaviano de Souza , Costa Renê Ferreira , Castro Sabrina Gonçalves Vieira de , Sousa Irisléia Pereira Soares de , Fernandes Martielle Batista , Matos Amanda Dayanne Malta , Lima Maria Clara Chaves , Madureira Mariana Rabelo , Santos Leandra Cristina Soares
Published: July 2024

Objetivou-se avaliar as características fermentativas e valor nutritivo de híbridos de sorgo em quatro estádios de maturação. O experimento foi conduzido na Embrapa Milho e Sorgo, em Sete Lagoas – MG. Foram avaliadas silagens de cinco híbridos (BRS 610, BRS 655, BRS 658, BRS659 e VOLUMAX) em quatro idades de corte: 88, 95, 102 e 108 dias. Houve efeito significativo para a interação cultivar x idade (P<0,05) para teor de MS e FDN, que apresentaram efeito linear crescente com aumento médio diário de 0,556; 0,417; 0,131 e 0,389 % no teor de MS; e comportamento linear decrescente com redução de 0,498; 0,36; 0,2030; 0,375 e 0,364 % diária no teor de FDN. Não houve diferença estatística entre híbridos (P>0,05), que apresentaram efeito linear crescente com incremento diário de 0,038% no teor de PB. Os teores de FDA das silagens mantiveram-se constantes com a maturidade (média = 36,88%). A MSPD das silagens dos híbridos de sorgo em quatro apresentou redução linear (P<0,05) com o avançar da idade de corte da planta. Não houve interação (P>0,05) entre híbrido e idade de corte para o teor de pH e NH3/NT. Em relação às idades de corte, os híbridos apresentaram efeito linear. O híbrido BRS610 destacou-se em relação aos demais híbridos por apresentar maior fração potencialmente digestível, independente da idade de corte. A média do teor de matéria seca indica que a melhor época de corte para os híbridos de sorgo é quando se encontra com 102 e 108 dias.
Implementation Of Clara Clustering Algorithm On Modis Data For Detection Of Forest Fire Potential In Indonesia
Authors: Petty Holbed Joshua , Prasetyo Sri Yulianto Joko
Published: July 2024

Forest fires are a recurring issue every year in various countries, especially in those with extensive forests like Indonesia. An initial step in fire prevention is detecting the potential occurrence of fires, which can be achieved by utilizing satellite data, such as MODIS data. In this study, clustering or grouping of MODIS data in Indonesia for the years 2021 and 2022 was conducted using the CLARA algorithm due to its robustness against outliers and efficiency in handling large datasets. The application of clustering with the CLARA algorithm on both datasets resulted in two clusters, and the evaluation using the Silhouette Coefficient yielded values of 0.89 and 0.88 for both years. The analysis revealed that both clusters in both datasets exhibited similar characteristics. In the data for the years 2021 and 2022, the first cluster displayed a moderate to high potential for fire, while the second cluster indicated a low potential for fire. The results of this study can be used as a reference for authorities to identify the level of forest/land fire potential from observed hotspots in Indonesia, thus enabling early prevention measures such as early extinguishment to prevent further spread of the fire.
The Role of Digital Literacy in Increasing Understanding of the Qur'an among People Islamic Students
Authors: Hanik Hidayati  , Wibowo Harry Sugiharto
Published: July 2024

Digital literacy among Islamic students has an important role in deepening understanding of the Koran. This research uses a literature study method to review the influence of digital literacy on understanding the Al-Qur'an. The results show that digital literacy provides access to quality Koranic information, broadens understanding and tolerance for religious diversity, and strengthens interfaith relations. Digital literacy also helps solve complex social and environmental problems, enriches Islamic knowledge, and develops critical thinking skills. In the educational context, digital literacy can improve the quality of Islamic education, strengthen cooperation between religious communities, and form responsible, fair and ethical individuals. Educators and educational researchers need to pay attention to the importance of digital literacy in supporting the personal, social and professional development of Islamic students in their lives. Thus, digital literacy is a key factor in increasing understanding of the Koran among Islamic students.
A New Deneutrosophication Method Proposal for Use in Delphi Methods: Application in Ancestral Knowledge Analysis
Authors: Edwin Edwin ,   Technical University of Cotopaxi, Ecuador,   Technical University of Cotopaxi, Ecuador,   Tashkent State University of Economics, Uzbekistan, Palma Gabriela Beatriz Arias , Mirzaliev Sanjar
Published: July 2024

This study explores the rich cultural heritage of indigenous peoples and communities, whose traditions and adaptability have intrigued scholars interested in understanding their relevance in modern times. The Neutrosophic Delphi Method emerges as a vital tool in this research, offering a dynamic and versatile approach to address the inherent complexity of indigenous activities. By investigating the uncertainty and ambiguity in decision-making, this method enables a thorough examination of cultural practices. The interdisciplinary methodology employed focuses on the interaction between traditional and modern aspects, examining the main activities that define the daily lives of indigenous communities. The use of the Neutrosophic Delphi Method is highlighted for its ability to handle diverse perspectives and complex data, and the deneutrosophication process to improve precision and clarity in the findings. This technique ensures an accurate and harmonized representation of indigenous knowledge with modern scientific research. This effort seeks not only to enhance the academic legacy but also to foster international dialogue, promoting the recognition and appreciation of cultural diversity. By empowering indigenous populations to contribute to the generation of knowledge about their experiences, the study advocates for a more inclusive and equitable approach in scientific inquiry, acknowledging the invaluable contributions of indigenous communities to the cultural richness of our world.
Reformáció Itáliában a 16. században
Authors: Hajnóczi Kristóf
Published: July 2024

A cikk egy rövid összefoglalása a protestáns reformáció eszméi itáliai megjelenésének, elterjedésének, visszahúzódásának majd eltűnésének a 16. században. Áttekinti a kapcsolódó itáliai szellemi áramlatokat, megmutatja az érintett területeket, végighalad a vonatkozó olasz szakirodalom által követett korszakoláson, ismertet számadatokat, bemutatja a legjelentősebb érintett csoportokat és közösségeket, valamint röviden ismerteti a bibliafordításokat és a két legjelentősebb művet, melyek az itáliai protestáns reformációhoz köthetők.
A piarista uradalom története 1806 és 1848 között
Authors: Balla János
Published: July 2024

A 19. század elején súlyos anyagi nehézségekkel küszködő piarista rend I. Ferenc adományából megkapta az egyik legnagyobb múltú magyarországi nagybirtokot, a Szent István király által létrehozott székesfehérvári őrkanonokság ingatlanvagyonának (kusztodiátus) mintegy 50 000 holdnyi földterületét. A szerzetesek által irányított uradalom állandó jövedelmet biztosított a rendtartománynak, amit az iskolák fenntartására fordíthattak. A feudális gazdálkodásba lassan beletanuló szerzeteseknek azonban több kihívással kellett szembenézniük 1848-ig: a reformkori országgyűlések a régi gazdasági rend kereteit feszegették, megpróbálták újraszabályozni a földesúr és jobbágy kapcsolatát. Mindeközben a reformellenzék körében egy komplex társadalmi, gazdasági, politikai program bontakozott ki, ami egy polgári Magyarország megteremtését szorgalmazta. A piaristák ugyan követték a változásokat, mégis váratlanul érte őket az áprilisi törvények által teremtett új helyzet, ami – a rend szempontjából – bizonytalanságot, sőt az uradalom szempontjából több megoldhatatlannak látszó problémát vetett fel.
Major Issues and Optimization Paths in the Implementation of Green Fiscal and Tax Policies
Authors: Liu Dong
Published: July 2024

In the current era of accelerating global economic development, countries worldwide are facing the challenge of resource shortages, giving rise to the concept of green sustainable development. This has also become an important principle for China in its pursuit of sustainable development. Against this backdrop, the nation has proposed corresponding green fiscal and tax policies. However, there are deficiencies in the implementation of Chinas green fiscal and tax policies. This paper analyzes the main issues in the implementation process of green fiscal and tax policies, aiming to optimize the implementation paths of green fiscal and tax policies by enhancing supervision of green tax policy execution, improving publicity of green fiscal and tax policies, and refining green tax incentives.
A Comparative Study on the Paths of Digital Economic Development under the Old and New Development Patterns
Authors: Li Yufan , Ni Yiming , Ren Yuheng
Published: July 2024

The world is undergoing a major change not seen in a century, which has a profound impact on the digital development of China's economy. Global trade wars, financial crisis and other unstable factors also pose challenges to the digital development of the Chinese economy. The uncertainty and instability of these factors have brought about certain impacts on the digital development of Chinese enterprises, which requires strengthening the monitoring and response capacity of the global economy. Therefore, this paper takes the rapid development of digital economy as the background, compares the digital development paths under China's old and new development modes, and comprehensively considers the impact of digitalisation on production, circulation, consumption, finance, and high-quality development, etc. On this basis, it comprehensively considers the impact of the development of digital economy on the formation of the new development mode, so as to provide theoretical support for the development trend of China's digital economy as well as the relevant decision-making.
The Importance and Implementation Strategies of M&A Financing
Authors: Pan Yujie
Published: July 2024

Financing is a crucial financial aspect of mergers and acquisitions (M&A). The effective selection and utilization of financing tools can significantly impact the financial performance and progress of M&A. This study, employing literature review and case examples, emphasizes the importance of M&A financing and explores three main issues from a dynamic perspective: financing needs for M&A pricing, obtaining financing, and repaying M&A financing. The findings suggest that meeting M&A funding needs, focusing on financing repayment, and controlling financing risks are critical for orderly M&A financing activities and performance optimization. Drawing on successful cases, the study designs strategies to facilitate orderly M&A financing, providing some references for related research.
Analysis of Economic Factors Affecting Consumer Purchase Intentions on E-commerce Live Streaming Platforms
Authors: Lin Jiong
Published: July 2024

With the rapid rise of e-commerce live streaming platforms, consumer purchase intentions are influenced by various economic factors. This study takes a close look at the five main economic factors that affect people's decisions to buy: their disposable income, how products are priced, their economic expectations, their ability to get credit, and their economic knowledge and financial management skills. It does this by interpreting the relevant economic factor concepts and reviewing previous research. These factors influence consumers' decision-making processes through a variety of mechanisms. Disposable income directly influences purchasing power; product pricing strategies and credit availability affect price sensitivity and timing of purchases; and economic expectations and financial management skills influence risk assessment and purchasing behavior. This analysis helps understand the complexities of consumer behavior in the context of e-commerce live streaming, providing valuable insights for platform operators and market strategists.
Systematic study of lightweight for object detection
Authors: Liu Haoyu
Published: July 2024

Object detection is a critical task in computer vision that has been studied for many years. However, existing object detection models are often computationally expensive and require high-end hardware to achieve real-time performance. To address this issue, many efforts aim to propose lightweight detection deep models to meet the requirements of resource-critical applications. YOLO is such a representative work that relies on a one-stage detection design and a series of optimization strategies. Correspondingly, related object detection works aiming at lightweight requirements based on YOLO are continuously proposed. Nevertheless, there is still a lack of a systematic study on the development of YOLO-based works, making it difficult to understand the lightweight trend for the object detection task. In this paper, we bridge the gap between YOLO's lightweight design and object detection. We comprehensively studied numerous representative works in this field and analyzed their concerns, approaches, performance, and application scenarios in detail. Moreover, we discussed the impacts of these efforts from multiple dimensions and proposed future directions for lightweight detection. Our study and findings are expected to provide valuable suggestions for object detection in low-power and resource-intensive devices.
Adaptive recommendation systems: A comparative analysis of KNN-based algorithms and hybrid models
Authors: Yu Jianing , Wang Anbang
Published: July 2024

This study presents a comprehensive comparative analysis of various recommendation algorithms, focusing on their efficacy in predicting user preferences. The algorithms examined include KNNBaseline, KNNWithMeans, KNNBasic, KNNWithZScore, and Singular Value Decomposition (SVD), each representing distinct methodologies within the collaborative filtering paradigm. Performance was evaluated using two error metrics: Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), reflecting both the magnitude and absolute values of prediction errors. The results reveal subtle differences in performance across the algorithms, with no single method demonstrating marked superiority. These findings underscore the importance of understanding the nuanced behavior of different algorithms and their suitability for specific applications or contexts. The study contributes valuable insights to the field of recommendation systems, enhancing the understanding of algorithmic behavior, and offers guidance for practitioners in selecting and optimizing algorithms to meet specific needs and objectives. Future research directions include the exploration of additional algorithms, diverse datasets, and alternative evaluation metrics.
DeepFM-based rating prediction for second-hand product sellers
Authors: Yang Gangzhi
Published: July 2024

With the advancement of e-commerce, online exchanges have gradually supplanted offline trading, leading to a surge in second-hand commodity transactions. A growing number of individuals are opting to sell and purchase unused items on second-hand trading platforms. Consequently, an escalating need arises for research into recommendation systems tailored to second-hand transaction data. Nevertheless, in contrast to conventional transaction data, second-hand transaction data is more attuned to high-level information, such as historical reviews, relative prices, and their implicit relationships. Conventional recommendation algorithms struggle to adequately extract implicit features from such information, thereby hindering their ability to achieve satisfactory results. This study delves into a recommendation system for second-hand transaction data based on the DeepFM model. The DeepFM model leverages a fusion of Factorization Machines (FM) and Deep Neural Networks (DNN) to capture both low-order and high-order interactions among features. Through experiments conducted on a curated second-hand transaction dataset sourced from the Taobao platform, we compare the performance of our proposed model with that of traditional algorithms. The results demonstrate the effectiveness of our approach in enhancing recommendation accuracy.
An overview of knowledge graph-based recommendation systems
Authors: Ye Yutao
Published: July 2024

Recommendation systems have emerged as effective tools for mitigating information overload. Traditionally, recommendation systems employ various models such as Collaborative Filtering, Matrix Decomposition, and Logic Decomposition. Among these, Collaborative Filtering stands out due to its high efficiency. However, it encounters challenges related to cold start and sparse data. To address these challenges, the integration of Knowledge Graphs with recommendation systems has demonstrated significant advantages. This paper classifies Knowledge Graph-based recommendation systems into two categories: enhanced classical recommendation models and novel recommendation models integrated with Knowledge Graphs. We provide explanations for each category and compare them with traditional methods to draw conclusions. To inspire future research endeavors, this article identifies potential research areas and highlights unresolved issues.
An empirical study of prompt mode in code generation based on ChatGPT
Authors: Guo Huaiyu
Published: July 2024

In recent years, with the continuous advancement of technologies such as Large Language Models (LLMs) and Chat Generative Pre-trained Transformer (ChatGPT), an increasing number of developers have turned to AI-assisted code generation. However, in the context of code generation, simple question-and-answer approaches may not yield the desired results. To address this challenge, we introduce prompt engineering as a means to construct efficient prompting methods for guiding models in generating the intended code. This paper empirically explores the impact of different prompting methods on code-generation tasks. We introduce several prompt-sensitive code tasks in our experiments and assess the effectiveness of various prompt methods in terms of the quality of generated code. Ultimately, we find that guiding the model from a specific role perspective yields the best results, while other methods exhibit varying degrees of effectiveness. This research provides valuable insights into the application of prompt engineering in code generation, encouraging future efforts to further optimize prompting methods and enhance the accuracy and practicality of generated code.
Adaptive bird's eye view description for long-term mapping and loop closure in 3D point clouds
Authors: Andrew Qu,
Published: July 2024

In this paper, we proposed a robust, adaptive bird eye view descriptor for loop closure detection and long term mapping in 3D point cloud. Loop closure detection plays a crucial role in SLAM context, enhancing the quality and the accuracy of constructed point cloud map. While visual-based loop closure detection methods are vulnerable against perceptual and lightness variances, LiDAR-based methods are robust against such variance. Therefore, to construct our BEV descriptor, we accumulate and register each LiDAR scan to form a key-frame, and develop an algorithm to radially and azimuthally partition and encode the key-frame into a 2D BEV pixel image. Then, similarity scores between every BEV descriptor are calculated to find the best candidate for loop closure. The best candidate is further validated through ICP-based geometric verification, and its newly established restriction is the used for pose graph optimization to improve the quality of the point cloud map. In our experiments, we compare our loop closure detection method with Scan Conext, a state-of-art global descriptor, on public dataset (KITTI) and our private dataset (collected with a Livox solid-state LiDAR). The results of both datasets show that our proposed descriptor has stronger adaptability in different types of environment (both indoor and outdoor) and LiDAR, with a greatly improved accuracy and overall performance.
Health insurance claim amount prediction using statistical methods
Authors: Wen Shiran
Published: July 2024

Healthcare can be costly in the United States. In 2021, the average cost of health insurance for a family of four was $22,221 per year. The average cost of health insurance for an individual was $7,739 per year (Keiser Family Foundation). With the aim of helping the public know more about what factors contribute the most to the expensive cost of their health insurance, this study conducted the analysis to identify the top contributing factors that increase health insurance claim amounts. Various statistical and machine learning tools were used to answer the proposed question: data conversion, correlation, partial correlation, k-means clustering, principal component analysis, multiple regression, lasso regression, support vector machine, and random forest. Consistent results that BMI (Body Mass Index), blood pressure, and smoking are the three variables that contribute the most to the increase in health insurance claim amount were found.
Research on the human-computer interaction principles and play-ability---Taking Pro Evolution soccer game as an example
Authors: Sun Xu
Published: July 2024

With the continuous advancement of technology, an increasing number of games have entered people's fields of vision. Football, as one of the world's most popular sports, has also spawned numerous related football games, including the Pro Evolution Soccer Game. However, for many people, this game still remains relatively unfamiliar. This study aims to analyze the principles of human-computer interaction in the Pro Evolution Soccer game and explain why it offers higher playability and entertainment value. Furthermore, there is a limited amount of comprehensive analysis regarding human-computer interaction principles and the Pro Evolution Soccer game. Firstly, this paper will introduce the basic concepts and development history of the Pro Evolution Soccer Game. Then, this paper will delve into the principles and characteristics of human-computer interaction in the game by studying its interface design, control methods, and artificial intelligence systems. Finally, this paper will assess the playability of the Pro Evolution Soccer game through real-life case studies and provide suggestions for improvements. This research adopts the methodology of a literature review. The findings indicate that the Pro Evolution Soccer game enhances its playability through carefully designed settings and interactivity, providing players with increased entertainment value. These settings include intuitive game interfaces, diverse control methods, and intelligent virtual players. Overall, this study provides important references and guidance for understanding the principles of human-computer interaction in the Pro Evolution Soccer game and enhancing its playability.
A comparative analysis of feature selection for IoT device identification in machine learning algorithms
Authors: Guo Zhiming
Published: July 2024

In recent years, IoT security has become a critical concern, prompting the integration of artificial intelligence and cybersecurity. State-of-the-art research has focused on applying machine learning and deep learning techniques, emphasizing the crucial role of data preprocessing for effective results. One of the most common and significant data preparation techniques, feature selection is now an essential step in the machine learning process. It involves finding pertinent characteristics and deleting unimportant, low-correlation, redundant, or noisy data. In peoples daily lives, noise can interfere with the sound of our normal lives. Similarly, in machine learning, they can also interfere with the process of correctly recognizing the laws of things, leading to the influence of many parameters such as learning rate. This feature selection process improves predictive accuracy and increases comprehensibility. This paper evaluates the performance of four machine learning algorithms for embedded feature selection and compares them. In the end, it was found that XGBoost outperformed the other three algorithms in terms of performance. After feature selection, the accuracy remained almost unchanged, and even with only a few features (about 10% of the original data), it still had a high accuracy
A survey of recommendation system via Graph Neural Network and collaborative filtering
Authors: Zhu Mingkai
Published: July 2024

Recommendation systems play a vital role in today's information-rich digital environment, where users are overwhelmed with a vast number of choices and content. Because of the limitation of formal recommendation system models, Graph representation learning has become the mainstream. Among all different approaches, GNN-based CF is a state-of-art method that shows great performance. This study aims to offer a comprehensive overview of the fundamentals of GNN-based CF. Specifically, The study provides a taxonomy according to Graph Construction, Neighborhood Aggregation, Information Update, and Final Node Representation. Moreover, this survey summarizes different ways to enhancing. Meanwhile, a close classification of datasets are mentioned. This survey seeks to deepen the understanding of GNN-based CF, offering a roadmap for researchers and practitioners to navigate this exciting field and push the boundaries of recommendation system in our data-rich world.
AccuRapidGuard: Advancing phishing website detection with high performance and efficiency
Authors: Sun Qi
Published: July 2024

Cyber attackers continually develop sophisticated techniques to create deceptive phishing websites, resulting in financial losses and the exposure of personal information. As a result, there is a growing demand for phishing detection solutions that are practical for everyday use. However, current deep learning-based models offer good accuracy and generalization but require extensive computational resources, making them impractical due to long training times and slow inference speeds. To overcome the limitation, we propose AccuRapidGuard, a deep learning model that utilizes parallel CNN layers and an RNN layer with attention mechanism. Extensive experiments demonstrate that AccuRapidGuard not only achieves great performance with high accuracy, low false positive rates, and excellent generalization, but also provides outstanding efficiency, significantly reducing training time and inference speed compared to state-of-the-art baseline models. This combination of exceptional performance and superior efficiency positions AccuRapidGuard as a highly valuable and practical solution for phishing detection.
An exploration of reinforcement learning and deep reinforcement learning
Authors: Fan ZiSheng
Published: July 2024

Today, machine learning is evolving so quickly that new algorithms are always appearing. Deep neural networks in particular have shown positive outcomes in a variety of areas, including computer vision, natural language processing, and time series prediction. Its development moves at a very sluggish pace due to the high threshold. Therefore, a thorough examination of the reinforcement learning field should be required. This essay examines both the deep learning algorithm and the reinforcement learning operational procedure. The study identifies information retrieval, data mining, intelligent speech, natural language processing, and reinforcement learning as key technologies. The scientific study of reinforcement learning has advanced remarkably quickly, and it is now being used to tackle important decision optimization issues at academic conferences and journal research work in computer networks, computer graphics, etc. Brief introductions and reviews of both types of models are provided in this paper, along with an understanding of some of the most cutting-edge reinforcement learning applications and approaches.
CCC--Uno AI agent auxiliary system
Authors: Zhang Chi , Chen Zhengxiao
Published: July 2024

In this paper, we explore the design and implementation of a UNO game AI Agent based on the Mini-Max algorithm, and we named it "CardCraft Champ". UNO, as a popular card game, is filled with strategies and uncertainty, making it challenging to design an AI Agent that can efficiently handle different game scenarios. Our goal is to develop an AI system that can intelligently select cards, respond to opponents strategies, and achieve victory in UNO games. The core idea of "CardCraft Champ" is to use the Mini-Max algorithm and Alpha-Beta pruning for decision-making. By conducting deep searches and evaluations on the game state, our AI Agent can predict various possible game progressions and make optimal decisions based on expected outcomes. The Alpha-Beta pruning technique can accelerate the search process even further, enabling our AI to find the optimal solution within a limited time and make wise decisions in the game. Our research not only focuses on the implementation of the algorithm but also on its application to actual UNO games. Through comparative experiments with other existing UNO game AIs, we demonstrate the superior performance of "CardCraft Champ" in different scenarios. The experimental results show that our AI Agent can minimize risks in the game while maintaining a high winning probability against various opponent strategies and game scenarios. Additionally, we consider the scalability and applicability of "CardCraft Champ". Our system performs well in battles of different difficulty levels, proving its adaptability and stability. We also explore possible directions for improvement, such as introducing deep learning techniques to enhance the AI's decision-making capabilities.
Enhancing road sign recognition using Convolutional Neural Networks
Authors: Zhuang Yuning
Published: July 2024

The significance of road sign recognition technology has grown in tandem with the development of autonomous driving systems. However, traditional methods have encountered limitations when faced with intricate road scenarios. In contrast, CNNs have the capacity to autonomously glean complex features from image data, leading to a substantial enhancement in the stability and accuracy of road sign recognition. In this research, we employ a road sign dataset obtained from Kaggle and apply image preprocessing techniques, coupled with data augmentation methods. Furthermore, we employ transfer learning by utilizing ResNet34 to construct a robust road sign recognition model. The experimental outcomes vividly demonstrate the efficacy of this model in acquiring intricate features of road signs, resulting in the augmented accuracy and robustness in recognition. Nevertheless, we do observe certain limitations, including recognition instability and shortcomings in generalization. Nonetheless, on the whole, CNNs exhibit significant potential for application within the realm of road sign recognition.
Improving breast cancer classification using histopathology images through deep learning
Authors: Liu Ziming
Published: July 2024

Breast cancer is a serious threat to human health. Image analysis of pathological sections of tumors has become an important means of detection. Assisting physicians in early detection of potential breast cancer has been a prominent topic in the field of medical image diagnostics. On this basis, a method based on depth neural network is proposed to solve the difficulties and limitations of manual extraction in traditional methods, and to automatically recognize breast disease images. On this basis, the improved methods of data enhancement and fine adjustment are adopted to solve the problem of small sample training based on depth neural network, improve the accuracy of image classification, and better meet the needs of the medical field.
Review of artificial intelligence applications and technologies in cybersecurity
Authors: Li Zimeng
Published: July 2024

With the rapid development of information technology and network technology, the Internet not only brings a lot of convenient resources to people's lives but also brings potential threats to people's network security, and the development of artificial intelligence provides new solutions for network security governance. The article focuses on common problems in network security, systematically summarizes the network security solutions based on AI technology and the empowering effects brought in recent years, analyzes the application of AI technology in common security problems, summarizes the advantages of AI in the application of network security, and puts forward the potential problems and challenges. Overall, AI technology has improved the accuracy, scalability, reliability and performance of network security applications in practical applications, and will become an essential engine of empowerment in the future development of network security technology.